text:
\section{Introduction}

One of the most successful language model pre-training tasks is masked language modeling (MLM; \citealt{bert}).
First, we randomly mask some input tokens in a sentence. Then the encoder learns to recover the masked tokens given the corrupted input.
\ele{}~\cite{electra} argues that MLM only produces supervision signals at a small proportion of positions (usually $15\%$), and uses the replaced token detection task as an alternative.
Specifically, \ele{} contains a generator and a discriminator. The generator is a masked language model, which substitutes masks with the tokens sampled from its MLM predictions. The discriminator learns to distinguish which tokens have been replaced or kept the same.
Experimental results on downstream tasks show that \ele{} can largely improve sample efficiency.

Despite achieving compelling performance, it is usually difficult to balance the training pace between the generator and the discriminator.
Along with pre-training, the generator is expected to sample more hard replacements for the detection task in a curriculum manner, while the discriminator learns to identify the corrupted positions.
Although the two components are designed to compete with each other, there is no explicit feedback loop from the discriminator to the generator, rendering the learning games independent.
The absence of feedback results in sub-efficient learning, because many replaced tokens have been successfully trained while the generator does not know how to effectively sample replacements.
In addition, a well trained generator tends to achieve reasonably good MLM accuracy, where many sampled replacements are correct tokens.
In order to relieve the issue of oversampling correct tokens, \ele{} explored tweaking the mask probability larger, raising the sampling temperature, and using a manual rule to avoid sampling original tokens.

In this paper, we propose two methods, namely hardness prediction and sampling smoothing, to tackle the above issues.
First, the motivation of \textit{hardness prediction} is to sample the replacements that the discriminator struggles to predict correctly.
We elaborate on the benefit of a good replacement mechanism from the perspective of variance reduction.
Theoretical derivations indicate that the replacement sampling should be proportional to both the MLM probability (i.e., language frequency) and the corresponding discriminator loss (i.e., discrimination hardness).
Based on the above conclusion, we introduce a sampling head in the generator, which learns to sample by estimating the expected discriminator loss for each candidate replacement.
So the discriminator can give feedback to the generator, which helps the model to learn what it has not acquired.
Second, we propose a \textit{sampling smoothing} method for the issue of oversampling original tokens.
We adopt a focal loss~\cite{focalloss} for the generator's MLM task, rather than using cross-entropy loss.
The method adaptively downweights the well-predicted replacements for MLM, which avoids sampling too many correct tokens as replacements.

We conduct pre-training experiments on the WikiBooks corpus for both small-size and base-size models.
The proposed techniques are plugged into \ele{} for training from scratch.
Experimental results on various tasks show that our methods outperform \ele{} despite the simplicity.
Specifically, under the small-size setting, our model performance is $0.9$ higher than \ele{} on MNLI~\cite{mnli2017} and $4.2$ higher on SQuAD 2.0~\cite{squad}, respectively.
Under the base-size setting, our model performance is $0.26$ higher than \ele{} on MNLI and $0.52$ higher on SQuAD 2.0, respectively.

\section{Related Work}

State-of-the-art NLP models are mostly pretrained on a large unlabeled corpus with the self-supervised objectives~\cite{elmo,albert,t5}.
The most representative pretext task is masked language modeling (MLM), which is introduced to pretrain a bidirectional BERT~\cite{bert} encoder.
RoBERTa~\cite{roberta} apply several strategies to enhance the BERT performance, including training with more data and dynamic masking.
UniLM~\cite{unilm, unilm2} extend the mask prediction to generation tasks by adding the auto-regressive objectives.
XLNet~\cite{xlnet} propose the permuted language modeling to learn the dependencies among the masked tokens.
Besides, \ele{}~\cite{electra} propose a novel training objective called replaced token detection which is defined over all input tokens.
Moreover, \electric{}~\cite{electric} extends the idea of \ele{} by energy-based cloze models.

Some prior efforts demonstrate that sampling more hard examples is conducive to more effective training.
\citet{focalloss} propose the focal loss in order to focus on more hard examples.
Generative adversarial networks~\cite{gan} is trained to maximize the probability of the discriminator making a mistake, which is closely related to \ele{}'s training framework.
In this work, we aim at guiding the generator of \ele{} to sample the replacements that are hard for the discriminator to predict correctly, therefore the pre-training process of the discriminator can be more efficient.

\section{Background: \ele{}}
\label{sec:electra}

\begin{figure*}[t]
\centering
\includegraphics[width=0.98\linewidth]{new_electra.pdf}
\caption{An overview of \ele{}.
The MLM head of the generator learns to perform MLM and samples replacements at each masked position from the MLM distribution.
For the corrupted sequence, the discriminator learns to distinguish which tokens have been replaced or kept the same.}
\label{fig:ele_model}
\end{figure*}

\begin{figure*}[t]
\centering
\includegraphics[width=0.98\linewidth]{new_our_model.pdf}
\caption{An overview of our model.
The generator has two prediction heads.
The MLM head learns to perform MLM through the focal loss instead of the cross entropy loss.
The sampling head is trained to estimate the discriminator loss over the vocabulary.
Our model samples the replacements from a new distribution, which is proportional to both the MLM probability and the corresponding discriminator loss.
The discriminator is trained to distinguish input tokens and the loss feedback is transferred to the generator for the sampling head to learn.}
\label{fig:our_model}
\end{figure*}

An overview of \ele{} is shown in Figure~\ref{fig:ele_model}.
The model consists of a generator \emph{G} and a discriminator \emph{D}.
The generator is trained by masked language modeling (MLM).
Formally, given an input sequence $\boldsymbol{x} = x_1 \cdots x_n$, we first randomly mask $k=\lceil 0.15n \rceil$ tokens at the positions $\boldsymbol{m} = m_1 \cdots m_k$ with \sptk{MASK}.
The perturbed sentence $\boldsymbol{c}$ is denoted as:
\begin{align*}
&m_i \sim \mathrm{uniform}\{1, n\} \quad \mbox{for} \; i=1 , \cdots , k  \\
&\boldsymbol{c} = \mathrm{replace}(\boldsymbol{x}, \boldsymbol{m}, \sptk{MASK})
\end{align*}
where the $\mathrm{replace}$ operation conducts masking at the positions $\boldsymbol{m}$.
The generator encodes $\boldsymbol{c}$ and performs MLM prediction.
At each masked position $i$, we sample replacements from MLM output distribution $p_{\textnormal{\textsc{g}}}$:
\begin{align*}
&{x'}_{i} \sim p_{\textnormal{\textsc{g}}}(x_{i}'|\boldsymbol{c}) \quad \mbox{for} \; i \in \boldsymbol{m}  \\
&{\boldsymbol{x}}^{\rm R} = \mathrm{replace}(\boldsymbol{x}, \boldsymbol{m}, {\boldsymbol{x'}})
\end{align*}
where masks are replaced with the sampled tokens.
Next, the discriminator encodes the corrupted sentence ${\boldsymbol{x}}^{\rm R}$.
A binary classification task learns to distinguish which tokens have been replaced or kept the same, which predicts the probability $D(x^{\rm R}_t, \boldsymbol{x}^{\rm R})$ to indicate how likely $x^{\rm R}_t$ comes from the true data distribution.

The overall pre-training objective is defined as:
\begin{equation*}
{
\begin{aligned}
&\underset{\theta_G, \theta_D}{\rm min}\!\sum_{\boldsymbol{x} \in \mathcal{X}}\!\underset{i \in \boldsymbol{m}}{\rm \mathbb{E}}[\mathcal{L}_G^{\theta_G}(x_i,\! \boldsymbol{c})]\!+\!\lambda\!\underset{t \in [1,n]}{\rm \mathbb{E}}[\mathcal{L}_D^{\theta_D}(x^{\rm R}_t\!,\! \boldsymbol{x}^{\rm R})] \\
&\mathcal{L}_G(x_i, \boldsymbol{c})\!=\!-\!\log p_{\textnormal{\textsc{g}}}(x_i|\boldsymbol{c}) \\
&\mathcal{L}_D(x^{\rm R}_t, \boldsymbol{x}^{\rm R})\!=\!
\begin{cases}
    \!-\!\log D(x^{\rm R}_t, \boldsymbol{x}^{\rm R}) &x^{\rm R}_t\!=\!x_t \\
    \!-\!\log (1\!-\!D(x^{\rm R}_t, \boldsymbol{x}^{\rm R}))&x^{\rm R}_t\!\neq\!x_t \\
\end{cases}
\end{aligned}
}
\end{equation*}
where $\mathcal{X}$ represents text corpus, and $\lambda$ $=50$ suggested by \citet{electra} is a hyperparameter used to balance the training pace of generator and discriminator.
Once pre-training is finished, only the discriminator is fine-tuned on downstream tasks.

\section{Methods}
\label{sec:methods}

\subsection{Hardness Prediction}
\label{sec:hardness}

The key idea of hardness prediction is to let the generator receive the discriminator's feedback and sample more hard replacements.
Figure~\ref{fig:our_model} shows the overview of our method.
Besides the original MLM head in the generator, there is an additional sampling head used to sample replaced tokens.

Given a\footnote{For notation simplicity, we assume only one token is masked in each sentence.} replaced token $x'$ in the input sequence $c$, let ${\mathcal{L}}_D(x',c)$ denote the discriminator loss for the replacement.
Rather than directly sampling replacements from the MLM prediction $p_{\textnormal{\textsc{g}}}$, we propose to sample from $p_{\textnormal{\textsc{s}}}$:
\begin{align}
p_{\textnormal{\textsc{s}}}(x'|c) &= \frac{p_{\textnormal{\textsc{g}}}(x'|c)  {\mathcal{L}}_D(x',c)}{{\rm E}_{p_{\textnormal{\textsc{g}}}(x^*|c)}[{\mathcal{L}}_D(x^*,c)]} \label{eq:opt_ps} \\
{\boldsymbol{x}}^{\rm R} &= \mathrm{replace}(\boldsymbol{x}, \boldsymbol{m}, {\boldsymbol{x'}}) \quad {x'} \sim p_{\textnormal{\textsc{s}}}(x'|c) \nonumber
\end{align}
where the corrupted sentence ${\boldsymbol{x}}^{\rm R}$ is obtained by substituting the masked positions $\boldsymbol{m}$ with sampled replacements $\boldsymbol{x'}$.
The first term $p_{\textnormal{\textsc{g}}}(x'|c)$ implies sampling from the data distribution.
The second term ${\mathcal{L}}_D(x',c)$ encourages the model to sample more replacements that the discriminator has not successfully learned.

Notice that \eqform{eq:opt_ps} uses the actual discriminator loss ${\mathcal{L}}_D(x',c)$, which can not be obtained without feeding ${\boldsymbol{x}}^{\rm R}$ into the discriminator.
As an alternative, we use the estimated loss value $\hat{\mathcal{L}}_D(x',c)$ to sample replaced tokens, which approximates the actual loss for the candidate replacement.
During pre-training, we use the actual loss as supervision, and simultaneously train the sampling head.
We describe the detailed implementations of loss estimation in Section~\ref{sec:implment:hardness}.

By considering detection hardness in replacement sampling and giving feedback from the discriminator to the generator, the components are no longer independently learned.
\ele{}~(\citealt{electra}; Appendix F) also attempts to achieve the same goal by adversarially training the generator.
However, it underperforms the maximum-likelihood training, because of the poor sample efficiency of reinforcement learning on discrete text data.
More importantly, their generator is trained to fool the discriminator, rather than guiding the discriminator by data distribution, which breaks the \ele{} training objective.
In contrast, we still retain the MLM head, and decouple it from replacement sampling. So we can take the advantage of the original training objective.

\subsubsection{Perspective of Variance Reduction}

We show that the proposed hardness prediction method is well supported from the perspective of variance reduction.

\begin{proposition}
Sampling replacements from $p_{\textnormal{\textsc{s}}}(x'|c)$ can minimize the estimation variance of the discriminator loss.
\end{proposition}

\begin{proof}
At each masked position, the expectation of the discriminator loss we aim to estimate can be summarized as $\mathrm{Z} = {\rm E}_{p_{\textnormal{\textsc{g}}}(x^*|c)}[\mathcal{L}_D(x^*, c)]$.
Under $p_{\textnormal{\textsc{g}}}$, the estimation variance of the discriminator loss is:
\begin{align*}
& \qquad \mathrm{Var}_{p_{\textnormal{\textsc{g}}}(x^*|c)}[\mathcal{L}_D(x^*, c)] \\
&= \sum_{x^* \in \text{vocab}} p_{\textnormal{\textsc{g}}}(x^*|c) (\mathcal{L}_D(x^*,c) - \mathrm{Z})^2 \\
&= \sum_{x^* \in \text{vocab}} p_{\textnormal{\textsc{g}}}(x^*|c) \mathcal{L}_D(x^*, c)^2 - \mathrm{Z}^2
\end{align*}

Similar to importance sampling, we can select an alternative distribution $p_{\textnormal{\textsc{s}}}$ different from $p_{\textnormal{\textsc{g}}}$, then the expectation $\mathrm{Z}$ is rewritten as:
\begin{align*}
&\qquad {\rm E}_{p_{\textnormal{\textsc{g}}}(x^*|c)}[\mathcal{L}_D(x^*, c)] \\
&= \sum_{x^* \in \text{vocab}} p_{\textnormal{\textsc{g}}}(x^*|c) \mathcal{L}_D(x^*,c) \\
&= \sum_{x^* \in \text{vocab}} p_{\textnormal{\textsc{s}}}(x^*|c)  \frac{p_{\textnormal{\textsc{g}}}(x^*|c)}{p_{\textnormal{\textsc{s}}}(x^*|c)} \mathcal{L}_D(x^*,c) \\
&= {\rm E}_{p_{\textnormal{\textsc{s}}}(x^*|c)}[\frac{p_{\textnormal{\textsc{g}}}(x^*|c)}{p_{\textnormal{\textsc{s}}}(x^*|c)} \mathcal{L}_D(x^*,c)]
\end{align*}
By making a multiplicative adjustment to $\mathcal{L}_D$, the estimation variance of $\mathrm{Z}$ under the new sampling distribution $p_{\textnormal{\textsc{s}}}$ is converted to:
\begin{align*}
    &\qquad \mathrm{Var}_{p_{\textnormal{\textsc{s}}}(x^*|c)}[\frac{p_{\textnormal{\textsc{g}}}(x^*|c)}{p_{\textnormal{\textsc{s}}}(x^*|c)} \mathcal{L}_D(x^*, c)] \\
    &= \sum_{x^* \in \text{vocab}} p_{\textnormal{\textsc{s}}} (\frac{p_{\textnormal{\textsc{g}}}(x^*|c)  \mathcal{L}_D(x^*,c)}{p_{\textnormal{\textsc{s}}}})^2 - \mathrm{Z}^2 \\
    &= \sum_{x^* \in \text{vocab}} \frac{(p_{\textnormal{\textsc{g}}}(x^*|c)  \mathcal{L}_D(x^*,c) - p_{\textnormal{\textsc{s}}}(x^*|c) \mathrm{Z} )^2}{p_{\textnormal{\textsc{s}}}(x^*|c)}
\end{align*}

Based on the above derivation, it is obvious that we obtain a zero-variance estimator when we choose $p_{\textnormal{\textsc{s}}}(x^*|c)=p_{\textnormal{\textsc{g}}}(x^*|c) \mathcal{L}_D(x^*,c) / \mathrm{Z}$ as \eqform{eq:opt_ps}.
This theoretically optimal form provides us insights into designing the above sampling scheme.
\end{proof}

\subsubsection{Two Implementations of Hardness Prediction}
\label{sec:implment:hardness}

We design two variants of the sampling head.
The first one is to explicitly estimate the discriminator loss (HP$_{\rm Loss}$).
The second method is to approximate the expected sampling distribution (HP$_{\rm Dist}$).

\paragraph{HP$_{\rm Loss}$} guides the generator to learn the probability predicted by the discriminator that the sampled token $x'$ is an original token.
In this case, the output layer of the sampling head is actually a $\mathrm{sigmoid}$ function same as the discriminator:
\begin{equation*}
\hat{D}(x',c) = \mathrm{sigmoid} (\boldsymbol{w}(x') \cdot \boldsymbol{h}_S(c))
\end{equation*}
where $\boldsymbol{h}_S(c)$ denotes the contextual representations projected by the sampling head, and $\boldsymbol{w}$ denotes the projection parameters.
Then the loss of the sampling head at the masked position is:
\begin{align*}
\mathcal{L}_S(x',c) = (\hat{D}(x',c) - D(x',c))^2
\end{align*}

When sampling replacements over the vocabulary, the estimated discriminator probability $\hat{D}(x',c)$ can be easily rewritten to the estimated discriminator loss $\hat{\mathcal{L}}_D(x',c)$:
\begin{align*}
&\hat{\mathcal{L}}_D(x',c) =
\begin{cases}
- \log \hat{D}(x',c) & x' = x \\
- \log (1 - \hat{D}(x',c)) & x' \neq x \\
\end{cases}
\end{align*}
Multiplying the MLM probability factor $p_{\textnormal{\textsc{g}}}$, we obtain the sampling distribution:
\begin{align*}
p_{\textnormal{\textsc{s}}}(x'|c) &= \frac{p_{\textnormal{\textsc{g}}}(x'|c) \hat{\mathcal{L}}_D(x',c)}{\sum_{x^* \in \text{vocab}} p_{\textnormal{\textsc{g}}}(x^*|c) \hat{\mathcal{L}}_D(x^*,c)} \\
&= \frac{p_{\textnormal{\textsc{g}}}(x'|c) \hat{\mathcal{L}}_D(x',c)}{{\rm E}_{p_{\textnormal{\textsc{g}}}(x^*|c)}[\hat{\mathcal{L}}_D(x^*,c)]}
\end{align*}

\paragraph{HP$_{\rm Dist}$} aims to directly approximate the expected sampling distribution as in \eqform{eq:opt_ps}, instead of the discriminator loss.
In this case, the sampling head produces an output probability of the token $x'$ with a $\mathrm{softmax}$ layer:
\begin{equation}
\label{eq:hp:dist:softmax}
p_{\textnormal{\textsc{s}}}(x'|c) = \frac{\exp (\boldsymbol{e}(x') \cdot \boldsymbol{h}_S(c))}{\sum_{x^* \in \text{vocab}} \exp (\boldsymbol{e}(x^*) \cdot \boldsymbol{h}_S(c))}
\end{equation}
where $\boldsymbol{e}$ represents the token embeddings.
For the sampled token $x'$, we define the loss of the sampling head as:
\begin{align*}
\mathcal{L}_S(x',c) = - \frac{p_{\textnormal{\textsc{g}}}(x'|c)}{p_{\textnormal{\textsc{s}}}(x'|c)} \mathcal{L}_D(x',c) \log p_{\textnormal{\textsc{s}}}(x'|c)
\end{align*}
Then we show that minimizing the above loss $\mathcal{L}_S(x',c)$ pushes sampling distribution of \eqform{eq:hp:dist:softmax} to our goal.
Specifically, the loss expectation over the whole vocabulary is:
\begin{align*}
&{\rm E}_{p_{\textnormal{\textsc{s}}}(x^*|c)}[\mathcal{L}_S(x^*,c)] = \\
&\!-\!\sum_{x^* \in \text{vocab}} p_{\textnormal{\textsc{s}}}(x^*|c)\!\frac{p_{\textnormal{\textsc{g}}}(x^*|c)}{p_{\textnormal{\textsc{s}}}(x^*|c)} \mathcal{L}_D(x^*,c)\!\log p_{\textnormal{\textsc{s}}}(x^*|c) \\
&= - \sum_{x^* \in \text{vocab}} p_{\textnormal{\textsc{g}}}(x^*|c) \mathcal{L}_D(x^*,c) \log p_{\textnormal{\textsc{s}}}(x^*|c)
\end{align*}
According to the Lagrange Multiplier method, the optimal solution $\tilde{p}_{\textnormal{\textsc{s}}}$ of the loss function $\mathcal{L}_S(x',c)$ is consistent with \eqform{eq:opt_ps}:
\begin{align*}
\tilde{p}_{\textnormal{\textsc{s}}}(x'|c) &= \frac{p_{\textnormal{\textsc{g}}}(x'|c) \mathcal{L}_D(x',c)}{\sum_{x^* \in \text{vocab}} p_{\textnormal{\textsc{g}}}(x^*|c) \mathcal{L}_D(x^*,c)} \\
&= \frac{p_{\textnormal{\textsc{g}}}(x'|c) \mathcal{L}_D(x',c)}{{\rm E}_{p_{\textnormal{\textsc{g}}}(x^*|c)}[\mathcal{L}_D(x^*,c)]}
\end{align*}

\subsection{Sampling Smoothing}
\label{sec:focal}

Along with the learning process, the masked language modeling tends to achieve relatively high accuracy.
As a consequence, the generator oversamples the correct tokens as replacements, which renders the discriminator learning inefficient.

In order to address the issue, we apply an alternative loss function called focal loss~\cite{focalloss} for MLM of the generator.
Compared with the vanilla cross-entropy loss, focal loss adds a modulating factor for the weighting purpose:
\begin{align*}
\mathcal{L}^{{\rm fc}}_G(x, c) = - (1 - p_{\textnormal{\textsc{g}}}(x|c))^{\gamma} \log p_{\textnormal{\textsc{g}}}(x|c)
\end{align*}
where $\gamma \geq 0$ is a tunable hyperparameter.
Besides using a constant $\gamma$, we try the piecewise function $\gamma = \mathbbm{1}(p_{\textnormal{\textsc{g}}}\!>\! 0.2) * 3 + \mathbbm{1}(p_{\textnormal{\textsc{g}}} \leq 0.2) * 5$ in our experiments as suggested by~\citet{calibfocal}.

In other words, the focal loss is used to adaptively down-weight the well-classified easy examples and thus focusing on more difficult ones.
When applying the focal loss to the MLM head for the generator, we notice that if a token is easy for the generator to be predicted correctly, i.e., $p_{\textnormal{\textsc{g}}}(x|c) \rightarrow 1$, the modulating factor is greatly decreased.
In contrast, if a token is hard to predict, the focal loss approximates to the original cross entropy loss.
Therefore, we propose to employ the focal loss in order to smooth the sampling distribution, which in turn relieves oversampling correct tokens as replacements.

\begin{table*}[t]
\centering
\scalebox{0.92}{
\begin{tabular}{l c c c c c c c c c}
\toprule
\multirow{2}{*}{\bf Model}   & \textbf{MNLI} & \textbf{QNLI} & \textbf{QQP} & \textbf{RTE} & \textbf{SST} & \textbf{MRPC} & \textbf{CoLA} & \textbf{STS}  \\
&      -m/-mm      &      Acc       &      Acc      &      Acc      &      Acc      &      Acc      &      MCC      &      PCC      \\ \midrule
~~\emph{Small-size} & \\
\ele{} (\emph{reimplementation}) & 80.3/80.6 & 89.0 & 89.2 & 62.2 & 89.0 & \textbf{87.3} & 59.6 & 86.3
\\
\midrule
\ele{}+HP$_{\rm Loss}$+Focal & \textbf{81.2}/81.7 & 89.1 & \textbf{89.6} & \textbf{66.1} & \textbf{89.2} & 86.6 & 59.3 & 86.7  \\
\ele{}+HP$_{\rm Dist}$+Focal & 81.0/\textbf{81.8} & \textbf{89.3} & \textbf{89.6} & 62.5 & \textbf{89.2} & 86.8 & \textbf{59.7} & \textbf{86.8} \\
\midrule
~~\emph{Base-size} & \\
BERT~\cite{bert} & 84.5/- & 88.6 & 90.8 & 68.5 & 92.8 & 86.0 & 58.4 & 87.8 \\
RoBERTa~\cite{roberta} & 84.7/- & - & - & - & 92.7 & - & - & -  \\
XLNet~\cite{xlnet} & 85.8/- & - & - & - & \textbf{93.4} & - & - & - \\
\ele{}~\cite{electra} & 86.2/- & 92.4 & 90.9 & 76.3 & 92.4 & 87.9 & 65.8 & 89.1  \\
\electric{}~\cite{electric} & 85.7/- & 92.1 & 90.6 & 73.4 & 91.9 & 88.0 & 61.8 & 89.4 \\
\ele{} (\emph{reimplementation}) &  86.7/86.5 & 92.6 & 91.4 & 80.4 & 92.6 & 89.1 & 66.5 & \textbf{91.0} \\
\midrule
\ele{}+HP$_{\rm Loss}$+Focal & \textbf{87.0}/\textbf{86.9} & \textbf{92.7} & \textbf{91.7} & \textbf{81.3} & 92.6 & \textbf{90.7} & 66.7 & \textbf{91.0}  \\
\ele{}+HP$_{\rm Dist}$+Focal & 86.8/86.8 & 92.3 & 91.6 & 80.0 & 92.7 & 89.8 & \textbf{67.3} & 90.9 \\
\bottomrule
\end{tabular}}
\caption{Comparisons between our models and previous pretrained models on GLUE dev set.
Reported results are medians over five random seeds.
}
\label{tbl:glue_res}
\end{table*}

\subsection{Pre-Training Objective}

Adopting the above two strategies, we jointly train the generator and the discriminator together as the original \ele{} model.
The word embeddings of them are still tied during the pre-training stage.
Formally, we minimize the combined loss over a large corpus $\mathcal{X}$:
\begin{equation*}
{
\begin{aligned}
&\underset{\theta_G, \theta_S, \theta_D}{\rm min} \sum_{\boldsymbol{x} \in \mathcal{X}} \Big( \underset{i \in \boldsymbol{m}}{\rm \mathbb{E}}[\mathcal{L}_G^{{\rm fc}, \theta_G}(x_i, \boldsymbol{c})] + \\ &\lambda_1 \underset{i \in \boldsymbol{m}}{\rm \mathbb{E}}[\mathcal{L}_S^{\theta_S}(x^{\rm R}_i, \boldsymbol{c})] + \lambda_2 \underset{t \in [1,n]}{\rm \mathbb{E}}[\mathcal{L}_D^{\theta_D}(x^{\rm R}_t, \boldsymbol{x}^{\rm R})] \Big) \\
\end{aligned}
}
\end{equation*}
where $\lambda_1, \lambda_2$ are two hyperparameters to adjust three parts of the loss.
We only search $\lambda_1$ value and keep $\lambda_2=50$ for the fair comparison with \ele{}.
After pre-training, we throw out the generator and only fine-tune the discriminator on the downstream tasks.

\section{Experiments}

\subsection{Setup}

We implement \ele{}+HP$_{\rm Loss}$/HP$_{\rm Dist}$+Focal on both the \emph{small-size} setting and the \emph{base-size} setting.
The two prediction heads share both the generator and the token embeddings, which avoids the unnecessary increase in model complexity.
We follow most settings as suggested in \ele{}~\citep{electra}.
In order to enhance the \ele{} baseline for a solid comparison, we add the relative position~\cite{t5}. Experimental results show that our methods can improve performance even on the enhanced \ele{} baseline.

We pretrain our models on the same text corpus as \ele{}, which is a combination of English Wikipedia and BooksCorpus~\cite{wikidata}.
We also adopt the N-gram masking strategy which is beneficial for MLM tasks.
The models are trained for 1M steps for small-size models and 765k steps for base-size models, so that the computation consumption can be similar to baseline models~\cite{electra}.
The base-size models are pretrained with $16$ V100 GPUs less than five days.
The small-size models are pretrained with $8$ V100 GPUs less than three days.
We use the Adam~\cite{adam} optimizer ($\beta_1=0.9, \beta_2=0.999$) with learning rate of 1e-4.
The value of $\lambda_2$ in the training objective is kept fixed at $50$ for a fair comparison with \ele{}.
For HP$_{\rm Loss}$, we search $\lambda_1$ in $\{5, 10, 20\}$, the best one is $5$.
For HP$_{\rm Dist}$, we keep $\lambda_1=1$.
We search the focal loss weight $\gamma$ in $\{1, 4\}$ on both the base-size and small-size model, the best configuration is $\gamma=1$.
The detailed pre-training configurations are provided in the supplemental materials.

\subsection{Results on GLUE Benchmark}

The General Language Understanding Evaluation (GLUE) benchmark~\cite{glue} is a collection of diverse natural language understanding (NLU) tasks, including inference tasks (MNLI, QNLI, RTE; \citealt{rte1,rte2,rte3,rte5,mnli2017, squad1}), similarity and paraphrase tasks (MRPC, QQP, STS-B; \citealt{mrpc2005, sts-b2017}), and single-sentence tasks (CoLA, SST-2; \citealt{cola2018, sst2013}).
The detailed descriptions of GLUE datasets are provided in the supplementary materials.
The evaluation metrics are Spearman correlation for STS-B, Matthews correlation for CoLA, and accuracy for the other GLUE tasks.

For small-size settings, we use the hyperparameter configuration as suggested in~\cite{electra}.
For base-size settings, we consider a limited hyperparameter searching for each task, with learning rates $\in \{$5e-5, 1e-4, 1.5e-4$\}$ and training epochs $\in \{3, 4, 5\}$.
The remaining hyperparameters are the same as \ele{}.
We report the median performance on the dev set over five different random seeds for each task.
All the results come from the single-task fine-tuning.
For more detailed fine-tuning configurations, please refer to the supplementary materials.

Results are shown in Table~\ref{tbl:glue_res}.
With the same configuration and pre-training data, for both the small-size and the base-size, our methods outperform the strong reimplemented \ele{} baseline by 0.6 and 0.4 on average respectively.
For the most widely reported task MNLI, our models achieve 87.0/86.9 points on the matched/mismatched set, which obtains 0.3/0.4 absolute improvements. The performance gains on the small-size models are more obvious than the base-size models, we speculate that is due to the learning of the small-size generator is more insufficient and suffers from the above issues more significantly.
The results demonstrate that our proposed methods can improve the pre-training of \ele{}.
In other words, sampling more hard replacements is more efficient than the original masked language modeling.

\subsection{Results on SQuAD 2.0}

The Stanford Question Answering Dataset (SQuAD; \citealt{squad}) is a reading comprehension dataset, each example consists of a context and a question-answer pair.
Given a context and a question, the task is to answer the question by extracting the relevant span from the context.
We only use the version 2.0 for evaluation, where some questions are not answerable.
We report the results of both the Exact-Match (EM) and F1 score.
When fine-tuning on SQuAD, we add the question-answering module from XLNet on the top of the discriminator as~\citet{electra}.
All the hyperparameter configurations are the same as \ele{}.
We report the median performance on the dev set over five different random seeds.
Refer to the appendix for more details about fine-tuning.

Results on SQuAD 2.0 are shown in Table~\ref{tbl:sq2_res}.
Consistently, our models perform better than \ele{} baseline under both the small-size setting and the base-size setting.
Under the base setting, our models improve the performance over the reimplemented \ele{} baseline by 0.6 points (EM) and 0.5 points (F1).
Especially under the small setting, our models outperform the baseline by a remarkable margin.
\ele{}+HP$_{\rm Dist}$+Focal obtains 4.3 and 4.2 points absolute improvements on EM and F1 metric.

\begin{table}[t]
\centering
\begin{tabular}{l c c }
\toprule
\multirow{2}*{\textbf{Model}} & \multicolumn{2}{c}{\textbf{SQuAD 2.0}} \\
& EM & F1 \\ \midrule
~~\emph{Small-size} & \\
\ele{} (\emph{reimplementation}) & 68.9 & 71.3  \\
\midrule
\ele{}+HP$_{\rm Loss}$+Focal & 71.8 & 74.3\\
\ele{}+HP$_{\rm Dist}$+Focal & \textbf{73.2} & \textbf{75.5}\\
\midrule
~~\emph{Base-size} & \\
BERT~\cite{bert} & 73.7 & 77.1 \\
RoBERTa~\cite{roberta} & - & 79.7 \\
XLNet~\cite{xlnet} & 78.2 & 81.0 \\
\ele{}~\cite{electra} & 80.5 & 83.3 \\
\electric{}~\cite{electric} & 80.1 & - \\
\ele{} (\emph{reimplementation}) & 82.4 & 85.1  \\
\midrule
\ele{}+HP$_{\rm Loss}$+Focal & \textbf{83.0} & \textbf{85.6} \\
\ele{}+HP$_{\rm Dist}$+Focal & 82.7 & 85.4 \\
\bottomrule
\end{tabular}
\caption{Comparisons between our models and previous pretrained models on SQuAD 2.0 dev set.
Reported results are medians over five random seeds.}
\label{tbl:sq2_res}
\end{table}

\begin{table}[t]
\centering
\begin{tabular}{l c c}
\toprule
\textbf{Model}   & \textbf{MNLI-m} & \textbf{SQuAD 2.0}   \\
\midrule
\ele{} & 80.3 & 71.3 \\
\midrule
\small{\emph{\ele{} + HP$_{\rm Loss}$}}    \\
$\lambda_1 = 5$    & 80.9 & 74.2 \\
$\lambda_1 = 10$   & 81.1 & 75.1 \\
$\lambda_1 = 20$   & 81.0 & 74.1 \\
\midrule
\multicolumn{2}{c}{\small{\emph{\ele{} + HP$_{\rm Loss}$ ($\lambda_1 = 5$) + Focal} }}  \\
$\gamma=
\scaleobj{0.75}
{
\begin{cases}
3& p_{\textnormal{\textsc{g}}} > 0.2\\
5& p_{\textnormal{\textsc{g}}} \leq 0.2
\end{cases}}$   & 81.0 & 74.7 \\
$\gamma = 1.0$   & 81.2 & 74.3 \\
$\gamma = 4.0$   & 80.9 & 74.2 \\
\bottomrule
\end{tabular}
\caption{Ablation studies on small-size models.
We analyze the effect of the hardness prediction loss weight $\lambda_1$ and the focal loss factor $\gamma$.
Reported results are medians over five random seeds.}
\label{tbl:ablation}
\end{table}

\subsection{Ablation Studies}

We conduct ablation studies on small-size \ele{}+HP$_{\rm Loss}$+Focal models.
We investigate the effect of the loss weight $\lambda_1$ of the sampling head and the focal loss factor $\gamma$ in order to better understand their relative importance.
Results are presented in Table~\ref{tbl:ablation}.

We first disable the focal loss and only understand the effect of $\lambda_1$.
As shown in Table~\ref{tbl:ablation}, no matter what the value of $\lambda_1$ is, our models exceed the baseline by a substantial margin, which demonstrates that the hardness prediction can indeed improve the pre-training and our methods are not sensitive to the loss weight hyperparameter.
Next, we fix $\lambda_1$ at 5 and understand the effect of the focal loss factor $\gamma$.
We observe that the application of the focal loss with piecewise $\gamma = \mathbbm{1}(p_{\textnormal{\textsc{g}}}\!>\!0.2) * 3 + \mathbbm{1}(p_{\textnormal{\textsc{g}}} \leq 0.2) * 5$ and $\gamma=1$ can improve the performance on two datasets, which proves the effectiveness of the sampling smoothing.

\section{Analysis}
To better understand the main advantages of our models over \ele{}, we conduct several analysis experiments.

\subsection{Impacts on Sampling Distributions}

\begin{figure}[t!]
\centering
\begin{subfigure}[t]{0.46\textwidth}
\centering
\includegraphics[width=\textwidth]{pg_conf.pdf}
\caption{Sampling Distribution of \ele{}}
\label{fig:conf_pg}
\end{subfigure}
\hfill
\begin{subfigure}[t]{0.46\textwidth}
\centering
\includegraphics[width=\textwidth]{ps_conf.pdf}
\caption{Sampling distribution of our models}
\label{fig:conf_ps}
\end{subfigure}
\caption{The distribution of the maximum probability at the masked positions under the sampling distributions of \ele{} and our models.}
\label{fig:conf}
\end{figure}

We first provide a comparison between the sampling distributions of \ele{} and our models illustrate the effect of our proposed methods.
We conduct evaluations on a subset of the pre-training corpus.
Figure~\ref{fig:conf} demonstrates the distribution of the maximum probability of the two sampling distributions at the masked positions.
We observe that the ratio of the maximum value under \ele{} sampling distribution between $[0.9, 1]$ is much higher than that of our models.
In other words, the original distribution suffers from over-sampling the high-probability tokens and the discriminator is forced to learn from these easy examples repeatedly.
In contrast, the distribution of the maximum value of our models in each interval is relatively more uniform than \ele{}, which indicates that our methods can significantly reduce the probability of sampling the well-classified tokens and smooth the whole sampling distribution.

\subsection{Estimation Quality}

\begin{table}[t]
\centering
\begin{tabular}{l | c c c}
\toprule
\textbf{Token Type} & {Original} & {Replaced} & {All}  \\
\midrule
\textbf{Corr. Coeff.} &  0.78 & 0.61 & 0.64 \\
\bottomrule
\end{tabular}
\caption{Correlation coefficient between the actual discriminator loss and the estimated value for \ele{}+HP$_{\rm Loss}$+Focal. ``Original'': sampling correct tokens as replacements. ``Replaced'': the positions that are substituted to incorrect tokens.}
\label{tbl:corr}
\end{table}

In order to measure the estimation quality of discriminator loss, we evaluate our models on a held-out set of pre-training corpus and compute the correlation coefficient between the actual discriminator loss $\mathcal{L}_D(x,c)$ and the estimated value $\hat{\mathcal{L}}_D(x,c)$.
The results of \ele{}+HP$_{\rm Loss}$+Focal are shown in Table~\ref{tbl:corr}.
We report the estimation quality of the original tokens and the replaced tokens separately.
The correlation coefficient value is $0.64$ over two types of tokens, which proves that $\mathcal{L}_D(x,c)$ and $\hat{\mathcal{L}}_D(x,c)$ correlate well.
Furthermore, we observe that the estimation quality over the original tokens is relatively higher than the replacements.
We speculate that the sampling probability of the original tokens is generally higher than the replacements, so the sampling head tends to receive more feedback from these original tokens.

\subsection{Prediction Accuracy of the Discriminator}

\begin{table}[t]
\centering
\begin{tabular}{l c c}
\toprule
\textbf{Model} & \textbf{Masked Positions} & \textbf{All Positions}  \\
\midrule
\ele{} &  0.81 & 0.96 \\
Ours &  0.72 & 0.95 \\
\bottomrule
\end{tabular}
\caption{Replacement detection accuracy of \ele{} and \ele{}+HP$_{\rm Loss}$+Focal.
The models are evaluated on $15$\% masked positions and all input tokens respectively.
Our method samples more hard examples.
}
\label{tbl:pred_acc}
\end{table}

In order to verify the claim that the sampling distribution of our models indeed considers the detection difficulty, we evaluate the prediction accuracy of the discriminator under the two sampling schemes of \ele{} and \ele{}+HP$_{\rm Loss}$+Focal.
Results are listed in Table~\ref{tbl:pred_acc}.
No matter evaluating at all positions or only at the masked positions, the detection accuracy under our sampling distribution is relatively lower than under masked language modeling in original \ele{}.
Because the unmasked tokens constitute the majority of input examples, the difference of the all-token accuracy between two models is not so distinct compared to the masked tokens.
This phenomenon is consistent with our original intention. It proves that our models can sample more replacements that the discriminator struggles to make correct predictions.
In contrast, the replacements sampled from \ele{} are easier to distinguish.

\section{Conclusion}

We propose to improve the replacement sampling for \ele{} pre-training.
We introduce two methods, namely hardness prediction and sampling smoothing.
Rather than sampling from masked language modeling, we design a new sampling scheme, which considers both the MLM probability and the prediction difficulty of the discriminator.
So the generator can receive feedback from the discriminator.
Moreover, we adopt the focal loss to MLM, which adaptively downweights the well-classified examples and smooth the entire distribution.
The sampling smoothing technique relieves oversampling original tokens as replacements.
Results show that our models outperform \ele{} baseline.
In the future, we would like to apply our strategies to other pre-training frameworks and cross-lingual models.
Moreover, we are exploring how to integrate the findings and insights of the proposed method into the masked language modeling task, which seems also quite promising.

\bibliographystyle{acl_natbib}

redpajama_set_name: RedPajamaArXiv


"Oh, if only life could be nothing more than looking at guns and gear all day followed by drinking and talking with friends all night? Real life can be such a pain."
It's 4am Saturday, and I'm getting ready for work.
As soon as I saw this it struck a cord with me. Glad you like it too Andy.


Gain overview of how QB works and how to get around in it.
Edit company Chart of Accounts, add costumer & job and vendor.
Learn how to work with register and reconcile QB bank account/ credit card.
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The use of the reporting and customization.
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Progressive invoice and how to prepare collection letter.
Attendees must have their own version of Quickbooks on their laptops.
He holds a MBA and masters in EE degrees.


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In today's scenario, agents and property professionals are facing a major shift in the way they reach clients. The present system encouraged buyers, vendors and clients to demand detailed information immediately. Coupled with a complex range of selling options, increasing competition the real estate industry is even more challenging.
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text:
Лецидеевые () — семейство лишайников порядка .

Описание
Слоевище накипное или чешуйчатое, эпилитное или эндолитное, реже эпигейное, эпиксильное или эпибриофитное. Апотеции обычно чёрные, реже чёрно-коричневые, с краем, лишённым клеток водорослевого симбионта, поверхностные или погружённые в слоевище. Аски булавовидные, восьмиспоровые, 25—95×6—25 мкм. Парафизы простые, реже разветвляющиеся, с булавовидными концами. Споры одноклеточные, неокрашенные, почти шаровидные или продолговатые. Пикнидии погружённые, шарообразные или кувшинообразные. Конидии палочковидные, верхушечные.

Фитобионты — требуксиофициевые водоросли.

Ареал
Типовой род семейства обладает космополитичным ареалом, отсутствуя только в пустынях.

Классификация
Согласно базе данных Catalogue of Life на декабрь 2022 года семейство включает следующие роды:

Примечания

Литература

Лишайники
Леканоромицеты
Семейства грибов

redpajama_set_name: RedPajamaWikipedia


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Every component of this kit is 100% made in the USA and has been crafted for a precise fit.
Please note that welding is required for installation.


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text:
Wellness / 8 days Ago
Marriott CRN Properties Step Up Their Gym Game
by Christoph Trappe
Marriott Marquis Chicago Fitness Center, Credit: Christoph Trappe
Sure, some hotels just put a gym into an empty guest room. Or perhaps it's a couple of machines in another room that was never meant to be a hotel gym. But many travelers, including meeting attendees, are demanding more when it comes to hotel fitness centers.
On a recent trip to Marriott Marquis Chicago, I sat down with Steve Conklin, director of group sales at the hotel, to chat about how properties in the Marriott Convention and Resort Network (CRN) are stepping up their gym game.
Listen to Christoph Trappe's full conversation with Steve Conklin:
For example, the J.W. Marriott in Chicago has kinetic treadmills and dumbbells up to 100 pounds. That caters to many gym rats, including NFL team players in town to face the Chicago Bears at Soldier Field.
Marriott Marquis Chicago has its own set of extensive gym amenities in an impressive fitness center to cater to increasing customer demand.
"It is all about listening to our customers, and our customers are all about wellness," Conklin said. "When they're on the road traveling away from family, they want to continue their life the way it is, including their fitness routine, and they don't want to have to go off property and search for a club."
Conklin said Marriott is focused on offering wellness-oriented amenities as part of the customer experience, such as premier gyms.
"We started wellness with the JW brand and also with the Renaissance brand and now with the Marquis brand," he said. "The Marriott Marquis New York re-energized the fitness aspect by moving meeting space and a restaurant to build a fitness center. Here at the Marriott Marquis in downtown Chicago, we moved the pool to add more of a fitness center."
The property now has a 6,000-square-foot fitness center with all the latest and greatest in terms of free weights and machines, including plenty of ellipticals, according to Conklin.
Free Weights on Display at the Marriott Marquis Chicago, Credit: Christoph Trappe
"What I love is that it's got natural light and a patio, so you can go outside and do yoga in the warmth of Chicago," he added.
[Related Content: 2-Team Baseball Towns Offer More Options for Event Planners]
Setting aside the right amount of space for a well-equipped gym is an important aspect of fitness center planning, according to Conklin.
"It's all about learning from the past with our other hotels, and Marriott corporate focuses a lot on what our customers are saying," Conklin said.
Marriott Marquis Chicago Fitness Center Cardio Machines, Credit: Christoph Trappe
When the company built the Marriott Marquis Chicago, which opened in 2017, one focus area was having ample fitness center space for a hotel that size, according to Conklin.
"This 6,000-square-foot center is used every single day, and the great thing is that it's 24-hour key access," Conklin said. "In Chicago, we get a lot of international travelers who get here at all times of the day, and their body clocks are working at all different times, so if you want to have a workout at 3 o'clock in the morning or 11 o'clock at night, you can have it."
Conklin added that even though the fitness center is not revenue-producing, it's one of the most important parts of the customer experience.
Read More About Marriott's Chicago CRN Properties:
How to Book a Luxury Suite at Chicago Marriott Downtown Magnificent Mile
New CRN Member—The Westin Michigan Avenue—Delivers Flexible Meetings
Meetings Today Podcast: 4 Things to Ask the Chef About When Planning Your Event
Christoph Trappe
Chief Content Officer
Christoph Trappe, a top 25 content marketer and author, is chief content officer at Stamats and Meetings Today.

redpajama_set_name: RedPajamaCommonCrawl


text:
Q: Textviews inside ConstraintLayout I have 2 textview inside a layout. The layout will be used in recyclerview.
First the layout is like this:

and my code is:
<?xml version="1.0" encoding="utf-8"?>
<android.support.constraint.ConstraintLayout xmlns:android="http://schemas.android.com/apk/res/android"
    xmlns:app="http://schemas.android.com/apk/res-auto"
    xmlns:tools="http://schemas.android.com/tools"
    android:id="@+id/mViewRoot"
    android:layout_width="match_parent"
    android:layout_height="wrap_content"
    android:background="?android:attr/selectableItemBackground"
    android:clickable="true"
    android:focusable="true">

    <TextView
        android:id="@+id/mTextViewTitle"
        android:layout_width="wrap_content"
        android:layout_height="wrap_content"
        android:layout_marginLeft="16dp"
        android:layout_marginRight="16dp"
        android:layout_marginTop="16dp"
        android:text="title"
        android:textColor="@android:color/black"
        android:textSize="18sp"
        app:layout_constraintHorizontal_bias="0.0"
        app:layout_constraintLeft_toLeftOf="parent"
        app:layout_constraintRight_toRightOf="parent"
        app:layout_constraintTop_toTopOf="parent" />

    <TextView
        android:id="@+id/mTextViewDate"
        android:layout_width="wrap_content"
        android:layout_height="wrap_content"
        android:layout_marginBottom="16dp"
        android:layout_marginLeft="0dp"
        android:layout_marginRight="16dp"
        android:layout_marginTop="8dp"
        android:text="date"
        app:layout_constraintBottom_toBottomOf="parent"
        app:layout_constraintHorizontal_bias="0.0"
        app:layout_constraintLeft_toLeftOf="@+id/mTextViewTitle"
        app:layout_constraintRight_toRightOf="parent"
        app:layout_constraintTop_toBottomOf="@+id/mTextViewTitle"
        app:layout_constraintVertical_bias="0.0" />
</android.support.constraint.ConstraintLayout>

The result become clipped like this. It seems like the margin right is not functioning.

I've also tried making the width of textviews become 0dp.

The code is:
<?xml version="1.0" encoding="utf-8"?>
<android.support.constraint.ConstraintLayout xmlns:android="http://schemas.android.com/apk/res/android"
    xmlns:app="http://schemas.android.com/apk/res-auto"
    xmlns:tools="http://schemas.android.com/tools"
    android:id="@+id/mViewRoot"
    android:layout_width="match_parent"
    android:layout_height="wrap_content"
    android:background="?android:attr/selectableItemBackground"
    android:clickable="true"
    android:focusable="true">

    <TextView
        android:id="@+id/mTextViewTitle"
        android:layout_width="0dp"
        android:layout_height="wrap_content"
        android:layout_marginLeft="16dp"
        android:layout_marginRight="16dp"
        android:layout_marginTop="16dp"
        android:text="title"
        android:textColor="@android:color/black"
        android:textSize="18sp"
        app:layout_constraintHorizontal_bias="0.0"
        app:layout_constraintLeft_toLeftOf="parent"
        app:layout_constraintRight_toRightOf="parent"
        app:layout_constraintTop_toTopOf="parent" />

    <TextView
        android:id="@+id/mTextViewDate"
        android:layout_width="0dp"
        android:layout_height="wrap_content"
        android:layout_marginBottom="16dp"
        android:layout_marginLeft="0dp"
        android:layout_marginRight="16dp"
        android:layout_marginTop="8dp"
        android:text="date"
        app:layout_constraintBottom_toBottomOf="parent"
        app:layout_constraintHorizontal_bias="0.0"
        app:layout_constraintLeft_toLeftOf="@+id/mTextViewTitle"
        app:layout_constraintRight_toRightOf="parent"
        app:layout_constraintTop_toBottomOf="@+id/mTextViewTitle"
        app:layout_constraintVertical_bias="0.0" />
</android.support.constraint.ConstraintLayout>

The result become like below. The textview below doesn't match the constraint above it.

How to solve this problem?

A:
<?xml version="1.0" encoding="utf-8"?>
<android.support.constraint.ConstraintLayout xmlns:android="http://schemas.android.com/apk/res/android"
    xmlns:app="http://schemas.android.com/apk/res-auto"
    xmlns:tools="http://schemas.android.com/tools"
    android:id="@+id/mViewRoot"
    android:layout_width="match_parent"
    android:layout_height="wrap_content"
    android:background="?android:attr/selectableItemBackground"
    android:clickable="true"
    android:focusable="true">

    <TextView
        android:id="@+id/mTextViewTitle"
        android:layout_width="0dp"
        android:layout_height="wrap_content"
        android:layout_marginLeft="16dp"
        android:layout_marginRight="16dp"
        android:layout_marginTop="16dp"
        android:text="asd;fjalksd fjlkasdjflasdflkasdlfkja lsdkfjals dfalskdfj alsdkfja sdlfkjalsdfj"
        android:textColor="@android:color/black"
        android:textSize="18sp"
        app:layout_constraintLeft_toLeftOf="parent"
        app:layout_constraintRight_toRightOf="parent"
        app:layout_constraintTop_toTopOf="parent" />

    <TextView
        android:id="@+id/mTextViewDate"
        android:layout_width="0dp"
        android:layout_height="wrap_content"
        android:layout_marginBottom="16dp"
        android:layout_marginLeft="0dp"
        android:layout_marginRight="16dp"
        android:layout_marginTop="8dp"
        android:text="date asdf asdf asdf asdf asdf adasdf asdf asdf asdf asdf asdf asdfasdf"
        app:layout_constraintBottom_toBottomOf="parent"
        app:layout_constraintLeft_toLeftOf="@+id/mTextViewTitle"
        app:layout_constraintRight_toRightOf="parent"
        app:layout_constraintTop_toBottomOf="@+id/mTextViewTitle"  />
</android.support.constraint.ConstraintLayout>

redpajama_set_name: RedPajamaStackExchange


text:
Mai Toquem Junts és un quartet reusenc de rock dur. Enregistraren un primer disc (Aerosomnis) el 1999, influït per grups comercials com Aerosmith.

Referències

Enllaços externs
 Biografia musical de Mai Toquem Junts.
 Fotografies, discografia i lletres de les cançons de Mai Toquem Junts.

Grups de rock catalans
Entitats culturals de Reus


The subject of salary negotiation is easier for some people than it is for others. While salary negotiation is supposed to be a routine part of taking a job or remaining happy in one, the fact that there is money on the line can make these conversations uncomfortable, tense or even downright unpleasant.
The good news is, that it doesn't have to be this way. As long as you are using proper workplace diplomacy, stating and proving your case, and honoring your own value, you can engage in salary negotiations without fear of failure or a negative reaction from your current or future employer.
They say that you attract more flies with honey than vinegar, and you'll certainly have a better chance of getting the salary you want when you're sweet instead of sour. That being said, being too sweet or complimentary during a salary negotiation comes across as a cheap, transparent bid for favor in negotiations and will not get you anywhere.
Practicing proper workplace diplomacy during a salary negotiation will be a huge factor in the success of these negotiations, as the person you're negotiating with will always respond better to people who approach this discussion in a straightforward, diplomatic way.
Knowing who you're dealing with– This is easier if you've worked with the person you're negotiating with, but you should have a good idea of who you're dealing with if you've gotten to the salary negotiation stage of a job offer. Work with their personality, not against it, and be sure that you don't use any of their communication "pet peeves" during salary negotiations (being indirect, talking too much, etc.). You should also keep their perspective in mind: how are you useful to them and why should they give you the salary you're asking for?
Using the right tone– The tone of the salary negotiation should be pleasant or even friendly if possible. If you find yourself becoming frustrated or "sour," adjust course immediately, as the tone of negotiations turning negative or confrontational will hurt your chances of getting the salary you want. Also, as we said before, avoid being "too nice," as this may be taken as professional flattery and hurt your chances.
Choosing the right time– If you're negotiating the salary of a job that you're about to take, there will often be a set time to discuss your salary. If this conversation arises earlier than you expect and you're unprepared, request a rain-check until you've had time to build the case for your desired salary and/or research the salary range for this position in your industry. If you're asking for a raise, always choose a time when you have many examples of recent, successful work that you can use as evidence for why you deserve a raise.
We all want to be paid more, but why should it be you? What have you done to deserve the salary you're asking for?
Don't be daunted by this questions, because it's the question you'll be answering for anyone that you're negotiating your salary with and the way you answer is important.
For instance, if you say something like "I'd like a raise because I've been working very hard and going the extra mile for my teammates," then you probably aren't getting that raise. While this statement is true, and a good starting point, it does not have the information needed to prove your value.
The more specific you can get, the better, especially when linking your hard work to successful outcomes for your team or department. Always bring evidence for why you should get the salary you're asking for and lay out your case in an objective, well-reasoned way.
You have been doing (or will do) valuable work for the person you're negotiating with, so don't sell yourself short. Though over-confidence won't be very helpful for getting the salary you want, some confidence is required when you ask for a better salary.
You also need to be asking for a salary that is realistic and in-line with what competitors are paying someone in your position and of your experience level. This information can easily be found through a quick internet search or by visiting Glassdoor.
Remembering your value also means not backing down from difficult questions or from a boss who is reluctant to give you the salary you're asking for. You shouldn't be confrontational, but you should be firm when stating your case and negotiating your salary. Maybe you won't be able to get the salary you were looking for now, but showing your boss that you understand your value will prove that you can't be put on the back burner.
By demonstrating that you know your value, you put yourself in the power position for negotiating your salary. As long as you remember your workplace diplomacy and build a strong case for your desired salary with evidence of your valuable work, you have a good shot at getting a salary closer to the "best case scenario" you've been dreaming of.


Every civilization needs to reflect on what is or is not important. In times gone by, it was knowledge, love, or honour. Things have changed.
At the dawn of Western society, great thinkers and writers such as Plato, Aristophanes, and Euripides bent their minds to the many problems that beset our fledgling civilization. Their toil played no small part in making the West what it is today. Time marches on, however, and Western civilization has evolved and changed. Sadly, Plato and his toga-wearing contemporaries are no longer all that relevant to your average iPod toting consumer.


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Enter Domino! the world's largest dominoes community! in the search bar and press on the icon of the app to enter on the app details.
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HAMBURG — Talks on global trade at the Group of 20 summit proved very difficult and differences on climate change also were clear, German Chancellor Angela Merkel said Friday, as police and protesters clashed throughout the day in the summit's host city of Hamburg.
Merkel told leaders of the G-20 economic powers that they must be prepared to make compromises as she worked toward a summit outcome that everyone present could accept.
That is a challenging task at a time when President Donald Trump's Trump's "America First" rhetoric and decision last month to withdraw from the Paris accord against climate change have caused widespread concern.
Negotiators "still have a great deal of work ahead of them" to formulate a passage on trade in the summit's closing communique, Merkel said after the first day of meetings.
She added that most participants called for "free but also fair trade" and underlined the significance of the World Trade Organization, though she didn't specify which ones did not support the trade language.
"The discussions are very difficult, I don't want to talk around that," Merkel said.
"It will be very interesting to see how we formulate the communique tomorrow and make clear that, of course, there are different opinions in this area because the United States of America regrettably … wants to withdraw from the Paris accord," Merkel said.
Germany has been keen to preserve the G-20's tradition of making decisions by consensus. Merkel has rejected calls from some to push for a strong "G-19" statement — without the U.S. — on climate change.
The leaders did make a joint statement on fighting terrorism, an issue on which there are few differences. They called for ensuring that there are "no 'safe spaces' for terrorist financing anywhere in the world" and pledged to work with internet providers and app administrators to combat the web's use for terror propaganda and financing.
Merkel noted that the countries at the summit represent two-thirds of the world's population, four-fifths of the globe's gross domestic product and three-quarters of world trade.
The G-20 comprises Argentina, Australia, Brazil, China, Germany, France, Britain, India, Indonesia, Italy, Japan, Canada, South Korea, Mexico, Russia, Saudi Arabia, South Africa, Turkey, the United States and the European Union.
Also attending are the Netherlands, Norway, Spain, Guinea, Senegal, Singapore and Vietnam.
Merkel said the threat posed by North Korea's missile tests was brought up at Friday's meetings by the leaders of South Korea and other countries in the region, and all hoped that "the U.N. Security Council will find an appropriate answer" to Pyongyang's violation of U.N. resolutions.
The summit was also a forum for a flurry of bilateral meetings, including Trump's first encounter with Russian President Vladimir Putin.
Outside the security cordon around the downtown congress centre, anti-globalization activists set dozens of cars ablaze and tried unsuccessfully to block national delegations from entering the summit.
The city already had boosted its police with reinforcements from around the country and had 20,000 officers on hand to patrol Hamburg's streets, skies and waterways. Another 900 were called in to cope with the clashes.
Merkel thanked them for their work.


In public health and infectious diseases, anyone who repeatedly and regularly shares the living space of someone with a contagious disease.
The sharing of body fluids and blood with another person constitutes close contact, as does the bathing, clothing, and toileting of the sick person. Close contacts with contagious people may need prophylactic treatment, quarantine, or observation to ensure their health and the health of others. Those who have casual contact with a sick person typically do not require these measures.
We have worked closely with the university to identify and offer close contacts antibiotic prophylaxis and to provide information to the wider student population".
The zoo identified 19 close contacts, all of whom had TSTs at [greater than or equal to] 8 weeks after exposure; 13 were negative.
It requires prolonged and close contact with somebody who has TB, such as someone living in the same house, and it can be completely cured with a course of antibiotics.
The Health Ministry said the two were in prolonged and close contact.
He remains in stable condition and those in close contact with him have shown no abnormalities.
She said: "Screening of close contacts in the college is under way in accordance with the Guidelines for the Control and Prevention of Tuberculosis in Ireland 2010.
A close contact on an aircraft was defined as a passenger sitting within 3 rows in front and 3 rows behind the index case-patient.
The United States said Tuesday it will maintain close contacts with Japan on a row over where a major U.
A report in a local newspaper said the probe was being conducted because the patient had not travelled or been in close contact with someone suffering from the H1N1 virus.
Antivirals are offered as treatment to people who are ill and to aid prevention to people who are well but have been in close contact with a case.
The National Public Health Service for Wales (NPHS) said the risk of TB, which can only be spread by very close contact, was 'very low'.


Dragon Nest M – SEA APK Download The buoy going to the Holy Paradise is going to leave, blazing the memory of greatness. You warriors, return for enterprise!
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"From the dates if our latest fashion shows, to tips on our favourite London hot spots, Topshop Concierge will help you get the best out of your shopping trip!" reads the flyer.
UK high street fashion retailer push the envelope with a concierge service from their flagship Oxford Street store. Staff at the store will provide advice on where to go in London for shopping and entertainment. The price? Oh, for free!


text:
DC WRITERS' SALON
Member Who's Who
The DC Writers' Salon is a studio and community for writers. The Who's Who helps members know what others are working on so they can connect further in and out of the Salon. If you're seeking fellow
Want an introduction? Email us at hello@dcwriterssalon.com and we'd be happy to connect you.
Ali C.
I'm working on ay and stand-up comedy on self storytelling and social norms
2023 Intentions: To get an essay published into the world.
Current inspiration: I
Project(s): In between working on freelance articles, I write short stories.
Intention(s): To make more time for personal writing.
Inspiration: Ali Cherry's stand up comedy bits, obviously
In between working on freelance articles, I write short stories.
In 2023, I want to make more time for
In between working on freelance articles, I write short stories about wild animals.
In 2023, I want to make more time for personal writing.
Right now, I'm super inspired by Ali Cherry's stand up comedy bits, obviously.
Salon programs
Intro to the Salon
Writing resources
Questions and policies
Join a Finishers Group
Affinity Circles
Book club
Member-Led Fridays
(we don't post much because, well, it's distracting to writing : )
1807 18th Street, NW Washington, DC 20009
To keep everyone safe, the Salon has a 100% Covid boosted policy. Please decline to join in person if you're not vaccinated.


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This is an original 1899 halftone print of an interior view of the Mine and Mining Building at the 1893 Chicago World's Fair (also referred to as the Columbian Exposition).
This 112+ year old Item is rated Near Mint +. Light aging throughout. No creases. No natural defects. No surface rub. No tears. No water damage.


With an impeccable track record of more than 25 years, we have successfully provided management support in significant procurement and industrial participation programs in global markets. We provide our client base with services designed to improve their industrial participation performance, to comply with complex regulations and to reduce their costs, while delivering measurable and sustainable results, in fields such as: cyber security, wind energy, system solutions, manufacturing & critical infrastructure development.
The scope of services of EFA Ventures spans from conducting industry analysis and formulating industrial participation proposals, to complete program and contract management, implementation and crediting services. More specifically, we provide turn-key solutions to our clients and partners, through the provision of direct, indirect and support services aimed at the fulfillment of their industrial participation commitments. Through this process we wish to transform our clients into our industrial partners.


text:
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	<classpathentry kind="con" path="org.eclipse.jdt.launching.JRE_CONTAINER/org.eclipse.jdt.internal.debug.ui.launcher.StandardVMType/JavaSE-1.7/"/>
</classpath>

redpajama_set_name: RedPajamaGithub


text:
Mental Health Summit 2023
It is our great pleasure to warmly invite all the participants to attend the "8th International Conference on Mental Health and Psychiatry" held on during August 16-17, 2023 in Frankfurt, Germany. This is the time and place to meet, speak, hear and discuss on subjects we love, know so much about, and which are so veritably important, for our understanding of the factors affecting and controlling our living conditions at this event to bandy and produce mindfulness about credited beneficial and technical beneficial advanced researches and treatments in the field of psychiatry. Which is an owing assembly for the worldwide prominent professionals in the field of Psychiatry and Psychology where Administrators, Students, Scientists, Professors, Researchers, and Delegates are about to partake their research work and accepting newly evolving developments in the conference areas.
Psychiatry and Psychology
Women Mental Health
Suicide and Self Harm
Neurology and Neurological Disorder
Tweets Box
Supporting Journals
Conference Series Organizes 2000+ Global Events Every Year across USA, Asia & Europe with support from 1000 more scientific societies and Publishes 600+ Open access journals which contains over 100000 eminent personalities, reputed scientists as organizing committee members and editorial board. The conference series website will provide you list and details about the conferences organize worldwide.
@Conferenceseries. Simple theme. Powered by Blogger.


The Yukon 800 Mile Marathon, the grandfather of Alaska's contemporary endurance races dating back 47 years, is a 2-day high speed riverboat race. Run on the Tanana and Yukon Rivers, traveling through hundreds of miles of vast Alaskan wilderness. It is billed as the longest, roughest and toughest speed boat race in the world.
Competitors live in Fairbanks and communities along the rivers, where they build the low-slung 24 foot long boats from scratch. These race boats are powered with 50 horsepower regulation engines that can reach speeds in excess of 70 miles per hour.
The Yukon 800 begins in Fairbanks in late June, around the time of summer solstice, and finishes after 800 grueling miles the next day back in Fairbanks.
Unpredictable weather such as high winds, rain and hail, blowing sand from river sandbars, smoke from nearby forest fires and fog can limit visibility. Plus, large trees, logs and other drift wash from the riverbanks and ride the current down the rivers. All can be a determining factor in the most meticulous plans of even the most seasoned Captain and crew.
According to historians, the Yukon 800 evolved from races held along the Nenana River and on a gravel pit outside of Fairbanks in the mid 1950s. Then came talk of a larger, grandeur form of competition. During a bar-room conversation in 1959, an idea emerged for a riverboat race that would decide who the best riverboat racers were.
The race began at Circle City on the Yukon River and was dubbed the Arctic Circle Marathon because its course traveled above the Arctic Circle for 20-30 miles near the village of Fort Yukon. The inaugural race in late May of 1960 started with 21 metal riverboats 24 to 30 foot long and powered by 40 horsepower engines and finished with 18 boats. The winner, Ray Kasola and crew ran the course from Circle City to Fairbanks in 26 hours, 26 minutes and 55 seconds.
The desire to become "King of the River" fueled many competitors to design faster and lighter boats, looking to get more power and speed out of their engines. Today's boats are specifically designed from Sitka Spruce for the framework and 1/8" to 1/4" plywood for the hull.
In 1964 the Fairbanks Outboard Association decided to move the start and finish of the race to Fairbanks to increase spectator and sponsor participation. The race would run to the village of Ruby on the Yukon River as the half way point and return, making the total race 700 miles round trip.
In 1972, the FOA extended the race course to Galena, increasing the race to 800 miles round trip. The racers began spending the night in Galena, returning to Fairbanks the next day.
This 800 mile marathon race known as "The Survival of the Fittest", traverses 800 miles of torturous, treacherous, glacier-fed rivers dotted with sandbars, gravel bars and floating debris. It starts in Fairbanks on the Chena River and runs to its confluence with the Tanana River. The racers follow the Tanana River past the villages of Nenana, Old Minto, Manley Hot Springs, Tanana and Ruby. FOA charters a plane that follows the course, carrying race officials, media and family.
The next morning the three person crew, after having survived the beating to the body and doing repairs to the boat, prepare to leave Galena. The only time the FOA allows a mass start is when the Yukon 800 leaves Galena. The racers eagerly eye the river and each other, calculating wind, water, mechanics and tactics.
The finish at Chena Pump Campgrounds is a cause for holiday. The family, friends and media gather to see the first "rooster tail" coming around the corner.
Copyright © 2004 Fairbanks Outboard Association . All Rights Reserved.


YGH completed a master plan and 300,000 square foot, multi-phase expansion and renovation effort at PCC's Cascade Campus. The expansion incorporated open and underused parcels and creates a consistent design vocabulary to reinforce the College's identity while maintaining the character of the neighborhood. New buildings include an Arts and Humanities Building, Technology Education Building and Physical Education Building. Additions to the science building, the student center and a classroom building increased the density of the campus and cost-effectively improved existing classroom, office and support areas.


The use of dietary supplements has become increasingly popular among members of the military. While some supplements may provide benefits to health, others could carry adverse effects that might compromise the readiness and performance of service members. The U.S. Department of Defense, the Samueli Institute, the National Institutes of Health (NIH), with additional support from the Food and Drug Administration (FDA), requested that the Institute of Medicine (IOM) convene an ad hoc Committee on Dietary Supplement Use Among Military Personnel. The committee was charged with reviewing the use of dietary supplements by military personnel, recommending a framework to identify the need for management of dietary supplement use within the military, and developing an approach to report adverse health events.
Key activities to ensure the success of this approach are information sharing as well as educational and outreach efforts. The committee recommends that a forum is established for the exchange of data and information related to dietary supplements. The committee also recommends that military service members, commanders, and health care personnel are educated about dietary supplements, their effects, and the importance of adverse event reporting.
The committee also recommends that the U.S. Department of Defense (DoD) designate a committee or an entity to be responsible for the oversight and coordination of dietary supplement related activities, and provide guidance to the military leadership about other activities related to the management of dietary supplement use (e.g. research, outreach and education, adverse event reporting).


followed by an open session until 22h00.
Bring family, friends – come and enjoy!
The Nationals Championship has its own dedicated website.
Monday 7 May 14h00 – 21:00 and Tuesday 8th May 14:00 – 22:00 at Grove Mall, Pretoria.
Online tickets are cheaper and available at Quicket.
Adults R80. Children 13 and younger R20.
Tickets at the door: Adults R120, children 13 and younger R30.
Ticket prices are per session.
The 2018 Provincial Championships is a joint event between the Gauteng North and Central Gauteng Figure Skating Associations.
The entry deadline for competitors is 1o March.
Quicket tickets for you and your family. Remember to print out or bring it on your mobile device!
R60 online for adults. SAVE 50% on normal fee!
* Please note that an additional R5 fee is payable to the online service provider.
Academy has started again this week and is looking for skaters, especially senior skaters to volunteer to help with coaching.
The hours are: Wednesday 18:00-19:00 and Saturday 8:00-10:00. Terms are 8 weeks long.
There is a small remuneration for those coaching.
For more information, please contact Yolande Giering.


Prior to World War 1, New Zealand's agricultural production and the farming enterprises responsible for them were largely in balance. Most towns had their own dairies, real ones, that made and supplied their communities with beautiful milk (in glass bottles), butter to die for, and cream that made any home baker's scones taste better. Margarine wasn't even visible on the horizon. There was artisanal cheese as well. Food was wholesome, natural, unprocessed, delicious and contained high levels of nutrition. Superb lamb was a birthright, and veges and eggs often came straight from the back yard. Bread was wholesome, unprocessed, and came from the local artisan baker. Nothing came in plastic bags.
A regenerative colleague of mine recently shared a story with me about how he was telling his 93-year old mum about all the cool things we were planning to do on farm. She reportedly said, "That's nice son, that's exactly what we used to do in the 1930s when your father and I were growing up."She reportedly went on to tell my colleague about the ice deliveries and the wonderful meat from the butcher that was delivered by Bob's young fella. The non-chemically impacted meat arrived wrapped in brown paper. The fresh asdaily milk arrived in glass bottles on the front doorstep. There was no plastic in sight, no industrial scale farming and the distribution of profit was equitable and fair from farmer to customer.
The question of course is how did we go from that superb system to the disconnected mess we have now? In today's world, things are very different. Most people don't know their suppliers, and few know where anything comes from. Supermarkets have very deliberately disconnected us from our food. Even the staff that work in supermarkets don't know their suppliers or where anything comes from. I asked the 'meat specialist' behind the supermarket's posh meat counter the other day where the $59.00 per kilo Scotch Fillet came from. He said it came from Processor X. Great…thanks, I'll give it a miss. Frustrating, disappointing, but let's move on for now.
At this time, on the farming side exporting was well established, particularly in the sheep industry. Its successful pursuit initially enabled by some very clever, innovative, New Zealanders and New Zealand companies in Dunedin. Their entrepreneurship had allowed Kiwis back in the day to enjoy one of the highest standards of living in the world. Life was very, very good and so was our health and the health of our environment. There were some early signs of the train wreck that lay ahead, but at the time, in the moment, it was all good. No one had linked ripping out all of the trees for pasture to feed grazing animals to climate change or its impacts. Talk of melting polar ice caps, sea level rise, increasingly damaging storms, warming oceans and very annoyed polar bears was still a long way in the distance. Climate change, what climate change. Nothing to see here, move along.
Of course, if it isn't broken, don't fix it. This led to the creation of a mindset that rationalized a mantra that suggested, "If it's working so well, why not do more of it." Inevitably, export strategy eventually met scale and New Zealand hasn't been the same since. Whilst Britain may have deserted New Zealand farmers in the early 1970's, joining the European Union to stave off economic demise, the scale mindset Britain had helped to foster persists to this day. It is embedded in the psyche and DNA of many farmers, and by default it still influences farming decisions and operations today.
Many of course will disagree and that is fine, but what else can explain the tenuous financial performance of some of New Zealand's major commodity supplying red meat and dairy cooperatives? Horticulture, fishing, forestry and other platforms have not been immune to the vagaries of commodity trading either. Venison, salmon and mussels have all suffered. The export at scale strategy idea that has brought us to the present day has some question marks over its business model. Doing more of the same under the mantra of transitioning-to- value reeks of myopia too, and history is littered with stark warnings of failing to learn from the past.
Does New Zealand have the right strategies for the future? I'm not sure, there are some super innovative companies out there and many are doing some fantastic things, but are there enough of them? In the end, farmers will choose their own paths and time and consumers will arbitrate. In 2050, and 2100, New Zealanders and others will deliver the verdict as they look back with the benefit of hindsight. They may have some interesting insights about commodity driven industrial farming. I would suggest time is not on our side and the premium consumers we say we covet today are currently having a good, long, hard look. These days they have plenty of choices and substitutes are growing rapidly, taking market share in markets traditional farming once owned. The more questions they ask, the harder it is going to get and you can't hide a brand story in a plastic bag…even a really nice one.
In response, New Zealand producers of all persuasions may need to start asking some very hard and potentially very unpalatable questions about how to respond to disruption and change in their environments and in their markets. In reality, and there is always a reality, it would appear that many of New Zealand's farmer owned co-operatives have and are struggling. Why? Their financial non-performance over the last decade strongly suggests they have created operational environments that are allowing capital rich enterprises positioned upstream in the supply chain to reverse integrate. In other words, our customers are buying us. One might also argue they are buying our silver with profits that by rights should have been ours. A cynic might say the current cooperatives are architecting their own demise.
So, back to the David and Goliath discussion. Which food system currently feeds and nurtures your loved ones? Which system provides the best nutrition? Which system is best for the planet? People? Animals? Our future? Make no mistake, looking after our land means looking after ourselves, and sadly, so far, we haven't proven to be the greatest custodians of our shared and very rare, blue planet. On this point, it's harder to disagree. What's broken and how badly it's broken is another debate. Fixing things might not be easy either as the primary production space in New Zealand is often mired in politics, bureaucracy, tradition, controversy, emotion, bias, lobbying and self-interest, a very powerful cocktail, which locks in behavior and negatively impacts the ability to respond to competition and change.
Scientists, ecologists, poets, writers, artists, meat lovers, chefs, vegetarians, environmentalists, climatologists, activists, governments, nutritionalists, doctors, farmers, industry bodies and the widely and globally concerned have been debating for decades. Most have an immovable position and that usually comes with a self-interested or political bias. Neither are particularly helpful to effective collaboration, which makes disruptive change even more challenging.
Are we listening? Is anyone listening? Maybe reflect for a minute or two and answer those two questions for yourself. Of course, this is a very emotive space and there is conflict even among the concerned. In essence though, there appears to be two key pathways or approaches to tackling the future. Option one is to continue with an industrialized and extractive approach. This strategy aims to provide volume and scale for commodity trading. At the beginning of the last century this was a successful strategy for New Zealand. It isn't any longer. Option two, is transitioning to regenerative and sustainable farming practices. This strategy is consumer driven and aims to provide premium quality produce to discerning consumers. Most marketers would understand that in markets where one product looks very much like another, price is the clearance mechanism. This is how commodity markets work and that is where most of New Zealand's export products are traded. When we play this game, the price is never set by us and we will always be…the price takers.
The late Sir Paul Callaghan and his co-writing colleague Shaun Hendy stated in the opening chapter of their seminal Get off the Grass publication that, "New Zealanders work harder and earn less than most other people in the developed world." Is this really where we want to be? Industrialized farming born over a century ago worked right up until it didn't…now, something has to give. What will that be?
In finishing, I point to what Sir David Attenborough and many others have been desperately trying to explain to us. We have one planet, and one planet only. We all need to share and care for it and it contrary to many current industrial agricultural practices, it is not infinitely expandable. Our beautiful home cannot be supersized to suit rampant population growth. Estimates suggest the global population will reach 10 billion by 2050. It was 3 billion in the 1960s. It is currently 7.7 billion. There are 353,000 new babies joining us each and every day. Christchurch's population is 374,000 people for those wanting some perspective, so we're nearly adding a Christchurch sized population to the planet every day. That kind of increase is going to be quite a challenge and as they say, when it rains, it pours…or doesn't. Will peak population synchronize with peak climate change? You will need to ask the experts. The key question still remains though. Who will you back? David, or Goliath?
Part 2: Is Red Meat a Commodity Product, a Luxury Product, or a Lifestyle Product?
Part 1: Is Red Meat a Commodity Product, a Luxury Product, or a Lifestyle Product?


text:
Q: Mocha-allure-reporter not working with node js I am facing issue when I run my tests with mocha-allure-reporter ,the tests are running successfully with mocha ,but mocha --reporter mocha-allure-reporter returns the following error
> notifications-api@1.0.0 test /app
> mocha --reporter mocha-allure-reporter

2016-11-15T06:13:26.762Z - info: Notification API up and running on port 4000
2016-11-15T06:13:26.773Z - error: Tue, 15 Nov 2016 06:13:26 GMT uncaughtException
2016-11-15T06:13:26.775Z - error: TypeError: test.currentRetry is not a function
at Runner.<anonymous> (/app/node_modules/mocha-allure-reporter/index.js:29:19)
at emitOne (events.js:77:13)
at Runner.emit (events.js:169:7)
at next (/app/node_modules/mocha/lib/runner.js:517:10)
at Runner.runTests (/app/node_modules/mocha/lib/runner.js:556:3)
at /app/node_modules/mocha/lib/runner.js:637:10
at next (/app/node_modules/mocha/lib/runner.js:283:14)
at Immediate._onImmediate (/app/node_modules/mocha/lib/runner.js:319:5)
at processImmediate [as _immediateCallback] (timers.js:383:17)
npm ERR! Test failed.  See above for more details.

Can anyone help to fix this issue?

A: The issues can be resolved by updating your mocha to the latest  version probably to v3 or above and allure reporter too. If the issue still persists regarding TypeError: test.currentRetry is not a function , please replace the line of code
if (!test.currentRetry()) {

with the following
if (typeof test.currentRetry !== "function" || !test.currentRetry()) {


text:
Beyond Bygone
Epic Power
Candid Talk
Mapping the states of India
/sunday-post/beyond-bygone/sunday-post/epic-power/fact-files/ians-feeds/sundaypost/in-retrospect/mapping-the-states-of-india/puja-special/punjab/Reminiscence/sponsored/sunday-post/wellness/sundaypost/global-eye/world/opinion/entertainment/technology/Safari/Sundaypost/exclusive-news/features/video-news/big-stories/sundaypost/trending/gastronomy/sundaypost/trending/nation/latest-news/breaking-news/business/sundaypost/inland/sundaypost/routes/sundaypost/beacon/sundaypost/insight/sundaypost/trending/candid-talk/sports/delhi/movies/books/editordesk/anniversaryissue/style/sundaypost/globaleye/bizwiz/forecast/capi-talk/sunday-post/game-on/editorial/kolkata/safari/diwali-special/visual-stories
Home > Opinion > Tale of a lost paradise
Tale of a lost paradise
Zafar Choudhary19 Feb 2013 11:40 PM GMT
IN 1947-48 Kashmir was the only issue between India and Pakistan. The Simla agreement of 1972 neutralised that centrality to a large extent. Half a century after the composite dialogue process, the first ever structural engagement between the two countries, the importance of Kashmir was reduced to one of the eight issues India and Pakistan need to resolve. Transforming Kashmir from 'the issue' to 'one of the issues' is a rich reflection of India's diplomatic craftsmanship. However, Pakistan continued to harp on Kashmir as the core issue between the countries.
On the eve of the Agra summit the then External Affairs Minister aptly summed up the national consensus opinion on Kashmir: 'What Pakistan calls as core issue is actually the core of Indian nationhood'. Really! This 'core business' needs investigation, the earlier the better for India's whole concept of Kashmir being central to Indian nationhood. New Delhi's diplomatic craftsmanship notwithstanding, it is important to acknowledge Kashmir's internal drive leading to change in the dynamics of the conflict.
In 2013 there are not many Kashmiris who think Kashmir is a dispute between India and Pakistan. In 2010, Syed Ali Shah Geelani dropped Pakistan out of his five-point framework of engagement with New Delhi. Geelani's agenda was clearly influenced by the ground realities of an average Kashmiri seeking to renegotiate relations with the rest of India on the basis of mutual respect and human dignity. That is the point that was once again been grossly missed in New Delhi while the secretive manner in which Parliament attack convict Afzal Guru's hanging was planned and executed.
Afzal Guru's truth lies buried with him behind the high walls of Tihar, South Asia's largest prison. Condemned to death in 2006 for his alleged role in the 2001 Parliament attack case, in his prison cell Guru was a powder keg for competing sets of nationhood and different ideologies. His impending hanging promised a political fortune for different parties to eke out. That is what has been simply happening since 9 February. For the rightwing nationalists the hanging has come as message of India's strength and capacity to fight terrorism and for the centrists it is an answer to the rightwing clamour.
The collective conscience of the society has been talked about a thousand times in the past week. If Kashmir is not part of it then one must admit that the Indian nation's collective conscience has a sense of satisfaction and gratification over the latest hanging at Tihar. The question is not the legality of the execution as we repose trust in the Indian judicial system. The question here is of Schadenfreude between Kashmir and the rest of India. We must understand that except for Guru himself and other than the claims the prosecution presented before the courts, no one on earth would have known the exact status of his involvement in the Parliament attack case. Therefore, we have not heard anyone in Kashmir saying with absolute confidence that an innocent was hanged for satisfying the 'collective conscience of the society'. The questions in Kashmir are only about fair trial and the right of family to meet the convict before being sent to the gallows.
We have grown up hearing about alienation in Kashmir. In equal measures we have heard of all things possible done for Kashmir's emotional integration with the rest of India. While sifting through the pages of history one comes across two types of questions many times over –Why Kashmiris erred? Why New Delhi erred? Six decades later the same questions continue. In the latest case there were, at the most, two questions an average Kashmiri could have asked. Instead of upholding the claimed democratic traditions of India by letting them ask the two questions, their voices have been muffled and they have been virtually imprisoned.
The two questions that Kashmiris could have asked are those which two important persons have said shouldn't have happened in the first place. The questions are about fair trial to the satisfaction of any law-knowing person and the family's right to know about the execution and opportunity to meet the convict before he is hanged. See, who has asked these questions. Gopal Subramaniam, the lawyer instrumental in securing the death sentence for Guru, has described the secret hanging as violation of human rights. He has said that the convict should have been given the opportunity of exploring the last legal option available to him. The second question comes from none other than the Prime Minister, who has made his displeasure known at the way the family was not informed of the executionwell in time.
With these questions staring at the face of what Home Minister Sushilkumar Shinde says 'following the due process of law', no one can stop people within and outside India from attributing political motives to the execution. As the communication blockade keeps Kashmiris isolated, their job is being done by the rightwing nationalists who are asking the Congress government of motives behind the timing of the execution. The Home Minister's Hindu terror remark will always be connected with Guru's hanging. Pending since 2006 the execution was just a matter of time, but the timing has made wide sections of society feel that Kashmir –the core of Indian nationhood – has been made use of to address some political questions the ruling regime had been facing. Indeed, a new chapter has been added to Kashmir's alienation. CBM or the Confidence Building Measure is the most abused term in the case of Kashmir.
A basic right of every Indian national, for example the travel document, is denied at the first instance or its issuance is prolonged. Then travel documents become a part of the issues to be discussed in a peace process and directions for speedy clearance comes as 'Kashmir-specific political and security CBMs'. (IPA)
Zafar Choudhary