ADDIS ABABA: Egypt, Sudan and Ethiopia have agreed to set up a scientific committee to study a dam Ethiopia is building on a tributary of the Nile, an Ethiopian minister said Thursday. The announcement broke a long impasse in a dispute over Egyptian fears that the $4-billion (3.2-billion-euro) Grand Ethiopian Renaissance Dam, being built on the Blue Nile, will affect the river´s downstream flows. The three countries´ foreign and irrigation ministers, as well as heads of intelligence, met in Addis Ababa on Tuesday to discuss the scheme. The meeting concluded on Wednesday at 3:00 am on a "high note", said Sileshi Bekele, minister of energy, irrigation and electricity. "We managed to actually find a number of win-win approaches," he told reporters in Addis Ababa. Previous rounds of talks had ended acrimoniously. The project will feed a reservoir for a hydroelectric scheme producing 6,000 megawatts of power, equal to six nuclear-powered plants.


text:
Q: Adding a density plot to a pgfplotstable I was able to replicate the example in http://www.pgfplots.org/tikz/examples/graph-in-table/. Now, I'd like to ask two concrete questions:

*

*How can I add a vertical space between rows that applies to the plots as well?

*Is there a way to plot a normal density on top of each interval?

Thanks!

A: You can change \arraystretch. To synchronize the plot, you need to redefine y to become y=-\arraystretch\baselineskip. I also added some Gaussian curves centered around the means and with the widths being controlled by the errors.
\documentclass[border=10pt]{standalone}
%%%<
\usepackage{verbatim}
\usepackage{filecontents}
\begin{filecontents}[overwrite]{data.txt}
name z p mean lci uci
Afear -0.96  0.33 -0.42 -1.28 0.44
Anofear 0.09 0.93 0.04 -0.85 0.94
B+2 0.29 0.78 0.10 -0.59 0.79
B+1   0.84  0.40  0.30 -0.40 1.00
B1:1   2.19  0.03  0.80 0.08 1.52
B-1   1.02  0.31  0.37 -0.33 1.07
B-2   -0.10  0.92  -0.03 -0.72 0.65
C+2   -1.11  0.27  -0.30 -0.83 0.23
C+1   1.15   0.25  0.32 -0.22 0.86
C1:1   -1.34  0.18  -0.38 -0.93 0.17
C-1   0.43  0.67  0.12 -0.42 0.66
C-2   -0.37  0.71  -0.10 -0.63 0.43
D+2   0.41  0.68  0.12 -0.44 0.67
D+1   -0.69  0.49  -0.20 -0.77 0.37
D1:1   -1.33  0.18  -0.39 -0.97 0.19
D-1   -1.21  0.23  -0.35 -0.92 0.22
D-2   0.32  0.75  0.09 -0.46 0.65
\end{filecontents}
%%%>
\usepackage{pgfplots}
\pgfplotsset{compat=1.17}
\usepackage{pgfplotstable}
\usepackage{booktabs}
\usepackage{multirow}
\begin{comment}
:Title: Graph within a table
:Tags: 2D;PGFPlotstable;Styles
:Author: Jake
:Slug: graph-in-table

We would like to plot a graph within a table column, similar to
http://texample.net/tikz/examples/weather-stations-data/ .

We will use the booktabs package for good table design,
and the multirow package.

The data is read using PGFPlotstable and the plot is typeset dynamically.

This code was written by Jake on TeX.SE.
\end{comment}

% Read data file, create new column ``upper CI boundary - mean''
\pgfplotstableread{data.txt}\data
\pgfplotstableset{create on use/error/.style={
    create col/expr={\thisrow{uci}-\thisrow{mean}
    }
  }
}

% Define the command for the plot
\newcommand{\errplot}{%
  \begin{tikzpicture}[trim axis left,trim axis right]
    \begin{axis}[y=-\arraystretch\baselineskip,% <- added \arraystretch
        scale only axis,
        width             = 6.5cm,
        enlarge y limits  = {abs=0.01},%<-
        axis y line*      = middle,
        y axis line style = dashed,
        ytick             = \empty,
        axis x line*      = bottom
      ]
      % ``mean'' must be present in the datafile,
      %``error'' is the newly generated column
      \addplot+[only marks][error bars/.cd,x dir=both, x explicit]
        table [x=mean,y expr=\coordindex,x error=error]{\data};
      \pgfplotsforeachungrouped\irow in {0,...,\the\numexpr\numberofrows-1}
      {\pgfplotstablegetelem{\irow}{error}\of\data
      \let\myerror\pgfplotsretval
      \pgfplotstablegetelem{\irow}{mean}\of\data
      \let\mymean\pgfplotsretval
      \addplot[forget plot,domain=-1.5:1.5]
      {0.5+\irow-exp(-(x-\mymean)*(x-\mymean)/(2*\myerror*\myerror))};
      }
    \end{axis}
  \end{tikzpicture}%
}

\begin{document}
% Get number of rows in datafile
\pgfplotstablegetrowsof{\data}
\let\numberofrows=\pgfplotsretval

% Print the table
\renewcommand\arraystretch{1.4}
\pgfplotstabletypeset[columns={name,error,z,p,mean,ci},
  % Booktabs rules
  every head row/.style = {before row=\toprule, after row=\midrule},
  every last row/.style = {after row=[3ex]\bottomrule},
  % Set header name
  columns/name/.style = {string type, column name=Name},
  % Use the ``error'' column to call the \errplot command in a multirow cell
  % in the first row, keep empty for all other rows
  columns/error/.style = {
    column name = {},
    assign cell content/.code = {% use \multirow for Z column:
    \ifnum\pgfplotstablerow=0
    \pgfkeyssetvalue{/pgfplots/table/@cell content}
    {\multirow{\numberofrows}{6.5cm}{\errplot}}%
    \else
    \pgfkeyssetvalue{/pgfplots/table/@cell content}{}%
    \fi
    }
  },
  % Format numbers and titles
  columns/mean/.style = {column name = Mean, fixed ,fixed zerofill, dec sep align},
  columns/z/.style    = {column name = $z$, fixed, fixed zerofill, dec sep align},
  columns/p/.style    = {column name = $p$, fixed, fixed zerofill, dec sep align},
  columns/ci/.style   = {string type, column name = 95\% CI},
  % Create the ``(x to y)'' format, use \pgfmathprintnumber with `showpos`
  % to make things align nicely
  create on use/ci/.style={
    create col/assign/.code={\edef\value{(
      \noexpand\pgfmathprintnumber[showpos,fixed,fixed zerofill]{\thisrow{lci}}
      to \noexpand\pgfmathprintnumber[showpos,fixed,fixed zerofill]{\thisrow{uci}})}
      \pgfkeyslet{/pgfplots/table/create col/next content}\value
    }
  }
]{\data}
\end{document}

redpajama_set_name: RedPajamaStackExchange


text:
Why work might have to be halted on major £25m M1 junction upgrade
Work has started but it could have to be put on pause
Dan MartinPolitics Reporter
The upgrade of junction 23 May be suspended
A £25 million upgrade of an M1 junction in Leicestershire could be halted because of a shortage of cash.
Leicestershire County Council has started work on a major project to improve Junction 23 of the motorway, near Loughborough, and the A512 but says it has not yet secured a deal to get the £8 million it was expecting to get from housing developers.
Site clearance and tree felling is already well underway near the junction but County Hall highways officers have warned they now may have to suspend the work until at least the autumn.
The scheme is intended to cut existing congestion around the junction and increase its capacity to deal with the nearby development of 3,000 homes between Loughborough and Shepshed as well the growth of the Loughborough University Science and Enterprise Park (LUSEP).
Some £12 million has been secured from the Leicester and Leicestershire Enterprise Partnership (LLEP) and £5 million from Highways England.
The council says it was prepared to put up the remaining millions but has not yet nailed down an agreement with the developers to recover it.
While it says it thinks it will get that deal by the end of this month it is also preparing to put the scheme on hold because if it continues it reduces the chances of ever getting the cash back.
Officials are looking at fall-back positions if the developer cash cannot be secured.
Traffic and travel - what you need to know
'Race track' A6
Latest traffic and travel news
What to do if you hit a pothole
Join our traffic and travel group
These include removing elements of the scheme that benefit the developers, leaving it to the developers to deliver those elements on a piecemeal basis or finding other sources of funding.
Labour North West Loughborough county councillor Max Hunt said: "We had been led to believe the agreement for the developer contributions for this scheme had been nailed down last a year ago but now we hear officers suggesting a major and much needed project, that is well underway, could be suspended.
"It's worrying that we have launched into a scheme knowing what it will cost but also knowing we don't have all the money for it."
What County Hall says
County council cabinet member for environment and transport Blake Pain said: "We're confident that the agreements will be in place by the end of the month as we continue to have discussions with the developers.
"We will be progressing with the preparatory works previously announced to keep the programme on track while these talks continue.
"To minimise disruption, we're trying to deliver this work in one go and that does involve working to different time scales and with different people looking to get the most benefit out of their funding."
What's happening in politics?
Latest politics news
City council news
County council news
Latest from Sir Peter Soulsby
He added: "This does present different challenges from, previously, when the county council would receive all necessary funds from one source, however, all parties believe this can be resolved.
"The scheme is vital to the future of the area's economy as it supports sustainable housing and employment to the west of Loughborough and in Shepshed as well as improving journey times and accessibility and safety, particularly linked to vehicles queueing from the M1.
In the NewsBicycle fitted with petrol engine and fuel tank 'tied on with string'
A scooter also had a petrol engine fitted to it

redpajama_set_name: RedPajamaCommonCrawl


text:
Clifford Thompson
Faculty, Postgraduate Writers' Conference
Clifford Thompson is the author, most recently, of What It Is: Race, Family and One Thinking Black Man's Blues, just out from Other Press and selected by Time magazine as "One of the Most Anticipated Books" of the fall, 2019 season. He received a Whiting Writers' Award for nonfiction in 2013 for Love for Sale and Other Essays, published by Autumn House Press, which has also published his memoir, Twin of Blackness (2015).
Clifford's personal essays, poems, and writings on books, film, jazz, and American identity have appeared in publications including The Washington Post, The Wall Street Journal, The Village Voice, The Times Literary Supplement, The Threepenny Review, The Georgia Review, Cineaste, The Los Angeles Review of Books, and Best American Essays 2018. He is also the author of a novel, Signifying Nothing.
For over a dozen years he served as the editor of Current Biography, and he has taught creative nonfiction writing at the Bennington Writing Seminars, New York University, Columbia University, Queens College, and Sarah Lawrence College. He lives in Brooklyn. cliffordthompson.info »


"The contemplation of Christ's face cannot stop at the image of the Crucified One. He is the Risen One!"(29) The Rosary has always expressed this knowledge born of faith and invited the believer to pass beyond the darkness of the Passion in order to gaze upon Christ's glory in the Resurrection and Ascension. Contemplating the Risen One, Christians rediscover the reasons for their own faith (cf. 1Cor 15:14) and relive the joy not only of those to whom Christ appeared – the Apostles, Mary Magdalene and the disciples on the road to Emmaus – but also the joy of Mary, who must have had an equally intense experience of the new life of her glorified Son. In the Ascension, Christ was raised in glory to the right hand of the Father, while Mary herself would be raised to that same glory in the Assumption, enjoying beforehand, by a unique privilege, the destiny reserved for all the just at the resurrection of the dead. Crowned in glory – as she appears in the last glorious mystery – Mary shines forth as Queen of the Angels and Saints, the anticipation and the supreme realization of the eschatological state of the Church.


text:
Conferences CVPR
Diversified Texture Synthesis with Feed-forward Networks
Yijun Li1, Chen Fang2, Jimei Yang2, Zhaowen Wang2, Xin Lu2, and Ming-Hsuan Yang1
1University of California, Merced 2Adobe Research
Recent progresses on deep discriminative and generative modeling have shown promising results on texture synthesis. However, existing feed-forward based methods trade off generality for efficiency, which suffer from many issues, such as shortage of generality (i.e., build one network per texture), lack of diversity (i.e., always produce visually identical output) and suboptimality (i.e., generate less satisfying visual effects). In this work, we focus on solving these issues for improved texture synthesis. We propose a deep generative feed-forward network which enables efficient synthesis of multiple textures within one single network and meaningful interpolation between them. Meanwhile, a suite of important techniques are introduced to achieve better convergence and diversity. With extensive experiments, we demonstrate the effectiveness of the proposed model and techniques for synthesizing a large number of textures and show its applications with the stylization.
Figure 1: Architecture of the proposed multi-texture synthesis network. It consists of a generator and a selector network.
Impressive neural style transfer results by Gatys et al. [14] have recently regained great interests from computer vision, graphics and machine learning communities for the classic problem of texture synthesis [10, 9, 35] . Considering the expensive optimization process in [14] , a few attempts have been made to develop feed-forward networks to efficiently synthesize a texture image or a stylized image [19, 32] . However, these methods often suffer from many issues, including shortage of generality (i.e., build one network per texture), lack of diversity (i.e., always produce visually identical output) and suboptimality (i.e., generate less satisfying visual effects).
In this paper, we propose a deep generative network for synthesizing diverse outputs of multiple textures in a single network. Our network architecture, inspired by [28, 7] , takes a noise vector and a selection unit as input to generate texture images using up-convolutions. The selection unit is a one-hot vector where each bit represents a texture type and provides users with a control signal to switch between different types of textures to synthesize. More importantly, such a multi-texture synthesis network facilitates generating new textures by interpolating with the selection units. Meanwhile, the noise vector is intended to drive the network to generate diverse samples even from a single exemplar texture image.
However, learning such a network is a challenging task. First, different types of textures have quite different statistical characteristics, which are partially reflected by the varied magnitudes of texture losses (i.e., the Gram matrix-based losses introduced in [13, 14] to measure style similarity) across different feature layers. Second, the convergence rates for fitting different textures are inherently different due to their drastic visual difference and semantic gaps. As a result, the overall difficulty of learning such a network is determined by the variation among the textures and the complexity of individual textures. Third, the network often encounters the "explain-away" effect that the noise vector is marginalized out and thus fails to influence the network output. Specifically, the network is not able to generate diverse samples of a given texture, and it often means overfitting to a particular instance.
In this work, we propose a suite of effective techniques to help the network generate diverse outputs of higher quality. We first improve the Gram matrix loss by subtracting the feature mean, so that the newly designed loss is more stable in scale and adds stability to the learning. Second, in order to empower the network with the ability of generating diverse samples and further prevent overfitting, we aim to correlate the output samples with the input noise vector. Specifically, we introduce a diversity loss that penalizes the feature similarities of different samples in a mini-batch. Third, we show that a suitable training strategy is critical for the network to converge better and faster. Thus we devise an incremental learning algorithm that expose new training textures sequentially to the learner network. We start from learning to synthesize one texture and only incorporate next new unseen texture into the learning when the previous texture can be well generated. As such, the network gradually learns to synthesize new textures while retaining the ability to generate all previously seen textures.
The contributions of this work are threefold:
We propose a generative network to synthesize multiple textures in a user-controllable manner.
A diversity loss is introduced to prevent the network from being trapped in a degraded solution and more importantly it allows the network to generate diverse texture samples.
The incremental learning algorithm is demonstrated to be effective at training the network to synthesize results of better quality.
2 Related Work
Traditional synthesis models.
Texture synthesis methods are broadly categorized as non-parametric or parametric. Parametric methods [17, 27] for texture synthesis aim to represent textures through proper statistical models, with the assumption that two images can be visually similar when certain image statistics match well [20] . The synthesis procedure starts from a random noise image and gradually coerces it to have the same relevant statistics as the given example. The statistical measurement is either based on marginal filter response histograms [3, 17] at different scales or more complicated joint responses [27] . However, exploiting proper image statistics is challenging for parametric models especially when synthesizing structured textures.
Alternatively, non-parametric models [10, 9, 22, 34] focus on growing a new image from an initial seed and regard the given texture example as a source pool to constantly sample similar pixels or patches. This is also the basis of earlier texture transfer algorithms [9, 23, 1, 18] . Despite its simplicity, these approaches can be slow and subject to non-uniform pattern distribution. More importantly, these methods aim at growing a perfect image instead of building rich models to understand textures.
Synthesis with neural nets.
The success of deep CNNs in discriminative tasks [21, 29] has attracted much attention for image generation. Images can be reconstructed by inverting features [26, 6, 5] , synthesized by matching features, or even generated from noise [16, 28, 4] . Synthesis with neural nets is essentially a parametric approach, where intermediate network outputs provide rich and effective image statistics. Gatys et al. [13] propose that two textures are perceptually similar if their features extracted by a pre-trained CNN-based classifier share similar statistics. Based on this, a noise map is gradually optimized to a desired output that matches the texture example in the CNN feature space.
Subsequent methods [19, 32] accelerate this optimization procedure by formulating the generation as learning a feed-forward network. These methods train a feed-forward network by minimizing the differences between statistics of the ground truth and the generated image. In particular, image statistics was measured by intermediate outputs of a pre-trained network. Further improvements are made by other methods that follow either optimization based [24, 11, 12] or feed-forward based [25, 33] framework. However, these methods are limited by the unnecessary requirement of training one network per texture. Our framework also belongs to the feed-forward category but synthesizes diverse results for multiple textures in one single network.
A concurrent related method recently proposed by Dumoulin et al. [8] handles multi-style transfer in one network by specializing scaling and shifting parameters after normalization to each specific texture. Our work differs from [8] mainly in two aspects. First, we employ a different approach in representing textures. We represent textures as bits in a one-hot selection unit and as a continuous embedding vector within the network. Second, we propose diversity loss and incremental training scheme in order to achieve better convergence and output diverse results. Moreover, we demonstrate the effectiveness of our method on a much larger set of textures (e.g., 300) whereas [8] develops a network for 32 textures.
3 Proposed Algorithm
We show the network architecture of the proposed model in Figure 1. The texture synthesis network (bottom part) has two inputs, a noise vector and a selection unit, while the upper part in blue dash line boxes are modules added for extending our model to style transfer. The noise vector is randomly sampled from a uniform distribution, and the selection unit is a one-hot vector, where each bit represents a texture in the given texture set. The network consists of two streams: the generator and the selector. The generator is responsible for synthesis and the selector is guiding the generator towards the target texture, conditioned on the activated bit in the selection unit.
Given M target textures, we first map the M dimensional selection unit to a lower dimensional selection embedding. Then we compute the outer product of the noise vector and selection embedding. After the outer-product operation, we reshape the result as a bunch of 1×1 maps and then use the SpatialFullConvolution layer to convolve them to a larger spatial representation with numerous feature maps. After a series of nearest-neighbor upsampling followed by convolutional operations, this representation is converted to a 256×256×3 pixel image. On the selector stream, it starts with a spatial projection of the embedding, which is then consecutively upsampled to be a series of feature maps which are concatenated with those feature maps in the generator, in order to offer guidance (from coarse to fine) at each scale.
Finally, the output of the generator is fed into a fixed pre-trained loss network to match the correlation statistics of the target texture using the visual features extracted at different layers of the loss network. We use the 19-layer VGG [31] model as the loss network.
3.1 Loss function
We employ two loss functions, i.e., texture loss and diversity loss. The texture loss is computed between the synthesized result and the given texture to ensure that these two images share similar statistics and are perceptually similar. The diversity loss is computed between outputs of the same texture (i.e., same input at selection unit) driven by different input noise vectors. The goal is to prevent the generator network from being trapped to a single degraded solution and to encourage the model to generate diversified results with large variations.
Figure 2: Comparisons between using G and mean subtracted ¯¯¯¯G. We show results of two 3-texture networks (left and right). Top: original textures, Middle: synthesized results using G based texture loss, Bottom: synthesized results using ¯¯¯¯G based texture loss.
Texture loss.
Similar to existing methods [13, 19, 32] , the texture loss is based on the Gram matrix (G) difference of the feature maps in different layers of the loss network as
Ltexture=∥Ggt−Goutput∥1 , Gij=∑kFikFjk , (1)
where each entry Gij in the Gram matrix is defined as the inner product of Fik and Fjk, which are vectorized activations of the ith (and jth) filter at position k in the current layer of the loss network. We use the activations at the conv1_1, conv2_1, conv3_1, conv4_1 and conv5_1 layer of the VGG model.
The Gram matrix based texture loss has been shown demonstrated to effective for single texture synthesis. However, for the purpose of multiple textures synthesis, we empirically find that the original texture loss (defined as Eq. 1) poses difficulty for the network to distinguish between textures and thus fails to synthesize them well. In the middle row of Figure 2, we show a few examples of textures generated using the original texture loss in two experiments of synthesizing 3 textures with one network. Note the obvious artifacts and color mixing problems in the synthesized results. We attribute this problem to the large discrepancy in scale of the Gram matrices of different textures.
Motivated by this observation, we modify the original Gram matrix computation by subtracting the mean before calculating the inner product between two activations:
¯¯¯¯Gij=∑k(Fik−¯¯¯¯F)(Fjk−¯¯¯¯F) , (2)
where ¯¯¯¯F is defined as the mean of all activations in the current layer of the loss network and the rest of terms remain the same with those in the definition of the Gram matrix (1). Without re-centering the activations, we notice that during training the values of losses and gradients from different textures vary drastically, which suggests that the network is biased to learn the scale of Gram matrix, i.e., ¯¯¯¯F, instead of discriminating between them. In the bottom row of Figure 2, we provide the same textures synthesized with the re-centered Gram matrix, which clearly shows improvements compared to the middle row.
Figure 3: Comparisons between without and with the diversity loss. Left: original textures, Middle: outputs (w/o diversity) under there different noise inputs, Right: outputs (w/ diversity) under the same set of different noise inputs.
Diversity loss.
As mentioned above, one of the issues with existing feed-forward methods is being easily trapped to a degraded solution where it always outputs that are visually identical (sometimes with less satisfying repetitive patterns) [30] . When trained only with the texture loss, the proposed network has the same issue. We show several examples in the middle panel of Figure 3. The results under different noise input are nearly identical with subtle and unnoticeable difference in pixel values. This is expected because the texture loss is designed to ensure all synthesized results to have the similar style with the given texture, but does not enforce diversity among outputs. In other words, each synthesized result is not correlated with the input noise.
In order to correlate the output with input noise, we design a diversity loss which explicitly measures the variation in visual appearance between the generated results under the same texture but different input noise. Assume that there are N input samples in a batch at each feed-forward pass, the generator will then emit N outputs {P1,P2,...,PN}. Our diversity loss measures the visual difference between any pair of outputs Pi and Pj using visual features. Let {Q1,Q2,...,QN} be a random reordering of {P1,P2,...,PN}, satisfying that Pi≠Qi. In order to encourage the diversity in a higher level rather than lower level such as pixel shift, the diversity loss is computed between feature maps at the conv4_2 layer of the loss network Φ as follows:
Ldiversity=1NN∑i=1∥Φ(Pi)−Φ(Qi)∥1, (3)
The results generated by our method with this diversity loss are shown in the right panel of Figure 3. While being perceptually similar, the results from our method contain rich variations. Similar observations are found in [30] which also encourages diversity in generative model training by enlarging the distance among all samples within a batch on intermediate layers features, while our method achieves this with the diversity loss.
Figure 4: Incremental training strategy. Each block represents an iteration and the number in it is the sampled texture id for this iteration (also the bit we set as 1 in the selection unit). We use K=1000 in the experiments.
Figure 5: Comparisons of the final texture loss when converged between the random and incremental training on 60-texture synthesis.
The final loss function of our model is a combination of the texture loss and the diversity loss as shown in (4). As the goal is to minimize the texture loss and maximize the diversity loss, we use the coefficients α=1, β=−1 in our experiments.
L=αLtexture+βLdiversity, (4)
3.2 Incremental training
We discuss the training process for the proposed network with focus on how to sample a target texture among a set of predefined texture set. More specifically, we address the issue whether samples should randomly selected or in certain order in order to generate diversified textures. Once a target texture is selected, we set the corresponding bit in the selection unit as 1 and the corresponding texture is used to compute the texture loss.
Figure 6: Comparisons between random training and incremental training. Top: original textures, Middle: synthesis with random training, Bottom: synthesis with incremental training. The model is handling 60 textures in Figure 5 and we show the synthesized results of 10 textures here.
Empirically we find that the random sampling strategy typically yields inferior results and it becomes difficult to further push down texture losses for all texture images after certain number of iterations. We train a 60-texture network as an example and show the converged results (10 out of 60) with random sampling in the middle row of Figure 6. The artifacts are clearly visible. Major patterns of each texture are captured, however the geometry is not well preserved (e.g., hexagons in the first texture), and colors are not well matched (i.e., mixing with colors from other textures).
We attribute this issue to the constant drastic change in the learning objective caused by random sampling within a diverse set of target textures. In other words, although the network gains improvement toward a sampled texture at every iteration, the improvement is likely to be overwhelmed in the following iterations, where different textures are optimized. As a consequence, the learning becomes less effective and eventually gets stuck to a bad local optimum.
Therefore we propose an incremental training strategy to help the learning to be more effective. Overall our incremental training strategy can be seen as a form of curriculum learning. There are two aspects in training the proposed network incrementally. First, we do not teach the network to learn new tasks before existing the network learns existing ones well. That is, we start from learning one texture and gradually introduce new textures when the network can synthesize previous textures well. Second, we ensure that the network does not forget what is already learned. Namely, we make sure that all the target textures fed to the network so far will still be sampled in future iterations, so that the network "remembers" how to synthesize them.
Specifically, in the first K iterations, we keep setting the 1st bit of the selection unit as 1 to let the network fully focus on synthesizing Texture 1. In the next K iterations, Texture 2 is involved and we sample the bit from 1 to 2 in turn. We repeat the same process to the other textures. We illustrate this procedure in Figure 4. After all the textures are introduced to the network, we switch to the random sampling strategy until the training process converges. In Figure 5 and 6, we show the comparison of both the final texture loss and synthesized visual results between the random and incremental training strategies in the 60-texture network experiment. Clearly the incremental training scheme leads to better convergence quantitatively and qualitatively.
(a) Texture 20 (b) Texture 30
(c) Texture 40 (d) Texture 50
Figure 7: Comparisons between the random and incremental training on a single texture during a 60-texture network training. Note that some sudden drastic changes on the loss curve appear when a new texture is firstly involved which causes a short-term oscillation in the network.
Interestingly, we observe that the network learns new textures faster as it sees more textures in later training stages. To demonstrate that, we record the texture losses for each texture when it is sampled and show two examples in Figure 7 when training the 60-texture network. Take Texture 20 (Figure 7(a)) as an example, the network learned with incremental training quickly reaches lower losses compare to the one with random sampling strategy. We hypothesize that the network benefits from the shared knowledge learned from Texture 1-19. This conjecture is supported by later introduced textures (Figure 7(b-d)) where incremental training gets relatively faster at convergence as it learns more textures.
Figure 8: Synthesized results of a 300-texture network. In each panel, Left: original texture, Right: synthesized result. We show results of 20 (out of 300) textures as examples here. For each texture, we only show one synthesized result.
4 Experimental Results
In this section, we present extensive experimental results to demonstrate the effectiveness of our algorithm. We experiment with synthesizing a large number of textures using a single network and then show that our model is able to generate diverse outputs and create new textures by linear interpolation.
4.1 Multi-texture synthesis
In addition to the 60-texture network trained for illustration purpose in Section 3, we experiment with a larger 300-texture network to further validate the robustness and scalability of our model. We map the 300-dimensional selection unit to a 128-dimensional embedding and use a 5-dimensional noise vector. The network is trained with both texture and diversity loss under the incremental training strategy. Texture images used in our experiments are from the Describable Textures Dataset (DTD) [2] . Figure 8 shows the synthesized results of 20 textures.
Figure 9: Comparisons of diverse synthesized results between the TextureNet [32] (middle) and our model (right).
Figure 10: Texture interpolation (or transition) with the 300-texture network. Top: Texture 20 to Texture 19, Bottom: Texture 19 to Texture 12. Images in the leftmost and rightmost are original textures.
4.2 Diversity
By sampling different noise in the noise vector, our network can generate diverse synthesized results for each texture. Existing single-texture networks [32] can also generate diversity to a certain extent. However, the diversity is still limited because their network is trained with the texture loss only. The diversity in [32] is mainly enforced by injecting multiple noise maps at different scales (from 8×8 to 256×256). Without explicit constraints to push diversity, such a huge variation will be reduced or absorbed by the network, which still leads to limited diversity in outputs. We compare the diverse outputs of our model and [32] in Figure 9. Note that the common diagonal layout is shared across different results of [32] , which causes unsatisfying visual experience. In contrast, our method achieves diversity in a more natural and flexible manner. With the diversity loss, our model enables diverse outputs with low dimensional noise input, which gives us the ability to generate continuous transition between those outputs.
4.3 Interpolation
Equipped with a selection unit and a learned M-texture network, we can interpolate between bits at test time to create new textures or generate smooth transitions between textures. We show two examples of interpolation with our previously trained 300-texture network in Figure 10. For example in the top row of Figure 10, we start from Texture 20 and drive the synthesis towards Texture 19. This is carried out by gradually decreasing the weight in the 20th bit and increasing the weight in the 19th bit with the rest bits all set as zero. Such a smooth transition indicates that our generation can go along a continuous space.
The method in [14] is also able to synthesize the interpolated result of two textures. In [14] , if we denote G1 and G2 as the Gram matrix of two textures, the interpolated texture is generated by matching some intermediate Gram matrix a×G1+(1−a)×G2 through optimization (e.g., a=0.5). We show the interpolation comparison between [14] and our method in Figure 11. It is observed that the results by [14] are simply overlaid by two textures while our method generates new textural effects.
Texture I Texture II [14] Ours
Figure 11: Interpolation comparison between [14] and our method.
4.4 Extension to multi-style transfer
We extend the idea of multi-texture synthesis to the multi-style transfer for image stylization. Given a style image and a content image, the style transfer aims at synthesizing an image that preserves the global content while transferring the colors and local structures from the style image. For presentation clarity, we will use the term style instead of the texture.
The network architecture is shown in Figure 12. We use an autoencoder network similar to [19] and incorporate our idea of introducing a selection unit to handle the transferring of different styles. More specifically, for each bit in the selection unit, we generate a corresponding noise map (e.g., from the uniform distribution) and concatenate these maps with the encoded features from the content, which are then decoded to the transferred result. When one style is selected, only the noise map that corresponds to it is randomly initialized while other noise maps are set to zero. The content loss is computed as the feature differences between the transferred result and the content at the conv4_2 layer of the VGG model as in [14] . The style loss and diversity loss are defined in the same way as those in texture synthesis. We train a 16-style transfer network and show the transferred results in Figure 13. We also perform the luminance-only transfer as in [12, 15] and show the color-independent transferred results in the bottom row of Figure 13. More results can be found in Figure 18-19.
In addition, we compare our multi-style transfer model with existing methods in Figure 15. We adjust the style weight such that all methods have similar transferring effects. It clearly shows that our multi-style transfer model achieves improved or comparable results.
Figure 12: Architecture of the multi-style transfer network.
With the selection unit, we interpolate between styles by adjusting the weights of different bits in the selection unit and generate the style interpolation (or transition) results in Figure 14. Specifically, if we denote s1 and s2 as the bit value of two styles and N1 and N2 as the corresponding noise map, the interpolation is generated by feeding s1×N1+s2×N2 as the selection input.
Diverse transfer results are shown in Figure 16. Different from the case of texture synthesis, the global structure of images is constrained by the demand of preserving content. Therefore the diversity is exhibited at local visual structures. Notice the slight but meaningful differences among these outputs.
Figure 13: Transferred results on test content images of a 16-style network. We show results of 8 (out of 16) styles as examples. Top: style images, Leftmost: content image, Middle: transferred results. Bottom: color-independent transferred results.
Figure 14: Style interpolation (or transition) with the 16-style network. Images in the leftmost and rightmost are original styles. The content images used in the middle are from Figure 13 and Figure 16.
Content [14] [32] [19] Ours Style
Figure 15: Comparison of style transfer results between existing methods and ours.
Selector network.
In our model, we introduce a selector network (Figure 1) in order to drive the network towards synthesizing the desired texture only. The selector injects guidance to the generator at every upsampling scale and helps the model distinguish different textures better during the synthesis. We show an example of training a 60-texture network w/o and w/ the selector network and compare these two settings in Figure 17. We present the loss curves of two textures as examples. Figure 17 clearly shows that with the selector, the network training achieves better convergence and thus generates better synthesized results.
Figure 16: Diverse transferred results of our 16-style transfer network. Left: content images, Middle: diverse transferred results, Right: style images. Note the difference in the beak and sky.
Embedding.
Starting with a one-hot selection unit to represent each texture with one bit, we first map it to a lower dimensional embedding via a linear projection and aim at learning a better representation of given textures. In our presented 60-texture and 300-texture model, we map the 60-D and 300-D selection unit to a 32-D and 128-D embedding respectively. Our synthesized results show that the embedding can still distinguish different textures for synthesis, which indicates that the original hand-crafted one hot representation is redundant. In addition, as we have shown that new textures can be created through interpolation in a feed-forward way, it poses an open question that whether we can find the coefficients in a backward way to represent a given new texture as a weighted combination of learned embeddings. We leave this as a future direction to pursue.
Figure 17: Comparisons of the loss curve between our framework without and with the selector network.
In this paper, we focus on synthesizing multiple textures in one single network. Given M textures, we propose a deep generative feed-forward network which can synthesize diverse results for each texture. In order to train a deep network for multi-texture synthesis, we introduce the diversity loss and propose an incremental leaning scheme. The diversity loss helps the network to synthesize diverse textures with the same input, and the incremental learning scheme helps effective and efficient training process. Experimental results demonstrate the effectiveness of model, which generates comparable results compared to existing single-texture networks but greatly reduces the model size. We also show the extension of our multi-texture synthesis model to multi-style transfer for image stylization.
[1] N. Ashikhmin. Fast texture transfer. IEEE Computer Graphics and Applications, 23(4):38–43, 2003.
[2] M. Cimpoi, S. Maji, I. Kokkinos, S. Mohamed, , and A. Vedaldi. Describing textures in the wild. In CVPR, 2014.
[3] J. S. De Bonet. Multiresolution sampling procedure for analysis and synthesis of texture images. In SIGGRAPH, 1997.
[4] E. L. Denton, S. Chintala, R. Fergus, et al. Deep generative image models using a Laplacian pyramid of adversarial networks. In NIPS, 2015.
[5] A. Dosovitskiy and T. Brox. Generating images with perceptual similarity metrics based on deep networks. In NIPS, 2016.
[6] A. Dosovitskiy and T. Brox. Inverting visual representations with convolutional networks. In CVPR, 2016.
[7] A. Dosovitskiy, J. Tobias Springenberg, and T. Brox. Learning to generate chairs with convolutional neural networks. In CVPR, 2015.
[8] V. Dumoulin, J. Shlens, and M. Kudlur. A learned representation for artistic style. arXiv preprint arXiv:1610.07629, 2016.
[9] A. A. Efros and W. T. Freeman. Image quilting for texture synthesis and transfer. In SIGGRAPH, 2001.
[10] A. A. Efros and T. K. Leung. Texture synthesis by non-parametric sampling. In ICCV, 1999.
[11] O. Frigo, N. Sabater, J. Delon, and P. Hellier. Split and match: Example-based adaptive patch sampling for unsupervised style transfer. In CVPR, 2016.
[12] L. A. Gatys, M. Bethge, A. Hertzmann, and E. Shechtman. Preserving color in neural artistic style transfer. arXiv preprint arXiv:1606.05897, 2016.
[13] L. A. Gatys, A. S. Ecker, and M. Bethge. Texture synthesis using convolutional neural networks. In NIPS, 2015.
[14] L. A. Gatys, A. S. Ecker, and M. Bethge. Image style transfer using convolutional neural networks. In CVPR, 2016.
[15] L. A. Gatys, A. S. Ecker, M. Bethge, A. Hertzmann, and E. Shechtman. Controlling perceptual factors in neural style transfer. arXiv preprint arXiv:1611.07865, 2016.
[16] I. Goodfellow, J. Pouget-Abadie, M. Mirza, B. Xu, D. Warde-Farley, S. Ozair, A. Courville, and Y. Bengio. Generative adversarial nets. In NIPS, 2014.
[17] D. J. Heeger and J. R. Bergen. Pyramid-based texture analysis/synthesis. In SIGGRAPH, 1995.
[18] A. Hertzmann, C. E. Jacobs, N. Oliver, B. Curless, and D. H. Salesin. Image analogies. In SIGGRAPH, 2001.
[19] J. Johnson, A. Alahi, and L. Fei-Fei. Perceptual losses for real-time style transfer and super-resolution. In ECCV, 2016.
[20] B. Julesz. Visual pattern discrimination. IRE transactions on Information Theory, 8(2):84–92, 1962.
[21] A. Krizhevsky, I. Sutskever, and G. E. Hinton. Imagenet classification with deep convolutional neural networks. In NIPS, 2012.
[22] V. Kwatra, A. Schödl, I. Essa, G. Turk, and A. Bobick. Graphcut textures: image and video synthesis using graph cuts. In SIGGRAPH, 2003.
[23] H. Lee, S. Seo, S. Ryoo, and K. Yoon. Directional texture transfer. In NPAR, 2010.
[24] C. Li and M. Wand. Combining markov random fields and convolutional neural networks for image synthesis. In CVPR, 2016.
[25] C. Li and M. Wand. Precomputed real-time texture synthesis with markovian generative adversarial networks. In ECCV, 2016.
[26] A. Mahendran and A. Vedaldi. Understanding deep image representations by inverting them. In CVPR, 2015.
[27] J. Portilla and E. P. Simoncelli. A parametric texture model based on joint statistics of complex wavelet coefficients. IJCV, 40(1):49–70, 2000.
[28] A. Radford, L. Metz, and S. Chintala. Unsupervised representation learning with deep convolutional generative adversarial networks. In ICLR, 2016.
[29] O. Russakovsky, J. Deng, H. Su, J. Krause, S. Satheesh, S. Ma, Z. Huang, A. Karpathy, A. Khosla, M. Bernstein, et al. Imagenet large scale visual recognition challenge. IJCV, 115(3):211–252, 2015.
[30] T. Salimans, I. Goodfellow, W. Zaremba, V. Cheung, A. Radford, and X. Chen. Improved techniques for training GANs. In NIPS, 2016.
[31] K. Simonyan and A. Zisserman. Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556, 2014.
[32] D. Ulyanov, V. Lebedev, A. Vedaldi, and V. Lempitsky. Texture networks: Feed-forward synthesis of textures and stylized images. In ICML, 2016.
[33] D. Ulyanov, A. Vedaldi, and V. Lempitsky. Instance normalization: The missing ingredient for fast stylization. arXiv preprint arXiv:1607.08022, 2016.
[34] L.-Y. Wei and M. Levoy. Fast texture synthesis using tree-structured vector quantization. In SIGGRAPH, 2000.
[35] L.-Y. Wei and M. Levoy. Texture synthesis over arbitrary manifold surfaces. In SIGGRAPH, 2001.
Style 1 Style 2 Style 3 Style 4 Style 5
Style 6 Style 7 Style 8 Style 9 Style 10
Style 11 Style 12 Style 13 Style 14 Style 15 Style 16
Content 1 Content 2 Content 3
Figure 18: 16 styles used for our 16-style transfer model training and 3 test content images (bottom). All of them are resized to 512 but the aspect ratio is kept. Note that as our multi-transfer model is fully convolutional, it is able to handle the content image of arbitrary sizes.
Figure 19: Transferred results of Style 1-16.


Have you read the Book of Ruth and thought, "What a marvelous love story! Ruth was so kind, and Boaz so lucky, and poor pitiful old Naomi." Would it surprise you to discover that hidden deep inside the Book of Ruth is the timeline for all mankind – with each of us a player in this MASTERpiece performance? Ruth, the "allegory," has slept concealed through three millennia, only to be awakened in these last days. She brings with her God's end-time agenda: Jewish-Christian relations. Ruth 3,000 Years of Sleeping Prophecy Awakened will thrill you as you step back into history and astound you as you step forward into the future.
Besides the twelve chapters that will take you on a verse by verse journey through the book of Ruth, there is bonus material on Jewish customs, traditions and feasts that shed additional light on Ruth. For those considering teaching this material, please note that the companion workbook available for purchase, as well as free downloads of my own personal teaching notes. My sincere desire is that you be fully equipped for your best study ever.
Also know that I would be honored to share this material in condensed or expanded sessions in any venue God provides to meet whatever time is allotted. If this is of interest, please feel free to contact me. I welcome you and ask the Lord to bless you as you seek to know Him more intimately.


text:
Home/World News/South Korea urges EU to team up against US — RT Business News
South Korea urges EU to team up against US — RT Business News
newsnetdailyDecember 10, 2022
Seoul trade minister says Washington's actions will have discriminatory impact on South Korean and European producers
Seoul and Brussels should coordinate their responses to a large-scale subsidy package adopted by the United States, South Korean Commerce Minister Dukgeun Ahn said in an interview with EURACTIV on Friday.
The Inflation Reduction Act (IRA), a $430 billion climate protection and social investment plan passed by the US Congress in August, is designed to fight inflation and reduce energy prices, as well as to combat climate change.
However, it also includes massive subsidies for US-produced electric cars and batteries, raising serious concerns that European and South Korean manufacturers will be significantly disadvantaged.
Ahn said South Korea and the European Union should work together to develop a response to the IRA, which was passed by Congress in August. The EU says the law violates competition rules because it offers incentives to buyers of US electric vehicles.
"We have huge leeway for the EU and Korea to work together and do the [IRA] system more compatible with the WTO, so as not to cause unnecessary problems for strategically important parts of our industry", Ahn said.
However, he expressed doubts about the possibility of changing the law in the short term.
Ahn suggested that the EU and South Korea could work with the US administration "to minimize the discriminatory impact of the IRA", which offers generous tax breaks to American companies that invest in clean energy and significant subsidies for household electric vehicles, batteries and renewable energy projects.
"If it becomes apparent that there is nothing they can do or show no willingness to work with us to find an appropriate solution, we will have to find another alternative," he said.
European Commission President Ursula von der Leyen had previously called for "action" and increased state aid for her own businesses in response to the US measures. Bernd Lange, head of the European Parliament's trade committee, said the EU should file a complaint with the World Trade Organization (WTO) against the law.
For more stories on economics and finance, visit RT's business section
NJPW Wrestle Kingdom 17: Live Scores, Omega Sephiroth, Sasha Banks Arrives & More
Jeremy Renner's snow plow injury was 'a tragic accident,' sheriff says
Myanmar junta to release more than 7,000 prisoners on Independence Day: report


In this quick blog post - we talk about how we can tweak the "Modified By" user field and "Modified" datetime field in SharePoint list and document libraries using Microsoft Flow.
This technique follows the very detailed article by MVP Andrew Koltyakov on LinkedIn - he describes how to call it with C#, JSOM and REST. He also has detailed examples of every possible field and what format you'll need to run this with.
Also, before you read any further - please repeat the title of the blog post loudly, really fast 3 times.
Requirements - why do we want this?
You have updated a list item - you don't really want the items to all say "Updated by System Account".
You've copied a bunch of files but you don't want it to all say "Modified (now)".
You want to modify the item but you don't want to create a new version.
You uploaded a file but you don't want to have two versions when you tweak the metadata.
I need this. You also need this.
I don't think we need to explain why you really really need this.
Here we have an item - modified a few seconds ago by me.
We want to update two values - the Modified By and the Modified (date time) fields.
Note the User is a JSON object that needs to be serialized by string() inside another JSON object.
The modified date is a date value, formatted to the "g" format. This format is usually MM/dd/yyyy HH:MM AA in EN locales. It is DIFFERENT if your environment has different locales. You can use "g" as date time string format to get the correct one for your locale.
Here is when it runs. Notice the Editor field is escaped JSON string.
The modified date is now Feb 18, 2018!
And the user is someone else - my minion Gandalf.
So this date is a year before right now, with the 'g' date formatting string.


The sign on link feature allows users to log in to ZelloWork on iOS and Android devices automatically. This means no more confusion over the username, password errors, or unknown network names.
The link may be sent via email or SMS text at any time. A QR code can also be scanned to automatically sign in or to get a link if you are using an alternate app to scan the code.
The link and code may be accessed during the manual user creation process or through the User Details page. More on this topic here: How do I send a SignOn Link?
For security purposes, links are valid for 48 hours from the time of creation. This setting can be modified through the ZelloWork console by going to Settings>Common>Expiration time for sign on links.
Please note that links do not affect standard login in any way, users are still able to manually enter their username and password if needed.


text:
Abrego: Farmers market moving to Malchow Plaza starting May 8
The farmers market in Aberdeen will look different this year, including having a new home, a new day and increased capacity.
Hours for the market will be from 9 a.m. to 1 p.m. Saturdays starting May 8 at the newly constructed Malchow Plaza, 506 S. Main St.
This year, there will be the opportunity for 27 booths at a time. And each 10-by-10 foot space will have access to electricity at the plaza.
In past years there have been different incarnations and organizers, but farmers markets have generally been on Thursday afternoons at local parks.
The new farmers market now has roughly 10 confirmed vendors, but that number is expected to grow. The Market on the Plaza, a business at Malchow Plaza, is organizing the farmers market and is asking vendors to register ahead of time.
"We want to have confirmed vendors, we want to know who's coming ahead of time so that we can advertise and prepare. We'll be staffed every weekend," said Market on the Plaza Operations Manager Nichole Heinz. "So if we have 27 vendors out there, we want to make sure that we have a staff inside to support those vendors."
For the first few weeks, Heinz and her team aren't sure what to expect, but they hope they will get more vendors showing up each weekend.
"If we're not full on the plaza, which I don't know that we will be right away, I think the vendors want to see kind of how it's going to go," she said. "And we'll show them the turnout was great, let's have a better weekend next weekend, and it's our first year and so we're learning as we go."
Along with the potential for 27 booths, the concrete plaza is thick enough for two food trucks to set up shop, Heinz said.
For the first few weeks, it's likely there will not be food trucks available.
"People are booked for graduation and private parties, so the certain food trucks that we have been having are just kind of already booked those first few days, which is a bummer," Heinz.
She she hopes to get the Dempsey's hot dog cart to serve food during the farmers market.
The Market on the Plaza also has alternative plans in the event that the farmers market outgrows initial capacity. That would require an event contract with the city, according to Heinz, to expand the market to Fifth Avenue Southwest.
Heiz hopes to get back to supporting local farmers and their homemade goods.
"There's so many people that do things like jams and jellies, and cheese and dairies and so forth," she said. "Those are the kinds of things that were missing."
She said she also hopes to get livestock producers to reserve booths at the farmers market.
"We have several farmers that will be bringing like veggies and herbs and things like that, we have quite a few beef vendors or meat vendors," Heinz said.
Youth entrepreneurship booths
Another change to this year's farmers market is the addition of youth entrepreneurship booths. They will be free to kids under the age of 18.
"(It's) so important because we have so many talented kids in the community 18 and under that make things, that bake things, that create things, and we really want to feature them, too, and give them the opportunity to kind of showcase their talent," Heinz said.
One space each Saturday will be designated for a vendor under the age of 18, and the vendor must grow or create all goods for sale.
The market will be held almost every Saturday through Oct. 16, which will be called Pheasant Fest since the pheasant season opens that day. There will be no farmers market July 3, Aug. 21, Sept. 4 and Oct. 2.
"We don't want to do like the average anymore, we want to make this an above-average farmers market that the community has really been asking for," Heinz said. "So we're working hard to make it what the people want."
Vendors who are interested in having a booth can contact Heinz at nichole@marketontheplaza.com or events@marketontheplaza.com. The Market on the Plaza will open today.
The Aberdeen Downtown Association announced that it has completed a sale of The Workshop to Bob and Andrea Baker of Liquid Capital. Additional office space is being added on the first floor, but the front two offices will be occupied by the Aberdeen Downtown Association.
The Workshop is at 208 S. Main St. It leases downtown office space to business professionals, entrepreneurs, creatives, freelancers and university students.
Vendors interested in the farmers market can contact The Market on the Plaza Operations Manager Nichole Heinz at nichole@themarketotheplaza.com or 605-824-4189. Venders should register in advance.


僕のところは呼び出し電話だ. The only number I can give you is that of a neighbor who lets me use his phone.
対訳 usher who calls the names of wrestlers, sweeps the ring, etc.
The detective decoyed the culprit out of his hiding-place.


This course takes you back into the principles of physics, that you just scratched the surface of on your first diving course. You will have to learn and understand the laws of Boyle-Mariotte, Henry and Dalton to be able to calculate all aspects of a dive with Nitrox, up to 40% Oxygen, to reduce risks and to prevent accidents.
The PADI EAN Diver Licence allows you to dive with Nitrox up to EAN40.
1/2 day, theory only. This course can be started any day.


Cheney Pond is probably the easiest to reach backcountry pond that is located off Boreas Road in the Vanderwhacker Mountain Wild Forest of the central Adirondacks. A rough dirt road travels to the pond from Boreas Road that can be used as a hiking or biking trail. There is a lean to located across the pond, accessible via boat.
The road is open to vehicles, however travel on the road isn't recommended unless you have a high clearance truck. The start of the road near the junction with Boreas Road looks like it is a good gravel road. Don't let this fool you as the dirt road quickly deteriorates with washouts, ruts and muddy sections. It's a really easy hike to reach the pond, so leave the vehicle up the road. The whole way to the pond is downhill with an elevation change of about 175 feet.
The start of the trail is located along Boreas Road, west of the bridge over the Boreas River on the south side of the road and east of Tahawus Road. The GPS coordinates are N43 56.441 W73 59.204 for the start of the trail for those who have a GPS devise. There is a DEC sign at the intersection of the gravel road and Boreas Road. The old DEC sign stated that it is 1.0 miles to the pond (pic below). It is only 0.5 miles to reach the pond and the DEC has replaced the signage with the correct distance. There is parking along the side of the gravel road near the intersection. This is also the starting point for the hike to Lester Dam.
Follow the dirt road for 0.4 miles. At this point you reach a junction. To the right is the trail to Lester Dam. To the left is Cheney Pond. You'll reach the end of the road in another 500 feet, where there is a campsite and a place to launch a small boat on the pond. There are other campsites located along the shore of the pond, accessible by boat including a lean to. This is a popular destination due to the ease to reach the pond, especially those willing to drive the rough road.
Look below for a few pictures and a couple of maps of this popular pond in the Adirondacks.


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GGGI Council's 2nd session launched at WFES' summit
Posted on January 18, 2013 Author ANE	Comment(0)
WAM ABU DHABI: The second session of the Dhabi, UAE, 17 January 2013: The second session of Council of the Global Green Growth Institute (GGGI) was held yesterday in Abu Dhabi on the sidelines of the sixth World Future Energy Summit hosted in Abu Dhabi from 15 – 17 January 2013.
The meeting was headed by Lars Lokke Rasmussen, the former Prime Minister of Denmark, Chair of the GGGI Council; and Dr. Sultan Ahmed Al Jaber, UAE Special Envoy for Energy '&' Climate Change , CEO of Masdar.
"Supported by the vision of our wise leadership, the UAE has always been an instrumental in adopting and supporting different creative initiatives in all fields. We are honoured to be one of the founding members and financially supporting the GGGI and joining forces to develop its plans and programmes which contributed to convert the institute into a leading international organization", Dr. Al Jaber said.
"Held for the first time outside its headquarters in Seoul, hosting the second council meeting of the GGGI in Abu Dhabi indicates our tireless efforts in this direction", Al Jaber added.
The list of participants at the meeting featured a number of officials and representatives of the member countries, including Mr. Christian Friis Bach, Denmish Minister for Development Cooperation; Dr Rene Castro-Salazar, Costa Rica's Minister for Environment, Energy and Telecommunications; H.E. Kim Sung-Hwan, Minister of Foreign Affairs and Trade of the Republic of Korea; Oystein Djupedal, Norwegian County Governor of Aust-Agder county; Sean Batten, Director of the Sustainable Development Fund in Australia; Mr. Kim Sang-hyup, Senior Secretary to the President for Green Growth, Republic of Korea, and Richard Samans, Director-General of GGGI The meeting's agenda discussed wide range of crucial issues including questions related to the countries that have ratified the Establishment Agreement, as well as the latest developments related to signing the Headquarters Agreement between the GGGI and the Korean government. Other important items discussed included issues about the immunities, privileges and regulations on human resources and procurement rules and disclosure policies as well as discussions about the latest developments in member countries, identifying the projected budget for the fiscal year 2013 and taking effective and concrete measures for the development of the Council's subsidiary and advisory committees.
GGGI is currently exploring the possibility of submitting proposal work on strengthening the principles of accountability, transparency and operational efficiency as well as enhancing the GGGI's organizational structure.
GGGI also aims to strengthen the strategic partnership between its head office in Seoul and its branch offices in Abu Dhabi, Copenhagen and London, and prepare appropriate plans to attract professionals and experts and boost the relationships among the Institute's eighteen member countries.
GGGI held its first session of the Council at the Inaugural Meetings of the Assembly and the Council on October 23, 2012 in Seoul, the Republic of Korea. As part of the organization's new governance structure as a multi-stakeholder, hybrid international organization, representatives from all 18 founding member nations convened to adopt the rules and procedures of the Assembly and Council as well as nominated and appointed GGGI's Director-General, Chairman of the Council, and President of the Assembly. The Institute's Establishment Agreement, signed by all founding member nations, details GGGI's new governance structure, which consists of an Assembly, Council, Advisory Committee and Secretariat.
GGGI is dedicated to pioneering and diffusing a new paradigm of economic growth: green growth. The Institute is a new kind of international organization driven by emerging and developing countries. Headquartered in Seoul, and with offices in Abu Dhabi, Copenhagen and London, GGGI has received financial support from Australia, Denmark, Germany, Japan, Korea, the UK and the UAE The Institute is also associated with a number of partnerships with Brazil, Ethiopia, Indonesia, Cambodia, Kazakhstan, UAE, and other countries with the purpose to develop green economic development plans.
WAM/MMYS
ويسكي كافلان ينضم إلى يافطات الإعلان في ساحة تايمز بمدينة نيويورك
Posted on August 18, 2016 Author ANE
الشركة التايوانية المنتجة للويسكي تصل إلى نيويورك تايبي، تايوان، 18 آب/أغسطس، 2016 / بي آر نيوزواير / — بعض من الـ 330,000 زائر يومي لساحة تايمز بنيويورك خلال الأشهر الخمسة القادمة سيرون صورة لزجاجة ويسكي تشع بضوئها عليهم من أعلى، ليس من يافطة واحدة، بل من 11 من اليافطات الإلكترونية الضخمة المنصوبة في ساحة تايمز. […]
US airstrike kills four militants in Somalia — Africom
Posted on March 10, 2020 Author ANE
WASHINGTON, March 9 (KUNA) — The US Africa Command (Africom) conducted an airstrike against "al-Shabaab terrorists" near Janaale town, southeast Mogadishu, Somalia, on Monday.The airstrike, launched in coordination with the Federal Government of Somal…
Abu Dhabi Traffic Police promote traffic safety through Facebook, Twitter and YouTube
Posted on July 14, 2013 Author ANE
WAM Abu Dhabi, 13th July 2013 (WAM) – The Traffic and Patrols Directorate at Abu Dhabi Police has launched an introductory campaign to familiarise community members with its program to reduce traffic accidents, titled "Together" on Facebook, Twitter an…Read More
Abu Dhabi IREC unites over 160 countries in call for doubling of world's renewable energy
سعر سلة خامات (الاوبك) يصل الى 108.01 دولار للبرميل يوم امس ..


text:
using System;
using System.Net;
using System.Net.Http;
using System.Web.Http.Controllers;
using System.Web.Http.Filters;

namespace OfficeDevPnP.Core.WebAPI
{
    public class WebAPIContextFilterAttribute : ActionFilterAttribute
    {
        public override void OnActionExecuting(HttpActionContext actionContext)
        {
            if (actionContext == null)
            {
                throw new ArgumentNullException("actionContext");
            }

            if (WebAPIHelper.HasCacheEntry(actionContext.ControllerContext))
            {
                return;
            }
            else
            {
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redpajama_set_name: RedPajamaGithub


After many EP releases and two full length albums, Paranormal Activity on Power House Records and New Vision on Goa Records we presents this digital anthology release of some of the early years from French Progressive Psytrance producer and dj Elepho. Anthony Gence was born in France in 1982 and from an early age music and its environment attracted him. After spending years listening to pop and rock while learning Guitar, in 2000 his artistic universe changed to the direction of electronic music. House music was one of his first steps, DJing in clubs, leading to his discovery of trance music festivals, which later became his passion. In 2011, his first musical output appeared with the song 4U on a vinyl record for Incorporated records. Since that first Elepho tracks were releases he has been to play the four corners of France as a producer and deejay with Stéréorganik, and appeared in compilations alongside the biggest global artists in Progressive trance.


can i tell you something that might make you throw up in your mouth a little bit? i made breakfast in bed for eggboy a lot during harvest. multiple times a week. really elaborate and adorable ones complete with miniature things and cute flatware.
speaking of mum's birthday: it's tomorrow!!!!! yayayayay! i sent her a copy of steph's book that eggboy and i annotated with fun notes pointing out our favorite recipes and pictures. and it's no coincidence that i'm posting this the day before her birthday in case anyone in chicago (hint hint, stoop) is reading this and wants to make her breakfast in bed.
something sweet: a cookie, a bite of nougat, a shot of cake batter. the best days start with a little something sweet.
carefully packaged liquids: coffee mugs that aren't too full, juice in a jar with a lid, a traveler mug, hell, a juice box. sleepy people are clumsy people. sleepy people in beds are really clumsy people.
extra napkins or a towel: just in case.
condiments and seasonings: to be perfectly honest, my little tabasco bottles and maldon tin and baby bon maman jars are a disproportionately large reason why i love making breakfasts in bed. they give me a cute attack every time i look at them. and they're obviously useful because if you actually think that any mother of mine would eat an unsalted, un-hot-sauced egg, even if it were delivered to her in bed, you are cray.
things that don't take too long to make: do you know how awful it is to be mid-breakfast-in-bed-prep and have the person you're making it for wake up and walk into the kitchen? mega awful.
things that aren't too noisy to make: this is an obvious one. be mindful of kitchen timers, blenders, coffee grinders, and those god-awful microwave beeps.
in this breakfast in bed, we have: grape ricotta focaccia (recipe coming soon!), an egg, dala horse cookies, tabasco, maldon salt, yogurt with nuts and honey, green juice, a nell & mary napkin, a little gold spoon, and obviously coffee.
that tray, plate, bowl, and jar are all part of a special breakfast in bed set from pacific merchants, and guess what-- i'm giving one away!!!! yayayayayayayyyyy! to enter this giveaway, leave a comment with your ultimate breakfast in bed! (open to u.s. residents only.) update: this giveaway is now closed.


I've helped a LOT of buyers and sellers over the years…so I understand this isn't just about finding you the right home or selling your home quickly…it's about getting you there as smoothly as possible. I'm also very familiar with the stresses and concerns of moving to Los Angeles and setting up base, as I moved here from London over 19 years ago. So give me a call and we can have a chat and you can see if I'd be the right fit for you.


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FRNT
Wellington, FL14814 Grand Prix Village Drive
Rental Price$425,000
Subdivision GRAND PRIX VILLAGE SOUTH
Secure your equestrian rental for next season. Situated on 4-acres of lush land, is an amazing 20-stall barn with 4 wash stalls, 2 tack rooms, a laundry room, and a feed room. The owners' lounge has a beautiful fireplace as the focal point, as well as a kitchen with great room for entertaining and a wonderful view of the 220' x 120' all-weather ring with superior custom footing. Property also includes six paddocks and a separate grooms' apartment with 4 bedrooms and a large kitchen.
Listing courtesy of Engel & Volkers Wellington
Wellington, FL3905 Gem Twist Court
Just a stone's throw from the Winter Equestrian Festival, this Grand Prix Village facility is absolutely stunning. Situated on 4 acres, the property includes 4 paddocks, a full property generator, as well as an 80' by 210' covered arena with state of the art footing and underground watering system. The barn features 20 stalls, 4 wash/grooming stalls, 2 tack rooms, 2 feed rooms, 1 large storage room, 2 powder rooms, and 2 offices, one of which includes a kitchenette; ''spacious'' is an understatement! The private, 2,500 sq ft owner's quarter is just up the elevator and includes a gorgeous custom kitchen and bathroom featuring state-of-the-art appliances and finishes, as well as a covered screened-in balcony. In a separate building, there are groom's/staff quarters and a garage.
Delray Beach, FL2325 S Ocean Boulevard
Subdivision Delray Beach
Sitting atop 1 acre of the Delray dunes stands the magnificent and mighty Mar Pietra. Travel through the steel gate, pass under the 3 bedroom guesthouse, and around the gorgeous stone fountain and motor court and you have arrived at Mar Pietra. Feel like a VIP as you make your way up stone steps to the steel front doors or enter through an 8 car garage any car enthusiast will appreciate. Even at 31,387 sqft there is no shortage of aesthetic appeal. Enjoy roman style stone columns, archways, frescos, custom wood work, and more. Take in the sunny south Florida weather by the private pool and spa just steps from the ocean or take a stroll to your own 0.5 acre private oasis, comprised of a pool, garden labyrinth, fire pits, covered cabana, and private dock resting upon the intracoastal. DISCLAIMER: Information published or otherwise provided by the listing company and its representatives including but not limited to prices, measurements, square footages, lot sizes, calculations and statistics are deemed reliable but are not guaranteed and are subject to errors, omissions or changes without notice. All such information should be independently verified by any prospective purchaser or seller. Parties should perform their own due diligence to verify such information prior to a sale or listing. Listing company expressly disclaims any warranty or representation regarding such information. Prices published are either list price, sold price, and/or last asking price. The listing company participates in the Multiple Listing Service and IDX. The properties published as listed and sold are not necessarily exclusive to listing company and may be listed or have sold with other members of the Multiple Listing Service. Transactions where listing company represented both buyers and sellers are calculated as two sales. The listing company's marketplace is all of the following: Vero Beach, Town of Orchid, Indian River Shores, Town of Palm Beach, West Palm Beach, Manalapan Beach, Point Manalapan, Hypoluxo Island, Ocean Ridge, Gulf Stream, Delray Beach, Highland Beach, Boca Raton, East Deerfield Beach, Hillsboro Beach, Hillsboro Shores, East Pompano Beach, Lighthouse Point, Sea Ranch Lakes and Fort Lauderdale. Cooperating brokers are advised that in the event of a Buyer default, no commission will be paid to a cooperating Broker on the Deposits retained by the Seller. No commissions are paid to any cooperating broker until title passes or upon actual commencement of a lease. Some affiliations may not be applicable to certain geographic areas. If your property is currently listed with another broker, please disregard any solicitation for services. Copyright 2020 listing company All Rights Reserved.
Listing courtesy of Premier Estate Properties, Inc
Palm Beach, FL473 N County Road
Stunning Oceanfront Estate. Available Summer 2021.
Listing courtesy of Christian Angle Real Estate
Boca Raton, FL3682 Princeton Place
Subdivision PRINCETON EST
Your own personal resort! 27,000+ total square feet Mediterranean-styled estate, nestled on 1.6+ acres of land in the ultra-exclusive, gated Princeton Estates, an enclave of Woodfield Country Club (20 luxury tennis courts and soon to be finished, newly renovated 18-hole golf course), the most exclusive family-friendly country club. The estate is centrally located to many of Boca Raton's finest offerings including South Florida's finest public and private schools, the ultra-luxury Town Center Mall, the urban retail/entertainment village of Mizner Park, extensive city-owned parks, the beaches of the Atlantic Ocean, the Palm Beach Int'l Airport, Fort Lauderdale Int'l Airport and minutes from Boca Raton's Executive Airport, complete with its own U.S. Customs facility for international passenge Offering the utmost in privacy and security, the estate can only be accessed by going through two 24/7 manned security gates or by landing a helicopter on its massive lawn (owner has obtained all required permits). The estate offers all the finest amenities including a Crestron Home Technology system, which provides home automation and covers security, temperature, lighting, audio, security cameras (80 throughout the house), and the pool from touchscreen devices located throughout and accessible remotely. In addition, the house is equipped with 2 large generators that can be powered by both propane tanks or natural gas. The main entrance consists of double 12-foot mahogany doors that enter a grand two-story foyer with circular staircase and motorized chandelier. There is a 1,500 bottle, air-conditioned, open-view wine room directly behind the formal dining area with controllable lighting. A themed private movie theater can also be found in the estate with an outside concession/full bar area that can rival any movie theater. The theater contains a 10-foot movie screen, 3-D projector, and seating for 11 people. The immense main kitchen of the estate was designed for function and entertaining in mind with 5 feet of space between the center island and the rest of the kitchen cabinetry. The kitchen contains plenty of marble/wood countertop space, multiple sinks, two Subzero refrigerators and one Subzero freezer, two Miele ovens with warming trays, Miele steamer, a walk-in pantry, two Wolf ovens with four gas burners and one griddle each, over-the-stove water pot filler, two microwaves, an ice maker, Miele coffee maker, two dishwashers, and more. Within the immediate kitchen area, one can find two more dining areas including a semi-formal dining area (can seat 10-12) and a breakfast dining area (can seat 4-6). Moving outside there is a covered patio, consisting of two separate sections. One section is 33 feet wide by 33 feet deep and is accented by cast stone columns and stone clad archways. The other section is 60 feet wide by 42 feet deep, and screen-enclosed area with entertainment kitchen and features automated retractable screens, automated metal storm shutters, and electronic and infrared heaters. There is also a patio bar, topped with marble counters, a wood/charcoal grill, a gas grill (all under a commercial vent), icemaker, refrigerator, flat screen TVs and a glass atrium with retractable shading. The patios all have views of the resort-style 60 feet by 20 feet pool with waterfall and heated spa (fits about 20 people). To the side of the pool is an outdoor side yard which measures 180 feet by 90 feet and contains a multi-purpose sports court. Continuing back to the estate, the first floor also features a massive recreational room that is 55' long by 45' wide with the main ceiling more than 25 feet. The recreational room features a full bar with dishwasher and sink and a 150-gallon built-in fish tank. The room is currently housing a large arcade (arcade games do not convey) but it can be converted for a variety of uses including a large fitness center, yoga studio, dance room, or even an indoor sports court. Off the recreation room, one can access one of the garages, which was part of a secondary addition. The garage contains a Hanmecson hydraulic lift system which allows two additional cars to be housed, which in total allows 9 cars to be housed in addition to a golf cart. There is also ample outdoor parking for one's entertainment needs. The second floor of the estate, which is concrete construction and 12+ feet ceilings, can be accessed via elevator or by one of two staircases. The second floor contains a 15 feet by 48 feet multipurpose room with access to a covered exterior patio. In addition to the multipurpose room, the second floor contains the master suite complete with morning kitchen, his and her bathrooms, his and hers walk-in closets, several bedrooms, and a large laundry room. Additional features of the estate include, but are not limited to: 13 a/c units, 4 booster wall-mounted a/c units, wrought iron railings, multiple central vacuum systems throughout, a commercial fire-suppression sprinkler system that is a pre-action system that does not have any water in the pipes until it senses a fire situation, IP Phone System (Klystron/Lutron), a crawlspace that runs across the middle of the house for cabling and maintenance access, and monitored plumbing systems including the A/C that can detect leaks and automatically notify the owner and/or shut down to prevent damage. The estate, designed by Randall Stofft and built by Terence Cudmore Builders, is being sold unfurnished, but all light fixtures and built-in appliances will stay (appliances in the catering kitchen excluded). It has been excellently maintained and there are no deferred maintenance issues. If an issue may arise, there are extensive roof tiles and other building materials located off the property area should repairs be needed. As mentioned, the estate is part of Woodfield Country Club which provides 24/7 manned guard gates, renovated main clubhouse, 18-hole Championship Golf Course, 20-court tennis center, commercial quality fitness center, swim park and grill bar, massage room, and children play facilities and child care. Information herein deemed reliable but not guaranteed and subject to error.
Listing courtesy of Marc Bell Real Estate
Wellington, FL15495 Sunnyland Lane
Subdivision PALM BEACH POINT
Sunnyland Lane is a beautiful equestrian oasis nestled perfectly on 5.5 acres. The State of the Art Barn is architecturally significant hosting 12+1 custom English style stalls. The barn is fitted with cobble stone pavers and has amenity to fit the equestrians' standards. The barn includes a spectacular Owners Lounge and an exquisitely finished 2 Bedroom Penthouse equipped with a large veranda that over looks the Grand Prix horse ring. The property is located in the exclusive and private equestrian community of Palm Beach Point.
Listing courtesy of Equestrian Sotheby's International Realty
Wellington, FL4600 Garden Point Trail
Unique opportunity to operate out of one of Palm Beach Points exclusive farms for the season. No detail was missed at gorgeous Lucky Day Farm. Available for the 2020/2021 Season. One twelve stall center aisle barn with tack room, feed room, laundry room and powder room. Treadmill pavillion with lights and fans. Tractor and drag, 130x250 ring. 3/2 guest cottage fully renovated kitchen, gas range and fenced yard.
Listing courtesy of Keller Williams Realty - Wellington
Palm Beach, FL1438 N Ocean Boulevard
Subdivision ESPLANADE ESTATES
Currently it is rented through May 2021. Available June 1, 2021. Live like a true North End local in the completely renovated Sea Captain's house directly on the ocean and overlooking the largest, most pristine beach on the island. The nine bedrooms, two kitchens, top-of-the-line fixtures and furnishings make this the perfect Palm Beach retreat. Entertain inside or out with a luxurious dining room, multiple living spaces, terraces and various outdoor loggias, including a generous heated pool and cabana. Additional features include: the private entrance off the quiet low traffic street, indoor/outdoor fireplaces, marble bathrooms, elevator, impact glass and more. Available for annual rental--price to be determined.
Listing courtesy of Tina Fanjul Associates, Inc.
Palm Beach, FL15 S Lake Trail
Absolutely stunning recently restored and generous 7 Bedroom in the heart of town is now available for Seasonal rental. All new gourmet kitchen, baths, furnishings - all beautifully finished! Great pool area, gardens and dock. Downstairs master bedroom. By far, this is Palm Beach's best rental property! Minimum of 6 months lease. Call for details!
Delray Beach, FL9192 Rockybrook Way
Subdivision Stone Creek Ranch
Rockybrook Estate in Delray Beach is an epic residential statement unlike anything seen in South Florida. Set in prestigious Stone Creek Ranch, an exclusive luxury community with only 37 estates, this 31,000-square-foot architectural marvel is an ode to both modern classicism and throwback glamour. In the same breath, the inviting seven-bedroom, 11-full-bath layout captivates with family friendly flourishes that double as entertainment spaces. It's the best of all worlds--sophisticated design, jaw-dropping detail and tangible warmth, flawlessly executed under one roof. Conceptualized by Brenner Architecture, Rockybrook dazzles at first dramatic sight with a grand foyer and custom-designed open-arms staircase that recalls a scene from a Hollywood classic. It's a hint at what's to come - one space after another that stirs the senses. A stately living room with marble flooring and 32-foot walls. A dining room that seats 16 with white agate backlit walls and white mother-of-pearl ceiling treatment. A glass-encased, temperature-controlled wine room. A Zen-like garden area with a waterfall treatment and koi pond. Original lighting fixtures by the likes of artists Dale Chihuly and Nicolas Terrain. A long hallway off the foyer eventually leads to a gourmet Downsview kitchen with state-of-the-art appliances; a striking double-column, stainless-steel range hood; and a separate island counter with seating for six and playfully oversized lighting. And that's not even the only place to prepare meals. Rockybrook also boasts a separate commercial catering kitchen. Upstairs, the master bedroom has its own separate wing, including a study lounge and kitchenette. The main balcony of the suite features a fireplace, whirlpool bath and sun deck, all overlooking the property's lush 2.5 acres and unrivaled outdoor spaces. Exquisite details also abound in the master baths; hers is adorned with amethyst and boasts a center bathtub with a hanging crystal chandelierand both baths have their own balconies. For all of its eye-catching opulence, Rockybrook also manages to enchant with a resort-like vibe that makes every day feel like a getaway. Look no further than the 250,000-gallon heated pool with black mosaic glass waterline tile and a black Pebble Tec finish, an ode to the infinity-edge splendor at Wynn Las Vegas. The tropically landscaped pool areacomplete with waterfalls, fire pits, a covered pavilion and dining area, a grotto with a rain feature, and a fully equipped summer kitchenprovides an entertainment epicenter for all seasons. But that's hardly the only place to play at Rockybrook. The estate also features a club room with a bar, card table and billiards table; a cigar room; a cinema-style theater roomand even a two-lane bowling alley in the massive children's lounge. Rockybrook also sports health and beauty areas, including a spa treatment room, salon, gym and sauna, a championship tennis court, and a blissful meditation space overlooking the tropical garden. The residence also is designed with hurricane-resistant windows, doors and exterior construction. In addition, it comes with a Creston smart home system and security cameras. Welcome to Rockybrook Estate - an American original.
Boca Raton, FL140 NE 5th Avenue
Subdivision KINNEY & GATES
140 NE 5th Avenue is a stunning, waterfront single-family home in East Boca Raton. Part of the 5th Avenue Enclave Estates, the property encompasses 11,111 total square feet with 7,910 square feet of interior living space. Living space includes two bedroom options: 5 bedrooms/5.5 bathrooms with a 952 square foot bonus room OR 7 bedrooms/7.5 bathrooms with no bonus room. The massive 6-car, air conditioned garage totals 2,038 square feet with electric car charging capabilities. Designed by Affiniti Architects, the home comes fully finished and furnished with customized interior design by renowned Interiors by Steven G . Ideally located on the main intracoastal in the heart of East Boca Raton!
Listing courtesy of Compass Florida LLC
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The previous 5.0-liter V8 engine with 510 PS was capable to push the vehicle from 0 to 100 km/h (62 mph) in 4.9 seconds. The model powered by the 3.0-liter engine delivers 275 PS and 600 Nm of torque, enough to sprint from 0 to 100 km/h (62 mph) in 6.4 seconds. This engine will also be used on the upcoming F-TYPE sports car, which will be available starting in 2013. The suspension refinement of the XJ has improved and so does the performance. The powertrain line-up all models in the range would benefit from the re-calibrated spring and damper tunes. It also comes with a remapped damper software.
With all these highlights, the ride isolation has been enhanced too. Ride isolation and passenger comfort have improved as well. The XJ now features the cabin with DAB as standard equipment. It also got a new premium sound system. The engineers at Jaguar have been collaborating with Meridian, a British audio specialist. The Meridian system has much lower levels of harmonic distortion.
This means that to achieve the high levels of audio clarity inside the vehicle, a lower power output is necessary. The range-topping speaker 825W system is offered as standard on Portfolio and Supersport XJ models while it is an option on other derivatives. In its class, the XJ has one of the most best-selling cars in its class, with an 11% increase in sales last year. The XJ gets advanced construction, luxury and performance.
The XJ range was just recently expanded to feature the new Sport and Speed packs as well as the presentation of the new 'Ultimate' model, such as a bespoke rear seat environment and exclusive exterior styling. Pricing starts at £56,260 for the XJ 3.0 Diesel Luxury and at £59,260 for the long wheelbase model.
Jaguar has announced details of its 2013 model year generation XJ. It benefits from Jaguar's relentless research and development programme to improve efficiency and introduce the latest comfort and functional technologies.
Significant transmission, engine and dynamic developments mean Jaguar's most technologically advanced car becomes more efficient and refined.
To maximise efficiency from the all-aluminium architecture of the XJ, Jaguar has introduced an eight-speed ZF transmission with Intelligent Stop-Start as standard. As seen on the XF, these developments lead to lower fuel consumption and emissions. The new XJ is expected to achieve 47 MPG while emitting 159g/km of CO2, a 14% improvement.
The 13MY XJ marks the introduction of a new 3.0-litre, supercharged V6 powertrain. Producing 340PS and 450Nm of torque - equating to almost 127PS per litre - the highest specific output of any Jaguar engine, the new engine is refined and powerful. Making its debut in the XJ and XF, this engine will also power the forthcoming F-TYPE sports car, available from 2013.
For 13MY, XJ has improved suspension refinement and performance. Alongside the exciting new powertrain line-up all models in the range benefit from re-calibrated spring and damper tunes as well as remapped damper software - offering enhanced ride isolation and passenger comfort on all road surfaces.
Enhancements extend to the XJ's award-winning cabin with DAB now fitted as standard and a new premium sound system.
Teaming up with British audio specialist Meridian, Jaguar engineers have developed a 825W sound system. Tested using the most scrupulous audio measurements the Meridian system boasts significantly lower levels of harmonic distortion - meaning a lower power output is required to achieve the high levels of audio clarity within the vehicle. The top-of-the range 20-speaker 825W system comes as standard on Portfolio and Supersport XJ models while offered as an option on other derivatives.
The XJ continues to be among the best-selling cars in its class, reporting an 11% per cent rise in sales in 2011. Combining advanced construction, luxury and performance, the XJ range has recently expanded to include the new Sport and Speed packs as well as the introduction of the new 'Ultimate' model, including a bespoke rear seat environment and unique exterior styling.


See why residents here love their new homes.
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CEEREAL welcomes its new company member Emco spol. s r. o., founded by Mr. Zdeněk Jahoda in 1990 Emco is one of the leading food company in the Czech Republic's food industry. The focus on healthy products, high quality of raw materials and end products, modern technology, innovation and continuous monitoring of customer needs form the main pillars of Emco's development and success.
The vision and objective of Emco is to become a global player on the cereal market while maintaining a strong local character: since its foundation, the company has been run by the Jahoda family who place their business emphasis on being local and building trust with customers, making Emco a family business ready for the future.
CEEREAL represents the breakfast cereal and oat milling industry towards the European Union and its institutions, industry and consumers associations as well as consumers. CEEREAL was established in 1992 by 8 breakfast cereal associations, and counts 9 member associations in 8 European countries and 12 company members.