text:
\section{Introduction}
The problem of signal recovery from nonlinear measurements arises in various imaging and signal processing tasks \cite{shechtman2015phase,maiden2009improved,millane1990phase,rodenburg2008ptychography,candes2015phasediff}. Conventional methods for solving such inverse problems use an iterative method to recover the signal from given measurements. In this paper, we present a framework to optimize over the measurement parameters to improve the quality of signals recovered by the given iterative method. In particular, we learn illumination patterns to recover the signal from coded diffraction patterns (CDP) using a fixed-cost alternating minimization method.

Coded diffraction imaging is a specific instance of Fourier phase retrieval problems. Phase retrieval refers to a broad class of nonlinear inverse problems where we seek to recover a complex- (or real-) valued signal from its phase-less (or sign-less) measurements \cite{fienup1978reconstruction,candes2013phaselift,fienup1982phase,jaganathan2015phase,shechtman2015phase,muminov2020small}. In practice, these problems often arise in coherent optical imaging where an image sensor records the intensity of the Fourier measurements of the object of interest. In coded diffraction imaging, the signal of interest gets modulated by a sequence of known illumination patterns/masks before observing the Fourier intensity at the sensor \cite{candes2013phaselift,jaganathan2015phase,shechtman2015phase}.
Applications include X-ray crystallography \cite{watson1953structure,millane1990phase,harrison1993phase}, astronomy \cite{fienup1987phase,gonsalves2014perspectives}, microscopy \cite{misell1973method,miao2008extending,rodenburg2008ptychography,tian2014multiplexed}, speech processing and acoustics \cite{rabiner1993fundamentals,balan2006signal,balan2010signal,jaganathan2016stft}, and quantum mechanics \cite{corbett2006pauli,reichenbach1998philosophic}.

We can model the sensor measurements for coded diffraction imaging as follows. Let us denote the signal of interest as $x \in \mathbb{R}^n$ or $\mathbb{C}^n$ that is modulated by $T$ illumination patterns $D = \{d_1, \ldots, d_T\}$, where $d_t \in \mathbb{R}^n$ or $\mathbb{C}^n$. The amplitude of sensor measurements for $t$th illumination pattern can be written as
\begin{equation}
    y_t =  |\mathcal{F} (d_t \odot x)|,
    \label{eq:yt}
\end{equation}
where $\mathcal{F}$ denotes the Fourier transform operator and $\odot$ denotes an element-wise product.
We note that real sensor measurements are proportional to the intensity of the incoming signal (i.e., square of the Fourier transform). In practice, however, solving the inverse problem with (non-square) amplitude measurements provides better results; therefore, we use the amplitude measurements throughout the paper.

\begin{figure}[!th]
    \centering
    \includegraphics[width=1\columnwidth]{fig/intro_v7.pdf}
    \caption{The block diagram of our proposed framework to learn illumination patterns while solving coded diffraction imaging using $K$ iterations of a phase retrieval algorithm. The iterative algorithm is represented as an unrolled network with $K$ layers. Steps at every iteration are fixed and depicted as an unrolled network (details can be found in Algorithm~\ref{algo:test}). Phase retrieval algorithm uses the measurements $Y = \{y_t\}$ and  illumination patterns $D = \{d_t\}$ to provide an estimate $x^K$ after $K$ iterations. Our goal is to learn the illumination patterns $D$ to minimize the error between the estimated $x^K$ and the ground truth.}
    \label{fig:intro}
\end{figure}

To recover the signal $x$ from the nonlinear measurements, we can solve the following optimization problem:
\begin{equation}\label{eq:PR}
    \min_x \; \sum_{t=1}^T \|y_t - |\mathcal{F}(d_t \odot x)| \|_2^2.
\end{equation}
In recent years, a number of iterative algorithms have been proposed for solving the problem in \eqref{eq:PR}, which includes lifting-based convex methods, alternating minimization-based nonconvex methods, and greedy methods \cite{candes2013phaselift,gross2017improved,netrapalli2013phase,hyder2019alternating,Jagatap2017}.

Our goal is to learn a set of illumination patterns to optimize the recovery of an alternating minimization (AltMin) algorithm for solving the problem in \eqref{eq:PR}. The AltMin method can be viewed as an unrolled gradient descent network, as shown in Fig.~\ref{fig:intro}, where we fix the steps at every iteration and the total number of iterations for AltMin. One forward pass through the unrolled network is equivalent to $K$ iterations of the AltMin algorithm. To minimize the computational complexity of the recovery algorithm, we keep the total number of iterations very small (e.g., $K=50$). At the training stage, we optimize over the illumination patterns to minimize the error between the AltMin outputs after $K$ iterations and the ground truth training images. At the test time, we solve the problem in \eqref{eq:PR} using $K$ AltMin iteration with the learned illumination patterns (equivalent to one forward pass).
We evaluated our method on different image datasets and compared against existing methods for coded diffraction imaging. We demonstrate that our proposed method of designing illumination patterns for a fixed-cost algorithm outperforms existing methods both in terms of accuracy and speed.

\section{Related Work}

\textbf{Phase Retrieval and Coded Diffraction Patterns:} Fourier phase retrieval problem arises in a number of imaging systems because standard image sensors can only record intensity of the observed measurements. This problem has been extensively studied over last five decades in optics, signal processing, and optimization \cite{gerchberg1972practical, fienup1982phase, millane1990phase, rodenburg2008ptychography,maiden2009improved, shechtman2015phase, hand2018phase,nayer2019phaseless}. Coded diffraction imaging is a physically realistic setup in which we can first modulate by signal of interest and then collect the intensity measurements \cite{candes2015phasediff,chandra2017phasepack}. The modulation can be performed using a spatial light modulator or custom transparencies \cite{miao2008extending,rodenburg2008ptychography,ptychTCI}. The recovery problems involves solving a phase retrieval problem; the presence of modulation patterns makes this a more tractable problem compared to classical Fourier phase retrieval \cite{candes2015phasediff}.

The algorithms for solving phase retrieval problem can be broadly divided into non-convex and convex methods. Classical algorithms for phase retrieval rely on solving the underlying non-convex problem using alternating minimization \cite{fienup1982phase,bauschke2002phase,netrapalli2013phase}. Amplitude flow \cite{wang2016sparse,wang2016solving},  Wirtinger flow \cite{candes2015phase,zhang2016reshaped,chen2015solving,cai2016optimal}, alternating minimization (AltMin) \cite{netrapalli2013phase,zhang2016reshaped,Jagatap2017,hyder2019alternating} are recent methods that solve the non-convex problem.
Convex methods usually lift the nonconvex problem of signal recovery from quadratic measurements into a convex problem of low-rank matrix recovery from linear measurements
\cite{candes2013phaselift,soltani2016fast,fazel2008compressed}.
The PhaseLift algorithm \cite{candes2013phaselift} and its variations \cite{gross2017improved, candes2015phasediff} can be considered under this class.
We can also incorporate prior knowledge about the signal structure (e.g., sparsity, support, or positivity) in the recovery process  constraints \cite{ohlsson2012cprl,li2013sparse,bahmani2015efficient,jaganathan2012recovery,netrapalli2013phase,cai2016optimal,wang2016sparse}.

\textbf{Data-Driven Approaches for Phase Retrieval:}
A number of papers have recently explored the idea of replacing the classical (hand-designed) signal priors with deep generative priors for solving inverse problems \cite{bora2017compressed,hand2016compressed,ulyanov2018deep,van2018compressed}. Some of the generative prior-based approaches for phase retrieval are presented in \cite{hyder2019alternating,jagatap2019algorithmic, hand2018phase,shamshad2018robust,metzler2020deep}.

Another growing trend is to apply deep learning to solve inverse problems (including phase retrieval) in an end-to-end manner, where deep networks are trained to learn a mapping from sensor measurements to the signal of interest using a large number of measurement-signal pairs. A few examples demonstrating the benefit of the data-driven approaches include robust phase retrieval \cite{metzler2018prdeep}, Fourier ptychographic microscopy \cite{kellman2019data}, holographic image reconstruction  \cite{rivenson2018phase}, and correlography for non-line-of-sight imaging \cite{metzler2020deep2}.

While our method is partially driven by data, our goal is not to learn a signal prior or a mapping from measurements to signal. We use data to learn the illumination patterns for a fixed recovery algorithm. The number of training images required by our method is extremely small (32 or 128 images only). Furthermore, the patterns we learn on one class of images provide good results on other types of images (see Table~\ref{table:generalzation}). Apart from the great flexibility, our method uses a well-defined AltMin routine, where we know exact steps for every iteration as opposed to the black-box deep models.

\textbf{Unrolled Network for Inverse Problem:}
Iterative methods for solving the inverse problems, such as AltMin or other first-order methods, can be represented as unrolled networks. Every layer of such a network performs the same steps as a single iteration of the original method  \cite{kellman2019physics,diamond2017unrolled,gregor2010learning,wang2016proximal,hammernik2018learning,sun2016deep,kamilov2016learning,bostan2018learning,monga2019algorithm,liang2019deep}. Some parameters of the iterative steps can be learned from data (e.g., step size, denoiser, or threshold parameters) but the basic structure and physical forward model are kept intact.

\textbf{Learn to Sense:} Deep learning methods have also been recently used to design the sensing system; especially in the context of compressive sensing and computational imaging \cite{mousavi2017learning,wu2019learning, Bergman:2020:DeepLiDAR,wang2020learning}. The main objective in these methods is similar to ours, which is to select sensor parameters to recover best possible signal/image from the sensor measurements. The sensor parameters may involve selection of samples/frames, design of sampling waveforms, or illumination patters as we discuss in this paper. In principle, the sensor can be treated as the first layer of the network with some physical constraints on the parameters \cite{kellman2019physics}. In contrast to most of the existing methods that learn a deep network to solve the inverse problem, our method uses a predefined iterative method as an unrolled network while learning the illumination patterns using a small number of training images.

\section{Proposed Method}
We use $N$ training images ($x_1,\ldots, x_N$) to learn $T$ illumination patterns that provide best reconstruction using a predefined (iterative) phase retrieval algorithm. Furthermore, to ensure that the illumination patterns are physically realizable, we constrain their values to be in the range $[0,1]$. We use a sigmoid function over unconstrained parameters $\Theta = \{\theta_1, \ldots, \theta_T\}$ to define the illumination patterns; that is, $d_t = \text{sigmoid}(\theta_t)$ for all $t=1,\ldots,T$.

Our proposed method for learning illumination patterns can be divided into two parts: The first (inner) part involves solving the phase retrieval problem with given coded diffraction patterns using AltMin as an unrolled network (see block diagram in Fig.~\ref{fig:intro}); Second part is updating the illumination patterns based on backpropagating the image reconstruction loss. These two parts together yield optimal image reconstruction and illumination patterns. Pseudocodes for both parts are listed in Algorithms~\ref{algo:train},\ref{algo:test}.

\begin{algorithm}[tb]
   \caption{Learning illumination patterns}
   \label{algo:train}
\begin{algorithmic}
   \State {\bfseries Input:} Training set $X$ with $N$ images $X=\{x_1, \ldots, x_N\}$.

   \State {\bfseries Initialize: } Initialize the optimization variables for $T$ patterns as $\Theta = \{\theta_1,\ldots, \theta_T \}$ from a uniform distribution.
   \For{$\text{epoch} = 1, 2, ..., M$}         \Comment{M epochs}
        \State Generate illumination patterns in the range [0,1] as $d_t = \text{sigmoid}(\theta_t)$ for all $t$.
        \For{$n=1, 2, ..., N$}      \Comment{N samples}
            \State $Y^n = \{y_1^n, \ldots, y_T^n ~|~y_t^n=|\mathcal{F} (d_t \odot x_n)| \}$
            \State $x_n^K(\Theta) \gets \texttt{solveCDP($Y^n$,$D$)}$ \Comment{\texttt{solveCDP} for each sample}
        \EndFor
        \State $L_\Theta = \sum_{n=1}^{N} \| x_n - x_n^K(\Theta)\|_2^2$

    	\State $\Theta \leftarrow \Theta - \beta \nabla_{\Theta}L_{\Theta}$ \Comment{Optimize over $\Theta$}
     \EndFor
     \State {\bfseries Output:} Optimal illumination patterns ${D} = \{d_1, \ldots, d_T ~|~ d_t = \text{sigmoid}(\theta_t)\}$.
\end{algorithmic}
\end{algorithm}

\begin{algorithm}[tb]
   \caption{\texttt{solveCDP($Y,D$)} via AltMin}
   \label{algo:test}
\begin{algorithmic}
   \State {\bfseries Input:}  Measurements $Y = \{y_1, \ldots, y_t\}$ and illumination patterns $D =\{d_1,\ldots, d_T\}$.

   \State {\bfseries Initialization:}  Initialize estimate $x^0 = 0$.
	\For{$k = 1, 2, ..., K$}    \Comment{K iterations of AltMin}
        \State $p_t^{k-1} \gets \text{sign}(\mathcal{F}(d_t \odot x^{k-1})$
        %

        %
        \State $\nabla_x L_{x,p} = \frac{2}{T}\sum_{t=1}^T [|d_t|^2\odot x^{k-1} - d_t^* \odot \mathcal{F}^*(p_t^{k-1}\odot y_t)]$
        %
    	\State $x^k \leftarrow x^{k-1} - \alpha\nabla_{x}L_{x,p} $
	\EndFor
   \State {\bfseries Output:} Estimated signal $x^K$

\end{algorithmic}
\end{algorithm}

\textbf{Phase retrieval as alternating minimization (AltMin):} Given measurements $Y = \{y_1,\ldots, y_T\}$ and illumination patterns $D = \{d_1,\ldots,d_T\}$, we seek to solve the CDP phase retrieval problem by minimizing the loss function defined in \eqref{eq:PR} as
\begin{equation}\label{eq:Lx}
    L_{x} = \frac{1}{T}\sum_{t=1}^{T} \|  y_t - |\mathcal{F} (d_t \odot x)| \|_2^2.
\end{equation}
Even though the loss function in \eqref{eq:Lx} is nonconvex and nonsmooth with respect to $x$, we can minimize it using the well-known alternating minimization (AltMin) with a single gradient descent step \cite{netrapalli2013phase,zhang2016reshaped}. We define a new variable for the  estimated phase of linear measurements as $p_t = \text{phase}(\mathcal{F} (d_t \odot x))$ and reformulate the loss function in \eqref{eq:Lx} into
\begin{equation}\label{eq:Lx_p}
    L_{x,p} = \frac{1}{T}\sum_{t=1}^{T} \|  p_t \odot y_t - \mathcal{F} (d_t \odot x) \|_2^2.
\end{equation}
The gradient step can be computed as
\begin{equation}
    \nabla_{x}L_{x,p} = \frac{2}{T}\sum_{t=1}^{T}  |d_t|^2 \odot x - d_t^* \odot \mathcal{F}^*(p_t \odot  y_t). \label{eqn:grad_x}
\end{equation}

We initialize the image estimate as a zero vector and update the estimate at every iteration as $  x^k = x^{k-1} - \alpha_{k-1}\nabla_{x}L_{x,p},$
where $\alpha_{k-1}$ denotes the step size. In our implementation, we used a fixed step size $\alpha$ for all iterations.
We use $K$ iterations of the gradient descent in the unrolled network and denote the final estimate as $x^K$. We summarize the steps of CDP phase retrieval routine as \texttt{solveCDP} in Algorithm~\ref{algo:test}.

\textbf{Learning illumination patterns:} To learn a set of illumination patterns that provide the best reconstruction with the predefined iterative method (or the unrolled network), we seek to minimize the difference between the original training images and their estimates. In this regard, we minimize the following quadratic loss function with respect to $\Theta$:
\begin{equation}\label{eq:L_theta}
    L_\Theta = \sum_{n=1}^N \|x_n - x_n^K(\Theta)\|_2^2,
\end{equation}
where $x_n^K(\Theta)$ denotes the \texttt{solveCDP} estimate of $n$th training image for the given values of $\Theta$.
Note that for a given values of $\Theta$, we can define illumination patterns as $d_t = \text{sigmoid}(\theta_t)$ and sensor measurements for $x_n$ as $y^n_t = |\mathcal{F}(d_t\odot x_n)|$ for $t=1,\ldots, T$ and $n = 1,\ldots, N$.
We use Adam optimizer in PyTorch \cite{kingma2014adam,paszke2019pytorch} to minimize the loss function in \eqref{eq:L_theta}. A summary of the algorithm for learning the illumination patterns is also listed in Algorithm \ref{algo:train}.

\section{Experiments}
\textbf{Datasets.}
\label{section:dataset}
We used MNIST digits, Fashion MNIST (F. MNIST), CIFAR10, SVHN and CelebA datasets for training and testing in our experiments. We use 128 images from each of the datasets for training and another 1000 images for testing. To make the tiny-image datasets uniform, we reshaped all of them to $32\times32$ size with grayscale values. Images in CelebA dataset have $218\times178$ pixels, we first converted all the images to grayscale, cropped $178\times178$ region in the center, and resized to $200\times200$. In Fig. \ref{fig:classical}, we evaluated the performance of our method on images used in \cite{metzler2018prdeep}.

\textbf{Measurements.} We use the amplitude of the 2D Fourier transform of the images modulated with $T$ illumination patterns as the measurements. Unless otherwise mentioned, we used noiseless measurements. We report results for measurements with Gaussian and Poisson noise in Fig.~\ref{fig:test_noise}.

\textbf{Computing Infrastructure.} Intel Core i7-8700 CPU and NVIDIA TITAN Xp GPU.

\subsection{Setup and hyper-parameter search}
The hyper-parameters include the number of iterations ($K$), step size $\alpha$, and the number of training samples $N$.
We set the default value of $K=50$, but we will show later that $K$ can be adjusted as a trade-off between better reconstruction quality and shorter run time.
We tested all methods for $T = \{2,4,8\}$ to evaluate three cases where signal recovery is hard, moderate, and easy, respectively. Through grid search, we found that it provides the best results over all datasets when $\alpha*T = 4$. We also studied the effect of the number of training images and found that illumination patterns learned on $32$ randomly selected images provide good recovery over the entire dataset. The test accuracy improves slightly as we increase the number of training samples. To be safe, we used 128 training images in all our experiments.

\subsection{Comparison between random and learned illumination patterns}
To demonstrate the advantages of our learned illumination patterns, we compare the performance of learned and random illumination patterns on five different datasets. We learn a set of $T=\{2,4,8\}$ illumination patterns on 128 training images from a dataset and test them on 1000 test images from the same dataset. For random patterns, we draw $T$ independent patterns from Uniform(0,1) distribution and test their performance on the same 1000 samples that we used for the learned case. We repeat this process 30 times and choose the best result to compare with the results for the learned illumination patterns. The average PSNR over all 1000 test image reconstructions is presented in Table~\ref{table:compare_learned_random}, which shows that the learned illumination patterns perform significantly better than the random patterns for all values of $T$. In addition to that, we can observe a transition in the performance for $T=4$, where random patterns provide poor quality reconstructions and learned patterns provide very high quality reconstructions.

To highlight this effect, we show a small set of reconstructed images and histograms of PSNRs of all the reconstructed images from learned and random illumination patterns in Fig.~\ref{fig:compare} for $T=4$ patterns. The result suggests that the learned illumination patterns demonstrate consistently better performance compared to random illumination patterns.

\begin{table*}[t]
\caption{Average PSNR for learned and random illumination patterns tested on different datasets.}
\label{table:compare_learned_random}
\vskip 0.15in
\begin{center}
\begin{small}
\begin{tabular}{@{}ccccccc@{}}
\toprule
\multirow{2}{*}{PSNR}      & \multicolumn{2}{c}{2 Illumination Patterns} & \multicolumn{2}{c}{4 Illumination Patterns} & \multicolumn{2}{c}{8 Illumination Patterns} \\ \cmidrule(l){2-7}
         & Random & Learned & Random & Learned & Random & Learned \\ \midrule
MNIST    & 13.59  & 27.77   & 28.61  & 101.50  & 46.43  & 111.79  \\
F. MNIST & 16.26  & 25.17   & 30.05  & 92.44   & 52.05  & 108.85  \\
CIFAR10  & 13.40  & 22.93   & 27.51  & 82.02   & 60.64  & 104.61  \\
SVHN     & 12.24  & 20.27   & 26.24  & 84.43   & 65.33  & 109.25  \\
\multicolumn{1}{l}{CelebA} & 12.67        & 18.49        & 21.67        & 78.98        & 41.49        & 90.29        \\ \bottomrule
\end{tabular}

\end{small}
\end{center}
\vskip -0.1in
\end{table*}

\begin{figure*}[!ht]
    \centering
    \begin{subfigure}[t]{0.24\linewidth}
    \includegraphics[width=1\linewidth]{fig/figs_mnist_v2.pdf}
    \caption{MNIST}
    \end{subfigure}
    \hfill
    \begin{subfigure}[t]{0.24\linewidth}
    \includegraphics[width=1\linewidth]{fig/figs_fmnist_v2.pdf}
    \caption{F. MNIST}
    \end{subfigure}
    \hfill
    \begin{subfigure}[t]{0.24\linewidth}
    \includegraphics[width=1\linewidth]{fig/figs_cifar_v2.pdf}
    \caption{CIFAR10}
    \end{subfigure}
    \hfill
    \begin{subfigure}[t]{0.24\linewidth}
    \includegraphics[width=1\linewidth]{fig/figs_celeba_v2.pdf}
    \caption{CelebA}
    \end{subfigure}
    \caption{Selected ground truth (GT) images, corresponding reconstructed images using random and learned illumination patterns. PSNR is shown on top of every reconstruction. Below each dataset, we show the histograms of the PSNRs of all images with random patterns (shown in blue) and learned patterns (shown in orange). The dashed vertical line indicates the mean of all PSNRs. We used $T=4$ illumination patterns. Random illumination patterns are selected best out of 30 trials. The learned illumination patterns are trained on 128 training images.}
    \label{fig:compare}
\end{figure*}

\subsection{Effect of number of iterations/layers (K)}
Figure~\ref{fig:diff_k} shows the performance of the learned and random illumination patterns as we increase $K$ to 200 at test time using the patterns learned for $K=50$. We observed that, with the learned patterns, the image reconstruction process converges faster and is more stable (smaller variance) than the case with random patterns. The  red curve in Fig.~\ref{fig:diff_k} has a steeper slope and narrower shades. Besides the default setting for $K=50$, we also learn the illumination patterns for different values of $K$.
Figure~\ref{fig:diff_k_train_test} shows that we can recover images in a small number of iterations if we use learned illumination patterns.
We also observe that we can perform better if we use more iterations in testing than in training. We have chosen $K=50$ for most of the experiments as a trade-off between computational cost and reconstruction performance.

\begin{figure}[!ht]
    \centering
    \begin{subfigure}[t]{0.24\linewidth}
    \includegraphics[width=1\linewidth]{fig/fig_diff_k_mnist.pdf}
    \caption{MNIST}
    \end{subfigure}
    \hfill
    \begin{subfigure}[t]{0.24\linewidth}
    \includegraphics[width=1\linewidth]{fig/fig_diff_k_fmnist.pdf}
    \caption{F. MNIST}
    \end{subfigure}
    \hfill
    \begin{subfigure}[t]{0.24\linewidth}
    \includegraphics[width=1\textwidth]{fig/fig_diff_k_cifar.pdf}
    \caption{CIFAR10}
    \end{subfigure}
    \hfill
    \begin{subfigure}[t]{0.24\linewidth}
    \includegraphics[width=1\textwidth]{fig/fig_diff_k_celeba.pdf}
    \caption{CelebA}
    \end{subfigure}
    \caption{Comparison of the reconstruction quality with  random and learned illumination patterns for different values of $K=1,\ldots,200$. We plot the average PSNR in bright color and the PSNR of randomly selected 100 samples in light shadows. \textbf{Learned} represents the reconstruction PSNR with learned illumination patterns (shown in red), and \textbf{Random} represents PSNR for random illumination patterns (shown in blue). The number of illumination patterns is $T=4$. Random illumination patterns are selected best out of 30 trials. The learned illumination patterns are trained on 128 training images and number of iterations $K=50$ during training.}
    \label{fig:diff_k}
\end{figure}

\begin{figure*}[!ht]
\centering
\begin{subfigure}[t]{0.3\linewidth}
\includegraphics[width=1\linewidth]{fig/diff_k/TrainingK=10.pdf}
\caption{Training K=10}
\end{subfigure}
\hfill
\begin{subfigure}[t]{0.3\linewidth}
\includegraphics[width=1\linewidth]{fig/diff_k/TrainingK=20.pdf}
\caption{Training K=20}
\end{subfigure}
\hfill
\begin{subfigure}[t]{0.3\linewidth}
\includegraphics[width=1\textwidth]{fig/diff_k/TrainingK=TestK.pdf}
\caption{Training K=Test K}
\end{subfigure}
\caption{Reconstruction quality vs number of iterations (layers) at test time (i.e., $K$ is different for training and testing with $T=4$). We show error bar of $\pm 0.25\sigma$ for each dataset. In (a) and (b), we fixed K (K=10, 20) and tested using different K. In (c), we trained and tested using the same number of layers.}
\label{fig:diff_k_train_test}
\end{figure*}

\subsection{Generalization of learned patterns on different datasets}

To explore the generalizability of our learned illumination patterns, we use patterns learned on one dataset to recover images from another. The results are shown in Table.~\ref{table:generalzation}. As we can see in the table, the diagonal numbers are generally the best, and off-diagonal numbers are generally better than the ones with random illumination patterns.

We have also tested the learned illumination patterns on several classical images. Some results are shown in Fig.~\ref{fig:classical}. We used illumination patterns learned on 128 celebA images, but we can see that the learned illumination patterns perform better than the randomly chosen illumination patterns for classical images which indicates some generalizability of our learned illumination patterns.

\begin{table*}[]
\vspace{2mm}
\caption{Reconstruction performance of illumination patterns learned and tested on different datasets. Every column corresponds to patterns learned on a fixed dataset and tested on all. Rand column reports the performance of random illumination patterns.}
\vspace{-3mm}
\label{table:generalzation}
\begin{center}
\begin{small}
\begin{tabular}{@{}ccccccccccc@{}}
\toprule
 &
  \multicolumn{5}{c}{4 Illumination Patterns} &
  \multicolumn{5}{c}{8 Illumination Patterns} \\ \cmidrule(l){2-11}
\multirow{-2}{*}{\begin{tabular}[c]{@{}c@{}}Test\\ \textbackslash{}Train\end{tabular}} &
  MNIST &
  FMNIST &
  CIFAR &
  SVHN &
  {\color[HTML]{656565} Rand} &
  MNIST &
  FMNIST &
  CIFAR &
  SVHN &
  {\color[HTML]{656565} Rand} \\ \midrule
MNIST &
  {\color[HTML]{000000} 101.50} &
  {\color[HTML]{000000} 83.13} &
  {\color[HTML]{000000} 78.04} &
  {\color[HTML]{000000} 54.83} &
  {\color[HTML]{656565} 28.61} &
  {\color[HTML]{000000} 111.79} &
  {\color[HTML]{000000} 92.66} &
  {\color[HTML]{000000} 82.75} &
  {\color[HTML]{000000} 74.86} &
  {\color[HTML]{656565} 46.43} \\
FMNIST &
  {\color[HTML]{000000} 64.47} &
  {\color[HTML]{000000} 92.44} &
  {\color[HTML]{000000} 85.14} &
  {\color[HTML]{000000} 76.41} &
  {\color[HTML]{656565} 30.05} &
  {\color[HTML]{000000} 82.37} &
  {\color[HTML]{000000} 108.85} &
  {\color[HTML]{000000} 85.80} &
  {\color[HTML]{000000} 93.09} &
  {\color[HTML]{656565} 52.05} \\
CIFAR &
  {\color[HTML]{000000} 31.77} &
  {\color[HTML]{000000} 46.79} &
  {\color[HTML]{000000} 82.02} &
  {\color[HTML]{000000} 68.12} &
  {\color[HTML]{656565} 27.51} &
  {\color[HTML]{000000} 52.75} &
  {\color[HTML]{000000} 84.94} &
  {\color[HTML]{000000} 104.61} &
  {\color[HTML]{000000} 87.85} &
  {\color[HTML]{656565} 60.64} \\
SVHN &
  {\color[HTML]{000000} 40.04} &
  {\color[HTML]{000000} 55.31} &
  {\color[HTML]{000000} 87.77} &
  {\color[HTML]{000000} 84.43} &
  {\color[HTML]{656565} 26.24} &
  {\color[HTML]{000000} 65.88} &
  {\color[HTML]{000000} 100.00} &
  {\color[HTML]{000000} 104.69} &
  {\color[HTML]{000000} 109.25} &
  {\color[HTML]{656565} 65.33} \\ \bottomrule
\end{tabular}

\end{small}
\end{center}

\end{table*}

\begin{figure}[!t]
    \centering
    \includegraphics[width=0.6\textwidth]{fig/fig_classical.pdf}
    \caption{\textbf{First Row:} Ground truth images from image processing standard test datasets. \textbf{Second Row:} Reconstruction using random illumination patterns with uniform random distribution [0, 1] (we selected $T=4$ patterns that provided best results on celebA test images in \textbf{30 trials}).  PSNR numbers are shown on the top of reconstructed  images. \textbf{Third Row:} Reconstruction using the patterns trained on celebA dataset. Each image has $200\times 200$ pixels and the number of illumination patterns is $T=4$.}
    \label{fig:classical}
\end{figure}

\subsection{Robustness to noise}
To investigate the robustness of our method to noise, we train our illumination patterns on noiseless measurements obtained from the training datasets. We then added Gaussian and Poisson noise at different levels to the measurements from the test datasets. Poisson noise or shot noise is the most common in the imaging systems. We model the Poisson noise following the approach in \cite{metzler2018prdeep}. Let us denote the $i^{th}$ element of measurement vector corresponding to $t^{th}$ illumination pattern, $y_t$ as
\begin{equation}
    y_t(i)=|z_t(i)|+ \eta_t (i) \;\;\;\forall i=1,2,\ldots, m
\end{equation}
where $ \eta_t (i) \sim \mathcal{N}(0,\lambda|z_t(i)|)$ and $z_t=\mathcal{F}(d_t\odot x)$. We varied $\lambda$ to generate noise at different signal-to-noise ratios. Poisson noise affects larger values in measurements with higher strength than the smaller values. Since the sensors can measure only positive measurements, we kept the measurements positive by applying ReLU function after noise addition. We expect the reconstruction to be affected by noise as we did not use any denoiser. We observe the effect of noise in Figure~\ref{fig:test_noise}. Even though noise affects the reconstructions, we can get reasonable reconstruction up to a certain level of noise. The relationship between noise level and reconstruction performance also indicates that our phase retrieval system is quite stable.

\begin{figure}[!t]
\centering
\begin{subfigure}[t]{0.35\linewidth}
\includegraphics[width=1\textwidth]{fig/noise/noise_curve_gaussian.pdf}
\caption{Gaussian}
\end{subfigure}
\centering
\begin{subfigure}[t]{0.35\linewidth}
\includegraphics[width=1\textwidth]{fig/noise/noise_curve_poisson.pdf}
\caption{Poisson}
\end{subfigure}
\caption{Reconstruction quality of the test images vs noise level of the measurements for different datasets. Here we show shaded error bar of  $\pm 0.25\sigma$ for each dataset. We learn the illumination patterns ($T=4$) on 128 noiseless training images of corresponding datasets.}
\label{fig:test_noise}
\end{figure}

\if 0
\subsection{Adding a denoiser}
The unrolling network enables us to use plug-n-play denoiser for additional regularization \cite{any paper here?,venkatakrishnan2013plug,?}. From the noise curves in Fig.~\ref{fig:test_noise}, we observe that at increasingly high noise level, our method show linearly low reconstruction performance. In case of high noise level, if we use a denoiser, it may potentially give better performance than the case when we do not use a denoiser. We investigate such behaviour by adding a convolutional autoencoder after every layer of the unrolling network. We demonstrate some reconstruction results in Fig~\ref{fig:denoiser_rec}. We can observe that the reconstructions are better when we add a denoiser for a very high noise level (10 dB SNR Poisson noise). However, due to the generation capacity of the denoiser, it is not expected to give us better performance at low noise level. For 10 dB SNR Poisson noise, the reconstruction quality without denoiser is 16.41 dB for CIFAR10, 17.58 dB for SVHN and 20.71 dB for Fashion MNIST. These values are average reconstruction performance over 1000 test images of the corresponding datasets. Whereas with an  autoencoder (denoiser) trained on corresponding training datasets, we can reach 19 dB for CIFAR10, 21.81 dB for SVHN and 22.56 dB for Fashion MNIST.
\begin{figure}[!t]
\centering
\begin{subfigure}[t]{0.32\linewidth}
\includegraphics[width=1\textwidth]{fig/denoiser/fashion_denoiser.pdf}
\caption{Fashion MNIST}
\end{subfigure}
\hfill
\begin{subfigure}[t]{0.32\linewidth}
\includegraphics[width=1\textwidth]{fig/denoiser/cifar_denoiser.pdf}
\caption{CIFAR10}
\end{subfigure}
\hfill
\begin{subfigure}[t]{0.32\linewidth}
\includegraphics[width=1\textwidth]{fig/denoiser/svhn_denoiser.pdf}
\caption{SVHN}
\end{subfigure}
\caption{Denoiser reconstruction performance for high noise level. Illumination Patterns are learned for clean images. We have added 10 dB Poisson noise to the test images. (i) The ground truth, (ii) reconstruction without using denoiser during testing and (iii) reconstruction using denoiser while testing. The results are for measurements using 4 illumination patterns. }
\label{fig:denoiser_rec}
\end{figure}
\fi

\begin{table}[]
\caption{Reconstruction PSNR and run time (sec) of our and existing methods on different datasets.}
\label{table:compare_existing_methods}
\vskip 0.15in
\begin{center}
\begin{small}
\begin{tabular}{ccccccccccc}
\hline
\multirow{2}{*}{Algorithm} &
  \multicolumn{2}{c}{MNIST} &
  \multicolumn{2}{c}{F. MNIST} &
  \multicolumn{2}{c}{CIFAR10} &
  \multicolumn{2}{c}{SVHN} &
  \multicolumn{2}{c}{CelebA} \\ \cline{2-11}
            & PSNR  & Time & PSNR  & Time & PSNR  & Time & PSNR  & Time & PSNR  & Time  \\ \hline
Fienup      & 13.17 & 0.31 & 23.28 & 0.30 & 32.79 & 0.29 & 40.57 & 0.29 & 31.57 & 5.42  \\
GS          & 12.96 & 0.31 & 23.28 & 0.30 & 32.71 & 0.29 & 40.99 & 0.29 & 31.69 & 5.22  \\
WirtFlow    & 10.74 & 0.07 & 13.72 & 0.06 & 16.28 & 0.05 & 19.65 & 0.04 & 16.93 & 1.26  \\
AmpFlow     & 17.09 & 0.07 & 32.83 & 0.05 & 42.47 & 0.05 & 47.43 & 0.04 & 37.76 & 1.24  \\
Kaczmarz    & 11.50 & 0.05 & 13.46 & 0.04 & 15.31 & 0.03 & 18.39 & 0.03 & 16.63 & 0.94  \\
Deep Models & 31.73 & 8.41 & 22.33 & 8.45 & 25.63 & 8.40 & 27.81 & 8.25 & 22.31 & 10.55 \\
Ours &
  \textbf{101.50} &
  \textbf{0.02} &
  \textbf{92.44} &
  \textbf{0.02} &
  \textbf{82.02} &
  \textbf{0.02} &
  \textbf{84.43} &
  \textbf{0.02} &
  \textbf{78.98} &
  \textbf{0.04} \\ \hline
\end{tabular}
\end{small}
\end{center}
\end{table}

\subsection{Comparision with existing methods}
We compare our method with various existing methods on different datasets and show the reconstruction PSNR (dB) and run time (seconds) per image in Table.~\ref{table:compare_existing_methods}. The description of the datasets can be found in section \ref{section:dataset}. We compare with Fineup  \cite{fienup1978reconstruction}, Gerchberg-Saxton  \cite{gerchberg1972practical}, Wirtinger Flow  \cite{candes2015phase}, Amplitude Flow  \cite{chen2015solving}, Kaczmarz  \cite{wei2015solving} and deep generative models \cite{metzler2020deep}. For \cite{fienup1978reconstruction,gerchberg1972practical,candes2015phase,chen2015solving,wei2015solving}, we use the code collected in PhasePack \cite{chandra2017phasepack}. In PhasePack, we restricted all the illumination patterns in the range of $[0,1]$ and set the maximum number of iterations to be $50$ which is the same as our default setting. We note that all of the methods in PhasePack use optimal spectral initialization; in contrast, we only use zero initialization. For deep generative models, we use a modified version of the publicly available code for \cite{metzler2020deep}. The code only provided pretrained DCGAN models for MNIST and F. MNIST; therefore, we trained our DCGAN models on the other datasets. This method is noticeably time-consuming because it optimizes over the latent vector for the deep model and uses 2000 iterations for each image where each iteration requires a forward and backward pass through the deep model.
For all of the methods, we selected the best PSNR from 5 trials and report the average time. For our method, we report the run time with batch size equal to 1, which aligns with other methods.
We observe from the table that our method with learned patterns performs significantly better than all the other algorithms in terms of both reconstruction quality and run time.

redpajama_set_name: RedPajamaArXiv


Avoid feeding pets outdoors (if you must feed pets outside, feed during the daytime and remove the uneaten food as soon as the animal has finished).
Feeding wildlife and feral cats can attract coyotes. In addition to coyotes eating the food, mice and other animals will be drawn to leftovers, which can subsequently attract predators such as foxes and coyotes.
To discourage coyotes from associating people with safety and food, eliminate the food sources around your yard and engage in hazing if you see a coyote on your property. Hazing is a process used to reinforce a coyote's natural wariness without harming them. To haze, be big and loud: Wave your arms, shout, use noisemakers, throw non-edible objects in its direction (but not at it), or spray the animal with a hose. It shouldn't take much for the coyote to get the memo -- just be persistent and maintain eye contact. Do not haze if a coyote is sick, injured, with pups, or is in its territory or out at night.
Keep small pets inside when possible and monitor them while outside. While walking dogs, use a secure, 4-6 foot leash and do not let dogs explore vegetation that you can't see through. It is advised that pets be fed indoors. If pets must be fed outdoors, feed during the daytime and remove food as soon as your pet is finished.
For more information on hazing and pet safety, visit: http://www.austintexas.gov/department/coyotes-central-texas. For immediate assistance, or to report a coyote that is sick or injured, call 311.
Article courtesy of City of Austin's Animal Services.


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text:
Wayfair-ing Stranger
The pro-business Trump administration is inexplicably supporting states' efforts to tax distant firms.
Economy, finance, and budgets
Years before the rise of online retailing, states tried to force out-of-state merchants who sold goods via catalogs to levy sales taxes on their residents. The Supreme Court, citing the principle that a state can require tax collection only from firms with a physical presence in their jurisdiction, struck down those efforts. But now South Dakota, encouraged by an opinion from Justice Anthony Kennedy, is urging the Court to allow it to collect sales taxes from online retailers. If the Court agrees in South Dakota v. Wayfair, many e-commerce firms could face unprecedented new burdens requiring them to track and file taxes in up to 45 states and potentially exposing them to audits in any of them. For some small firms, such changes could be fatal.
Even more troubling, though, is the role of the Trump administration's Justice Department, which intervened with a brief in support of South Dakota, in which the Solicitor General argues that the Court should expand the definition of what constitutes tax "nexus"—that is, the authority to levy taxes—from physical presence in a state to virtual presence. The Justice Department brief is so vague and inexact on what "virtual" means that were the court to follow its advice, it might subject almost any enterprise that does business online—in other words, just about any business—not only to sales tax obligations but also, possibly, to corporate income taxes in places where these businesses don't operate. It's difficult to imagine a more unwise, uninformed approach to a complex issue.
The Supreme Court has interpreted the Constitution's Commerce Clause—which gives the federal government the right to regulate interstate trade—as limiting the ability of states to tax businesses from other jurisdictions. A business must have a physical presence—a local sales office, a distribution facility, a warehouse—in a state to establish tax nexus. States have long tried to get around the court's rulings, for instance by declaring that any firm trying to sell goods to in-state customers was automatically present in the state, even if the seller was located out of state. To clarify the issue, in 1959 Congress passed the Interstate Income Act, prohibiting states from applying their corporate taxes to firms if those businesses' only activity was to solicit orders in a state.
As catalog retailing expanded, local governments also tried forcing out-of-state firms to collect sales tax. The court swatted these efforts down several times, most recently in 1992, when North Dakota claimed that Quill, an Illinois cataloger, had established a physical presence in the state when it sent floppy disks with product listings to in-state customers. The court ruled against North Dakota but noted that as technology changed the terms on which firms were doing business, Congress should intervene with new laws clarifying what constituted in-state presence.
Ironically, the Quill decision created an opening for states because it applied only to taxes that out-of-state firms might be required to pay on transactions—it said nothing about taxes on earnings or revenues, such as corporate income and gross-receipts levies. As technology evolved, states expanded the definition of what they considered "physical presence" for the purpose of grabbing corporate taxes from firms. Several dozen states now say that if a company's website is hosted on a server in-state, even if the firm has no other presence there, then that constitutes enough nexus to levy income taxes on the firm. Similarly, because the Interstate Income Act applies only to tangible goods, states are trying to dun out-of-state businesses that sell intangible products, like software and cloud-computing services, to residents or businesses in their jurisdictions. In testimony before Congress, for instance, the owner of South Carolina firm ProHelp Systems described how New Jersey forced it to pay corporate income tax for licensing a software program to an in-state firm. The fee that the software firm earned was less than the minimum corporate income tax; when other states began doing the same thing, the small firm decided to go out of business.
Meanwhile, as e-commerce has exploded, states complain that they're losing sales tax to remote sellers. Though Amazon and other big online retailers now collect and pay sales tax because they have physical operations in many places, states saw an opportunity to grab revenue from smaller firms after Justice Kennedy wrote in a 2015 opinion that the Court should reevaluate the Quill decision. President Trump's selection of Neil Gorsuch to the Supreme Court also buoyed states because Gorsuch had expressed similar sentiments while serving on the Tenth Circuit Court of Appeals. Several states, including South Dakota, passed new laws requiring out-of-state e-commerce firms to collect taxes with the express purpose of generating a court challenge. The parties argued the case last week.
Settling this matter through the courts, however, is a recipe for trouble, because the justices can't possibly deal with all the nuances of the issue, which require legislation. The chair of the House Judiciary Committee, Robert Goodlatte, along with other members of Congress, has urged the court in an amicus brief to deny South Dakota's petition because it is Congress's job to sort this out. Acknowledging that Congress has struggled to find an answer, Goodlatte nonetheless observes that "the Court should recognize that a lasting solution will require compromise, and respect and accommodate the ongoing, diligent efforts of Congress to find a fair solution consistent with Constitutional norms." Of course, the White House would participate in any legislative negotiations, and the president would have to sign a new bill into law. Why, instead, the Justice Department seems willing to hand over to the court a responsibility that the Constitution gives to Congress and the executive branch is baffling.
Even worse, the administration's brief urges the court to set a new definition for physical presence that could rewrite the rules of tax nexus dramatically. The brief begins by alleging that e-commerce firms create ubiquitous "virtual" existences online and then urges the court to use virtual presence as a way of defining whom a state can tax. "Internet retailers can establish a pervasive and continuous presence—including by maintaining virtual storefronts that seek to replicate the experience of shopping in a physical store, available to every state resident 24 hours a day—even in States where they have no employees or physical property," the brief declares. But the Solicitor General never explains how Wayfair (an ecommerce retailer of home goods) might be specifically targeting South Dakota residents. It is merely the business's general web presence, accessible to any American with a computer and online connection, that makes it vulnerable to the tax man. While the Solicitor General's brief concerns itself only with the sales taxes at issue in this case, the notion of physical presence applies to a much broader range of taxes. The Trump administration's argument, then, not only opens up small firms to assessing and collecting sales tax in dozens of states but also serves as an invitation to states to extend their hands into the pockets of distant firms via business levies like the corporate income tax or gross receipts tax. The bureaucratic burdens alone are daunting to imagine.
It's not easy to understand what would prompt an administration that bills itself as business-friendly and anti-bureaucratic to take such a position on something as central as the federal government's role in interstate commerce. One can only hope that cooler heads—Trump economic adviser Larry Kudlow, say, or Treasury Secretary Steven Mnuchin, or Treasury Under-Secretary David Malpass—will talk some sense into the Justice Department, if it isn't already too late.
Steven Malanga is the senior editor of City Journal, the George M. Yeager Fellow at the Manhattan Institute, and the author of Shakedown: The Continuing Conspiracy Against the American Taxpayer.
Photo: AndreyPopov/iStock
Philip Howard's Endgame
Steven Malanga A longtime analyst of the public sector offers a provocative solution to America's government-union problem.
Black Ink Brings Red-State Tax Cuts
Steven Malanga Record budget surpluses produced a second year of reductions, mostly in GOP-leaning locales.
Why the Middle Class Flees States That Tax the Rich
Steven Malanga Such places favor bigger, more pervasive government.
Fiscal Fault Lines
Allison Schrager A risk-based case for taking inflation and the national debt more seriously
Unfreezing the Debt-Ceiling Debate
Fred Bauer Expanding the range of policies on the bargaining table could help facilitate a deal.
Strong Hiring Figures Send a False Signal
Milton Ezrati Despite bullish job numbers, we could still be headed for a recession.
Strange Solidarity
Catholic bishops' support for AFSCME in a Supreme Court case makes little legal or moral sense.
Steven Malanga April 10, 2018

redpajama_set_name: RedPajamaCommonCrawl


text:
Brett McGurk
Executive Branch, Secretary of State, White House December 24, 2018
State Dept. mocks McGurk's claim he left 'on principle;' says 'he should have been gone' earlier this year
The State Department is pushing back against former ISIS coalition diplomat Brett McGurk's claim that he decided to leave his position 'on principle' over POTUS Donald Trump's decision to pull all U.S. forces out of…
Bureaucracy, Current Events, Deep State, Executive Branch, Foreign Policy, Geopolitics, President Donald Trump, World December 22, 2018
POTUS Trump's Syria policy continues to claim casualties — in the DC Swamp
The announcement last week by POTUS Donald Trump that he would pull all 2,000 U.S. troops out of Syria continues to claim casualties, but not in that war-torn shell of a country. The casualties are…


With the DASA DevOps SkillsMap initiative, we aim to provide guidance to high performing DevOps teams that wish to improve in any of the 12 skill and competence areas of the DASA DevOps Competence Model.
The DASA DevOps SkillsMap offers focused guidance on which certifications helps individuals to build capabilities to address current gaps in the skill gaps as outlined in the Competence Model for DevOps teams and professionals.
The DASA DevOps SkillsMap provides a searchable, user-friendly structure that allows anyone to find training and certification programs that are required to work effectively in DevOps teams.
DASA's goal is to help the community at large adopt DevOps practices. That also means that we recognize there are many training and certification programs in the market that address specific competence requirements, which are great candidates to be included in the DASA DevOps SkillsMap.
If you are aware of a training and certification program that helps close the skills gap, the DASA DevOps SkillsMap structure provides you the option and guidance to create a mapping yourself, so it can be added to the official DASA DevOps SkillsMap.
Any training program that meets the following requirements can be mapped to the competence model and included to the DASA DevOps skillsmap. DASA is open, all inclusive, and does not exclude any mappings.
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In case the organization who provided the mapping can no longer support updating the mapping, DASA may consider removing the mapping.
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text:
Two Morgantown Boys Wheel to Wheeling and Adventure
Story by Frank M. Heaster
The Morgantown Post - Sunday, August 28 1966
TWO MORGANTOWN
youths who have newspaper routes recently bicycled to Wheeling to see their grandmother and had a few experiences they won't forget for some time.
Taking Route 19 from Morgantown, Wayne and Michael Breiding, 12 and 14 years of age respectively, then turned off on 70 at Little Washington, and then Rt 40 into Wheeling. The adventurous youths were riding (and pedaling and pushing) three geared bikes with hand brakes and are the sons of Mr. and Mrs. George H. Breiding of 616 Eighth St.
Recreation and physical exercise is nothing new to the boys their father is an advocate of such practices since he is state extension program leader in outdoor recreation, WVU Cooperative Extension Service.
Mom had packed on ample lunch for the trip to Wheeling and the boys stopped at a small roadside restaurant for a snack. During a rain, they sheltered in a grocery store in Waynesburg.
Reaching Wheeling, they were stopped by a policeman and asked if they were running away from home. They presented library cards for identification but after some questioning, the officer called the father who told the boys to hurry for Grandmother was not expecting them. Needless to say they weren't too impressed by the police treatment.
Mike had one accident when he ran over a pop bottle. He was thrown on his side and later had a flat tire which was repaired free by a friendly attendant at a gas station in Little Washington.
The trip to Wheeling began on a Tuesday morning and took 17 and a half hours. They admitted there were several miles in the Waynesburg area which required pushing the bikes to the summit. Wayne and Mike played it careful and got off the road when meeting or being passed by tractor-trailers.
The return trip home was on Friday and took only 12 and a half hours.
"Would we do it again? Sure, we're going to go again next summer!"
ERT - Home
Contact Mike Breiding


text:
MLB team values continue to rise despite shutdown
By Gabe Lacques USA Today
Major League Baseball is bracing for a multi-billion dollar hit to its industry this year.
Yet if there's one group that can withstand a financial wallop wrought by the novel coronavirus, it is the game's 30 ownership groups.
Forbes released its annual list of franchise values Thursday and projected that 28 of 30 clubs saw appreciation in 2019, with the New York Yankees once again leading the pack in total value ($5 billion) and appreciation (9%).
Only the Pittsburgh Pirates and Miami Marlins experienced drops, of 1% and 2% respectively, with the Marlins, at $980 million, the only franchise outside the billionaire club. The Kansas City Royals, the only club to change hands last year, with John Sherman buying them for $1 billion, are valued at $1.025 billion.
The overall values aren't surprising in an industry distributing an estimated $10.5 billion in revenues, and its proprietary television and online inventory maintaining high value despite the existential threat to the TV industry. Still, there's a few takeaways we can glean from the values, which are unconfirmed and often rebutted by MLB:
•Why has tanking been such a trend? Consider the Washington Nationals and Baltimore Orioles, who share a geographical region and a sports network. The Nationals spent big again and won the World Series. The Orioles saw attendance bottom out at Camden Yards as they intentionally fielded one of the worst teams in their history. Yet both teams' value increased by 9%.
•The Tampa Bay Rays won 96 games, made the American League Championship Series yet experienced virtually flat attendance (an increase of 23,000) as they floated the concept of partial relocation to Montreal. Still, they saw a 4% increase in franchise value, to $1.05 billion, as a new cable deal that doubles in value to $60 million annually kicks in.
•Is on-field success tied to franchise riches? Largely — six of the top 12 most valuable teams (Yankees, Dodgers, Cardinals, Nationals, Astros, Braves) comprised more than half the 10-team playoff field. Yet, the No. 18 Twins, No. 26 Athletics and No. 28 Rays also made the postseason, a possible testament to their front-office smarts, the value of revenue-sharing or, more likely, the cyclical nature of contention that mid-market teams experience.
MLB's class structure may also determine who emerges from a 2020 campaign wreaked — or wrecked — by the COVID-19 pandemic. Smaller-market teams are generally more reliant on ticket sales and gameday revenue, which will be slowest to return once the game does as concerns about large gatherings — or even the ability to gather in public — loom into 2021.


text:
Global university rankings: one Australian university makes the top 20
The good performance of ANU is no big surprise. It is Australia's most lavishly funded Uni. It basically can afford any faculty members it wants. Still, Australia is a small community on the world stage so to rank so highly out of thousands of universities worldwide is a considerable achievement
It may be worth noting what the QS rankings are based on. They are reputational rankings. They tell us how highly a university is thought of. That is important information but rankings of academic output are possibly more important. The Leiden ranking is notable for the latter. Several Australian universities get into the top 50 according to Leiden but the ANU is not among them. It is way down at 191. That does suggest the the high reputation of the ANU might not be fully deserved
By contrast, the fact that University of Sydney, University of Melbourne and University of Queensland score highly in both sets of rankings is persuasive. It indicates a deserved high reputation for them. As I am a graduate of two of those three I am pleased about that. I think it is clear that the teaching there is world-class
One Australian university is among the world's top 20
universities and five are in the top 50, according to a major global ranking that shows Australian universities have made overall improvements in all measures, including teaching, employability and research.
The University of Sydney was the only Australian university to drop down the list, slipping four places from 46, while six of Australia's top universities improved in their rank from last year.
Australian National University is the highest ranked in the country at 20th place in the 2018 QS World University Rankings.
It is followed by the University of Melbourne, ranked at 41, the University of New South Wales at 45, the University of Queensland at 47 and the University of Sydney at 50.
Belinda Robinson, chief executive of peak sector body Universities Australia, said the ranking is especially important to international students choosing a university.
"Global rankings are a major factor for many international students in deciding where to study, so they're also very important to the $22.4 billion a year that international students bring into Australia's economy," Ms Robinson said.
"These impressive rises underscore the global competitiveness of Australia's universities and the excellent quality of our education and research on the world stage."
Massachusetts Institute of Technology (MIT) is the top ranked university in the world for the sixth consecutive year, followed by Stanford University, Harvard University, the California Institute of Technology, the University of Cambridge, the University of Oxford, University College London, Imperial College London, the University of Chicago and the Swiss Federal Institute of Technology.
MIT has been described as "the nucleus of an unrivalled innovation ecosystem" by QS Quacquarelli Symonds, the education analysis firm behind the ranking, which notes that companies created by the university's alumni have a combined revenue of $2 trillion, the equivalent of the world's 11th largest economy.
Despite the ongoing dominance of US and UK universities in the top 10, QS Quacquarelli Symonds also notes that many other universities from those countries are now being outperformed by "the best of Australia, Russia, China, Singapore and India – among others".
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The Christmas Island annual crab migration is coming to Google Street View
By Mark Austin	 December 10, 2017
Every year, tens of millions of red crabs embark on an annual migration across Christmas Island in Australia, from their burrows in the forest to the coast, where they breed and release eggs into the sea. It's a spectacular sight, lasting two weeks or so, and the annual event draws tourists from around the world.
For the first time ever, you'll be able to experience this amazing natural event yourself on Google Street View in the coming year. As Google recently posted on its blog, Dr. Alasdair Grigg of Parks Australia will be recording images of this yearly trek using the Street View Trekker.
It's an arduous process, as swarms of the tiny critters fill the roads, pathways, and beaches of Christmas Island. The tourism board estimates there are 50 million crabs on the island, and they outnumber humans 300,000 to 1.
The crabs have the right of way, and care needs to be taken with every step. The government has even built bridges across major thoroughfares for the crabs, to ensure they arrive at their destination safely.
Grigg told CNET that he wants the Street View project to educate people around the world about this extraordinary annual event.
"We hope people can get a taste of the magnificent nature and the red crab migration through the eyes of the Google Trekker," Grigg said. "We also hope they are inspired to appreciate the world-class conservation values of the Island"
The crabs spend most of their lives in burrows, staying out of the sunlight and conserving moisture for their annual marathon scuttle. Once the tides, rains, and moon cycle are in alignment, they emerge and begin their march to the coast to spawn and lay eggs in the sand.
Over the past few years, the Google Street View Gallery has explored some of the most remote places on Earth, joining thousands of sheep in the Faroe Islands in the North Atlantic, exploring Macchu Picchu in the Andes, and visiting the rim of a volcanic lake in Australia. The tech giant has even partnered with NASA for a tour of the International Space Station.
Google says you'll be able to experience this spectacular phenomenon yourself with imagery from the Christmas Island collection in early 2018.
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A powerful true story of forgiveness, redemption, and amazing grace.
A woman comes back from self-imposed exile to seek revenge against those responsible and the system that let them go free.
A security expert must infiltrate a burning skyscraper.
Five years after the events of Mamma Mia!


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Michael Jackson's Children, Family set to honor the King of Pop at Grauman's Chinese Theatre in Hollywood
Hollywood, Calif. – Michael Jackson's three children Prince, Blanket and Paris Jackson, will be on hand to today to immortalize and honor the King of Pop's legacy. The ever more public trio will use their father's glove and dancing shoes to make the imprints in front of the Grauman's Chinese Theatre in Hollywood, near the Michael Jackson's star of the Hollywood Walk of Fame.
The ceremony coincides with Cirque du Soleil's "Michael Jackson The Immortal World Tour," which makes its Los Angeles debut Friday night at at the Staples Center (January 27-29) in Los Angeles.
"Blanket and Paris JacksonMichael Jackson's three children Princenear the Michael Jackson's star of the Hollywood Walk of Fame.will be on hand to today to immortalize and honor the Kind of Pop's legacy.will be on hand to today to immortalize and honor the Kind of Pop's legacy. The ever more public trio will use their father's glove and dancing shoes to make the imprints in front of the Grauman's Chi
Previous PostAulani, a Disney Resort & Spa in Hawaii, Announces 'Sixth Night Free' Offer to Enjoy All the Fun This Spring. Guests Receive a Sixth Night Free Plus Daily Continental Breakfast When Booking a Five-Night Consecutive Stay at New Disney Resort on OahNext PostMichael Jackson Immortalized at the Grauman's Chinese Theatre in Hollywood. Prince, Paris and Blanket carry the torch.


The curriculum is well structured and meets the basic requirements for quality work. Work programs are designed in accordance with developments in the European Higher Education Area (EHEA). The nature and specifics of these programs promises achievement in conformity with labor market needs. Programs for each subject have their basic structure together with goals, objectives, methodology, content, assessment and literature.
Studies are regular and part time (without parting from work).


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Home Europe Formula One Drivers Sebastian Vettel Childhood Story Plus Untold Biography Facts
Sebastian Vettel Childhood Story Plus Untold Biography Facts
CB presents the Full Story of a German racing driver with the nickname "Baby Schumi". Our Sebastian Vettel Childhood Story plus Untold Biography Facts brings you a full account of notable events from his childhood time to date.
The analysis involves his early life, family background, life story before fame, rise to fame story, relationship life, personal life, family facts, lifestyle and other little-known facts about him.
Yes, everyone knows he is one of the greatest drivers in the history of the sport. However, only a few consider Sebastian Vettel's biography which is quite interesting. Now, without further ado, let's begin.
Sebastian Vettel Early Life and Family Background:
Race driver Sebastian Vettel was born on the 3rd day of July 1987 at Heppenheim in West Germany. He is the third of four children born to his mother Heike Vettel and to his father Norbert Vettel.
The German national of white ethnicity with little known roots is a bonafide native of Heppenheim where he was raised alongside his older sisters; Melanie and Stefanie as well as his younger brother Fabian.
Growing up at Heppenheim, young Vettel understood the recreational need for speed at a very young age by accompanying his father to racing events. By the time the racing enthusiast was aged 3, he became committed to driving Karts at home and looked forward to the day he could legally take to the road.
Sebastian Vettel Education and Career Buildup:
As Vettel's passion for racing got intense by the years, he could extensively manage anything that dealt with speed except keeping pace with the intensive demands of academics.
Little wonder he performed terribly in academics but passed his leaving exams at Starkenburg-Gymnasium secondary school with a respectable grade that might have screamed – don't go to college.
Sebastian Vettel Early Career Life:
Vettel's history in competitive racing dates as far back to 1995 when the then 8-year-old began racing in Kart series. The talented manner with which he drove eventually made him arrive Red Bull's Junior team as an 11-year-old.
While with the Junior team, Vettel laid hands-on numerous titles including the 2001 Junior Monaco Kart cup which he recalls holding and smiling like an overgrown 14-year-old kid in a candy shop.
Sebastian Vettel Road To Fame Story:
2003 was the year which Vettel's career officially started after he was promoted to open-wheel cars. The then-teenage driver went on to secure significant wins and earned promotions in the ensuing 4 years amidst stalling challenges.
The first unfortunate challenge saw Vettel being fined for exceeding the recommended speed lane limit on re-entering the racing track while subsequent stagnating incidents on his road to fame took the form of serious crashes and poor finishes.
Sebastian Vettel Rise To Fame Story:
Not one to snap under the pressure, Vettel got better with each passing challenge and eventually recorded his first breakthrough at a 2008 Italian Grand Prix where he became the youngest driver to win a Grand Prix at the time.
He went on to win the 2010 Brazilian Grand Prix as well as the Abu Dhabi Grand Prix to become the youngest world champion in Formula One at the time. The rest, as they say, is history.
Sebastian Vettel Relationship Life Facts:
There are a few names which strike chords in Vettel when mentioned, especially that of his childhood friend turned wife – Hanna Prater. The lovebirds who were an inseparable pair through Velvet's career buildup to breakthrough.
Their union is blessed with two beautiful daughters at the time of writing. They include Emily Sprater (born January 2004) and Matilda Vettel born (born 2015). Both kids were born years before Vettel officially got married to Hanna in June 2019.
Sebastian Vettel Family Life:
Sebastian Vettel cherishes family and has an amazing family life. Please find below details about members of his family.
About Sebastian Vettel's father:
Norbert Vettel is Sebastian's dad. He worked as a carpenter and had a keen interest in racing during Sebastian's early life. Norbert, who introduced Sebastian to racing before he was aged 3 never misses out on providing paternal support to the racer and of course, takes out time to celebrate with him after every wins.
About Sebastian Vettel's mother:
Heike Vettel is Sebastian mom. She helped raised the racer and ensured that his early karting endeavours did not put him in harm's way. Fast forward to date, Heike continues to double as Vettel most passionate fan in words and deeds.
About Sebastian Vettel's siblings:
Vettel has two older sisters and a young brother. His sisters include Melanie who works as a dental technician at the time of writing as well as Stefanie who is a physiotherapist for disabled children. On his part, Sebastian's younger brother Fabian has a less illustrious career in racing.
About Sebastian Vettel's relatives:
Proceeding to Sebastian Vettel's extended family life there are no records of his paternal and maternal grandparents while his uncles, aunts, cousins, nephews and nieces are largely unknown at the time of writing.
Sebastian Vettel Personal Life:
Personality traits that define Sebastian Vettel are of Cancer Zodiac sign. They include his proclivity for competition and creativity. In addition, he possesses an impatient persona and hardly reveals details about his personal and private life.
Regarding Vettel's interests and hobbies, he has quite a number of pastime activities that include listening to music, keeping up with football and basketball games, watching movies as well as spending time with his adorable family and friends.
Sebastian Vettel Lifestyle:
"Baby Schumi's" estimated net worth of over $120 Million and annual salary of $40 million – at the time of writing – speaks well of the lucrative potentials of racing and explains why top drivers of the sport like Vettel spend big to live a luxurious lifestyle
Strong pointers to such lifestyle include lavish cars like a Ferrari California T and Mercedes SL65 AMG which are parked in Vettel's little known house located at the canton of Thurgovia in Switzerland.
Sebastian Vettel Untold Facts:
To bring our Sebastián Vettel childhood story and biography to an end, here are untold or lesser-known facts about the awesome driver.
Love for singing:
Do you know that Sebastian Vettel could have become a musician. He grew up loving music and had pop icons like Michael Jackson as idols. Fortunate for the sport of Formula 1, Vettel made the decision to build a career in racing because he lacked the voice to become a musician.
Racing Polyglot:
Vettel speaks three languages at the time of writing. They include his native German as well as English which is the most common language of communication in Formula One. In addition, Vettel has a significant level of proficiency in Italian.
Smoking and drinking:
No thanks to Vettel's preferences for living a private life, it cannot be established whether he smokes as he has not be captured holding cigarettes or cannabis. However, he is given to drinking alcohol albeit in moderation.
The reason behind his nickname:
"Baby Schumi" has long been associated with Vettel because his style of driving is not any different from that of German legendary driver Michael Schumacher. However, Vettel has often played down the comparison stressing that he prefers to be called "New Vettel".
Tattoos:
If body arts doesn't look weird on Formula One drivers then Vettel would probably be the weirdest driver in the sport as he is yet to seriously entertain the thought of getting ink on his skin.
Vettel is not religious and he is yet to give clues about his beliefs at the writing. In addition, he has not made statements or moves that give him away as a non-believer. Thus his position of matters of faith lies somewhere between privacy and focus on racing.
FACT CHECK:
Thanks for reading our Sebastian Vettel Childhood Story plus untold biography facts. At childhood biography, we strive for accuracy and fairness. If you see something that doesn't look right in this article, please place your comment or contact us!
Childhood Biography Diary- for FORMULA ONE DRIVERS
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Making the finest nylon string guitars from Spain since 1965
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Cesar Rosas
::More info::
César Rosas is a singer, songwriter and guitarist for Los Lobos. Rosas also participates in the Latin super group Los Super Seven. Perhaps the most recognizable member of Los Lobos, Rosas is known for his trademark black sunglasses and slicked-back, black hair. His singing voice is reminiscent of Chicago electric blues legend John Lee Hooker.
CEM DURU�Z
Cem Duruöz has performed at the Carnegie Hall three times. Most recently for the D'Addario Foundation and Alhambra Guitars. He also gave a recital titled "Guitar in Turkey" for the Moon and Stars Project.
www.duruoz.com
EDUARDO MINOZZI
A Brazilian musician of outstanding facility and musical insight, Eduardo plays with an enthusiasm and energy that captures his audiences' imagination and draws them into a world of spontaneous and creative wonder.
He completed his Bachelor's Degree in guitar performance at Brazil leading University - The University of São Paulo, under Dr. Edelton Gloeden and also studied with Paulo Porto Alegre and Everton Gloeden at EMM. Subsequently, Eduardo was awarded a cultural visit to the USA under the sponsorship of the São Paulo United States Consulate General. This trip resulted in a successful audition at the University of Arizona and a full scholarship to pursue both Master's and Doctoral Degrees in Guitar Performance. He has been a Teaching Assistant (GTA) to Prof. Thomas Patterson in the School of Music at the U of A since 2006.
www.guitarimpact.com
PATXI ANDI�N
Spanish famous songwriter with a different voice sometimes provocative and others times intimate and seductive.
www.facebook.com/patxi.andion.pagina.oficial
FAREED HAQUE
Fareed Haque is a modern guitar virtuoso. Steeped in classical and jazz traditions, his unique command of the guitar and different musical styles inspire his musical ventures with tradition and fearless innovation.
http://fareed.com
RENNY JACKSON
Originally from Birmingham (U.K.), Renny Jackson is an up-and-coming singer-songwriter in the European acoustic music scene. He is known for his heartfelt songs, skilful playing and lyrics that combine traditional folk elements with more contemporary rhythms and harmonic progressions as a result of his flamenco studies. Currently based in Seville (Spain) where he performs regularly with backing band 'The Unmade Road'
http://www.facebook.com/rennyjacksonmusic
RICARDO COBO
Widely regarded as one of the leading virtuosos of the new Classic Guitar generation.Ricardo Cobo gave his professional debut with the Orquesta Filarmónica de Bogotá at age seventeen on a nationwide telecast for an audience of over nine million people. Celebrated for his passionate and mesmerizing performances worldwide, he is in high demand as soloist, chamber musician, lecturer and recording artist.
www.ricardocobo.com
SERENADE DUO
Founded in 1988, the multi-award winning ensemble Serenade Duo featuring Michelle LaPorte, flutist and Gerry Saulter, guitarist bring concert music of the world to life with their warm presence and engaging interpretations of unique repertoire.
www.serenadeduo.com
Patrick Abbate
Patrick was born in Oristano, Sardinia, Italy. He started playing when he was 11. He plays Electric, Acoustic and Classic guitars. Studied experimental composition and electronic music at the Conservatory of Cagliari , electric guitar at Percento Musica in Rome and participate in several master classes with amazing teachers as Frank Gambale. Patrick has been Steve Vai′s translator and assistant during his master class in Italy.
His main influences are Steve Vai, Slash, The Edge, SRV, Hendrix, David Gilmour, Dimebag, Jason Becker, Gary Moore, Satriani and Malmsteen.
In 2007 Patrick received the Award as "Randall and Washburn European Artist of the Year" at the NAMM Show in Los Angeles from the USMusic Corporation.
He played with Willie Basse′s "Black Sheep", "Kee Marcello and Patrick Abbate Band", Megan McCauley, Irene Nelson, Marco Baz Bazzoni (Gianni Cyano), Mino Mereu, Giangi Cappai (Alma Matris),Roshon Fegan and many more.
Now he lives in Los Angeles and he is about to publish his new instrumental solo record produced by David Z Rivkin (Prince) and mastered by Giangi Cappai.
www.patrickabbate.com
www.youtube.com/bandaner
www.facebook.com/patrickabbate
ALHAMBRA USA
Alhambra U.S.A. Distributor
Hoshino U.S.A. Inc.
1726 Winchester Road
alhambra@hoshinousa.com
www.alhambrausa.com
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Manufacturas ALHAMBRA
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Apdo. de Correos, 63
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If you're farming points for more honors, drop bodies into ventilation shafts and the like, and you'll get more points for hiding them, in addition to the points you get from assassinations.
Upgrade points are usually best spent on new assassination techniques, as they open up new possibilities for how to get through a level, as well as making the game more fun.
Don't be afraid of terrorizing a guard if you can't get past him. The game is pretty good about educating you on how to go about this.
Don't underrate smoke bombs, they're incredibly useful on any level with laser traps.
Remember that you can do almost any triggered action (grappling to ledges, throwing knives, throwing smoke bombs) while in your slow-mo aiming mode. Abuse this to no end. It makes some impossible situations beatable.


If your rights were violated, under the Fair Debt Collection Practices Act ("FDCPA") you may be entitled to up to $1000, plus costs and attorneys fees.
The "Debt Collector" is the company that contacted you to collect the alleged debt.
Enter all phone numbers that you have for the Debt Collector above.
Call you after 9:00 P.M.
Tell us what happened in your own words. Include dates if you can.
Please keep it brief (under 5 sentences), as you will not be able to SUBMIT this form if you write too much. We will contact you to learn more.
ex. "On 11/4/2015 Acme Debt Collector Inc. called me after 11:00P.M. and threatened to get me arrested."
Write "N/A" below and skip to SUBMIT if you do not know the Original Creditor.
ex. Credit Card, Gym Membership, Hospital Bill, Personal Loan etc.
This form was created inside of Charles Pascal Cohen.


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Information for Campground Owners about COVID-19. Learn More
Join CCRVC
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CCRVC Pre-Budget Consultation Submission in Advance of the 2019 Budget
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Economic Growth – Ensuring Canada's Competitiveness
Canadian Camping and RV Council
2019 Pre-Budget Submission
That the Federal Government amend the Income Act of Canada or by any other appropriate measure to clearly define, identify and recognize that income earned by small Private Campgrounds is to be considered as "active business income" for the purpose of determining their eligibility for the small business tax deduction.
About the CCRVC
The Canadian Camping and RV Council (CCRVC) is a national association that represents Provincial Private Campground Owners' Associations and Private Campgrounds across Canada including British Columbia Lodging and Campgrounds Association, Parks and Campgrounds Owners' Association of Alberta, Manitoba Association of Campgrounds and Parks, Campgrounds in Ontario, Campgrounds in Quebec, New Brunswick Campground Owners Association and the Association of Atlantic Parks and Campgrounds.
CCRVC's mission is to support the Canadian Camping and RV Industry, foster projects to enhance the camping experience for both Canadians and international visitors, work with federal decision makers to ensure a thriving Canadian tourism sector and to serve as Private Campground Owner's Advocacy Voice on a National level.
As part of a $14.5-billion-dollar RV and Camping Industry, Private Campgrounds in Canada make a considerable contribution to our economy. In 2014, 5,768,650 or 22% of the total Canadian adult population in an economic survey were estimated to enjoy camping as a lifestyle. In the same survey, annual travel related expenditures totalled $2 billion, with another $850 million in non-travel related camping expenditures. The RV and Camping Industry creates over 60,420 full-time jobs for Canadians and generates $2.9 billion in total salary and wages.
Current Landscape and 2019 Budget Recommendation
Despite these robust financial figures which indicate that owning a Private Campground is a lucrative business and successful tourist destination in Canada, the classification of a Private Campground as being a Passive "Specified Investment Business" as opposed to an "Active Business" remains to be a considerable threat to our future.
Under the current Income Tax Act, the Small Business Tax Deduction is not available to a corporation that carries on as a Specified Investment Business if the business has a principal purpose of the business to earn income from real or immoveable property and employs LESS than 5 full time employees year-round.
We reference the CRA Bulletin released August 2016 and its arbitrary nature which describes how campgrounds may or may not be able to qualify for the small business tax deduction: http://www.cra-arc.gc.ca/nwsrm/stmnt/2016/st160823-eng.html
Although the current tax classification has remained unchanged in decades (1970's), a significant number of campgrounds across Canada have recently been denied the Small Business Tax Deduction and re-assessed as an Investment Business in the past 24 months. All of the impacted campgrounds have been in business for several years and had previously qualified for the Small Business Tax Deduction by the Canada Revenue Agency indicating that CRA has unquestionably changed their interpretation of the rule.
It is unrealistic to expect Private Campgrounds who operate on average for only 152 days in a calendar year, to maintain seasonal workers throughout the off season when they are not in operation so that they meet the minimal threshold of 5 full time employees year-round and qualify for the SBD under the tax rule as written.
However, there is an exception whereby a Campground could qualify for the SBD if they can prove to the CRA that they provide a number of "services and activities" as summarized in the CRA communication.
Private Campgrounds generally do provide several of the services/activities which have been listed as deciding factors in determining if a "specified investment business" is deemed "active" by the CRA: laundromat, snack bar and/or restaurant, washroom, showers, swimming pool, beach area, playground, recreation centre, Wi-Fi, propane, organized activities, and many more but we have several examples where this factor is now being either disregarded or overlooked by the Canada Revenue Agency.
Furthermore, families that own small private campgrounds are extremely active in their business performing accounting, marketing, customer service and other daily activities throughout the year unlike any investment business. Additionally, the vast majority of private campgrounds have invested significant infrastructure costs including water treatment systems, sewage and septic systems, electrical infrastructure, amongst others, that have improved their land use to that of a campground which should be considered to be a dividing factor from the perception of some as just collecting "rent" from a "farmer's field".
We estimate that 75% of our 2347 private campgrounds employ less than five full-time employees and thus are required the tedious task to prove annually that they are indeed an Active Business to CRA. The current rules and unclear tax obligation has impacted the financial forecasting of hundreds of small family run private campgrounds across Canada who are now withholding hiring additional part-time employees, infrastructure improvements and capital expenditures until the current threat is removed.
If a small family run Private Campground has the SBD denied by CRA when filing their Corporate Tax Return, it results in more than a 300% Tax Increase (15% vs 50%) to the Private Campground which is unsustainable.
Campgrounds are recreational facilities offering the travelling and vacationing public access to campsites on a defined overnight and seasonal basis. However, under the current tax rule, private campgrounds are unfairly included in the same tax category as say an Apartment Building which is inappropriate and makes little sense. An Apartment Building or a Mobile Home Park offers year-round permanent living whereas a Private Campground offers seasonal and temporary recreational stays to travelling consumers.
Additionally, Private Campgrounds do not charge "rent" as described in the definition of a "specified investment business" in the Income Tax Act, but rather overnight campsite and amenity/service fees to temporary and seasonal camping enthusiasts.
CRA's unclear tax distinction is adversely affecting the Camping and RV industry's growth prospects and the threat could lead to campground closings and of equal concern, restrict needed improvements for the industry to remain competitive and attract foreign visitors which may be no longer affordable.
This will harm the entire industry ultimately leading to: reduced number of campgrounds available for domestic and international tourism, loss of jobs throughout the industry, substantial loss of tax income to all levels of Government, and diminished economic benefits in small communities across the country. Currently the RV and Camping Industry needs more campgrounds and we cannot possibly afford to lose camp sites that are necessary to facilitate the growth in the RV and Camping Lifestyle.
It is becoming increasingly apparent that a tax classification change that would have family owned private campgrounds qualify for the SBD that provides a clear understanding and policy for the Canada Revenue Agency to follow is necessary to finally remove any and all ambiguity that currently exists.
We hereby ask that the House of Commons Standing Committee of Finance include the following recommendation in your Report to the Minister of Finance ensuring our Private Campgrounds remain financially viable and competitive with similar camping destinations located in the United States.
Link to House of Commons Standing Committee on Finance – http://www.ourcommons.ca/Committees/en/FINA/StudyActivity?studyActivityId=10153612
British Columbia's Northern Coastal Glaciers May Be Saved by Climate Action
British Columbia, which is abode to 1,033 provincial parks, recreation areas, conservancies, ecological reserves, and protected areas covering more than 14 million hectares, is home
Modern Campground January 21, 2023
CCRVC Releases Criteria for 2023 COHCE Award Nominations
As outdoor hospitality providers, parks always strive to deliver the best possible service to campers. Next month, outstanding members of the Canadian outdoor accommodations sector
Akari Nakano January 20, 2023
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Fully remodeled condo w/spectacular views. Marble & wood floors. Custom made walk-in closets, ample storage, pantry& laundry rooms. Access to screened balcony from master, living or family room. Kitchen opens to family w/ great water view. Great floor plan. Master is totally apart from other two bedrooms for privacy. Enjoy a truly resort style living. Gated, tennis, basketball, soccer,gym & a lot more. A+schools.
For more information or make an appointment to see property ID A10611743.