Whew! After two years of dreadfulness, I can finally say it: The market for foodservice equipment and supplies is beginning to recover. The foodservice market at the operator level has already turned north. The E&S market typically lags operator fortunes by six to 18 months. But both hard numbers and anecdotal reports already make it clear the E&S market is starting its comeback. Let's review the evidence.
Start with operator trends. We and others have said that we would know the worst was over for commercial operators when casual dining and other full-service operations turned the corner. It's full service, particularly at the higher end, that has been most brutally battered during this more than two-year foodservice recession, the worst in our lifetimes. But blended full-service same-store sales began to improve in the fourth quarter of 2009, according to data from Technomic Inc., and that trend has continued into '10. Individual chain operators are still reporting negative sales and traffic, but many also have seen dramatic improvement.
Quick-service chains didn't bottom out until the last quarter of '09 or even first quarter this year. And that includes McDonald's, which has outperformed everyone throughout this entire recession. But same-store sales and traffic now appear to be improving in QSR, too. After vacillating between -1% and +1% to 2% from October through March, McDonald's has reported U.S. same-store-sales gains of 3.8% and 3.4% in April and May.
All this improvement is reflected in two key data points. The National Restaurant Association's Restaurant Performance Index moved into expansion territory in March for the first time since October '07. And it stayed above the 100 level the index uses as a tipping point in April. In May, Technomic raised its forecast of total foodservice sales for this year by two percentage points. While real sales are forecast to remain negative by nearly 1%, the current dollar sales prediction rose from -1.6% in the January revision to +0.6% in May.
These improvements on the operator side will take a while to turn into E&S purchases, but we already have hard numbers indicating the E&S market has turned the corner. Revenues for E&S public companies bottomed out in the third quarter of '09 at a breathtaking -19% and have improved since. The first quarter '10 decline was only 3.4% and would have been nearly flat without one company's tough chain roll-out comparisons. The Business Barometer maintained by the Manufacturers' Agents Association for the Foodservice Industry shows a similar pattern. Rep sales were off only 5.3% in the first quarter, a marked improvement over the double-digit decreases reported all last year.
So, yes, the E&S market is improving, finally. We haven't decided if we will revise our own forecast for '10 upwards. It currently stands at -5.1% in current dollars. If you'd like to be among the first to know, sign up for our annual Presidents' Preview E&S Market Forecast seminar Aug. 4. Details can be found at fermag.com. And let's all breathe a collective sigh of relief.


text:
Go Negosyo Supports Harvard Conference 2015
Joey Concepcion Founders Corners July 20, 2015
Cross-cultural interaction has never been as inevitable as it is nowadays. I am blessed to have travelled the world by visiting several countries with my family. Aside from the peaceful vacations and beautiful places that I have visited, my most favourite part of travelling is meeting and interacting with different kinds of people. They may be tourists, business travellers or local residents of their particular countries, but they all have very interesting stories to share about their lives and culture.
Through the years, Go Negosyo has collaborated with a number of foreign partners like the United Stated Embassy and Australian Embassy. In our intention to promote our home-grown talents and give exposure to the Philippines, we once again partnered with another international organization—Harvard Project for Asian and International Relations (HPAIR).
The HPAIR is student-run organization of the Harvard University Faculty of Arts and Sciences. HPAIR organizes Harvard University's largest annual student conferences in the Asia-Pacific region. Since 1991, it has continuously gathered a growing pool of international students from top universities, renowned academics, business professionals, and political leaders to engage in our rigorous educational events. The organization aims to provide a dynamic forum of exchange on international issues vital to Asia, foster long-term relationships among young leaders around the world and connect talented delegates with some of today's leaders in academia, business and government. (HPAIR.org)
In addition to its international conferences, HPAIR aims to promote understanding of the economic, political, social, and cultural issues facing the Asia-Pacific region within the Harvard community by organizing panels and other events on Harvard's campus.
This year, HPAIR's host country organizing committee is the De La Salle Project for Asian and International Relations (DLSPAIR).
The HPAIR Asia Conference 2015 will be held in Manila from August 20-24 with the theme "Confronting Asia's Development Challenges through Innovation." The last time the Philippines hosted the same conference was back in 1994.
Delegates from different notable universities such as Princeton University, Berlin School of Economics and Law, University of Santo Tomas, Nanyang Technological University, Duke University, New York University, The University of Hong Kong, University of British Columbia, Cornell University, Graduate School of Industrial Labor Relations (Distance Education Program), University of Sydney, Ateneo de Manila University, University of Delhi, Georgetown University, National University of Singapore, University of Asia and The Pacific (UA&P), University of Manchester, University of California, Berkeley, Keio University, London School of, Economics, and University of Cambridge will benefit from gaining a broader exposure to issues spanning multiple arenas, including political, social, economic, cultural, and business. As the world becomes more globalized as well as commercialized, the boundary between business and other aspects of society is increasingly less rigid. The Asia Conference seeks to capture this phenomenon and help delegates understand its impacts on the future of international relations.
The conference will tackle various relevant issues on corporate leadership, energy and sustainability, entrepreneurship, finance and world economy, health and social policy, human rights and security and diplomacy.
RFM Corporation and Go Negosyo will host the "Manila Night" dinner for the delegates on August 23 wherein we will showcase the Filipino culture and heritage. Several entrepreneurs and school partners are also invited. On this dinner, we would allow the delegates to have a taste of the Philippines through our delicious local delicacies, hear our beautiful music, see our stunning dances through a number of cultural dances and feel the warmth of our friendliness and hospitality by welcoming them.
On the following day, August 24, we will also conduct a Social Entrepreneurship Seminar with the theme: "Changing the World is our Business." We have invited reputable social entrepreneurs such as Pacita "Chit" Juan of Echostore, Nanette Medved- Po of Hope in a Bottle and Bryan McClelland, founder of Bambike to be the speakers of the event. After all the talks, the attendees will be given the opportunity to ask the panellists any question that they may have.
It is such an honor to be given an opportunity to present our country to the rest of the world. More than the festivities, we hope that our visitors also bring with them memories of friendship and cultural exchange when they go home to their respective countries. Rest assured that Go Negosyo will take good care of our guests as any Filipino would.
Go Negosyo Renews Partnership with DHL Express Philippines
DHL Express Philippines has been one of staunch partners of Go Negosyo for the past years. DHL provides logistics solutions for many micro, small and medium entrepreneurs. Yati Abdullah, DHL Express Philippines' Country Manager, shares that even online shops and small entrepreneurs may use their services to send products to their customers. DHL Express Philippines promises to help entrepreneurs in their growth and market expansion.
Pilipinas : Now is Our Time

redpajama_set_name: RedPajamaCommonCrawl


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redpajama_set_name: RedPajamaGithub


text:
Q: How to set in parametr funciton a List of any types? I have a function like this:
public static String getNumberOfItemsInList(Order[] orders) {
     return String.valueOf(objects.length);
}

I must use this function in many options:
getNumberOfItemsInList(list of ordres);
getNumberOfItemsInList(list of users);
getNumberOfItemsInList(list of services);

How to pass in the parameter of the function List that would take any type we give it? I don't want to create a function for any type of the list,
can somebody help me?

A: Write 2 methods, so you're covered regarding the definition of "list":
public static String getNumberOfItemsInList(Object[] array) {
     return String.valueOf(array.length);
}

public static String getNumberOfItemsInList(Collection<?> coll) {
     return String.valueOf(coll.size());
}

The first can handle all types of arrays, and the second can handle all types of collections, including all types of lists.

A: try this
public static <T>  String getLength(T[] items) {
    return String.valueOf(items.length);
}

redpajama_set_name: RedPajamaStackExchange


text:
\section{Introduction}

Neural architecture search (NAS) has emerged as a research problem of searching for architectures that perform well on target data and tasks. A key mystery surrounding NAS is what factors contribute to the success of the search. Intuitively, using the target data and tasks during the search will result in the least domain gap, and this is indeed the strategy adopted in early NAS attempts~\cite{Zoph2017,real2017large}. Later, researchers~\cite{zoph2018learning} started to utilize the transferability of architectures, which enabled the search to be performed on different data and labels (\emph{e.g}\onedot} \def\Eg{\emph{E.g}\onedot, CIFAR-10) than the target (\emph{e.g}\onedot} \def\Eg{\emph{E.g}\onedot, ImageNet). However, what has not changed is that \emph{both the images and the (semantic) labels} provided in the dataset need to be used in order to search for an architecture. In other words, existing NAS approaches perform search in the \emph{supervised} learning regime.

In this paper, we take a step towards understanding what role supervision plays in the success of NAS. We ask the question: How indispensable are labels in neural architecture search? Is it possible to find high-quality architectures using images only? This corresponds to the important yet underexplored \emph{unsupervised setup} of neural architecture search, which we formalize in Section~\ref{sec:unnas}.

With the absence of labels, the quality of the architecture needs to be estimated in an unsupervised fashion during the search phase. In the present work, we conduct two sets of experiments using three unsupervised training methods \cite{gidaris2018unsupervised,zhang2016colorful,noroozi2016unsupervised} from the recent self-supervised learning literature.\footnote{Self-supervised learning is a form of unsupervised learning. The term ``unsupervised learning'' in general emphasizes ``without \emph{human}-annotated labels'', while the term ``self-supervised learning'' emphasizes ``producing labels from data''.} These two sets of experiments approach the question from complementary perspectives. In \emph{sample-based experiments}, we randomly sample 500 architectures from a search space, train and evaluate them using supervised \vs self-supervised objectives, and then examine the rank correlation (when sorting models by accuracy) between the two training methodologies. In \emph{search-based experiments}, we take a well-established NAS algorithm, replace the supervised search objective with a self-supervised one, and examine the quality of the searched architecture on tasks such as ImageNet classification and Cityscapes semantic segmentation. Our findings include:

\begin{itemize}[itemsep=.1em,topsep=.5em]
\item The architecture rankings produced by supervised and self-supervised pretext tasks are \emph{highly correlated}. This finding is consistent across two datasets, two search spaces, and three pretext tasks.
\item The architectures searched without human annotations are \emph{comparable in performance} to their supervised counterparts. This result is consistent across three pretext tasks, three pretext datasets, and two target tasks. There are even cases where unsupervised search outperforms supervised search.
\item Existing NAS approaches typically use \emph{labeled} images from a \emph{smaller} dataset to learn transferable architectures. We present evidence that using \emph{unlabeled} images from a \emph{large} dataset may be a more promising approach.
\end{itemize}

We conclude that labels are not necessary for neural architecture search, and the deciding factor for architecture quality may hide within the image pixels.

\section{Related Work}

\subsubsection{Neural Architecture Search.} Research on the NAS problem involves designing the search space \cite{zoph2018learning,ying2019bench} and the search algorithm \cite{Zoph2017,real2019regularized}. There are special focuses on reducing the overall time cost of the search process \cite{liu2018progressive,pham2018efficient,liu2018darts}, or on extending to a larger variety of tasks \cite{chen2018searching,liu2019auto,ghiasi2019fpn,so2019evolved}. Existing works on NAS all use human-annotated labels during the search phase. Our work is orthogonal to existing NAS research, in that we explore the unsupervised setup.

\subsubsection{Architecture Transferability.} In early NAS attempts \cite{Zoph2017,real2017large}, the search phase and the evaluation phase typically operate on the same dataset and task. Later, researchers realized that it is possible to relax this constraint. In these situations, the dataset and task used in the search phase are typically referred as the \emph{proxy} to the target dataset and task, reflecting a notion of architecture transferability. \cite{zoph2018learning} demonstrated that CIFAR-10 classification is a good proxy for ImageNet classification. \cite{liu2018progressive} measured the rank correlation between these two tasks using a small number of architectures. \cite{kornblith2019better} studied the transferability of 16 architectures (together with trained weights) between more \emph{supervised} tasks.
\cite{kolesnikov2019revisiting} studied the role of architecture in several self-supervised tasks, but with a small number of architectures and a different evaluation method (\emph{i.e}\onedot} \def\Ie{\emph{I.e}\onedot linear probe).
Part of our work studies architecture transferability at a larger scale, \emph{across} supervised and unsupervised tasks.

\subsubsection{Unsupervised Learning.} There is a large literature on unsupervised learning, \emph{e.g}\onedot} \def\Eg{\emph{E.g}\onedot, \cite{Dosovitskiy2014,Doersch2015,Wang2015,zhang2016colorful,noroozi2016unsupervised,gidaris2018unsupervised,Oord2018,Wu2018a,Tian2019,he2019momentum}. In the existing literature, methods are generally developed to learn the \emph{weights} (parameters) of a fixed architecture without using labels, and these weights are evaluated by transferring to a target \emph{supervised} task. In our study, we explore the possibility of using such methods to learn the \emph{architecture} without using labels, rather than the weights.
Therefore, our subject of study is simultaneously the unsupervised generalization of NAS, and the architecture level generalization of unsupervised learning.

\section{Unsupervised Neural Architecture Search}
\label{sec:unnas}

The goal of this paper is to provide an answer to the question asked in the title: are labels necessary for neural architecture search? To formalize this question, in this section, we define a new setup called Unsupervised Neural Architecture Search ({UnNAS}{}). Note that UnNAS represents a general problem setup instead of any specific algorithm for solving this problem. We instantiate {UnNAS}{} with specific algorithms and experiments to explore the importance of labels in neural architecture search.

\subsection{Search Phase}
The traditional NAS problem includes a search phase: given a pre-defined search space, the search algorithm explores this space and estimates the performance (\emph{e.g}\onedot} \def\Eg{\emph{E.g}\onedot accuracy) of the architectures sampled from the space. The accuracy estimation can involve full or partial training of an architecture. Estimating the accuracy requires access to the labels of the dataset. So the traditional NAS problem is essentially a \emph{supervised learning} problem.

We define UnNAS as the counterpart \emph{unsupervised learning} problem. It follows the definition of the NAS problem, only except that there are no human-annotated labels provided for estimating the performance of the architectures. An algorithm for the UnNAS problem still explores a pre-defined search space,\footnote{Admittedly, the search space design is typically heavily influenced by years of manual search, usually with supervision.} but it requires other criteria to estimate how good a sampled architecture is.

\subsection{Evaluation Phase}

Generally speaking, the goal of the NAS problem is to find an architecture. The weights of the found architecture are \emph{not} necessarily the output of a NAS algorithm. Instead, the weights are optimized after the search phase in an evaluation phase of the NAS problem: it includes training the found architecture on a target dataset's training split, and validating the accuracy on the target dataset's validation split. We note that ``\emph{training} the architecture weights" is part of the NAS \emph{evaluation} phase---the labels in the target dataset, both training and validation splits, play a role of evaluating an architecture.

Based on this context, we define \emph{the evaluation phase of {UnNAS}{}} in the same way: training the weights of the architecture (found by an {UnNAS}{} algorithm) on a target dataset's training split, and validating the accuracy on the target dataset's validation split, both \emph{using} the labels of the target dataset. We remark that using labels during the evaluation phase does \emph{not} conflict with the definition of {UnNAS}{}: the search phase is unsupervised, while the evaluation phase requires labels to examine how good the architecture is.

\begin{figure}[t]\centering
\includegraphics[width=0.55\linewidth]{figs/teaser_ai}
  \caption{\textbf{Unsupervised neural architecture search}, or \mbox{UnNAS}, is a new problem setup that helps answer the question: are labels necessary for neural architecture search? In traditional unsupervised learning (top panel), the \emph{training phase} learns the weights of a fixed architecture; then the \emph{evaluation phase} measures the quality of the weights by training a classifier (either by fine-tuning the weights or using them as a fixed feature extractor) using supervision from the target dataset. Analogously, in \mbox{UnNAS} (bottom panel), the \emph{search phase} searches for an architecture without using labels; and the \emph{evaluation phase} measures the quality of the architecture found by an \mbox{UnNAS} algorithm by training the architecture's weights using supervision from the target dataset.
}
\label{fig:setup}
\end{figure}

\subsection{Analogy to Unsupervised Learning}

Our definition of {UnNAS}{} is analogous to unsupervised \emph{weight} learning in existing literature \cite{gidaris2018unsupervised,zhang2016colorful,noroozi2016unsupervised}. While the unsupervised learning phase has no labels for training weights, the quality of the learned weights is evaluated by transferring them to a target task, supervised by labels. We emphasize that in the {UnNAS}{} setup, labels play an analogous role during evaluation. See Figure~\ref{fig:setup} for an illustration and elaboration on this analogy.

Similar to unsupervised weight learning, in principle, the search dataset should be different than the evaluation (target) dataset in {UnNAS}{} in order to more accurately reflect real application scenarios.

\section{Experiments Overview}

As Section~\ref{sec:unnas} describes, an architecture discovered in an \emph{unsupervised} fashion will be evaluated by its performance in a \emph{supervised} setting. Therefore, we are essentially looking for some type of architecture level \emph{correlation} that can reach across the unsupervised \vs supervised boundary, so that the unsupervisedly discovered architecture could be reliably \emph{transferred} to the supervised target task. We investigate whether several existing self-supervised pretext tasks (described in Section~\ref{sec:pretext}) can serve this purpose, through two sets of experiments of complementary nature: \emph{sample-based} (Section~\ref{sec:sample-based}) and \emph{search-based} (Section~\ref{sec:search-based}). In \emph{sample-based}, each network is trained and evaluated individually, but the downside is that we can only consider a small, random subset of the search space. In \emph{search-based}, the focus is to find a top architecture from the entire search space, but the downside is that the training dynamics during the search phase does not exactly match that of the evaluation phase.

\subsection{Pretext Tasks}
\label{sec:pretext}

We explore three unsupervised training methods (typically referred to as \emph{pretext tasks} in self-supervised learning literature): rotation prediction, colorization, and solving jigsaw puzzles. We briefly describe them for completeness.

\begin{itemize}[itemsep=.1em,topsep=.5em]
\item \emph{Rotation prediction}~\cite{gidaris2018unsupervised} ({\texttt{Rot}}): the input image undergoes one of four preset rotations (0, 90, 180, and 270 degrees), and the pretext task is formulated as a 4-way classification problem that predicts the rotation.

\item \emph{Colorization}~\cite{zhang2016colorful} ({\texttt{Color}}): the input is a grayscale image, and the pretext task is formulated as a pixel-wise classification problem with a set of pre-defined color classes (313 in \cite{zhang2016colorful}).

\item \emph{Solving jigsaw puzzles}~\cite{noroozi2016unsupervised} ({\texttt{Jigsaw}}): the input image is divided into patches and randomly shuffled. The pretext task is formulated as an image-wise classification problem that chooses from one out of $K$ preset permutations.\footnote{On ImageNet, each image is divided into 3$\times$3$=$9 patches and $K$ is 1000 selected from 9!$=$362,880 permutations \cite{noroozi2016unsupervised}; on CIFAR-10 in which images are smaller, we use 2$\times$2$=$4 patches and $K$ is 4!$=$24 permutations.}
\end{itemize}

All three pretext tasks are image or pixel-wise classification problems. Therefore, we can compute the classification accuracy of the pretext task (on a validation set). Based on this pretext task accuracy, we can analyze its correlation with the supervised classification accuracy (also on a validation set), as in our sample-based experiments (Section~\ref{sec:sample-based}). Also, since these pretext tasks all use cross entropy loss, it is also straightforward to use them as the training objective in standard NAS algorithms, as done in our search-based experiments (Section~\ref{sec:search-based}).

\section{Sample-Based Experiments}
\label{sec:sample-based}

\begin{figure}[t]
\centering
\includegraphics[width=0.85\linewidth]
{figs/fig2_top}
\includegraphics[width=0.85\linewidth]
{figs/fig2_bottom}
  \caption{\textbf{Correlation between supervised classification accuracy \vs pretext task accuracy on CIFAR-10 (``C10'')}. Top panel: DARTS search space. Bottom panel: NAS-Bench-101 search space. The straight lines are fit with robust linear regression \cite{Huber2011} (same for Figure~\ref{fig:RC_IN1K} and Figure~\ref{fig:RC_across}).}
\label{fig:RC_C10}
\end{figure}

\subsection{Experimental Design}

In \emph{sample-based experiments}, we first randomly sample 500 architectures from a certain search space. We train each architecture from scratch on the pretext task and get its pretext task accuracy (\emph{e.g}\onedot} \def\Eg{\emph{E.g}\onedot, the 4-way classification accuracy in the rotation task), and also train the same architecture from scratch on the supervised classification task (\emph{e.g}\onedot} \def\Eg{\emph{E.g}\onedot, 1000-way classification on ImageNet).\footnote{These architectures only have small, necessary differences when trained towards these different tasks (\emph{i.e}\onedot} \def\Ie{\emph{I.e}\onedot, having 4 neurons or 1000 neurons at the output layer).}

With these data collected, we perform two types of analysis.
For the \emph{rank correlation} analysis, we empirically study the statistical rank correlations between the pretext task accuracy and the supervised accuracy, by measuring the Spearman's Rank Correlation \cite{spearman1904proof}, denoted as $\rho$.
For the \emph{random experiment} analysis, we follow the setup proposed in \cite{bergstra2012random} and recently adopted in \cite{radosavovic2019network}. Specifically, for each experiment size $m$, we sample $m$ architectures from our pool of $n$ $=$ 500 architectures. For each pretext task, we select the architecture with the highest pretext task accuracy among the $m$. This process is repeated $\lceil n/m \rceil$ times, to compute the mean and error bands ($\pm$ 2 standard deviations) of the top-1 accuracy of these $\lceil n/m \rceil$ architectures \emph{on the target dataset/task}.

These two studies provide complementary views. The \emph{rank correlation} analysis aims to provide a global picture for \emph{all} architectures sampled from a search space, while the \emph{random experiment} analysis focuses on the \emph{top} architectures in a random experiment of varying size.

We study two search spaces: the {{DARTS search space}} \cite{liu2018darts}, and the {{NAS-Bench-101 search space}} \cite{ying2019bench}. The latter search space was built for benchmarking NAS algorithms, so we expect it to be less biased towards the search space for a certain algorithm. The experiments are conducted on two commonly used datasets: CIFAR-10~\cite{Krizhevsky2009} and ImageNet~\cite{Deng2009}.

\subsection{Implementation Details}

Details of sampling the 500 architectures are described in Appendix.
In the DARTS search space, regardless of the task, each network is trained for 5 epochs with width 32 and depth 22 on ImageNet; 100 epochs with width 16 and depth 20 on CIFAR-10.
In the NAS-Bench-101 search space, regardless of the task, each network is trained for 10 epochs with width 128 and depth 12 on ImageNet; 100 epochs with width 128 and depth 9 on CIFAR-10. Please refer to~\cite{liu2018darts,ying2019bench} for the respective definitions of \emph{width} and \emph{depth}. The performance of each network on CIFAR-10 is the average of 3 independent runs to reduce variance.

We remark that the small-scale of CIFAR-10 dataset and the short training epochs on ImageNet are the compromises we make to allow for more diverse architectures (\emph{i.e}\onedot} \def\Ie{\emph{I.e}\onedot 500).

\begin{figure}[t]
\centering
\includegraphics[width=0.85\linewidth]
{figs/fig3_top}
\includegraphics[width=0.85\linewidth]
{figs/fig3_bottom}
\caption{\textbf{Correlation between supervised classification accuracy \vs pretext task accuracy on ImageNet (``IN'')}. Top panel: DARTS search space. Bottom panel: NAS-Bench-101 search space.}
\label{fig:RC_IN1K}
\end{figure}

\subsection{Results}

\subsubsection{High rank correlation between supervised accuracy and pretext accuracy on the \underline{same} dataset.}

\begin{figure}[t]
\centering
\includegraphics[width=\linewidth]{figs/fig4_top}
\includegraphics[width=\linewidth]{figs/fig4_bottom}
  \caption{\textbf{Correlation between ImageNet supervised classification accuracy \vs CIFAR-10 (``C10'') pretext task accuracy}. Rankings of architectures are highly correlated between supervised classification and three unsupervised tasks, as measured by Spearman's rank correlation ($\rho$). We also show rank correlation using CIFAR-10 supervised proxy in the rightmost panel.
Top panel: DARTS search space. Bottom panel: NAS-Bench-101 search space. }
\label{fig:RC_across}
\end{figure}

In Figure~\ref{fig:RC_C10} and Figure~\ref{fig:RC_IN1K}, we show the scatter plots of the 500 architectures' supervised classification accuracy (horizontal axis) and pretext task accuracy (vertical axis) on CIFAR-10 and ImageNet, respectively. We see that this rank correlation is typically higher than 0.8, regardless of the dataset, the search space, and the pretext task. This type of consistency and robustness indicates that this phenomenon is general, as opposed to dataset/search space specific.

The same experiment is performed on both the DARTS and the NAS-Bench-101 search spaces. The rank correlations on the NAS-Bench-101 search space are generally higher than those on the DARTS search space. A possible explanation is that the architectures in NAS-Bench-101 are more diverse, and consequently their accuracies have larger gaps, \emph{i.e}\onedot} \def\Ie{\emph{I.e}\onedot, are less affected by training noise.

Interestingly, we observe that among the three pretext tasks, colorization consistently has the lowest correlation on CIFAR-10, but the highest correlation on ImageNet. We suspect this is because the small images in CIFAR-10 make the learning of per-pixel colorization difficult, and consequently the performance after training is noisy.

\subsubsection{High rank correlation between supervised accuracy and pretext accuracy \underline{across} datasets.}
\label{sec:sample-based-across}

In Figure~\ref{fig:RC_across} we show the \emph{across dataset} rank correlation analysis, where the pretext task accuracy is measured on CIFAR-10, but the supervised classification accuracy is measured on ImageNet.

On the DARTS search space (top panel), despite the image distribution shift brought by different datasets, for each of the three pretext tasks (left three plots), the correlation remains consistently high ($\raise.17ex\hbox{$\scriptstyle\sim$}$0.8). This shows that across the entire search space, the relative ranking of an architecture is likely to be similar, whether under the unsupervised pretext task accuracy or under the supervised target task accuracy.
In the rightmost panel, we compare with a scenario where instead of using an unsupervised pretext task, we use a proxy task of CIFAR-10 supervised classification. The correlation is $\rho$ $=$ 0.90 in this case. As the CIFAR-10 supervised classification is a commonly used proxy task in existing NAS literature \cite{zoph2018learning}, it gives a reference on $\rho$'s value.

In the bottom panel we show more analysis of this kind by replacing the search space with NAS-Bench-101. Although the architectures are quite different, the observations are similar. In all cases, the self-supervised pretext task accuracy is highly correlated to the supervised classification accuracy.

\subsubsection{Better pretext accuracy translates to better supervised accuracy.}

\begin{figure}[t]\centering
\begin{subfigure}[t]{0.4\textwidth}
\captionsetup{justification=centering}
\centering
\includegraphics[width=1.0\linewidth]{figs/darts_random_search}
\end{subfigure}
~
\begin{subfigure}[t]{0.4\textwidth}
\captionsetup{justification=centering}
\centering
\includegraphics[width=1.0\linewidth]{figs/bench_random_search}
\end{subfigure}
\caption{\textbf{Random experiment efficiency curves}. Left panel: DARTS search space. Right panel: NAS-Bench-101 search space. We show the range of ImageNet classification accuracies of top architectures identified by the three pretext tasks and the supervised task under various experiment sizes. See text for more details. }
\label{fig:bench_random_search}
\end{figure}

In addition to the rank correlation analysis, we also perform the random experiment analysis. Figure~\ref{fig:bench_random_search} shows the \emph{random experiment efficiency} curve for DARTS and NAS-Bench-101 search spaces.
Again, the pretext accuracies are obtained on CIFAR-10, and the target accuracies are from ImageNet.
By design of this experiment, as the experiment size $m$ increases, the \emph{pretext} accuracies of the $\lceil n/m \rceil$ architectures should increase. Figure~\ref{fig:bench_random_search} shows that the \emph{target} accuracies of these $\lceil n/m \rceil$ architectures also increase with $m$.
In addition, at each experiment size, most unsupervised pretext objectives perform similarly compared to the commonly used supervised CIFAR-10 proxy. The overall trends are also comparable.
This shows that the architecture rankings produced with and without labels are not only correlated across the entire search space, but also towards the top of the search space, which is closer to the goal of {UnNAS}{}.

\section{Search-Based Experiments}
\label{sec:search-based}

\subsection{Experimental Design}

In \emph{search-based experiments}, the idea is to run a well-established NAS algorithm, except that we make the minimal modification of replacing its supervised search objective with an unsupervised one. Following the {UnNAS}{} setup, we then examine (by training from scratch) how well these unsupervisedly discovered architectures perform on supervised target tasks. Since all other variables are controlled to be the same, search-based experiments can easily compare between the supervised and unsupervised counterparts of a NAS algorithm, which can help reveal the importance of labels.

The NAS algorithm we adopt is DARTS (short for ``differentiable architecture search")~\cite{liu2018darts} for its simplicity. DARTS formulates the activation tensor selection and operation selection as a categorical choice, implemented as a softmax function on a set of continuous parameters (named $\alpha$). These parameters are trained in a similar fashion as the architecture weights, by backpropagation from a loss function. After this training, the softmax outputs are discretized and produce an architecture.

The self-supervised objectives that we consider are, still, those described in Section~\ref{sec:pretext}: rotation prediction ({\texttt{Rot}}), colorization ({\texttt{Color}}), and solving jigsaw puzzles ({\texttt{Jigsaw}}). For comparison, we also perform NAS search with supervised objectives, \emph{e.g}\onedot} \def\Eg{\emph{E.g}\onedot classification ({\texttt{Supv.Cls}}) or semantic segmentation ({\texttt{Supv.Seg}}). To help distinguish, we name the method {NAS-\texttt{DARTS}}{} if the search objective is supervised, and {UnNAS-\texttt{DARTS}}{} if the search objective is unsupervised.

\subsection{Implementation Details}

\paragraph{Search phase.}
We use three different datasets for architecture search: ImageNet-1K (IN1K)~\cite{Deng2009}, ImageNet-22K (IN22K)~\cite{Deng2009}, and Cityscapes~\cite{Cordts2016}. IN1K is the standard ImageNet benchmark dataset with 1.2M images from 1K categories. IN22K is the full ImageNet dataset that has $\raise.17ex\hbox{$\scriptstyle\sim$}$14M images from 22K categories. Cityscapes is a dataset of street scenes that has drastically different image statistics.
Note that {UnNAS-\texttt{DARTS}}{} will only access the images provided in the dataset, while {NAS-\texttt{DARTS}}{} will additionally access the (semantic) labels provided in the respective dataset. The search phase will operate only within the training split, without accessing the true validation or test split.

We report in Appendix the hyper-parameters we used. One major difference between our experiments and DARTS~\cite{liu2018darts} is that the images in the search datasets that we consider are much larger in size. We use 224$\times$224 random crops for search on IN1K/IN22K, and 312$\times$312 for search on Cityscapes following~\cite{liu2019auto}. To enable DARTS training with large input images, we use 3 stride-2 convolution layers at the beginning of the network to reduce spatial resolution. This design, together with appropriately chosen number of search epochs (see Appendix), allows {UnNAS}{} search to be efficient ($\raise.17ex\hbox{$\scriptstyle\sim$}$2 GPU days on IN1K/Cityscapes, $\raise.17ex\hbox{$\scriptstyle\sim$}$10 GPU days on IN22K, regardless of task) despite running on larger images.

\paragraph{Evaluation phase.}
We use two distinct datasets and tasks for UnNAS evaluation: (1) ImageNet-1K (IN1K) for image classification. The performance metric is top-1 accuracy on the IN1K validation set. (2) Cityscapes for semantic segmentation. We use the \texttt{train\_fine} set (2975 images) for training. The performance metric is mean Intersection-over-Union (mIoU) evaluated on the \texttt{val} set (500 images).

For IN1K evaluation, we fix depth to 14 and adjust the width to have \#FLOPs $\in$ [500,600]M. Models are trained for 250 epochs with an auxiliary loss weighted by 0.4, batch size 1024 across 8 GPUs, cosine learning rate schedule \cite{loshchilov2016sgdr} with initial value 0.5, and 5 epochs of warmup.
For Cityscapes evaluation, we fix depth to 12 and adjust the width to have \#Params $\in$ [9.5,10.5]M. We train the network for 2700 epochs, with batch size 64 across 8 GPUs, cosine learning rate schedule with initial value 0.1.
For both ImageNet and Cityscapes evaluations, we report the mean and standard deviation of 3 independent trainings of the same architecture.
More implementation details are described in Appendix.

We note that under our definition of the UnNAS setup, the same dataset should not be used for both search and evaluation (because this scenario is unrealistic); We provide the IN1K$\rightarrow$IN1K and Cityscapes$\rightarrow$Cityscapes results purely as a reference. Those settings are analogous to the linear classifier probe for IN1K in conventional unsupervised learning research.

\subsection{Results}

In search-based experiments, the architectures are evaluated on both ImageNet classification, summarized in Table~\ref{tab:imagenet250}, and Cityscapes semantic segmentation, summarized in Table~\ref{tab:cityscapes}.
We provide visualization of all {NAS-\texttt{DARTS}}{} and {UnNAS-\texttt{DARTS}}{} cell architectures in Appendix.

\newcolumntype{H}{>{\setbox0=\hbox\bgroup}c<{\egroup}@{}}
\begin{table}[t]\centering
\tablestyle{4.5pt}{1.05}\begin{tabular}{ll|lH|cc}
method & search dataset \& task & top-1 acc. & top-5 acc. & \ft{7}FLOPs (M) & \ft{7}params (M) \\
\shline
NAS-\texttt{DARTS}~\cite{liu2018darts} & CIFAR-10 {\texttt{Supv.Cls}}{} & 73.3 & 91.3 & 574 & 4.7 \\
NAS-\texttt{P-DARTS}~\cite{chen2019progressive} & CIFAR-10 {\texttt{Supv.Cls}}{} & 75.6 & 92.6 & 557 & 4.9 \\
NAS-\texttt{PC-DARTS}~\cite{xu2019pc} & CIFAR-10 {\texttt{Supv.Cls}}{} & 74.9 & 92.2 & 586 & 5.3 \\
NAS-\texttt{PC-DARTS}~\cite{xu2019pc} & IN1K {\texttt{Supv.Cls}}{} & 75.8 & 92.7 & 597 & 5.3 \\
NAS-\texttt{DARTS}$^\dagger$ & CIFAR-10 {\texttt{Supv.Cls}}{} & 74.9\pmstd{0.08} & 92.2\pmstd{0.06} & 538 & 4.7 \\
\hline
\hline
{NAS-\texttt{DARTS}} & IN1K {\texttt{Supv.Cls}}{} & {76.3}\pmstd{0.06} & {92.9}\pmstd{0.04} & 590 & 5.3 \\
\grayfy{{{UnNAS}-\texttt{DARTS}}} & \grayfy{IN1K {\texttt{Rot}}{}} & \grayfy{{75.8}\pmstd{0.18}} & \grayfy{{92.7}\pmstd{0.16}} & \grayfy{558} & \grayfy{5.1} \\
\grayfy{{{UnNAS}-\texttt{DARTS}}} & \grayfy{IN1K {\texttt{Color}}{}} & \grayfy{{75.7}\pmstd{0.12}} & \grayfy{{92.6}\pmstd{0.16}} & \grayfy{547} & \grayfy{4.9} \\
\grayfy{{{UnNAS}-\texttt{DARTS}}} & \grayfy{IN1K {\texttt{Jigsaw}}{}} & \grayfy{{75.9}\pmstd{0.15}} & \grayfy{{92.8}\pmstd{0.10}} & \grayfy{567} & \grayfy{5.2} \\
\hline
{NAS-\texttt{DARTS}} & IN22K {\texttt{Supv.Cls}}{} & {75.9}\pmstd{0.09} & {92.7}\pmstd{0.08} & 585 & 5.2 \\
{{UnNAS}-\texttt{DARTS}} & IN22K {\texttt{Rot}}{} & {75.7}\pmstd{0.23} & {92.7}\pmstd{0.11} & 549 & 5.0 \\
{{UnNAS}-\texttt{DARTS}} & IN22K {\texttt{Color}}{} & {75.9}\pmstd{0.21} & {92.8}\pmstd{0.10} & 547 & 5.0 \\
{{UnNAS}-\texttt{DARTS}} & IN22K {\texttt{Jigsaw}}{} & {75.9}\pmstd{0.31} & {92.8}\pmstd{0.13} & 559 & 5.1 \\
\hline
{{NAS-\texttt{DARTS}}} & Cityscapes {\texttt{Supv.Seg}}{} & {75.8}\pmstd{0.13} & {92.6}\pmstd{0.17} & 566 & 5.1 \\
{{UnNAS-\texttt{DARTS}}} & Cityscapes {\texttt{Rot}}{} & {75.9}\pmstd{0.19} & {92.7}\pmstd{0.07} & 554 & 5.1 \\
{{UnNAS}-\texttt{DARTS}} & Cityscapes {\texttt{Color}}{} & {75.2}\pmstd{0.15} & {92.4}\pmstd{0.05} & 594 & 5.1 \\
{{UnNAS}-\texttt{DARTS}} & Cityscapes {\texttt{Jigsaw}}{} & {75.5}\pmstd{0.06} & {92.6}\pmstd{0.08} & 566 & 5.0 \\
\end{tabular}
  \caption{\textbf{ImageNet-1K classification results of the architectures searched by NAS and {UnNAS}{} algorithms}. \grayfy{Rows in gray} correspond to invalid {UnNAS}{} configurations where the search and evaluation datasets are the same. $\dagger$ is our training result of the DARTS architecture released in \cite{liu2018darts}.}
\label{tab:imagenet250}
\end{table}

\begin{table}[t]\centering
\tablestyle{4.5pt}{1.05}\begin{tabular}{ll|l|cc}
method & search dataset \& task & mIoU & \ft{7}FLOPs (B) & \ft{7}params (M) \\
\shline
{NAS-\texttt{DARTS}} $^\dagger$ & CIFAR-10 {\texttt{Supv.Cls}}{} & 72.6\pmstd{0.55} & 121 & 9.6 \\
\hline
\hline
{{NAS-\texttt{DARTS}}} & IN1K {\texttt{Supv.Cls}}{}  & {73.6}\pmstd{0.31} & 127 & 10.2 \\
{{UnNAS-\texttt{DARTS}}} & IN1K {\texttt{Rot}}{} & {73.6}\pmstd{0.29} & 129 & 10.4 \\
{{UnNAS-\texttt{DARTS}}} & IN1K {\texttt{Color}}{} & {72.2}\pmstd{0.56} & 122 & 9.7 \\
{{UnNAS-\texttt{DARTS}}} & IN1K {\texttt{Jigsaw}}{} & {73.1}\pmstd{0.17}  & 129 & 10.4 \\
\hline
{{NAS-\texttt{DARTS}}} & IN22K {\texttt{Supv.Cls}}{} & {72.4}\pmstd{0.29} & 126 & 10.1 \\
{{UnNAS-\texttt{DARTS}}} & IN22K {\texttt{Rot}}{} & {72.9}\pmstd{0.23} & 128 & 10.3 \\
{{UnNAS-\texttt{DARTS}}} & IN22K {\texttt{Color}}{} & {73.6}\pmstd{0.41} & 128 & 10.3 \\
{{UnNAS-\texttt{DARTS}}} & IN22K {\texttt{Jigsaw}}{} & {73.1}\pmstd{0.59} & 129 & 10.4 \\
\hline
{{NAS-\texttt{DARTS}}} & Cityscapes {\texttt{Supv.Seg}}{} & {72.4}\pmstd{0.15} & 128 & 10.3 \\
\grayfy{{{UnNAS-\texttt{DARTS}}}} & \grayfy{Cityscapes {\texttt{Rot}}{}} & \grayfy{{73.0}\pmstd{0.25}}  & \grayfy{128} & \grayfy{10.3} \\
\grayfy{{{UnNAS-\texttt{DARTS}}}} & \grayfy{Cityscapes {\texttt{Color}}{}} & \grayfy{{72.5}\pmstd{0.31}} & \grayfy{122} & \grayfy{9.5} \\
\grayfy{\textbf{{{UnNAS-\texttt{DARTS}}}}} & \grayfy{\textbf{Cityscapes {\texttt{Jigsaw}}{}}} & \grayfy{\textbf{{74.1}\pmstd{0.39}}} & \grayfy{\textbf{128}} & \grayfy{\textbf{10.2}} \\
\end{tabular}
\caption{\textbf{Cityscapes semantic segmentation results of the architectures searched by NAS and {UnNAS}{} algorithms}. These are trained from scratch: there is no fine-tuning from ImageNet checkpoint. \grayfy{Rows in gray} correspond to an illegitimate setup where the search dataset is the same as the evaluation dataset. $\dagger$ is our training result of the DARTS architecture released in \cite{liu2018darts}. }
\label{tab:cityscapes}
\end{table}

\subsubsection{{UnNAS}{} architectures perform competitively to supervised counterparts.}

We begin by comparing {NAS-\texttt{DARTS}}{} and {UnNAS-\texttt{DARTS}}{} when they are performed on the same search dataset. This would correspond to every four consecutive rows in Table~\ref{tab:imagenet250} and Table~\ref{tab:cityscapes}, grouped together by horizontal lines.

As discussed earlier, strictly speaking the IN1K$\rightarrow$IN1K experiment is not valid under our definition of {UnNAS}{}. For reference, we \grayfy{gray out} these results in Table~\ref{tab:imagenet250}. {NAS-\texttt{DARTS}}{} on IN1K dataset has the highest performance among our experiments, achieving a top-1 accuracy of $76.3\%$. However, the {UnNAS}{} algorithm variants with {\texttt{Rot}}{}, {\texttt{Color}}{}, {\texttt{Jigsaw}}{} objectives all perform very well (achieving $75.8\%$, $75.7\%$ and $75.9\%$ top-1 accuracy, respectively), closely approaching the results obtained by the supervised counterpart. This suggests it might be desirable to perform architecture search on the target dataset directly, as also observed in other work~\cite{cai2018proxylessnas}.

Two valid {UnNAS}{} settings include IN22K$\rightarrow$IN1K and Cityscapes$\rightarrow$IN1K for architecture search and evaluation. For IN22K$\rightarrow$IN1K experiments, NAS and {UnNAS}{} results across the board are comparable. For Cityscapes$\rightarrow$IN1K experiments, among the {UnNAS}{} architectures, {\texttt{Rot}}{} and {\texttt{Jigsaw}}{}  perform well, once again achieving results comparable to the supervised search. However, there is a drop for {UnNAS-\texttt{DARTS}}{} search with {\texttt{Color}}{} objective (with a $75.2\%$ top-1 accuracy). We hypothesize that this might be owing to the fact that the color distribution in Cityscapes images is not as diverse: the majority of the pixels are from \emph{road} and \emph{ground} categories of gray colors.

In general, the variances are higher for Cityscapes semantic segmentation (Table~\ref{tab:cityscapes}), but overall {UnNAS-\texttt{DARTS}}{} architectures still perform competitively to {NAS-\texttt{DARTS}}{} architectures, measured by mIoU. For the Cityscapes$\rightarrow$Cityscapes experiment, we observe that searching with segmentation objective directly leads to inferior result (mean $72.4\%$ mIoU), compared to the architectures searched for ImageNet classification tasks. This is different from what has been observed in Table~\ref{tab:imagenet250}. However, under this setting, our {UnNAS}{} algorithm, in particular the one with the {\texttt{Jigsaw}}{} objective shows very promising results (mean $74.1\%$ mIoU). In fact, when the search dataset is IN22K or Cityscapes, all {UnNAS-\texttt{DARTS}}{} architectures perform \emph{better} than the {NAS-\texttt{DARTS}}{} architecture. This is the opposite of what was observed in IN1K$\rightarrow$IN1K. Results for Cityscapes$\rightarrow$Cityscapes are \grayfy{grayed out} for the same reason as before (invalid under {UnNAS}{} definition).

\subsubsection{NAS and {UnNAS}{} results are robust across a large variety of datasets and tasks.}

The three search datasets that we consider are of different nature. For example, IN22K is 10 times larger than IN1K, and Cityscapes images have a markedly different distribution than those in ImageNet.
In our experiments, {NAS-\texttt{DARTS}}{}/{UnNAS-\texttt{DARTS}}{} architectures searched on IN22K do not significantly outperform those searched on IN1K, meaning that they do not seem to be able to enjoy the benefit of having more abundant images. This reveals new opportunities in designing better algorithms to exploit bigger datasets for neural architecture search. For Cityscapes$\rightarrow$IN1K experiments, it is interesting to see that after switching to a dataset with markedly distinct search images (urban street scenes), we are still able to observe decent performance. The same goes for the reverse direction IN1K/IN22K$\rightarrow$Cityscapes, which implies that the search does not severely overfit to the images from the dataset.

In addition to this robustness to the \emph{search dataset} distribution, NAS and {UnNAS}{} also exhibit robustness to \emph{target dataset and task}. Classification on ImageNet and segmentation on Cityscapes are different in many ways, but among different combinations of search dataset and task (whether supervised or unsupervised), we do not observe a case where the same architecture performs well on one but poorly on the other.

\subsubsection{{UnNAS}{} outperforms previous methods.}

Finally, we compare our {UnNAS-\texttt{DARTS}}{} results against existing works.
We first note that we are able to achieve a better baseline number with the {NAS-\texttt{DARTS}}{} architecture (searched with the CIFAR-10 proxy) compared to what was reported in~\cite{liu2018darts}. This is mainly due to better hyper-parameter tuning we adopt from~\cite{chen2019progressive} for the \emph{evaluation phase} model training. This baseline sets up a fair ground for all the {UnNAS}{} experiments; we use the same evaluation phase hyper-parameters across different settings.

On ImageNet, our {UnNAS-\texttt{DARTS}}{} architectures can comfortably outperform this baseline by up to $1\%$ classification accuracy. In fact, the extremely competitive {UnNAS-\texttt{DARTS}}{} results also outperform the previous best result ($75.8\%$) on this search space, achieved with a more sophisticated NAS algorithm \cite{xu2019pc}.

On Cityscapes, there have not been many works that use DARTS variants as the backbone. The closest is Auto-DeepLab \cite{liu2019auto}, but we use a lighter architecture (in that we do not have the Decoder) and shorter training iterations, so the results are not directly comparable. Nonetheless, according to our evaluation, the {UnNAS}{} architectures perform favorably against the DARTS architecture released in \cite{liu2018darts} (discovered with the CIFAR-10 proxy). The best {UnNAS-\texttt{DARTS}}{} variant (Cityscapes {\texttt{Jigsaw}}{}) achieves $74.1\%$ mIoU, which outperforms this baseline by $1.5\%$ on average.
Overall, our experiments demonstrate that exploring neural architecture search with unsupervised/self-supervised objectives to improve target task performance might be a fruitful direction.

Outperforming previous methods was far from the original goal of our study. Nonetheless, the promising results of {UnNAS}{} suggest that in addition to developing new \emph{algorithms} and finding new \emph{tasks}, the role of \emph{data} (in our case, more/larger images) and \emph{paradigm} (in our case, no human annotations) is also worth attention in future work on neural architecture search.

\section{Discussion}

In this paper, we challenge the common practice in neural architecture search and ask the question: do we really need labels to successfully perform NAS? We approach this question with two sets of experiments. In sample-based experiments, we discover the phenomenon that the architecture rankings produced with and without labels are highly correlated. In search-based experiments, we show that the architectures learned without accessing labels perform competitively, not only relative to their supervised counterpart, but also in terms of absolute performance. In both experiments, the observations are consistent and robust across various datasets, tasks, and/or search spaces. Overall, the findings in this paper indicate that labels are \emph{not} necessary for neural architecture search.

How to learn and transfer useful representations to subsequent tasks in an unsupervised fashion has been a research topic of extensive interest, but the discovery of neural network architectures has been driven solely by supervised tasks. As a result, current NAS products or AutoML APIs typically have the strict prerequisite for users to ``\emph{put together a training dataset of labeled images}''~\cite{AutoML_API}. An immediate implication of our study is that the job of the user could potentially be made easier by dropping the labeling effort. In this sense, {UnNAS}{} could be especially beneficial to the many applications where data constantly comes in at large volume but labeling is costly.

At the same time, we should still ask: if not labels, then what factors are needed to reveal a good architecture? A meaningful unsupervised task seems to be important, though the several pretext tasks considered in our paper do not exhibit significant difference in either of the two experiments. In the future we plan to investigate even more and even simpler unsupervised tasks. Another possibility is that the architecture quality is mainly decided by the image statistics, and since the datasets that we consider are all natural images, the correlations are high and the results are comparable. This hypothesis would also suggest an interesting, alternative direction: that instead of performing NAS again and again for every specific labeled task, it may be more sensible to perform NAS once on large amounts of unlabeled images that capture the image distribution.

\section{Additional Details for Sample-Based Experiments}

In sampling the 500 DARTS architectures, we only keep those whose \#Params and \#FLOPs both fall into [80\%, 120\%] that of ResNet-56. Doing so prevents the influence of model capacity on the rank correlations, but the downside is that these architectures may not be representative of the entire search space. Therefore, in sampling the 500 NAS-Bench-101 architectures, we used a complementary strategy where we first rank all architectures based on the released accuracy, and then sample 50 architectures from each 10th percentile.

\section{Additional Details for Search-Based Experiments}

\subsection{Search Phase}

Recall that in the search phase of {NAS-\texttt{DARTS}}{}/{UnNAS-\texttt{DARTS}}{}, we consider 3 datasets: ImageNet-1K (IN1K), ImageNet-22K (IN22K), Cityscapes.

For IN1K, we follow \cite{liu2019auto,chen2019progressive} to postpone updating architecture parameters $\alpha$ for half of the total search epochs.
For IN1K/IN22K, the search phase lasts 2 epochs for IN1K and 1 epoch for IN22K. Since IN22K is approximately 10 times larger than IN1K ($\raise.17ex\hbox{$\scriptstyle\sim$}$14M vs $\raise.17ex\hbox{$\scriptstyle\sim$}$1.2M), the search on IN22K is approximately 5 times longer than the search on IN1K. Batch size is 64, learning rate is 0.1 (cosine schedule), weight decay is 0.00003.
For Cityscapes, the search phase lasts 400 epochs. Batch size is 32, learning rate is 0.1 (cosine schedule), weight decay is 0.0003.
Other than those listed here and in the main paper, all hyperparameters follow those used in the original DARTS.
In a few settings where the batch size above will exceed 32GB GPU memory, we divide batch size and learning rate (both for weights and for architecture parameters $\alpha$) by 2.

\subsection{Evaluation Phase}

When evaluating an architecture on Cityscapes semantic segmentation, since the task is pixel-level classification instead of image-level classification, we need to make minimal but necessary modifications. Different from the network used in IN1K classification, we (1) replace the last stride 2 layer with stride 1, to increase spatial resolution; and (2) remove the global average pooling and replace the fully connected classifier with the Atrous Spatial Pyramid Pooling (ASPP) module~\cite{chen2017rethinking} followed by bilinear upsampling to produce per-pixel classification at the original resolution. These are the same modifications the segmentation framework DeepLabv3~\cite{chen2017rethinking} made to a ResNet backbone.

\section{{NAS-\texttt{DARTS}}{} and {UnNAS-\texttt{DARTS}}{} Architectures}

Here we visualize all the {NAS-\texttt{DARTS}}{} and {UnNAS-\texttt{DARTS}}{} cell architectures: searched on ImageNet-1K (Figure~\ref{fig:darts_in1k_archs}), ImageNet-22K (Figure~\ref{fig:darts_in22k_archs}), Cityscapes (Figure~\ref{fig:darts_cityscapes_archs}).

\newcommand{0.9\linewidth}{0.9\linewidth}

\begin{figure}[!htp]\centering

\begin{subfigure}[t]{0.24\textwidth}
\centering
\includegraphics[width=0.9\linewidth]{figs/best_archs/darts/IN1K_Supv_normal}
\includegraphics[width=0.9\linewidth]{figs/best_archs/darts/IN1K_Supv_reduce}
\caption{NAS {\texttt{Supv.Cls}}{}}
\end{subfigure}
\begin{subfigure}[t]{0.24\textwidth}
\centering
\includegraphics[width=0.9\linewidth]{figs/best_archs/darts/IN1K_Rot_normal}
\includegraphics[width=0.9\linewidth]{figs/best_archs/darts/IN1K_Rot_reduce}
\caption{{UnNAS}{} {\texttt{Rot}}{}}
\end{subfigure}
\begin{subfigure}[t]{0.24\textwidth}
\centering
\includegraphics[width=0.9\linewidth]{figs/best_archs/darts/IN1K_Color_normal}
\includegraphics[width=0.9\linewidth]{figs/best_archs/darts/IN1K_Color_reduce}
\caption{{UnNAS}{} {\texttt{Color}}{}}
\end{subfigure}
\begin{subfigure}[t]{0.24\textwidth}
\centering
\includegraphics[width=0.9\linewidth]{figs/best_archs/darts/IN1K_Jigsaw3_normal}
\includegraphics[width=0.9\linewidth]{figs/best_archs/darts/IN1K_Jigsaw3_reduce}
\caption{{UnNAS}{} {\texttt{Jigsaw}}{}}
\end{subfigure}

\caption{Cell architectures (normal and reduce) searched on ImageNet-1K. }
\label{fig:darts_in1k_archs}
\end{figure}

\begin{figure}[!htp]\centering

\begin{subfigure}[t]{0.24\textwidth}
\centering
\includegraphics[width=0.9\linewidth]{figs/best_archs/darts/IN22K_Supv_normal}
\includegraphics[width=0.9\linewidth]{figs/best_archs/darts/IN22K_Supv_reduce}
\caption{NAS {\texttt{Supv.Cls}}{}}
\end{subfigure}
\begin{subfigure}[t]{0.24\textwidth}
\centering
\includegraphics[width=0.9\linewidth]{figs/best_archs/darts/IN22K_Rot_normal}
\includegraphics[width=0.9\linewidth]{figs/best_archs/darts/IN22K_Rot_reduce}
\caption{{UnNAS}{} {\texttt{Rot}}{}}
\end{subfigure}
\begin{subfigure}[t]{0.24\textwidth}
\centering
\includegraphics[width=0.9\linewidth]{figs/best_archs/darts/IN22K_Color_normal}
\includegraphics[width=0.9\linewidth]{figs/best_archs/darts/IN22K_Color_reduce}
\caption{{UnNAS}{} {\texttt{Color}}{}}
\end{subfigure}
\begin{subfigure}[t]{0.24\textwidth}
\centering
\includegraphics[width=0.9\linewidth]{figs/best_archs/darts/IN22K_Jigsaw3_normal}
\includegraphics[width=0.9\linewidth]{figs/best_archs/darts/IN22K_Jigsaw3_reduce}
\caption{{UnNAS}{} {\texttt{Jigsaw}}{}}
\end{subfigure}

\caption{Cell architectures (normal and reduce) searched on ImageNet-22K. }
\label{fig:darts_in22k_archs}
\end{figure}

\begin{figure}[!htp]\centering

\begin{subfigure}[t]{0.24\textwidth}
\centering
\includegraphics[width=0.9\linewidth]{figs/best_archs/darts/Cityscapes_Seg_normal}
\includegraphics[width=0.9\linewidth]{figs/best_archs/darts/Cityscapes_Seg_reduce}
\caption{NAS {\texttt{Supv.Seg}}{}}
\end{subfigure}
\begin{subfigure}[t]{0.24\textwidth}
\centering
\includegraphics[width=0.9\linewidth]{figs/best_archs/darts/Cityscapes_Rot_normal}
\includegraphics[width=0.9\linewidth]{figs/best_archs/darts/Cityscapes_Rot_reduce}
\caption{{UnNAS}{} {\texttt{Rot}}{}}
\end{subfigure}
\begin{subfigure}[t]{0.24\textwidth}
\centering
\includegraphics[width=0.9\linewidth]{figs/best_archs/darts/Cityscapes_Color_normal}
\includegraphics[width=0.9\linewidth]{figs/best_archs/darts/Cityscapes_Color_reduce}
\caption{{UnNAS}{} {\texttt{Color}}{}}
\end{subfigure}
\begin{subfigure}[t]{0.24\textwidth}
\centering
\includegraphics[width=0.9\linewidth]{figs/best_archs/darts/Cityscapes_Jigsaw3_normal}
\includegraphics[width=0.9\linewidth]{figs/best_archs/darts/Cityscapes_Jigsaw3_reduce}
\caption{{UnNAS}{} {\texttt{Jigsaw}}{}}
\end{subfigure}

\caption{Cell architectures (normal and reduce) searched on Cityscapes. }
\label{fig:darts_cityscapes_archs}
\end{figure}

redpajama_set_name: RedPajamaArXiv


text:
CFO Capital Advises Alpha Galvanizing in Sale to AZZ
CFO Capital served as the exclusive financial advisor to Alpha Galvanizing in its recent acquisition by AZZ, Inc. (NYSE: AZZ). Terms of the deal, which closed on February 1, 2016, were not disclosed.
Alpha Galvanizing, located in Atkinson, Nebraska, was founded in 1995 as a subsidiary of Olson Industries, Inc. Co-founder Dan Olson commented, "As we evaluated potential acquirers of Alpha Galvanizing, we wanted to make sure that we found the right partner for our employees, customers, and the local community. CFO Capital helped us find the right buyer for our company, as well as ensure a smooth transaction and integration."
The transaction did not include Olson Industries' main business nor its MWC division. Olson Industries is a fabricator of airport lighting canisters, center pivot irrigation systems, and trash containers. The remaining businesses will continue to operate at the company's headquarters in Atkinson.
AZZ, Inc. is a Fort Worth, Texas-based provider of galvanizing services, welding solutions, specialty electrical equipment and engineered services to the power generation, transmission, distribution, refining and industrial markets. The addition of Alpha Galvanizing brings AZZ's total galvanizing facility count to 43 across North America.
Tim Pendley, senior vice president and chief operating officer of Galvanizing Services of AZZ Inc., commented, "We are pleased to enter a new galvanizing market in Nebraska with the acquisition of the assets of Alpha Galvanizing. Our goal is to continue to expand our geographic coverage by offering a broad portfolio of metal protection services, unmatched quality and superior customer service. From a geographic standpoint, the new AZZ Galvanizing-Nebraska is ideally located to support our Midwestern and Denver locations and their customers as needed."
John Gustafson, Managing Director of CFO Capital, added "It was a pleasure to work with the sellers to ensure a strong strategic fit with the new owners, as well as working with AZZ to fulfill their objectives in the region while ensuring a smooth transition."


This is post number 1 highlighting three series I've loaded onto the site from 2004-2006 that have never been there before.
"Billings" was made the same summer I made the Old Trail Town series from Cody, Wyoming. The mid 2000's were heady years for me. I was now a full professor at Northeastern, I was starting to teach summers in Venice and photography had embarked on a sea change of a different way to capture light on a light sensitive material, from film to CCD sensors. Looking back at my work made during those years, the series I made at the end of the film era for me, the work looks large in scale, completely known in terms of the materials I was using and comfortable in the tools I used to make my images. Two of the series I have just placed on the site, Billings and Thomspon are large works, and look complex, involved, and are perhaps less accessible to many. If anything I can do as a photographic artist is close to "symphonic" then these are two groups that would represent that idiom best. As such, again looking back at them with years of perspective, I would rate them highly in terms of works being made by an artist at the height of his powers and secure in the materials being used to make his statements. If it sounds as though I am proud of these works, I am. Are they among my most beautiful series? Absolutely not. Are they for everybody? No. But they are rhythmic, melodic, arrive at a climax, finish with a conclusion and are intended to take the viewer somewhere. All three are mature works, not really possible for a young man to make.
So, Billings pays homage to my youth due to two summers spent in Wyoming and Montana as a teenager working on a ranch. In order to get to the ranch in those years, one flew to Billings, then drove from there to Cody and then went west to the ranch. Forty years later, in 2005, I rented an apartment in Cody and worked for five weeks in 8 x 10 and early digital. shooting daily, driving a rented Jeep. Looking back it was the last trip I took where I worked in the 8 x 10 inch format.
Cleary, I was photographing the big camera as I was thinking this way of making pictures was close to being over.
What was I photographing the 8 x 10 with? A Nikon D70, an early generation small chip DSLR that was very affordable and made a picture from a 6 mp file. In truth I had a blast with the Nikon, shooting things like a 4th of July parade in downtown Cody.
Looking back, the Nikon wasn't much of a camera in today's terms, but it does support the idea that for many people there is no need for a large file. The images seen at 72 dpi don't suffer a bit here. It is only when trying to make prints and large ones, that this camera's file is severely deficient.
I also worked in the 120mm film format on that trip and that is how I made the Old Trail Town series and Billings as well.
So where's Billings? It's coming up in the next post.


text:
'Wink' director tests waters of short films
by Pamela Powell
Now in its 20th year, the Los Angeles-based film festival, Dances With Films (DWF), lives up to its words of conception: a festival where 'who you know' doesn't matter, but the quality of your work does. First-time writer and director of a narrative short film, German-born Monika Petrillo jumps into the filmmaking waters with Wink. Her film's topic sounds a bit unusual—a frustrated and lonely suburban housewife and a goldfish—but Petrillo laughingly said, "How can you go wrong with a beautiful woman and a goldfish?"
The inspiration behind not only this film, but Petrillo's decision to become a filmmaker was her godmother, Li Erben. Erben's late husband, Russian-born French film director Victor Vicas had written a story about a blonde, a winking goldfish and a bath. After hearing that description, Petrillo could see the whole film. "I came home…and before I knew it, I had written the whole 12-page script."
Working with animals and children can be challenging, but fish? Petrillo didn't want to use CGI (Computer Generated Imagery), but she questioned how trainable goldfish could be. A little research and YouTube viewing and she had a plan, using real goldfish. She needed not just one double-finned aquatic genius, but four—stunt doubles, if you will. The "stunt" was eating. Once a fish is full, it no longer wants to eat, thus the need for the lookalikes. And finding four identical goldfish, Petrillo recalled, was more difficult than you can imagine!
This unusual story still retains basic components of reality which was very important to Petrillo. "I've always cared for films about people like you and me…I think making films about emotions that we all have and we all share is what really connects us as human beings." In the end, the message of Wink, according to Petrillo, is written on Melanie's (Caitlin Brandes) coffee mug in the film: "Do more of what makes you happy." She hopes this film can "…inspire people to break out of their routine…and do something crazy!"
Creating memorable and vibrant films like this as a female seated in the writer and director's chair isn't an easy one. Petrillo concurred that sadly, there is a very small number of women in these positions, although she has observed an increasing number of women producers. Petrillo had the opportunity to work with notable directors, both male and female, learning important lessons such as maintaining a sense of humor from Mike Judge and allowing a scene to develop naturally from Richard Linklater. However, she observed that female directors aren't "given…room to be indecisive, whereas a male director can easily say 'I don't know yet' without being judged as wishy-washy or incompetent."
Petrillo's "Wink" is anything but indecisive and it has a naturalness with a sense of humor, but most importantly, she shows us the importance of making each day memorable.
© Pamela Powell (6/10/17) FF2 Media
Photos: Monika Petrillo & Wink
Photo Credit: Fish Tale Films, LLC
Roza Melkumyan 2 years ago
Akerman's 'Je Tu Il Elle' and the Power of Ambiguity
Amelie Lasker 1 year ago
Spend 2022 in Rosalind Wise's Vibrant British Meadows
Giorgi Plys-Garzotto 4 years ago
Queer Your Queue: Lesbian period pieces to watch after 'The Favourite'
by Pamela Powell June 12, 2017
New York native film critic and film critic Pamela Powell now resides near Chicago, interviewing screenwriters and directors of big blockbusters and independent gems as an Associate for FF2 Media. With a graduate degree from Northwestern in Speech-Language Pathology, she has tailored her writing, observational, and evaluative skills to encompass all aspects of film. With a focus on women in film, Pamela also gravitates toward films that are eye-opening, educational, and entertaining with the hopes of making this world a better place.
Congratulations to Martha Gonzalez, a 2022 MacArth


text:
Q: Post byte array in parameter I am writing my http post header.
POST /\r\n
Content-Length=...\r\n
\r\n\r\n
file1=(bytearray data)&file2=(bytearray data)

I am not sure how I can put the bytearray data there

A: If you want to do it all by hand, you only need to encode your parameters properly :
byte[] array1 = new byte[10];
byte[] array2 = new byte[10];

StringBuilder builder = new StringBuilder();

builder.append("POST /\r\n");
builder.append("Content-Length=...\r\n");
builder.append("\r\n\r\n");
builder.append("file1=");
builder.append(URLEncoder.encode(new String(array1),"UTF-8"));
builder.append("&file2=");
builder.append(URLEncoder.encode(new String(array2),"UTF-8"));

Or you can use something more clear or more standard with HttpURLConnection:
byte[] array1 = new byte[10];
byte[] array2 = new byte[10];

StringBuilder parameters = new StringBuilder();

parameters.append("file1=");
parameters.append(URLEncoder.encode(new String(array1),"UTF-8"));
parameters.append("&file2=");
parameters.append(URLEncoder.encode(new String(array2),"UTF-8"));

String request = "http://domain.com";
URL url = new URL(request);
HttpURLConnection connection = (HttpURLConnection) url.openConnection();
connection.setDoOutput(true);
connection.setDoInput(true);
connection.setRequestMethod("POST");
connection.setRequestProperty("Content-Type", "application/x-www-form-urlencoded");
connection.setRequestProperty("charset","UTF-8");
connection.setRequestProperty("Content-Length",Integer.toString(parameters.toString().getBytes().length));

DataOutputStream wr = new DataOutputStream(connection.getOutputStream ());
wr.writeBytes(parameters.toString());
wr.flush();
wr.close();
connection.disconnect();

More info :

*

*HttpURLConnection


Design Express, is an easy to use statistics package that integrates with Compusense Five data collection software.
Have you every tried to design a fully-balanced round-robin experiment?
Are your designs balanced for carry-over effects and order effects?
Have you ever tried to balance order of trial in an incomplete block design from a textbook?
Design Express is the answer to all of these statistics problems and more.
Design Express has been designed by statisticians for use by non-statisticians. At the touch of a single button the software chooses from a wide range of designs for single and multi-session experiments.
A unique feature of Design Express is the ability to construct appropriate designs when a complete set of samples is presented to all consumers over a number of sessions.
Click here to download a demonstration copy of Design Express.
To buy a licence click here.


Ohhhhh boy i like that so much.
Man you got me. You made special rant. MARKING AS FAVORITE.
@Haxk20 you gotta love the creativity of people. And thanks for the ++es.
Is this some custom message or a package? This is nearly as good as the one server that work that has a 10% of starting a game and logging you out if you loose.
@YouAreAPIRate In our case, its added to /etc/motd as part of server initial configurations.


text:
415-584-8773 shop@holymtn.com
Rare Teas
Tea Brewing & Processing
Tea Types & Estates
Tea Discovery by Lydia Kung
Tea Tales
Tea & Health
Tea and Caffeine
Caffeine Content of Teas
Caffeine Content of Beverages, Foods & Drugs
Caffeine: the bitter truth
Theobromine & Caffeine: A Comparison
Theobromine Content of Beverages
Medical Research Involving Tea
Blanc de Chine
Taiwanese Ceramic
Japanese Tetsubin
The Making of a Tetsubin
Teapot & Tea References
Tea Art Gallery
Tea Ceramics
Sigiriya Maidens
Historical Buddha
Ganesha, Elephant God
Hanuman, Monkey King
Hotei, Laughing Buddha
Kukkuripa, the Dog Lover
Quan Yin, Goddess Mercy
Shiva, Lord of the Dance
Tara, One Who Saves
Tantric Deities
Yab-Yum, Father-Mother
Above the Clouds: Alpine Gallery
The Himalayas
Year of the Rat Predictions
Lunar Year Chart
Does Kombucha confer health benefits beyond those of conventional tea?
by holymtn | Jan 29, 2018 | Tea | 0 comments
In Japanese, kombucha means "seaweed tea"; it is a "tea" made out of the seaweed called kombu. The Japanese consider it to be very healthy. However, this is not the same kombucha tea that Westerners drink.
Kombucha is an unusual-tasting drink. Depending on what's added to it, it's best described as a sour apple cider, perhaps with vinegary notes.
Initially drunk in China more than 2,200 years ago for its detoxifying and energizing characteristics, this venerable beverage found its way to Russia and from there into other eastern European areas via trade route expansion. It became popular in Germany during WWII, and further demand for the drink increased in the 1950s in North Africa and France. As early as the 1960s Swiss scientists claimed kombucha was helpful for maintaining digestive health, much like yogurt.
How to make a Kombucha SCOBY
A green or a black tea base is used to make kombucha, then white sugar is added which has been fermented with a type of "tea fungus" called a symbiotic culture of acetic acid (vinegar) bacteria and yeast, or SCOBY, for one to two weeks. The taste of the kombucha changes during the fermentation process from a pleasingly fruity sour-like effervescent flavor, to a mild vinegary taste after a long gestation period.
The fermentation process is critical as the SCOBY changes the polyphenols –- compounds normally found in tea, fruits and vegetables –- into other natural compounds. This increases the acidity, which prevents other microorganisms from multiplying. These newly-created organic compounds are claimed to provide health benefits beyond those already found in green or black tea.
The development of fermentation
The fermentation process prolongs the serviceable life of kombucha, as it does with other classically popular foods and drinks such as cheese, yogurt, sauerkraut, kimchi, wine and beer. Although beer and wine become alcoholic during the fermentation process, kombucha usually consists of less than 0.5% alcohol, so is classified essentially as non-alcoholic. Ethanol develops when the yeasts and bacteria in the SCOBY interact via a process called glycolysis. The acetic acid bacteria in the SCOBY utilizes the ethanol to produce vinegar (acetic acid), which is partially responsible for its sour taste.
Lightly fermented foods contain healthy bacteria. There is a theory that these bacteria help settle our gastrointestinal system, which early research suggests may enhance a range of bodily functions from our mood and stress levels, to our weight and cravings for food. But merely consuming healthy bacteria won't automatically generate these bacteria to permanently live in, or colonize, the gut. To achieve any longstanding health advantages from foods containing live bacteria, known as probiotics, the current research advocates taking them constantly.
Although the impact on other diseases like inflammatory bowel disease is uncertain, there is evidence that probiotics can be advantageous in some groups of people, such as those who suffer from certain gut problems like C. difficile infection. However, it is still unknown whether probiotics, whether in the form of kombucha or yogurt, are beneficial to the wider population. To cultivate good gastrointestinal health, it is best to eat a wide variety of nutritious foods.
Kombucha's other health applications
There are many impressive health claims about kombucha, including detoxification of the blood, reduction of cholesterol levels, reduction of blood pressure, diabetes care, antibacterial effects, and anti-ageing abilities. There have been several "lab bench" studies looking at isolated cells and researching kombucha's effects on animals. This provides us with insufficient evidence, as human biological systems frequently work differently. Currently there are no published studies from human clinical trials of drinking kombucha. Two reviews of the lab bench and animal studies on kombucha in 2000 and 2014 indicate the drink may have antimicrobial, antioxidant, anti-cancer and anti-diabetic properties. The studies also indicate some provocative procedures by which kombucha could conceivably be used to heal gastric ulcers and high cholesterol, and promote the body's immune response and liver detoxification. But they do not yet tell us if they transmute to humans.
A chemical called DSL (D-Saccharic acid-1,4-lactone) is potentially the most beneficial component in kombucha. DSL has the capability to suppress an important enzyme, β-glucuronidase, which may be linked to cancer growth. But again, this has not been demonstrated in humans.
Lab-bench studies have nevertheless detected the existence of polyphenols, acids and vitamins that are also found in traditional green and black tea.
The health properties of kombucha are presumed to be similar to drinking tea or other fermented foods. If you enjoy kombucha, continue drinking it, but if you prefer the traditional black and green tea, there are excellent health reasons to continue drinking those. While some intriguing properties have been shown from lab bench and animal studies, it doesn't necessarily follow that we will see these same benefits in humans.
Remember that some of the kombuchas on the market have fruit juice added after the fermentation process, which will augment overall sugar intake. The amount of sugar in these varieties can range from 10-15g per serving (two to three teaspoons), so if you have regular tea without sugar, drinking kombucha will increase your sugar intake.
Holy Mountain Trading Company offers fine full-leaf gourmet teas and imported treasures from around the world, including gourmet foods, unique dinnerware, and Asian-inspired arts, gifts and accessories to satisfy your soul and enrich your home.
Rare Teas & Teaware
Featured Teas
© Holy Mountain Trading Company | 2018
Join our mailing list and get 15% off your first order, learn about new arrivals, enjoy special discounts and much more!


If you've spent any time in the produce section, you might have noticed that coupons for produce are hard to come by. Even more so if you're looking for organic produce!
The good news is that Driscoll's is offering a new way for you to get discounts on their berries with two new programs. There are discounts for all their types of berries, including organic varieties.
There are two programs, one is called the Rewards Club, and the other is called the Consumer Advisory Panel.
You can choose to be part of one or both of them, there is no restriction between the two.
They're also offering a $0.50 coupon just for registering.
Everybody is welcome in the Berry Community. Just type "Berry Community" into your favored search engine. If you want the exact URL, it is http://www.driscolls.com/community/berry-community/register.
It requires some information to register, specifically, your name, email address, and zip code.
Driscoll's holds regular contests that you can enter even if you aren't a member of the Berry Community!
The current sweepstakes is offering a year's worth of berries and a Breville sandwich press for the grand prize. They're also offering two separate first prizes that will give you a year's worth of berries, so it would be a good idea to enter the contest no matter what.
You can enter the sweepstakes at Driscolls.com/community/promotions.
The Consumer Advisory Panel gives you a coupon for every survey you complete.
The cool thing is that the more surveys you take, the more valuable coupons you earn. The stacking reward system goes as this: $0.50 for the first few surveys, $0.75 for the 5-15th surveys, and $1.00 for the 16th survey and for each survey after that.
You'll receive the coupon in an email, which you can then print.
I think if you buy Driscoll's products, this is a great way to save money on your grocery, or at least a part of it. Not only you get valuable coupons, you also get a chance to win stuff with their sweepstakes.
For some people, the best part may be the fact that they get to voice their opinion about products they use.
Either way, let me know what you think, and whether or not you are going to join. Or if you are already a member, let me know about your experience and how you like it so far.


text:
Cambridge City Council
'Fudged' answers over whether the public will be able to access historic Castle Mound
"I think it is incredibly poor that the county council have failed to give a clear and unequivocal guarantee on this"
Josh ThomasLocal Democracy Reporter
Shire Hall, the current County Council headquarters in Castle Street
Campaigners and councillors have slammed "fudged" answers over whether the public will be able to access historic Castle Mound in Cambridge after the site is vacated by the county council.
The mound, which is all that remains of the Norman castle in Castle Street, sits within the grounds currently occupied by Cambridgeshire County Council.
Campaigners are worried the mound will be off limits to the public when the site is vacated by the council, which is moving to a new headquarters in Alconbury in 2020.
The council has filed a landowner deposit which has led to fears there could be restrictions to public access to the area around Shire Hall, which includes Castle Mound.
A notice of landowner deposit signed by John Macmillan, strategic asset manager at Cambridgeshire County Council, reads: "Cambridge County Council wishes to bring to an end any period during which persons may have indulged as of right in lawful sports and pastimes on the whole or any part of the land shown edged in red on the accompanying map."
Cambridgeshire County Council
Working to maintain public access
Future of Willow Walk
Going plastic free
Temporary barriers along the busway
The council says the landowner deposit is a bid to protect the access enjoyed at the moment, but yesterday (February 21) Cambridge City Council's deputy leader slammed the county council, saying they were "fudging" answers when they should be reassuring residents.
Richard Swift, an executive member of the Friends of Castle Mound campaign group, said: "It is a Cambridge landmark. For centuries, it has been a place of local government and democracy. For centuries, local people in Cambridge and Cambridgeshire have climbed the mound, picnicked there, and engaged in sport and pastimes, and revelled in the scenic views.
"The landlord deposit appears to put an end to lawful sports and pastimes there. What support can the city council give to ensure the general public keeps free access to the site?"
Mr Swift added it was a "travesty" that the issue had to be discussed at all at a council meeting.
Cllr Anna Smith, deputy leader of the city council, told the chamber she was answering both as a councillor and a teacher.
"I think it is a real shame tonight we have had two questions that come to the same thing," said Cllr Smith. "Preserving the city of Cambridge and the things we all care about.
"I think it is incredibly poor that the county council have failed to give a clear and unequivocal guarantee on this. They do not need to fudge it, but they are, and they continue to fudge it.
"As a city council, we will do all we can to support the community."
Cllr Smith said one of the options on the table is pushing to have the area designated a protected "town green" which would help guarantee public access "in perpetuity".
"We don't have to do any of this if the county council makes a clear commitment to preserving it for the people of Cambridge."
Fears iconic Castle Mound could close to the public
New Cambridge mayor chosen after Nigel Gawthrope's tragic death


After a year of hard work by the Salmo designers and dozens of tests with use of 3D printing techniques we proudly introduce the all new Salmo Freediver 12 SDR. This is a floating/diving lure with unique, elongate shape and large size lip like no other on the market. The internal workings of the Freediver also sets it apart from all others. Two rattle chambers for a low and high pitch sound and Long Cast (LC) weight transfer system enables longer casts with fewer tangles. The all new Salmo Freediver writes the final chapter and sets new standards in how anglers will be fishing with a true mega deep diving lure. The use of the best quality components like excellent Mustad KVD Triple Grip and newest techniques to produce and finish the lures allows achievement of features unavailable in the other similar competitor baits. The tear strength is about 30 kg. Laboratory testing shows that this durability is a parameter that the competition can only dream of!
The effectiveness of Freediver was tested at several fisheries in Europe and in the US. In every one of them, the "new baby" of Salmo, shows its claws and entices the predators. The purpose of the saltwater variation of the bait is mainly to catch blue fish and snappers at extremely deep depths. The tests proves that the predator fish also appreciates the efforts of the Salmo designers. The new wobbler was used to catch numerous fish, including some spectacular specimens!
How to fish with Freediver – how many times do you think about extreme depth fishing? Take a deep breath, it has never been so easy like now! Salmo Freediver 12 Super Deep Runner (SDR) is equally effective whether trolled or cast. The best way to discover the special abilities of this lure is simply trying it. The Freediver 12, on a 0.16 braided line, when pulled 35 meters behind a boat at 3.2 km/h dives easily 9 meters. The increase of the distance between the bait and the aft of a boat causes the increased depth. As a result, the lure achieves an incredible depth of 12 m when it's 60 m behind the aft. That result is unachievable for the other lures with the similar parameters on the market! The Freediver is also equipped with very effective casting system that allows for really long casts during a traditional cast and retrieve method from ultra slow to super fast with always perfect action.


text:
Birth workers talk maternal mortality at Springfield, Mo. conference
By Nikki Ogle |
Posted: Sun 9:46 PM, Jul 14, 2019 |
Updated: Sun 9:46 PM, Jul 14, 2019
SPRINGFIELD, Mo. (KY3) - Missouri has one of the highest maternal mortality rates in the country. Birth workers and community members met at a conference Sunday to talk about how to lower that number.
One woman had personal reasons for attending.
"The greatest work of my life was this girl here. She was everything to me," said Rochelle Russell.
Rochelle Russell travels the county to tell her daughter's story. Breanna Banner had a stroke just days after giving birth.
"She died 19 days after having my granddaughter," Russell said.
Breanna was 20 years old.
"When she was pushing the baby, it over-flooded her head with pressure and fluid. It literally pushed her brain to the left," Russell said.
According to Poppy Daniels, OB/GYN, a stroke is just one of the risks for expecting mothers.
"Pre-eclampsia, stroke, blood clots, those are the most common causes of death," Daniels said.
Daniels said other complications are heart disease, diabetes, infection and and blood loss. According to the United Health Foundation, Missouri ranks 42 out of all 50 states for maternal mortality.
"Part of the problem is, we have a lot of rural Missourians who do not have good access to care. There's a lot of hospitals, especially rural hospitals that have closed," Daniels said.
She said a lot of people don't know how common maternal mortality is. The United Health Foundation states out of every 100,000 births in Missouri, around 32 of those mothers die. Daniels said African American women are three times more likely than white women to die during or after childbirth.
"I think unless something has happened to you or a family member, you're not aware of it," Daniels said.
She said expecting mothers and their families need to know the risks.
"You're able to do whatever you can to avoid them and ask your healthcare provider to help you understand what your risk is," Daniels said.
Russell said she wishes she'd asked more questions about her daughter's birth control. She said Breanna's stroke was caused from side effects doctors never told her about.
"Just be vigilant and ask the questions and if you're not comfortable with the answers, go to another doctor and ask them. You have the right to seek a second opinion. I learned that the hard way," she said.
Daniels said it's important for soon-to-be moms to be their own advocate or have a knowledgeable advocate with them at doctor appointments. She said many women are now using doulas or midwives for support during pregnancy.
Read the original version of this article at www.ky3.com.


text:
/**
 * Crafted by x22a on 06.02.17.
 */
import Relay from 'react-relay';

export default class RemoveSiteMutation extends Relay.Mutation {
	static fragments = {
		viewer: () => Relay.QL`
			fragment on Viewer {
				id
			}
		`
	};
	getMutation() {
		return Relay.QL`mutation{ removeSite }`;
	}
	getFatQuery() {
		return Relay.QL`
			fragment on RemoveSitePayload {
				deletedId,
				viewer {
					sites
				}
			}
		`;
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Buying a home in Hamilton costs more than the offer you make. There are numerous other expenses that will add to the amount that you'll need to spend. This purchase price checklist outlines all the costs you can expect. Please note that they can vary by province and are subject to change.
The starting point in your calculations… if you're like most first-time home buyers, you'll need a mortgage for the majority of this!
A tax payable to the provincial government by the purchaser upon the transfer of title from a seller. This tax is usually not expected by most homeowners. It can be sizeable. The amount varies from province to province and is generally a percentage of your purchase price.A tax payable to the provincial government by the purchaser upon the transfer of title from a seller. This tax is usually not expected by most homeowners. It can be sizeable. The amount varies from province to province and is generally a percentage of your purchase price.
Although fees vary across the nation, it can cost you up to $1,200 depending on whether you are re-mortgaging your existing home or buying new.
Fees paid to the provincial government for recording a title transfer, mortgage registration or other instruments such as an Assignment or Lien with the local authorities.
Must be purchased if you are buying a home for less than 20% down. A sliding fee scale applies, depending on the percentage of the purchase price required in a first mortgage (some minor exceptions).
Provincial "New Home Warranty Program" Premiums - New Homes Only!
A third party (provincial) warranty program between a builder and a buyer. With the exception of Ontario and Quebec, membership in such a program is voluntary for the builder. Through these programs, your home is guaranteed against defects for at least one year. All homes with a high-ratio insured mortgage (with greater than 80% loan to value) must be enrolled in such a program.
Some local utility companies (hydro, gas, water) charge a fee on closing to connect new buyers up to their service. More common, however, is an extra charge on the first billing.
*The above estimates are based on experience, however, I advise doing your own research and due diligence in case of discrepancies due to the current market.
Defined By Service And Expertise. Guiding Real Estate Buyers, Sellers And Investors To Make $mart Decisions.
Serving Ancaster, Dundas, Stoney Creek Hamilton Mountain, Hamilton East, Hamilton West, Hamilton Central, Hamilton Downtown. Single family homes, townhomes, real estate investments and commercial real estate.


Note: Figures are based on salaries reported for full-time jobs lasting at least a year. Cities with fewer than five law firm salaries reported are not included in this table. Only medians and means are shown if fewer than ten salaries were reported.


We were fortunate enough this year to go apple picking at one of our favorite orchards. Not sure if we love apple picking as much as we love their apple pie from the Elegant Farmer. Regardless, apple picking was something we always loved to do when the kids were growing up. Something about watching them eat a bunch of apples while climbing trees is a quick pleasure. This year we probably bought way too many apples, and as I would walk past the half bushel, I decided that I would come up with something and see if the family would try out the apple chips.
My wife was gratuitous enough to get me a food dehydrator some time ago. I've done everything from making beef jerky to vegetable chips in the dehydrator and I'll admit that I love it. So when the apples were in my zone, I decided on batching out some apples and making apple chips. So easy, so good, and a great way to use up some apples.
Yep, that's it. If you want to jazz them up, you could put some cinnamon and sugar mix onto them once they were cut up, but I went simple and simple was good.
Take your sliced apples and place them onto your dehydrator trays, leaving about 1/2 inch between each one. The goal is to not let them overlap or touch one another.
Once you have filled the trays, turn on your dehydrator and let them dry out for about 4-6 hours. It's ok to feel them, and take them out when they meet your desired crispness.
You can store these in a airtight container to keep their crispness, but in our house, they were gone that afternoon. I was a bit startled to say the least to see them go so quickly, but I was actually really glad because I knew that everyone had a great nutritious snack that day. So if you are looking for something delicious and easy, and a great snack to boot, give these dehydrated apple chips a try (and yes you can do these in the oven as well).
My family devoured dried fruit of all sorts: apple slices, banana chips, berries, fruit pastes dried and rolled (avoiding a brand name.) Your post makes me miss my dehydrator.
Thanks Wendy! I love the dehydrator, and it is a great one in the kitchen.
Great family pic Dax! I have long wanted a dehydrator and I think it's time, the apple chips look good!
Cool idea. I'll give it a try. Thanks.


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