text:
\section{Introduction}

\label{sec:intro}

In tidal-torque theory, the angular momentum of galaxies (galaxy \textit{spin}) is generated by the tidal torque due to the misalignment between the protohalo inertia tensor and the local gravitational tidal shear \citep{1969ApJ...155..393P,1970Ap......6..320D,1984ApJ...286...38W}.
Cosmological simulations have confirmed that the direction of dark matter halo spin is well described by the tidal-torque theory \citep{2002MNRAS.332..325P},
and that disk galaxies generally follow dark matter and gain similar spin directions as their host halos \citep{2015ApJ...812...29T,2019MNRAS.488.4801J}.
This makes galaxy spin a promising cosmological probe of various parameters including the initial condition of the Universe, primordial chirality and the neutrino mass \citep[see e.g.][for recent investigations]{2000ApJ...532L...5L,2001ApJ...555..106L,2019PhRvD..99l3532Y,2020PhRvL.124j1302Y,2021NatAs...5..283M,2022PhRvD.105h3512M,2022PhRvD.105h3504M}.

Recently, \citet{2021NatAs...5..283M} find a correlation between the observed galaxy spins and the initial density field of the Universe.
The same galaxy catalog has also been used to search for primordial chirality violations \citep{2022PhRvD.105h3512M}.
However, the signal to noise ratio is limited by the number of galaxies ($\sim15000$) with their spin directions observed.
To be able to fully exploit the galaxy spin as a cosmological probe and make good on any of the exciting prospects, it is necessary to develop new methods to measure the direction of galaxy spin accurately and efficiently.

Assuming that spiral galaxies are well approximated by circular disks, their three-dimensional spin directions can be determined through the position angles and axis ratios which are readily available from photometric observations, up to a fourfold degeneracy \citep[see Figure 2 of][for a visual illustration]{2021NatAs...5..283M}.
\citet{iye2019spin} visually inspect 842 spiral galaxies and confirm that (1) all the spirals are \textit{trailing}, i.e. Z-wise spirals rotate clockwisely, and (2) the dark, dust-lane-dominant side of the minor axis is closer to us.
Therefore, one can break the fourfold degeneracy if one can determine (1) whether the galaxy is a Z-wise or S-wise spiral, and (2) which side of the minor axis is darker and redder.\footnote{Alternatively, one can use the information regarding which side of the major axis is approaching us \citep[][]{1995ApJ...445...46H,2000ApJ...543L.107P,2021NatAs...5..283M}. This may be less ambiguous than visually deciding the dark side of the minor axis, but requires spectroscopic data and thus cannot be directly obtained from photometric surveys.}
In this paper, we focus on the classification of Z-wise vs S-wise spirals, and leave dark side vs bright side classification for future research.

Galaxy Zoo 1 (GZ1) is a citizen science project which classifies about $9\times10^5$ galaxies by members of the public \citep[][]{2008MNRAS.389.1179L,2011MNRAS.410..166L}.
\footnote{\url{http://zoo1.galaxyzoo.org/}}
It provides the information of clockwise or anticlockwise (Z-wise or S-wise spiral pattern) for galaxies from the Sloan Digital Sky Survey \citep[SDSS,][]{abazajian2009seventh,ahumada202016th} data, which leads to a $\sim3\sigma$ detection of the correlation between the galaxy spin field and cosmological initial conditions \citep[][]{2021NatAs...5..283M} and preliminary results for primordial chirality violations \citep[][]{2022PhRvD.105h3512M}.
However, the ongoing and forthcoming cosmological surveys, such as the Dark Energy Spectroscopic Instrument \citep[DESI,][]{aghamousa2016desi1,aghamousa2016desi2}, will map tens of more galaxies than those classified in GZ1, which are prohibitive to be again classified by human.
Machine learning (ML) based classification methods are required to efficiently identify the morphological properties of galaxies.

Deep Convolutional Neural Networks (CNN) have led to a series of breakthroughs in computer vision during the past ten years \citep{lecun1989backpropagation,krizhevsky2012imagenet,he2016deep,tan2019efficient}.
They are now regarded as the state-of-the-art image classification method and are widely used in general astrophysics applications  \citep{2010MNRAS.406..342B,2011A&A...525A.157H,2015MNRAS.450.1441D,he2019learning,2020arXiv201213083A,lin2020feature}.
In this paper, we present Chirality Equivariant Residual Network (CE-ResNet), a machine learning based classifier for the Z-wise vs S-wise spirals.
The remainder of this paper is organized as follows: we introduce the datasets in Section \ref{sec:dataset} and the architecture of our model in Section \ref{sec:arch}.
We present the training and classification results of our model in Section \ref{sec:train} with SDSS images, and in Section \ref{sec:desi-image} with DESI images.
The known asymmetry between Z-Spirals and S-Spirals in GZ1 is discussed in Section \ref{sec:violation}.
We conclude this paper in Section \ref{sec:discuss}.
The source code of our CE-ResNet model \footnote{\url{https://github.com/h3jia/galaxy_spin_classifier/}} and the classification catalogs \footnote{\url{https://zenodo.org/record/7170929/}} are publicly available.

\section{Galaxy Spin Datasets}

\label{sec:dataset}

\begin{deluxetable*}{cccccccc}

\label{table:pzps}
\tablecaption{The numbers of Z-wise and S-wise spirals in the different classification catalogs.
A galaxy is classified as a Z(S)-wise spiral if its $p_{\rm z}$($p_{\rm s}$) is larger than the cutoff value $p_{\rm cut}$.
See the corresponding sections for the details of each combination.
We also list the significance values for chirality violation based on the numbers of Z-wise and S-wise spirals; see Section \ref{sec:violation} for more details.}
\def1.1{1.1}

\tablehead{Catalog & Image Source & Classifier & $p_{\rm cut}$ & Z-wise & S-wise & $\sigma$-value & See}

\startdata
\multirow{5}{*}{Reduced GZ1} & \multirow{5}{*}{SDSS} & \multirow{5}{*}{GZ1 Humans} & 0.5 & 35068 & 37022 & 7.278 & \multirow{5}{*}{Section \ref{sec:dataset}} \\
& & & 0.6 & 27753 & 29535 & 7.445 & \\
& & & 0.7 & 22064 & 23488 & 6.672 & \\
& & & 0.8 & 16674 & 17921 & 6.704 & \\
& & & 0.9 & 10269 & 11283 & 6.907 & \\
\hline
\multirow{5}{*}{Reduced GZ1} & \multirow{5}{*}{SDSS} & \multirow{5}{*}{CE-ResNet} & 0.5 & 35327 & 35667 & 1.276 & \multirow{5}{*}{Section \ref{sec:train}} \\
& & & 0.6 & 27437 & 27737 & 1.277 & \\
& & & 0.7 & 21036 & 21283 & 1.201 & \\
& & & 0.8 & 15433 & 15542 & 0.619 & \\
& & & 0.9 & 9218 & 9442 & 1.640 & \\
\hline
\multirow{5}{*}{Reduced GZ1} & \multirow{5}{*}{DESI} & \multirow{5}{*}{CE-ResNet} & 0.5 & 44362 & 45432 & 3.571 & \multirow{5}{*}{Section \ref{sec:desi-image}} \\
& & & 0.6 & 36892 & 37619 & 2.663 & \\
& & & 0.7 & 29826 & 30217 & 1.596 & \\
& & & 0.8 & 22525 & 22804 & 1.310 & \\
& & & 0.9 & 13583 & 13775 & 1.161 & \\
\hline
\multirow{5}{*}{Preliminary DESI} & \multirow{5}{*}{DESI} & \multirow{5}{*}{CE-ResNet} & 0.5 & 55243 & 55526 & 0.850 & \multirow{5}{*}{Section \ref{sec:pre-desi}} \\
& & & 0.6 & 41977 & 42269 & 1.006 & \\
& & & 0.7 & 31508 & 31693 & 0.736 & \\
& & & 0.8 & 22012 & 22143 & 0.623 & \\
& & & 0.9 & 11649 & 11919 & 1.759 & \\
\enddata

\end{deluxetable*}

In the GZ1 project, public volunteers are asked to classify the SDSS galaxy images into six categories: ellipticals, Z-wise spirals, S-wise spirals, edge-on spirals, star / don't know, and mergers.
The catalog includes the vote counts of the six morphological types for 667,944 galaxies with SDSS spectra data available, and for 225,268 galaxies with no spectra data available.
The empirical probability that a galaxy belongs to each category can be estimated by the fraction of votes, while only the probabilities of Z-wise and S-wise spirals, $p_{\rm z}$ and $p_{\rm s}$, are relevant for this paper.
\footnote{Note that $p_{\rm z/s}$ throughout this paper is the probability that a random volunteer in the GZ1 project will decide that the galaxy is a Z(S)-Spiral, which is not exactly the same as the probability that the galaxy is actually a Z(S)-Spiral.
However, these two types of probabilities should be positively correlated, and the galaxies with $p_{\rm z/s}\to1$ are indeed very likely to be real Z(S)-Spirals (see the sample images in Appendix \ref{sec:sample}).
Therefore, the empirical probability estimated by the vote fractions is still a useful quantity for the classification of galaxy morphology.}
We refer to the empirical probabilities from the GZ1 catalog as the ``true" probability, in contrast to the ``predicted" probability given by the classifiers.
To keep the number of different kinds of galaxies roughly balanced, we downsample the galaxies with $p_{\rm m}\equiv\max\,(p_{\rm z},p_{\rm s}) \in [0,0.1]$ by a factor of 20 (i.e. only keep 1 of 20 such galaxies), the galaxies with $p_{\rm m} \in (0.1,0.2]$ by a factor of 5, and the galaxies with $p_{\rm m} \in (0.2,0.3]$ by a factor of 2.
We then query the SDSS DR16 SQL database with the RA and Dec of each galaxy in the GZ1 catalog, and apply the following cuts to remove the galaxies that are unlikely to be clear enough to identify their morphology:
\begin{enumerate}
    \item There should be exactly one PhotoObj within $1"$ of the location in the GZ1 catalog.
    \item The error of $r$ band magnitude should be in $(0,1)$.
    \item The $r$ band half light radius $r_{50}$ should be larger than $1"$.
    \item The $r$ band relative error of radius $\sigma_{r_{50}}/r_{50}$ should be in $(0",0.25")$.
\end{enumerate}
We find 173,097 galaxies that meet all the criteria (dubbed ``Reduced GZ1" catalog henceforth), and use $70\%$ of them for training, $15\%$ for validation, and $15\%$ for testing.
We obtain the jpeg images of these galaxies from both SDSS and DESI surveys using the Legacy Surveys Sky Viewer tool \citep{dey2019overview}.
\footnote{\url{https://www.legacysurvey.org/}}
The SDSS DR16 images are generated from the \textit{gri} bands, while the DESI DR9 images are generated from the \textit{grz} bands.
The numbers of $p_{\rm z}$ and $p_{\rm s}$ galaxies with different choices of cutoff values $p_{\rm cut}$ for the vote fraction are listed in Table \ref{table:pzps}.
Sample images for galaxies with different GZ1 morphology classification probabilities can be found in Figure \ref{fig:sam-gz-sdss}.

To assess whether our model can be robustly applied to surveys other than SDSS (with which the model is trained), we also collect all the galaxies in the DESI Legacy Survey Sweep Catalogs that are larger than $1"$ in half light radius and have spectroscopic redshifts available, leading to the ``Preliminary DESI" catalog of 1,953,246 galaxies.
For our model, the image field-of-view (FOV) is chosen as a multiple of the galaxy size, which however may deviate significantly between different survey measurements (see Figure \ref{fig:desi-spec}).
This requires that our classification model should be insensitive to the image FOV, which will be justified later in this paper (see Figure \ref{fig:stable} and the discussions therein).

\section{Network Architecture}

\label{sec:arch}

\begin{deluxetable}{ccc}

\label{table:resnet50}
\tablecaption{The structure of our Chirality Equivariant Residual Network (CE-ResNet).
It is based on the original ResNet-50 network in \citet{he2016deep}, but has four additional fully connected layers.
The network predicts the scores of Z-Spirals and Non-Spirals from the original images, and the scores of S-Spirals and Non-Spirals from the flipped images, which guarantees that it is equivariant under a parity inversion.
See Figure \ref{fig:arch} for a demonstration of the model workflow.}
\def1.1{1.1}

\tablehead{
\colhead{layer name} & \colhead{output size} & \colhead{54-layer CE-ResNet}
}

\startdata
conv1 & $80\times80\times64$ & $7\times7$, 64, stride 2 \\[0.05cm]
\hline \\[-0.35cm]
conv2\_x & $40\times40\times256$ & \makecell{$3\times3$ max pool, stride 2 \vspace{0.1cm} \\ $\left[
\begin{tabular}{c}
    1$\times$1, 64 \\[0.1cm]
    3$\times$3, 64 \\[0.1cm]
    1$\times$1, 256 \\
\end{tabular}
\right] \times 3$} \\[0.9cm]
\hline \\[-0.3cm]
conv3\_x & $20\times20\times512$ & $\left[
\begin{tabular}{c}
    1$\times$1, 128 \\
    3$\times$3, 128 \\
    1$\times$1, 512 \\
\end{tabular}
\right] \times 4$ \\[0.7cm]
\hline \\[-0.3cm]
conv4\_x & $10\times10\times1024$ & $\left[
\begin{tabular}{c}
    1$\times$1, 256 \\
    3$\times$3, 256 \\
    1$\times$1, 1024 \\
\end{tabular}
\right] \times 6$ \\[0.7cm]
\hline \\[-0.3cm]
conv5\_x & $5\times5\times2048$ & $\left[
\begin{tabular}{c}
    1$\times$1, 512 \\
    3$\times$3, 512 \\
    1$\times$1, 2048 \\
\end{tabular}
\right] \times 3$ \\[0.7cm]
\hline \\[-0.35cm]
fc & $2$ & \makecell{average pool, output $1\times1\times2048$ \vspace{0.1cm} \\ 512-512-64-64-2 fully connected} \\[0.4cm]
\enddata

\end{deluxetable}

\begin{figure}
    \epsscale{1.04}
    \plotone{img/CE-ResNet-1.pdf}
    \caption{The workflow of the CE-ResNet model.
    Each galaxy image is fed into the network twice: given the original image the network outputs the scores for Z-Spirals and Non-Spirals, while given the flipped image the network outputs the scores for S-Spirals and Non-Spirals.
    We then average the two output scores for Non-Spirals and apply a softmax function to get the probabilities for the three categories.}
    \label{fig:arch}
\end{figure}

Empirically, deeper neural networks are more expressive than their shallower analogs, at the cost of being more difficult to train \citep{mhaskar2017when,mehta2019high}:
as the depth of the network increases, its accuracy may get saturated and then eventually decreases rapidly, which is known as the \textit{degradation} problem \citep{he2015convolutional,srivastava2015training}.
This issue can be mitigated by deep Residual Networks \citep[ResNet,][]{he2016deep}, which allows the construction of extremely deep convolution networks and is still considered as the state-of-the-art method for computer vision tasks.
The key insight of ResNet is that, in principle deeper networks should perform at least as well as their shallower analogs, since if the subsequent layers are all identity, a deep network becomes equivalent to a shallow network.
The degradation problem then implies that it is not easy for neural networks to approximate the \textit{identity} function with nonlinear layers.
Therefore, instead of fitting the target function $\mathcal{H}(x)$ directly, it is advantageous to have each nonlinear layer fit $\mathcal{F}(x) \coloneqq \mathcal{H}(x)-x$, which can be implemented by a simple \textit{shortcut} connection \citep[see e.g. Figure 2 of][]{he2016deep}.
Then, if the linear weights and biases are all zero, we have $\mathcal{F}(x)\equiv0$ and $\mathcal{H}(x)$ is identity.
This leads to an explicit solution for deeper networks such that they should perform no worse than shallower networks.

\begin{figure*}
    \plotone{img/163175-da.pdf}
    \caption{The four types of data augmentation used during the training of the network, which increases the effective data size and mitigates overfitting to the irrelevant information in the galaxy images.}
    \label{fig:data-aug}
\end{figure*}

\begin{figure*}
    \epsscale{0.64}
    \plotone{img/loss-2-0.pdf}
    \caption{The training loss and validation loss of out network, evaluated on the Reduced GZ1 dataset with SDSS images.
    We terminate the training after 120 epochs as the validation loss no longer decreases.}
    \label{fig:loss-epoch}
\end{figure*}

We implement our Chirality Equivariant Residual Network (CE-ResNet) in \texttt{pytorch} \citep{pytorch}, based on the ResNet-50 model in \citet{he2016deep}.
The network structure is shown in Table \ref{table:resnet50}.
We use ReLU activation function for the convolution layers, and tanh activation function for the fully connected layers.
Our model is the same as the original ResNet-50 model, except for the following changes.
(1) The input image size is now $3 \times 160 \times 160$, and the output size for each layer is changed accordingly.
(2) We add four additional fully connected layers to improve its expressivity.
(3) Each galaxy image is fed into the neural network twice: the same network predicts the scores for Z-Spirals and Non-Spirals given the original image, and the scores for S-Spirals and Non-Spirals given the flipped image.
We then average the two estimates of the Non-Spiral score, and apply a softmax function to get the probabilities for the three categories (see Figure \ref{fig:arch}).
This guarantees that the network is purely parity-even, which is crucial for the cosmological galaxy spin analysis.

Note that we directly use the network to predict the scores and probabilities; we do not select a cutoff value and divide the galaxies into discrete categories of Z-Spirals, S-Spirals and Non-Spirals before training.
Unlike standard ML datasets such as MNIST \citep{lecun1998gradient}, where it is obvious whether one image is a handwritten number 8 or not, many galaxy images are not clear enough so that one can assert they belong to a certain morphology category.
Although galaxies with larger $p_{\rm z/s}$ are more likely to be real Z/S-spirals, there is no simple cutoff value such that all the galaxies with $p_{\rm z/s}$ above it are Z/S-spirals while all the galaxies below it are not: as shown in Figure \ref{fig:sam-gz-sdss}, the images just change continuously with respect to $p_{\rm z/s}$.
The value of $p_{\rm z/s}$ in the GZ1 catalog is indeed a stochastic variable: assuming that the vote on one single galaxy from each person follows some i.i.d. distribution based on its morphology and image quality, the fraction of total votes for each category does asymptotically converge with infinite vote size, but will always have finite noise when the vote size is limited.
If one pre-divides the galaxies into discrete categories, there are always mislabelled galaxies around the cutoff probability, which will be confusing to the classifier.
Therefore, we stick with estimating the scores directly, and leave the interpretation of the output probabilities to the user.

\section{Training and Results}

\label{sec:train}

\begin{figure*}
    \epsscale{1.15}
    \plotone{img/stable-2-1.pdf}
    \caption{We check the stability of our model, under a translation, rotation, zoom and permutation of color channels.
    The grey bands indicate the data augmentation used in the training of our network.
    Our model is completely insensitive to the rotation and color channel permutation of input images.
    Its performance does degrade when the images are translated by more than 0.7 galaxy radii or when the FOV is smaller than 3 galaxy diameters, probably because some useful information is cut out from the images.
    On the other hand, using a larger FOV has a smaller impact on the classification results.}
    \label{fig:stable}
\end{figure*}

\begin{figure*}
    \epsscale{1.1}
    \plotone{img/sdss-heatmap-2-0.pdf}
    \caption{2-dim histograms for the true and predicted $p_{\rm z/s}$, with the density normalized by each column.
    The heat map is close to the ideal diagonal case, with the dispersion likely dominated by the Poisson noise in the training data.}
    \label{fig:heat}
\end{figure*}

The input of our model are 3-channel RGB images of shape $3\times160\times160$, with the target galaxy centered and the image FOV equal to five times the galaxy half light diameter.
Since the chirality of a spiral galaxy should have no dependence on its location, orientation, size and color, we apply the following data augmentation during training, which are illustrated in Figure \ref{fig:data-aug}:
\begin{enumerate}
    \item The relative location of the galaxy is moved by up to 25\% of its half light radius along both directions, with the exact translation randomly sampled from the uniform distribution $U(-0.25\,r_{50},\,0.25\,r_{50})$.
    \item The galaxy is rotated by a random angle, sampled from the uniform distribution $U(0^{\circ},\,360^{\circ})$.
    \item The exact FOV of training images is sampled from a uniform distribution between four times and six times the galaxy half light diameter.
    \item We apply a random permutation to the three color channels, to mitigate the potential overfitting to the correlation between galaxy color and morphology \citep[e.g.][]{bamford2009galaxy}. This will also improve the model generalizability to images from other surveys, with possibly different filters, instrumental responses and color scales.
\end{enumerate}
Here we stick with 3-channel images and do not average the different bands or use only the band with the best signal-to-noise ratio: although the color of galaxy should contain no chirality information, it is possible that e.g. the spiral arms where more new stars are forming look bluer than the region between the arms, so that the spiral structure is less clear in single-channel images compared with the full 3-channel images.

We train our CE-ResNet model for 120 epochs using the cross entropy loss and the AdamW optimizer \citep{loshchilov2017decoupled}, which takes about one day on one NVIDIA V100 GPU.
We set the weight decay coefficient to 1 and use an initial learning rate of 0.0001, and after every 5 epochs we reduce the learning rate by a factor of $15\%$.
The training and validation losses are shown in Figure \ref{fig:loss-epoch}.
Sample images of different $p_{\rm z/s,pred}$ are shown in Figure \ref{fig:sam-cnn-sdss}, which are similar to those from the original GZ1 classification (Figure \ref{fig:sam-gz-sdss}).
The total number of spiral galaxies is also close to the original human classification (Table \ref{table:pzps}).

\begin{figure*}
    \epsscale{1.08}
    \plotone{img/systematics-2-1.pdf}
    \caption{The quantiles of $p_{\rm m} \equiv \max(p_{\rm z},p_{\rm s})$ prediction errors for galaxies within different bins of redshift $z$, half light radius $r_{50}$, $r$ band magnitude $m_r$, color $m_g-m_r$, aspect ratio $b/a$, and orientation $\phi$, with the corresponding number counts of galaxies in each bin.
    The legend shows the four bins for $p_{\rm m}$.
    We note that for the same $p_{\rm m}$ bin, the $p_{\rm m}$ prediction is roughly unbiased with the variance independent of the six parameters, except that the model slightly underestimate $p_{\rm m}$ for the galaxies that are distant visually, visually small, dim, and have a small aspect ratio.}
    \label{fig:sys}
\end{figure*}

We test the robustness of our model under the data augmentation transforms in Figure \ref{fig:stable}, with only one type of transform activated each time.
Note that in the first panel, we apply a stochastic transform similar to the one used during training, with the maximum translation indicated by the horizontal axis.
For the other three panels, the same deterministic transform is applied to all the images in each case.
The loss function is almost constant within the extent of training data augmentation indicated by the grey bands, meaning that our model is indeed insensitive to these transforms.
Beyond the grey bands, the loss function does increase when a large translation or a small FOV is used, probably because part of the galaxies are cut out from the images.
This implies that when the model is applied to another catalog without reliable galaxy radius measurements, one should consider setting the image FOV slightly larger rather than smaller, to make sure that the whole galaxy is contained in the image.

In Figure \ref{fig:heat}, we show the 2-dim histograms of the true vs predicted GZ1 vote probabilities for the Z-wise and S-wise spirals, which is roughly diagonal with a small dispersion of $\lesssim 0.1$.
Is this a good result?
Since our training data is from the volunteer vote fractions, the dispersion cannot be smaller than the Poisson noise in the data.
Assuming that the votes follow an i.i.d. binomial distribution, the empirical vote fraction will have a standard error of $\Delta p=\sqrt{p(1-p)/n}$, which equals to 0.073 if $p=0.8$ and $n=30$.
Therefore, the performance of our model is already close to the best allowed by the training data.
We also note an excess of galaxies in the $0<p_{\rm z/s,true}<0.1$, $0.1<p_{\rm z/s,pred}<0.3$ bins, due to the relative large number of galaxies with $0<p_{\rm z/s,true}<0.1$.
However, such small $p_{\rm z/s}$ galaxies are rarely relevant to practical applications, since their spin directions are mostly undetermined.

\begin{figure*}
    \epsscale{1.08}
    \plotone{img/desi-sdss-2-2.pdf}
    \caption{Similar to Figure \ref{fig:sys}, but for the difference in $p_{\rm m}$ when DESI images are used instead of SDSS images.
    The predicted $p_{\rm m}$ increases for almost all types of galaxies.}
    \label{fig:desi-sdss}
\end{figure*}

We check whether the classification accuracy systematically depends certain galaxy parameters in Figure \ref{fig:sys}.
All galaxies in the test dataset are binned according to their redshift $z$, half light radius $r_{50}$, $r$ band magnitude $m_r$, color $m_g-m_r$, aspect ratio $b/a$,  orientation $\phi$, as well as the true $p_{\rm m} \equiv \max(p_{\rm z},\,p_{\rm s})$.
We plot the error of $p_{\rm m}$ prediction and the number of galaxies for each bin.
Generally, the error is the smallest for $p_{\rm m}\in [0.85,\,1.0]$, since the Poisson noise follows $\Delta p=\sqrt{p(1-p)/n}$ and decreases with increasing $p$ when $p>0.5$.
We find that our model is overall unbiased, except for the galaxies with large $z$, small $r_{50}$, large $m_r$ and small $b/a$, whose $p_{\rm m}$ is slightly underestimated.
This is unlikely an issue for cosmological applications, however, as the number of such galaxies is relatively small.
Actually, such deviation will not complicate the cosmological analysis, since effectively our model just has a different \textit{selection function} than the humans in GZ1, meaning that a slightly different population of galaxies have their spin directions determined by the classifier.
In principle, one should take this into account to avoid making overconfident claims about our Universe, but the treatment of such selection effects should be similar, regardless of whether the galaxies are selected by humans or machines.

\begin{figure*}
    \epsscale{1.08}
    \plotone{img/desi-spec-2-2.pdf}
    \caption{Similar to Figure \ref{fig:sys}, but comparing $p_{\rm m,pred}$ and the measured half light radius, for all the galaxies in the Reduced GZ1 catalog that have one within-1" match in the Preliminary DESI catalog.
    The predicted $p_{\rm m}$ is close for most galaxies despite the difference in the radius measurement, except for the large, red galaxies whose radius measurement differs by about $\gtrsim 100\%$.}
    \label{fig:desi-spec}
\end{figure*}

\section{Application to DESI Images}

\label{sec:desi-image}
\label{sec:pre-desi}

Having trained our CE-ResNet model with SDSS images, we use the model to classify the same galaxies but with DESI images, which generally look redder than SDSS images as the \textit{z} band is used instead of the \textit{i} band for the red channel.
It turns out that our model performs well on these DESI images: we find 31.8\% more spiral galaxies (with $p_{\rm cut}=0.7$) relative to the original human classification (Table \ref{table:pzps}), as the predicted $p_{\rm m}$ increases for almost all kinds of galaxies except those already clearly classified with SDSS images (Figure \ref{fig:desi-sdss}).
Comparing the sample images in Figures \ref{fig:sam-cnn-sdss} and \ref{fig:sam-cnn-desi}, the DESI images are obviously clearer than the SDSS images, enabling better classification of the galaxy morphology.

We also apply our model to the ``Preliminary DESI" catalog, which indeed has a large overlap with the GZ1 catalog, as SDSS contributes most of the current galaxy spectra data before DESI spectra become available.
We find that 150,283 of the 173,097 galaxies in the Reduced GZ1 catalog can be matched to one galaxy within 1" in the Preliminary DESI catalog.
However, these galaxies have slightly different location and radius measurements in DESI Legacy Surveys, which are required to determine the image cut for our model input.
We thus use the ``Preliminary DESI" catalog to validate the accuracy of our model with DESI photometric measurements, since the future DESI spectra catalog will have the galaxy location and radius measured with the same pipeline.

We compare the predicted $p_{\rm m}$ for the galaxies matched between the Reduced GZ1 and Preliminary DESI catalogs.
In principle, $p_{\rm m,pred}$ should be close between these two cases, since we are classifying the same galaxies using the same imaging survey but just with different image cuts.
As shown in Table \ref{table:pzps}, the total number of Z-Spirals and S-Spirals are similar to the Reduced GZ1 catalog with DESI images, with slightly fewer galaxies if one chooses $p_{\rm cut}=0.9$ but more galaxies if one chooses $p_{\rm cut}\in[\,0.5,0.7\,]$.
We find that $\Delta p_{\rm m,pred}$ has a strong correlation with the difference in the measured galaxy radius: most of the galaxies have their radii measured larger by about 30\% in the DESI catalog, which however only leads to a small impact on the predicted $p_{\rm m}$ ($\lesssim 0.05$) since our model is insensitive to a reasonable amount of changes in the image FOV (Figure \ref{fig:stable}).
On the other hand, some large, dim galaxies have $\gtrsim 100\%$ difference in radius measurements, which leads to a larger dispersion in $\Delta p_{\rm m,pred}$.
This should only have limited effects on cosmological applications of our model, however, as the number of such galaxies is relatively small according to Figure \ref{fig:sys}.
Although in practice, one may further improve the performance by applying an empirical correction for the galaxy radii to compensate for the difference between surveys, based on e.g. the general trend of $r_{\rm DESI}/r_{\rm SDSS}$ in Figure \ref{fig:desi-spec}, which we leave for future research.

\section{Chirality Violation Due to Human Bias}

\label{sec:violation}

There is a known bias towards S-wise spirals in the GZ1 classification catalog, which has been attributed to human selection effects \citep{hayes2017nature}.
We check the symmetry between Z-Spirals and S-Spirals using the classification catalogs in Table \ref{table:pzps}.
We use the following statistics to determine the significance of chirality violation,
\begin{equation}
    T = \frac{n_z-n_s}{\sqrt{n_z+n_s}},
    \label{EQ:TEST}
\end{equation}
which should asymptotically follow the standard Gaussian distribution under the null hypothesis of no chirality violation.
See Appendix \ref{sec:test} for the derivation.

Note that our Reduced GZ1 catalog contains all the $p_{\rm m}>0.3$ galaxies in the full GZ1 catalog, while the $p_{\rm m}\leq0.3$ galaxies are downsampled.
Similar to \citet{hayes2017nature}, we find a $\sim 7\sigma$ asymmetry in the GZ1 Humans classification catalog, which disappears if the same SDSS images are classified by the parity-even CE-ResNet, implying that the asymmetry is due to a slight underestimate of $p_{\rm z}$ relative to $p_{\rm s}$ by GZ1 Humans.
However, when DESI instead of SDSS images are used for the Reduced GZ1 galaxies, there are again slightly more S-wise than Z-wise galaxies with $p_{\rm cut}=0.5$.
This is likely because some of the $p_{\rm z}\sim 0.5$ (with DESI images) galaxies are cut out from the Reduced GZ1 catalog since they may have $p_{\rm z}<0.3$ by GZ1 Humans.
When applied to the Preliminary DESI catalog, our CE-ResNet finds equal number ($<1.8\sigma$ asymmetry) of Z-Spirals and S-Spirals for all different choices of $p_{\rm cut}$, confirming that no real chirality violation between these two types of spirals exists in Nature.

\section{Discussions}

\label{sec:discuss}

In this paper, we present Chirality Equivariant Residual Network (CE-ResNet), a machine learning based model for the classification of Z-wise vs S-wise spirals.
Trained with Galaxy Zoo 1 (GZ1) data, our model gives similar predictions on the chirality of galaxies as the volunteers in the GZ1 project, but can be efficiently applied to the millions or even billions of galaxies that will be mapped in the near feature.
Our model is manifestly parity-even, since basically the same estimator is used the predict the probabilities of Z-Spirals and S-Spirals, using the trick that one gets a S-Spiral if one flips the image of a Z-Spiral.
We validate our model in Section \ref{sec:train}, and verify that our model can be directly applied to DESI images even though it is trained with SDSS images in Section \ref{sec:desi-image}.
We confirm that no real asymmetry between the numbers of Z-wise and S-wise spirals exists in Section \ref{sec:violation}.

We note a few related works in the literature.
\citet{hayes2017nature} develops an unbiased selector to demonstrate the origin of the excess of S-Spirals in the GZ1 catalog.
However, their Unbiased Machine spirality selector finds significantly fewer spirals than GZ1 Humans, such that it cannot maximally extract the information in the survey data for cosmological analysis.
Recently, \citet{tadaki2020spin} studies the classification of spiral galaxies with CNN.
However, their dataset only includes galaxies that are \textit{unambiguously} Z-spirals, S-spirals and non-spirals, whereas real world survey catalogs also include galaxies with \textit{unclear} morphological type due to the limitation of image quality.
Their model predictions on these unclear galaxies can be undefined as the network has never seen such galaxies during training, making it risky to be directly applied to full survey catalogs.
Also, the classifier in \citet{tadaki2020spin} is not manifestly parity-even, so one should be cautious about the possible inherent asymmetry between Z-Spirals and S-Spirals in their model.

The network used in this paper is equivariant under a reflection of the input image, which eliminates the potential bias caused by the difference between the Z- and S-type estimators.
Additionally, we augment the dataset by a random translation, rotation, scaling and permutation of color channels, to mitigate potential overfitting onto the irrelevant position, orientation, size and color information.
According to Figure \ref{fig:stable}, our network is indeed stable under such transforms.
We note that in principle, one can implement a more advanced equivariant network that manifestly accounts for all the relevant symmetries
\citep[e.g.][]{zhang2019making,weiler2019general,sosnovik2019scale,cesa2022a}.
Also, domain adaption techniques may help to improve the performance when the model needs to be applied to data from different surveys \citep{bendavid2010theory,alexander2021domain}.
We leave these directions for future research, since our current architecture already works well in the various benchmarks demonstrated in this paper.

\section{Acknowledgement}

We thank Jiaxuan Li, Mingwei Ma and Zhehao Xu for helpful discussions.
Ue-Li Pen receives support from Ontario Research Fund-Research Excellence Program (ORF-RE), Natural Sciences and Engineering Research Council of Canada (NSERC) [funding reference number RGPIN-2019-067, CRD 523638-18, 555585-20], Canadian Institute for Advanced Research (CIFAR), Canadian Foundation for Innovation (CFI), the National Science Foundation of China (Grants No. 11929301), Thoth Technology Inc, Alexander von Humboldt Foundation, and the National Science and Technology Council (NSTC) of Taiwan (111-2123-M-001-008-, and 111-2811-M-001-040-).

Computations were performed on the Mist supercomputer at the SciNet HPC Consortium and the SOSCIP Consortium's GPU computing platform.
SciNet is funded by: the Canada Foundation for Innovation; the Government of Ontario; Ontario Research Fund - Research Excellence; and the University of Toronto \citep[][]{Loken_2010}.
SOSCIP is funded by the Federal Economic Development Agency of Southern Ontario, the Province of Ontario, IBM Canada Ltd., Ontario Centres of Excellence, Mitacs and 15 Ontario academic member institutions.

The Legacy Surveys consist of three individual and complementary projects: the Dark Energy Camera Legacy Survey (DECaLS; Proposal ID \#2014B-0404; PIs: David Schlegel and Arjun Dey), the Beijing-Arizona Sky Survey (BASS; NOAO Prop. ID \#2015A-0801; PIs: Zhou Xu and Xiaohui Fan), and the Mayall z-band Legacy Survey (MzLS; Prop. ID \#2016A-0453; PI: Arjun Dey). DECaLS, BASS and MzLS together include data obtained, respectively, at the Blanco telescope, Cerro Tololo Inter-American Observatory, NSF's NOIRLab; the Bok telescope, Steward Observatory, University of Arizona; and the Mayall telescope, Kitt Peak National Observatory, NOIRLab. Pipeline processing and analyses of the data were supported by NOIRLab and the Lawrence Berkeley National Laboratory (LBNL). The Legacy Surveys project is honored to be permitted to conduct astronomical research on Iolkam Du'ag (Kitt Peak), a mountain with particular significance to the Tohono O'odham Nation.

NOIRLab is operated by the Association of Universities for Research in Astronomy (AURA) under a cooperative agreement with the National Science Foundation. LBNL is managed by the Regents of the University of California under contract to the U.S. Department of Energy.

This project used data obtained with the Dark Energy Camera (DECam), which was constructed by the Dark Energy Survey (DES) collaboration. Funding for the DES Projects has been provided by the U.S. Department of Energy, the U.S. National Science Foundation, the Ministry of Science and Education of Spain, the Science and Technology Facilities Council of the United Kingdom, the Higher Education Funding Council for England, the National Center for Supercomputing Applications at the University of Illinois at Urbana-Champaign, the Kavli Institute of Cosmological Physics at the University of Chicago, Center for Cosmology and Astro-Particle Physics at the Ohio State University, the Mitchell Institute for Fundamental Physics and Astronomy at Texas A\&M University, Financiadora de Estudos e Projetos, Fundacao Carlos Chagas Filho de Amparo, Financiadora de Estudos e Projetos, Fundacao Carlos Chagas Filho de Amparo a Pesquisa do Estado do Rio de Janeiro, Conselho Nacional de Desenvolvimento Cientifico e Tecnologico and the Ministerio da Ciencia, Tecnologia e Inovacao, the Deutsche Forschungsgemeinschaft and the Collaborating Institutions in the Dark Energy Survey. The Collaborating Institutions are Argonne National Laboratory, the University of California at Santa Cruz, the University of Cambridge, Centro de Investigaciones Energeticas, Medioambientales y Tecnologicas-Madrid, the University of Chicago, University College London, the DES-Brazil Consortium, the University of Edinburgh, the Eidgenossische Technische Hochschule (ETH) Zurich, Fermi National Accelerator Laboratory, the University of Illinois at Urbana-Champaign, the Institut de Ciencies de l'Espai (IEEC/CSIC), the Institut de Fisica d'Altes Energies, Lawrence Berkeley National Laboratory, the Ludwig Maximilians Universitat Munchen and the associated Excellence Cluster Universe, the University of Michigan, NSF's NOIRLab, the University of Nottingham, the Ohio State University, the University of Pennsylvania, the University of Portsmouth, SLAC National Accelerator Laboratory, Stanford University, the University of Sussex, and Texas A\&M University.

BASS is a key project of the Telescope Access Program (TAP), which has been funded by the National Astronomical Observatories of China, the Chinese Academy of Sciences (the Strategic Priority Research Program ``The Emergence of Cosmological Structures" Grant \#XDB09000000), and the Special Fund for Astronomy from the Ministry of Finance. The BASS is also supported by the External Cooperation Program of Chinese Academy of Sciences (Grant \#114A11KYSB20160057), and Chinese National Natural Science Foundation (Grant \#12120101003, \#11433005).

The Legacy Survey team makes use of data products from the Near-Earth Object Wide-field Infrared Survey Explorer (NEOWISE), which is a project of the Jet Propulsion Laboratory/California Institute of Technology. NEOWISE is funded by the National Aeronautics and Space Administration.

The Legacy Surveys imaging of the DESI footprint is supported by the Director, Office of Science, Office of High Energy Physics of the U.S. Department of Energy under Contract No. DE-AC02-05CH1123, by the National Energy Research Scientific Computing Center, a DOE Office of Science User Facility under the same contract; and by the U.S. National Science Foundation, Division of Astronomical Sciences under Contract No. AST-0950945 to NOAO.

redpajama_set_name: RedPajamaArXiv


text:
Q: when to cast objects in objective c? Is there a general rule to casting?
So far I have only casted in objective-c when I want the object to be of the type of a custom super class I created. Such as class: Car, subclass: Ford and I cast a Ford object to (Car *)
However I was going through some sample code and I found this:
- (BOOL)application:(UIApplication *)application didFinishLaunchingWithOptions:      (NSDictionary *)launchOptions
{
    UINavigationController *nav = (UINavigationController *) self.window.rootViewController;

    CoursesTableViewController *cvtc = (CoursesTableViewController *) [[nav viewControllers] objectAtIndex:0];

    cvtc.managedObjectContext = self.managedObjectContext;

    return YES;
}

Why is nav casted to a (UINavigationController *) when it already is? and same again for cvtc but for a custom subclass of a TableViewController

A: Type of self.window.rootViewController is UIViewController not UINavigationController. And UINavigationController inherits UIViewController. That is why it is typecasted.
Similarly, CoursesTableViewController is typecasted as return type of [[nav viewControllers] objectAtIndex:0]; is id.

A:
Why is nav casted to a (UINavigationController *) when it already is?

The self.window.rootViewController provides the content view of the window.
It will return the resulted content view as UIViewController. You may be assigned any class that derived from UIViewController as self.window.rootViewController.
So it will be your duty to cast it as per your code.

same again for cvtc but for a custom subclass of a TableViewController

The above explanation will cover this.

redpajama_set_name: RedPajamaStackExchange


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text:
Happy Birthday, Radio News
By Bob Welbaum September 1, 2020 Historical
Monday, August 31, was the 100th anniversary of the first radio news program, a milestone worth commemorating in this election year.
One hundred years ago, the users of radio were hobbyists. In Detroit, home of the Scripps newspaper family's The Detroit News, the company faced a dilemma. Radio was a threat to newspapers, yet it was also a logical way to disseminate news.
Would anyone listen to a news broadcast? As a test, they hired a teenager named Michael DeLisle Lyons to start up a radio station. It was established under his name so they wouldn't be embarrassed if it failed. Lyons got the necessary permits on August 20, 1920, and he played nonstop music for 10 days as a trial. The primary elections were August 31, and The Detroit News reported that returns would be broadcast that evening.
The report in the newspaper the next morning read: "The sending of the election returns by The Detroit News' radiophone Tuesday night was fraught with romance and must go down in the history of man's conquest of the elements as a gigantic step in his progress. In the four hours that the apparatus […] was hissing and whirring its message into space, few realized that a dream and a prediction had come true. The news of the world was being given forth through this invisible trumpet to the waiting crowds in the unseen market place."
Their radio station began under the call letters 8MK and still broadcasts today as WWJ, and it still uses an all-news format.
Taken from The Writer's Almanac of August 31, 2020 (https://www.spreaker.com/show/the-writers-almanac?).
Do We Bathe Too Much?
In Defense of… Parasites?

redpajama_set_name: RedPajamaCommonCrawl


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Many property-owners are surprised to discover their commercial roofs need to be replaced due to a lack of maintenance. Like other areas of the home, a roof must be maintained to ensure that it maintains its integrity. Some types of roofs require more maintenance than others. If you fail to routinely check your roof and repair problems when they are small, the situation can quickly escalate into the need to replace the entire roof.
Such problems might include pooling water, a piece of slipped flashing, or even a shingle that has torn loose. If you are not confident in inspecting your commercial or industrial roof on your own, invest in hiring an experienced roofing contractor to check it for you. Also, keep in mind that if you wish to decrease the amount of maintenance required for your roof, a Colorbond roof is an excellent option. A Colorbond roof is also an excellent choice for protection against high winds as well as bushfires.
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Spain has rejected plans for an EU and IMF bailout for one more time the last few hours and I'm sure they are going to do it many more times for the next few days. A déjà vu for me coming from tow years back when the Greek prime minister accompanied by Greek officials was saying exactly the same things just before IMF lands in Athens airport. But this is not a surprise and it is not like we didn't expect it, the part that comes as a surprise, especially after what Greece has gone through, is the anxiety of the Germans to see Spain joining the mechanism. The Germans that so snappishly used the acronyms PIGS to describe the in debt southern states, including Ireland, adding the stereotypes of the lazy, sunbathed beggars of the south.
Since last week especially I have to admit that I sense a certain contradiction in the voluntarily advices coming from Germany and Brussels to Spain. Contradictory because while you know that the measures in Greece have thunderously failed and you trying to find a way out you cannot advice the same measures to another state; furthermore to a state that holds a big part in the European economy and a state contrary to Greece that has an active heavy industry in many levels including investment and banking. And if the Greek bailout has made most of the European nations react badly under the heavy cost to their taxpayers' citizens and with a little help from all the contemporary populists – that apparently multiply fast lately – fighting any kind of bailout how Mrs. Merkel, Mr. Rehn or Mr. Hollande are going to deal with their internal reaction? Especially Mrs. Merkel who's leading al the austerity plans, demands obedience to the EU measures and has to deal with a strong internal opposition that might cost her the chancellorship soon.
In the past I have written articles pointing in the mystery of the German reunification and the unknown cost of it for Europe and especially the European Union and the more I see Germany's movements in Europe this moment the more I believe that whatever is going on this minute it has its roots in the events, at least the economic side, of the reunification. The reunification was coming but the actual events that led there surprise even the architects of the reunification. Suddenly a population of nearly 20 million with an economy in ruins and an industry trembling somewhere between deterioration and destruction with a provision to take at least thirty years before this part of the German nation to be able to stand up and be equal, at least financially, with the west part. Thirty years, from 1990 it makes 2020; in eight years. The same time in an extreme effect to help the people the German state exchanged the nearly nothing representing in gold East German Mark with the official analogy 1x3 (in the dark alleys the exchange reached 1x10) with the strong DM miraculously causing absolutely no problem to the German economy. On the contrary the reunification succeeded so much that inside a decade it was hard to contemplate – apart from the obvious internal inflation and unemployment that was also nearly zeroed after fifteen years – the differences between the two unified states.
In economics just like in mathematics miracles don't happen; things follow certain rules where even the mule event is predictable and calculated. Actually the contemporary situation in Greece, Spain, Portugal, Ireland, Italy and Spain was predictable since early 1980s; that the European leadership with political decisions decided to ignore all the warnings it shows only their incapability. So what has happened in Germany that revolutionized the theory and overturned the rules?
Past readers know well that I'm not fan of conspiracy theories and most of the time my opinions are the result of logical thinking but following the logic of a well known fiction persona, "when you have eliminated the impossible, whatever remains, however improbable, must be the truth." And the improbable in this case is laundering. Germany is laundering money using apparently mafia methods – and through the constant transmission and exchange and in the process clean them - and since the reunification and the transformation from the East German economy to the mighty West German there is a lot of pure paper without real value to lauder. Paper that was kept for long in safes but under the global circumstances and taking in account the American involvement or wish to get involved is about time to come out and give that extra boost the German economy to stay on the top and be able to stand the attack. But of course all is speculation … however improbable.


text:
Q: Will the Gigabyte GeForce GTX1050TI G1 Gaming 4G work with my motherboard? I want to upgrade my Acer Aspire XC-605. I want to get a better PSU, a bigger case and a better graphics card. I believe my motherboard is called MS-7869 DB.SRPCN.001 (I'm not sure). It has a PCI-Express 16x slot but the graphics card that I want to buy has a PCIe 3.0 x16 connection.
My question is: Will the MS-7869 DB.SRPCN.001 motherboard (with PCI-Express 16x slot) work with the Gigabyte GeForce GTX1050TI G1 Gaming 4G (with PCIe 3.0 x16 connection)
Thanks!

A: Yes. It will most definitely work. You have a PCIe 16x slot on your board, which any GFX card will fit into. Since PCIe is backwards compatible, most cards will work on most boards.
However, one thing to be wary of is TDP and your power supply. If you buy a 1000W power supply (which is more than enough and very pricey), you're going to be able to pull 1000W before that power supply gives up the ghost and shuts the system down. If you only get a 450W PSU, you'll only be able to pull 450W before the computer shuts down. If your CPU, GPU, RAM, etc. max out your power supply, it won't work, but that's an unrelated issue to compatibility.
I digress, YES, this card is compatible with this motherboard.


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Posted on January 2, 2014 at 9:23 am by [email protected] and filed under Hack All The Things.
Why hack only some of the things when you could hack them all? While many security hobbyists and professionals are often quite adept at a certain niche in security, having some cross-training in other areas is not only beneficial to your individual growth but is useful in a corporate situation where you may be asked to implement a particular security control or run a certain type of assessment.
Hack All The Things aims to teach readers of varying skill levels, new techniques, tools, concepts, and protips to help reinforce basic skills as well as introduce new skills.
Often being offensive in nature —the best defense is a good offense—articles will sometimes cover subject matter which is useful for prospective CCDC participants as well as anyone working in, or aiming to work in, a defensive security role.


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There are hundreds of tutorials on the subject of the OOP - Object Oriented Programming. Since they all begin with a strong emphasis on the technically-heavy features of the OOP, the approach may do more harm than good for a beginner, even if the motive of the author of the tutorials in question is to present it as the best as he or she can. In this context, this tutorial is going to follow a different path, way away from the well text-muted-beaten path.
That means, the features of the OOP will be discussed along with the development of the classes.
A collector wants to be a member of a particular antiques club. So, he wants to register himself by filling in an online form. If successful, a unique ID and a PIN will be sent by post. If he wants, he can register as a life member too - in order to become a special member; he has to fill in a different form for that purpose, though. Once registered, the member can login to the system and use the other services; for a life member, there is going to be a separate form for the same purpose.
This is a real life situation. The process of modelling the above with the use of software, leads to the concept of Object Oriented Programming - OOP.
To do that, let's recognize the nouns, their attributes and verbs.
A collector wants to be a member of a particular antiques-club. So, he wants to register himself by filling in an online form. If successful, a unique ID and a Pin will be sent by post. If he wants, he can register as a life-member too - in order to become a special member; he has to fill in a different form for that purpose, stating whether he is a life-member, though. Once registered, the member can login to the system and use the other services; for a life member, there is going to be a separate form for the same purpose.
The nouns are objects - member, special member.
The main elements are the objects. The methods and attributes are just parts of them. In order to model real-life objects - in this case, members - in OOP, we create a blueprint, which is known as a class.
The process of modelling an antiques collector in programming as a class, while assigning fields, properties and methods to it, is called Abstraction in the OOP.
In the following animation, the cookie press represents the class. It makes, of course, cookies - the objects.
The four variables - public string _regDate, protected Guid _memberID, public bool _islifeMember, protected int _pin, declared at the top of the class are known as fields in OOP.They store the data of an object, when created; public, private, protected are called access modifiers.
- variables can only be accessed by the code in the same class or a derived class from it - if the base class is Dad and the derived is Son both Dad and Son can access them.
In addition, string, int, bool and Guid are variable types in C#. Type GUID is particularly important, as it can create unique values, which are really important for distinct User IDs in a real-world application.
The two Member() methods, just below the declaration of fields, are called Constructors in the OOP. They are the first methods to be initialized in the event of an object being created from a class. They are important, as the values to be initiated are always placed inside constructors.
The first constructor is called default Constructor, as it does not take any parameter. The second constructor is called parameterized Constructor, as it takes two parameters - _regDate and _memberID.
Thanks to the parameterized constructor, the date of registration and assigning a unique ID to a member can easily be achieved at the time of creating an object from a class, which is known in the OOP as Instantiation.
memberID, Pin, RegDate and IsLifeMember are called properties of the class. Their values can be set or obtained with the aid of the fields.
- gives values to the object, when created.
- sets values of the object.
If the property only contains get section, it is called readonly. Its value cannot be set through objects. The memberID, for instance, is readonly for obvious reasons: it cannot be altered externally!
On the other hand, if it only contains set section, it is called writeonly, as is only for setting the values and not reading from an object.
Since fields contain data without directly exposing them to outside world, this feature is known as Encapsulation in the OOP. _memberID, for instance, contains - and hides - the unique ID of a member object. In short, fields hide data inside a 'capsule'.
Please not that Encapsulation is not the same as Abstraction, although the distinction is pretty vague.
Methods are actions that the objects can perform.
Both registerMember and Login are methods; they can register member and let a member login after registration respectively. The methods, when act as functions, return a value. The Login method, for instance, return a Boolean value. Methods often need parameters.
Of course, the same could be achieved by declaring two different methods.Overloading, however, is more efficient as it makes the code more readable and minimizes the lines of code, which are vital in creating - and maintaining - a big application.
Since the creation of the class, Member, is complete, let's create objects from this class. This is called instantiation.
Please note the power of GUID - Globally Unique Identifier - creator; it generates unique IDs.
This is the process of extending the functions and properties of a class to its child classes.
With the aid of the objects from lifeMember class, life members can be added to a separate database. In order to simulate it, please click the button below.
Sometimes, there is a need of overriding a method in a base class, while using the same in the derived class. In order to achieve this, however, the method in question must be declared as virtual in the base class. Then override keyword is used in the derived class.
The Login() method returns a different thing in the derived class, as it has been overridden by the 'new' Login() method in the derived class.
In the base class Login() returns a Boolean value, whereas in the derived class it returns a string, after being overridden.


text:
Home » Business » Turkey completes phase 1 of digital lira pilot, pledges to push ahead in 2023
Turkey completes phase 1 of digital lira pilot, pledges to push ahead in 2023
Business 4 January 2023
Turkey has become the latest country to announce a successful pilot of a central bank digital currency (CBDC), with its central bank revealing it had conducted its first transactions in the digital lira.
The Central Bank of the Republic of Türkiye (CBRT) announced that it had completed the first phase of the Digital Turkish Lira Project and executed the foremost payment transactions on the network.
Press Release on the Use of Digital Turkish Lira: https://t.co/48ulWfJqXw
— CentralBankofTürkiye (@CentralBank_TR) December 29, 2022
"The CBRT will continue to run the limited, closed-circuit pilot tests with technology stakeholders in the first quarter of 2023. Findings obtained from these tests will be shared with the public via a comprehensive evaluation report," CBRT stated.
The central bank ramped up its CBDC efforts in 2021, engaging the services of various tech firms, including Tubitak-Bilgem and Havelsan, to research, develop and test the digital lira.
These efforts have come amid the continued devaluation of the Turkish lira. Since the turn of the last decade, the lira has continued to struggle, a downward spiral that was accelerated in 2022. In September, $1 was worth 8 lira, but by December, it had shot up to 19 lira.
The lira's struggle has continued to push more Turks into digital assets, in line with citizens experiencing similar struggles, including in Venezuela and Zimbabwe. While the CBRT believes that a digital lira can remedy the situation, some, including the leaders of the country's opposition party, have cautioned that it's not a panacea for all that ails Turkey.
"Digital currency is exciting, but it is not the deus ex machina that will save the country's tanking lira," said Ali Babacan, the founder of DEVA, Turkey's opposition party.
Despite some skepticism, the CBRT has pledged to continue with its digital lira pilot in 2023.
"In 2023, the CBRT will expand the Digital Turkish Lira Collaboration Platform to involve selected banks and financial technology companies, and will unveil advanced phases of the pilot study that will further widen the participation," the bank said.
To learn more about central bank digital currencies and some of the design decisions that need to be considered when creating and launching it, read nChain's CBDC playbook.
Watch: The BSV Global Blockchain Convention presentation, CBDCs and BSV
title="YouTube video player" frameborder="0" allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture" allowfullscreen>
Central Bank Digital Currency
Central Bank of the Republic of Türkiye
digital lira


You're a night security guard and the only Christian on duty. Another guard suddenly sticks his head into your office. Pointing his finger he almost accuses, "You're one of those 'Christians,' right?" Nothing good ever follows that question. No one gives you a high five, says "good job," and goes about their business. They want to debate, challenge, or stump you. You hesitantly respond, "Yeah . . ." He crosses his arms, looks you square in the eye and then comes the challenge: "I do drugs. What would Jesus say about that?" How would you respond—in a way that might actually resonate?
For some, our gut response would be to look down, stammer, and ashamedly admit we don't know what Jesus would say. Maybe it's the outlandish honesty or the shock of a challenge at 2 a.m. Perhaps we have a hard time putting Jesus' response into words. Or our people-pleaser kicks in and we simply can't tell him the core of what we believe. A common response to this question is a blank stare. Put yourself in the shoes of the asker: "I don't know" looks like ignorance.
For others, the answer would stem from the moralistic, humanist culture we grew up in. Our answer is some form of Bob Newhart's MADtv sketch: a counselee admits a number of struggles, while Newhart "counsels" each with a blunt, "Stop it!" Even if we intellectually know Jesus is our savior, we function as if He is a good guy with ethical advice. Maybe we advise a few "good works." Perhaps we appeal to legality ("you'll get arrested"), personal welfare ("it might kill you"), heartstrings ("if you get arrested, can you imagine how your family will feel?"), or moralism ("you know it's wrong"). It could be that we even quote a verse: "He'd say 'you shall have no other gods before me'; that's the first commandment." Put yourself in the asker's shoes again: "'Stop it'" fits a view of God many already assume: a rule-giving, demanding, and impersonal deity.
A final common response acknowledges their need for the gospel. Maybe you've been praying for this guard. You're elated that God finally opened the door. So you gush the gospel many of us know well. "He'd tell you that God is perfect and heaven is perfect, but because of sin, you're not perfect. God sent Jesus to die for your sin so you can be reconciled to God and live eternal life with Him. If you accept Jesus He'll forgive your sin of drugs." This is true—and praise God it is! But if he's ignoring God, he doesn't care about heaven. If he's like much of the world, he doesn't believe he's too bad a person. If he's a common American, it's likely he doesn't fully understand sin or his need for Jesus. Even the objective, big-picture gospel is not a sufficient answer.
These responses fail to get to the heart of our faith. The first is empty. the second is moralistic. The third sees the gospel as merely a past event that greatly benefits my future, but that has nothing to do with today. Many who question the gospel need to know how it applies to them in their current situation. Behind the challenging question is a heart in need of applicable truth.
Futile attempts like these are not unique to our culture. Writing to first-century Ephesus, Paul explains the goal of Christian life is maturity, then gives three ways we cannot attain that goal: "every wind of doctrine, by human cunning, by craftiness in deceitful schemes." First, exclusively pursuing doctrinal trends, teachers, or head-knowledge of the Bible isn't enough. Second, we will always be let down by relying on our own power, to make new rules and fix each other. Third, false teachers deceive, spouting false hope and false ways to solve real issues.
Identity: Who or what shapes their understanding of themselves? Where do they find personal value and worth?
Brokenness: Where are things "different" than they're supposed to be? What are areas of pain, hurt, and frustration? Who or what's to blame?
Redemption: What or who do they look to, to fix the brokenness? What or who makes everything right? What or who's their functional redeemer?
Hope: What does "right" look like? What would everything look like once everything is fixed? What or who is the center of that hope?
When we identify false identity or hope in someone's life, see a misplaced view of brokenness, or hear the letdown of a false redeemer, we can point them toward a better story. We lead them to an identity and hope in God, not anything or anyone else. We define sin as the true brokenness, not any other problem. We point to Jesus as the only true Redeemer in the midst of the siren calls of false saviors. That loves them well, and speaks gospel truth in a way that addresses a direct need.
"I do drugs. What would Jesus say about that?" Based on today's content, how do we answer that question? What deeper need do the drugs really cover? What true struggle is he admitting? Put yourself in Nick's shoes: how does the objective gospel apply to the guard's subjective situation?
After thinking for a moment, Nick responded, "I think Jesus would tell you you're looking for hope in a place that lets you down. And you know it lets you down because you have to take a hit three times a day. So I think Jesus would tell you He's a better place to put your hope, because He promises He'll never let you down." Nick spoke the gospel truth into the basis of the guard's personal hope. In thousands of years of history, sixty-six written books, and millions of lives across history, God has proven that Jesus is our greatest hope. The guard didn't fall on his knees weeping that night. God didn't redeem his soul in that office. But he uncrossed his arms, shook his head, and told Nick, "No one has ever told me that before. That actually makes a lot of sense." That night, the guard walked having heard the gospel in a way that resonated with his present life and need.


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redpajama_set_name: RedPajamaGithub


This page is dedicated Zines that are currently in "print", at least within the last year (for annual zines).
The & Publishing Group is a dedicated group of globally distributed volunteers whose goal is to promote and keep active old school games, especially Advanced Dungeons & Dragons (AD&D). As part of the OSR — regardless if you call it "Old School Renaissance" or "Old School Revival" — the & Publishing Group publishes a variety of supplements useful for all TSR-era D&D games. This includes the flagship & Magazine, plus supplements and adventures.
As a zero revenue organization, all of our products are absolutely free! Our love of Dungeons & Dragons keeps us going!
Back to BasiX is a fanzine that "celebrates, highlights and supports one of the original roleplaying game systems of the 1980's, the Basic and Expert editions of Dungeons & Dragons. Each issue will provide or highlight content within the Holmes, Moldvay, Cook and Mentzer edited rules from TSR's Gary Gygax."
Produced by Thom Wilson at ThrowinGames LLC, Back to Basix is a quarterly Zine.
focused on Lamentations of the Flame Princess."
Black Pudding is an OSR Fanzine produced by J.V. West's under his label: Random Order Creations.
J.V. West is the writer, editor, and main artist for Black Pudding and each issue isn't layed out so much as drawn out. Full of original art, hand-drawn character sheets, and miscellaneous awesomeness, Black Pudding is available digitally as a Pay-What-You-Want title through One Book Shelf.
Dice Roll Zine is a 32-page old-fashioned amateur home-printed zine for OSR RPG games written by Steve C (Prismatic DM) and illustrated by some of the best OSR-influenced artists around.
Explosive Runes is a periodic zine that currently serves as "The Official Magazine of RPG Crossing", which is a medium online community of play-by-post role-players.
Explosive Runes is a PDF zine that currently appears to have a yearly release schedule. The current issue back through to issue three can be found at RPG Crossing.
FANTASTIC EXCITING IMAGINATIVE is full of "Additional Rules for Fantastic Medieval Role Playing Adventure Campaigns!"
Herein you will find well-over 50 new ideas for your game — all of it inspired by the art in the original book! New spells, new magic, new monsters and threats, new directions to explore, and plenty of twists on the classics.
This zine is produced by Inner Ham.
The Frontier Explorer is a web based magazine dedicated to providing fan generated material for "pen and paper" role playing games as well as fiction to the RPG community.
In the beginning, Frontier Explorer will have a definite focus on TSR's (now WoTC's) old science fiction RPG Star Frontiers since that game holds a special place in the hearts of the magazine's two founding editors.
The Manor, from Tim Shorts/GM Games, has been around since 2012 and the main author writes like he's running a game for adult players. To quote the lead author/editor "...I do this for fun, not to change minds or challenge gaming philosophies. I roll dice, laugh, and try to make my funny voices sound convincing."
Content is geared towards generic OSR, or a rules-light system easily converted to another OSR system.
RPG Pundit Presents is a weekly OSR Periodical "from the mind of RPGPundit".
Each issue covers a different topic, anywhere from Medieval-Authentic material to over-the-top gonzo stuff for introducing a level of zaniness to your games. The material is intended for use with Old School (OSR) roleplaying games, providing sufficient mechanics, but is likely easy to port over to any RPG.
Sanctum Secorum is a companion zine to the Sanctum Secorum podcast. Focused on DCC RPG, each issue is full of playable material inspired by the works of Appendix N. Best of all, it is not only monthly, it is also free.
Listen to the podcast online here. When a new zine issue is ready there will be a link on the podcast page or you can use this DriveThruRPG link.
Trolls Zine is very much a free fanzine for the Tunnels & Trolls role-playing game. Each issue is packed with typical zine fare: adventures, magic items, and assorted T&T articles.
The 1st eight issues were released from Flying Buffalo and subsequent issues were released from Khaghbboommm.
The Twisting Stair is a traditional OSR Zine available in print form only. Ordering information can be located on one of the author's blog "Grogdog's AD&D Blog"
2018.03.25 Black Pudding #4 is Out!
OSR Zine by Christopher Stogdill is licensed under a Creative Commons Attribution-NonCommercial-NoDerivs 3.0 United States License.


Studio Piraino's August 2015 Product of the Month is Unite SECOND Day Finishing Cream.
Using SECOND Day will give a lived in, earthy texture you can wear right now that will also look great tomorrow.
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When it comes to gummi candies, what do আপনি prefer?
What is your favortie type of candy?
Fun Dip অথবা Pop Rocks?
How many licks does it take to get to a center of a tootsie pop?
This is a তালিকা of 42 candys and some information!Enjoy!
1.)Snickers: Snickers is a চকোলেট bar with caramel,peanuts,and চকোলেট covered.They have a very big king size.
2.)Twizzlers:Twizzlers is liqourice that has four flavors.Red,orange,yellow,and black.There is also another liqourice brand that is called Red Vines.
3.)Tootsie Pop:Tootsie Pop is a lolipop that has দগ্ধ শর্করা inside of it.It has five flavors.Dark red,blue,orange,brown,and red.
যেভাবে খুশী উদ্ধৃতি about ক্যান্ডি চকোলেট and Chocolate!
1. GIVE ME THE চকোলেট AND NO-ONE GETS HURT!
3. When someone throws skittles at আপনি and says, "Taste the rainbow!" throw M&M's back and say, "I'm not afraid!"
4. People get wasted from alcohol. Me? I get wasted from candy.
Okay,So I found a way to get my bag back!I opened my door slowly,Looked around,Saw no one,I ran across the hall.I stopped at the couch,i got on my knees.Then I heard Matthew."Mom!Please!a few more!!"
once there was a 9 বছর old girl named,Sandra.Sandra loved,LOVED,LOVED!Candy!She ate ক্যান্ডি চকোলেট every day!Sandra had brown,long hair.Short Nails.And had one little brother,Matthew.And a mom.
"MOM!I'm প্রথমপাতা from school!"Sandra yelled."OKAY!"Her mother responded."Can I eat a Smackers Bar?"Smackers bar we're a চকোলেট bar with nuts,peanut butter,and caramel."NOT 'TIL আপনি EAT YOUR ডিনার LATER!!!"Mom's voice cut throw the air.
Milton Hershey was born on September 13, 1857, in a farmhouse near the Central Pennsylvania village of Derry Church. Milton was in the fourth grade when his Mennonite father, Henry Hershey, found his son a position as an printer's apprentice in Gap, Pennsylvania. Milton later became an apprentice to a candy-maker in Lancaster, Pennsylvania, and candy-making became a passion which Milton grew to love.
Snickers-Snickers is a চকোলেট bar made দ্বারা the Mars family. It consists of চিনাবাদাম মাখন nougat topped with roasted পিনাটস্ and caramel, covered with দুধ chocolate.Snickers is the best selling চকোলেট bar of all time and has annual global sales of US $2 billion.
Printable Coupons and coupon code for Valentine's দিন চকোলেট & Candy.
Need help with a ক্যান্ডি চকোলেট timeline!
ক্যান্ডি চকোলেট Buffets Tips, Tricks, How-to's, and Advice... please share!


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Wearables and Cloud-based Technology Effects: How People Understand and Pursue Their Health and Fitness Initiatives
by Irina Kravchenko
When most people think about working out, the first images that come to mind are probably some combination of darkly lit weight rooms, clanking barbells, or grunting body-builders in muscle tees slamming weights on the ground. While these archetypal images of fitness are not necessarily "wrong", they undermine the exponential digital growth the fitness and wellness industry has experienced in the last decade.
Maintaining a healthy lifestyle has departed from the traditional association that being fit means being a gym rat or a vegan: individuals now pursue their health initiatives on varying levels thanks to the rise of digital technology.
The impact of the technology surge in fitness has undoubtedly changed the landscape of the industry. Rather than relying on traditional methods of fitness, individuals are now granted a wide variety of options to stay healthy and fit in addition to more tools and software that can effectively gauge their effort and progress.
The main players that have helped cultivate this landscape are none other than wearable devices and cloud-based services. The technologies, though they can be used separately, typically work in tandem. Wearable devices utilize advanced sensors to collect an array of personal biometric data, giving the user real-time updates of their activity levels, as calories burned, distance traveled, or heart rate levels. User data is immediately collected and analyzed through wireless cloud-storage facilities, creating backlogs of activity data that can always be accessed from an array of the user's devices.
Due to the widespread adoption of common fitness technology, individuals now approach fitness with a broader perspective, which has made them more conscious and motivated to pursue overall healthier lifestyles.
Explosion of Wearables
For the better half of the last decade, wearables have solidified their place as the clear front-runner of all fitness trends and technologies.
Wearable fitness trackers have become so common that many devices, like smartphones, now come with them as a pre-installed application. Similarly, movement or strength tracking mobile applications consistently rank among the top downloaded fitness-related apps.
In the United States alone, 49% own a wearable device either from a specific fitness brand or on their phones. These figures correlate to the normalization for anyone to use a wearable regardless of their fitness levels.
Wearable technology has secured its place as the top fitness trend for the last several years while continuing to indicate high projections for the future. In fact, by 2022 the wearable device market is projected to quadruple, making them edge out smartphones as the highest-selling electronic device meant for consumer use.
A major contributor to this growth lies in their newly modified convenience. Although most commonly used as a device around the user's wrist, biometric trackers now come in alternative wearable options like shoes or sports bras, glasses, or even as stylish jewelry and more.
However, this surge of wearable technology carries more implications than merely keeping users more informed about their own personal fitness levels; how we use this data has changed the narrative and individual interaction with fitness as a whole.
A Change in Perspective
Technology has changed the spectrum of fitness to a constant as opposed to a segmented part of our days. A crucial aspect of gauging our fitness progress is not just what we do when we work out, but how our overall lifestyle and daily activities measure up compared to our exercise routines.
Until this adoption of fitness technology, the idea of fitness on a 24-hour scale was for the most part ignored. Before our Fitbits shamed us into getting 10,000 steps per day, it was much easier to dismiss how taking the stairs instead of the elevator could actually make a difference. Before our trackers buzzed every hour reminding us to get up and move around the office, many would not think twice about staying glued at our desks for hours on end.
Technology has brought a consciousness to fitness that changes our individual perspective, and therefore, attitudes towards it. This can be directly attributed to our access to data, and consequently how we can use this data to make collectively healthier decisions on a grander scale.
Furthermore, the importance of cloud-storage of our data is equally essential for maintaining this view and continued motivation. By tracking our progress concisely over time, we can tangibly see how small changes to our days make a difference or eventually form into new habits. Since motivation is especially easy to lose when we do not see results, maintaining a continuous archive of our biometric data negates this critical problem.
Additionally, cloud technology allows us to integrate data from various applications that can build a fuller picture of our well-being. For example, we can integrate data from food tracking applications, so they can monitor calories in vs calories out to gain an overall understanding of their daily activity.
Social Impact of Wearables
In addition to changing individual views, the impact of these technologies have also transformed mainstream perceptions of fitness in the following ways:
Fitness is "Trendy"
Since wearables have made individuals take a more vested interest in their fitness, they can be credited with helping make fitness "cool". The subculture of fitness plays a massive role in the mainstream media, whether through celebrity fitness influencers, athletic-inspired popular fashion, or the latest fad diet.
The cult-like social media fitness following and engagement speaks to how popular fitness as a whole has become. Wearables provide users with ideal content for sharing with their social networks, allowing them to connect with others who share their interests. Since engaging with varying aspects of fitness is so accessible, more and more individuals are encouraged to do so.
Lastly, the hype surrounding these latest gadgets has almost made them a status symbol. People love showing off their new Apple Watch or Fitbit, and in doing so, inspire others to want to buy these products as well.
The Gamification of Fitness
Although "making fitness fun" sounds like a tired cliche, wearables have brought an entertaining element to working out that appeals to our competitive sides. We can compete against ourselves to beat our personal "calories burned" record during a run or challenge ourselves to climb an extra flight of stairs per week.
Most wearables also allocate awards for personal milestones, like lifetime miles traveled or most steps taken in a day. These small yet validating achievements can still be the extra momentum needed to maintain our goals.
On an interactive level, wearables allow you to compete against others. You can connect your wearables with your friends to compete throughout the day or sync your device with your fitness club's network to see how your stats match up. This can turn essentially any gym activity or class into a game, and fitness clubs can similarly go the extra step by offering prizes or incentives based on member activity.
Fit-tech Revolutionizing the Fitness Club Experience
Unsurprisingly, health and fitness centers have made a general move to adopting cloud-based technology. This trend stems from the push for facilities to become more consumer-oriented, putting increased pressure on clubs to keep up with heightened expectations for their members.
Most gym attendees probably do not even recognize that cloud-based technology underlies most of the services that convenience them. For example, if your gym allows your sign-up for classes online, download a playlist you liked from a class or provides for a mobile application, you are using the cloud. Most modern equipment provides for blue-tooth capabilities to easily sync your workout details to your club's mobile app or your personal applications. Many gyms function entirely on cloud-based gym management software to safely store crucial information and avoid operational glitches.
USE CASE: Orangetheory, one of the most popular boutique clubs worldwide predicates its entire gym-model on cloud-based services. Orangetheory requires all members to wear its signature heart rate to monitor every class, which automatically syncs their data to the Orangetheory mobile app. The application allows users to easily access real-time progress reports as well as track workouts over time.
Other functionalities of the app include communicating directly between management and clients, settling outstanding payments, cooking classes, and rating instructors.
Bridging the Gap Between Fitness and Healthcare
The impact of this technology is not limited to individual health and wellness goals; it has already demonstrated to cross-over into imperative fields of healthcare and disease prevention.
For example, Fitbit recently established a partnership with Solera Health in the fight against Type 2 Diabetes. The relationship between the two respective organizations began in 2017 when Solera began offering Fitbits to its members who had enrolled in their own National Diabetes Prevention Program, which used digital coaching and support to help at-risk individuals minimize their threat of developing the disease.
The success of the program indicated that members who had used Fitbit devices lost more weight as well as exhibited more activity than participants who did not use them. The success of the primitive partnership laid the groundwork for the more extensive collaboration set to debut this year.
Using data acquired through Fitbit's platform, more individuals will not only be identified as at-risk for diseases like type-2 diabetes, high blood pressure, or varying heart diseases, but the scope of information provided by wearable trackers is a great first line of offense in actively combating those risks.
Potential for Fit-Tech in the Future
With fitness technology showing no signs of decline any time soon, the potential for the future shows great promise, especially considering the inevitability of this technology improving over time. Wearables and cloud-based services will continue to make people more active and cognizant about how their personal decisions impact their overall health and well-being.
On a macro level, widespread acceptance and utilization of these technologies demonstrate a cultural shift towards pursuing healthier lifestyles. Like Fitbit's involvement in fighting Type-2 Diabetes, bridging together fitness and medicine illustrate the promise of fitness technology and its untapped potential for the future.
Laura is a Digital Marketing Specialist for Perfect Gym Software. She is a native-born Los Angeleno recently relocated to Warsaw, Poland who spends most of her time at the gym, on an airplane, or online shopping.
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Marriott Courtyard hotel design approved without enthusiasm by Euclid Corridor Design Review Committee
Updated Nov 4, 2010 ; Posted Nov 4, 2010
View full sizeBraun & SteidlThe "after" view of the proposed University Circle Marriott Courtyard reflects changes in the design made in the past two weeks.
A moderately improved design for a new
Marriott Courtyard hotel
in University Circle won a lukewarm approval Thursday from the area's
Architects from the Akron firm of Braun & Steidl Architects changed the bricks on the building's facades from buff yellow to deep red, and added detail to metal panels on the corner above the main entrance facing Cornell Road.
It wasn't enough to please committee member John Wagner, who said the largely plain fa ade facing south toward a two-story brick building owned by University Hospitals and a parking garage, was "brutal" and "rough" and still needed work.
The committee's job is to make recommendations to the city's Planning Commission, whose approval is necessary for a building permit.
Two weeks ago, the Euclid Corridor Design Review Committee tabled a decision on the $27 million hotel project. In a rare move, the planning commission approved the earlier version of the proposal, but asked the architects to return to the design review committee.
On Thursday morning, a majority of the committee voted in favor of the design, but without enthusiasm. They asked the architects to return for subsequent meetings on improving the Mayfield facade, a rear entrance lobby and other details.
View full sizeBraun & SteidlThe "before" version of the Marriott Courtyard was tabled by the Euclid Corridor Design Review Committee two weeks ago, but approved by the City Planning Commission.
"This is a major site and a very important building," said committee member Richard Pace. "We can't say that three out of four facades are good."
Despite Pace's comment, it's fair to say that the mid-block site, which will face the east facade of the new University Hospitals cancer center across Cornell Road, is more of a background location.
Given this reality, it could be argued that the Braun & Steidl design fittingly will give the hotel a background building.
It's important, however, to realize that the dull facade facing northeast toward Mayfield will be very close to the new Museum of Contemporary Art Cleveland, which will occupy a dramatic, glass and black steel building designed by Farshid Moussavi of Foreign Office Architects in London.
MOCA, like other clients in University Circle, is trying to raise architectural standards in the area, the most economically dynamic part of Cleveland.
Braun & Steidl appear to be struggling to measure up. Their design seems to be part of a trend toward treating Mayfield Road as a less significant address than nearby Euclid Avenue.
As such, the hotel design is a poor reflection on the claims of local practitioners -- and readers of this blog -- who insist that local architects ought to get more prestigious jobs, such as designing a MOCA.
View full sizeBraun & SteidlThe proposed University Circle Marriott Courtyard as it would appear from Mayfield Road.
When a local firm can't overcome the challenges posed by a client with an apparently constrained budget and many functional requirements to satisfy, it's a poor reflection on the local design community.
Braun & Steidl has done some solid work in the area, including a new administrative building for Cleveland State University.
The hotel design has improved over the past couple weeks. But it is still a generic looking product. It makes you wonder whether something better would have been possible.