text:
\section{Introduction}
Efforts to understand deep generative models, \eg~generative adversarial networks~\cite{arjovsky2015wasserstein,goodfellow2014generative,gulrajani2017improved,miyato2018spectral,nowozin2016f,radford2016unsupervised,salimans2016improved}, and variational autoencoders~\cite{kingma2014semi,kingma2014auto,sohn2015learning}, have never stopped in recent years---motivated by the ultimate goal of interpretable and human-controllable artificial intelligence~\cite{doshi-velez2017towards,lakkaraju2016interpretable,rudin2019stop,zhang2018interpretable}.
The two main lines are: representation understanding (\eg, disentangling representation~\cite{gabbay2020demystifying,liu2020oogan,locatello2020disentangling,shu2020weakly}, seeking the correlation between objects and representations~\cite{bau2018gan}) and model understanding~\cite{al-shedivat2017contextual,jeon2018ib,kocaoglu2018causalgan,kulkarni2015deep,zhang2017interpreting,zhang2018interpretable,zhang2019interpreting}.

Learning interpretable representation, means to learn a disentangled representation which can separate the distinct, informative factors of variation in the data. Disentanglement related literature is filled with Bayesian generative models, such as VAE~\cite{kingma2014auto}, $\beta$-VAE~\cite{higgins2016beta}, and factor-VAE~\cite{kim2018disentangling}, where the representation can be learned through latent variable models with some structure prior.
In contrast, the work about disentanglement in GANs is very rare, even GANs have remarkable ability of generation.
Such a contrast forces us to think the reasons behind it, and we think the absence of sample likelihood and posterior inference in GANs seems to prohibit the forward step.
Then, a natural question arises, without a probabilistic framework in GANs, how to learn a factorized representation, especially disentangling the inter-class variation.

InfoGAN~\cite{chen2016infogan}, as one of the few choice for disentanglement methods in GANs, attempts to control the underlying factors by maximizing the mutualinformation (MI)~\cite{barber2003the} between the images and latent variables.
However, it has three obvious drawbacks: (\romannumeral1) computing the mutual information between factor and image appears not so reasonable, as they lie in different domains; (\romannumeral2) the optimization of InfoGAN is relatively complex and leads training instability; (\romannumeral3) it does not provide a way on how to utilize limited amount of supervision, as Locatello~\etal \cite{locatello2019challenging} recently have demonstrated that unsupervised disentanglement learning without inductive biases is theoretically impossible.

Following this line of thinking, we attempt to explore other ways that can address this issue.
Inspired by the definition of disentanglement, we have an intuition that the recent successful contrastive learning (CL)~\cite{sohn2016improved,2020A,2019Momentum, wu2018unsupervised,2020Supervised,2020Supervised} may open a door for us---the factor with the same value produce images with similar features and vise versa.

\begin{figure}[t]
	\centering
	\includegraphics[width=0.47\textwidth]{Fig1.pdf}
	\caption{The Network architecture of CD-GAN, which has one generator, one discriminator and one encoder. The input of Generator consists of two parts, one is the continuous latent code $\mathbf{z}$, which represents the content code, the other is one-hot latent factor $c$, which controls the category of generated images. The discriminator is used to determine whether an image is real or fake, while the encoder is constrained by contrastive loss and $\ell_2$ loss. The definitions of positives and negatives are images with the same class or not.}
	\label{fig:Struc}
\end{figure}

Motivated by this intuition, we propose a new disentanglement framework, namely contrastive disentanglement in generative adversarial networks (CD-GANs), which explore a new way for inter-class disentanglement, and provide possibility to utilize limited amount of supervision to the largest extent.
Specifically, we formulate a contrastive loss on class related features to guide the generated images to correlate to latent factors and analyze how such a loss potentially helps GANs training.
In addition, we provide a new way to make use of few labels to largest extent for improving inter-class disentanglement performance.

We demonstrate the efficacy of our CD-GAN on a number of well-known datasets: (\romannumeral1) MNIST~\cite{lecun2001gradient}, (\romannumeral2) Fashion-MNIST~\cite{xiao2017/online}, (\romannumeral3) CIFAR-10~\cite{Krizhevsky2009LearningML}, (\romannumeral4) dSprites~\cite{dsprites17} and (\romannumeral5) Faces~\cite{faces}. The extensive qualitative and quantitative results demonstrate the inter-class disentanglement capability of our method.
Importantly, the results with few labels in our framework can promote the disentanglement performance dramatically.

In summary, in this paper, we make the following contributions: (\romannumeral1) we propose a new inter-class disentanglement framework in GANs, which has the-state-of-the-art unsupervised disentanglement performance. (\romannumeral2) we analyze the advantage of contrastive loss for preventing mode collapse in GANs training. (\romannumeral3) we probe a new way, in complex data disentanglement, to make use of limited amount of supervision to the largest extent, rendering obvious performance improvement. (\romannumeral4) we demonstrate the efficacy of our CD-GAN on many well-known tasks and public datasets.

\section{Related Works}

This section reviews disentanglement in VAE and GANs, and contrastive learning.

\noindent\textbf{Disentanglement in VAE.}
VAE-based methods~\cite{chen2018isolating, higgins2016beta, kim2018disentangling} attempt to optimize the total correlation (TC) objective, which encourages the independence between each dimension of latent representation.
$\mathbf{\beta}$-VAE~\cite{higgins2016beta} uses a larger weight on the KL divergence between the variational posterior and the prior.
$\mathbf{\beta}$-TCVAE~\cite{chen2018isolating} estimates TC by minibatch-weighted sampling.
Factor-VAE~\cite{kim2018disentangling} utilizes a discriminator network to estimate the TC.
Recently, the work~\cite{locatello2020disentangling} has proposed to use few labels in disentangling factors of variation under the VAE framework.
These methods have been shown to be capable of disentangling factors with a successful disentanglement scores.
However, VAE-based methods has a limited capability of image generation caused by its $\ell_2$ norm loss.

\noindent\textbf{Disentanglement in GANs.}
Disentanglement methods in GANs~\cite{esser2020a, chen2016infogan, lin2020infogan, mukherjee2019clustergan, shen2020interpreting} can be categorized into two types: unsupervised and supervised.
InfoGAN~\cite{chen2016infogan}, as one typical unsupervised method, attempts to control the underlying factors by maximizing the mutual information(MI) between the images and latent vectors.
InfoGAN-CR~\cite{lin2020infogan} adds a contrastive regularizer over the latent codes.
While ClusterGAN~\cite{mukherjee2019clustergan} sample the one-hot latent vector and the continuous code with a reconstruct loss and a cross-entropy loss to achieve clustering in the latent space.
In the direction of supervised disentanglement, Esser~\etal ~\cite{esser2020a} proposed an invertible interpretation network to disentangled the representations of semantic concepts from hidden representations.

Recently, Locatello~\etal \cite{locatello2019challenging} has demonstrated that unsupervised
disentanglement learning without inductive biases is theoretically impossible and
that existing inductive biases and \emph{unsupervised methods do not allow to consistently
learn disentangled representations}.
On the other hand, some work~\cite{locatello2020disentangling} have pointed out that obtaining a limited amount of supervision, for example labelling extremely small number of samples, like 1\%, is effective in VAE based methods.
However, to the best of our knowledge, there is no related works on how to improve the disentanglement performance by virtue of little supervision in GANs.

\noindent\textbf{Contrastive Learning.} As a typical self-supervised representation learning, contrastive learning~\cite{sohn2016improved,2020A,2019Momentum, wu2018unsupervised,2020Supervised,2020Supervised} aims to maximize the characteristic similarity of positive pairs and to minimize it between negative pairs, then the learned representation can be applied to downstream tasks. Contrastive loss is inspired by noise contrastive estimation~\cite{2010Noise} or N-pair losses~\cite{sohn2016improved}, and it usually adopts cross-entropy loss, $\ell_2$ loss and cosine similarity loss.

Contrastive learning needs to construct positive and negative pairs for training.
SimCLR~\etal~\cite{2020A} introduces data augmentations such as rotation and cutout, and knowledge distillation to construct the single positive pair for every sample, and other images are regarded as negative samples for that sample.
Momentum Contrast (Moco)~\cite{2019Momentum, wu2018unsupervised} further demonstrates the effect of increasing the number of negative pairs on promoting the performance of contrastive learning.
On the contrast, supervised contrastive learing (SCL)~\cite{2020Supervised} uses image-level class labels as the basis for constructing positive and negative pairs, and show appropriately increasing the number of positive samples contributes to improve the performance of CL.
Our work is mainly inspired by SimCLR~\cite{2020A} and SCL~\cite{2020Supervised}, we introduce contrastive loss into GANs in the hope that it can guide GANs to fulfill inter-class disentanglement.

\section{Contrastive Disentanglement in Generative Adversarial Networks}
\label{CD-GAN}

Generative adversarial networks (GANs), as the fundamental deep generative model, directly learn a mapping (\ie~generator $G$) from input noise $\mathbf{z}$ to image, where discriminator $D$ is trained to discriminate real and fake images.
Specifically, it trains $D$ to maximize the probability of giving correct discrimination to both real and generative samples, and simultaneously train $G$ to maximize the probability of generated samples being real.
The objective function usually takes the form:
\begin{multline}
\min_{G}
\max_{D}
\mathbb{E}_{\mathbf{x} \sim p_{data}\left(\mathbf{x}\right)}
\left[ \log D\left( \mathbf{x}\right) \right]\\
+\mathbb{E}_{\mathbf{z} \sim p\left(\mathbf{z}\right)}
\left[ \log \left( 1-D( G\left( \mathbf{z}\right)\right) \right].
\label{eq:GAN}
\end{multline}
Here, $D\left(\mathbf{x}\right)$ can be viewed as the probability of $\mathbf{x}$ being real, and $D\left(G\left(\mathbf{z}\right)\right)$ is defined for the generated sample $G\left(\mathbf{z}\right)$.

As previously mentioned, disentanglement in GANs is not a trivial task, as latent variable model with data structure prior is hard to formulate in the scenario where no sample likelihood exists.
Towards breaking through this obstacle, we propose to disentangle the inter-class variation of data through comparing the features of generated images.
It is inspired by the intuition the same factor values render the same image feature related to that factor.

To disentangle the inter-class variation of data, which is the commonest in visual data, we define the input latent code consists of two parts: (\romannumeral1) $c\sim mul\left(\bm{\pi}\right)$, which controls the detailed class and (\romannumeral2) $\mathbf{z}\sim\mathcal{N}\left(\mathbf{0}, \sigma^2\mathbf{I}\right)$, which corresponds to intra-class variation.
Then, for each generated image $G\left(\mathbf{z}, c\right)$, given the discriminator $D$, the loss for generator now change to be:
\begin{equation}
\mathcal{L}_{GAN}=\mathbb{E}_{\mathbf{z} ,c \sim p\left(\mathbf{z}, c\right)}
\left[ \log \left( 1-D( G\left( \mathbf{z}, c\right)\right) \right].
\end{equation}
where $p\left(\mathbf{z}, c\right)$ can be rewritten as $p\left(\mathbf{z}\right)p\left(c\right)$ as $\mathbf{z}$ and $c$ are independent variables.

Inspired by the intuition that the class factor with the same values should produce images in the same class, we use an encoder to extract its class related representation $\mathbf{f}$, \ie, $\mathbf{f}=E_f\left(G\left(\mathbf{z},c\right)\right)$ and construct contrastive loss $\mathcal{L}_c$ with respect to feature $\mathbf{f}$.
We hope the same factor $c$ with the same values, even match with various $\mathbf{z}$, renders similar features $\mathbf{f}$, and vice versa.
Let $p_{pos}(\cdot,\cdot)$ be the positive distribution about $\mathbf{z}, c$ over $\mathbb{R}^{D+1}\times\mathbb{R}^{D+1}$, $p\left(\mathbf{z}, \text{neg}\left(c\right)\right)$ the distribution of negative samples, \ie, with different class factor value.
By virtue of contrasting positive and negative pairs, we formulate $\mathcal{L}_c$ as:
\begin{equation}
\displaystyle \mathop{\mathbb{E}}
_{\substack{\left(\mathbf{z}, c, \mathbf{z}^+, c^+\right)\sim p_{\text{pos}}\\ \left(\mathbf{z}_i,c^-_i\right)\stackrel{i.i.d.}{\sim} p\left(\mathbf{z}, \text{neg}\left(c\right)\right)}}
\left[-\log \frac{ e^{\left(\mathbf{f}^T\mathbf{f}^+/\tau\right)}}{e^{\left(\mathbf{f}^T\mathbf{f}^+/\tau\right)} +
\sum_i e^{\left(\mathbf{f}^T\mathbf{f}_i^-/\tau\right)}}\right]
\label{eq:CL}
\end{equation}
where $\tau > 0$ is a scalar temperature hyperparameter.
Here, $\mathbf{f}^+$ is the feature learned from positive sample, \ie, $E_f\left(G\left(\mathbf{z}^+,c^+\right)\right)$, $c^+ = c$, $\mathbf{z}^+$ is a sampling point from $p\left(\mathbf{z}\right)$ and $\mathbf{z}\neq \mathbf{z}^+$.
Similarly, $\mathbf{f}_i^-$ is the feature learned from negative samples, \ie, $E_f\left(G\left(\mathbf{z}^-_i,c_i^-\right)\right)$ and $c_i^- \neq c$.
Eq.~\ref{eq:CL} encourages the generated samples with the same latent code $c$ have similar features $\mathbf{f}$, and the samples with distinct latent code $c$ have unlike $\mathbf{f}$, as shown in Fig.~\ref{fig:Struc}.
Through such a contrastive loss, we can formulate the correlation between the class of generative images and latent code $c$.

Importantly, suppose the encoder, namely $E_f$, is ideal, $\mathbb{P}\left[\mathbf{f} = \mathbf{f}^+\right]=1$, the loss $\mathcal{L}_c$ changes to be:
\begin{equation}
\displaystyle \mathop{\mathbb{E}}
_{\substack{\left(\mathbf{z}, c\right)\sim p\left(\mathbf{z},c\right)\\ \left(\mathbf{z}_i,c^-_i\right)\stackrel{i.i.d.}{\sim} p\left(\mathbf{z}, \text{neg}\left(c\right)\right)}}
\left[ \log \left(e^{1/\tau}
+
\sum_i e^{\left(\mathbf{f}^T\mathbf{f}_i^-/\tau\right)}\right) \right]
\end{equation}
which is equivalent to maximizing the pairwise distance between $\mathbf{f}$ and $\mathbf{f}^-_i$ in the learned space $\mathcal{F}$, means pushing the images with different factor values (\eg~category) uniformly distributed.
This may alleviate the possibility of mode collapse in GANs training.

Except enforcing the class related features to be as similar as possible when factor $c$ has the same value, we also enforce the content not to change with class $c$ to guarantee the $c$ and $\mathbf{z}$ are distinct factors~\cite{mukherjee2019clustergan}.
Even we impose independent assumption on $\mathbf{z}$ and $c$, we also formulate a reconstruction loss $\mathcal{L}_z$ on the latent variables $\mathbf{z}$, which control the content of an image.
To this end, let $\mathbf{e}$ denote the class unrelated content, extracted as $\mathbf{e} = E_e\left(G\left(\mathbf{z}, c\right)\right)$, and define $\mathcal{L}_{\mathbf{z}}$ as:
\begin{equation}
\mathcal{L}_z = \displaystyle \mathop{\mathbb{E}}_{\left(\mathbf{z},c\right)\sim p\left(\mathbf{z},c\right)}
\|\mathbf{z}-\mathbf{e}\|_2^2
\end{equation}
where $E_e$ is the encoder responsible for extracting class unrelated content, and may share same layers with $E_f$.
The smaller $\mathcal{L}_z$ is, the better the content $\mathbf{z}$ has been preserved.

In addition, in previous Eq.~(\ref{eq:CL}), we set the number of positive pairs, compared with negative pairs, is 1.
This setting encourage each positive pair has large effect on calculating the contrastive loss, but not taking into account more positive pairs.
Similarly, adding larger numbers of negatives to the denominator provides increased contrast for the positives.
Following the work~\cite{2020Supervised}, we try to increase the number of positive pairs in contrastive loss formulation (\ie, Eq.~(3)) to weak this contrast, and may promote performance.
Thus, for any $\left(\mathbf{z},c\right)$, corresponding to $\mathbf{f}$, all positives, \ie, all samples with the same $c$ value, after sampling can be introduced to contribute the numerator, which may incur the relevant change of denominator.

Finally, the objective function for our CD-GAN takes account into the image reality (\ie, $\mathcal{L}_{GAN}$), the contrastive loss (\ie, $\mathcal{L}_c$) and the content consistency (\ie, $\mathcal{L}_{\mathbf{z}}$):
\begin{equation}
\mathcal{L}_G = \mathcal{L}_{GAN} + \beta_1 \mathcal{L}_c + \beta_2 \mathcal{L}_{\mathbf{z}}
\end{equation}
where $\beta_1$ and $\beta_2$ are two trade-off terms.
To constrain the flexibility, we prefer to learn $G$ and $E=\left\{E_f,E_e\right\}$ separately.
After optimizing $G$, we optimize $E$.
Finally, we update discriminator $D$ according $\mathcal{L}_D$, which takes the same form as in our chosen GANs.
The learning of CD-GAN is a two-step alternative optimization procedure: (\romannumeral1) update discriminator $D$, and (\romannumeral2) update generator $G$ and encoder $E=\left\{E_f,E_e\right\}$.

\begin{algorithm}
\small
\caption{Minibatch stochastic optimization for CD-GAN.}
\label{alg:Learning}
\begin{algorithmic}[1
\Require{Initialize $D$, $G$, and $E$.}
\For{number in training iterations}
  \State Generate minibatch of $2N$ examples: $\left\{\mathbf{x}^{fake}_n\right\}_{n=1}^{2N}$
  \State Sample minibatch of $2N$ examples: $\left\{\mathbf{x}^{real}_n\right\}_{n=1}^{2N}$
  \State Update $D$ by ascending gradient: $\nabla_{D}\mathcal{L}_D$
  \State Sample minibatch of $2N$ noise samples: $\left\{\left(\mathbf{z}_n,c_n\right)\right\}_{n=1}^N$
  \State Update $G$ by descending gradient with $E$ fixed: {\begin{center}  $\nabla_{G}\left\{\mathcal{L}_{GAN} + \beta_1\mathcal{L}_{c}+ \beta_2\mathcal{L}_{\mathbf{z}}\right\}$  \end{center}}
  \State Update $E$ by descending gradient with $G$ fixed: {\begin{center}  $\nabla_{E}\left\{\beta_1\mathcal{L}_{c}+ \beta_2\mathcal{L}_{z}\right\}$  \end{center}}
\EndFor
\end{algorithmic}
\end{algorithm}

\section{Using Few Labels}

Our key idea is, in our contrastive disentanglement framework, to introduce some real examples with labels as anchor points and drag generated samples to be close to them.
Compared with the unsupervised contrastive loss in Eq.~(\ref{eq:CL}), the main difference lies in the contrastive loss on positive pairs.
For each sample generated from $\left(\mathbf{z}, c\right)$, the contrast on positive pairs becomes:
\begin{equation}
 e^{\left(\mathbf{f}^T\mathbf{f}^+/\tau\right)}
+
e^{\left(\mathbf{f}^T\mathbf{f}_{real}^{+}/\tau\right)}
\end{equation}
where $\mathbf{f} = E_f\left(G\left(\mathbf{z}, c\right)\right)$, $\mathbf{f}^+ = E_f\left(G\left(\mathbf{z}^+, c^+\right)\right)$, and $\mathbf{f}^+_{real}$ is extracted from a real image, which is sampled from a very small subset of real images with labels.
Thus, for each generated image, we encourage its feature is close to one anchor point, which may make use of these few labels to the largest extent.

Similarly, the contrastive loss for negative pairs can take some examples from real images with different labels.
Thus, the contrast here does not only means contrast with positive and negative pairs, but also means contrast fake examples with anchor points (\ie, real images with few labels), which may largely promote the disentangling performance, even very few labels are involved.

\section{Experiments}

We evaluated the performance of our CD-GAN on a number of tasks and datasets to demonstrate its efficacy.

\subsection{Benchmark Datasets}
We use 5 benchmark datasets to evaluate our proposed CD-GAN, and the details of these datasets are described in the following:
(\romannumeral1) MNIST~\cite{lecun2001gradient}. It has 70,000 $28\times28$ grayscale images of digits ranging from 0 to 9.
The training set has 60000 samples.
(\romannumeral2) Fashion-MNIST~\cite{xiao2017/online}. It also has 70000 images with the size $28\times28$.
The 10 types of fashion products are: ankle boot, bag, coat, dress, pullover, sandal, shirt, sneaker, trouser and Tshirt.
(\romannumeral3) CIFAR-10~\cite{Krizhevsky2009LearningML}. It has 60,000 labeled $32\times32$  RGB natural images in 10 classless.
The 10 classes are: airplane, automobile, bird, cat, deer, dog, frog, horse, ship and truck.
There are 50000 images for training.
(\romannumeral4) dSprites~\cite{dsprites17}. It consists of 737,280 binary $64\times64$ images of 2D shapes.
It has 5 independent latent factors: shape, scale, rotation, $x$ and $y$ positions of a sprite.
The shape contains three categories: square, ellipse and heart.
(\romannumeral5) Faces~\cite{faces}. It contains many latent factors as azimuth (pose), elevation, and lighting.
We randomly generated 10,000 different faces and sampled 10 images at equal intervals from -90 degrees to 90 degrees for each face.
Thus, the total number of images is 100000.
\subsection{Experimental Setup}
\noindent\textbf{Evaluation Metrics.}
We used normalized clustering purity (ACC), normalized mutual information (NMI), and adjusted rank index (ARI) to evaluate our disentangling performance~\cite{mukherjee2019clustergan}.
ACC is designed to calculate the percentage of correct clusters in total samples.
NMI tend to evaluate cluster score by measuring the normalized mutual information between real labels and clustered labels.
ARI is a metric that measures the similarity of the two clusters by evaluating the proportion of the correct clustering in all clustering results.
Following the work~\cite{mukherjee2019clustergan}, given test images, we adopted $k$-means to cluster the features encoded by $E_f$.
Then, the above mentioned three metrics were used to evaluate performance.
The reported the best ACC, NMI and ARI scores from 5 runs.
In addition, to evaluate the generative performance, we use Inception Score (IS)~\cite{salimans2016improved} and Fr\'{e}chet Inception Distance (FID)~\cite{heusel2017gans} for evaluation.
The IS was calculated for 10 partitions of 50,000 randomly generated samples, and the FID was calculated for 50,000 samples.

\begin{figure*}[t]
	\centering
	\subfigure[MNIST(Unsupervised)]{\includegraphics[width=0.31\textwidth]{MNIST.png}}
	\hspace{.15in}
	\subfigure[Fashion-MNIST(Unsupervised)]{\includegraphics[width=0.31\textwidth]{Fashion-MNIST-Unsupervised.png}}
	\hspace{.15in}
	\subfigure[Fashion-MNIST(Few labels)]{\includegraphics[width=0.31\textwidth]{FashionMNIST.png}}
	\caption{Images generated by CD-GAN trained on different datasets. Each row corresponds to one latent factor value. \textbf{(a):} Trained on MNIST with unsupervised setting. The images in each row have the same category. \textbf{(b)}: Trained on Fashion-MNIST with unsupervised setting. The images in each row have the same category. \textbf{(c):} Trained on Fashion-MNIST with few labels. Obvious disentanglement performance can be found. }
	\label{fig:Mnist}
\end{figure*}
\noindent\textbf{Baselines.}
We compared our CD-GAN with the primary GANs based disentanglement methods, including: (\romannumeral1) GANs~\cite{goodfellow2014generative}, (\romannumeral2) InfoGAN~\cite{chen2016infogan}, (\romannumeral3) InfoGAN-CR~\cite{lin2020infogan}, (\romannumeral4) ClusterGAN~\cite{mukherjee2019clustergan}.
We did not compare CD-GAN with VAE based disentanglemet methods, as VAE based disentanglement has been well studied whereas GANs based disentanglement has been seldom explored.
In fact, the generation performance of GANs should obviously better than VAE as VAE has a $\ell_2$ reconstruction loss.

\noindent\textbf{Implementation Details.}
The parameter settings, such as trade-offs, batch size, network architecture and optimization details are described in the following.

\noindent\textbf{-MNIST.} When calculating $\mathcal{L}_c$, the number of positives and negatives for each sample is the number of samples having the same factor value or having the different values.
The network generator and discriminator architecture follows the work~\cite{mukherjee2019clustergan}, which was WGAN-GP~\cite{gulrajani2017improved}.
The encoder's architecture is almost the same as the Discriminator.
The batchsize for encoder, generator and discriminator were 512, 64, and 64.
The dimension of $\mathbf{z}$ was 30, and $\mathbf{c}$ was one-hot with the dimension of 10.
The dimension of the embedding $\mathbf{f}$ and $\mathbf{e}$ were set be 40 and 30, respectively.
$\beta_1$ and $\beta_2$ were set to be 50 and 0.0005.
The value of $\tau$ is set it be 0.07.

\noindent\textbf{-FashionMNIST.} The main parameters and experimental settings were set the same as the ones on the MNIST dataset.
Some differences are list in the following.
The batch size of encoder was set to be 1200, and the dimension of $\mathbf{z}$ was set to be 40.
The dimensions of $\mathbf{f}$ and $\mathbf{e}$ were 50 and 40 respectively, and $\beta_1=100$.
In the experiment of introducing few labels, $\beta_1$ were set to be 50, and $2.4\%$ of the training samples were with labels.
\begin{figure*}[t]
	\centering
	\subfigure[InfoGAN]{\includegraphics[width=0.31\textwidth]{cifar10-InfoGAN.png}}
	\hspace{.15in}
	\subfigure[ClusterGAN]{\includegraphics[width=0.31\textwidth]{cifar10-ClusterGAN.png}}
	\hspace{.15in}
	\subfigure[CD-GAN]{\includegraphics[width=0.31\textwidth]{cifar10-CDGAN.png}}
	\caption{Images (with size $32\times32$) generated by different methods on CIFAR-10. Each row corresponds to one latent factor value. \textbf{(a):} InfoGAN. The images of each row have no obvious disentanglement results and the image quality is poor.  \textbf{(b):} ClusterGAN. The images of each row have no obvious disentanglement results. \textbf{(c):} CD-GAN. Some obvious similar images can be found in one row, such as the 2rd, 8th and 9th rows.}
	\label{fig:cifar}
\end{figure*}

\noindent\textbf{-CIFAR-10.}
The network architecture was set according to the work ~\cite{miyato2018spectral}, which was SNGAN.
The encoder's architecture was ResNet18~\cite{he2016deep}.
The batch size of generator was 128, while it for discriminator and encoder were 64 and 256.
The dimension of $\mathbf{z}$ was 118, and the dimension of $\mathbf{c}$ was 10.
When calculating $\mathcal{L}_c$, the number of positives and negatives for each sample is the number of samples having the same factor value or having the different values.
The dimension of $\mathbf{f}$ and $\mathbf{e}$ were 128 and 118 respectively.
In addition, $\beta_1=1$, and $\beta_2=0.01$. The value of $\tau$ was 0.05.
In the experiment of introducing few labels, $5\%$ of the training samples were with labels, and $\beta_2$ were set to be 0.00005.

\noindent\textbf{-dSprites.} The shape variation was chosen as the target for disentanglement.
The scale, rotation and translation changes can be treated as intra-class variation.
The network architecture follows the work~\cite{lin2020infogan}, which was SNGAN.
The encoder's architecture follows the Q-network of the InfoGAN-CR~\cite{lin2020infogan} except the last layer.
The batch size for was set to be 300 for generator and discriminator, and 1200 for encoder.
727,280 images were used for training and the rest 10,000 for testing.
The dimension of $\mathbf{z}$ was 52, and $\mathbf{c}$ was one-hot with the dimension of 3.
The dimension of $\mathbf{f}$ and $\mathbf{e}$ were 40 and 52, respectively.
In addition, $\beta_1=1$, and $\beta_2=0.0001$.
In the experiment, $1.2\%$ of the training samples were set to have labels.
The value of $\tau$ was 0.07.

\noindent\textbf{-Faces.} The network architecture for GANs were set according to the work~\cite{chen2016infogan}, which was WGAN-GP.
The encoder's architecture was the same as the discriminator except the last layer.
The batch size of generator and discriminator were 180, while the batch size of encoder was 3,600.
The dimension of $\mathbf{z}$ was 118,  and the dimension of $\mathbf{c}$ was 10.
The dimension of the embedding $\mathbf{f}$ and $\mathbf{e}$ were 128 and 118, respectively.
In addition, $\beta_1=10$ ,and $\beta_2=10$.
$1\%$ of the training samples were with labels.
The value of $\tau$ was set to be 0.07.

\subsection{Ablation Study}
To test the validity of contrastive loss for disentangling inter-class variation, we set the value of $\beta_1$ to be zero.
Then, the objective function only have two parts: (\romannumeral1) the GANs loss $\mathcal{L}_{GAN}$, and (\romannumeral2) the content consistency loss $\mathcal{L}_z$.
From the results in Fig.~\ref{fig:Mnist}, we find there is no disentangling effect on both MNIST and Fashion-MNIST, even we also have a structured input, including content factor $\mathbf{z}$ and class factor $c$.
Such results obviously demonstrate, through contrastive loss, we can disentangle the discrete variation of some factor.
\begin{figure}[h]
	\centering
	\subfigure[MNIST]{\includegraphics[width=0.2\textwidth]{MNIST-Ablation.png}}
		\hspace{.15in}
	\subfigure[Fashion-MNIST]{\includegraphics[width=0.2\textwidth]{Fashion-MNIST-Ablation.png}}
	\caption{Images generated by CD-GAN without contrastive loss. Each row corresponds to one latent factor value, it is proved that disentanglement can not be resolved without contrast loss. \textbf{(a):} Trained on MNIST.  \textbf{(b):} Trained on Fashion-MNIST.}
	\label{fig:Ablation}
\end{figure}

\subsection{Quantitative Results}

\noindent\textbf{Disentangled Results.}
Fig.~\ref{fig:Mnist} shows the disentangled results with training data on the MNIST, and Fashion-MNIST datasets.
The images in each row are generated from fixed factor $c$ and various noise $\mathbf{z}$.
The results on MNIST as in Fig~\ref{fig:Mnist} (a) show the generation results of our unsupervised CD-GAN, where different values of $c$ controls digits from 0 to 9.
In addition, this hints unsupervised disentanglement method may be capable of disentangling simple structured data.
However, the results on Fashion-MNIST dataset changes to be worse as this dataset becomes more complex.
From the work~\cite{mukherjee2019clustergan}, we know unsupervised disentanglement methods fails to deal with this dataset to some extent.
From Fig.~\ref{fig:Mnist} (c), we observe our CD-GAN with few labels can dramatically improves the disentanglement performance on the Fashion-MNIST dataset.
This is due to the contrastive loss in CD-GAN, which can drag fake images to be close to real ones in the same class, even the real ones are very limited.

Fig.~\ref{fig:cifar} shows the disentanglement results of InfoGAN, ClusterGAN and CD-GAN on the CIFAR-10 dataset.
From the results, we find InfoGAN and ClusterGAN both fail to disentangle the correct inter-class variation, since CIFAR-10 is much more complex than MNIST and FashionMNIST.
Such results also demonstrate our claim that disentanglement learning should have limited amout of supervision.
From Fig.~\ref{fig:cifar}, where 5\% of training images have labels, we observe some inter-class variation can be disentangled.
For example, the 2nd, 6th and 8th rows correspond to ``automobile'',``dog'',``horse''.

\begin{figure}[t]
	\centering
	\subfigure[Square]{\includegraphics[width=0.2\textwidth]{dSprites0.png}}
	\hspace{.15in}
	\subfigure[Ellipse]{\includegraphics[width=0.2\textwidth]{dSprites1.png}}
	\hspace{.15in}
	\subfigure[Heart]{\includegraphics[width=0.2\textwidth]{dSprites2.png}}
	\hspace{.15in}
	\subfigure[ACC, NMI, ARI]{\includegraphics[width=0.2\textwidth]{dsprites.pdf}}
	\caption{Images generated by CD-GAN on dSprites. \textbf{(a), (b)}, and \textbf{(c)} represent the images corresponding to the three shapes, respectively. There is enough diversity in the images for each category.
	\textbf{(d)} is the result of the ACC, NMI and ARI score.}
	\label{fig:dsprites}
\end{figure}

Fig.~\ref{fig:dsprites} shows the disentangled results on dSprites dataset.
Fig.~\ref{fig:dsprites} (a), (b) and (c) show the generation results of changing factor $c$ and $\mathbf{z}$, where the shape ``square'', `` ellipse'', and ``heart'' are generated orderly.
These results reveal our CD-GAN with few labels is robust to intra-class changes, such as scale, rotation and translation.
This should own to the contrastive loss of our method, which not only contrasts the features associated with different factors, but also utilizes the few labels to the largest extent.
The ACC, NMI and ARI scores are all close to 1, even only 1\% of labels are involved.

\begin{figure}[t]
	\centering
	\includegraphics[width=0.38\textwidth]{Faces.png}
	\caption{Images generated by CD-GAN trained on Faces. Each row corresponds to one latent factor value. Experiments show that the continuous variation of the same attribute can be controlled by adding loss $\mathcal{L}_{\mathbf{z}}$.}
	\label{fig:faces}
\end{figure}

Fig.~\ref{fig:faces} shows the disentangled results on Faces dataset, where each row corresponds to different head pose.
We discretized the continuous pose change to discrete pose change, the angle gap between each class is $20^{\circ}$.
We observe the images in adjacent categories are very similar, compared with digits, objects or shapes.
The results show our CD-GAN has the capability to disentangle the inter-class variation, where the adjacent class are very similar.
This further demonstrate the efficacy of our method for inter-class disentanglement.

\noindent\textbf{t-SNE Results} To further study how CD-GAN disentangle inter-class variation, we visualized the features $\mathbf{f}$, the output of $E_f$, by t-SNE on Fashion-MNIST dataset.
We sampled $10000$ points from $p\left(\mathbf{z},c\right)$, and for each fixed $c$, the number of sampled points is the same.
In Fig.~\ref{fig:tsne}, Different color corresponds to different values of factor $c$.
We visualized the encoded features of unsupervised CD-GAN and CD-GAN with few labels.
\begin{figure}[h]
	\centering
	\subfigure[Fashion-MNIST(Unsupervised)]{\includegraphics[height=0.165\textwidth]{UnSupervised-Fashion-MNIST-tsne.png}}
	\subfigure[Fashion-MNIST (Few labels)]{\includegraphics[height=0.165\textwidth]{Supervised-Fashion-MNIST-tsne.png}}
	\caption{Visualization of the generated images by t-SNE on the Fashion-MNIST dataset. \textbf{(a):} Visualization of the generated images by CD-GAN with unsupervised training. \textbf{(b):} Visualization of the generated images by CD-GAN with limited amout of supervision. It shows that limited amount of supervision guides better disentanglement.}
	\label{fig:tsne}
\end{figure}

Fig.~\ref{fig:tsne} shows an obvious phenomenon, that is, CD-GAN with even approiximate 2\% labels have dramatically improved disentanglement result than unsupervised CD-GAN.
This is due to our contrastive loss design, where each generated image are compared with one real images with labels.
These real images with labels in CD-GAN disentanglement framework can be treated as anchor points, which may largely calibrate the generated images having the same class labels.

\subsection{Qualitative Results}
\begin{table}[t]
\begin{center}
	{
	\begin{tabular}{l l l l
	\hline \hline
	 Algorithm                    & ACC  & NMI  & ARI\\
	\hline
	\hline
	ClusterGAN~\cite{mukherjee2019clustergan}   &$\mathbf{0.95}$  & $0.89$ & $0.89$ \\
    Info-GAN~\cite{chen2016infogan}  &$0.89$  & $0.86$ & $0.82$ \\
    Info-GAN-CR~\cite{lin2020infogan}   &$0.90^*$  &$0.91^*$  &$0.92^*$        \\
    GAN with bp~\cite{mukherjee2019clustergan}              &$0.95$  &$0.90$ &$0.89$ \\
    GAN with Disc.$\phi$~\cite{mukherjee2019clustergan}     &$0.70$  &$0.62$ &$0.52$ \\
    CD-GAN (Unsupervised)                   &$0.91$  &$\mathbf{0.93}$ &$\mathbf{0.94}$\\
	\hline \hline
	\end{tabular}}
\end{center}
	\centering\caption{Comparison of disentanglement Result on MNIST. $*$ denotes the results obtained by ours running.}
	\label{tab:mnist}
\end{table}

\begin{table}[t]
\begin{center}
	{
    \begin{tabular}{l l l l
	\hline \hline
    Algorithm                    & ACC  & NMI  & ARI\\
	\hline
	\hline
    ClusterGAN~\cite{mukherjee2019clustergan}  &$0.63$  &$\mathbf{0.64}$ &$0.50$  \\
    Info-GAN~\cite{chen2016infogan}    &$0.61$  &$0.59$ &$0.44$ \\
    Info-GAN-CR~\cite{lin2020infogan}   &$0.61^*$  & $0.52^*$ &$0.41^*$   \\
    GAN with bp ~\cite{mukherjee2019clustergan}           &$0.56$  &$0.53$ &$0.37$ \\
    GAN with Disc.$\phi$~\cite{mukherjee2019clustergan}   &$0.43$  &$0.37$ &$0.23$ \\
    CD-GAN (Unsupervised)    &$\mathbf{0.66}$    &$0.62$  &$\mathbf{0.53}$   \\
    CD-GAN (Few labels)    &$\mathbf{0.82}$  &$\mathbf{0.72}$  &$\mathbf{0.68}$    \\
	\hline \hline
	\end{tabular}}
\end{center}
	\centering\caption{Comparison of disentanglement Result on Fashion-MNIST. $*$ denotes the results obtained by ours running.}
	\label{tab:fashion}
\end{table}

\begin{table}[t]
\begin{center}
	{
	\begin{tabular}{l l l l
	\hline \hline
    Algorithm      & ACC   & IS & FID\\
    \hline\hline
	ClusterGAN~\cite{mukherjee2019clustergan}  & $0.14^*$  &$7.26^*$ &$26.86^*$\\
	Info-GAN~\cite{chen2016infogan}   & - &$7.19^*$ &$28.20^*$ \\
    Info-GAN-CR~\cite{lin2020infogan} & - &$6.81^*$ &$29.82^*$  \\
    CD-GAN (Few labels)   &$\mathbf{0.41}$  &$\mathbf{7.65}$ &$\mathbf{23.97}$ \\
    \hline \hline
	\end{tabular}}
\end{center}
	\centering\caption{Comparison of disentanglement Result and evaluating Inception Score(IS) and Frechet Inception Distance(FID) on CIFAR-10. $*$ denotes the results obtained by ours running.}
	\label{tab:cifar}
\end{table}

The results in Tabs.~\ref{tab:mnist}, Tab.~\ref{tab:fashion} and Tab.~\ref{tab:cifar} show some tendencies in the comparison with other baseline methods.
(\romannumeral1) Our CD-GAN have achieve state-of-the-art performance, when compared with other baseline methods.
The ACC, NMI and ARI score are 0.91, 0.93, and 0.94 on MNIST and 0.66, 0.62, 0.53 for unsupervised settings on Fashion-MNIST.
As discussed in Sec.~3, the contrastive loss may have more powerful disentanglement capability, which not only considers the similarity of generated samples in one class, but also utilizes the dissimilarity of samples in different class.
(\romannumeral2) Our CD-GAN with few labels (usually 1-2\%) can dramatically promote the disentanglement performance.
As we discussed previously, this is due to the contrastive loss in CD-GAN, which provide us a way to have real images with labels as anchor points and calibrate generated images by virtue of them.
(\romannumeral3) On especially complex dataset, such as CIFAR-10, all unsupervised methods fails, where the ACC is close to 0.1.
Our CD-GAN with few labels (with 5\%) can have much better performance than ClusterGAN, which dramatically promote the disentanglement performance afforded by unsupervised methods.
This is also due to the contrastive disentanglement strategy.

Tab.~\ref{tab:cifar} also shows the FID comparison between CD-GAN, InfoGAN, InfoGAN-CR and ClusterGAN on CIFAR-10 dataset, where each method adopts SNGAN architecture.
This result shows our IS and FID for unsupervised setting are 7.38 and 25.40 while ClusterGAN's IS and FID are 7.26 and 26.86.
Obviously , such results are much better than InfoGAN and InfoGAN-CR, in which the mutual information regularization may limit diversity.
Moreover, by virtue of few labels, our CD-GAN further improved IS and FID.

\section{Conclusion and Future Work}
Contrastive loss was introduced in generative adversarial networks to construct a contrastive disentanglement framework for interpretable representation learning.
By virtue of contrastive loss, we formulated the correlation between the disentangled factor values and inter-class image variation.
Moreover, we analyzed the advantage of formulating contrastive loss for preventing mode collapse in GAN's training.
We provided a new way to make use of limited amount of supervision to the largest extent in disentangling complex data.
We demonstrated the efficacy of our CD-GAN on many well-known tasks and public datasets.

In the current work, we use a moderate batchsize in parameter learning, which may limit the capability of contrastive learning.
Although our results indicate such a choice is effective, it is worth to explore extremely large batchsize for performance promotion further.
In addition to apply contrastive learning for inter class variation disentanglement, we are currently investigating the possibility of applying it for more complex factor disentangling problem.

{\small
\bibliographystyle{ieee_fullname}

redpajama_set_name: RedPajamaArXiv


With more than 10 years of experience in Metal Scrap trading industry, we are one of the leading companies in Mindanao, Philippines. Our company is looking forward to have a business relationship with overseas metal scrap buyers.
At present, we have Aluminium Scrap for sale, for a quantity of 50 MT on a regular monthly basis. Metal scrap buyers who would like to buy Aluminium Scrap, please contact us with all required details, so that we can contact you back as per that information.


The Talon SRX motor controller is a CAN-enabled "smart motor controller" from Cross The Road Electronics/VEX Robotics. The Talon SRX can be controlled over the CAN bus or PWM interface. When using the CAN bus control, this device can take inputs from limit switches and potentiometers, encoders, or similar sensors in order to perform advanced control such as limiting or PID(F) closed loop control on the device.
Extensive documentation about programming the Talon SRX in all three FRC languages can be found in the Talon SRX Software Reference Manual on CTRE's Talon SRX product page.


text:
Q: Flask App runs in terminal but not in Docker Container So, I've got this flask app that works fine when I run it via the terminal, but for some reason, if I start up a docker container, it instantly exits with 'exit code zero'.
Here is the folder structure:
https://imgur.com/a/BOGCt6S
docker-compose.yml:
version: '3.2'

services:

    flask-app:
        build: ./Flask_App
        volumes:
            - ./Flask_App/static:/Flask_App/static
            - ./Flask_App/db:/Flask_App/db
        ports:
            - '5001:5001'
        # restart: unless-stopped

Dockerfile:
FROM python:3

# Setup env
COPY requirements.txt ./

RUN pip install -r requirements.txt

# Setup App
RUN mkdir -p /Flask_App

COPY . /Flask_App

WORKDIR /Flask_App

EXPOSE 5001

ENTRYPOINT [ 'python3' ]

CMD [ 'app.py' ]

and the app.py file: (I know its just imports, but it works fine when I run it via the terminal on the host, so it probably is working ok)
from flask import Flask
from flask_sqlalchemy import SQLAlchemy
from flask_login import LoginManager
from flask_bootstrap import Bootstrap

from forms import *

app = Flask(__name__)
app.config.from_pyfile('config.py')
Bootstrap(app)

db = SQLAlchemy(app)

login_manager = LoginManager()
login_manager.init_app(app)
login_manager.login_view = 'login'

from views import *

if __name__ == '__main__':
    app.run(host=app.config['HOST'], port=app.config['PORT'], debug=app.config['DEBUG'])

and just in case, here is a part of the 'config.py' file:
DEBUG = False
HOST = '0.0.0.0'
PORT = 5001

A: As said by David Maze in a comment, the single quotes(') I used in ENTRYPOINT and CMD should be changed to double quotes ("), since they are supposed to be JSON arrays.

redpajama_set_name: RedPajamaStackExchange


Mary Frances' forever popular bracelet bag. Golden green shimmer silk embellished with bronze, gold and golden green bead work. Magnetic top closure. Embroidered silk handle.
These are the coolest novelty bags, very popular and very expensive.


text:
"Music is better than medicine." That's what 11 year old Alma told Westchester legislators. Alma went to Albany with Sister Beth Dowd and ten other youngsters of Songcatchers on Valentine's Day to deliver some love songs and to ask for a boost in arts funding. Songcatchers is an organization that works out of St. Gabriel's basement in New Rochelle teaching music to hundreds of youngsters at $8 a lesson. How they do that is with lots of volunteers, mentors and prayer, all of which Sister Beth learned in the ministry as an educator and musician. Starting in 1997 in one room with 35 kids, eight volunteers and four instruments, Songcatchers now occupies four rooms, has 200 students, 70 volunteers and teaches 12 instruments. With the motto "Reaching for Peace Through Music" most youngsters participate in the choir and also play two instruments. Musikgarten, Choir Camp and Composers of the Future are just a few of the programs Sister Beth runs on a budget of $165,000, only $1,000 of which she received this year from an Arts Alive Grant from ArtsWestchester, which is funded by the New York State Council on the Arts (NYSCA).
I was curious as to how important that $1,000 was to Sister Beth, so I asked. "It's not much money, but it gets us lots of mileage," she said. I didn't understand. She elaborated. "We never could have taken the growth steps that we did without the support of ArtsWestchester, which over 12 years totaled $46,085. This support helped us raise private donations and secure additional grants. For example, the Arts Alive Grant allowed us to team up with Barry Hoffman's Westchester Chamber Orchestra on Composers of the Future." This program encourages kids to compose music, which is then performed by the orchestra. "The funds were never huge, but despite ups and down, they were steady."
Songcatcher's grant this year is one quarter of the $4,000 it was in 2000. Then too, NYSCA's budget for grants this year is $31.6 million as compared to a high of $54.3 million in 1990. The best news we have heard in a long time is that Governor Andrew Cuomo has recommended level funding for next year. Yet, statewide arts advocates at www.ArtsNYS.org are asking legislators to add another $4.5 million for programs like Sister Beth's throughout the state.
Neil Watson, director of the Katonah Museum, who traveled to Albany this year said, "For cultural organizations like Katonah Museum of Art, NYSCA support is a main artery. As important as the funds are, NYSCA support also represents a seal of approval that enables us to raise private funds, enlarging and diversifying our base of support." Dominick Balletta of the Jacob Burns Film Center echoed these sentiments. "The funds Jacob Burns receives from NYSCA are effectively redistributed throughout Westchester because people from all over Westchester come to the film center."
While no promises were made, there was a genuine recognition of the importance of NYSCA's community partnership. State Senator Andrea Stewart-Cousins told us, "New York State Council on the Arts is the smallest agency in the state, but its impact far exceeds its size. Because of NYSCA, the state has developed an arts sector that stimulates the economy, builds community and transforms individual lives. In Westchester, The Decentralization Program that makes possible Arts Alive Grants puts the arts at the heart of community life." Sister Beth agrees. "The arts build communities. It unites us."
Tags: Arts Alive Grant, ArtsWestchester, Jacob Burns Film Center, Katonah Museum, New York State Council on the Arts, NYSCA, Senator Andrea Stewart-Cousins, Songcatchers, Westchester, Westchester Chamber Orchestra, Westchester legislators
« The Tree People
Ted Mann Remembered »

redpajama_set_name: RedPajamaCommonCrawl


The titles "verkfrædingur" for chartered engineer and "tæknifrædingur". for technical engineer are protected by law in Iceland. Permission to use these titles is granted by the Ministry of Industry to those persons deemed qualified by the individual engineering associations.


text:
layout: archive
permalink: /tags/german
---

# Cuisine: German

<div class="tiles">
{% assign sorted_posts = site.tags.German | sort: 'title' %}
{% for post in sorted_posts %}
  {% include archive-single.html type="grid" %}
{% endfor %}
</div><!-- /.tiles -->

redpajama_set_name: RedPajamaGithub


text:
The science-fiction adventure follows two boys struggle to revive humanity after a mysterious crisis has left everyone in the world turned to stone for several millennia.
As regular high school students Taichi and Rin disappeared in a beam of light. When they came to, the two of them were already in a world of swords and…
Genre: Action & Adventure, Animation, Sci-Fi & Fantasy
Arifureta: From Commonplace to World's Strongest
Seventeen-year-old Hajime Nagumo is your average, everyday otaku. However, his simple life of pulling all-nighters and sleeping in school is suddenly turned upside down when he, along with the rest…
For a thousand years, the Vikings have made quite a name and reputation for themselves as the strongest families with a thirst for violence. Thorfinn, the son of one of…
Genre: Action & Adventure, Animation, Drama, War & Politics
Year 198 of the Solar Era in Tokyo, special fire brigades are fighting against a phenomenon called spontaneous human combustion where humans beings are turned into living infernos called "Infernals"….
Is It Wrong to Try to Pick Up Girls in a Dungeon? On the Side: Sword Oratoria
This is an adaptation of Sword Oratory, light novel side story of Dungeon neither Deai the Motomeru no wa Machigatte Iru Daru Ka. It covers the story of one of…
Genre: Action & Adventure, Animation, Comedy
After a traffic accident, Kazuma Sato's disappointingly brief life was supposed to be over, but he wakes up to see a beautiful girl before him. She claims to be a…
Grimgar of Fantasy and Ash
When Haruhito awakens, he's in the dark. He doesn't know where he is, why is he here, and where he came from. Around him there are several men and women…
20-something Angus MacGyver creates a clandestine organization where he uses his knack for solving problems in unconventional ways to help prevent disasters from happening.
Genre: Action & Adventure, Drama
Shigeo Kageyama, a.k.a. "Mob," is a boy who has trouble expressing himself, but who happens to be a powerful esper. Mob is determined to live a normal life and keeps…
In 2012, the world allegedly comes to an end at the hands of a human-made virus, ravaging the global populace and leaving only children untouched. It is at this time…
Normal high school kids by day, protectors of Paris by night! Miraculous follows the heroic adventures of Marinette and Adrien as they transform into Ladybug and Cat Noir and set…
The students of class 3-E have a mission: kill their teacher before graduation. He has already destroyed the moon, and has promised to destroy the Earth if he can not…
Aesthetica of a Rogue Hero
Known as a Rogue Hero in the fantasy world Arezard, Akatsuki decided to take upon the request of the defeated Demon King to take his daughter with him. After bringing…
Stigma of the Wind
Four years ago, Kazuma Kannagi was defeated by his cousin Ayano Kannagi for the right to be the successor of Enraiha, a sword passed down in the Kannagi family. His…
When Buy More computer geek Chuck Bartowski unwittingly downloads a database of government information and deadly fighting skills into his head, he becomes the CIA's most vital secret. This sets…
Genre: Action & Adventure, Comedy, Drama
Anime television series created, directed and written by Tensai Okamura and animated by Bones.
Genre: Action & Adventure, Animation, Mystery, Sci-Fi & Fantasy
In the distant future, people build their homes and raise domestic animals in subterranean caverns. As a result, they live in fear of the occasional earthquake and cave-in, and have…
Genre: Action & Adventure, Animation, Drama
Mugen is a ferocious, animalistic warrior with a fighting style inspired by break-dancing. Jin is a ronin samurai who wanders the countryside alone. They may not be friends, but their…
Genre: Action & Adventure, Animation, Comedy, Drama
What's New, Scooby-Doo?
Scooby-Doo and the Mystery, Inc. gang are launched into the 21st century, with new mysteries to solve.
Genre: Action & Adventure, Animation, Comedy, Mystery
Mobile Suit Gundam SEED
Mobile Suit Gundam SEED is an anime series developed by Sunrise and directed by Mitsuo Fukuda. As with other series from the Gundam franchise, Gundam SEED takes place in a…
.hack
.hack follows several young players as they navigate the vast, mysterious MMORPG known as "The World" – a place, it turns out, that is sometimes impossible to leave.
Genre: Action & Adventure, Animation, Mystery
Kim Possible is an American animated action-adventure comedy television series about a teenage crime fighter who has the task of dealing with worldwide, family, and school issues every day. The…
Genre: Action & Adventure, Animation, Comedy, Kids
Without a Trace was an American police procedural television drama series that originally ran on CBS from September 26, 2002 to May 19, 2009. The series follows the ventures of…
Genre: Action & Adventure, Drama, Mystery
The Adventures of Jimmy Neutron: Boy Genius
The Adventures of Jimmy Neutron: Boy Genius, often shortened to just Jimmy Neutron, is an American computer animated television series created by John A. Davis. It is the 2002 spin-off…
Made by Genndy Tartakovsky, this animated series tells the story of a great warrior displaced to the distant future by the evil shape-shifting wizard Aku. The world has become a…
This reality competition sees teams embark on a trek around the world to amazing destinations where they must compete in a series of challenges, some mental and some physical. Only…
Genre: Action & Adventure, Reality
During the mid-22nd century, a century before Captain Kirk's five-year mission, Jonathan Archer captains the United Earth ship Enterprise during the early years of Starfleet, leading up to the Earth-Romulan…
Genre: Action & Adventure, Drama, Sci-Fi & Fantasy
Black Lagoon is a Japanese manga series written and illustrated by Rei Hiroe. It has been published in Shogakukan's Monthly Sunday Gene-X since 2002, and nine collected volumes have been…
Genre: Action & Adventure, Animation, Crime, Drama
Hellsing, a secret organization of the British government, have long been battling supernatural threats to keep the people safe from the creatures of the night. The current leader, Integra Wingates…
Japan has been invaded and conquered by the Britannian Empire. Japan is now known as Area 11 and its citizens known as Elevens. The Britannian Empire takes away Japan's autonomous…
Ben 10 was an American animated series created by Man of Action, and produced by Cartoon Network Studios. The series is about a 10 year old boy named Ben Tennyson…
The Batman is an American animated television series produced by Warner Bros. Animation based on the DC Comics superhero Batman. It ran from September 11, 2004 to March 22, 2008,…
With the Ancients' city of Atlantis discovered in the Pegasus Galaxy by Stargate Command, Dr. Elizabeth Weir and Major Sheppard lead a scientific expedition to the ancient abandoned city. Once…
In a magical universe, witches, warriors begin fighting in the name of good .vs. evil! At a magic school, five teenage girls are selected to defend the universe with their…
After saving the life of the President, two secret service agents – Myka Bering and Pete Lattimer – find themselves assigned to the top secret Warehouse 13. The Warehouse is…
Genre: Action & Adventure, Comedy, Sci-Fi & Fantasy
Sterling Archer is the world's most daunting spy. He works for ISIS, a spy agency run by his mother. In between dealing with his boss and his co-workers – one…


text:
La temporada 1969-70 fue la vigésimo primera de los Philadelphia 76ers en la NBA, y la séptima en Filadelfia, Pensilvania, tras haber jugado hasta entonces en Syracuse bajo el nombre de Syracuse Nationals. La temporada regular acabó con 42 victorias y 40 derrotas, ocupando el cuarto puesto de la división Este, clasificándose para los playoffs en los que cayeron ante los Milwaukee Bucks en las Semifinales de División.

Elecciones en el Draft

Temporada regular

Playoffs

Semifinales de División
 Milwaukee Bucks vs. Philadelphia 76ers

Plantilla
{| class="toccolours" style="font-size: 95%; width: 85%; "
|-
! colspan="2" style="background-color:#D0103A;  color:#FFFFFF; text-align: center;" | Philadelphia 76ers 1969-70
|- style="background-color:#0046AD; color:#FFFFFF;   text-align: center;"
! Jugadores !! Entrenadores
|-
| valign="top" |
{| class="sortable" style="background:transparent; margin:0px; width:100%;"
! N.º !!  !! Pos. !! Nombre !! Altura !! Peso !! Origen

Estadísticas

Galardones y récords

Véase también

 Temporada 1969-70 de la NBA

Referencias

Enlaces externos
 1969-70 Philadelphia 76ers Roster

Temporadas de los Philadelphia 76ers
NBA 1969
NBA 1970

redpajama_set_name: RedPajamaWikipedia


Book in advance to take advantage of our discounted group prices. This rate is for a two hour entry for a group of between 10 and 19 flumers. Please enter the number of tickets you require, select the date and time and then click 'Book Now'.


text:
Hilton Hotel Salalah
The Hilton Salalah Resort is a relaxing retreat, surrounded by vibrant bougainvilleas and lush palm groves. This beachfront Salalah hotel is set amidst landscaped gardens and all of the Salalah hotel rooms and suites offer tranquil views of the Indian Ocean and Arabian Sea, as well as the dramatic Dhofar mountain range.
Cruise Ship Vacations All Inclusive The Carnival Cruise Ship Fantasy pulls into the Alabama Cruise Ship Terminal. scheduling regular cruises out of Mobile starting in November 2016. "It's a more all-inclusive product for the travel. Cruise ship deals for Alaska, Hawaii, Bahamas, Europe, or a Caribbean Cruise. Explore weekend getaways and find great cruise ship specials and enjoy Freestyle cruising
Hilton Salalah Resort, Salalah, Oman. 1.6K likes. Hotel. today I found a phone call on the beach belonging to Miss Fischer from the USA.
Salalah Gardens Hotel – Our customers like the bar and breakfast at this hotel. This 4-star hotel features 3 restaurants, free breakfast, and an outdoor pool. Crowne Plaza Resort Salalah – This 5-star hotel features a private beach, a golf course, and 3 restaurants. Expedia.
All children (a person under 12 years of age) staying at the Hotel must be ccompanied by an adult and must be supervised by an adult at all times.
Echoing his views Nader Halim, general manager, Hilton Salalah Resort, said that he was expecting almost full occupancy at his hotel during the Eid holidays. "We are optimistic that the numbers will.
The project is expected to play a major role in boosting tourism in Salalah, Oman's second-largest city, which is relatively underdeveloped. It has only three upmarket hotels: a Hilton, a Crowne Plaza.
By comparison, the pipeline for the entire GCC market — including Jeddah, Al Khobar, Kuwait, Manama, Muscat, Salalah and other important cities — totalled 199 hotels in 2007. three-star. Hilton.
Budget: Salalah Plaza Hotel from Dhs200 per night. www.facebook.com/salalahplaza Blow out: Hilton Salalah from Dhs1,211 per night. www.hilton.com Adventure, Romance, Beach, Foodie, Hours of flight – 4.
GAJ's ongoing projects also include work on Dubai's Arcadia Secondary School; Al Bait Hotel, Sharjah; the Vida Hotel Dubai Creek Harbour; DoubleTree by Hilton and Hilton Garden Inn hotels in Riyadh;.
Emille began her career in the hotel industry as a PR assistant. she still is a valuable help for me and my team," Nader Halim, general manager, Hilton Salalah Resort
Oct 12, 2018 by Natascha Kruger – Receptionist – Hilton Salalah , Hotel Management. Dear KettyFerri, Thank you very much for choosing to stay at Hilton Salalah Resort and for taking the time to let us know about your recent stay.
Registered under Federal Law No. (6) of 2007,Certificate No.14 dated 29th Dec 1984,Comm. Regn. 51814
See for yourself with the hotel's Family Affair package where the entire clan. per night until July 14 and from August 16-August 31 (+968 23 235 333; www.cpsalalah.com). The Hilton Salalah Resort.
Nov 29, 2018 · Now $135 (Was $̶2̶0̶8̶) on TripAdvisor: Hilton Salalah, Salalah. See 722 traveler reviews, 869 candid photos, and great deals for Hilton Salalah, ranked #5 of 26 hotels in Salalah and rated 4.
It's going to be a great night… Now book yourself a great room, with LateRooms.com. Find top hotel deals for all occasions. Tonight, or any night.
Just a 90-minute direct flight away from the UAE, or a gruelling drive of more than 1,000 kilometres from Al Ain or Muscat, Salalah and the wider Dhofar. Book a table At the Hilton, the open-air.
Our top choice in hotel is the Ramses Hilton, with a two-night stay starting at Dh1,600. its national carrier is offering bargain packages to Salalah for the monsoon season, as part of the city's.
The latest list of iconic projects makes it easy to understand why: Hilton Hotel. in Egypt, Salalah Rotana Resort in Oman, and Bristol Hotel, Versace Towers in both Beirut and Jeddah. These.
Marriott Hotels & Resorts is Marriott International's flagship brand of full-service hotels and resorts.The company, based in Bethesda, Maryland, is repeatedly included on the Forbes list of best companies to work for, and it was voted the fourth best company to work for in the UK by The Times in 2009. As of September 2017, there were 566 hotels and resorts operating under the brand.
Ups Boot Camp Dec 12, 2011 · Battling to cut cholesterol and get fighting fit – fast A healthy diet and boot camp sends high cholesterol levels tumbling inside a week. Nov 15, 2017. Before they arrive to boot camp, recruits are expected to train to meet the. a medium "satisfactory" score on their run, pushups, and curl-ups. Oct
Salalah Gardens Hotel in Salalah on Hotels.com and earn Rewards nights. Collect 10 nights get 1 free*. Read 76 genuine guest reviews for Salalah Gardens Hotel. 5 – Salalah Gardens Hotel, Salalah, Executive Suite with Balcony, Balcony; 6 – Salalah Gardens Hotel, Salalah, Safir Grand Suite ,
DoubleTree by Hilton Resort & Spa Marjan Island There are lots. From Dhs1,470. Ongoing. Salalah, Oman (02 322 8222). Radisson Blu Hotel Muscat Is a weekend really long enough to explore the wonders.
Currently Salalah has a number of hotels operated by international chains such as the Hilton, Marriott and Crowne Plaza – but not enough to accommodate the influx of tourists that arrive during the.
Having examined the needs and challenges of hotels and. Downtown and Rotana Salalah Oman with Aromatherapy Associates. In addition,to this, new spa menus have been created with existing partners.
Miatlushka fought off competition from HR professionals from Hilton Salalah Resort, Bonnington Jumeirah Lakes. because we had so many people and that's something the hotel and the company is really.
A five-star W Hotel is to be the first of three Starwood Hotels and Resorts planned to open in Muscat, Oman, it was reported. A construction tender for the lavish accommodation offering, which is part of a redevelopment of the InterContinental Muscat, was floated by.
Hilton Salalah Resort places you within a 10-minute drive of Salalah Gardens Mall and Port of Salalah. This 4-star hotel has 147 guestrooms and offers an outdoor pool, free self parking, and a fitness center.
QATAR AIRWAYS FACT SHEET (December 2017) AIRLINE OF THE YEAR 2011, 2012, 2015 & 2017 (Skytrax Global Survey) Description: Qatar Airways is the national carrier of the State of Qatar.Currently undergoing rapid expansion, Qatar Airways is one of the fastest growing
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Emirates Business Class Lounge @ Emirates Terminal 3, Dubai International Airport: Etihad Airways Premium Lounge @ Abu Dhabi International Airport
Chef George Morrone returns to Kimpton Hotels & Restaurants as director of culinary development. Earlier, he was executive chef of Kimpton's Fifth Floor restaurant in San Francisco. Most recently, he.
Bed And Breakfast In Key West On The Beach Tucked away in the Blue Ridge Mountains of Southwestern Virginia and located just minutes from I-77 and I-81 about half-way between Charleston, West Virginia and Charlotte, North Carolina, this historic Wytheville, Virginia bed and breakfast offers four elegant guestrooms plus a private garden cottage to provide the ideal lodging spot for Bristol NASCAR race fans,
In the northern summer, Salalah, in the southern region of Dhofar. and camels standing by the deserted road outside the Hilton hotel. Oman is not cheap, and its diet has not improved much since Ibn.
Schmid has worked with Hilton Dubai Jumeirah, Hilton Salalah Resort, Nile Hilton Hotel, Le Meridien Dubai, Sheraton Soma Bay Resort and other international hotels for over 11 years. Ma was earlier.
We're helping customers maintain the highest standards in critical manufacturing environments and provide comfortable, sustainable environments in select markets around the world.
2014 at the Convention Centre of the Hilton Hotel Jeddah. The Ministry's representative office in KSA will underscore the visit of around 17,000 Saudis to Oman during Salalah festival in 2013, in.
We invite you to experience this contemporary Italian restaurant that offers a beautiful view of the property's natural gardens. Antipasti, regional italian cuisine or dolce, there is an option for everybody.
When it comes to getting around the area you can either hop in a 4×4 – there are various hire car companies dotted throughout Salalah – or you can opt for a guided tour. The latter are available.
Hilton Salalah Resort vender mot Det indiske hav i Salalah. Gå direkte til hovedinnholdet. NOK. The hotel itself was lovely as were all the staff I came into contact with. The lobby area is very nice and I love the smell of frankincense / bukhour that wafts through the public areas. Hilton Hotels & Resorts De mest populære fasilitetene.
They said the pair spent the night together in a Land Rover, socialised during a barbecue on a beach in Salalah, Oman, and shared dinner and the following day's breakfast at the nearby Hilton hotel.
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Taking the present crisis in Greece as a departure point, Professor Kalyvas takes stock of the surprising number of critical junctures during the past two centuries, when Greece managed to become a focal point, both in negative and positive terms, of critical issues in European and global politics. This lecture also questions why a country so small and peripheral has come to play such a role, providing an interpretation based on a review of the country's history since it became an independent state in the third decade of the 19th century.
Stathis N. Kalyvas is Arnold Wolfers Professor of Political Science, Director of the Program on Order, Conflict, and Violence, and Co-Director of the Hellenic Studies Program at Yale University.
This event is free and open to all with no ticket required. Entry is on a first come, first served basis. For any queries contact Ioanna Antonopoulou by email i.antonopoulou@lse.ac.uk or phone 020 7107 5326. For further information visit theHellenic Observatory website.


Everyone can learn from Mr. Holmes!
Last night on DYI, Mike Holmes was helping some ladies find a new home. These ladies are quite special as they provide a home for young girls that need love and a stable home life. Last night, Mike and the ladies looked at several homes that needed new roofs. The homes had some visible repairs to the actual roof and water stains on the ceiling due to leaks. Your home inspection should always inspect the roof, ceilings, and attic space to look for potential concerns.
WC Home Inspection LLC is proud to announce we are now an affiliate member of Coastal Carolina Association of Realtors. Being a member will allow us to better service our clients.
In particular, we now have Supra key access for Horry and Georgetown counties in South Carolina.
Last night on "Holmes Buy It Right"
Last night on DIY's "Holmes Buy It Right", Mike was looking at some notched joists. We will not bore you with the details of allowable notching but this picture shows a clear case of excessive notching. This amount of excessive notching could lead to significant structural issues. Just another example of the importance of having an inspection by a qualified home inspector before making a purchase decision.
My threshold is rotten/water damaged. What should I do?
The quick answer is replace the damaged wood but that is not enough. I recommend installing a product such as Blueskin over the new wood. Blueskin is a plastic film material that sticks to the wood and water can not penetrate.
Also, if possible a storm door or overhang/porch will provide additional protection for the exterior door. But many times these options are not practical.
As you can see this vent needs some TLC. Besides the TLC, the vent is closed which can lead to problems such as excessive moisture in the crawlspace. We recommend keeping vents open except during extended periods of temperatures below freezing. When the temperature dips many homeowners close all their vents until spring. If you feel you must close vents in the winter only close a few. Keep a vent open on each side of the crawlspace as a bare minimum. Also, during extreme cold weather keep a light bulb lite in the crawlspace to generate some heat to prevent frozen pipes.
Some states do not require training or a license to perform inspections.
If you live in a state that does not have a licensing program for inspectors, we recommend using care when selecting a home inspector. Many of these individuals are not qualified to be doing home inspections. Ohio and New Jersey are examples of states that do not require a license. If you live in central Ohio and need help, we might be able to help so give us a call. If you live in a state without a licensing program and need help, please give us a call.
Watch Mike Holmes New Show Tonight!


2.Wearing seazon:Summer&Fall,Warm weather.PERFORMANCE : UV protection;Anti-static;Windproof,Waterproof;Wear-resisting;Breathable;Quick dry;Perfect for all the outdoor sports,travelling,jogging,walking,camping,fishing,climbing,tennis,golf,hunting,cycling,hiking,running,beach wear,ect.
3.Due to manual measurement, please allow will be 0.5-1.0 inch/1-3cm error.As the products,we show you the US size on the hang tag stickers,please kindly note it.
CLASSIC DESIGN : Hooded;Zipper closure;Two side pockets for convenient to put something;Various colors with couple style make you sweet in the summer.
PERFORMANCE : UV protection;Breathable;Quick dry;Anti-static;Windproof,Waterproof;Wear-resisting;Perfect for all the outdoor sports,travelling,jogging,walking,camping,fishing,climbing,tennis,golf,hunting,cycling,hiking,running,beach wear,ect.
NOTE : As the products,we show you the US size on the hangtag sticker.Please contact us free if you have any question.Thank you!


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Aga Khan IV (British spiritual leader and patron, born 1936)
Note: Spiritual leader of Isma'ili muslims and patron of architecture. He holds British citizenship.
Aga Khan IV (preferred,V,LC)
Aga Khan, IV (V,display)
Aga Khan, Karim (V)
Karim, Shah (V)
Khan, Karim Aga (V)
Shah, Karim al-Husayn (V)
British (modern) (preferred)
leader (preferred)
........ Aga Khan IV (I,U)
(British spiritual leader and patron, born 1936) ..... [VP Preferred]
(leader, born 1936) ..... [AVERY]
(undetermined leader) ..... [AVERY]
Aga Khan, IV ........ [AVERY Preferred, VP]
.......................... Avery Authority files (1963-)
Aga Khan IV ........ [VP Preferred]
........................ Encyclopedia Britannica Online (2002-) accessed 6 January 2010
........................ Library of Congress Authorities online (2002-) NAFL50046055, accessed 6 January 2010
Aga Khan, Karim ........ [AVERY]
Karim, Shah ........ [AVERY]
........................ Avery Authority files (1963-)
Khan, Karim Aga ........ [AVERY]
Shah, Karim al-Husayn ........ [VP]
........................................... Encyclopedia Britannica Online (2002-) accessed 6 January 2010
Subject: ........ [AVERY, Gallery Systems, VP]
.................... McMillan, Aga Khan, Canada.com (2008) accessed 6 January 2010
English .......... [AVERY, VP]
.......... McMillan, Aga Khan, Canada.com (2008) accessed 6 January 2010


As an example, imagine we are on an island together with ten other families. Let's trade values. I'll fix your hut roof for two hours, whilst you husk coconuts for me for two hours. Fair? Sure. No problem.Tomorrow, I'll fish for my family and your family for five hours as long as you collect firewood and water for your family and my family for five hours. Okay? Yes. No problems here.
There's nothing wrong with this way of going on, and we could exist for centuries like this. In fact this is exactly how primitive societies did (and still do) operate. But there is no progress.Why is progress desirable? Because without it we are all still working for sixteen hours a day in mindless, numbing physical toil - just as people did for centuries, just as they are still doing now in many parts of the world. The fact that we swap jobs (trade values) doesn't actually improve our lives very much apart from a slight efficiency due to division of labor.
Indeed, you intend to create an honest, tradable value which will make you wealthy, but also make everyone else better off too.This is the essential point about getting wealthy through creating an honest value - everyone benefits.
Note one vital principle here. You are not motivated by altruism to improve the lot of others. You are solely interested in improving your own lot, and that of your family. You are acting selfishly. How can you act in any other way? This is what gets you out of bed in the morning.You are rational, because you know that the only sustainable way of becoming wealthy is to create something of lasting benefit to others (an honest value) otherwise they won't 'buy' it. The only alternative is for you to use force to enslave the population of the island to your desires, or to con them out of their values.
Okay, so what are you going to do? Through your ingenuity, your creativeness and your honest toil during what should be your rest period, you are going to create a neat piping system of bamboo cane which brings water down from the stream right into the village.You plan, you scheme, you work and sweat and toil. You sacrifice your leisure and a portion of your life. You take risks - it might not work. You place yourself in danger - the mountain is steep and slippery. Of course, you need to conduct a market survey, so you gather all the villagers together and say this:"For centuries the women have walked two miles a day to that hill with their water jars to fetch the daily water for their families. You all know that each family spends three hours a day in this pursuit. If I could bring you the same water, here, into the village and you could collect it in five minutes instead of three hours, would each family work for one hour a day on various tasks dictated for my family?"
Now of course the resounding answer would be "Yes!" Note the vital point here: everyone is a winner. Each family gains two precious hours a day for nothing - absolutely free, for zero effort on their part. They can use this time to grow more crops, fix up the hut, or whatever else they want to do. The net effect is that their lives are enriched and their standard of living rises, all due to your ingenuity, risk, and discipline.In return for your effort, ingenuity, skill and daring, you become a wealthy man. How? Because you now have ten families working one hour a day for you and so you can 'retire.'In other words, because you created values for others which they willingly bought from you, you have freed yourself from the need ever to work again. There is no money on our island, but money is merely a token of so many units of human labor.There are several important points here.
In fact you freed the people from two full hours of soul-destroying donkeywork each and every day of their lives, and the price you charged them for these two hours was - zero.Secondly you did it for you, not for them, and you're proud to admit it. You're also proud of your water system. You're trying to add filtration and perhaps design an automatic coconut husker too.
Instead of spending three hours fishing, we now spend one hour, and pay him one hour in rent. So we are all better off to the tune of another hour a day, with no drop in living standards. In other words, we work less hard for the same amount of fish and fresh water. Or, of course, we can choose to work the same hours as before and get more fish and fresh water. In other words, everyone has become wealthier.


text:
District At-A-Glance
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HOPKINTON PUBLIC SCHOOLS
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Welcome to Hopkinton Public Schools
The mission of the Hopkinton Public Schools is to learn, create, and achieve together.
We believe that the goal of learning is understanding
Hopkinton is Organized by District Level Departments
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Dear Families, Students, and Community Members:
I would like to share the procedures used in cancelling school, delaying the opening of school, and sending students home early during severe weather conditions or other emergency situations.
A. CANCELLING SCHOOL – During inclement weather, I stay in direct contact with several resources: the Schools' Director of Buildings and Grounds, the Hopkinton Public Works Department, and the Hopkinton Police Department. Each lends insight about the conditions of roads and access to the schools. If the roads and sidewalks are judged to be unsafe for travel, I make the decision to cancel school. The television stations listed below will broadcast this information, as will Twitter and Facebook. I will also send out an automated message from the school district's emergency notification system and post the cancellation on the HPS website.
Television Station
Website/Alerts
www.hopkinton.k12.ma.us
SchoolMessenger Email/SMS
SchoolMessenger Phone Call
CH 10 (Hopkinton Educational Access)
CH 25 (Fox)
PLEASE DO NOT call the Police or Fire Department on bad weather days. They need their lines to be open for emergencies.
B. DELAYED OPENING – Rather than close school for a whole day during short-term weather situations, there may be days when the beginning of school will be delayed one or two hours. When the opening of school is delayed, Facebook, Twitter, the district website, and the television stations listed above will broadcast whether school will start one or two hours later than usual. Bus pickup, therefore, will also occur one or two hours later. During a one-hour delayed opening, for example, a child who would usually be picked up at 8:35 A.M. would be picked up at 9:35 A.M. On these days morning preschool will be cancelled. Lunch will be available on days when there is a delayed opening, and student dismissals will be at the regularly scheduled times.
C. EMERGENCY DISMISSAL – If it is necessary to dismiss schools prior to regular times, announcements will be made on the television stations used for school cancellation. Emergency dismissal information will also be posted on Twitter, Facebook, and the schools' website. In addition, families will receive an automated message from the school district's emergency notification system. Although it is very unlikely that we would need to dismiss early, it does happen, and families will need to ensure there will be adult supervision at home. Accordingly, now would be a good time to review with your child, your extended family, and your friends and neighbors where your child should go if you cannot be home. During worsening weather situations that warrant early dismissal, afternoon preschool is likely to be cancelled.
Naturally, your child's safety is our greatest concern. With your cooperation, we can keep inconveniences to a minimum. Please note that even when a decision is made to open school during inclement weather, as a parent or guardian you can always decide to keep your child home if you do not feel that road conditions are safe. The school district respects your right to do so.
Thank you for your assistance.
Carol Cavanaugh, PhD
89 Hayden Rowe Street Hopkinton, MA 01748
webmaster@hopkinton.k12.ma.us
Non-Discrimination Policy: The Hopkinton Public School System does not discriminate on the basis of race, color, religion, national origin, age, gender, gender identity, sexual orientation, or disability in admission to, access to, employment in, or treatment in its programs and activities. The Hopkinton Public School System is committed to maintaining a school environment free of harassment based on race, color, religion, national origin, age, gender, gender identity, sexual orientation, or disability. Harassment by administrators, certified and support personnel, students, vendors and other individuals at school or at school-sponsored events is unlawful and is strictly prohibited. The Hopkinton Public School System requires all employees and students to conduct themselves in an appropriate manner with respect to their fellow employees, students and all members of the school community.
If you would like to report a bullying or discrimination incident please call the superintendent's office at 508-417-9360 or via email at reportbullying@hopkinton.k12.ma.us


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October 1, 2017 October 1, 2017 Author
Studio Visit: Roland Reiss, Chromatic Joy
Roland Reiss. Photo Credit Gary Brewer.
By Gary Brewer
"Colors have their own distinctive beauty that you have to preserve, just as in music you try to preserve sounds. It is a question of organization, of finding the arrangement that will keep the beauty and freshness of color."
"There are always flowers for those who want to see them."
"All artwork is about beauty; all positive work represents it and celebrates it. All negative art protests the lack of beauty in our lives. When a beautiful rose dies, beauty does not die because it is not really in the rose. Beauty is an awareness in the mind."
Agnes Martin, Beauty is the Mystery of Life
Color energy – vibrant color chords that dazzle – the unrestrained expression of joy; it is a serious enterprise to give weight and presence to the music of the heart and soul. In the 'visual arts' we live in a world where beauty is often trammeled underfoot by burdensome theories that negate experience in exchange for vapid conceptual conceits. To create works that express an ethos in which authenticity is a profound achievement and where one seeks to find something that they recognize as true. One often finds themselves in a defensive position, to rationalize the 'raison d'être' for that which is as pure as sunlight on a flower.
Roland Reiss has chosen this path, or I should say, it has chosen him. To make paintings that follow Matisse's "wish to create a spiritual remedy;" works that comfort and soothe the spirit. Roland is on a serious journey, to look long and hard into the nature of color and form and to find chromatic chords that capture his experience; that reflect the joy of 'being'.
As we spoke, I said to him, 'It is almost as if you are pushing back time, that you are using the energy of light to expose a new dimension.' He said of this, "As I grow older paintings become a way to expand the joyful experience of life!"
The current development of work out of his "je t'aime" series is an expression of these convictions. Last year Roland spent some time in the hospital. While there his wife Dawn Arrowsmith brought him a bouquet of flowers, and in his drugged delirium he looked at the bouquet and said aloud, "When I get out of here I am going to paint those flowers!" It was an assertion of an intense desire to get back to the work he loves; painting in his studio.
When he returned home he set to work. He created a small painting with an Etruscan red background, the colors of the vase and flowers fully saturated; a nod to Matisse's late paper cutouts. The shapes of the leaves and petals are mostly flat with areas of subtle gradations. They are painted in vibrant colors and at the edges there is a slight feathering, a soft brushy edge that give the forms a softer presence. The field of red surrounding the vase and flowers is unbroken, isolating them in a central position. There is a sincerity to this painting that captures the genuine radiant joy and gratitude Roland felt for being healthy again and back in his studio. It was a joy that was palpable enough that when his dealer, Diane Rosenstein, came for a visit and saw the painting, she said in no uncertain terms, "That painting is about love!"
Roland has continued with this 'je t'aime' series, a theme and variations exploring the power of color. The newer ones are called "Unrepentant Flowers." He sometimes repeats the same design in different colors exploring the effect the chromatic changes have on the composition. Each painting shares the same format surrounded on all sides by a field of color, the flower and vase are centered, floating in a chromatic sea of light. There are maybe 15 paintings hung on one wall in his studio in three rows, each painting is 30×24 inches. The effect is almost musical. The dazzling energy coming off of each painting is a different color chord. There are complex internal relationships of shape and color that establish high-energy visual vibrations that have a radiant, uplifting emotional tenor. Indeed the room itself feels physically lighter, like gravity has been lessened. There is a lifetime of experience and knowledge compressed into these small vibrant paintings. They assert 'I am' with a passion and soulfulness that is true and deep.
Years earlier, just before he embarked on his floral series, Roland had been thinking about artists' mature work and how it contained everything that they knew about painting. He had been thinking specifically about Manet and his late flower paintings. He said of those, " You can see in these small works his entire history as an artist. Every invention and innovation is in those paintings, you do not need to see "Luncheon on the Grass." In one small painting of flowers he has summed it all up. I was intrigued by this and its possibilities supported the future development of my work."
The series of floral paintings began in 2007, when Roland decided that he wanted to create works that would encompass much of his artistic journey up to that point. He wanted to find an armature that would allow him to include references and images from many different phases of his career and life. It was a tall order but considering possible solutions he thought, "well why not flowers, they are fresh territory for me. I could arrange them on a grid like scaffolding and fill in the spaces behind them and in between with fragments from my life. It would embody some of the various kinds of work that I have produced throughout my career." He decided to float the compositions in the middle of a color field, leaving space around on all side. The background colors are subtle nuanced earth tones that create an atmosphere for the galaxies of Roland's life and work to float within, suspended in a space free from gravity – a space just outside of 'time'. These works are elegant in their design and restraint. These are complex, thoroughly considered compositions, with hidden narratives tucked in the foliage or behind a flower petal. These paintings are deeply beautiful in their conception and resolve.
His next series in the 'Floral' paintings are full of bold color. A group of red, yellow and blue paintings that juxtapose the flat vibrant purity of primary colors against the thick impasto brush stokes that Roland carefully placed to frame and 'block in' the flowers and vases. It was a dialogue between the energy of a stroke of paint from his hand in relation to the pure color relationships discerned by his knowing eye. These are bold powerful paintings that take on other 20th century artists for whom the primary colors was their 'terroir', namely Mondrian and Barnett Newman. It was a way to go beyond their efforts, to add something to the dialogue with history as well as to represent the concerns of what the hand and eye can capture of the artists individual spirit. It is a philosophical dialogue to simultaneously challenge oneself and to take on history. To challenge ones heroes, not to best them but to contribute a line to the narrative thread.
Roland's life as an artist is long and rich. A brief synopsis takes us from Los Angeles to his early years teaching painting at the University of Colorado, to his move back to Los Angeles where he chaired the art program at Claremont University where he also became known for his miniatures, and then to the present.
I asked Roland about his early works that explored the minimalist-material zeitgeist of the late 1960's, I was curious to know how he got from that body of work to the miniatures for which he became known when he moved back to LA. Roland responded, "I had been working in a reductive abstract approach for years, exploring materials, plastics and other ways of making paintings and objects. I was given a retrospective at the University of Colorado's Denver Art Center and was happy and excited to see my work represented in a large survey. At this point my life had been filled with family and great students, I was expecting to be thrilled with the show, but as I sat in the gallery the work began to feel vacuous. I could not find my life in the work. I decided I needed to discover a way to bring everything into my art. I had been interested in holograms and was thinking about some holographic scientific objects I had seen in small Plexiglas boxes. My interest in film had an influence as well. I was a foreign film sponsor at the University – Fellini, Bergman, Antonioni – and the idea of film sets, fractured narratives, and the holograms in the Plexiglas boxes all came together. I decided I would make all this happen in miniatures and set to work. I thought of them as 3 dimensional paintings."
These eccentric, obsessive and wonderful stage sets of the imagination would take up the next 15 years of his artistic output. He filled them with everything from his life and work; humorous, playful and open to interpretation they 'contained multitudes', and were an antidote to the emptiness that he felt when viewing his retrospective.
Following the miniatures there was a long period when he returned to his first love, painting. He was focused on light and had returned to using plastics, exploring its ability to capture and reflect light. These works were largely abstract but referenced figurative elements as well; a silhouette of something in his studio, the refection of his lights on a sheet of Plexiglas. These paintings explored light and color in an open dialogue between pure abstraction and representation. This was the last series of paintings he did before he began his new adventure into floral painting and intensified color.
All of Roland's varied works throughout his career share a common quality in their conception and realization. He has consistently sought what is called a "peak experience" a subjective recognition of when a work reaches its zenith, when will and intuition persuade matter to yield to ones spirit and capture the synaptic sparks of consciousness. There is a radiance that Roland exudes in both his person and his work. It is a state of joy and gratitude, which literally keeps him young at heart. Older now, his mind and spirit exude an enthusiasm and intellectual curiosity that is a gift to all who know him or were his students.
He said of this, "As a teacher I was invigorated by my interaction with students. Their energy and enthusiasm were boundless and it continually fed me. It gave me an endless source of inspiration to create my own work and to explore new avenues of thought and artistic practice. I gave a lot to my students but it was a symbiotic relationship in which my giving was rewarded with their bright minds giving back to me as well." On this note Roland told me of a mythic tale of endurance and sublime possession. Occasionally he would have the graduate students come for a lecture on color. Roland would bring wine and the students would bring popcorn. The lecture would open late in the day and continue into the morning hours. An incantation in the spirit of the mythic oral tradition of ancient times, a sagacious being in Dionysian splendor intoning the infinite possibilities of light and color, the metaphysical and metaphoric pursuit of seeing and knowing, of analyzing our perceptual experience through the prism of the light of mind and spirit.
Roland is a cultural treasure. His works mine the depths of his soul, bringing to light the treasures we all share, the treasure that we as humans are made from. Roland is a humanist in the deepest sense of the word, he believes that our world, as tragic as it can be, is a gift to appreciate and to share, to intone in words and deeds a gratitude and thanks for this brilliant 'consciousness' that we all possess, where memory and dreams yield great works of art.
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katie phillips says:
Roland is an inspiration to us all and the master of image and materials
Vincent Suez says:
ROLAND, Love those flowers! Best to you and Dawn. Vincent
Carolyn Friend says:
Roland, I love this work! All the very best to you and to Dawn. Carolyn