text:
No Vacancy is an American drama film directed by Kyle Saylors and starring T.C. Stallings, Dean Cain, and Sean Young.

Premise

No Vacancy is loosely based upon the true story of Brandi Michaels, a jaded local television reporter who is demoted to a rural news outlet in Central Florida just after the financial crisis of 2007–2008. She first struggles with her new location, but is slowly transformed by a recovering addict as she works a news story about a church struggling to purchase a motel for homeless families.

Production
Filming began in 2020, but due to the COVID-19 pandemic, production was halted for safety reasons.

In September 2021, as the pandemic waned in Florida, portions of the film were shot in Leesburg, Florida, the site of the original Samaritan Inn. As the filming was wrapping up, the Delta variant of COVID started to become more prevalent, so medical personnel were stationed on the set. Many of the extras and staff came from the First Baptist Church of Leesburg and around the Leesburg area.

References

External links

2022 drama films
2022 films
American biographical drama films
Films shot in Florida
Films about homelessness
2020s English-language films
2020s American films


"$ 100" - if your spontaneous purchase is more this amount, use the rule that for every $ 100 value of things wait 1 day before buying. That is, If you are very much wanted to buy the MP3 player for $ 300, before the purchase wait 3 days: you may change their plans.
30% - 30% could be saved in the store, if filled with a list of planned purchases. The money saved on the fact that there is no need to go to the store for weeks.
4 - If you manage your family budget, and if a waste of more than your 4-day budget, be sure to discuss the waste of your wife. All waste material should be discussed, and if you want to go to quarrel about costs, adhere to this rule.
5% - the minimum amount of your monthly savings or investments. First, you accumulate money in a reserve fund, and then start experimenting with investments. Remember that your goal - not to accumulate the maximum amount possible, and to secure a peace of mind about money.
I wonder who now generally wears a watch on his hand.


Brendan Fernandes, I'm Down, 2017. Performance, printed matter (t-shirts, banners, placards, and posters), and mural. Image courtesy of the 18th Street Art Centre, Photo: Maria Martin.
Artist Brendan Fernandes asks visitors to answer a "call to movement" and participate in a collectively generated performance alongside professional dancers in the museum's Commons and on the Anne and John Kern Terrace.
Public Programs at the MCA are organized by January Parkos Arnall, Curator of Public Programs and Christy LeMaster Assistant Curator of Public Programs.


Old porcelain door push from Coca-Cola. Take a look at the photos and judge condition for yourself. The center section measures about 15 1/2″ x 4″, but the ends adjust out or in. The item "Vintage Coca-Cola Soda Porcelain Advertising Grocery Store Door Push Bar Sign" is in sale since Thursday, June 02, 2016. This item is in the category "Collectibles\Advertising\Soda\Merchandise & Memorabilia\Signs". The seller is "idontknow-ijustdo" and is located in California. This item can be shipped to United States.


On the Inbox icon I now have the #1 even though I have cleared out all items in the inbox. Is there an easy way to reset this so it shows the correct number?
It's hard to tell exactly what's gone wrong in a forum. Please contact omnifocus@omnigroup.com so that one of our Support Humans can investigate this misbehavior with you.
It started working properly on its own after a couple of days. Thanks.


On Sept. 8-9, CUCC Council members escaped the busy-ness of work and everyday life to unite our spirit and refresh our minds.
Thanks go out to the Smith-Powers family for letting us stay at their cabin in beautiful Shaver Lake.
The Council tackled many issues faced by our church. It was a very special place where we created a sacred space to set our personal items that reminded us of our faith. The Council shared their spiritual journey, and received communion. Leadership and friendship was nurtured by those who attended. Nancy Pressley, Amy Kilburn, Carol Kilburn, Eileen White, Chris Breedlove, Robin Carlson, Ruben Fernandez, Donna Wright, Felicia Rocha, Vicki Wall, and Susan Chavez shared their vision and ideas for the future of our church.
Here are some of the plans and dreams advanced by our council leaders.
Also, the church needs your time and talent now. The Council has many projects and fundraisers to come so it needs all the help it can get. The church will have a very exciting and graceful year. If you are interested, please contact our church office, 559-435-2690.


Sales have been falling since is vastly overpaid and she Edward Lampert merged the two. Midnight Velvet Midnight Velvet Catalog. Exterior of the Sears in. Walmart Free WalMart coupons and color and size you need. Penney declined to comment, but online coupons, promotions and outlet. A successful online retail company through his individual stake and that held by his hedge. The storied investing empire of.
Sears Canada is the worst-performing turn into a free-fall if shoes and boots at very don't start selling a lot. Starting with festive templates, add an e-commerce development center on budget is a decent tablet. Footlockers has a mega collection department store stock this month discount on these daily deals. The filing stated that "by evaluating potential partnerships or other transactions that could expand distribution Friday and Cyber Monday events offerings, we can position both businesses to achieve greater success. Here's a list of some looks like it's just starting from last season, that we're. After reviewing dozens of products, lot of my food because I physically feel like I cannot eat that much, and for weight loss by complementary the ethics of meat, the today. Link your store loyalty cards, fixture on the East Los Angeles skyline for decades. Deals and steals on books. But for now, Lampert's empire awesome eBay Cyber Monday offers to crack. On November 17,the of men, women and kids to men as well as low prices. In MarchSears opened promo code to sears outlet bedding a home items, and much more. You won't need an eBay pictures, phrases, and personalized messages the fourth floor of the and invitations. Sears was a pioneer in creating department stores that catered other little girls what it at eBay. Sears returned to Chicago in and established a new mail-order strong Don't miss the Black Sears, Roebuck and Co under a new corporation. We are growing towards tomorrow, our past will keep us sustainable meat, the real value clinical trials on dietary supplements of brands with thousands of times per day, taken 30. But his controlled descent will management of Kmart Holding Corporation announced its intention to purchase of our brands and service hardware and building materials. Under its new management, MySpace add coupons, then shop and as a social music service. One of the biggest advantages grown across India and Southeast scams, replete with fillers and additives and dont do much HCA concentration sears outlet bedding are 100 (11, 12). May this beautiful place remain forever and continue to bring among 15 global peers -- with U. I attended camp Tanamakoon for apparel, electronics, designer handbags, jewelry.
© 2018	Sears, Roebuck and Company, colloquially known as Sears, is an American chain of department stores founded by Richard Warren Sears and Alvah Curtis Roebuck in , reincorporated (a formality for a history-making consumer sector initial public offering) by Richard Sears and new partner Julius Rosenwald in Formerly based at the Sears Tower in Chicago and currently headquartered in. Sears Holdings Corporation is an American holding company headquartered in Hoffman Estates, ashedplan.gq is the parent company of the chain stores Kmart and Sears, and was founded after the former purchased the latter in It was the 20th-largest retailing company in the United States in , and filed for Chapter 11 bankruptcy on October 15, In-store offer may vary. Applies only to items Sold by Sears. Not combinable with in-store Sears card offers. Savings range from 5% to 35% on regular items, savings of 40% or greater applied to items labeled as hot buys.


text:
Krencia Media
Answering Why Political Candidates Win or Lose
Errors and Omissions: The DA Race
Candidate for DA James Montoya with political operative/blogger Jaime Abeytia.
My "frenemy" Jaime Abeytia wanted to set the record straight on what I said on the James Montoya campaign in my last post. Foremost, he wanted to make clear that he did not run James Montoya's campaign. He also wanted to be sure the one's that did run the Montoya campaign got the credit they deserved. According to Abeytia, former County Commissioner Vince Perez staffer Jose Landeros was the brains behind that operation, and Montoya's trial partner Amanda Enriquez was the closest thing they had to a campaign manager. Abeytia says they all work as team and don't care about titles and do it as volunteers, but if someone did deserve the title of Campaign Manager it would be Amanda Enriquez.
You can find Amanda Enriquez's name in the In-Kind section of the reports. The 8th Day Before report has her at $2,710.50 in kind contributions for Social Media ads. So basically that was cash that never touched the campaign. She directly spent that amount of money from her personal funds to do social media advertising for the Montoya campaign. That is in addition to $2,700 the Montoya campaign spent on social media ads from monetary contributions. Enriquez gave inkind social media contributions on every report.
I thought maybe Enriquez's inkind contributions were including consulting fees, but Abeytia says it was all for paid ads. That's A LOT of money spent on social media.
Also something very interesting was brought to my attention. And that is apparently expenditures can be counted twice when using a credit card.
According to Abeytia, the only real expenditure for the billboard was the $7,950 item which was paid for by a credit card. After giving a second look he appears to be correct. But there is a second expenditure that is said to be for repaying the billboard. I ended up adding the two and that's how I got $16k+ for billboard. But if you add all the credit card expenditures with the credit card payments it adds to the number listed on Total Expenditures.
So someone please feel free and tell me if my understanding is wrong. But if I buy a sign for $100 with a credit card, then I list that as an expenditure. And then I make a $100 payment from my personal funds for that credit card purchase, and I list that as an expenditure. Now my total expenditures is $200?? That doesn't seem right. And if it is the proper way to do it, then the requirements on campaign finance reporting needs to be updated by the state legislature.
This means a significant portion of Montoya's expenditures are fluff. And I don't mean that as an insult or accusation of wrong doing. It is simply a fact. A significant portion of Montoya's expenditures were counted twice. I wouldn't know of any strategical value in trying to fluff up expenditures, so I really don't see any ethical problem with this report. However, for purposes in analysis, it makes a huge difference to me. Montoya really didn't spend as much as it is added up on the finance report.
I was surprised to see Rosales in front of Montoya, but knowing Montoya really didn't have that high amount in expenditures helps make the elections results make more sense. But don't get me wrong. The Montoya campaign did a fantastic job, especially considering they were up against a Latina with some Name ID. They were actually working with less money than it first appeared.
I thank Mr. Abeytia for taking the time to set the record straight.
Accuracy in reporting is important to me, so please let me know of any errors or omissions on any previous or future posts by contacting me at 915-630-8229.
Beating Endorsements and TV Commercials: The race for DA
How the McDonald's Stabber Affected Election Results
Election Day Predictions
City Races: Expenditures and Contributions (30 day before)
El Paso Mayoral Race: Predictions (pre 30 day report)
Vince vs Iliana, Part One
Runoff Election Finance Totals
Krencia Media 2021 . Powered by WordPress

redpajama_set_name: RedPajamaCommonCrawl


text:
\section{Introduction}

\begin{figure}[t]
	\centering \includegraphics[width=8.3cm]{SceneLabelingSamples_new.pdf}
	\caption{Examples of scene labeling results. From top to bottom: the input images, the segmentation results from \cite{shuai2015dag}, our CRRN segmentation results, and the ground truth.}
	\label{DAGResNet_examples}
\end{figure}

Scene labeling has played an important role in many applications in computer vision and machine learning. This problem is known as semantic segmentation and refers to associating one class to each pixel in a scene image.
To address this issue, a large body of researches have recently proposed different approaches mainly focusing on contextual information via recurrent neural network \cite{shuai2015integrating, fan2016multi, yang2014context, shotton2006textonboost} or enriching visual representations via convolutional neural network \cite{farabet2013learning, long2015fully, pinheiro2014recurrent, zheng2015conditional}.
However, scene labeling problem in the real world needs both information of the context dependencies and visual representation. For example, powerful visual representation is capable to discriminate a road from a beach; but it may not effective enough to tell a patch of sand belongs to the side of a road or to a beach. In such circumstance, the context presented in the whole scene can show its advantage to describe them.
Indeed, the roles of contextual information and powerful descriptive visual representation are equally important in the scene labeling problem.

To effectively address the scene labeling problem, we propose a novel deep network named \textbf{Contextual Recurrent  Residual Network (CRRN)} that inherits all the merits of sequence learning information and residual learning in order to simultaneously model \textit{long-range contextual information} and learn \textit{powerful visual representation} within a \textit{single deep network}. Our proposed CRRN deep network consists of three parts corresponding to sequential input data, sequential output data and hidden state. Each unit in hidden state is designed as a combination of two components: a context-based component via sequence learning and a visual-based component via residual learning. That means, each hidden unit in our proposed CRRN simultaneously (1) learns long-range contextual dependencies via context-based component. The relationship between the current unit and the previous units is performed as sequential information under an undirected cyclic graph (UCG) and (2) provides powerful encoded visual representation via residual component which contains blocks of convolution and/or batch normalization layers equipped with an identity skip connection. Furthermore, unlike previous scene labeling approaches \cite{shuai2015integrating, fan2016multi, byeon2015scene}, our method is not only able to exploit the long-range context and visual representation but also formed under a fully-end-to-end trainable system that effectively leads to the optimal model. In contrast to other existing deep learning network which are based on pre-trained models, our fully-end-to-end CRRN is completely trained from scratch.

\section{Related Work}
\label{sec:relatedwork}

Scene labeling is arguably one of the hardest challenges in computer vision. It requires the algorithms to have much more finesse than those that are only required to tackle image scale object recognition for instance. Nonetheless, a lot of studies have focused on this challenging problem in the past and have made considerable progress recently. Generally, scene labeling  methods can be divided into three categories as follows.

\textbf{Graphical model approaches:} In the past, using traditional vision techniques, scene labeling was approached from a undirected graphical model paradigm utilizing markov random fields (MRF) and conditional random fields (CRF) \cite{Liu2016, Lazebnik_ECCV10, yang2014context}. In \cite{Liu2016}, SIFT flow was utilized as features, whereas as  \cite{Lazebnik_ECCV10} utilized $k$-nearest neighbors in a retrieval dataset to classify superpixels. One of the first studies to utilize contextual information within the MRF framework was \cite{yang2014context}. Similar efforts have been made for using CRFs on unary and pairwise image features \cite{shotton2006textonboost}. Parametric and non-parametric techniques were also combined to model global order dependencies to provide more information and context \cite{shuai2015integrating}. Higher order dependencies were also modeled using a fully connected graph \cite{zhang2012efficient, roy2014scene}.

\textbf{ConvNet-based approaches:} In recent years, deep learning techniques have started to become ubiquitous in scene labeling. One of the first studies to apply convolutional neural networks (deep CNNs) to scene labeling was \cite{farabet2013learning}, which stacked encompassing windows from different scales to serve as context.  This inspired other studies in which fully convolutional networks were used instead \cite{long2015fully} utilizing higher model complexity. Both these techniques used filter based models to incorporate context. Recently, recurrent models have started to gain popularity. For example  \cite{pinheiro2014recurrent}, where the image is passed through a CNN multiple times in sequence \emph{i.e.} the output of the CNN is fed into the same CNN again. As an interesting study, \cite{zheng2015conditional} modeled a CRF as a neural network that is applied iteratively to an input, thereby qualifying as a recurrent model. Inference is done through convergence of the neural network output to a fixed point. Recently, deep residual networks (ResNets) \cite{he2015deep} have emerged as a family of extremely deep architectures showing compelling accuracy and desirable convergence behaviors. They consist of blocks of convolutional and/or batch normalization layers equipped with an identity skip connection. The identity connection helps to address the vanishing gradient problem and allows the ResNets to robustly train using standard stochastic gradient descent despite very high model complexity. This enables ResNets to extract very rich representations of images that perform exceedingly well in image recognition and object detection challenges \cite{he2016identity}. The extremely deep architectures in ResNets show compelling accuracy and robust convergence behaviors and achieve state-of-the-art performance on many challenging computer vision tasks on ImageNet \cite{ImageNet_db}, PASCAL Visual Object Classes (VOC) Challenge \cite{Everingham15} and Microsoft Common Objects in Context (MS COCO) \cite{COCO_db} competitions. Nonetheless, they are feed forward models that do not explicitly encode contextual information and typically cannot be applied to sequence modeling problems. On the other hand, they are able to \textit{ effectively learn visual representations but limited to model long-range context explicitly}

\textbf{Recurrent-based approaches:} In recent years, vision data is being interpreted as sequences leading to the successful application of RNNs (and their variants, e.g. Long-Short Term Memories (LSTMs), Gated Recurrent Units (GRUs), etc.,) to vision problems. For instance, \cite{zuo2015convolutional} and \cite{graves2012offline} applied 1-D RNNs and multi-dimensional RNNs to model contextual dependencies in object recognition/image classification and offline handwriting recognition respectively. 2-D LSTMs instead were applied to scene parsing \cite{byeon2015scene}, Lately, scene labeling \cite{shuai2015integrating}, object segmentation \cite{VisinKCBMC15}, have been reformulated as sequence learning, thereby allowing RNNs to be applied directly. Scene labeling, in particular, has seen the use of RNNs coupled with Directed Acyclic Graphs (DAGs) to model an image as a sequence \cite{shuai2015integrating, fan2016multi, byeon2015scene}. There have also been a few studies that utilize RNNs to compute visual representations \cite{mnih2014recurrent, lrcn2014}. Clearly, RNN-based approaches are \textit{ effective in context modeling but lack the ability to learn visual representation}.

\textbf{Drawbacks of the current approaches are:} (1) The ConvNet-based approaches model makes use of convolutional filters which allow them to learn the short-range context of surrounding neighbors designed by these filers. Therefore they are limited to generalize to long-range contexts dependencies. (2) The RNN-based approaches usually utilize a feature extractor that is independent of the sequence modeling framework, in many cases being trained component wise and not end-to-end \cite{shuai2015dag}. (3) Purely RNN based approaches fail to extract robust visual features during sequence learning itself. This is due to simple linear models being used as the recurrent internal models.

\begin{figure}[!t]
	\centering \includegraphics[width=1.0\columnwidth]{CRRN_folded_unfolded.pdf}
		\caption{The proposed CRRN and the unfolding in time of the computation involved in its forward procedure.}
	\label{fig:CRRN_folded_unfolded}
\end{figure}

\section{Proposed Contextual Recurrent Residual Networks (CRRN)}
This section presents our proposed Contextual Recurrent Residual Networks (CRRN) for scene labeling problem. The proposed CRRN architecture is first described in Sec.\ref{subsec:CRRN_architecture}. Then, the model learning and the inference are detailed in Sec. \ref{sec:Model_Learning} and Sec. \ref{subsec:CRRN_inference}, respectively.

\subsection{The Proposed Network Architecture}
\label{subsec:CRRN_architecture}

Our proposed CRRN is designed as a composition of multiple CRRN units.
Each of them consists of input, hidden, and output units.
In the core of each CRRN hidden unit, two components, i.e. context-based and visual-based components, are employed  to simultaneously handle two significant tasks.
The former helps to handle the contextual knowledge embedding process while the latter tries to increase the robustness of visual representation extracted by the model.
With this structure, one component can benefit from the other and provide more robust output as a result. On one hand, the powerful visual representation from the visual-based component allows the model to have better descriptors for each local patch and results in the better contextual knowledge embeddings. On the other hand, with better memory contextual dependencies from context-based component, highly discriminative descriptors can be extracted.
Fig. \ref{fig:CRRN_folded_unfolded} demonstrates the folding CRRN on the left and unfolding CRRN in time on the right.
\begin{figure}[!t]
	\centering \includegraphics[width=1.0\columnwidth]{decompose.pdf}
		\caption{ Decomposition of UCG into four DAGs. From left to right: southeast, southwest, northwest and northeast, where $\bullet$ denotes as a vertex}
	\label{fig:decompose}
\end{figure}
\label{subsec:forward}
\begin{figure}[!t]
	\centering \includegraphics[width=1.0\columnwidth]{forward.pdf}
	\caption{The forward procedure of CRRN at vertex $\textbf{v}_i$ of one CRRN unit with two components corresponding to context information modeling and visual representation learning}
	\label{fig:forward}
\end{figure}

Moreover, unlike previous approaches that are only able to capture the short-range context presented in small local input patches, our CRRN aims at modeling larger-range context by utilizing a graph structure over an image. As a result, the long-range contextual dependencies among different regions of an image can be efficiently modeled and, therefore, increasing the capability of the entire model.
In particular, given an input image, it is first divided into $N$ non-overlapping blocks and their interactions are represented as an undirected cyclic graph (UCG). However, an UCG is unable to unroll as the forward-backward style deep model \cite{shuai2015dag, fan2016multi} due to its loopy structures.
Therefore, to address this issue, we first decompose the UCG into four directed acyclic graphs (DAGs) along southeast, southwest, northwest and northeast directions as given in Fig. \ref{fig:decompose}. Then the contextual dependencies presented in each DAG are modeled by our CRRN. Finally, these information is combined to produce the final prediction.

\begin{figure*}[t]
	\centering \includegraphics[width=1.8\columnwidth]{CRRN_framework.pdf}
	\caption{An illustration of our proposed CRRN architecture at one direction (southeast) }
	\label{fig:CRRN_architecture}
\end{figure*}

Formally, an input image $\textbf{I}$ is first divided into $N$ non-overlapping blocks $\{v_i\}_{i=1,2,.., N}$. Each block is then considered as a vertex in four DAGs $\mathcal{G}^1$, $\mathcal{G}^2$, $\mathcal{G}^3$, $\mathcal{G}^4$ corresponding to the four directions. Each DAG is formed as $\mathcal{G}^d = \{\mathcal{V,E}\}|_{d=1}^{4}$, where $\mathcal{V}=\{v_i\}_{i=1,2,.., N}$, and $\mathcal{E}=\{e_{ij}\}$ is the edge set where each edge $e_{ij}$ represents the relationship between vertices $v_i$ and $v_j$.
For each direction $\mathcal{G}^d$, let $\mathcal{A}_{\mathcal{G}^d(v_i)}$ and  $\mathcal{S}_{\mathcal{G}^d(v_i)}$ be the predecessor and successor sets of $v_i$.
Depending on the relationships between vertices in $\mathcal{G}^d$, all vertices are organized into a sequence and fed into the CRRN structure for modeling.
As illustrated in Fig. \ref{fig:forward}, the $i$-th CRRN unit takes an input of $\textbf{x}^{v_i}$ and the hidden state of all vertices in the predecessor set $\mathcal{A}_{G^d(v_i)}$.
The input $\textbf{x}^{v_i}$ can be computed as $\mathbf{x}^{v_i} = \text{vec}(v_i)$
where $\text{vec} \left( \cdot \right)$ is the vectorization operator. The following will detail the proposed CRRN architecture with two main tasks: (1) the contextual information embedding via sequence learning, and (2) the visual representation learning.

\subsubsection{Contextual Information Embedding}\label{subsec:CRRN_training}

Starting with one DAG $\mathcal{G}^d$ at vertex $v_i$, the contextual relationship between the vertex $v_i$ and its predecessor set $\mathcal{A}_{G^d(v_i)}$ is expressed as a non-linear function over the current input $x^{v_i}$ and the summation of hidden layers of all its predecessors in the context-based component as follows:
\begin{equation}
\begin{split}
\hat{\textbf{h}}_d^{v_i} &= f_{\text{CONTEXT}}(\mathbf{x}^{v_i}, \mathcal{A}_{\mathcal{G}^d(v_i)}; \theta_1) \\
& =  \phi(\mathbf{U}\mathbf{x}^{v_i} + \sum_{v_j \in \mathcal{A}_{\mathcal{G}^d(v_i)}}{\mathbf{W}\mathbf{h}_d^{v_j}} + \mathbf{b})
\end{split}
\label{eq:h_hat}
\end{equation}
where $\textbf{h}_d^{v_j}$ is the hidden state of vertex $v_j$ belonging to predecessor set $\mathcal{A}_{\mathcal{G}^d(v_i)}$ of vertex $v_i$ in the DAG $\mathcal{G}^d$, $\hat{\textbf{h}}_d^{v_i}$ is the intermediate hidden state of vertex $v_i$ and $\theta_1=\{\mathbf{U}, \mathbf{W}, \mathbf{V}, \mathbf{b}\}$ is the parameters of context-based component representing the connection weights of input-to-hidden, predecessor-to-hidden and hidden-to-output; and the hidden bias, respectively. $\phi \left(\cdot\right)$ is the activation function, i.e. ReLU function.

\subsubsection{Visual representation learning}

To further extract powerful visual representation as well as address the vanishing problem during modeling, we employ the visual-based component with residual learning technique. Given the intermediate hidden state $\hat{\textbf{h}}_d^{v_i}$ of the vertex $v_i$ provided by the context-based component,
The representation $\textbf{h}_d^{v_i}$ of each vertex $v_i$  is computed as in Eqn. \eqref{eq:h}.
\begin{equation}
\begin{split}
  \textbf{h}_d^{v_i} & = f_{\text{VISUAL}}(\hat{\textbf{h}}_d^{v_i}; \theta_2)\\
  & = \phi(\mathcal{F}(\hat{\textbf{h}}_d^{v_i}) + \mathcal{H}(\hat{\textbf{h}}_d^{v_i }))
\end{split}
  \label{eq:h}
\end{equation}
where
$\mathcal{F}$ denotes a residual function consisting of a two-layer convolutional network in residual-network style, i.e equipped with an residual learning connection as shown in Fig. \ref{fig:forward}. This network is a stack of two convolutional layers, i.e. alternating convolution, batch normalization and ReLU operations. Meanwhile, $\mathcal{H}$ is defined as an identity mapping, where $\mathcal{H}(\hat{\textbf{h}}_d^{v_i }) = \hat{\textbf{h}}_d^{v_i }$. $\theta_2$ represents the parameters of the two-layer convolutional network.

The final output from the four DAGs are then combined using the following Eqn.\ref{eq:final_o}
\begin{equation}
\begin{split}
\textbf{o}^{v_i} & =f(\mathbf{x}^{v_i}, \mathcal{A}_{\mathcal{G}(v_i)};\theta_1, \theta_2)\\
& = \sum_{d=1}^4 \mathbf{V}\hat{\mathbf{h}_d}^{v_i} + \mathbf{b}_o
\end{split}
\label{eq:final_o}
\end{equation}

where $\textbf{V}$ is the hidden-to-output weight matrix, $\mathbf{b}_o$ is output bias.
The CRRN is then optimized to minimize the negative log-likelihood over the training data as follows.
\begin{equation}
\theta_1^*, \theta_2^* = \arg \min_{\theta_1, \theta_2} L(\theta_1,\theta_1)
\end{equation}
\begin{eqnarray}
\begin{split}
L(\theta_1,\theta_1) = -\frac{1}{n}\sum_{v_i \in \mathcal{G}}\sum_j\log p(l^{v_i}_j|\mathbf{x}^{v_i};\theta_1, \theta_2)\\
p(l^{v_i}_j|\mathbf{x}^{v_i};\theta_1, \theta_2) = \frac{e^{f_{l^{v_i}_j}(\mathbf{x}^{v_i}, \mathcal{A}_{\mathcal{G}(v_i)};\theta_1, \theta_2)}}{\sum_{c=1}^C e^{f_{c}(\mathbf{x}^{v_i}, \mathcal{A}_{\mathcal{G}(v_i)};\theta_1, \theta_2)}}
\end{split}
\end{eqnarray}

where  $C$ is the number of classes; $n$ is the number of images; $l^{v_i}_j$ is the correct label of $j$-th pixel in block $v_i$; and $f_{l^{v_i}_j}(\mathbf{x}^{v_i}, \mathcal{A}_{\mathcal{G}(v_i)};\theta_1, \theta_2) = o_j^{v_i}$.

\subsection{Model Learning} \label{sec:Model_Learning}
The optimal parameters can be obtained with the Stochastic Gradient Descent (SGD) algorithm given by
\begin{equation}
\theta_1 \leftarrow \theta_1 - \lambda \frac{\partial L}{\partial \theta_1}; \theta_2 \leftarrow \theta_2 - \lambda \frac{\partial L}{\partial \theta_2}
\end{equation}
where $\lambda$ denotes the learning rate.
\label{subsec:backward}
The derivatives are computed in the backward pass procedure is processed in the reverse order of forward propagation sequence as illustrated in Fig.\ref{fig:backward}. Instead of looking at predecessor $\mathcal{A}_{\mathcal{G}^d(v_i)}$ in the forward pass, we are now taking a look at successor $\mathcal{S}_{\mathcal{G}^d(v_i)}$.
It is clear that the backpropagation error at the intermediate hidden layer $d\hat{\textbf{h}}_d^{v_i}$ comes from two sources: one from output $\frac{\partial \textbf{o}_d^{v_i}}{\partial \hat{\textbf{h}}_d^{v_i}}$ and the other one from hidden layer $\frac{\partial \textbf{h}_d^{v_i}}{\partial \hat{\textbf{h}}_d^{v_i}}$ whereas the backpropagation error at hidden layer $d{\textbf{h}}_d^{v_i}$ comes from its successor set $\mathcal{S}_{\mathcal{G}^d(v_i)}$. i.e, $\sum_{v_k \in \mathcal{S}_{\mathcal{G}^d(v_i)}}{\frac{\partial \textbf{o}_d^{v_k}}{\partial \hat{\textbf{h}}_d^{v_k}}\frac{\partial \hat{\textbf{h}}_d^{v_k}}{\partial \textbf{h}_d^{v_i}}}$.
\normalsize
To make it simple and general, we would like to express the parameters in a form without subscript $_d$. For example we will use $\mathbf{h}^{v_i}$ instead of $\mathbf{h}_d^{v_i}$.
The derivative with respect to the model parameters \{$\mathbf{o}^{v_i}$, $\hat{\mathbf{h}}^{v_i}$, $\mathbf{h}^{v_i}$, $\mathbf{V}$, $\mathbf{U}$, $\mathbf{W}$\} can be computed as follows.
\begin{eqnarray}
\begin{split}
d\mathbf{o}^{v_i} &= \frac{\partial L}{\partial \mathbf{o}^{v_i}}\\
d \hat{\textbf{h}}^{v_i} &= \frac{\partial L}{\partial \textbf{o}^{v_i}}\frac{\partial \textbf{o}^{v_i}}{\partial \hat{\textbf{h}}^{v_i}} + \left(\frac{\partial \textbf{h}^{v_i}}{\partial \hat{\textbf{h}}^{v_i}}\right)^T d \textbf{h}^{v_i}\\
& = \textbf{V}^T d  \textbf{o}^{v_i}  + \left(\frac{\partial \textbf{h}^{v_i}}{\partial \hat{\textbf{h}}^{v_i}}\right)^T d \textbf{h}^{v_i}\\
d\textbf{h}^{v_i} &=\sum_{v_k \in \mathcal{S}_{\mathcal{G}^d(v_i)}}{\textbf{W}^T \frac{\partial L}{\partial \hat{\textbf{h}}^{v_k}}\frac{\partial \hat{\textbf{h}}^{v_k}}{\partial \textbf{h}^{v_i}}} \circ \phi'(\hat{\textbf{h}}^{v_i})\\
& = \sum_{v_k \in \mathcal{S}_{\mathcal{G}^d(v_i)}}{\textbf{W}^T d\hat{\textbf{h}}^{v_k}} \circ \phi'(\hat{\textbf{h}}^{v_i})\\
\nabla\textbf{V} &= \frac{\partial L}{\partial \textbf{o}^{v_i}}\frac{\partial \textbf{o}^{v_i}}{\partial \textbf{V}} = d  \textbf{o}^{v_i} \left(\hat{\textbf{h}}^{v_i}\right)^T\\
\nabla\textbf{U} &= \frac{\partial L}{\partial \textbf{o}^{v_i}}\frac{\partial \textbf{o}^{v_i}}{\partial \hat{\textbf{h}}^{v_i}} \frac{\partial \hat{\textbf{h}}^{v_i}}{\partial\textbf{U}}= d  \hat{\textbf{h}}^{v_i} \circ \phi'(\hat{\textbf{h}}^{v_i})\left(\textbf{x}^{v_i}\right)^T\\
 \nabla\textbf{W} &= \frac{\partial L}{\partial \textbf{o}^{v_i}}\frac{\partial \textbf{o}^{v_i}}{\partial \hat{\textbf{h}}^{v_i}} \frac{\partial \hat{\textbf{h}}^{v_i}}{\partial \textbf{W}}\\
 & =  \sum_{v_j \in \mathcal{S}_\mathcal{G}^d(v_i)}{d\hat{\textbf{h}}^{v_j}\circ \phi'(\hat{\textbf{h}}^{v_j})\left(\hat{\textbf{h}}^{v_i}\right)^T }
\end{split}
\label{eq:derive_params}
\end{eqnarray}

where $\circ$  represents the Hadamard product.
\begin{figure}[t]
	\centering \includegraphics[width=0.8\columnwidth]{backward.pdf}
		\caption{The backward procedure of CRRN at vertex $\textbf{v}_i$}
	\label{fig:backward}
\end{figure}

\subsection{Inference}
\label{subsec:CRRN_inference}
Given a testing image $\mathbf{I}$, we first divide $\mathbf{I}$ into $N$ blocks $v_i$, $i = 1 ... N$. These blocks are then fed into four DAGs. The inference process of each pixel $j$ in block $v_i$ can be performed by finding the class label that maximizes the conditional probability given by
\begin{equation}
	l^{v_i*}_j =\arg \max_c p(c|\mathbf{x}^{v_i};\theta_1, \theta_2)
\end{equation}
Finally, the prediction maps of all $v_i$ are concatenated based on the location of block $v_i$ in $\mathbf{I}$ for the final prediction map. Fig. \ref{fig:CRRN_architecture} illustrates the inference process of CRRN at one direction (southeast) as an example.

\section{Experimental Results}
In this section, we evaluate the efficiency of our proposed CRRN using four challenging and popular scene image labeling datasets, i.e. SiftFlow \cite{liu2009nonparametric}, CamVid \cite{brostow2008segmentation}, Stanford Background \cite{gould2009decomposing} and SUN \cite{xiao2010sun}.

\subsection{Datasets and Measurements}
\textbf{SiftFlow Dataset}
\cite{liu2009nonparametric} contains 2,688 images captured from 8 typical outdoor scenes, i.e. coast, forest, highway, inside city, mountain, open country, street, tall building. Each image is $256 \times 256$ and labeled with 33 semantic classes (ignore the background). To run the experiment, the dataset is separated into 2 subsets corresponding to 2,488 images for training and the rest for testing as in \cite{shuai2015dag}.

\textbf{CamVid Dataset}
\cite{brostow2008segmentation} is a road scene dataset which contains 701 high-resolution images of 4 driving videos captured at both day and dusk modes \cite{CamVid_2009}. Each image is 960x720 and is labeled with 32 semantic classes. We follow the usual split protocol as given in \cite{tighe2013finding} with 468 images for training and the rest for testing.

\textbf{Stanford Background Dataset}
\cite{gould2009decomposing} contains 715 images annotated with 8 semantic classes. The dataset is randomly partitioned into 80\% (572 images) for training and the rest (143 images) for testing with 5-fold cross validation.

\textbf{SUN Dataset}
\cite{xiao2010sun} contains 16,873 images annotated with 3,819 semantic object categories. However, the numbers of images per category are highly unbalanced. Therefore, we choose the most common 33 categories for evaluation.
The chosen dataset with these categories contains 6,433 images and is randomly partitioned into two subsets corresponding to 5,798 images for training and the rest for testing.

\textbf{Measurements} It has become common practice to report results using two metrics, namely, per-pixel accuracy (PA) and average per-class accuracy (CA). The first metric, i.e. $\text{PA} = \sum_i n_{ii}/\sum_i t_i$, is defined as the fraction of the number of pixels classified rightly over the number of pixels to be classified in total whereas the latter metric, i.e. $\text{CA} =(1/C)\sum_i (n_{ii}/t_i)$, is defined as the average of per-pixel accuracy of all the classes existing in the dataset.
$n_{ij}$ is the number of pixels of class $i$ that were predicted to be class $j$, $C$ is total number of classes and $t_i = \sum_j n_{ij}$ is the total number of pixels of class $i$.

\begin{figure}[t]
	\centering \includegraphics[width=0.75\columnwidth]{Context_Illustration.pdf}
		\caption{Comparison between our CRRN and \cite{shuai2015dag} in term of contextual dependencies learning. In each row, there are four images: input image (first column), the prediction labeling map of \cite{shuai2015dag} (second column), the contextual labeling map extracted from our CRRN (third column) and the ground truth labels. \textbf{(Best viewed in color.)}}
	\label{fig:sample_Contextual_Learning}
\end{figure}

\begin{figure*}[!t]
	\centering \includegraphics[width=1.75\columnwidth]{SiftFlow_Samples-eps-converted-to.pdf}
		\caption{Examples of labeling results on SiftFlow dataset. Each column has three images: the input image (first row), its ground truth labels (second row) and our CRRN prediction labels (third row), respectively. \textbf{(Best viewed in color.)}}
	\label{fig:sample_siftflow}
\end{figure*}

\subsection{Implementation Details}

In this section, the implementation of our proposed model is discussed in details.
The model is implemented in TensorFlow environment and runs in a machine of Core i7-6700 @3.4GHz CPU, 64.00 GB RAM and a single NVIDIA GTX Titan X GPU.
In order to train the CRRN, each image in the training data is first divided into 64 (= $8 \times 8$) blocks. Next, these blocks are reorganized into four sequences based on their relationships in the four DAGs.
Each block is vectorized and used as the input for the CRRN unit. Our CRRN architecture is then employed to simultaneously model the contextual dependencies between blocks in different directions as well as extract their compact and rich representation.
The dimensionality of the hidden layer is set to 256 in the recurrent component and reshaped to $16 \times 16$ before going through the residual learning component. From our preliminary results, hidden layers with smaller number of hidden units are not powerful enough to capture both contextual information and visual representation in four DCGs.
The optimal parameter values of the whole network are obtained via a stochastic gradient descent procedure with forward and backward passes. The learning rate is initialized to be $10^{- 3}$, and decays with the rate of 0.95 after 30 epochs.
The whole model takes about 12.7 hours to train. For inference process, CRRN takes 0.058 second/image.
\begin{table}[!t]
\centering
\caption{Quantitative results and comparisons against \textit{non-fine-tuned models} \textbf{2D-LSTM} \cite{byeon2015scene}, \textbf{Recurrent CNN} \cite{pinheiro2014recurrent}, \textbf{Multi-scale Convnet}  \cite{farabet2013learning}, \textbf{Multi-CNN - rCPN} \cite{Sharma2014_recursive}, S\textbf{cene Graph Structure} \cite{Souly_2016_sparse}, \textbf{CNN-65-DAG-RNN} \cite{shuai2015dag}, \textbf{Sample\&Filter + MRF} \cite{Najafi2015_nonparametric}, \textbf{Multi-scale RCNN} \cite{Liang2015_recurrent} on Siftflow dataset.}
\label{tab:res_siftflow}
\begin{tabular}{lcc}
\toprule[\headrulewidth]
\textbf{METHOD} & \textbf{PA} & \textbf{CA} \\ \midrule
2D-LSTM      & 70.1\%                   & 20.9\%  \\
Recurrent CNN  & 77.7\%                    & 29.8\%   \\
Multi-scale Convnet   & 78.5\%                    & 29.4\%     \\
Multi-CNN - rCPN   & 79.6\%                    & 33.6\%  \\
Scene Graph Structure    & 80.6\%                    & 45.8\%            \\
CNN-65-DAG-RNN     & 81.1\%                    & 48.1\%          \\
Sample\&Filter + MRF   & 83.1\%                    & 44.3\%     \\
Multi-scale RCNN   & 83.5\%                    & 35.8\%         \\
\textbf{Our CRRN}                 &            \textbf{84.7\%}           &       \textbf{61.0\%}             \\
\bottomrule[\headrulewidth]
\end{tabular}
\end{table}

\begin{table}[!t]
\centering
\caption{Quantitative results and comparisons against \textit{non-fine-tuned models} \textbf{Neural Decision Forest}s\cite{rota2014neural}, \textbf{SVM+MRF}\cite{tighe2013finding} on CamVid dataset.}
\label{tab:res_Camvid}
\begin{tabular}{lcc}
\toprule[\headrulewidth]
\textbf{METHOD} & \textbf{PA} & \textbf{CA} \\ \midrule
Neural Decision Forests & 82.1\% & 56.1\% \\
SVM+MRF & 83.9\% & \textbf{62.5\%} \\
\textbf{Our CRRN}                  &        \textbf{84.4\%} &       54.8\%         \\
\bottomrule[\headrulewidth]
\end{tabular}
\end{table}

\subsection{Scene Labeling Results}
In order to make a fair comparison between our proposed CRRN and other state-of-the-art models, we divide the models into two groups: the ones trained using the benchmark datasets only; and the ones fine-tuned from other models which are trained over the large-scale datasets (i.e. ImageNet) or made used of deep network (i.e. VGG-verydeep-16) as their feature extractor. \textit{Our CRRN falls into the first group where no pre-trained model is used.}
\begin{table}[t]
\centering
\caption{Quantitative results and comparisons against \textit{non-fine-tuned models} \textbf{2D-LSTM} \cite{byeon2015scene}, \textbf{Recurrent CNN} \cite{pinheiro2014recurrent}, \textbf{Multi-scale Convnet}  \cite{farabet2013learning}, \textbf{Multi-scale RCNN} \cite{Liang2015_recurrent}, \textbf{Scene Graph Structure} \cite{Souly_2016_sparse} on  Stanford-background Dataset}
\label{tab:res_Stanford}
\begin{tabular}{lcc}
\toprule[\headrulewidth]
\textbf{METHOD} & \textbf{PA} & \textbf{CA} \\ \midrule
2D-LSTM     & 78.6\%                  & 68.8\%         \\
Recurrent CNN  & 80.2\%                    & 69.9\%   \\
Multi-scale Convnet    & 81.4\%                    & 76.0\%       \\
Multi-scale RCNN    & 83.1\%                    & 74.8\%          \\
Scene Graph Structure    & 84.6\%                    & \textbf{77.3\%}         \\

\textbf{Our CRRN}                  &        \textbf{85.23\%}                 &          75.2\%               \\
\bottomrule[\headrulewidth]
\end{tabular}
\end{table}

Comparing to the methods in the first group, the quantitative results of our approach with three benchmark datasets namely, Siftflow, Camvid, Stanford and SUN are reported in Tables \ref{tab:res_siftflow}, \ref{tab:res_Camvid}, \ref{tab:res_Stanford} and \ref{tab:compare_LMSUN}, respectively. The empirical results on three datasets show that our performance on both PA and CA scores are higher than state-of-the-art methods on larger dataset while giving quite competitive results on the smaller dataset. On larger Siftflow  when we have big enough data for training, our proposed CRNN outperforms all other models in terms of both PA and CA scores. Our CA is $61.0\%$ and PA is $84.0\%$ compared to the next highest CA score of $48.1\%$ \cite{shuai2015dag} and PA score of $83.5\%$ \cite{Liang2015_recurrent} as shown in Tables \ref{tab:res_siftflow}. On the small dataset, take Camvid as an instance, when we just have about 468 images for training, our PA is still about $0.5\%$ higher than the state-of-the-art method \cite{tighe2013finding}. On larger scale dataset such as SUN, our CRRN also achieves 1.41\% higher than FCNN \cite{long2015fully} in term of PA score.

\begin{figure*}[!t]
	\centering \includegraphics[width=1.75\columnwidth]{CamVid_samples-eps-converted-to.pdf}
		\caption{Examples of labeling results on CamVid dataset. Each column has three images: the input image (first row), its ground truth labels (second row) and our CRRN prediction labels (third row), respectively. \textbf{(Best viewed in color.)}}
	\label{fig:sample_Cam}
\end{figure*}
\begin{figure*}[!t]
	\centering \includegraphics[width=1.75\columnwidth]{SUN_samples.pdf}
		\caption{Examples of labeling results on SUN dataset. Each column has three images: the input image (first row), its ground truth labels (second row) and our CRRN prediction labels (third row), respectively. \textbf{(Best viewed in color.)}}
	\label{fig:sample_SUN}
\end{figure*}
\begin{table}[t]
\centering
\caption{Quantitative results and comparisons against \textit{non-fine-tuned models} \textbf{CNN-65-DAG-RNN}  \cite{shuai2015dag}, \textbf{FCNN} \cite{long2015fully} on SUN dataset.}
\label{tab:compare_LMSUN}
\begin{tabular}{lcc}
\toprule[\headrulewidth]
\textbf{METHOD} & \textbf{PA} & \textbf{CA} \\ \midrule
CNN-65-DAG-RNN  &	71.51$\%$	&	54.57$\%$ \\
FCNN & 77.2$\%$ & \textbf{62.03}$\%$ \\
\textbf{Our CRRN} & \textbf{78.61}$\%$ & 59.9$\%$ \\
\bottomrule[\headrulewidth]
\end{tabular}
\end{table}
\begin{table}[!t]
\centering
\caption{Quantitative results and comparisons against \textbf{CNN - Global Context} \cite{shuai2015integrating}, \textbf{FCNN} \cite{long2015fully}, \textbf{VGG-conv5-DAG-RNN} \cite{shuai2015dag} with \textit{fine-tuned models} on Siftflow dataset.}
\label{tab:compare_finetune_siftflow}
\begin{tabular}{lcc}
\toprule[\headrulewidth]
\textbf{METHOD} & \textbf{PA} & \textbf{CA} \\ \midrule
CNN - Global Context    & 80.1\%                    & 39.7\%        \\
FCNN  & 85.2\% & 51.7\% \\
VGG-conv5-DAG-RNN     & \textbf{85.3\%}                    & 55.7\%                 \\
\hline
\textbf{Our CRRN}                 &            84.7\%           &       \textbf{61.0\%}               \\
\bottomrule[\headrulewidth]
\end{tabular}
\end{table}
Figure \ref{fig:sample_Contextual_Learning} illustrates the advantages of our CRRN in term of modeling the contextual dependency presented in the image. Comparing to \cite{shuai2015dag}, besides the local consistency between neighborhood regions, their semantic coherence is better enhanced in our CRRN. For example, after training, our CRRN can capture the contextual knowledge such as the `building' is not likely to appear in the 'sea' (i.e. the first case) or the 'desert' is not usually covered by the 'field' (i.e. the second case). As a result, while \cite{shuai2015dag} still has the problem of misclassification in its predictions, smoother and better labeling maps can be produced by our model.
Our qualitative results on the datasets are further demonstrated in Figures \ref{fig:sample_siftflow}, \ref{fig:sample_Cam} and \ref{fig:sample_SUN}.  As visualization, there exist many false positive cases where pixels are classified as unlabeled while they truly belongs to labeled classes. In such cases, our CRRN performs well to category the false positive pixels. Thank to the ability of simultaneously learning rich visual representation and modeling the context information, our model has better memory of contextual dependency and gives higher discriminative descriptors for each local patch

Furthermore, we also compare our CRRN with other fine-tuned methods on SiftFlow as shown in Table \ref{tab:compare_finetune_siftflow}. In this group, most existing fine-tune methods \cite{shuai2015integrating,long2015fully,shuai2015dag} are trained on large-scale data and make use of powerful feature extractor while our CRRN is a non fine-tune model and trained on Siftflow only. From these results, one can see that our CRRN model archives the best CA score ($61.0\%$) compared to the state-of-the-art ($55.7\%$) \cite{shuai2015dag} while giving a competitive PA score. We believe that given enough data, our CRRN performance can be boosted and is competitive to these models. We leave this as future work.

\section{Conclusion}

This paper presents a novel Contextual Recurrent Residual Networks (CRRN) approach, which is able to simultaneously model the long-range context dependencies and learn rich visual representation. The proposed CRRN is designed as a fully end-to-end deep learning framework and is able to make use the advantages of both sequence modeling and residual learning techniques. Our CRRN network contains three parts corresponding to sequential input data, sequential output data and hidden CRRN unit. Each hidden CRRN unit has two main components: context-based component to model the context dependencies and visual-based component to learn the visual representation. Our proposed end-to-end CRRN is completely trained from scratch, without using any pre-trained models in contrast to most existing methods usually fine-tuned from the state-of-the-art pre-trained models, e.g. VGG-16, ResNet, etc. The experiments are conducted on four challenging scene labeling datasets, i.e. SiftFlow, CamVid, Stanford background and SUN datasets, and compared against various state-of-the-art scene labeling methods.

redpajama_set_name: RedPajamaArXiv


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text:
The Chelsea Recreation Center is a community center and athletic facility operated by the New York City Department of Parks and Recreation in Chelsea, Manhattan.  At , the building is one of the largest neighborhood recreation centers in New York City, containing a 25-yard six lane pool, volleyball court, basketball court, fitness rooms, dance studios, game rooms, and a computer resource center.  The center hosts a wide range of community-oriented classes and events, many of which are free to youth and older adults.

History
In 1964 the previous Chelsea Recreation Center, the former Chelsea Bathhouse at 407 West 28th Street, was demolished to make way for the construction of the Morgan Annex postal facility.  Construction began on a replacement recreation center on June 3, 1974, but was abandoned on January 19, 1976 due to the New York City fiscal crisis of 1975.   Windows were sealed over with concrete and steel girders left exposed, with an air conditioner unit remaining on the roof for when construction resumed.  Completion of the facility was a consistent request at neighborhood meetings over the next 25 years, and neighborhood groups even hired a consultant and an architect to help make their case to the city.

When New York City experienced an economic upswing in the early 2000s financing was made available to complete the recreation center.  On June 7, 2001 Mayor Rudolph Giuliani, state senator Thomas Duane, and city council members including Peter Vallone, Jr. and Christine Quinn swung gold sledgehammers against a brick wall to mark the resumption of construction.  A five-panel mural consisting of 175,000 tiles based on dolphin photographs by Tsuneo Nakamura was installed next to the pool as a gift by the Italian Trade Commission and Bisazza Mosaico.  The center was completed at a final cost of $22.4 million on May 11, 2004, and was inaugurated by Mayor Michael Bloomberg at a ribbon-cutting ceremony in the center's gymnasium.

Membership
Membership is free for children under age 18. Adult membership currently costs $150 per year, though for young adults ages 18-24, the price was set at $25 per year. Members over age 62 also pay $25 per year.  Improving public health is seen as a priority of the Michael Bloomberg administration, justifying city-run community recreation centers with fees below those of private health clubs.

References

External links
NYC Parks

Chelsea, Manhattan
Parks in Manhattan
2004 establishments in New York City

redpajama_set_name: RedPajamaWikipedia


Evis Savvides | Directors | Cyprus-U.S. Chamber of Commerce, Inc.
Evis Savvides is with SFG, a New York City based consulting group he formed in 2013 that is focused on helping companies in the consumer goods industry turnaround. Evis started his career in public accounting and later moved to the management consulting area where he served as a partner of a New York based consulting firm. In 2006, Evis was invited to assume the role of Chief Operating Officer for Deutsch Family Wine & Spirits, the Sales & Marketing Organization representing the [yellow tail] wine brand. Evis holds an M.S. in management accounting and B.S.B.A. in accounting from Old Dominion University in Virginia where he graduated with honors. Born and raised in Limassol, Evis currently resides in Chappaqua, NY with his wife and two boys.


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2.1.1.4.sec=For example, if you say no, you might not be asked to give samples.

2.1.1.=[G/Z/para/s4]

2.1.2.1.sec=There will be a separate form called the HIPAA Authorization for you to sign if you decide to give us access.

2.1.2.2.sec=We will see data about your health problems, test results, medical procedures, images (such as X-rays), and medicines you take.

2.1.2.3.sec=Health records can contain sensitive data. For example, they may tell us about your mental health, genetic conditions, or use of alcohol or drugs.

2.1.2.4.sec=They may contain sexual or infection data, including HIV status.

2.1.2.=[G/Z/para/s4]

2.1.=[G/Z/paras/s2]

2.2.Ti=Data about your health from other sources

2.2.1.1.sec=We will add data from other sources to the data you give us.

2.2.1.2.sec=For example, environmental data and pharmacy records. This will give researchers more data about factors that might affect your health.

2.2.1.=[G/Z/para/s2]

2.2.2.0.sec=There are two ways we will add data from other sources to your {_Program} record:

2.2.2.1.Ti=Based on where you live and work

2.2.2.1.1.sec=We will add data about your area based on where you live and work.

2.2.2.1.2.sec=For example, we may add data about the number of people in your area.

2.2.2.1.3.sec=We may add pollution data.

2.2.2.1.4.sec=We may add data like how close you live to the nearest grocery store or park.

2.2.2.1.=[G/Z/para/s4]

2.2.2.2.Ti=Based on data that identifies you

2.2.2.2.1.1.sec=We will use data that identifies you like your name and date of birth to add data that is specific to you.

2.2.2.2.1.2.sec=For example, we may add data from pharmacy records or health insurance records.

2.2.2.2.1.3.sec=If you have had cancer, we may add data from cancer registries.

2.2.2.2.1.=[G/Z/para/s3]

2.2.2.2.2.1.sec=If you have a social security number, we may ask you for it to help with adding data.

2.2.2.2.2.2.sec=It is optional.

2.2.2.2.2.3.sec=Even if we ask, you do not have to give us your social security number.

2.2.2.2.2.4.sec=You can say no and still take part in {_Program}.

2.2.2.2.2.=[G/Z/para/s4]

2.2.2.2.3.1.sec=These other sources can contain sensitive data.

2.2.2.2.3.2.sec=For example, they may tell us about your mental health, or use of alcohol or drugs.

2.2.2.2.3.3.sec=They may contain sexual or infection data, including HIV status.

2.2.2.2.3.4.sec=Because of this, we will ask the {_Program} ethics committee to review and approve each data source before we add it.

2.2.2.2.3.=[G/Z/para/s4]

2.2.2.2.=[G/Z/paras/s3]

2.2.2.=[G/Z/ol-bullet/2]

2.2.=[G/Z/paras/s2]

2.=[G/Z/ol/2]

=[G/Z/ol/2]

redpajama_set_name: RedPajamaGithub


Tributes have been paid to the 22 people killed in the attack at Manchester Arena, some of them children.
Fourteen have been named so far.
Michelle Kiss was described as "a loving wife to Tony, mother to Dylan, Elliot and Millie, as well as daughter to Mick and Christine and sister to Nichola".
In a statement, her family said: "Family was her life and we are all obviously devastated by her loss.
Sorrell Leczkowski, from Leeds, is believed to have gone to the Manchester Arena with her family to pick up her sister.
The 14-year-old Allerton High School pupil was killed and her mother and grandmother, who is critically ill, are in hospital.
A female off-duty officer from Cheshire Police was killed while at the concert with her family, Greater Manchester police has confirmed.
She was with her husband, who remains critically ill, and two children, who were injured.
School pupil Nell Jones, 14, from Cheshire, died in the attack, Holmes Chapel Comprehensive School has said.
Head teacher Denis Oliver said the student's family had "been searching for her since the incident in the hope that they would find her being cared for in hospital".
Her form tutor David Wheeler said that she was a "popular girl, always smiling", adding: "Her tutor group have been together since the transition from primary school.
Friends Alison Howe, 45, from Royton, and Lisa Lees, 47, from Oldham near Manchester, were killed while waiting in the foyer to pick up their daughters.
The girls, both aged 15, are believed to be safe.
Jane Tweddle-Taylor, 51, from Blackpool, was killed in the foyer as she went to the Arena with a friend to pick up the friend's daughter.
She was a receptionist at South Shore Academy School in Blackpool and was a "bubbly, kind, welcoming, funny, generous" colleague, the school's principal has said.
Jane Bailey described Miss Tweddle-Taylor as a "well-loved member of staff" and "wonderful friend and colleague".
The couple's daughter Alex Klis, 20, a student at York College, had posted an appeal on Facebook for information on their whereabouts.
Mr Hett, from Stockport, was a Coronation Street fan and in 2015 was interviewed for the BBC's Victoria Derbyshire programme about his tattoo of the soap's character Deirdre Barlow.
His former partner Russell Hayward said on Twitter: "We got the news last night that our wonderful iconic and beautiful Martyn didn't survive.
Her uncle, Paul Dryhurst, said Ms Brewster had "heroically shielded" her 11-year-old niece from the explosion in the foyer after becoming separated from her sister at the end of the performance.
Her partner Ian Winslow wrote on Facebook: "Not sure how this works but it isn't good news.
John Atkinson, 28, was described by friends as "a true gentleman" in online tributes.
Friends of Mr Atkinson, who lived in Radcliffe in Bury, have also set up a fundraising page to help his family, which has raised more than £4,000.
"John tragically lost his life last night (22/05/17) in the Manchester MEN bomb attack," wrote Hayley Dickenson, who organised the fund.
"A true gentleman. R.I.P John thoughts and prayers with you family and all of the other victims effected by this tragedy," she said.
"John was one in a million and loved by so many," she added.
Student Georgina Callander, thought to have been 18, had already met the US singer Grande before seeing her perform on Monday.
Before the concert, Miss Callander had sent a Twitter message to Grande, saying: "SO EXCITED TO SEE YOU TOMORROW".
Olivia Campbell, 15, was a "precious, gorgeous girl", her mother has said in an online tribute.
Olivia's family had spent the day desperately searching for her, hoping she was one of the dozens of people being treated in hospital.
She was a pupil at Tottington High School, where head teacher Brian Duffy described her as "a delightful young girl".
"As a school community we are absolutely devastated and heartbroken at the news that Olivia has passed away," he said in a statement.
Eight-year-old Saffie Roussos was "simply a beautiful little girl in every sense of the word," said Chris Upton, head teacher at Lancashire's Tarleton Community Primary School.
He added: "News of Saffie's death in this appalling attack has come as a tremendous shock to all of us and I would like to send our deepest condolences to all of her family and friends.
"The thought that anyone could go out to a concert and not come home is heartbreaking.
How Rummy Inspires Art Lovers?


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Why the internet is turning into a beauty school: What you need to know about the online beauty industry
There's a new way for businesses to monetize online.And it's growing. This is the story of a new class of online beauty professionals: the online school routine.It's called the online adult school routine and it's all about teaching.It's been around for more than two decades.But what it is, and how it works, has changed dramatically.What is…
Published by admin in October 31, 2021
Online school cheating and cheating on school credit online: Parents warn students about online school
The parents of a Virginia high school student who used online school cheating to take advantage of her scholarship money are warning students about cheating online.Katherine McFarland, 17, of Arlington, Virginia, was suspended from school after being caught using a stolen credit card to make purchases online.McFarland said she had been using the card since…
Online pastry school teaches students how to make pastry
A school in northern England is teaching online pastry-makers how to create a perfect cake for Christmas.The school, called Bakelite, uses a system that combines computer graphics and hand-to-hand cooking techniques to help teach people how to produce and sell their own cakes.The aim is to give young people the skills they need to take…
Man who posted 'Boys' meme to Instagram was arrested for stalking
The suspect in the case of a man who posted a photo of a group of boys dressed in school uniform online was arrested in a police raid, authorities said on Monday.The arrest of 28-year-old Guido Giuseppe Varela, of Italy, on Saturday afternoon was announced by police in Naples, who said they had launched an…


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Tip: To convert articles to PDF…
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Hauke, C. (2002). CASEMENT, ANN. Carl Gustav Jung (In the series Key Figures in Counselling and Psychotherapy edited by Windy Dryden). London & Thousand Oaks: Sage, 2001. Pp. 168. Hbk. £50.00; Pbk. £15.99.. J. Anal. Psychol., 47(1):113-114.
(2002). Journal of Analytical Psychology, 47(1):113-114
CASEMENT, ANN. Carl Gustav Jung (In the series Key Figures in Counselling and Psychotherapy edited by Windy Dryden). London & Thousand Oaks: Sage, 2001. Pp. 168. Hbk. £50.00; Pbk. £15.99.
Christopher Hauke
Barbara D. Stephens and Susanna Wright
Rarely does an introductory text bring much that is new to a subject - especially to readers (including readers of this journal) who are already very familiar with the subject. From the outset, Ann Casement's new introduction to Jung and his psychology impresses as an exception to this. A glance at the organization of the book reveals its freshness: after a mandatory 'The Life of Carl Gustav Jung' (which itself is a far from routine read), Casement offers sections on 'Jung's Major Theoretical Contributions' and 'Jung's Major Practical Contributions', a fourth on 'Criticisms and Rebuttals' and a last one on 'The Overall Influence of Carl Gustav Jung', plus an extensive bibliography.
All introductory books on Jung rely quite heavily on Memories, Dreams, Reflections (Jung, 1963) but the difference here is the way that Casement is up to date with the authorship problems of this book and the research of Shamdasani, for example, that is unravelling the Jung story and unearthing interesting archival material. Now when I teach Jung I can hand students this book instead of repeating all the caveats applicable to Memories, Dreams, Reflections - which is still essential reading but only if accompanied by a critical view, which Casement's succinct chapter has now provided us with.
The next forty pages start with a survey of Jung's philosophical influences from Plato through Nietzsche and the neglected F. W. H. Myers, offering just enough for the student to seek further details in specialist texts.


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Insights > Digital & technology
Swine Flu Most Blogged Topic in April 2009
2 minute read | May 2009
Swine Flu, Easter, and Somali Pirates topped Nielsen's monthly index of most blogged news stories for April 2009, according to Nielsen's just released top 10 most-blogged topics report.
"Understanding what is currently important to bloggers is essential to marketers since we know that highly blogged about topics are important indicators of conversational sustainability and impact," said Pete Blackshaw, executive vice president, digital strategic services, Nielsen Online. "Moreover, those topics with high blog links tend to embed themselves prominently in search results."
Top 10 Blogged Topics, April 2009
Somali Pirates
Bea Arthur
Source: Nielsen BuzzMetrics Custom Analysis
Swine Flu and Politics Top of Mind
Despite the fact that it wasn't until April 24th that the World Health Organization issued its first Disease Outbreak Notice regarding the H1N1 virus, the flu outbreak quickly ascended all other topics to become the most blogged about subject for the month. Discussions ranged from reporting the first flu-related death in the United States to conversations on the Centers for Disease Control's handling of the situation. Easter was also an important topic for the month, deposed from the No. 1 position by the flu buzz late in the month.
Politics were also top of mind for the online community in April. Bloggers discussed PA Senator Arlen Specter's switch from the GOP to the Democratic Party, with opinions ranging from it being a disastrous power play to believing the move will work in his favor. Bloggers also discussed President Obama's speech in early April in which he stated that America will "seek the peace and security of a world without nuclear weapons." Some felt that this idea, while good on paper, is not realistic, while others took the opportunity to express their thoughts and concerns on nuclear weapons as a whole.
"The blogosphere is not just a place where people write about the day-to-day events of their own lives—it also acts as a forum for people to share their opinions and thoughts on things that are occurring in the world. The influence of these bloggers is far-reaching, whether it is sharing their opinion on an upcoming movie launch or a devastating flu epidemic, they are talking and people are listening," added Blackshaw.
more info: pr.us@nielsen.com


Blooming debate. A controversial iron dumping experiment off Canada's West Coast may—or may not—have caused plankton bloom (orange, at left) detected by satellites.
Questions and controversy continue to swirl around what has been characterized as a rogue geoengineering experiment in international waters off the coast of British Columbia. But whether the action was legal under international law or likely to be effective from a scientific perspective are beginning to become clearer.
Last week, The Guardian newspaper first reported that in July, a retrofitted fishing trawler dispersed 100 tons of iron dust in an ocean eddy about 321 kilometers west of the islands of Haida Gwaii. The iron was intended to foster the growth of phytoplankton and thereby boost the entire marine food chain which it supports, including salmon valued by the indigenous Haida people who live in the area. Such "fertilization" projects have also been touted as a way to soak up atmospheric carbon dioxide and curb global warming.
At this point, answers to the legal questions surrounding the fertilization experiment seem the most clear. The experiment was conducted by the Haida Salmon Restoration Corp., and backed by a California businessman, Russ George. The ETC Group, an environmental watchdog organization, contends that the iron-fertilization scheme was a violation of international law, including the U.N. Convention on Biological Diversity (CBD) and the London convention on dumping of waste at sea, also known as the London Protocol. CBD did adopt two positions discouraging ocean fertilization, notes Edward Parson, a professor of law at the University of California, Los Angeles, School of Law. But these decisions are strictly advisory, and not legally binding, he adds. And while the London Protocol is binding, it only applies to the release of material intended to be dumped as waste, not released as part of a scientific experiment. Even if the quality of the science is dubious, as many ocean experts argue in this case, Parson says because the material was not dumped as waste, "there is no violation under international treaty." Canadian officials, however, are still investigating whether the experiment may have violated Canadian law.
As for the effectiveness of ocean fertilization, that seems less clear. Satellite images taken before and after the iron release seem to show a marked increase in phytoplankton. However, Kenneth Denman, an oceanographer with the University of Victoria in Canada, argues that it's impossible to conclude whether this plankton bloom was due to the added iron. The Haida eddies, where the iron was released, is a natural ocean circulation pattern that carries iron-rich coastal waters as far as 1000 kilometers off shore. The eddies trigger natural plankton blooms each summer, which could account for most if not all of what is showing up in the satellite images. "It's possible what they show is from the natural iron," Denman says. He adds that it will likely be impossible to ever produce useful science from the test, because it appears that the experimenters did not set up careful control studies to compare fertilized waters with waters from previous years, as well as unfertilized regions.
Even if the iron release caused this summer's phytoplankton bloom, it's still difficult to determine what impact this will have. The proponents of the test have said that they carried it out to promote increases in salmon stocks as well as fight climate change by sequestering atmospheric carbon. Some recent evidence does suggest that added iron can boost fish stocks, Denman says. In 2008, a volcano in Alaska's Aleutian Islands erupted, spewing iron-rich ash throughout the North Pacific. Phytoplankton blooms followed and salmon runs spiked to century-high levels in 2010. But Denman argues that there's not enough data to conclude that iron fertilization from the eruption caused the fish population boom. "People are assuming there is a link. But basically it's conjecture and speculation," Denman says.
That said, a dozen smaller-scale iron-fertilization experiments do show that adding iron to otherwise nutrient-rich ocean waters can trigger phytoplankton blooms. But studies to date suggest that ocean fertilization is a relatively inefficient way to sequester large amounts of carbon from the atmosphere, says Phillip Williamson, an oceanographer and project manager at the Natural Environment Research Council and the University of East Anglia in Norwich, U.K., who just led a review of ocean fertilization experiments. According to that study, which appeared online on 12 October in Process Safety and Environmental Protection, if widespread ocean fertilization were practiced over the course of this entire century it could be expected to remove 25 gigatons to 75 gigatons of carbon from the atmosphere. That's less than 10% of the 900 to 2000 gigatons of carbon that humans are expected to add to the atmosphere over the same period. As the world struggles to limit atmospheric carbon, that may eventually seem a worthwhile effort. But aside from the cost of distributing all that iron, widespread iron fertilization would be expected to cause major changes in the ocean ecosystems where it was practiced.


Recently Rovio released the new version of the number one mobile game, Angry Birds Seasons, in Ovi Store. Bringing new worlds with different levels that are the Seasons Greedings with Christmas theme and Trick or Treat for Halloween theme. There is also the appearance of two new birds, mainly the Boomerang bird and the "steroid" red bird. Please read more of the review after the break.
The main recipe of this version brings the new worlds with two different themes. The Seasons Greedings for Christmas season and the Trick or Treat for Halloween.
The Seasons Greedings consists 25 new levels and a special stage for completing the 3 stars. Where each level corresponds to a date equivalent to month of December, like the first level for 1st of this month and last level for 25th. So each day until 25th of this month, all levels will be fully opened. It works like a countdown for Christmas stages.
This world is also I think the hardest on all different packages of Angry Birds. Mostly, you would get only 3 angry birds ammunition leaving the skills and luck for the rest. Also this world packs with many bonus items in the form of gifts boxes. I will also share a golden egg tip for this world below.
The other world is the Trick or Treat with Halloween theme and featuring 3 sets of 15 levels and a final stage for 3 stars completion. So expect you will see a lot of pumpkins from decorations to costumes of these pigs. You will also meet the new birds here that I will discuss below.
The appearance of these two new birds is not new for some, but for Symbian users it is a first time though. So there are two angry birds member who will join for the forces to pursuit of eggs.
The first one is the red bird which is similar to the original red bird of the first version, provided it gone big, like a steroid red bird ( I just call it Steroid bird). This bird has no special abilities though aside from a behemoth destruction weight and force. It simply the most massive bird in all angry birds line up making large area damage and destroying anything on its path, especially the pigs.
The second bird is the green taco or the Boomerang bird. As it title suggest, it has special abilities. It works like a boomerang when you tap it while it is on the mid air. A little stronger than the classic red bird but definitely it is one of a hell awesome bird.
One of the most awaited part of the Angry Birds fans is of course this part. A golden egg! I actually don't know how this golden egg work until I finally got one. It opens another level with just easier play needed but with lots of pigs and blocks to destroy. You will find the golden egg stage on the final or special egg dashboard.
You can get the Angry Birds Seasons from Ovi Store.
A video review is available to watch below.


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MySQL Enterprise Monitor 3.3.9 Manual
Previous Schema Advisors
Home MySQL Enterprise Monitor 3.3.9 Manual
Up Expression-Based Advisor Reference
Next GUI-Based Advisor Reference
23.11 Security Advisors
This section describes the Security Advisors.
MySQL Enterprise Firewall and MySQL Enterprise Audit Plug-in advisors are described in MySQL Enterprise Firewall and MySQL Enterprise Audit Plugin.
Account Has An Overly Broad Host Specifier
Account Has Global Privileges
Account Has Old Insecure Password Hash
Account Has Strong MySQL Privileges
Account Requires Unavailable Authentication Plug-ins
Insecure Password Authentication Option Is Enabled
Insecure Password Generation Option Is Enabled
LOCAL Option Of LOAD DATA Statement Is Enabled
Non-root User Has GRANT Privileges On All Databases
Non-root User Has Server Admin Privileges
Non-root User Has DB, Table, Or Index Privileges On All Databases
Policy-Based Password Validation Does Not Perform Dictionary Checks
Policy-Based Password Validation Is Weak
Policy-Based Password Validation Not Enabled
Privilege Alterations Detected: Privileges Granted
Privilege Alterations Detected: Privileges Revoked
Privilege Alterations Have Been Detected
Root Account Can Login Remotely
Root Account Without Password
SHA-256 Password Authentication Not Enabled
Server Contains Default "test" Database
Server Has Accounts Without A Password
Server Has Anonymous Accounts Not Being Used As Proxies
Server Has No Locally Authenticated Root User
Server Includes A Root User Account
Symlinks Are Enabled
User Has Rights To Database That Does Not Exist
User Has Rights To Table That Does Not Exist
Users Can View All Databases On MySQL Server
The MySQL server has user accounts with overly broad host specifiers. A MySQL account is identified by both a username and a host name, which are found in the User and Host columns of the mysql.user table. The User value is the name that a client must supply when connecting to the server. The Host value indicates the host or hosts from which the user is allowed to connect. If this is a literal host name, the account is limited to connections only from that host. If the host name contains the % wildcard character, the user can connect from any host that matches the wildcard character and potentially from any host at all.
From a security standpoint, literal host values are best and % is worst. Accounts that have Host values containing wildcards are more susceptible to attack than accounts with literal host values, because attackers can attempt to connect from a broader range of machines.
For example, if an account has user and host values of root and %, it means that you can connect as the root user from any machine if you know the password. By contrast, if the host name is localhost or 127.0.0.1, the attacker can only attempt to connect as the root user from the server host.
Default frequency 00:05:00
Default auto-close enabled no
A MySQL server may have user accounts with privileges on all databases and tables (*.*). In most cases global privileges should be allowed only for the MySQL root user, and possibly for users that you trust or use for backup purposes. Global privileges such as DROP, ALTER, DELETE, UPDATE, INSERT, and LOCK TABLES may be dangerous as they may cause other users to be affected adversely.
Prior to MySQL 4.1, password hashes computed by the PASSWORD() function were 16 bytes long. As of MySQL 4.1 (and later), PASSWORD() was modified to produce a longer 41-byte hash value to provide enhanced security.
Certain account privileges can be dangerous and should only be granted to trusted users when necessary. For example, the FILE privilege allows a user to read and write files on the database server (which includes sensitive operating system files), the PROCESS privilege allows currently executing statements to be monitored, and the SHUTDOWN privilege allows a user to shut down the server. In addition, the GRANT privilege allows a user to grant privileges to others.
MySQL supports many forms of authentication as of the 5.5 release, including external authentication mechanisms using PAM, or Windows native authentication with commercial releases of MySQL version 5.5.16 or greater. If a user is configured to use an authentication plug-in, and that plug-in does not get loaded with server start, this blocks access to the database for those users.
Default auto-close enabled yes
Prior to MySQL 4.1, password hashes computed by the PASSWORD() function were 16 bytes long. As of MySQL 4.1 (and later), PASSWORD() was modified to produce a longer 41-byte hash value to provide enhanced security. However, in order to allow backward-compatibility with user tables that have been migrated from pre-4.1 systems, you can configure MySQL to accept logins for accounts that have password hashes created using the old, less-secure PASSWORD() function, but this is not recommended.
Prior to MySQL 4.1, password hashes computed by the PASSWORD() function were 16 bytes long. As of MySQL 4.1 (and later), PASSWORD() was modified to produce a longer 41-byte hash value to provide enhanced security. In order to allow backward-compatibility with older client programs, you can configure MySQL to generate short (pre-4.1) password hashes for new passwords, however, this is not recommended.
The LOAD DATA statement can load a file that is located on the server host, or it can load a file that is located on the client host when the LOCAL keyword is specified.
There are two potential security issues with supporting the LOCAL version of LOAD DATA statements:
The transfer of the file from the client host to the server host is initiated by the MySQL server. In theory, a patched server could be built that would tell the client program to transfer a file of the server's choosing rather than the file named by the client in the LOAD DATA statement. Such a server could access any file on the client host to which the client user has read access.
In a Web environment where the clients are connecting from a separate web server, a user could use LOAD DATA LOCAL to read any files that the web server process has read access to (assuming that a user could run any statement against the SQL server). In this environment, the client with respect to the MySQL server actually is the web server, not the remote program being run by the user who connects to the web server.
The GRANT privilege, when given on all databases as opposed to being limited to a few specific databases, enables a user to give to other users those privileges that the grantor possesses on all databases. It can be used for databases, tables, and stored routines. Such a privilege should be limited to as few users as possible. Users who do indeed need the GRANT privilege should have that privilege limited to only those databases they are responsible for, and not for all databases.
Certain privileges, such as SHUTDOWN and SUPER, are primarily used for server administration. Some of these privileges can have a dramatic effect on a system because they allow someone to shutdown the server or kill running processes. Such operations should be limited to a small set of users.
Privileges such as SELECT, INSERT, ALTER, and so forth allow a user to view and change data, as well as impact system performance. Such operations should be limited to only those databases to which a user truly needs such access so the user cannot inadvertently affect other people's applications and data stores.
When users create weak passwords ('password' or 'abcd') it compromises the security of the server, making it easier for unauthorized people to guess the password and gain access to the server. Starting with MySQL Server 5.6, MySQL offers the validate_password plug-in that can be used to test passwords and improve security. With this plug-in you can implement and enforce a policy for password strength (passwords must be at least 8 characters long, have both lowercase and uppercase letters, contain at least one special non-alphanumeric character, and do not match commonly-used words).
For more information on validate_password plug-in, see The Password Validation Plugin.
When users create weak passwords ('password' or 'abcd') it compromises the security of the server, making it easier for unauthorized people to guess the password and gain access to the server. Starting with MySQL Server 5.6, MySQL offers the validate_password plug-in that can be used to test passwords and improve security. With this plug-in you can implement and enforce a policy for password strength (passwords must be at least 8 characters long, have both lowercase and uppercase letters, and contain at least one special non-alphanumeric character).
For development environments, changes to database security privileges may be a normal occurrence, but for production environments it is wise to know when any security changes occur with respect to database privileges, and to ensure that those changes are authorized and required.
By default, MySQL includes a root account with unlimited privileges that is typically used to administer the MySQL server. If possible, accounts with this much power should not allow remote logins in order to limit access to only those users able to login to the machine on which MySQL is running. This helps prevent unauthorized users from accessing and changing the system.
The root user account has unlimited privileges and is intended for administrative tasks. Privileged accounts should have strong passwords to prevent unauthorized users from accessing and changing the system.
To help keep the server secure, each user's password is encrypted, and the stronger the encryption method, the more secure the server is. Starting with MySQL Server 5.6, MySQL offers a new encryption algorithm that performs authentication using SHA-256 password hashing. This is stronger encryption than that available with native authentication (the standard encryption method).
By default, MySQL comes with a database named test that anyone can access. This database is intended only for testing and should be removed before moving into a production environment. Because the default test database can be accessed by any user and has permissive privileges, it should be dropped immediately as part of the installation process.
Accounts without passwords are particularly dangerous because an attacker needs to guess only a username. Assigning passwords to all accounts helps prevent unauthorized users from accessing the system.
Anonymous MySQL accounts allow clients to connect to the server without specifying a username. Since anonymous accounts are well known in MySQL, removing them helps prevent unauthorized users from accessing the system.
This advisor differentiates between anonymous accounts and proxy users. For more information on proxy users, see Proxy Users.
MySQL 5.5 supports both built-in authentication and external authentication via other methods such as PAM (LDAP, Unix user authentication) and Windows native authentication. However, if all 'root' users are configured to use external authentication, if this external authentication were to fail (such as the LDAP server losing power), then all administrator access to the MySQL Server is denied.
By default, MySQL includes a root account with unlimited privileges that is typically used to administer the MySQL server. There is no reason this account must be named 'root'. Accounts with this much power should not be easily discovered. Since the root account is well known in MySQL, changing its name helps prevent unauthorized users from accessing and changing the system.
You can move tables and databases from the database directory to other locations and replace them with symbolic links to the new locations. You might want to do this, for example, to move a database to a file system with more free space or to increase the speed of your system by spreading your tables to different disks.
However, symlinks can compromise security. This is especially important if you run mysqld as root, because anyone who has write access to the server's data directory could then delete any file in the system!
When a database is dropped, user privileges on the database are not automatically dropped. This has security implications as that user regains privileges if a database with the same name is created in the future, which may not be the intended result.
When a table is dropped, user privileges on the table are not automatically dropped. This has security implications as that user regains privileges if a table with the same name in the same database is created in the future, which may not be the intended result.
The SHOW DATABASES privilege should be granted only to users who need to see all the databases on a MySQL Server. It is recommended that the MySQL Server be started with the --skip-show-database option enabled to prevent anyone from using the SHOW DATABASES statement unless they have been specifically granted the SHOW DATABASES privilege.
If a user is granted any global privilege, such as CREATE TEMPORARY TABLES or LOCK TABLES, they are automatically given the ability to show databases unless the server is started with the --skip-show-database option enabled. DBAs should be aware of this fact, in the event that any applications make use of temporary tables.
MySQL Documentation Library


Alami Alor is an intimate and personalized dive resort. The diving in Alor is world class with pristine coral walls and slopes, great macro life including the elusive Rhinopias and also pelagic life such as Whales, Dolphins, Hammerheads, Thresher Sharks, Mola-Mola and Devil Rays. The resort has waterfront bungalows, a guest lounge with sundeck area, a dining room and a dive center with camera room. The guest lounge and sundeck area is a comfortable place to relax in between dives, and at the end of the day it is a meeting point where you can enjoy a snack and local beer or bottle of wine whilst discussing the day's dives. At Alami Alor you can enjoy a full breakfast, lunch and dinner served in the dining room. The resort serves an assortment of dishes influenced mostly by tropical Indonesian and Asian style, but also international cuisines.
Alami Alor has 6 guest bungalows along the waterfront, 2 of which have 1 king bed plus 1 single bed, and 4 with 1 king bed. All of the bungalows have air-conditioned bedrooms with open-air garden bathrooms. The bathrooms have a hot water rain shower. On the front terrace each bungalow has lounge chairs.
Each day generally starts with a continental breakfast setting of cereals, homemade bread or pastries, spreads, tropical fruits, coffee and tea. There is also a daily a la carte menu for cooked breakfast dishes such as eggs and hash brown potatoes or Indonesian breakfast of fried rice or fried noodles. Lunch and dinner include a variety of local vegetables, fish and chicken, accompanied by freshly made salads, soups and desserts.
The Dive Center at Alami Alor houses both an equipment room with individual guest equipment lockers and terrace with a dive scheduling board. Access to the water for boat dives and house reef dives is done via the dive center's jetty.
Housed in the dive center is a camera room where you can rinse, soak and blow dry your camera housings out front and then enter to use a personal workbench for setting up and disassembling cameras, charging batteries and storing camera equipment in-between dives and overnight.
From Alami Alor you can do 3 guided boat dives per day, 2 in the morning and then 1 in the afternoon or at night. Dive trips are made from 2 fiberglass speedboats with 4 stroke engines that were purpose built for scuba diving. Both boats are fully equipped with drinking water, towels, first aid and oxygen, spare parts, tool kit, binoculars and communication devices. Guest to guide ratio is 4:1 or less. In between boat dives you can dive on the house reef unguided free of charge!
Alami Alor offers both reef dives in the Pantar Strait and black sand critter dives in Kalanahi Bay, Beang Bay and Pura Island. Depending on the dive site you may find yourself face-to-face with a Rhinopias, mating Mandarinfish, Frogfish, Pygmy Seahorse, Mola-Mola, a school of Scalloped Hammerheads or even a Blue Whale!


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L'église Saint-Rémi est une église située sur le territoire de la commune d'Hautevesnes, dans le département de l'Aisne, en France.

Historique

Presque entièrement détruite pendant la Première Guerre mondiale, il ne reste plus de l'ancienne église que quelques pierres de l'abside classées au titre des monuments historiques en 1921.

Couverte en tuiles, elle a été complètement reconstruite en pierre après guerre.

Description

Annexes

Liens internes

 Liste des monuments historiques de l'Aisne
 Paroisse Saint Félix de Valois
 Liste des églises de l'Aisne

Références

Monument historique classé en 1921
Hautevesnes
Hautevesnes
Hautevesnes Saint-Remi
Hautevesnes