Are you new to driving? Do you want to fast track your driving lesson journey without cutting corners on safety and driving basics? You are sure in the right place because, in as little time as possible, I will put you through what you need to be one of the best drivers on the road. You may be confused about a lot of things going on on the roads with no one to explain them to you. They might not even make sense to you at the moment, but after spending time with me, you will get a better understanding of what such things are.
One of such things I will be discussing today is hard shoulders. This is one thing many drivers and road users still get confused about. You may have witnessed a situation where you are in a car and your driver wants to overtake another vehicle while maintaining the approved speed limit without the person in front of you pulling to the left side to make way for you. That left side is called hard shoulders and could also be on either side of the road. Many people do not make use of the hard shoulders because they are afraid they may end up with a charge if they pull to the hard shoulder.
In order to avoid repeating the same mistake when you eventually start driving, it is important you take note that it is alright to drive onto the hard shoulder to let the other person overtake. This is as long as you are not deemed to be aiding a crime which could land you in prison for 12 years. For the avoidance of doubt, always check to make sure that no cyclist, pedestrian is using the hard shoulder before you move onto that part of the road. Also, you should make sure that no entrance or junction is close by as contained in page 67 of Rules of the Road. This important information is particularly helpful for driving lessons Dublin, driving lessons Tallaght, and driving lessons Churchtown.
Will driving onto hard shoulders burst my car?
Another misconception people have is that when they drive onto the hard shoulder, their car will burst into flames. To some, the broken yellow lines of the hard shoulder contain explosives that have been put in place by council workers. Well, no such thing can happen. Hard shoulders are safe to use and will not have your car going up in flames when you drive onto them.
In fact, not letting a driver safely overtake you could land you into trouble. That is what hard shoulders are meant for. As long as you are driving within the rules of the road, you will be doing yourself more harm than good when you prevent another driver from overtaking you. It could be that they are in a hurry and not as relaxed as you are when driving. Maybe they have an emergency to attend to, and your refusal to let them overtake costs them something important. Just make sure that you have checked for what I stated above before driving onto the hard shoulder to allow the other person overtake you.
I have a feeling you have learned something very important today. The essence of making sure you one of the best drivers in the world is not just dishing out instructions to you. you can expect to get more of this type of lecture when you enroll with us for your driving lessons. We will teach you what others are ignorant of which are considered as road offense. Remember, if you interested in driving lessons Tallaght, and , driving lessons Dublin, driving lessons Churchtown or anywhere in Dublin, you can always count on us to deliver the very best lessons that will turn you into an amiable driver.
Please contact National Driving School today if you would like to book a lesson.


All applicants to MEDEX Northwest must complete the General Personal Statement section of the Supplemental Application. Please do not duplicate the CASPA Application personal statement. MEDEX will receive the CASPA application that includes this information.
Copy and paste unformatted text or type the personal statements directly in the space provided in the application. Please limit the General Personal Statement to no more than 1000 words.
Tell us how your values and experience make you a good fit for MEDEX. Why is now the right time to apply to MEDEX?
What are the biggest challenges you face in returning to school, and what will you contribute to your MEDEX classmates?
Do you believe your academic history accurately reflects your academic ability to successfully complete PA school? Please explain.
The prompts listed above may change. Please review the prompts listed on the application directly in the year that you apply to ensure that you address all questions asked.


text:
#13 Kurt Warner - QB
Kurt Warner Career NFL Stats 1998-2009
COMP %
RYDS
Kurt Warner Childhood
Kurtis Eugene Warner was born on June 22, 1971 in Burlington, IA. He is the son of Sue and Gene Warner. Kurt's parents parted ways when he was very young and was raised primarily by his mother. Although times were hard, Kurt was still able to participate in sports. He was an athletic kid and he exceled at football from a young age.
Kurt Warner High School Stats and Awards
Warner attended Regis High School. He played varsity for four years and finished as one of the best prep stars in Iowa history. Although he received the accolade, scholarships from big time programs was a different matter. Warner didn't get any offers from Division I schools. Instead Warner accepted a scholarship offer to the University of Northern Iowa, a Division I-AA school.
Passing Yards Title:
Super Bowl Champion
XXXIV
Kurt Warner College Stats and Notes
Warner's football journey at Northern Iowa didn't start well as he was a backup for the first three years of his college career. However, he finished on a high note as he was named Gateway Conference Player of the Year as a senior.
Kurt Warner NFL Career Stats and Notes
Warner went undrafted in the 1994 NFL Draft. After a stint in the Arena Football League, he finally made it on to an NFL team with the St. Louis Rams in 1998.
His rise in the league started due to an injury to starter Trent Green prior to the 1999 season. Once given the opportunity however, he took full advantage like no other quarterback in history (more on this later). Warner played for the St. Louis Rams for six years. In 2004, he headed east to play for the New York Giants for one season. Warner then headed west and joined the Arizona Cardinals. He would finish out his career in the desert, retiring after the 2009 season. In 2017 Warner was inducted into the Pro Football Hall of Fame.
Kurt Warner 1999 NFL MVP And Super Bowl XXXIV MVP Performance
To go from undrafted status, to playing in the Arena Football League, to backup to Hall of Fame isn't the type of road travelled for 99 percent of NFL players. However Warner made the road to the NFL worth it. His meteoric rise was nothing short of amazing. In 1999 during his first season as a full-time starter, Warner threw for 4,353 yards and 41 TDs. He led the once downtrodden Rams to an improbable 13-3 record. The Rams completed their Cinderella season with a 23-16 Super Bowl XXXIV victory over the Tennessee Titans. Warner, who was the regular season MVP, went 24 for 45 for a then Super Bowl record 414 yards and 2 TDs. He would take home Super Bowl MVP honors.
During the next two years, the Rams were no longer a surprise team. They were a juggernaut offense that was called 'The Greatest Show on Turf'. Warner continued to put up historic stats not seen since Hall of Famer Dan Marino in the mid-1980s. In 2001 he threw for 4,830 yards and 36 TDs and won his second NFL MVP in three seasons.

redpajama_set_name: RedPajamaCommonCrawl


All generalities are false, including this one!
If your answer to the question "What mistake are you making" is "none at the moment", then that is the mistake you're making: believing that you're not making any. Or at least, it is if you're growing, or if you're trying to grow.
Over recent few years I have been working on developing my public speaking, and a recent highlight was to be given the opportunity to share the stage with someone who is a mentor, coach, and leadership role model for me.
This was at the International Certification event for the John Maxwell Team last August in Orlando, Florida, in the summer of 2018. These events are held twice a year, and I was due to have a quiet chat with John Maxwell, on the main stage in front of some 3,500 people in the afternoon of the first full day of the conference.
I arrived at the conference a couple of days before it started, and one of my fellow coaches asked me what I was hoping to get out of this conference. My reply was to say; "I am hoping not to screw up on Monday". Fortunately I recognised my mistake pretty quickly. I was delivering a bout of negative self-talk right there and I was going to have to put a stop to that.
There were 10 of us going up to have s similar chat with John as part of the conference on that Monday afternoon, and over the course of the next couple of days we had several practice runs. During those practices all of us managed to mess up at least once, and we were all encouraging and cheering each other on all the way.
I hadn't learned my lesson too well, however. When Monday afternoon came I was sitting in line at the end of the 10 seats waiting to take my turn and as each of my fellow coaches went on stage and did an awesome job, I had this thought running through my head: "I'll be the last on stage and the first to screw up". Again, I had to catch my mistake, arrest my negative self-talk and turn it around to tell myself that I was going to the be the last on stage, I was going to knock it out of the park and be the best of the bunch.
I'm not sure that I was the best of the bunch but I did manage to get through without any screw-ups or mistakes and had an absolutely awesome time in the process.
In the run up to this event I had made many mistakes in my public speaking journey. I've had the experience of coming to deliver a talk and found that over half of the people in the audience were at a gig I had done a few weeks earlier where I had prepared exactly the same talk; I have had the experience of being stood in front of an audience part way through a speaking gig with my mind going totally blank, having absolutely no idea what I had intended to say next, and nothing to offer as an alternative.
However, I also know that these have been some of the most significant learning opportunities, and without them and without the challenge of having gone through them I would never have even imagined attempting the things that have become some of my greatest triumphs, and that I have reached places I would never have reached without facing these challenges and coming through the other side – such as the awesome experience of sharing the stage with John.
I guess that oftentimes the biggest danger is that we fear failure so much that we work to avoid the possibility of failure and completely miss our greatest triumphs.
The step first is to be honest and realistic and face up to the things we find challenging. This involves looking again at times when we didn't achieve the results we wanted to achieve, examining ourselves and asking the question "what could I have done differently", and then taking responsibility for the mistakes we have made along the way.
I suspect that for most people, when we were learning to ride a bike, we didn't give up the first time we fell off and grazed a knee, we didn't give up learning to drive the first time we stalled the car, and we didn't give up give up trying to walk the first time we fell over as a toddler. If we're learning to lead we'll inevitable fail sooner or later if we want to make progress.
The only mistake that is all bad is the mistake we don't learn from. Some fabulous inventions ad discoveries have come from mistakes, including the microwave oven, x-rays, Superglue, Teflon, Velcro and Play-doh!
We need to ask the question: "What mistake have we made recently", and if the answer is "nothing" then that's the mistake we have made.


text:
\section{Introduction}
	\label{sec:introduction}

	Affordances are properties of regions of scenes or objects which indicate their relevance for a certain functional interaction. Examples are \textit{holdable} for the external part of a mug or \textit{drivable} for a road.
	Localizing affordances is therefore an important task for autonomous systems that interact with the environment \cite{Koppula2013} as well as assistive systems that support visually impaired people~\cite{blind}.

	Segmenting affordance regions, however, is a more difficult task than classical semantic image segmentation, which focuses on objects or categories that summarize regions of similar appearance like sky or grass.

	Affordances are not only much more fine-grained than object categories, they represent a more abstract concept that generalizes across object categories. This requires that an affordance segmentation approach recognizes affordance for a previously unseen object class. For instance, it should generalize \textit{cuttable} from the blades of scissors or knives to the blade of a saw. Furthermore, affordance segmentation is a multi-label segmentation problem since affordance regions spatially overlap. This is in contrast to classical semantic image segmentation where the categories are mutually exclusive. This is in particular for weakly supervised learning, as it is addressed in this work, a big challenge.

	Since acquiring pixelwise segmentation masks for training is very time consuming, methods for weakly supervised learning have been proposed that learn to segment object categories either from image labels \cite{weak_deeplab,SEC} or keypoint annotations~\cite{WTP}.
	Our work builds on~\cite{cvpr} where an approach for affordance segmentation has been proposed that uses only keypoint annotations as weak supervision for training. The approach employs an iterative approach alternating between updating the parameters of a convolutional neural network and estimating the unknown segmentation masks of the training images. During this process, the predictions of the network need to be binarized. Since thresholding at the 50\% decision boundary, as it is done in a fully supervised setting, does not work for weakly supervised learning, an additional binary segmentation step is used in~\cite{cvpr}.

	In this work, we propose an adaptive approach that determines the threshold for binarization for each training image and affordance class. Our approach not only avoids the additional segmentation step used in~\cite{cvpr} but also increases the affordance segmentation accuracy substantially. Since the initialization of the affordance segments based on the keypoints has a high impact on the accuracy, we show further how the parameters for initialization can be determined by cross validation using an approximation of the Jaccard index based on the given keypoints.
	We evaluate our approach on the CAD 120 affordance dataset~\cite{cvpr} and the UMD part affordance dataset~\cite{UMD} using two different network architectures. In all settings, our approach outperforms~\cite{cvpr}. On the CAD 120 affordance dataset, the mean accuracy is increased by up to 17 percentage points compared to~\cite{cvpr}.

	\section{Related Work}
	\label{sec:related_work}
	Our work is related to affordance modeling as well as weakly supervised semantic segmentation methods. An affordance is an attribute of an object part that implies the possible usage of this object.
	Assigning affordances to object parts is not trivial, while \cite{cvpr} and \cite{UMD} simply let a human annotator decide, others use more sophisticated statistics like mining of word co-occurrences \cite{Chao2015} or object attribute graph structures \cite{Zhu2014}.\\
	Modeling affordances can be the final goal or an intermediate step. In \cite{Castellini2011}, affordances of an object are defined in terms of hand poses during interaction. These affordances are used along with object appearance features for object classification. \cite{Kjellstroem2011} apply implicit affordance modeling for simultaneous hand action and object detection. While \cite{Zhu2015} combine object affordances with physical observables and human pose to obtain a generative model for object tasks, \cite{Koppula2016} use object affordance, object appearance and human poses for action detection.\\
	Since recognizing affordances is crucial for the constructive manipulation of objects by robots, several approaches that require full supervision have been proposed.

	While some use geometric information like orientation of object surfaces \cite{Katz2014}, 3d point clouds \cite{Kim2014}, or normal and curvature features \cite{UMD}, others rely only on appearance.
	\cite{Hermans2011} predict attributes from appearance and affordances from attributes, \cite{Desai2013} measure similarity between query and training image by the location of object parts, \cite{cvpr} train a deep model on RGB data. RGB-D data is exploited by \cite{Lenz2015} who propose a two stage cascade to model graspable regions and \cite{Roy2016} who train a CNN to predict depth information and affordances from RGB data simultaneously.
	CNNs were also used in \cite{Nguyen2016} for a pixelwise affordance segmentation in RGB-D data and in \cite{Kumra2017} to predict grasps. \cite{Grabner2011,Jiang2013,Koppula2014} exploit human poses to localize object affordances.

	Weakly supervised semantic image segmentation faced rapid progress in recent time.
	\cite{weak_deeplab} use an expectation-maximization (EM) approach with area constraints to train a CNN. \cite{WTP} use keypoint annotations and incorporate objectness into their loss function, \cite{SEC} exploit localization cues from an image level classifier, area constrains and CRFs. \cite{Hou2016} rely on superpixels and \cite{saliency_segmentation} learn a model from image labels and saliency predictions. In \cite{Durand2017}, an approach based on pooling of classwise heat maps along with image labels was proposed. A weakly supervised affordance segmentation approach based on EM similar to \cite{weak_deeplab} was proposed in \cite{cvpr}. Our approach builds on this work.

	\section{Weakly Supervised Affordance Segmentation}
	\label{sec:Methodology}
Our approach for weakly supervised affordance segmentation extends the approach~\cite{cvpr} by adaptive binarization and approximated cross validation for estimating hyperparameters. We therefore briefly describe~\cite{cvpr} first and then describe in Section~\ref{subsec:adaptive_thresholding} the adaptive binarization and in Section~\ref{subsec:approximated_cross_validation} approximated cross validation.

	\subsection{Method}
	\label{sec:Methodology_a}
The approach~\cite{cvpr} extends fully convolutional neural networks like \cite{deeplabVGG} or \cite{deeplabResnet} for the task of affordance segmentation. In contrast to semantic image segmentation, where only one label per pixel needs to be predicted, affordance segmentation requires to predict a set of labels per pixel since an object region might contain multiple affordance types. The approach predicts $P(Y|I;\theta)$ where $I$ denotes the input image, $\theta$ denotes the parameters of the model, i.e.\ the weights of the neural network, and $Y = \{y_{i,l}\}$ with $y_{i,l} \in \mathcal \{0,1\}$ is the pixelwise segmentation. If $y_{i,l}=1$ the affordance type $l$ is predicted for pixel $i$.
Due to the multi-label problem, the network uses a sigmoid layer instead of a softmax layer~\cite{deeplabVGG,deeplabResnet}:
	\begin{align} \label{eq:supervised_dcnn_pixel_label}
	P(y_{i,l}=1|I;\theta) = \frac{1}{1+\exp\left(-g_{i,l}\left(y_{i,l}|I;\theta\right)\right)},
	\end{align}
where $g_{i,l}$ is the value of the previous layer of the neural network.
For segmentation, the predicted probabilities $P(y_{i,l}|I;\theta)$ need to be binarized. In \cite{cvpr}, this is achieved by the standard 50\% threshold:
	\begin{align}\label{eq:thres}
	\hat{y}_{i,l} = \begin{cases} 1 & \text{if } P(y_{i,l}=1|I;\theta) \geq 0.5 \\0&\text{otherwise.}\\ \end{cases}
	\end{align}

The model parameters $\theta$ are determined during training. In the strongly supervised setting, training means optimizing the log-likelihood:
	\begin{align}\label{eq:J}
	\centering
	J(\theta) = \log P(Y|I;\theta) = \sum_{i=1}^{n} \sum_{l\in\mathcal{L}} \log P(y_{i,l}|I;\theta).
	\end{align}
In the weakly supervised setting, the log-likelihood can not be calculated since $Y$ is not given during training. In~\cite{cvpr}, it was proposed to train the model only from a set of keypoints $Z=\{(l_k,i_k)\}$, which denote the presence of the affordance $l_k$ at pixel $i_k$, using expectation-maximization (EM). During training, both $Y$ and $\theta$ need to be estimated from $Z$. The approach starts with an initial estimate $\hat{Y}$, which is derived from the keypoints $Z$ by labeling all pixels within a radius of $\sigma$ around a keypoint:
\begin{align}\label{eq:sigma}
\hat{y}_{i,l} = \begin{cases} 1 & \text{if } \vert \{ (l_k,i_k){\in}Z : l_k{=}l \land \vert i_k-i\vert{\leq}\sigma \}\vert > 0 \\0&\text{otherwise.}\\ \end{cases}
\end{align}
In contrast to \cite{cvpr} that uses fixed values for initialization, we discuss in Section \ref{subsec:approximated_cross_validation} how $\sigma$ can be estimated by approximated cross validation.

After $\hat{Y}$ is estimated, the weights of the network $\theta$ are updated by optimizing $J(\theta) = \log P(\hat{Y}|I;\theta)$. Given the new weights $\theta$, the CNN predicts $P(y_{i,l}|I;\theta)$ for each training image and $\hat{Y}$ is refined by binarization of the CNN predictions. The 50\% threshold used in~\eqref{eq:thres}, however, is only valid for the fully supervised setting. While in \cite{cvpr} an additional GrabCut step is used to address this issue, we propose an adaptive approach that determines the threshold for binarization for each training image and affordance class. This not only increases the accuracy, but it also reduces the training time since an additional GrabCut step is not needed anymore by our approach. The approach for adaptive binarization is discussed in Section~\ref{subsec:adaptive_thresholding}. Our weakly supervised approach for affordance segmentation is illustrated in Figure~\ref{fig:pipeline}.

To reduce overfitting and perform approximated cross validation as described in Section~\ref{subsec:approximated_cross_validation}, we split the training set into three equally sized subsets A, B, and C. During the M-step, we train the convolutional network on each of the tuples (A,B), (B,C), and (C,A). During the E-step, each network predicts $P(y_{i,l}|I;\theta)$ for the set that was not used for training. As in \cite{cvpr}, we use two EM iterations to obtain $\hat{Y}$ for all training images. The final CNN model is then obtained by optimizing $J(\theta) = \log P(\hat{Y}|I;\theta)$ on the entire training set.

        %

        %

	\begin{figure*}
	{
\centering
\includegraphics[height=90mm]{pipeline2.png}
\caption{\label{fig:pipeline}
Illustration of our approach for affordance segmentation using key points as weak supervision. The CNN is trained by iteratively updating the segmentation masks for the training images (E-step) and the parameters of the network (M-step).}
}
\end{figure*}

\subsection{Adaptive Binarization}
\label{subsec:adaptive_thresholding}
We first want to explain why the binarization as described in \ref{eq:thres} is not optimal for the weakly supervised case. Let us first consider an optimal classifier that separates two classes perfectly in the training data. In this case, $P(y_{i,l}=1|I;\theta)\geq 0.5$ if a pixel is annotated by $y_{i,l}=1$ and $P(y_{i,l}=1|I;\theta)< 0.5$ if it is annotated by $y_{i,l}=0$.
Hence, using 50\% as threshold for binarization is optimal. For weakly supervised learning, $\hat{Y}$ is in particular after the initialization only a poor estimation of the unknown ground truth segmentation masks $Y$ of the training data such that $\hat{y}_{i,l}\neq y_{i,l}$ for many pixels. This means that the optimal threshold is unknown. However, we can use the keypoints $Z$ to obtain an estimate of the threshold:
        \begin{align}
        \label{eq:threshold}
	    {\hat{y}}_{i,l} = \begin{cases} 1 & \text{if } P(y_{i,l}=1|I;\theta) \geq t \\0&\text{otherwise}\\ \end{cases}
	\end{align}
	where
	\begin{align}
        \label{eq:threshold2}
	    t = \min\left\{0.5,f\left(\{P(y_{i_k,l}=1|I;\theta)\}_{l_k=l}\right)\right\}.
	\end{align}
$P(y_{i_k,l}=1|I;\theta)\}_{l_k=l}$ are the predictions of the classifier for all keypoints in the training image $I$ with label $l$ and $f$ computes either the mean or median of \mbox{$P(y_{i_k,l}=1|I;\theta)\}_{l_k=l}$}. In our default experimental setting, we will have only one keypoint for each affordance occurring in an image. In general, one can expect that the threshold is below $0.5$ since
the ratio $\tfrac{\vert\{i:y_{i,l}=0 \land \hat{y}_{i,l}=1\}\vert}{\vert\{i:y_{i,l}=0\}\vert}$ is usually lower than $\tfrac{\vert\{i:y_{i,l}=1 \land \hat{y}_{i,l}=0\}\vert}{\vert\{i:y_{i,l}=1\}\vert}$. As soon as the threshold reaches $0.5$, we can replace the adaptive threshold by $0.5$. We therefore limit the threshold by $0.5$.

	\subsection{Approximated Cross Validation}
	\label{subsec:approximated_cross_validation}
		In the fully supervised setup, hyperparameters can be optimized by cross-validation on the training set using the same measure that is also used for evaluation.  Since the ground truth masks $Y$, however, are unknown in the weakly supervised setup, exact cross validation is not possible. We therefore propose to approximate the Jaccard index, which measures the intersection over union between the ground-truth $Y$ and the prediction $\hat{Y}$, on the validation set. Since the Jaccard index is computed per affordance class $l$ and then averaged over all classes, we discuss only the binary case with $y_i \in \{0,1\}$. Let $P(y_{i}=1) = \tfrac{\vert\{i:y_{i}=1\}\vert}{\vert\{i\}\vert}$ be the unknown percentage of pixels with $y_i = 1$ and $P(\hat{y}_{i}=1) = \tfrac{\vert\{i:\hat{y}_{i}=1\}\vert}{\vert\{i\}\vert}$ the known percentage of pixels that have been classified with $\hat{y}_i = 1$.
We can approximate $P(\hat{y}_{i}=1|y_{i}=1)$ by measuring how often a keypoint annotated by the affordance class has been correctly classified. Similarly, $P(\hat{y}_{i}=1|y_{i}=0)$ is given by the percentage of keypoints that have been misclassified. This gives the relation
\begin{align}
P(\hat{y}_{i}=1) =& P(\hat{y}_{i}=1|y_{i}=1)P(y_{i}=1) \\ \nonumber
                  &+ P(\hat{y}_{i}=1|y_{i}=0)(1-P(y_{i}=1))
\end{align}
and thus
\begin{align}
P(y_{i}=1)=\frac{P(\hat{y}_{i}=1)-P(\hat{y}_{i}=1|y_{i}=0)}{P(\hat{y}_{i}=1|y_{i}=1)-P(\hat{y}_{i}=1|y_{i}=0)}.
\end{align}
The Jaccard index which is
\begin{align}
J=\frac{ \vert\{i:y_{i}=1 \land \hat{y}_{i}=1\}\vert}{\vert\{i:y_{i}=1\}\vert + \vert\{i:y_{i}=0 \land \hat{y}_{i}=1\}\vert}
\end{align}
can then be approximated by
\begin{align}\label{eq:approxJ}
J_{approx} = \frac{P(\hat{y}_{i}=1|y_{i}=1)P(y_{i}=1)}{P(y_{i}=1)+P(\hat{y}_{i}=1|y_{i}=0)(1-P(y_{i}=1))}.
\end{align}
As mentioned in Section~\ref{sec:Methodology_a}, we split the training set into three subsets for approximate cross-validation.

\begin{table*}
\centering
\begin{tabular}{lllllllll}
\hline
CAD 120 & Bck & Open & Cut & Contain & Pour & Support & Hold & Mean \\ \hline
non-adaptive (VGG) & 0.62 & 0.09 & 0.20 & 0.41 & 0.35 & 0.11 & 0.40 & 0.31  \\ \hline
adaptive (VGG) & 0.68 & 0.10 & 0.23 & 0.44 & 0.36 & 0.50 & 0.47 & 0.40  \\ \hline
\end{tabular}
\caption{\label{tab:cad_nat} Comparison of adaptive binarization with non-adaptive binarization. The Jaccard index is reported for the object split of CAD 120 affordance dataset. }
\end{table*}

\begin{table*}
\centering
\begin{tabular}{lllllllll}
\hline
UMD & Grasp & Cut & Scoop & Contain & Pound & Support & Wgrasp & mean\\ \hline
non-adaptive (VGG) & 0.32 & 0.12 & 0.48 & 0.46 & 0.08 & 0.33 & 0.69 & 0.36\\ \hline
adaptive (VGG) & 0.31 & 0.18 & 0.56 & 0.49 & 0.08 & 0.41 & 0.66 & 0.38\\ \hline
\end{tabular}
\caption{\label{tab:umd_nat} Comparison of adaptive binarization with non-adaptive binarization. The Jaccard index is reported for the novel split of the UMD part affordance dataset.}
\end{table*}

	\section{Experiments}
	\label{sec:Experiments}

	For evaluation, we use the CAD 120 affordance dataset~\cite{cvpr} and the UMD part affordance dataset~\cite{UMD}. We use the splits separating the object classes (novel split on UMD and object split on CAD) and the splits which do not separate the object classes (category split on UMD and actor split on CAD). As measure, we use the Jaccard index. We report the results using the VGG architecture \cite{deeplabVGG} and the ResNet architecture \cite{deeplabResnet} as underlying convolutional network.
	First, we conduct ablation experiments to show the impact of our two key components, adaptive binarization and approximated cross validation. Second, we compare our approach with other weakly supervised segmentation approaches. If not otherwise specified, we use our approach based on the VGG architecture with adaptive binarization and approximate cross validation to determine $\sigma$~\eqref{eq:sigma}. As in~\cite{cvpr}, we use one keypoint per affordance class and training image. In Section~\ref{sec:keypoints}, we also evaluate the impact of the number of keypoints.

\begin{table*}
\centering
\begin{tabular}{lllllllll}
\hline
CAD 120 & Bck & Open & Cut & Contain & Pour & Support & Hold & Mean \\ \hline
Max thres. 1.0 (VGG) & 0.62 & 0.08 & 0.21 & 0.34 & 0.33 & 0.39 & 0.19 & 0.31  \\ \hline
Max thres. 0.5  (VGG) & 0.68 & 0.10 & 0.23 & 0.44 & 0.36 & 0.50 & 0.47 & 0.40  \\ \hline
\end{tabular}
\caption{\label{tab:cad_min}
Impact of limiting the adaptive threshold \eqref{eq:threshold} by 0.5. The Jaccard index is reported for the object split of the CAD 120 affordance dataset.
}
\end{table*}

\begin{table*}
\centering
\begin{tabular}{lllllllll}
\hline
UMD & Grasp & Cut & Scoop & Contain & Pound & Support & Wgrasp & mean\\ \hline
Max thres. 1.0 (VGG) & 0.32 & 0.04 & 0.36 & 0.42 & 0.05 & 0.23 & 0.64 & 0.29\\ \hline
Max thres. 0.5 (VGG) & 0.31 & 0.18 & 0.56 & 0.49 & 0.08 & 0.41 & 0.66 & 0.38\\ \hline
\end{tabular}
\caption{\label{tab:umd_min}
Impact of limiting the adaptive threshold \eqref{eq:threshold} by 0.5. The Jaccard index is reported for the novel split of the UMD part affordance dataset.}
\end{table*}

\subsection{Adaptive Binarization}
\label{subsec:minimum}
First we evaluate the impact of adapting the binarization to each training image and affordance class in comparison to using a constant threshold for each affordance class. To this end, instead of using $\min\left\{0.5,f\left(\{P(y_{i_k,l}=1|I;\theta)\}_{l_k=l}\right)\right\}$ as an individual threshold for each image $I$, we take the average of these thresholds over all images in the training set labeled with the affordance class $l$. Note that $f\left(\{P(y_{i_k,l}=1|I;\theta)\}_{l_k=l}\right){=}P(y_{i_k,l}=1|I;\theta)$ in this experiment since we use only one keypoint per affordance $l$ and image $I$.

The results for the object split of the CAD 120 affordance dataset and the novel split of the UMD part affordance dataset are shown in Tables \ref{tab:cad_nat} and \ref{tab:umd_nat}, respectively. Compared to the proposed adaptive binarization approach, the accuracy decreases for all affordance classes and the background, which are regions annotated without any affordance class. In average, the accuracy decreases by $-9\%$. On UMD, the decrease is smaller but still $-2\%$. The effect on CAD is larger since the size of the affordance regions varies more across the training images in comparison to UMD.

As discussed in Section~\ref{subsec:adaptive_thresholding}, we limit the adaptive threshold by $0.5$, which is the optimal threshold for a fully supervised trained model. Tables \ref{tab:cad_min} and \ref{tab:umd_min} show the results when the threshold is not limited, i.e., the adaptive threshold can even get close to one. As expected, the accuracy drops for both datasets by $-9\%$ since a threshold above $0.5$ would produce even in the fully supervised case too small affordance segments.

\subsection{Approximated Cross Validation}
The initialization depends on the value $\sigma$, which determines the initial affordance segments around the keypoints~\eqref{eq:sigma}. This is shown in the last column of Table \ref{tab:approximated_cross_validation_CAD} where we report the mean Jaccard index for three values of $\sigma$. Note that $\sigma$ is set proportional to the image width $w$. The results show that the accuracy strongly depends on the initialization. The strongest variation can be observed for the category split of the UMD part affordance dataset where the accuracy varies between $0.44$ to $0.61$. The approximated Jaccard index computed from the keypoints in the training set, which is reported in the second column of Table \ref{tab:approximated_cross_validation_CAD}, however, correlates with the Jaccard index on the test set. This shows that using approximated cross validation to determine $\sigma$ works very well in practice. Note that the values between the Jaccard index and its approximation differ since the first measure is computed over the test set and the second over the training set. In all experiments except of Table \ref{tab:approximated_cross_validation_CAD}, we have determined $\sigma$ by approximated cross validation.

	\begin{table*}
	\centering
	\begin{tabular}{l|lll||lll}
	& \multicolumn{3}{c||}{approx.\ Jaccard train} & \multicolumn{3}{c}{Jaccard test} \\ \hline
	$\sigma$ relative to image width\; & 0.03$w$ & 0.06$w$ & 0.12$w$ & 0.03$w$ & 0.06$w$ & 0.12$w$  \\ \hline \hline
	CAD actor split & 0.38 & \textbf{0.40} & 0.30 & 0.41 & \textbf{0.42} & 0.37   \\ \hline
	CAD object split  & 0.48 & \textbf{0.50} & 0.39 & 0.38 & \textbf{0.40} & 0.35   \\ \hline
	UMD category split & 0.57 & \textbf{0.58} & 0.44 & \textbf{0.61} & 0.59 & 0.44   \\ \hline
	UMD novel split & \textbf{0.66} & 0.62 & 0.44 &  \textbf{0.38} & \textbf{0.38} & 0.35   \\ \hline
	 \end{tabular}
	 \caption{\label{tab:approximated_cross_validation_CAD} Impact of $\sigma$ \eqref{eq:sigma}. The second column contains the approximated Jaccard index \eqref{eq:approxJ} computed on the training data for three values of $\sigma$. The approximated Jaccard index is used to determine $\sigma$. The third column contains the Jaccard index computed on the test data for three values of $\sigma$.}
	\end{table*}

	\begin{figure*}[t]
	{
\centering
\includegraphics[height=53mm]{multiple_points.png}
\caption{\label{fig:multiple_points}
Affordance segmentation with more than one keypoint per image and affordance. For the function $f$ \eqref{eq:threshold}, we compare average and median. The mean Jaccard index is plotted over the number of keypoints.}
}
\end{figure*}

\subsection{Varying Number of Keypoints}\label{sec:keypoints}
Our approach also works with multiple keypoints per affordance class in an image. In this case, we compare two functions for $f\left(\{P(y_{i_k,l}=1|I;\theta)\}_{l_k=l}\right)$  \eqref{eq:threshold}, namely taking the average or the median of $\{P(y_{i_k,l}=1|I;\theta)\}_{l_k=l}$.
The results are reported in Figure \ref{fig:multiple_points}. For the object split of the CAD 120 affordance dataset, average and median perform similar and the accuracy increases only slightly after three keypoints. A similar behavior can be observed for the novel split of the UMD part affordance dataset, but the accuracy increases more if the median is used.

\subsection{Comparison to the State-of-the-art}
We finally compare our approach with other weakly supervised semantic segmentation approaches~\cite{SEC,WTP,weak_deeplab,cvpr}. The results for both splits on the CAD 120 affordance dataset are reported in Table~\ref{tab:cad120_dataset_results}, while the results for the UMD part affordance dataset are reported in Table \ref{tab:umd_dataset_results}. The methods~\cite{SEC,weak_deeplab} use only image labels and therefore weaker supervision. It is therefore expected that methods that use more supervision in form of keypoints achieve a higher accuracy. For the methods \cite{WTP,cvpr} and our approach, we use one keypoint for each affordance class in an image. The parameter $\sigma$ has been determined by approximated cross validation. We also report the results as in \cite{cvpr} for the VGG architecture and the ResNet architecture. Our approach outperforms \cite{cvpr} and the other methods on both datasets. While our approach  achieves with the ResNet architecture on all datasets and splits a better mean accuracy than VGG, this is not the case for \cite{cvpr} where VGG is sometimes better. For the actor split of the CAD 120 affordance dataset, the mean accuracy is improved by $+17\%$ compared to \cite{cvpr}. This shows the benefit of adaptive binarization for weakly supervised affordance segmentation.
Qualitative results are shown in Figure~\ref{fig:example}.

	\begin{table*}
	 \centering
	 \begin{tabular}{lllllllll}
	  CAD 120 & Bck & Open & Cut & Contain & Pour & Support & Hold & Mean \\ \hline\hline
 \multicolumn{9}{l}{image label supervision - actor split} \\ \hline\hline
Area constraints \cite{weak_deeplab} & 0.53 & 0.11 & 0.02 & 0.09 & 0.09 & 0.07 & 0.15 & 0.15 \\ \hline
SEC \cite{SEC} & 0.53 & 0.43 & 0.00 & 0.25 & 0.09 & 0.02 & 0.20 & 0.22 \\ \hline\hline
\multicolumn{9}{l}{keypoint supervision - actor split} \\ \hline\hline
WTP \cite{WTP} & 0.53 & 0.13 & 0.00 & 0.10 & 0.08 & 0.11 & 0.22 & 0.17 \\ \hline
 \cite{cvpr} (VGG) & 0.61 & 0.33 & 0.0 & 0.35 & 0.30 & 0.22 & 0.43 & 0.32  \\ \hline
Proposed (VGG) & 0.71 & 0.47 & 0.0 & 0.36 & 0.37 & 0.56 & 0.49 & 0.42  \\ \hline
\cite{cvpr} (ResNet) & 0.60 & 0.25 & 0.00 & 0.35 & 0.30 & 0.17 & 0.42 & 0.30  \\ \hline
 Proposed (ResNet) & 0.77 & 0.50 & 0.00 & 0.43 & 0.39 & 0.64 & 0.56 & 0.47  \\ \hline\hline
\multicolumn{9}{l}{image label supervision - object split} \\ \hline\hline
Area constraints \cite{weak_deeplab} & 0.59 & 0.03 & 0.03 & 0.01 & 0.02 & 0.02 & 0.28 & 0.14 \\ \hline
 SEC \cite{SEC} & 0.54 & 0.04 & 0.09 & 0.13 & 0.09 & 0.08 & 0.13 & 0.16 \\ \hline\hline
\multicolumn{9}{l}{keypoint supervision - object split} \\ \hline\hline
 WTP \cite{WTP} & 0.57 & 0.01 & 0.00 & 0.02 & 0.09 & 0.03 & 0.19 & 0.13 \\ \hline
 \cite{cvpr} (VGG) & 0.62 & 0.08 & 0.08 & 0.24 & 0.22 & 0.20 & 0.46 & 0.27  \\ \hline
 Proposed (VGG) & 0.68 & 0.10 & 0.23 & 0.44 & 0.36 & 0.50 & 0.47 & 0.40  \\ \hline
 \cite{cvpr} (ResNet) & 0.69 & 0.11 & 0.09 & 0.28 & 0.21 & 0.36 & 0.56 & 0.33  \\ \hline
  Proposed (ResNet) & 0.74 & 0.15 & 0.21 & 0.45 & 0.37 & 0.61 & 0.54 & 0.44  \\ \hline
	 \end{tabular}
	 \caption{\label{tab:cad120_dataset_results} Comparison of our method to the state-of-the-art on the CAD 120 affordance dataset. The Jaccard index is reported.}
	 \end{table*}

	\begin{table*}
	 \centering
	 \begin{tabular}{lllllllll}
	 UMD & Grasp & Cut & Scoop & Contain & Pound & Support & Wgrasp & mean\\ \hline\hline
	 \multicolumn{9}{l}{image label supervision - category split} \\ \hline\hline
	  Area constraints \cite{weak_deeplab} & 0.06 & 0.04 & 0.10 & 0.14 & 0.22 & 0.04 & 0.37 & 0.14 \\ \hline
	  SEC \cite{SEC}& 0.39 & 0.16 & 0.27 & 0.13 & 0.35 & 0.19 & 0.07 & 0.22\\ \hline\hline
	 \multicolumn{9}{l}{keypoint supervision - category split} \\ \hline\hline
	 WTP \cite{WTP}& 0.16 & 0.14 & 0.20 & 0.20 & 0.01 & 0.07 & 0.13 & 0.13\\ \hline
	 \cite{cvpr} (VGG) & 0.46 & 0.48 & 0.72 & 0.78 & 0.44 & 0.53 & 0.65 & 0.58\\ \hline
	 Proposed (VGG)  & 0.55 & 0.48 & 0.72 & 0.76 & 0.49 & 0.48 & 0.67 & 0.59\\ \hline
	 \cite{cvpr} (ResNet) & 0.42 & 0.35 & 0.67 & 0.70 & 0.44 & 0.44 & 0.77 & 0.54\\ \hline
	 Proposed (ResNet) & 0.57 & 0.54 & 0.71 & 0.70 & 0.43 & 0.54 & 0.69 & 0.60\\ \hline\hline
	 \multicolumn{9}{l}{image label supervision - novel split} \\ \hline\hline
	  Area constraints \cite{weak_deeplab} & 0.05 & 0.00 & 0.04 & 0.16 & 0.00 & 0.01 & 0.32 & 0.09 \\ \hline
	 SEC \cite{SEC}& 0.12 & 0.03 & 0.06 & 0.23 & 0.07 & 0.12 & 0.25 & 0.13\\ \hline\hline
	 \multicolumn{9}{l}{keypoint supervision - novel split} \\ \hline\hline
	 WTP \cite{WTP}& 0.11 & 0.03 & 0.18 & 0.11 & 0.00 & 0.02 & 0.23 & 0.10\\ \hline
	 \cite{cvpr} (VGG) & 0.27 & 0.14 & 0.55 & 0.58 & 0.02 & 0.37 & 0.67 & 0.37\\ \hline
	  Proposed (VGG) & 0.31 & 0.18 & 0.56 & 0.49 & 0.08 & 0.41 & 0.66 & 0.38\\ \hline
	  \cite{cvpr} (ResNet) & 0.25 & 0.21 & 0.62 & 0.50 & 0.08 & 0.43 & 0.67 & 0.40\\ \hline
	  Proposed (ResNet) & 0.34 & 0.34 & 0.58 & 0.40 & 0.07 & 0.42 & 0.77 & 0.42\\ \hline
	 \end{tabular}
	 \caption{\label{tab:umd_dataset_results} Comparison of our method to the state-of-the-art on the UMD part affordance dataset. The Jaccard index is reported.}
	\end{table*}

	\begin{figure*}
	{
\centering
\includegraphics[height=90mm]{qualitative_example.png}
\caption{\label{fig:example}
Qualitative comparison of our approach (second row) with \cite{cvpr} (first row). Our approach localizes even small affordance parts while the GrabCut step in \cite{cvpr} merges the cap with the entire object.}
}
\end{figure*}

\section{Conclusion}
In this work, we have proposed an approach for affordance segmentation that requires only weak supervision in the form of sparse keypoints. Our approach builds on the method \cite{cvpr}, but it does not require an additional graph cut segmentation step. This has been achieved by an adaptive approach for binarizing the predictions of a convolutional neural network during training. By approximating the Jaccard index based on the keypoints, we are also able to optimize parameters for the initialization. This approach could also be used to optimize other hyperparameters. We evaluated our approach on the CAD 120 affordance and the UMD part affordance dataset. Our approach outperforms the state-of-the-art for weakly supervised affordance segmentation. On the CAD 120 affordance dataset, the mean accuracy is increased by up to 17 percentage points compared to \cite{cvpr}.

\clearpage

{\small
\bibliographystyle{ieee}

redpajama_set_name: RedPajamaArXiv


text:
https://www.thetelegraph.com/news/article/Futrell-named-to-airport-board-14857816.php
Futrell named to airport board
Published 2:02 pm CST, Saturday, November 23, 2019
EAST ALTON — Steve Futrell, owner of E-Rate Funding Solutions, LLC in East Alton, has been appointed as a commissioner for St. Louis Regional Airport.
The Madison County Board selected Futrell to serve until May 2021 to complete the term of Dennis Dubbledee who died earlier this year.
"I am appreciative of the appointment to the Airport Board of Commissioners," said Futrell. "As a life-long resident of the area I am keenly interested in its economic development. My goal is to take an active role in the vision of the Airport Authority and to assist in every way possible to implement strategies to realize the vision."
E-Rate Funding Solutions provides FCC E-Rate and technology consulting services to schools, school districts and libraries. Before opening the business, Futrell was Chief Technology Officer for Special School District of St. Louis County and Director of Technology for St. Louis Public Schools.
A graduate of Roxana High School, he earned a master's degree in Education from Southern Illinois University Edwardsville and a bachelor's degree from Welch College in Nashville, Tennessee.
"We are delighted to have Steve's passion for economic development and professional expertise on the board," said David C. Miller, Director of Aviation at St. Louis Regional. "We expect his experience in technology, business and the public sector to be extremely valuable as we continue to expand the airport's 600-acre business park."
For more on St. Louis Regional Airport, visit www.stlouisregional.com.


What is your plan for after you complete your CELTA or other TEFL course? Perhaps, you want to move abroad and just teach for a few years? Maybe you intend to train others on how to become English teachers in the future? Perhaps you would really like to become a Director of Studies? Whatever your goal, it is important that you continue working on your teacher development after your TEFL course.
Before you accept a teaching position, find out from your potential employer what continuing professional development opportunities are offered at the school or what the school does to ensure staff development. Asking about ways to continue your teacher development will not only make you sound keen to your potential employer but will also help you to assess if the school will be able to provide you with the support you need.
In an ideal situation, the school where you teach would have a support network in place for new teachers. You would be an assigned a mentor, someone to whom you can address questions and who would introduce you to the school and explain how things are done. You would have your lessons observed, would be given constructive feedback and you would have an opportunity to observe the lessons of senior teachers. The school would also run workshops or other training events for staff. However, if that is not the case, do not panic. There are things you can do on your own.
One of the best ways to improve your teaching is to reflect on your lessons. Take stock at the end of each lesson, or at the end of the day. Think about what went well and not so well during your lessons. What would you do again and what would you do differently next time? Keeping a journal with notes about your lessons will help you to figure out your strengths and things you need to work on. It will also help you to remember good practices which you can reuse in lessons. From time to time you could also ask your students for feedback on your teaching.
Keep up to date with developments in the teaching world and don't be scared to introduce new methods and ideas to your teaching. The internet is a great source of information, with many blogs, podcasts and websites dedicated to English teaching. For example, this Cambridge English website is aimed at English teachers keen on using digital resources in teaching. If you do not have time for regular online research, you can sign-up for some mailing lists and get pre-selected, relevant information emailed directly to you. If you use social media, you can follow like-minded people interested in English teaching for inspiration. These days there are also specialist online teacher development courses and MOOCs available, which you can take to improve your teaching knowledge and practise.
To get articles like this one and our latest job opportunities delivered directly to your inbox, scroll down, and sign up for our newsletter. You can also like us on Facebook and follow us on Twitter.


Bemag Transformer offers energy efficient transformers generating low losses.
Transformers can be specifically designed for all custom type loads.
(loose laminations can cause humming).
Progressive step lapping of the majority of the units reduces core losses and noise.
In order to provide our customers with the highest quality product, epoxy impregnation provides excellent bonding strength. Assemblies have a temperature rating of 220°C.


text:
Билл Томас (; 20 июня 1983, Питтсбург) — американский хоккеист, крайний правый нападающий. В настоящее время является игроком зальцбургского «Ред Булла», выступающего в Австрийской лиге ().

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Игроки «Аризона Койотис»
Игроки «Питтсбург Пингвинз»
Игроки «Флорида Пантерз»
Игроки «Сан-Антонио Рэмпэйдж»
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Игроки «Лейк Эри Монстерз»
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Игроки ХК МОДО

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Dedicated to those who are also wandering on the road of Specialized Kinesiology, offering you means and products you might find interesting to investigate. Specializing on the experience of muscle testing since 1989.
Orthokin.nl – You can read more on what I do with Specialized Kinesiology @ orthokin.nl. And while you already show interest in the subject of Specialized Kinesiology you also might take a peek at The Art of Muscle Testing, a page where you may find a not everyday explanation of how to muscle test, since you know there is much more to it then the eye can see.
Our goal – This site – in fact any other site produced – has the main goal to get you information. Information on the "things" that are important, the "things" that need to be passed on. So you can make up your own mind about the things that matter to you. Because you matter to me. Information I have is may be interesting to you too. So there you go!


text:
Q: topojson.py issue converting Point objects from geopandas created geojson I'm trying to convert information from a geopandas df into a topojson with python, using topojson.py. In this case, all of the geometries it the gpd df are shapely Points. geo_df.head() looks like this:
    geometry
0   POINT (61.39313083609281 34.73857657446109)
1   POINT (61.68715178647459 34.62839735680247)
2   POINT (61.02182917574118 34.6419671306646)
3   POINT (60.9017205031241 32.07261235932085)
4   POINT (61.0167260004123 32.15043416326984)

I can convert to geojson using:
gpd_df.to_file("points.json", driver="GeoJSON")

But when I try to convert that geojson to topojson like so:
topojson("points.json", "pointstopo.json", quantization=1e6, simplify=0.0001)

I get the following error:
~/anaconda3/lib/python3.6/site-packages/topojson/mytypes.py in
Point(self, point)
     35             self.polygon(coordinate);
     36     def Point(self,point):
---> 37         self.point(point['coordinates'])
     38     def Polygon(self,polygon):
     39         self.polygon(polygon['coordinates'])
TypeError: point() takes 1 positional argument but 2 were given

When I look at the geojson, point coordinates are given as two values:
"type": "Feature", "properties": { }, "geometry": { "type": "Point",
"coordinates": [ 62.269649605592754, 32.429278041035452 ] } },

It seems like topojson.py is expecting a shapely Point (or something like it), which has only one argument for a set of coordinates, rather than the geojson I have.
Am I correct in this understanding of the topojson.py error? If so, any thoughts on a workaround? Am I using topojson.py correctly?
I understand that I'm not getting any benefit from the topojson format if I'm just encoding Point geometries, but I plan to use it in other cases with both points and polys.

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text:
When You Are Near
I love the West and everything country. This book fits the bill as I have visited Cody, Wyoming and went to the Buffalo Bill's Museum. The museum has on display many items from his Wild West Shows. This Book brought back happy memories of my visit.
Who doesn't love a story where a young woman is strong but has a hidden weakness or secret that must be worked out? Each main character has a story of their own to be told. Lizzy is dealing with her losses the best way she can. Mary is determined to find out the truth. Ella is trying to escape and make it in her own.
Throw in a good looking cowboy...and you have this delightful story.
I received this book for free to review from Celebrate Lit.
Book: When You Are Near
Author: Tracie Peterson
Genre: Christian Historical Romance
After her father's death, Lizzy Brookstone, a trick rider in the all-female Brookstone Wild West Extravaganza, wants nothing more than to quit her role as the star of the show and live a quiet life on her family's ranch. But returning home would bring Wesley DeShazer, the ranch's foreman and the man who broke her heart, back into her life. In addition, the Brookstones' new manager fears that Lizzy's departure could doom the already cash-strapped show.
Hope grows in Lizzy during a stopover at Fleming Farm. Ella Fleming not only has a natural talent for trick riding and could easily replace Lizzy, but she's eager to join the show to flee a forced betrothal. However, while there, one of the show's wranglers is killed in a manner Lizzy finds suspicious. And during her escape from the farm, Ella stumbles across a secret that could affect all of their lives, as well as the future of the Brookstone Extravaganza.
Tracie Peterson (www.traciepeterson.com) is the bestselling, award-winning author of more than 100 novels. Tracie also teaches writing workshops at a variety of conferences on subjects such as inspirational romance and historical research. She and her family live in Montana.
Guest Post from Tracie
Introducing…When You Are Near!
I'm excited about the new Brookstone Brides series and had so much fun researching for the various characters and locations. This is a story about a fictional Wild West show with all female performers. Prior to the rodeos we see today, the Wild West shows traveled throughout America and Europe to entertain people and give city dwellers a taste of what the American frontier was like. At least their version.
Buffalo Bill Cody was famous for his shows, which brought Native Americans and their culture into the world of people who might otherwise never have even seen an "Indian." Pawnee Bill was another entertainer who sought to keep these shows alive.
For the fictional show in my novels, I wanted to focus on three female characters who became a part of the Brookstone Wild West Extravaganza for varying reasons. Lizzy Brookstone is the heroine in book one, When You Are Near, and as the daughter of one of the show's owners, she has enjoyed a starring role as a trick rider.
For research I went to Pueblo, Colorado to the ranch of Karen Vold, a famous trick rider and rodeo star. Linda Scholtz, a famed trick rider, was teaching a clinic there along with Karen at the Red Top Ranch Trickriding School.
These are two remarkable Christian trick riders who have long worked to keep the sport alive. I spent three days getting to know them and what they do and learned so much. And just to prove that we weren't all work and no play, I thought I'd share my attempt at trick riding. What I won't do for research!
The series will have three books, as usual, and each book will focus on a different lady and her skills. Mary is a trick shooter like Annie Oakley, and Ella is a Roman rider who stands on the backs of two horses for her part in the act. It was so interesting learning about each of these types of performers, and I hope you'll get a chance to read all three and enjoy the stories of the Brookstone Wild West Extravaganza.
Among the Reads, March 26
The Power of Words, March 26
Christian Author, J.E. Grace, March 26
Inklings and notions, March 26
The Meanderings of a bookworm, March 27
Chas Ray's Book Nerd Corner, March 27
Library Lady's Kid Lit, March 27
Vicky Sluiter, March 27
Carpe Diem, March 28
The Avid Reader, March 28
Genesis 5020, March 28
The Lit Addict, March 29
Jeanette's Thoughts, March 29
Back Porch Reads, March 29
Just the Write Escape, March 30
Splashes of Joy, March 30
Happily Managing a Household of Boys, March 30
Emily Yager, March 31
Daysong Reflections, March 31
Truth and Grace Homeschool Academy, March 31
Life of Literature, April 1
EmpowerMoms, April 1
For the Love of Literature, April 1
Abba's Prayer Warrior Princess, April 2
Through the Fire Blogs, April 2
Stephanie's Life of Determination, April 2
For Him and My Family, April 3
Remembrancy, April 3
Inspiration Clothesline, April 3
Stories Where Hope and Quirky Meet, April 4
Locks, Hooks and Books, April 4
Pause for Tales, April 4
Older & Smarter?, April 5
Stories By Gina, April 5
Book by Book, April 5
Bigreadersite, April 6
Book Bites, Bee Stings, & Butterfly Kisses, April 6
Connect in Fiction, April 6
Godly Book Reviews, April 7
Debbie's Dusty Deliberations, April 7
Texas Book-aholic, April 7
Blossoms and Blessings, April 8
janicesbookreviews, April 8
A Reader's Brain, April 8
The Becca Files, April 8
To celebrate her tour, Tracie is giving away a grand prize of When You Are Near, Tracie's A $25 Barnes and Noble Gift card, and the three books in The Heart of Alaska series and three other winners will receive a copy of When You Are Near!!
Be sure to comment on the blog stops for nine extra entries into the giveaway! Click the link below to enter. https://promosimple.com/ps/dcdd/when-you-are-near-celebration-tour-giveaway
at March 28, 2019 2 comments:
A Very Austen Valentine
I am a big loved of anything Jane Austen. I was excited to read this wonderful collection of stories based on her books. How can you not get lost in these stories?
I had a favorite: My Favorite Valentine. I could not get Colin Firth's face out of my mind. Not a bad thing!!!!
I was sent back to another time where there were certain expectations in their society. Manners were important. People were to behave a certain way. This doesn't happen anymore. Sometimes you are ridiculed when you expect decency and respect.
I loved this collection and would highly recommend it to my daughter (A huge Jane Austen fan) and to others.
Book: A Very Austen Valentine
Author: Robin Helm
Genre: Historical Romance (Regency), Religious and Inspirational
Six beloved authors deliver romantic Valentine novellas set in Jane Austen's Regency world. Robin Helm, Laura Hile, Wendi Sotis, and Barbara Cornthwaite, together with Susan Kaye and Mandy Cook, share variations of Pride and Prejudice, Persuasion, and Sense and Sensibility, featuring your favorite characters in sequels, adaptations, and spinoffs of Austen's adored novels.
Experience uplifting romance, laugh-out-loud humor, and poignant regret as these authors deftly tug on your heartstrings this Valentine's Day.
I Dream of You by Robin Helm
Newly-married Elizabeth Darcy has a plan: to charm her too-busy husband into desiring her company as much as he did when he was courting her. A series of romantic dreams gives her just the push she needs to put that plan into action.
Sir Walter Takes a Wife by Laura Hile
Faced with a lonely future and finding himself strapped for cash, Persuasion's Sir Walter Elliot manfully decides to marry again. But his careful plans go sadly awry! A lighthearted Valentine mash-up featuring two of Jane Austen's worst snobs.
My Forever Valentine by Wendi Sotis
Jane and Charles Bingley have married, even though Miss Elizabeth Bennet remains certain Mr. Fitzwilliam Darcy gave his best effort to keep them apart. After Mr. Darcy refused to stand up with Bingley and did not attend the wedding, she despises the gentleman more than ever and finds his company intolerable. How will she endure her visit to Kent if Mr. Darcy turns up everywhere she goes?
Pretence and Prejudice by Barbara Cornthwaite
A chance encounter with a handsome stranger forces Elizabeth to resort to subterfuge in order to discover his true intentions.
My Valentine by Mandy H. Cook
Little Charlotte was always determined and independent, traits which served her well as she battled a serious childhood illness and later as she took on Polite Society. Will those traits now deprive her of true love? Or would her lifelong Valentine win her heart?
The Lovers' Ruse by Susan Kaye
In this Persuasion alteration, Anne is so altered by Wentworth's love in the summer of 1806, she refuses to give him up when both her godmother and father try to persuade her. "The Lovers' Ruse"follows Frederick and Anne through their whirlwind courtship and their secret engagement. When Wentworth returns for his Annie girl, the cat comes out of the bag.
Robin Helm's books reflect her love of music, as well as her fascination with the paranormal and science fiction.
Previously published works include The Guardian Trilogy: Guardian, SoulFire, and Legacy (a guardian angel protects a supernaturally gifted girl), the Yours by Design series: Accidentally Yours, Sincerely Yours, and Forever Yours (Fitzwilliam Darcy switches places in time with his descendant, Will Darcy), and Understanding Elizabeth (Regency sweet romance).
She contributed to A Very Austen Christmas: Austen Anthologies, Book 1, an anthology featuring like-minded authors, in 2017, and A Very Austen Valentine: Austen Anthologies, Book 2which was released on December 29, 2018.
New releases for 2019 include More to Love, a standalone historical sweet romance dealing with body image; Lawfully Innocent, a historical U.S. Marshal romance book in the Lawkeepers series; Maestro, a historical sweet romance featuring a brilliant musician and his student; and A Very Austen Romance: Austen Anthologies, Book 3.
She lives in sunny South Carolina where she teaches piano and adores her one husband, two married daughters, and three grandchildren.
More from Robin
Do you love Historical romance, but avoid "those" covers like the plague?
Would you enjoy an anthology featuring six Valentine-themed novellas set in the Regency period written by Christian authors?
In A Very Austen Valentine, stories range from a young wife searching for a way to bring her marriage back to the way it was, to a laugh-out-loud courtship between Jane Austen's worst snobs.
Misunderstandings, concealed identities, and romance abound.
There is even a sequel featuring the children of the Darcys and the Brandons.
Every reader will find something to love in this wonderful collection.
Blossoms and Blessings, March 25
Christian Bookaholic, March 25
Blogging With Carol, March 26
Britt Reads Fiction, March 26
Reflections from My Bookshelves,March 27
For Him and My Family, March 27
Godly Book Reviews, March 28
Connect in Fiction, March 29
The Christian Fiction Girl, March 30
Abba's Prayer Warrior Princess, March 30
Moments, March 31
Mary Hake, March 31
Truth and Grace Homeschool Academy, April 1
Creating Romance, April 2
Library Lady's Kid Lit, April 4
Just the Write Escape, April 6
Inklings and notions, April 7
To celebrate her tour, Robin is giving away
Grand Prize: a $50 Amazon gift card
1st Place: An autographed paperback of A Very Austen Valentine: Austen Anthologies, Book 1
2nd Place: An autographed paperback of A Very Austen Christmas: Austen Anthologies, Book 2
3rd Place: A set of three ebooks – Reader's Choice of any three of my books and
4th Place: a $5 Amazon gift card!!
Be sure to comment on the blog stops for nine extra entries into the giveaway! Click the link below to enter. https://promosimple.com/ps/ddfa/a-very-austen-valentine-celebration-tour-giveaway
Mending Fences - Suzanne Woods Fisher
There has to be another book in this series. I just read the last page and said "what" ?
I can't believe I have to wait to find out Izzy and Luke's future.
I enjoyed reading of Luke's growth and his journey of asking for forgiveness. It clearly was not easy. We might not have a list of people to ask for forgiveness. But is there one person? What would you say? How would it make you feel if your apology was rejected? Or if you were actually forgiven?
Suzanne Woods Fisher is one of the authors that I eagerly look forward to reading. She has a special talent to take me on a journey of discovery. This Book was about Izzy and Luke and what they discovered about themselves and others.
Book: Mending Fences
Author: Suzanne Woods Fisher
Genre: Christian fiction, Amish, Mennonite
Every saint has a past. Every sinner has a future.
Luke Schrock is a new and improved man after a stint in rehab, though everyone in Stoney Ridge only remembers the old Luke. They might have forgiven him, but nobody trusts him. He has been allowed to live at Windmill Farm under two conditions. First, he must make a sincere apology to each person he's hurt. Second, he must ask each victim of mischief to describe the damage he caused.
Simple, Luke thinks. Offering apologies is easy. But discovering the lasting effects his careless actions have caused isn't so simple. It's gut-wrenching.
And his list keeps growing. Izzy Miller, beautiful and frustratingly aloof, also boards at Windmill Farm, and Luke's clumsy efforts to befriend her only insult and annoy her. Eager to impress, Luke sets out to prove himself to her by locating her mother. When he does, her identity sends shock waves through Stoney Ridge.
"A funny, heartwarming story of friendship, love, and the possibility of happily ever after."—Amy Clipston, bestselling author of Seat by the Hearth
"Suzanne Woods Fisher has written a sweet and poignant story you won't want put down. Definitely a must read!"—Kathleen Fuller, bestselling author of the Amish of Birch Creek series
Click here to purchase your copy.
Suzanne Woods Fisher is an award-winning, bestselling author of more than two dozen novels, including the Nantucket Legacy, Amish Beginnings, The Bishop's Family, and The Inn at Eagle Hill series. She is also the author of several nonfiction books about the Amish, including Amish Peace and The Heart of the Amish. She lives in California. Learn more at www.suzannewoodsfisher.comand follow Suzanne on Facebook @SuzanneWoodsFisherAuthor and Twitter @suzannewfisher.
More from Suzanne
"A real friend is one who walks in when the rest of the world walks out." –Walter Winchell
Luke Schrock was nearly friendless. He returned to Stoney Ridge in Mending Fences after a stint in rehab only because his bishop, David Stoltzfus, strongly encouraged him. And everybody knows you didn't say no to your bishop.
But nobody else in town wanted anything to do with Luke. They forgave him for the trouble he'd caused in his last downward spiral, the one that went too far. The one that even scared Luke. Forgiving him was the easy part. The Amish of Stoney Ridge were intentional forgivers. It was their trust—that's what he would have to earn. Trust was a fragile thing. Once broken, it wasn't easily mended.
Somehow, David was still able to look past the behavior to see the best of Luke Schrock. He didn't stop there. He found a family who was willing to take Luke in, and he spent hours with him—at times as a mentor, at times as a father figure, but mostly as a friend.
A loyal friend can have a powerful impact. Consider those friends in the town of Capernaum, who carried their paralyzed friend to Jesus for healing—so determined to get to Him that they dragged his stretcher up onto a roof, broke through the roof tiles, and lowered him down. Can you imagine being in the crowd, listening to Jesus, as straw and tile pieces and branches and bugs started to drop down from the ceiling? A shocking display…of devoted friendship. And what a miraculous outcome for that paralyzed man!*
I won't tell you how Luke's story ends—only that he works hard to find ways to make amends to those he hurt, to regain trust. Especially the hard-to-earn trust of a brown-eyed beauty named Izzy.
Do you have a real friend like David Stoltzfus, who walks in when the rest of the world has walked out? Or maybe the better question, for you, for me, is to ask ourselves if we are friends to others like David Stoltzfus was to Luke Schrock. I know I want to be.
*This miracle is reported in three gospels: Mark 2:1-12, Matthew 9:1-8, Luke 5:17-26.
The Power of Words, March 4
The Avid Reader, March 4
Quiet Quilter, March 5
CarpeDiem, March 5
Among the Reads, March 6
Girls in White Dresses, March 6
A Reader's Brain, March 6
Blossoms and Blessings, March 7
Jeanette's Thoughts, March 7
Southern Gal Loves to Read, March 7
Bigreadersite, March 8
Blogging With Carol, March 8
Eat, Read, Teach, Blog, March 8
God's Little Bookworm, March 9
Just the Write Escape, March 9
EmpowerMoms, March 10
Book Bites, Bee Stings, & Butterfly Kisses, March 10
Christian Chick's Thoughts, March 11
Locks, Hooks and Books, March 11
Because I said so — and other adventures in Parenting, March 12
Debbie's Dusty Deliberations, March 12
Random Thoughts From a Bookworm, March 13
Caffeinated Christian Raves – N – Reviews, March 14
Lighthouse Academy, March 14
Texas Book-aholic, March 14
janicesbookreviews, March 15
Literary Reflections Book Blog, March 16
Pause for Tales, March 16
Inspiration Clothesline, March 16
Have A Wonderful Day, March 17
Hallie Reads, March 17
Little Homeschool on the Prairie, March 18
For The Love of Books, March 18
The Becca Files, March 18
To celebrate her tour, Suzanne is giving away a grand prize of an Amazon Kindle!!
Be sure to comment on the blog stops for nine extra entries into the giveaway! Click the link below to enter. https://promosimple.com/ps/de7d/mending-fences-celebration-tour-giveaway
at March 06, 2019 No comments:


The video shows Mayer traveling around the countryside, enjoying life, playing his acoustic, and hanging out with the locals. This song will be off of Mayer's forthcoming album, Born and Raised, released through Columbia Records on May 22. We cannot wait for this album to hit the shelves.
Please watch "Shadow Days" by simply heading through and let UTG know if you have picked up your pre-order of Born and Raised!


The Clean Bin Project was a year-long project whose goal was to get a share household to buy nothing and reduce the amount of garbage produced to as close to zero as possible, undertaken by Vancouverites Jen, Grant and Rhyannon. Starting on Canada Day 2008 (July 1st for those not Canadian) the trio pledged to "try to buy no more 'stuff' and produce zero landfill waste for one year." The purpose was to have less "stuff" (i.e. non-essential items) making its way into their home and eventually into the landfill.
Hop Studios offered services to them for free in order to show support for their work.
Along the way Jen blogged about their experiences of trying to live as garbage-free as possible. The site is a hosted WordPress blog.
We mapped the wordpress.com url to its own domain, cleanbinproject.com, and also set them up on twitter to connect with people and promote their project. We gave them a few tweaks to the CSS of their blog to improve the navigation and make the site a little, dare we say it, less cluttered.


Tiny diamantes outline Marie-Antoinette's initials on this pretty heart ornament. Add a touch of French elegance to your home with this decoration!
Behold the French Queen who lost her head after the French Revolution! (1755-1793)..
Is your daughter going to her first ball this year? Why not treasure the special event with this pre..


OK everyone. Where is this, and what is its connection with Red Dwarf?
As Cappsy correctly guessed, this is the Midland Hotel in Manchester, where the hotel scenes in Stasis Leak were filmed, and where the cast stayed.
You can tell it's the same place just from looking in the door even though I've never actually been in, as people wielding cameras would probably get thrown out again.
It's about 5 minutes walk from the BBC building in Manchester.


When the weather begins to turn from hot and muggy to cold and dry, having an efficient running heating system is essential for comfort and to maintain your health. If your heating unit at home or your commercial HVAC system is not working correct, you can depend on Chill Air Concept to service your heating system quickly, effectively and affordably. Since 1999, Chill Air Concept has been the go-to source for commercial and residential heating services throughout Parker. Whether you have an older furnace or Energy Star® rated central heat units at home, or a business with a faulty heating system, we have the bandwidth to complete all services you need.
Commercial and residential heating systems are much different in design, and the methods they are serviced. Regardless of whether you have a commercial property with a complex HVAC system or a single-family home owner with a new or older heating system, our team of qualified, certified and insured technicians can service any heat unit possible. We have years of experience with both residential and commercial heating systems and have completed several repairs, tune-ups and installations in Parker and surrounding communities. If you require any heating services in Parker – give us a phone call today!
If you require your indoor filters replaced and belts tightened at your house in Parker, or you have a commercial HVAC system that requires weekly or monthly service, our company can complete any heating system maintenance you need. We will customize a maintenance program that fits your needs and budget. By customizing the maintenance program, you'll be able to receive the best possible maintenance for your individual system and the best price. Contact our sales team today to ask them about our heating maintenance agreements, and let's create a program that makes sense.
When the heating system breaks down in Parker, at home or the office – contact Chill Air Concept to diagnose and repair your unit. Our team can repair all commercial and residential heating systems, of all brands, makes and models. From minor issues to major system failures, we will find the root system failure, and recommend the repair that will resolve this problem. If you have any questions about our heating repairs, or need to set up an appointment – contact us today.
Replacing an older heating system is made easy when you work with our team. Chill Air Concept is a long-time distributor of York and Carrier heating systems, including Energy Star® rated systems. We offer both commercial and residential systems and offer the best prices since we work directly with these manufacturers. Our certified heating technicians can install your new heating system professionally, which ensures correct operation for your heating system for years.
We have established strong relationships with several lenders locally and across the US to create an affordable financing program that will fit your individual budget. Our goal is to set up a financing program that simply makes sense and won't put a dent in your pocket book. We offer programs for home owners and businesses, and will work with you to simplify the process and ensure you get the perfect heating system. Our professional sales team is ready to speak to you about any heating services in Parker you require. Contact us at (972) 699-8080 to schedule an appointment or to allow us to answer any questions you might have.


Review of Andy powered by the Slant community.
What are the best open-source Android emulators?
What are the best Android emulators for Windows?
To root Andy download this rootkit, extract the archive and run AndyRootkit30.exe. It comes with frequently installed root applications that can be installed in the process and also allows unrooting Andy.
As long as virtualization is enabled in BIOS, the setup is straightforward. Simply download, install, run. First time the software runs it will ask to either log in with a Google account or create one allowing it to download software directly from the Play Store.
With some tinkering, Andy can be set up to run with up to 3GB of RAM, the current Android 32bit RAM limit. It allows for a smooth experience and offers enough power to have multiple apps running simultaneously. The VM can even add more virtual CPU cores to Andy, doubling it's processing power or more.
Andy is built on top of the powerful Genymotion VHD. It takes advantage of OpenGL capable graphics cards allowing it to be one of the fastest Android emulators.
Andy comes with a custom clever implementation of ADW. It is theme-able, while functions very well without any modifications. The app sorting is a nice touch. Andy is compatible with most Android launchers.
Andy is constantly optimizing for applications to perform better on their system. Their support staff has a reporting setup to get the right information to the developers as fast as possible.
It can't stream media to a phone, but it can work as a game controller. It allows for accelerometer- and touch-based interactions.
Andy manages support mostly through a Facebook group where both knowledgeable admins and smart community members help answer questions.
Andy supports all traditional Android widgets and notifications. It can even use apps like Push Bullet to send those notifications to their other devices.
The new launcher provided for, it seems, limitless number of Andy tablets. Have a Andy just for CoC with 3GB and 4CPUs to maximize your CoC play to the next level. Then make a full copy of that machine for a full backup. Have an Andy for the children, be able to lock each Andy with a password lock-screen. And manage all this with less only 7 buttons ;) Again, Simple and powerful.
As of this latest version, users can be notified of new versions and incremental changes. It's possible to update only certain pieces or parts, rather than downloading an entirely new build. Andy is also exploring OTA update possibilities.
Andy is available in over 40 languages.
For a full list of supported languages, see here.
While setting up Andy, 1ClickSync will ask for Google Account credentials that allow keeping apps and their settings in sync between Andy and an Android device.
Andy is available on Windows 7 and 8.1 PCs with OS X support in development.
Downloading Andy requires only one click with no email address or account needed.
Which is where windows is installed. What's more, it does not let you change the installation path.


text:
module Dry
  module Types
    module Rails
      module TypesRegistration
        REGISTERED_TYPES = Concurrent::Array.new

        def register(name, type = nil, &block)
          return super unless type
          return if TypesRegistration::REGISTERED_TYPES.include?(name)

          super.tap do
            # Check to see if we need to remove the registered type
            autoloaded = ActiveSupport::Dependencies.will_unload?(type)
            # ActiveSupport::Dependencies.will_unload?(klass) won't return true yet
            #   if it's the first time a constant is being autoloaded
            #   so we have to see if we're in the middle of loading a missing constants
            autoloaded |= caller.any? { |line| line =~ /\:in.*?new_constants_in/ }

            TypesRegistration::REGISTERED_TYPES << name if autoloaded
          end
        end
      end

      Dry::Types.module_eval do
        class << self
          prepend(TypesRegistration)
        end
      end

      class Railtie < ::Rails::Railtie
        config.to_prepare do
          types = TypesRegistration::REGISTERED_TYPES.dup
          types.each do |type|
            TypesRegistration::REGISTERED_TYPES.delete(type)
            type = Dry::Types.identifier(type)
            Dry::Types.container._container.delete(type)
            Dry::Types.type_map.delete(type)
          end
        end
      end
    end
  end
end

redpajama_set_name: RedPajamaGithub


text:
Home MLS Toronto FC Mariner's fiery halftime speech inspires TFC rally against the Rapids
Mariner's fiery halftime speech inspires TFC rally against the Rapids
By Mark Polishuk
Paul Mariner
Toronto FC was playing its 10th game in 33 days on Wednesday night against the Colorado Rapids and, in the first half, it looked as if the busy schedule had finally caught up to the Reds. The club was frustrated and down a goal after the first 45 minutes, and yet in the second half, the Reds rebounded to equalize and then pull ahead on Andrew Wiedeman's first career goal to give Toronto the 2-1 result.
With the Reds on a three-game winning streak, suddenly the club may wish that it had yet another league game in a few days in order to keep its roll going.
"We take them as they come," said defender Doneil Henry. "The boys have been putting in great shifts and it's hard but we've been handling it well. We just keep grinding."
The Reds' congested schedule coincided with Paul Mariner's hiring as head coach, and TFC are now 4-2-4 under their new gaffer, as opposed to their gruesome 1-9 record under Aron Winter over the season's first 10 games. Mariner praised how his players responded after he "gave them both barrels at halftime."
"The resilience of these boys is off the charts," Mariner said. "We've had a ridiculous run of games and we've basically been playing with the same players. They've been playing hurt and some of those guys don't want to be taken out of the team. My hat is off to those guys again."
TFC missed a golden opportunity in the opening minutes when Luis Silva was pulled down in the box, giving the Reds a penalty kick. Ryan Johnson took the kick, rather than captain Torsten Frings, and the change didn't work, as Johnson sailed his shot off the right post.
Toronto tried to keep up the pressure but were deflated by Conor Casey's goal in the 23rd minute. Rapids defender Hunter Freeman sent a quality cross towards the box and Casey managed to escape Henry's marking long enough to leap and head the ball into the net for his second score of the year.
Johnson had a couple more chances in the first half but couldn't convert, leading to the Reds' half-time deficit and Mariner's fiery locker-room speech.
"He's a great motivator and he told us at the end of the first half that we should be winning the game," Silva said. "You could tell in the second half that we picked it up and we fought until the last minute."
Silva was getting the start at forward, playing up front in place Danny Koevermans, out for the season with a torn ACL. The rookie made a good impression in his first game as a striker, getting two shots on goal and creating all sorts of problems for Colorado's defence.
Case in point, TFC's first goal. Silva and Johnson weaved their way into the box, and when Silva attracted two defenders, Silva passed to his fellow forward, who made a canny touch to put the ball past Rapids goalkeeper Matt Pickens.
Silva's development as a forward is a good sign for a Toronto side that is thin up front in the wake of Koevermans' injury and the departures of Joao Plata and Nick Soolsma. Mariner said that he was impressed with Silva's performance despite some remaining holes in his game.
"Sometimes the young players, when they get into the attacking third, they don't know the defenders are a little bit reticent to go and tackle them," Mariner said. "[Silva] was sort of losing the ball too much for my liking but you can see the qualities. In that final third he does freeze defenders and he's got beautiful feet."
Just as Johnson found redemption after his penalty kick miss, TFC's second goal also provided a player with atonement for a first-half error. In the 67th minute, Henry was fighting for possession in the right corner and made a great effort to both win the ball and keep it from going out of bounds. Henry had time to cross into the box and found Wiedeman for the left-footed touch and the goal.
"I was trying to put as much pressure on the back line as I could," Henry said. "I went in to try and get a cross and when I broke through I saw [Wiedeman] at the top of the box. I saw him, picked him out and he got a goal.
"I just had to find my confidence, especially after the first goal and I know I should've been there with better marking. It didn't stop me, I wanted to just go in there and get the three points."
Wiedeman joined the Reds on Friday as part of a trade that saw Julian de Guzman go to FC Dallas. Though the deal was primarily made so TFC could free up some cap space and get rid of a player who seemed to have worn out his welcome in Toronto, Mariner believes Wiedeman has the tools to make the trade a steal for his club.
"[Wiedeman] is deceptively quick. He gets around the field very very well, he's a very intelligent footballer," Mariner said. "He's got wonderful feet, his touch is very good and I've been told he's probably one of the best finishers in MLS but he's been behind some very good players in Dallas. We were fortunate to pick him up."
For his part, Wiedeman describes himself as a "poacher" around the net and his first MLS strike "was definitely a poacher's goal."
With Silva and Wiedeman both impressing on Wednesday, Mariner and TFC director of team and player operations Earl Cochrane could again change their focus heading towards the close of the MLS transfer window. Koevermans' injury created the assumption that the Reds would pursue a striker, but if their current crop of forwards prove they're up to the job, Mariner and Cochrane could instead look to fill their oft-stated need for an experienced centre back.
"We're still light up front and I'm not going to be say we're light at the back because the back four have been absolutely spectacular but you do need some players in to give them some rest," Mariner said. "It's just a fact of football life. We can't continue playing 20-plus more games, it's going to be tough. You just can't keep asking players to keep performing at the level we're asking of them."
A roster move could come as early as Thursday, as Mariner hinted that "we may have an announcement tomorrow."
Now that their compacted stretch of 10 league games is complete, the Reds don't play another MLS match until July 28 against Houston. In the interim, however, is Saturday's friendly against Liverpool in front of what is expected to be a large crowd at the Rogers Centre. While Mariner is looking forward to facing a world football power, he admitted he isn't a fan of the scheduling.
"If I'm brutally honest, it couldn't have come at a worse time because I've got some sore men in there, battle-harmed and beaten up a little bit," Mariner said. "But it's Liverpool, you know?"
TagsAndrew WiedemanColorado RapidsConor CaseyDoneil HenryEarl CochraneHunter FreemanLuis SilvaMatt PickensMLSPaul MarinerRyan JohnsonToronto FCTorsten Frings
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Tired of reading about Chris Christie? Take a 90-second break and watch this ADORABLE polar bear cub from the Toronto Zoo try and take its first steps.
It's the best thing you'll see all week, until you get to happy hour after work on Friday.


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text:
Politics & Policy COVID and Schools Coronavirus Detroit
Michigan leads lawsuit against DeVos over coronavirus funds for private schools
By Koby Levin and Lori Higgins
Dana Nessel
Tribune News Service via Getty Images
Michigan and California are leading a multistate lawsuit against U.S. Education Secretary Betsy DeVos, alleging she is wrongfully requiring millions of dollars in coronavirus relief to be sent to private schools.
DeVos' rules are an unlawful interpretation of the federal COVID-19 relief package, which Congress passed in March, said Dana Nessel, Michigan's attorney general.
That package provided nearly $31 billion in aid to K-12 schools to help them plug some budget holes caused by the steep drop in state revenue because of the pandemic, and to purchase needed personal protective equipment for students and staff. Nearly $400 million was earmarked for Michigan, with $350 million going directly to local school districts and $40 million to the state education department.
Part of that money would have provided services such as music classes or physical therapy to private school students from low-income families. But DeVos' interpretation of the formula would mean Michigan schools could lose out on at least $16 million. It could also mean that in some cases, students attending wealthy private schools would receive more aid than students attending less wealthy public schools, Nessel said.
"Public schools are at risk of losing millions and millions in federal funds to their private school neighbors," she said during a press conference on Tuesday.
She said the Grand Rapids school district would lose $2.6 million, while the Flint school district would lose $1.4 million.
"Michigan kids simply cannot afford for Betsy DeVos to play politics with their education," she added later.
The suit was filed in a federal court on Tuesday by attorneys general in Wisconsin, Maine, California, New Mexico, and the District of Columbia.
Here's what Angela Morabito, press secretary for the U.S. Department of Education, which is also named in the suit, had to say:
"While the Department does not comment on pending litigation, the Secretary has said many times, this pandemic affected all students, and the CARES Act requires that funding should be used to help all students," Morabito said in a statement. "There is no reasonable explanation for debating the use of federal funding to serve both public and private K-12 students when federal funding, including CARES Act funding, flows to both public and private higher education institutions."
The lawsuit comes a day after information released by the federal treasury agency showed dozens of private schools in Michigan received loans from the Paycheck Protection Program. These loans present an additional opportunity for federal relief funds for private and charter schools. The forgivable loans, aimed at maintaining payroll, weren't available to public school districts. (Chalkbeat is also a nonprofit and received federal paycheck protection funding.)
DeVos, a west Michigan native who for decades has been a big proponent of school choice, said in May that the coronavirus pandemic has offered a chance to advance her longstanding goal of using public dollars to support access to private schools.
"For more than three decades that has been something that I've been passionate about," she said on a radio program. "This whole pandemic has brought into clear focus that everyone has been impacted, and we shouldn't be thinking about students that are in public schools versus private schools."
In May, Michigan's state superintendent, Michael Rice, issued a memo telling school districts to hold off on setting aside extra money for private schools under DeVos's guidance.
"We're going to be watching this closely and we're prepared to insert ourselves as need be," he told Chalkbeat in a May interview.
Gov. Gretchen Whitmer said the pandemic has shined a light on the inequities in the public school system. Schools in less affluent areas of the state struggled to provide remote learning because many of their students don't have the necessary technology to participate. She said the relief package rightly directed money to those types of districts.
"Betsy DeVos … has a different plan," Whitmer said. "Schools in these areas deserve a government that will support them in this crisis. She doesn't share our priorities."
Tina Kerr, executive director of the Michigan Association of School Administrators, said in a statement that she supports the lawsuit.
"At a time when public schools need additional resources, Secretary DeVos is once again using illegal tactics to advance her personal privatization agenda. These funds should do what Congress intended – support the students who need it most."
Beth DeShone, the executive director of the Great Lakes Education Project, said she is discouraged that the state is moving forward with a lawsuit, saying the virus didn't discriminate based on the kind of schools students attend. She said it ignores the fact that there are private schools that serve disadvantaged students.
"Students across the board in communities have been hit hard by this pandemic," DeShone said. Students attending private schools, "deserve this as much as their neighbor down the street."
Read the lawsuit here:
Page 1 of CARES Act Private School Suit Complaint
Contributed to DocumentCloud by Koby Levin of Chalkbeat • View document or read text


text:
Propelling space titans
load typo
Propelling
space titans
Design and production of cryogenic tanks and equipment,
production of industrial gases that act as a propellant, operational support
and provision of services associated with the launch site…
Thanks to its know-how in cryogenics and gas applications, Air Liquide is involved in every
major stage of the life of a launcher from design to lift off, on ground and on board.
On the ground: on the launch site
Production and supply of propulsion fluids
Supply of launch pad and mast distribution systems
Supervision and maintenance of the oxygen and hydrogen tanks
Production and supply of propulsion and inerting fluids (oxygen, hydrogen, helium, nitrogen, compressed air)
In French Guiana, Air Liquide supplies the propulsion and implementation fluids (liquid oxygen and hydrogen) of the Ariane, Vega and Soyouz launchers. For example, in the case of Ariane 5, these fluids are supplied in semimobile tanks positioned near the launch base and connected to the launcher through highly insulated pipes. Every launch requires 420,000 liters of liquid oxygen and 800,000 liters of liquid hydrogen
The launch pad is an enormous mobile metal structure that, on its own, weighs 850 tons. It is equipped with an umbilical mast, which stands at a height of 58 meters and houses the installations necessary to feed and control the launcher. Air Liquide supplies and operates all of the distribution systems that supply liquid oxygen and hydrogen to the cryogenic main stage (EPC).
The mast also supports the two cryogenic arms needed to fill and empty the cryogenic upper stage (ESC-A).
On behalf of Arianespace and the French National Space Agency (CNES), Air Liquide ensures the supervision and complete maintenance of the cryogenic equipment, EPC and ESC-A tanks as well as all of the tubes that connect the launcher, the arms and the pipelines to the installations on the ground.
Gaseous helium is used to inert the transfer lines, particularly the liquid oxygen and hydrogen lines of Ariane 5. In its liquid state, helium is used to maintain the pressure of the EPC tanks, which contain liquid hydrogen and oxygen, as they are emptied.
Compressed air is used for different parts of the launcher.
Finally, nitrogen is supplied in its gaseous state, at 250 bar, through a pipeline that supplies the launch zone.
5, 4, 3, 2, 1... Lift off! Ignition of the cryogenic main stage (EPC) and then of the two solid propellant boosters (EAP) that surround it to propel the rocket out of the Earth's atmosphere. Pyrotechnic systems later detach the EAPs from the rocket.
Separation of the nose cone
Once out of the Earth's atmosphere, the rocket's nose cover is released. The engine continues to power the rocket. The nose cone and the tanks then come away, releasing the cryogenic upper stage (ESC-A).
Separation of the upper stage and satellite
Propulsion continues for approximately 15 minutes before stopping. The rocket - or more specifically, the "payload" (man-made satellite or space probe that the rocket has to place in orbit) - freed from the elements that surround it, continues its ballistic flight before deploying its two satellites in a geostationary orbit.


Mix flour, cayenne pepper and salt. Spread on large platter and dredge each fillet, shaking off excess. Set aside on sheet of waxed paper. Heat half the oil and butter in a skillet large enough to accommodate 3 fillets. When butter is foaming but not brown, add fillets and saute on one side for about 4 minutes, until light golden. Turn fillets and spread the browned side with sauce; continue to saute until underside is browned, about another 4 minutes. Cover skillet for a few minutes to melt sauce. Remove fish to platter. Add remaining oil and butter and cook remaining fillets.
Place parsley in food processor and process until coarsely chopped, turning machine off and on and scraping down sides. Add all other ingredients except salt, and process until mixture makes a smooth paste. Store, tightly covered in refrigerator.