healthy weight loss | To Health With That!
HELP! Weight Loss Stopped and I Don't Know Why.
I can't begin to tell you how many of you I've had in my office literally sobbing your eyes out because you're doing everything right and you were losing weight, but weight loss stopped. It's a horrible feeling to feel like you're "failing" at weight loss. Things were going great – the pounds were steadily coming off and then all of a sudden everything stopped moving. A few weeks went by and things kept not moving. Eek! This is when people freak out.
I want you to take a deep breath, and relax. You are doing awesome. AWESOME. This is totally normal. It's just that your plan for how things are going to go and your body's plan for how things are going to go are two really different things, and they have to be because your body's number one priority is keeping you alive. Believe me, that's a really good thing.
Losing weight is not just a matter of digesting a few fat cells for your body. It's a big deal! Say you lose 15 pounds. For the "average" person (arbitrarily picking 150 lbs as average) that's 10% of their body weight.
Weight Loss Stopped – What Is Really Happening?
Take a look around your living room or your office or wherever you happen to be sitting right now and picture taking 1/10th (that's 10%) of the stuff out. So if there are 10 pictures on the wall, take out one. If there are 10 pieces of furniture, take one out. For every 10 books, decorative items, pillows, etc… take out one. So, for everything in the room, for every 10 pieces of everything, one piece has to go. Seriously. Once you've done that take a look around. If your house is anything like mine, taking out 10% of everything makes it look like a gap-toothed jack-o-lantern.
After I take 10% of everything out of the room, then I have to take some time to re-organize. I've got to move stuff around and sort through what's left to make the room work again. Your body has to do the exact same thing with weight loss – it's got to re-organize. So if weight loss stopped it's probably because your body has a bigger priority at this moment. Did you know every pound of extra fat has about a mile of extra blood vessels supplying it? Eek! Your body has to do something with all of that too, because frankly in the grand scheme of things it's more important to circulate your blood effectively than it is to meet your weight loss goals.
Weight loss stopped? It's because your body has to take the time to rearrange the furniture. Looks good, right?
Losing weight isn't a quick and easy process for your body – it's actually tremendously hard work and takes a lot of shifting of hormones, blood vessels, musculoskeletal support, inflammatory levels and toxins. Weight loss in real-life is different from what you see on TV and if you don't remember about that then read through this to remind yourself. If you're hitting a plateau in your weight loss, and weight loss stopped suddenly, that is completely okay. In fact, it's great! It means that you have lost enough that your body needs to move stuff around so just let it happen and keep doing what you're supposed to be doing. The weight loss will start again – your body just needs to take a minute to collect itself.
So, It's Normal That Weight Loss Stopped – How Long Until It's A Problem?
If after 6 weeks you're still stalled out, then talk to your practitioner or whoever you are working with to lose the weight. In that case there may be something blocking you like a hormone issue, a detox issue or a food sensitivity – if you aren't familiar with weight loss blockers take a look at this. But seriously let your body have six weeks before you freak out and every day keep reminding yourself that your body is a beautiful thing that is doing it's best every day to keep you alive. Your body wants you to be happy and healthy and whole. It's changing everything in the background so that you can be healthy and wonderful and then move on to the next phase of weight loss.
The bottom line is: go easy on your body. Go easy on yourself. Breathe and let this whole process unfold the way it's going to – it doesn't have to look exactly like you thought it would for it to be going exactly right. Sure, weight loss stopped – now thank your body for it because it's busy keeping you alive. Your body is making sure your heart, lungs, hormones, neurotransmitters and everything else are keeping up with the process. Your body is taking care of a whole lot of stuff that keeps you moving on a daily basis and it's really important to honor that process and allow your body a little room when it needs to reorganize the furniture.
This entry was posted in Natural Health, Weight loss and tagged healthy weight loss, natural weight loss, real-life weight loss, weight loss, weight loss blockers, weight loss stopped on May 28, 2015 by amyneuzil.


text:
\section{Introduction}
With the development and popularization of personal intelligent terminal technology and social networks, the importance of constructing a dialogue system that can comprehend user emotions and intentions and conduct effective dialogue interactions has increased significantly. A critical module in the dialogue system is the natural language understanding module that analyzes user behaviors like intents or emotions. Analyzing user sentiments with contextual relationships is an advanced step for simple sentiment classification tasks and is more suitable for usage scenarios in the real world with more research value. The task of emotion recognition in conversation (ERC) is to assign emotion labels to all the utterances in a historical dialogue with a contextual relationship. At the same time, each historical dialogue contains interactions between multiple different speakers, as illustrated in Figure \ref{task_intro}.

\begin{figure}[t]
\centering
\includegraphics[width=0.9\columnwidth]{task_introduction.pdf}
\caption{The conversation flow chart in multi-person dialogue emotion recognition. The solid line indicates that the previous utterance directly influences the current speaker's emotion. The dashed line signifies that the same speaker is influenced by other utterances and expresses different emotions.}
\label{task_intro}
\end{figure}

There are three challenges for ERC. (1) The first challenge is that the emotion of each utterance may be affected by contextual information. For example, specific emotions will depend on certain utterances of the context. Meanwhile, utterances with the same expression may have completely different emotions in various contexts. Therefore, effectively modeling the context dependency and the speaker dependency is the main factor distinguishing this task from traditional sentiment classification. (2) The second challenge is that each speaker's emotion is influenced by the utterance of other speakers in the conversation, so there may exist a sudden change in a speaker's emotion. (3) The third challenge lies in semantically similar but different categories of emotions, such as ``frustrated'' to ``sad'', ``happy'' to ``excited'', etc. It is difficult to distinguish these semantically similar sentiment categories.

Recent related work addressed contextual dependencies and speaker relations using various graph networks \cite{dag-erc, ghosal-etal-2019-dialoguegcn, rgat, sheng-etal-2020-sumagggin}. However, as the number of layers deepens, the phenomenon of over-smoothing \cite{over-smoothing-aaai-20} starts to appear, resulting in the representation of similar sentiments tending to be indistinguishable.

This work deals with the above challenges by better modeling the context and speaker information and auxiliary generation task.

Firstly, we introduce a dialogue-level Transformer \cite{transformer-nips-2017} layer to model the long-range context dependencies between utterances. A pre-trained language model captures the representation of each utterance. Compared to previous approaches that only adopt pre-trained models as a feature extractor \cite{roberta} and employ the extracted features as the node representation of downstream graph networks, a pure Transformer structure makes fewer prior structural assumptions \cite{transformer_survey}.

Secondly, we adopt supervised contrastive learning (SCL) \cite{khosla2020supervisedcl} to alleviate the difficulty in categorizing similar emotions, which makes samples with same sentiments cohesive and different sentiments mutually exclusive under the fully utilization of label information. Compared with the cross-entropy loss for noisy labels, the supervised contrastive loss can increase the stability of training and improve the generalization of the model \cite{scl-fine-tune}.
Unlike the regular SCL, we copy the hidden state of all samples in a batch and detach off its gradient as its multiview representation. The reason is that the categories in existing ERC datasets are highly unbalanced, and some categories may exist in a batch with only one sample. If only the original SCL is used, it will lead to incorrect loss calculation.

Thirdly, we introduce an auxiliary response  generation task to enhance the ability of capturing the context information for ERC. The prediction of the following utterance makes the model fully consider contextual dependencies, thus forcing the model to consider the information in the context and rely on the current utterance itself when recognizing the sentiment in the conversation. Moreover, by splicing the speaker directly before utterance as a hint for speaker information, the dependency between speakers and utterances is modeled adequately without additional parameters.

Finally, we utilize BART \cite{lewis-etal-2020-bart}, a pre-trained Transformer with an encoder-decoder structure, as our backbone model and enhance it by contrastive and generative loss. Our proposed \textbf{CO}nstrastive-and-\textbf{G}eneration-enhanced BART (CoG-BART) obtains state-of-the-art results on four ERC datasets compared to the baseline models. Additionally, ablation experiments and case studies prove the effectiveness of the contrastive and generative losses in the ERC task.

To summarize, our main contributions can be concluded as follows:
\begin{itemize}
\item To the best of our knowledge, we utilize supervised contrastive learning for the first time in ERC and significantly improve the model's ability to distinguish different sentiments.
\item By incorporating response generation as an auxiliary task, the performance of ERC is improved when certain contextual information is involved.
\item Our model is easy-to-implemented since it does not depend on external resources, like graph-based methods.
\end{itemize}

\begin{figure*}[t]
\centering
\includegraphics[width=0.95\textwidth]{main_model.pdf}
\caption{The overall framework of CoG-BART. The utterance is fed into BART for $N$ utterances in a batch to get its hidden state. The representation of the utterance obtained after max-pooling the hidden state of each utterance is fed to the upper-level dialogue-level Transformer for modeling context dependencies. The obtained context-dependent utterance representations are utilized to compute the cross-entropy loss and supervised contrastive loss. In addition, the two adjacent utterance pairs are used for the auxiliary response generation.}
\label{fig1}
\end{figure*}

\section{Related Work}
This section will introduce related works in ERC. Due to context-dependency and speaker dependency properties, it is natural for researchers to employ graph neural networks. Therefore, many works have constructed various task-specific graph structures. Meanwhile, with the excellent performance of the pre-trained model in diverse downstream tasks, an increasing number of works adopt the pre-trained model as the feature extractor for the input of the downstream model or directly fine-tune it with downstream datasets. Therefore, this section divides the related work into two categories: graph-based models and pre-train-based models.

\subsection{Dialog Emotion Recognition}
\subsubsection{Graph-based model}
Considering the unidirectionality of information interaction, DAG \cite{dag-erc} utilizes directed acyclic graphs to model the information interaction between utterance and speaker. DialogGCN \cite{ghosal-etal-2019-dialoguegcn} adopts the basic graph neural network to model the relationship between contexts. SumAggGIN \cite{sheng-etal-2020-sumagggin} adds an aggregation module based on DialogGCN to additionally consider phrase-level information other than utterance-level. By simulating the process of human reasoning, DialogCRN \cite{dialogcrn} proposes to apply several reasoning modules to extract and integrate clues of emotional reasoning. To make the model better understand the additional general information involved in the dialogue process, KET \cite{zhong-2019-ket} combines external knowledge with a hierarchical Transformer. By appending sequence information into the graph network, RGAT \cite{rgat} uses relational position encoding to combine position information into the graph network structure to consider the dependency between speakers. TODKAT \cite{todkat} integrates topic detection into the pre-training model and fuses commonsense knowledge into Transformer \cite{transformer-nips-2017}.

\subsubsection{Pre-train-based model}
Suppose each utterance is regarded as an independent sentence, regardless of its context-dependence and speaker information. In that case, the problem can be transformed into a simple sentence classification so that pre-trained models \cite{ptm_survey} such as BERT \cite{devlin-etal-2019-bert}, BART \cite{lewis-etal-2020-bart}, and RoBERTa \cite{roberta} can be used directly for fine-tuning. HiTrans \cite{li-etal-2020-hitrans} adopts BERT to extract utterance features, followed by transformer structure for modeling context. Considering speaker dependence, the auxiliary task of judging whether two utterances are the same speaker is used to model the speaker information. COSMIC \cite{ghosal-etal-2020-cosmic} exploits RoBERTa as the feature extractor of each utterance and model the dependency of the context with RNN. In addition, the common knowledge transformer COMET \cite{bosse-20-comet} is incorporated to introduce world knowledge. Based on XLNet, DialogXL \cite{dialog-xl} changes the segment-level structure to utterance-level and uses memory to record the historical context. Meanwhile, by adopting different mask mechanisms on different attention heads, each attention head pays attention to different aspects of dialogue information. \citet{gen-and-cls-naacl} trained BART with both generation and classification in a multi-task format, though their method focused on response generation, treating emotion recognition as an auxiliary task. However, we focus on ERC and apply supervised contrastive loss as an additional optimization goal.

\subsection{Contrastive Learning}
\subsubsection{Unsupervised contrastive learning}
In the field of computer vision, SimCLR \cite{ChenK0H20-simclr} takes pictures obtained from the same image through randomly different data augmentation methods as positive samples and other pictures as negative samples, thereby optimizing contrastive loss. The naive sentence representation obtained by BERT has poor performance in semantic text similarity tasks. Therefore, ConSERT \cite{YanLWZWX20-consert} introduces self-supervised contrast loss in the fine-tuning stage of BERT. MBERT \cite{kim-21-mbert} does not use data augmentation to construct positive samples but uses BERT with frozen parameters and fine-tunable parameters as a special siamese model to construct positive samples.
\subsubsection{Supervised contrastive learning}
To make full use of label information, \citet{khosla2020supervisedcl} extends it to supervised contrastive learning based on self-supervised training so that samples belonging to the same label are gathered in the embedding space while pushing samples of different categories away. Given that cross-entropy loss may cause model training instability and converge to a local optimum, SCL \cite{scl-fine-tune} introduces supervised contrastive loss in the fine-tuning stage, which greatly improves the model's performance in few-shot learning scenarios. SimCSE \cite{simcse} uses entailment pair in the annotated NLI dataset as the positive sample and the contradict pair as the negative sample in supervised contrastive learning.

\section{Methodology}
\subsection{Problem Definition}
In dialogue emotion recognition, the data is composed of multiple conversations $ \{c_1, c_2, \cdots, c_N\} $, with each conversation composed of several utterances $ c_i= \left[ u_1, u_2, \cdots, u_m \right] $ and emotion labels $ \mathcal{Y}_{c_i}=\{y_1, y_2, \cdots, y_m\}\in S$, where $S$ indicates the categories of emotions. For an utterance, it is comprised of several tokens $ u_t=\left[w_{t,1},w_{t,2}, \cdots, w_{t,n}\right] $. Every utterance in a conversation $c_i$ is uttered by one speaker which can be represented as $ p(c_i)=\left[p(u_1),\cdots,p(u_i),\cdots,p(u_m)\right]$ and $p(u_i)\in P $, where $P$ indicates the categories or names of the speakers. Accordingly, the whole problem can be expressed as getting the emotional label of each utterance based on the context and speaker information in a piece of conversation: $ \mathcal{Y}_{c_i}=f(c_i, p(c_i)) $.

\subsection{Supervised Contrastive Learning for ERC}
\subsubsection{Utterance Encoding}
To model the dependencies between speaker and utterance, for a certain utterance $u_{t}$ in a conversation, we splice the speaker's name or category before the utterance.
After tokenizing the utterance prepended with the speaker information, we get:
\begin{flalign}
    & \tilde{u}_{t}=\left[\langle s\rangle,w_{t,1},\cdots, w_{t,i},\cdots, w_{t,|n_{t}|}, \langle /s\rangle\right],
\end{flalign}

\noindent where $\langle s\rangle$ and $\langle /s\rangle$ are treated as special tokens to indicate the beginning and end of an utterance.
Then the token sequence after tokenization is fed to the shared embedding layer of BART to acquire the hidden state of each token in utterance before sending it to the encoder and decoder of BART. After sending $H_t$ to BART, the representation of the current utterance $\widehat{H}_{t}$ is acquired:
\begin{flalign}
     H_{t}&=\mathrm{EmbeddingLayer}(\tilde{u}_{t}), \\
     \widehat{H}_{t}&=\mathrm{BART\textrm{-}Model}(H_{t}),
\end{flalign}

\noindent where $H_t,\widehat{H}_t \in\mathbb{R}^{s\times d}$, and $s, d$ indicates the length of the sequence and hidden dimension respectively.

\subsubsection{Dialogue Modeling}

The representation $\widehat{H}_{t}$ obtained by the BART-Model is max-pooled to obtain the aggregated representation of the utterances as follows:
\begin{flalign}
    & \check{h}_{t}=\mathrm{max\textrm{-}pooling}(\widehat{H}_{t}).
\end{flalign}

To model the historical context information of the dialogue, we exploit a dialogue-level Transformer \cite{transformer-nips-2017} layer as the context encoder. The multi-head attention mechanism can capture the interaction between different dialogues in multiple rounds of dialogue and aggregate different features to obtain the final implicit representation, thereby fully modeling the complex dependence between different utterances and context relations. For all utterances in a context, the multi-head attention score of the hidden state between two different utterances in a conversation $\check{h}_{j}$, $\check{h}_{k}$ can be calculated by the following formulas:

\begin{flalign}
    &\mathrm{Atten}(Q,K,V)=\mathrm{softmax}(\frac{QK^T}{\sqrt{d_k}})V, \\
    &head_i=\mathrm{Atten}(\check{h}_{j}W_i^Q, \check{h}_{k}W_i^K,\check{h}_{k}W_i^V), \\
    &\mathrm{MultiHead}(Q,K,V)=[head_1;\cdots; head_n]W^O,
\end{flalign}

\noindent where $W_i^Q\in \mathbb{R}^{d\times d_q}$, $W_i^K\in \mathbb{R}^{d\times d_k}$, $W_i^V\in \mathbb{R}^{d\times d_v}$ and $ W^O\in \mathbb{R}^{d\times d}$ are parameters that can be optimized, $d_q$, $d_k$ and $d_v$ are dimensions of query, key and value vectors, $n$ indicates the number of heads.

Therefore, the utterance representation that models the context-dependence can be obtained through the above-mentioned dialogue-level Transformer:
\begin{flalign}
    {H}_{win}&=[\check{h}_t, \check{h}_{t+1}, \cdots, \check{h}_{t+bs-1}], \\
    {H}_{d\textrm{-}win}&=\mathrm{Dialogue}\textrm{-}\mathrm{Transformer}({H}_{win}),
\end{flalign}

\noindent where ${H}_{win}\in\mathbb{R}^{bs\times d}$ indicates utterances in a conversation within the window size $bs$ and ${H}_{d\textrm{-}win}\in\mathbb{R}^{bs\times d}$ denotes the utterances after context modeling.

\subsubsection{Supervised Contrastive Learning}

Supervised contrastive learning assumes that some crucial aspects get attention and allows few-shot learning to be more stable when fine-tuned on pre-trained models \cite{scl-fine-tune}.
The typical contrastive learning uses only one pair of positive examples, while all other samples are treated as negative examples. Supervised contrastive learning treats all examples with the same label in the batch as positive examples by making full use of label information.

For ERC, the number of samples in each category in some datasets \cite{li-etal-2017-dailydialog} is highly unbalanced, while the supervised contrastive learning will mask itself when calculating the loss. If only one sample exists for a category in the batch, it cannot be directly applied to calculate the loss. Therefore, a copy of the hidden state of the utterance ${H}_{d\textrm{-}win}$ is made to obtain $\overline{H}_{d\textrm{-}win}$, and its gradient is detached. Hence the parameter optimization is maintained stable.

For a batch with $N$ training samples, each sample is operated by the above mechanism to obtain multiview $2N$ samples, then the supervised contrastive loss of all samples in a batch can be expressed by the following equation:

\begin{flalign}
    X&=[{H}_{d\textrm{-}win},\overline{H}_{d\textrm{-}win}], \\
    \mathcal{L}_{\mathrm{SCL}}&=\sum_{i\in I}\frac{-1}{|P(i)|}\sum_{p\in P(i)}\textrm{SIM}(p,i), \\
    \textrm{SIM}(p,i)&=\log\frac{\exp((X_i\cdot X_p)/\tau)}{\sum_{a\in A(i)}\exp(X_i\cdot X_a/\tau)},
\end{flalign}

\noindent where $X\in \mathbb{R}^{2N\times d}$, $i\in I=\{1,2,\cdots,2N\}$ indicate the index of the samples in a multiview batch, $\tau\in R^+$ denotes the temperature coefficient used to control the distance between instances, $P(i)=I_{j=i}-\{i\}$ represents samples with the same category as $i$ while excluding itself, $A(i)=I-\{i, N+i\}$ indicates samples in the multiview batch except itself.

\subsection{Auxiliary Response Generation}
To facilitate the model to consider richer contextual information when determining utterance sentiment, the model is required to generate its following utterance $u_{t+1}$ given the current utterance $u_t$. The output hidden state of each token in $u_{t+1}$ is generated by the BART decoder sequentially.

\begin{flalign}
     \acute{H}_t&=\textrm{BART-Encoder}(H_t), \\
     \grave{h}_j^d&=\textrm{BART-Decoder}(\acute{H}_t;\grave{h}_{<j}^d), \\
     u_{t+1,j}&=\textrm{Softmax}(\grave{h}_j^d), \\
     \mathcal{L}_{\mathrm{Gen}}&=-\sum_{i=1}^N\log p(u_{t+1}|u_t, \bm{\theta}),
\end{flalign}
\noindent where $\bm{\theta}$ is the parameters of BART need to be optimized.

\subsection{Model Training}
The loss of model training consists of three parts: the hidden state ${H}_{d\textrm{-}win}$ obtained after context modeling passes through a multilayer perceptron to obtain logits for calculating cross-entropy loss. The other part is the supervised contrastive loss and the loss of response generation. The loss is a weighted sum of the three components, and the sum of their weights equals one. The overall framework of CoG-BART is illustrated in Figure \ref{fig1}.

\begin{flalign}
    P_i&=\mathrm{Softmax}(W_s {H}_{d\textrm{-}win,i}+b_s), \\
    \hat{y}_i&=\mathrm{argmax}(P_i), \\
    L_{\textrm{CE}}&=-\frac{1}{N}\sum_{i=1}^N\sum_{c=1}^Cy_{i,c}\cdot \log \hat{y}_{i,c}, \\
    \mathcal{L}&=(1-\alpha-\beta)\mathcal{L}_{\mathrm{CE}}+\alpha \mathcal{L}_{\mathrm{SCL}}+\beta\mathcal{L}_{\mathrm{Gen}},
\end{flalign}

\noindent where $y_{i,c}$ represents the label of a certain utterance, $\hat{y}_{i,c}$ indicates the probability distribution of category $c$ output by the dense layer, $\alpha$ denotes the weight for supervised contrastive loss and $\beta$ is the weight for loss of response generation.

\begin{table}[t]
\centering
\resizebox{.95\columnwidth}{!}{

\begin{tabular}{clcccc}
\toprule
{Dataset} &       & DD    & MELD & ENLP & IEMOCAP \\
\midrule
{\#Dial}  & Train & 11118 & 1038 & 713  & 120     \\
{}        & Dev   & 1000  & 114  & 99   & 120     \\
{}        & Test  & 1000  & 280  & 85   & 31      \\
{\#Utter} & Train & 87170 & 9989 & 9934 & 5810    \\
{}        & Dev   & 8069  & 1109 & 1344 & 5810    \\
{}        & Test  & 7740  & 2610 & 1328 & 1623    \\
\midrule
{\#CLS}   &       & 7     & 7    & 7    & 6       \\
\bottomrule
\end{tabular}
}
\caption{Statistics of four benchmark datasets. }
\label{dataset_desc}
\end{table}

\begin{table*}[t]
\centering
\resizebox{\textwidth}{!}{
\setlength{\tabcolsep}{.65mm}
\begin{tabular}{lcccccccc}
\toprule
Dataset      & \multicolumn{2}{c}{MELD}                                   & \multicolumn{2}{c}{EmoryNLP}
             & \multicolumn{2}{c}{IEMOCAP}                                & \multicolumn{2}{c}{DailyDialog}      \\
\midrule
Model        & Weighted           & Micro-F1                              & Weighted          & Micro-F1
             & Weighted           & Micro-F1                              & Weighted          & Micro \\
{}           & -Avg-F1            &                                       & -Avg-F1           &
             & -Avg-F1            &                                       & -F1-neural   & -F1-neutral \\
\midrule
BERT         & 62.28              & 63.49                                 & 34.87             & 41.11
             & 60.98              & -                                     & 53.41             & 54.85             \\
RoBERTa      & 62.51              & 63.75                                 & 35.90             & 40.81
             & 63.38              & -                                     & 52.84             & 54.33             \\
HiTrans      & 61.94              & -                                     & 36.75             & -
             & 64.50              & -                                     & -                 & -                 \\
DialogXL     & 62.41              & -                                     & 34.73             & -
             & 65.94              & -                                     & -                 & 54.93             \\
XLNet        & 61.65              & -                                     & 34.13             & -
             & 61.33              & -                                     & -                 & 53.62             \\
BART-large   & 63.57              & 64.41                                 & 35.98             & 38.93
             & 56.14              & 56.67                                 & 54.83             & 55.34            \\
\midrule

CoG-BART     & \textbf{64.81} ($\pm$0.19)       & \textbf{65.95} ($\pm$0.44) & \textbf{39.04} ($\pm$0.10)      & \textbf{42.58} ($\pm$0.94) & \textbf{66.18} ($\pm$0.45)                  & \textbf{66.71} ($\pm$0.49)            & \textbf{56.09} ($\pm$0.01)             & \textbf{56.29} ($\pm$0.17)                  \\
\bottomrule
\end{tabular}

}
\caption{The overall results of CoG-BART with pre-train-based baseline models on four datasets. }
\label{main_results}
\end{table*}

\begin{table}[t]
\centering
\resizebox{1\columnwidth}{!}{
\setlength{\tabcolsep}{.3mm}
\begin{tabular}{@{}lcccc@{}}
\toprule
Dataset      & \multicolumn{1}{c}{MELD}                                   & \multicolumn{1}{c}{EmoryNLP}
             & \multicolumn{1}{c}{IEMOCAP}                                & \multicolumn{1}{c}{DailyDialog}      \\
\midrule
Model        & Weighted                                                   & Weighted
             & Weighted                                                   & Micro            \\
{}           & -Avg-F1                                                    & -Avg-F1
             & -Avg-F1                                                    & -F1-neutral      \\
\midrule
KET          & 58.18                                                      & 34.39
             & 59.56                                                      & 53.37            \\
RGAT         & 60.91                                                      & 34.42
             & 65.22                                                      & 54.31            \\
RGAT+RoBERTa & 62.80                                                      & 37.89
             & 66.36                                                      & 59.02            \\
DialogGCN    & 58.10                                                      & -
             & 64.18                                                      & -                \\
DialogCRN    & 58.39                                                      & -
             & 66.20                                                      & -                \\
COSMIC       & 64.28                                                      & 37.10
             & 63.05                                                      & 56.16            \\
DAG-ERC      & 63.65                                                      & 39.02
             & \textbf{68.03}                                             & \textbf{59.33}   \\
\midrule
CoG-BART     & \textbf{64.81} ($\pm$0.19)                                 & \textbf{39.04} ($\pm$0.10)
             & 66.18 ($\pm$0.45)                                          & 56.29 ($\pm$0.17)                  \\

\bottomrule
\end{tabular}
}
\caption{Comparison with graph-based models.}
\label{main_results_graph_model}
\end{table}

\section{Experimental Settings}
This section will elaborate on the datasets, baseline models, experimental conditions, and parameter settings adopt in the experiment.
\subsection{Experimental Setup}
The code framework and initial weight of BART come from Huggingface's Transformers \cite{wolf-etal-2020-transformers}. The optimizer applied for model training is AdamW with a linear-scheduled warm-up strategy. The parameters adjusted in this experiment include batch size, learning rate, warm-up ratio, $\alpha$, and $\beta$. We conducted a hyperparameter search for model training through the reserved validation set. The results on the test set come from the best checkpoint in the validation set, and we average the scores from five different random seeds. All experiments are performed on GeForce RTX 3090 GPU.

\subsection{Datasets}
This section will introduce four benchmark datasets: MELD \cite{poria-etal-2019-meld}, EmoryNLP \cite{emorynlp}, DailyDialog \cite{li-etal-2017-dailydialog}, and IEMOCAP \cite{busso2008iemocap} for comparison with the baseline models.
\subsubsection{MELD}
This dataset comes from the dialogue content of the characters in the American drama \textit{Friends}. MELD originally contained multi-modal data, but we used only the text data for the experiments.
\subsubsection{EmoryNLP (ENLP)}
This dataset also comes from \textit{Friends}, and the difference from MELD is the annotation of utterance's emotional label category. The emotional tags contained in this dataset are: \textit{joyful}, \textit{neutral}, \textit{powerful}, \textit{mad}, \textit{sad}, \textit{scared}, and \textit{peaceful}.
\subsubsection{DailyDialog (DD)}
Manually compiled data sets about daily communication. The annotation method used in this data set is Ekman's emotion type \cite{ekman1993facial}, which includes six basic emotion tags, including \textit{happiness}, \textit{surprise}, \textit{anger}, \textit{disgust}, \textit{fear}, and \textit{sadness}.
\subsubsection{IEMOCAP}
Like MELD, it is a multi-modal dataset. The content is derived from the lines in the scripts of the two actors, and the emotional tags included are \textit{excited}, \textit{neutral}, \textit{frustrated}, \textit{sad}, \textit{happy}, and \textit{angry}.

The detailed statistics of the four datasets are shown in Table \ref{dataset_desc}, where ``\#Dial" indicates the number of dialogue in train/dev/test, ``\#Utter" represents the number of all utterances in dialogue, and ``\#CLS" denotes the number of categories of each dataset.

\subsection{Metrics}
For MELD, EmoryNLP and IEMOCAP, we adopt weighted average F1 as the evaluation metrics. In that ``neutral" occupies the majority in DailyDialog, micro-F1 is employed as the evaluation metric for this data set, and we ignore the label ``neutral" when calculating the results as in the previous works \cite{todkat, dag-erc}.

\section{Results and Analysis}
\subsection{Main Results}

Table \ref{main_results} and \ref{main_results_graph_model} record the results of comparing CoG-BART with the baseline models on four datasets.

Among the pre-train-based models and their variants, the selected baseline models are BERT \cite{devlin-etal-2019-bert}, RoBERTa \cite{roberta}, HiTrans \cite{li-etal-2020-hitrans}, DialogXL \cite{dialog-xl} and XLNet \cite{xlnet}.
In MELD \cite{poria-etal-2019-meld}, CoG-BART has an approximate absolute 1.24\% improvement over the previous state-of-the-art BART-large \cite{lewis-etal-2020-bart}.

For graph-based models, KET \cite{zhong-2019-ket}, RGAT \cite{rgat}, DialogGCN \cite{ghosal-etal-2019-dialoguegcn}, DialogCRN \cite{dialogcrn},  COSMIC \cite{ghosal-etal-2020-cosmic}, and DAG-ERC \cite{dag-erc} are listed.

Compared to the graph-based model, CoG-BART improves 0.53 points over COSMIC \cite{ghosal-etal-2020-cosmic}. It is worth noting that RoBERTa-large is used as the feature extractor in COSMIC, while CoG-BART only adopts BART-large as the backbone structure to obtain competitive results, indicating that adequate knowledge transfer of pre-trained models which  effectively model the dependencies between contexts can also yield promising results in MELD.

We can observe from the results in EmoryNLP \cite{emorynlp} that the graph-based model using the pre-trained model as the feature extractor works better overall than the model applying only the pre-trained model as the backbone network. Meanwhile, CoG-BART still achieves results with significant improvement. Also, the graph-based model can obtain higher F1 overall on IEMOCAP \cite{busso2008iemocap} compared to the pre-trained based models. The reason is that the number of utterances contained in one context of IEMOCAP is much larger than the other three datasets, so pre-trained models are usually incapable of handling excessively long contexts. However, graph network models can better model context dependencies. In comparison, CoG-BART also achieves results similar to those of graph-based models, demonstrating the capability of CoG-BART to model the context-dependence.

The micro-F1 values of CoG-BART in DailyDialog are lower compared to the results of some graph neural network models. Still, it can achieve similar results to some pre-train-based models such as BERT \cite{devlin-etal-2019-bert}, RoBERTa \cite{roberta} and DialogXL \cite{dialog-xl}. Therefore, the graph-based model may have the advantage over pre-train-based models by more adequately modeling context dependencies on this dataset.

\begin{figure}[t]
\centering
\includegraphics[width=0.98\columnwidth]{effect_of_cl.pdf}
\caption{The t-SNE visualization results of the model output when $\alpha$ is 0 and 0.8, respectively.}
\label{effect_of_scl}
\end{figure}

\subsection{The Potency of Supervised Contrastive Learning}
\subsubsection{Qualitative Analysis of SCL}
To conduct a qualitative analysis of supervised contrastive learning, we utilize t-SNE \cite{t-sne-2002} to visualize the distribution of high-dimensional hidden states obtained by the model trained with supervised contrastive loss. By controlling different sizes of $\alpha$, the ratio of supervised contrastive loss is controlled to 0\% and 80\%, respectively, to obtain the hidden state output by the model under different levels of supervised contrastive learning.

As illustrated in Figure \ref{effect_of_scl}, when the supervised contrastive loss is not exploited, that is, when the cross-entropy loss function is completely adopted, the overlap rate of samples between different labels is particularly high, especially for some samples with similar emotions, which increase the difficulty of learning the decision boundaries. As the proportion of supervised contrastive loss increases, it can be distinctly observed that the degree of coupling between different classes is gradually enlarged, and the same classes begin to cohesive. It is worth mentioning that although the distance within the class has been reduced, the uniformity \cite{align_and_uniform} between samples has been well maintained, indicating that the information has been well preserved and no representation collapse has occurred.

\begin{table}[t]
\centering
\resizebox{1\columnwidth}{!}{
\begin{tabular}{lcccccc}
\toprule
{Metric}                  & \multicolumn{6}{c}{Weighted Average F1}                                                             \\
\midrule
{Datasets}                & $\alpha$=0.2    & $\alpha$=0.4   & $\alpha$=0.6 & $\alpha$=0.8     & $\beta$=0.1    & $\beta$=0.2   \\
\midrule
MELD                      & \textbf{64.57}  & 63.99          & 64.42        & 61.84            & 64.83          & 63.70         \\
IEMOCAP                   & 64.38           & \textbf{66.18} & 65.12        & 63.38            & 66.18          & 63.54         \\
EmoryNLP                  & \textbf{39.04}  & 36.68          & 36.90        & 35.24            & 37.45          & 37.57         \\
\bottomrule
\end{tabular}
}
\caption{The F1 scores for different values of $\alpha$ and $\beta$}
\label{alpha_of_scl}
\end{table}

\begin{figure*}[t]
\centering
\includegraphics[width=0.95\textwidth]{generation_analysis.pdf}
\caption{Case studies show that response generation enables the model to correctly predict the emotion based on context.}
\label{effect_of_response_generation}
\end{figure*}

\subsubsection{Quantitative Analysis of SCL}
The effects of different proportions of supervised contrastive learning on CoG-BART are illustrated in Table \ref{alpha_of_scl}, where the weighted average F1 of CoG-BART with different proportions of SCL loss is recorded. Different $\alpha$ have a large impact on the outcomes, e.g., there exists a 2.8 points difference in F1 values between $\alpha$ equals 0.4 and 0.8 for IEMOCAP, reflecting the significant positive effect of supervised contrastive learning for this dataset. Meanwhile, different datasets have different values of $\alpha$ in obtaining the relatively best gain effect. For instance, CoG-BART performs best when $\alpha=0.2$ in MELD, while achieving the best result when $\alpha=0.4$ in IEMOCAP.

\subsection{Effect of Response Generation}
Response generation has a facilitating effect on modeling context dependence to some extent. As the two cases in Figure \ref{effect_of_response_generation} illustrate, if only the current utterance itself is considered, the expression may cause the model to misjudge the sentiment of the current utterance, while generating responses leads the model to pay more attention to contextual information, thus making correct predictions which consistent with the scenario. As for the impact of different weights of response generation loss, Table \ref{alpha_of_scl} illustrates that when fixing $\alpha$ and adjusting $\beta$, there is also a slight impact on the model's overall performance.

\subsection{Ablation Analysis}

\begin{table}[t]
\centering
\resizebox{.95\columnwidth}{!}{
\begin{tabular}{lll}
\toprule
Dataset                  & \multicolumn{1}{c}{MELD}              & \multicolumn{1}{c}{IEMOCAP}              \\
\midrule
Methods                  & \multicolumn{2}{c}{Weight-Avg-F1}                                               \\
\midrule
CoG-BART                 & \multicolumn{1}{c}{64.81}             & \multicolumn{1}{c}{66.18}               \\
\quad -Gen               & 64.26 ($\downarrow$0.55)              & 64.74 ($\downarrow$1.44)                \\
\quad -SCL loss          & 64.28 ($\downarrow$0.53)              & 64.23 ($\downarrow$1.95)                \\
\quad -Speaker           & 64.14 ($\downarrow$0.67)              & 55.41 ($\downarrow$10.77)                \\
\quad -Gen, SCL loss     & 63.57 ($\downarrow$\textbf{1.24})              & 62.90 ($\downarrow$3.28)                \\
\quad -SCL loss, Speaker & 63.72 ($\downarrow$1.09)     & 54.83 ($\downarrow$\textbf{11.35})       \\
\quad -Gen, Speaker      & 64.02 ($\downarrow$0.79)              & 54.95 ($\downarrow$11.23)                \\
\quad -Dialog-Trans      & 64.40 ($\downarrow$0.41)              & 64.19 ($\downarrow$1.99)                \\
\bottomrule
\end{tabular}
}
\caption{Ablation study to evaluate the impact of different components on the overall performance of the model on MELD and EmoryNLP}
\label{ablation_study}
\end{table}

To investigate the impacts of individual modules and combinations of several components on the overall effect of the model, this section conducts an ablation study on three modules in CoG-BART. As illustrated in Table \ref{ablation_study}, the selected datasets are MELD and IEMOCAP, where ``-" indicates the removal of the single method or several methods, ``Gen" denotes the auxiliary task of response generation, ``SCL loss" means supervised contrastive loss, and ``Speaker" indicates the splicing of speaker label before utterance.

From the results of MELD, removing any of the three modules makes the overall performance worse, while discarding the supervised contrastive loss and response generation has the greatest impact on the performance of CoG-BART in MELD. These indicate that supervised contrastive loss leverage label information better compared to cross-entropy loss, thus effectively distinguishing different sentiments.

Consistent results are also obtained in IEMOCAP, indicating that the improvement in model performance from these three modules transfers well across these datasets. However, the more unexpected finding was that removing the speaker's information made CoG-BART most degraded in IEMOCAP. By analyzing this  dataset, we found that it involved 302 speakers, so it may be crucial to fully model the speaker information for this dataset. It also proves the effectiveness of the simple method of splicing the speaker information directly in front of the utterance. Furthermore, removing the supervised contrastive loss alone degrades the performance by 1.95 points on IEMOCAP, indicating that supervised contrastive learning significantly impacts CoG-BART on this dataset. The results after removing Dialog-level Transformer suggest that this module improves overall performance by modelling longer contextual dependencies.

\section{Conclusion}
We propose supervised contrastive learning with response generation as an auxiliary task for BART, namely CoG-BART, for emotion recognition in conversation (ERC). First, supervised contrastive learning is introduced into the training process to distinguish similar emotions, reducing intra-class distance and increasing inter-class variance. Meanwhile, the response generation is adopted as an auxiliary task; hence, the model categorizes utterances with similar semantics but different emotions by considering the context. The results obtained on four datasets compared with the current state-of-the-art baseline methods demonstrate the proposed method's effectiveness. Furthermore, ablation studies demonstrate that supervised contrastive learning can effectively improve the model's efficacy in recognizing emotions, thus improving the overall performance. Also, response generation as an auxiliary task helps the model fully consider the context to discern the emotions of semantically similar utterances in varying contexts.

\section{Acknowledgments}
We are very grateful to the reviewers for their diligent and rigorous attitude towards our work and their valuable suggestions for improvement during the whole review process. This work was supported by the National Key Research and Development Program of China (No. 2020AAA0108702) and the National Natural Science Foundation of China (NO. 62022027).

redpajama_set_name: RedPajamaArXiv


Before it was like there was a ladder and everyone is climbing, but I was stuck at the bottom. Now I am starting to climb.
Many of o Many of our pragmatic Youth program expenses do not qualify for core funding, which is why we launched our "Youth Matters" fundraising campaign. 'Youth Matters' raises funds to ensure youth in our community, your community, continue to have access to high quality, client centered mental health supports.
Often the youth we serve exhibit complex mental illnesses such as schizophrenia, bi-polar disorder and depression, among others. They frequently suffer the added burden of substance abuse, poverty, homelessness and family discord/estrangement. When these problems are mixed with the typical turbulence of adolescence it is easy to predict poor outcomes.
Many transitional youth in our community are in desperate need of services and are at risk of 'falling through the cracks' upon exiting children's services and entering into adolescence/young adulthood if they do not receive the necessary care during this transitional phase of their lives; hence the term 'transitional'. When care is not accessible to this age group they will often not engage in adult mental health services until it is too late and permanent, life altering damage has been done; This 'damage' is often manifested in justice/criminal involvement, drug use, marginalization, lengthy hospitalizations and suicidal ideation.
In 2008 our Youth Program was established in order to meet demands for specific mental health services and supports to youth aged 16-24. In response to that demand, in 2009, we began internally supporting a Youth Pilot Program with 3 youth.
Today we have 2 full time youth mental health case managers and serve over 20 youth per year. The popularity of the Youth Pilot Program was evident and it became vital for both the youth we served and their families that we continue offering these services. Our commitment to our communities youth is extremely important to us – as today's youth, are tomorrow's adults.
Our Youth Program caters to youth aged 16-24 which addresses a critical window of treatment time for youth suffering from mental illness; care and support during this time is crucial to their future success as early intervention is key to preventing the poor outcomes typically associated with lack of treatment.
An external third party evaluation of our Youth Pilot Program was undertaken and we are proud of the results. We welcome you to view and share the findings of our initial Youth Program Evaluation Report here.
We are also delighted to let you know that the first graduate of our Youth Pilot Program now lives independently and has graduated from Algonquin College after receiving a scholarship!
Like most teens, our son doesn't listen to us. This program allows him to live his life and have privacy… while also getting support.


India's monsoon season started in June, but the death toll in Kerala has soared in the last 24 hours.
India floods: 'I'll have nothing if I leave my house' Saving flood water to get through the droughts.
The government has urged people not to ignore evacuation orders, and is distributing food to tens of thousands who have fled to higher ground.


text:
Q: How to create Objects in database if does not exists with JPA? I have a web application that I use JPA / Hibernate I want to know how to hibernate can generate the  non-existent tables from entities.
the persistence file is as follows:
What I can add as property ?
<persistence xmlns="http://java.sun.com/xml/ns/persistence"
    xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance"
    xsi:schemaLocation="http://java.sun.com/xml/ns/persistence
        http://java.sun.com/xml/ns/persistence/persistence_1_0.xsd"
    version="1.0">
    <persistence-unit name="BankingApp">
        <provider> org.hibernate.ejb.HibernatePersistence</provider>
        <properties>

            <property name="hibernate.connection.driver_class" value="oracle.jdbc.driver.OracleDriver"/>
            <property name="hibernate.connection.url" value="jdbc:oracle:thin:@localhost:1521:XE"/>
            <property name="hibernate.connection.username" value="user"/>
            <property name="hibernate.connection.password" value="password"/>
            <property name="hibernate.dialect" value="org.hibernate.dialect.Oracle10gDialect"/>

        </properties>
    </persistence-unit>
</persistence>

A: I found the solution, simply add the following property:
<property name="hibernate.hbm2ddl.auto" value="create"/>

A: You can add this property
<property name="hibernate.hbm2ddl.auto" value="update"/>

The possible values for this property are:

*

*validate: Only validates the schema

*update: Update the schema

*create: Create the schema, override previous data

*create-drop: Create the schema and drop the schema at the end of the session

redpajama_set_name: RedPajamaStackExchange


text:
Frankfurt, Munich, Hamburg, Stuttgart, Berlin, Düsseldorf - the most important airports in Germany. What's going on there and at other German airports and what's new - in this section.
Extensive information about new routes, projects and what else is going on in German aviation can be found in the "Germany" section. In the main menu you can also switch to the sub-headings of the individual airports.
Tip: There is news from here Austriathe Switzerland and the rest of the world.
International | Military | Austria | Germany | Switzerland | Tourism | Trip reports | Travel Tips | Service & Jobs | Newsletter | "To Go" offline magazine
Erfurt expects 140.000 passengers
Erfurt Airport had around 2022 passengers between March and the end of October 119.900. Compared to the same period last year, it was possible to increase by 93 percent. The most passengers were with 25.400 travelers in October 2022. The busiest
Continue reading "
Rene Steuer 4 November 2022 09:46
Summer holidays 2022: Erfurt records a significant increase in passengers
Erfurt-Weimar Airport has been used by 62.371 passengers since the start of the local summer holidays. In a direct comparison with the same period last year, the number of passengers tripled. Holiday flights to Antalya, Mallorca,
Rene Steuer 10 August 2022 15:06
Photo gallery: European Air Charter new with MD-82 from Erfurt to Burgas
The Bulgarian airline European Air Charter is now offering non-stop flights from Erfurt to Burgas. According to the airport, there were 82 passengers on board the first flight, which was carried out with a McDonnell Douglas MD-155. tickets can
Rene Steuer 18 July 2022 18:03
Holiday start at the central German airports
No chaos is expected at the three airports in Dresden, Erfurt and Leipzig-Halle. The preparation for this article started yesterday morning at Rhein-Main Airport in Frankfurt/Main with a canceled flight to Berlin. on
Stephen Lorenz 16 July 2022 08:03
Albastar reconnects Erfurt-Weimar with Mallorca
As of today, the Balearic island of Mallorca is again an integral part of Erfurt-Weimar Airport's summer flight schedule. The Spanish airline Albastar flies Boeing 737-800 aircraft with 189 seats on behalf of the Duisburg tour operator Schauinsland-Reisen GmbH
Granite Pireci 17 June 2022 15:04
Erfurt: Fire brigade trains on a mobile aircraft fire simulator
The company fire brigade at the Erfurt-Weimar regional airport is currently the first customer to train on a mobile aircraft fire and recovery simulator from Arff Services GmbH. National and international authorities therefore require regular training. In addition to the basic course "Aircraft firefighting for regional airports".
Rene Steuer 31 March 2022 17:03
Guided tour of the site: Erfurt-Weimar Airport invites you
For the last time this year, Erfurt Weimar Airport will open its doors and invite all interested parties to a 90-minute tour of the airport grounds. The tour will take place on Tuesday, December 28.12.2021th, 10 at 00:XNUMX a.m. Together with
Granite Pireci 22 December 2021 19:02
Erfurt: Albastar takes on Erfurt-Palma de Mallorca
On behalf of the tour operator Schauinsland-Reisen, the Spanish airline Albastar will fly between Erfurt and Palma de Mallorca in summer 2022. The charter flights are to be operated with Boeing 737-800 machines. Schauinsland is currently planning trips
Rene Steuer 15 December 2021 19:39
Sundair is back in Erfurt
The German holiday airline Sundair started a non-stop connection between Erfurt-Weimar and Heraklion on Thursday. A total of three new destinations will be added from the airport in Thuringia in the course of September. Sundair will make the maiden flight on Friday
Rene Steuer 3 September 2021 18:02
Gerd Stöwer is hired on a long-term basis in Erfurt-Weimar
Erfurt-Weimar Airport will continue to be managed by Gerd Stöwer until at least the end of 2023. Following a recommendation from the Supervisory Board, the manager received a contract extension from the shareholders' meeting. Options have already been agreed for the period thereafter. Stöwer
Jan Gruber 13 August 2021 19:35
«Recent articles Page1 Page2 older articles »

redpajama_set_name: RedPajamaCommonCrawl


text:
Tagged: ethics
Faculty Conduct Research on Compassion
Five Santa Clara faculty members are featured contributors to a new book on compassion, featuring a study of Santa Clara students showing that programs like Arrupe increases student compassion.
Movie Aims to Prevent Campus Sexual Assault
Santa Clara alumni acted in a movie that premiered on campus last Friday to raise awareness of the issue of sexual assault on college campuses.
Forum Talks Sexual Assault Culture on Campus
A 2012 anonymous survey showed that a substantial number of Santa Clara students had non-consensual sex that went unreported to university officials.
Ethical Implications of Hobby Lobby Ruling Discussed
Leslie Griffin, a law professor from the University of Nevada, Las Vegas, spoke to a full audience at Santa Clara about the Supreme Court Hobby Lobby decision and how it limited both women's rights and religious freedom.
Netflix Invades Privacy, Pays Out
Two Santa Clara hubs for Internet ethics have been named as recipients of a portion of $9 million-settlement from a class-action lawsuit brought against Netflix.
Controversial Year Leaves School Values in Question
We bring inspiring leaders like Dolores Huerta, Paul Farmer and the Dalai Lama to campus to speak about compassion worldwide, but I see something of a double standard when we neglect issues of social justice on our own campus.
App Weighs Right and Wrong
Fourteen hundred people have downloaded Santa Clara's new iPhone application, which helps users make ethical choices in everyday life.
The Markkula Center for Applied Ethics' new app is designed to aid users in the decision-making process, allowing them to analyze every ethical aspect of a potential judgement.
Dalai Lama Makes Historical Appearance
In one of the most highly anticipated events in recent years, the university welcomed the 14th Dalai Lama to campus on Monday to discuss the role of compassion and ethics in business.


Squat Rack with Pull-up bar are durable and safe frames for weightlifting. It can be equipped with storage systems for sports inventory and safety bars. Squat Rack with Pull-up bar is used for all kinds of exercises both with the weightlifting bar or using pull-up bar and is made of high grade materials.
The squat rack is perfectly suited for different attacments - such as dip bars, core trainers, half-safety bars and grip trainers.


text:
Weekend of Traffic Chaos Predicted
Local road and tube closures could mean major jams
Chiswick residents Face Months of Traffic Chaos
Council announces three major sets of road works in area
Travel Updates for Your Area
Sign up for email newsletters from ActonW3.com, EalingToday.co.uk, ChiswickW4.com
Comment on this story on the forum
The May Bank Holiday weekend looks as though it could be the start of a miserable time for people looking to travel around West London.
The North Circular is set to close northbound between Chiswick Roundabout and Gunnersbury Avenue by Pope's Lane for 10 days beginning at 8pm on Friday night (April 30th). This is due to Thames Water needing to do repair work.
During this period, northbound traffic will be diverted from the Chiswick Roundabout via Chiswick High Road, Acton Lane and Uxbridge Road, before rejoining the A406 Hanger Lane. Southbound traffic will not be affected.
Over the Bank Holiday weekend, public transport in the area will also be hit. The District Line is shut Saturday to Monday between Ealing Broadway and Turnham Green and the Central line is suspended between Ealing Broadway and North Acton. The London Overground service between Richmond and Stratford is also not operating on Sunday and Monday. There are reports that the Piccadilly line may be stopping at Turnham Green but we have yet to have that officially confirmed by Transport for London.
Passengers wishing to travel to Ealing Broadway should change at Hammersmith for the Piccadilly Line and then change onto local bus services at Ealing Common to complete their journey.
Steve Shine, Thames Water's Chief Operating Officer, said, "We understand this work will cause inevitable disruption to road users and local residents, but we have to carry out these repairs urgently to stop the leak getting any worse. We would like to assure road users and local residents that we will be working around the clock to complete this work as quickly as we can."
In order to maximise the potential of this road closure British Telecom is to carry out upgrade works while the carriageway is closed.
Alan Bristow, TfL's Traffic Director, said, "We ask motorists to bear with us while these works are carried out and we will do our best to ensure that disruption is kept to a minimum.''


Hello again! Another month under my belt in the ever-forward-moving progression towards my big 3-0.
Ridiculous relief as blessed Gordy from the Apple Store, Buchanan Street, saved my poor MacBook Pro's life (by replacing its logic board), and thusly my harddrive and all my many things. Thanks Gordy!!! I've since backed everything up again and will be more diligent, scout's honor! Have also upgraded my OS from Leopard (retro!!) to Maverick and upgraded RAM so feeling slick and shiny in the new year. *my tech teeth are tingling*.
December has been pretty eventful: we completed our Race for charity, I went Glassblowing!, we spent loads of time playing boardgames with pals (and enjoying our friends' new super-cute puppy!), and we racked up another few hours at karaoke (+2hrs with Stuart & +2hrs with my Bball team on our night out – danger!). Supercube gang stamped up my point card as well so looking forward to trying to squeeze in another cheeky hour+ before 2014!).
So many Christmas parties – work, bball team, pals & family. Handsome S and family were very good to me this Christmas, and I am looking forward to many upcoming adventures and trying new things (including scuba diving lessons!, trips abroad and tasty meals). I am also immensely grateful for all the glorious free time. I have read lots and played lots of video & board games and generally gotten loads done that I've been putting off as work has been so manic lately. There is nothing as wonderful as free time.
I managed to finish a poem called 'On the first day'; it's pretty pants (a sci-fi villanelle… will never see the light of day!). But at least it's finished!
And my Coding class starts in January!
Looking forward to dedicating Jan-Feb to 30 days of proper Paleo (hopefully making a dent in 'cut 30lbs'.
I think it's my last chance to try and plant garlic to grow this year – will get on this in January too.
We've also booked our Iceland holiday in February – Northern Lights boat trip and tickets to 3-day Sonar Festival sorted! V excited = understatement of the year.
Still to plan is our LA trip (hopefully including a 'mommy & me' element), and how to squeeze in the many, many remaining adventures before August. Bring it!
In other news, a teacher-friend asked on FB recently about winter traditions around the world, and it got me thinking about all the amazing festivities in Scotland around Christmas & New Year (Hogmanay). Reflecting on this made me really grateful for what an amazing opportunity it is and has been to live in this city, with so many kind and friendly people. I am so lucky and so fortunate; I promise I don't take it for granted.
Was watching the Russian space walk the other day and even they've got GoPro cameras now. The future is all around us.
Lastly, to get you in the spirit of the season, be sure to check out Google's 2013 zeitgeist! It is tops.
And keep an eye out for my wee Lion&Sloth 2013 in review – to be posted shortly!
See you in the New Year! Here's to family & friends & living a content-rich life. Lang may yer lum reek!


Librarian of Congress Carla Hayden has announced the addition of 25 films to the library's National Film Registry, selections deemed worthy of preservation for their "cultural, historic and/or aesthetic" significance. This year's collection brings the number of registry films to 725 and includes such beloved mainstream movies as the historical romance "Titanic" (1997) and "Die Hard" (1988).
Other popular works include "Dumbo" (1941), "Superman" (1978), "The Goonies" (1985), "Field of Dreams" (1989) and "Memento" (2000). Among this year's more esoteric fare is "The Sinking of the Lusitania," a 1918 short by cartoonist and animator Winsor McCay about the German submarine attack that contributed to the United States' entry into World War I, and an archive of amateur home movies from the 1920s and '30s about life in the Mexican American community of Corpus Christi, Tex.
Hayden selected the films with input from members of the National Film Preservation Board and other specialists at the library. The public also was invited to weigh in.
As timely as it may be to see this honor bestowed on "Titanic" — now celebrating its 20th anniversary — or "Die Hard" — a holiday staple — there could be no more opportune selection than "4 Little Girls." Nominated for a best documentary feature Oscar, Spike Lee's 1997 film about the Birmingham church bombing of Sept. 15, 1963, was met by a reopening of the long-dormant criminal case by the FBI — a case that just so happens to have been subsequently prosecuted — successfully — by an Alabama lawyer named Doug Jones. At press time, Jones was locked in a close race for the U.S. Senate with the controversial former judge Roy Moore.
On Tuesday — Election Day in Alabama — Lee phoned from New York to say that he always cherishes the National Film Registry honor. (Two of his narrative features were previously selected: "Do the Right Thing" and "Malcolm X.") Lee also said he wanted to dedicate the film's selection to the murdered girls: Addie Mae Collins, Carol Denise McNair, Carole Robertson and Cynthia Wesley.
But mostly, the filmmaker took the opportunity to unleash a torrent of unfiltered invective about Moore. After making a few circumspect comments about the power of storytelling to "hold a mirror up to the ugliness that we have become," Lee acknowledged that he was troubled by what's happening in America today, epitomized by the Alabama election, before acknowledging that the word "troubled" was probably too mild.
But Lee said current affairs had not caused him to lose hope, as a human or as a filmmaker. "Like my man Jesse [Jackson] says, you've got to keep hope alive," he said. Never modest, the cinematic bombthrower said he thinks that this latest honor may not be the last time one of his films enters the registry and that his 2006 Hurricane Katrina documentary "When the Levees Broke: A Requiem in Four Acts" could be the next.
Then there's Lee's drama "Black Klansman," a fact-based biopic, due out next year, about a black Colorado police officer who infiltrated the Ku Klux Klan.


Rihanna is set to follow in the footsteps of fellow pop star Gwen Stefani and launch her own clothing range.
The "Disturbia" singer, who is a frequent front-row guest at runway shows, has confirmed she is working on her own designs.
The star has also previously teamed up with retail chain H&M to design a range of casualwear as part of the Fashion Against Aids campaign.


The Anderson Software Group, Inc. was proud to participate in Devoxx in November 2015 in Antwerp, Belgium. In our session we show how JavaFX binding makes desktop client application development easier and faster to develop. If you were not able to attend our session, you can view it in its entirety here. Enjoy!


3 bedroom 1 bath brick house for sale in Northwest Atlanta.
3 bedroom 2 bath house.
3 bedroom 2 bath in Hammond Park.
House is in livable condition.
3 bedroom 1 bath house.
4 bedroom 1.5 bath house in southeast Atlanta.
House is in good condition but just needs a little upgrades.
2 bedroom 1 bath house in center Hill.
3 bedroom 2 bath fire damage split level house in a nice area.
3 bedroom 1 bath house on 1.6 acres of land in a high comps area.
3 bedroom 1 bath ranch house in Southeast Atlanta.
4 bedroom 2 bath 2 story house inear downtown Griffin.
2 bedroom 1 bath house in Southeast Atlanta .
4 bedroom 1.5 bath brick house in Decatur.
3 bedroom 2 bath brick house in Southwest Atlanta.
3 bedroom 2 bath brick house in a nice area.
House is in decent condition.
2 bedroom 2 bath brick house in a nice community.
2 bedroom 1 bath house in good condition.
3 bedroom 2.5 bath Townhome .
3 bedroom 1.5 bath house with 1 car garage.
3 bedroom 1.5 bath brick house near Eastlake Golf club.
House is currently occupied. Please do not disturb the tenant.
3 bedroom 1 bath house in Southeast Atlanta.
4 bedroom 2.5 bedroom in Marietta.
2 bedroom 1 bath ranch house with a bonus room.
3 bedroom 1 bath brick in Northwest Atlanta.
2 bedroom 1 bath house in Grove park.
3 bedroom 1 bath house in Northwest Atlanta.
2 bedroom 1 bath house in Se Atlanta .
3 bedroom 3 bath Tri Level Town House in Clarkston.
4 bedroom 2 bath ranch house.
3 bedroom 1 bath house in Southwest Atlanta GA.
3 bedroom 1 bath ranch house in Florida Hts.
2 bedroom 1 bath attached house in Sylvan Hill.
4 bedroom 2 bath house in Florida Hts.
2 bedroom 1 bath house on unfinished basement.
3 bedroom 1 bath brick house in SE Atlanta.
3 bedroom 1 bath house in Pittsburgh.
3 large bedroom 2 bath house in a nice area .
House needs upgrades. The living space is 1850 sqft.
3 bedroom 2 bath house in East Point.


text:
All Sports News
All Area Teams
Sports Live Stream
All Local Living
Florida Finds
Faith + Inspiration
Print edition locations
GET THE LATEST LOCAL NEWS DELIVERED TO YOUR INBOX
Applicant for West End Golf rezoning asks for 60-day deferral
Suzette Cook, Staff Reports
West End Golf Course hole 6 sign
The civil engineering firm representing a developer's application for the rezoning of the former West End Golf property has requested a 60-day deferral in order to prepare information.
According to Alachua County Senior Planner for Growth Management Gerald Brewington, Jay Brown, Jr., P.E., President of JBrown Professional Group made the following request.
"Our team feels like we need more time to communicate with the neighboring property owners and to provide staff with additional information in support of our application. We would therefore request that you defer our pending Comprehensive Plan Amendment and Rezoning applications for at least 60 days. We will stay in touch with ACGM staff and let you know the status of our additional efforts. Please reply that you have received this email and the deferral of our application is acceptable to County staff."
Brewington said that as a result of the request, there is no estimated time at this point," for a date to proceed with a hearing.
"Until such time as the applicant is ready to proceed, we are basically in a holding pattern," Brewington said about the application that would convert the 75 acres of recreation zoned property into 487 homes and commercial space.
The potential buyer of the property is Developer Sayed Moukhtara who has the property under contract with Owner Peter Min. The deal won't go through unless the Board of County Commission approves of the rezoning. The 75 acres is currently listed at $6.25 million by Min who represents the property under Pepine Realty.
Min closed the golf course almost a year ago and has allowed the buildings and greens to go unkempt causing concern to homeowners who paid premium prices to live adjacent to the course for easy access and views of the greens.
Because of the deferral, the next stages of the rezoning process might not happen until early 2021.
"If they should amend their request in any way, we would need to re-evaluate any proposed changes," Brewington said. "As such, there is no timeframe for when public hearings will take place, although a 60 day deferral would most likely, at a minimum, push hearings into next year."
JBrown held a neighborhood meeting in July in which more than 300 comments were made against the development and even Brown said it was the highest attended meeting in his career. That meeting lasted four hours and no neighbor was in favor of the rezoning.
Since then, a group of more than 50 "Friends of West End" have united to defeat the rezoning request arguing that the area can't handle the traffic that will be created by people occupying 487 more homes and that the county should keep the recreation space as it is… only one of two green spaces located in West Alachua County.
Don't miss our top stories every weekday in your inbox.
The Friends of West End have started a GoFundMe account, hired an attorney and have held two protests urging the BOCC to deny the application.
Tags:none
Suzette Cook
Local news in your inbox.
Got a news tip or story?
Special guest visits Newberry Elementary
Branford man dies in single-vehicle crash
Gas prices surge in Florida
LCPD car burglary investigation leads to arrest
BOCC OKs plans for two subdivisions
BBQ and Bacon Fest to be held in Newberry
Bicyclist dies from hit-and-run injuries
All Area: Union County paces small school offense
Mainstreet Daily News is a local, independent news outlet dedicated to telling the story of Gainesville and its surrounding area. Our mission is to inform, engage, and inspire our readers to make a difference in the communities and neighborhoods we serve.
mainstreetdailynews.com
100 NW 76th Drive #2
Email: editor@mainstreetdailynews.com
Public Notice Certification
chevron-rightfacebook-squaretwitterangle-downinstagramcrossmenuwpDiscuz
We would love your thoughts — please comment.x


text:
Star wars risk clone wars edition rules. RISK Star Wars
Star wars risk clone wars edition rules Rating: 8,4/10 1649 reviews
Risk: Star Wars Clone Wars Edition
During your turn you get new troops, and you can play cards to get more troops or ships to get more power when attacking. It seemed like the perfect combination—a great strategy board game based on the world of the greatest science fiction movie franchise of all time. The number or armies received depends on the type of set turned in. Ships in general are built using cards, and players can buy one ship each turn. If you had to watch anything animated that is related to Star Wars, then the Clone Wars series is certainly it.
Risk Star Wars Edition Game
The Republic player does not know which token represents which leader. While playing the version, the Republic armies and the Separatist armies have different card decks. If a player takes over an opposing army's territory, that player gets a card at the end of the turn. Monopoly Star Wars Saga Edition takes these powers and applies them as follows: Players can call upon the Force when they roll doubles at anytime during the game. But what does not seem to get talked about enough is the Star Wars: Clone Wars animated series, which was actually great in its own right.
Star Wars Risk: The Clone Wars Edition
The addition of Order 66 makes the game quite unbalanced as it easily turns the tide of the battle. This represents Anakin's fall to the Dark Side. Buy properties as you land on them. Anakin cards are available to both the Republic and Separatist armies, representing his struggle between the Light and Dark sides of the Force. Fighters can prevent die rolls of 1, making them essential in increasing your chances in initiating successful attacks.
If you play as the Rebel Alliance, it's to destroy the Death Star! Some effects include the ability to add extra reinforcements to a defending territory, remove units from a territory, or alter die rolls during battle. The Imperial armies include two Death Stars—special units that can protect Imperial-controlled planets from attack and can destroy other planets, just like in the movies. If that territory is ever taken over by the Republic, the Republic wins the game. The Star Wars universe is populated by billions of people from hundreds of species, so building simple houses and hotels is insufficient. Once Order 66 is called, however, Republic players cannot use the special text on Anakin cards; they can use the cards only for spacecraft and reinforcements. Each zone contains a grouping of game squares which represent a planet in the Star Wars Universe. Once Order 66 is called, all Separatist tokens are removed from the game, and the Republic player can no longer capture them.
Some cards have a red colored background, which represent the Jedi. Meanwhile, capital ships let players roll 8-sided dice instead of 6-sided dice, thus providing advantage. It also has territories and continents, but set in space and are instead called planets and regions respectively, making it consistent with the Star Wars setting. Ships add another dimension of strategy in the game as their positioning can be a major factor in battle. The game is over if Republicans or Separatists gain control over all the planets.
Star Wars Risk The Black Series
Although the instructions remain the same, the artwork and the orders are written to suit the Star Wars style. However, the decision to call Order 66 must be made carefully. Thankfully playing Risk online fills that void. There are 3 types of Skill Cards—Republic Skill Cards, Separatist Skill Cards, and Anakin Skill Cards. With the Galactic Republic engaged in all-out war against the Confederacy of Independent Systems, chaos and confusion have seeped into every part of the Republic and its citizens. Plastic pieces include dice 5 six-sided , 5 eight-sided and tokens depicting the armies and Darth Sidious. And they thought, they were winning! Check out this review of Star Wars Risk The Black Series.
Star Wars Risk is pretty good, also not Risk
Finally, since the Darth Sidious token is placed on the gameboard after all territory control changes have been made, it could give the Republic an easy victory. Each space on the track has a list of numbers from 1 to 8 next to it. They are earned by defeating enemy armies. Unfortunately most of them really suck, so I set out to find and review the best sites to play Risk online. Playing the Game Roll the dice and move your token the appropriate number of squares. At first we played it as classic Risk, but the extra rules are really not that hard, and they really add to the fun. Double 3: Jedi Mind Trick: Collect 200 Credits from every player.
All Separatists have to do is to turtle until Order 66 can be executed, after which they can then snowball until the game is won. The Separatist Leader Tokens give the Republic a key advantage at the beginning of the game, as they are able to draw cards much faster than the Separatists, which allows them to build their forces more quickly. Double 2: Jedi Mind Trick: Collect 200 Credits from the Bank. Special powers and what they do are written on the Cards, so you must read them to understand how they can be used correctly. With the release of the new Star Wars movie, The Force Awakens, we look back on the past installments of Star Wars, but through a different lens. Star Wars Risk is a great boardgame. Setting Up Place the Jedi and Sith cards face-down on their respective places on the board.
Playing Star Wars Risk
The Republic does get a head start, receiving advantage through Separatist Secret Tiles, which let Republic players draw extra Cards at the turn in which they are taken. Pay the Banker for each property in exchange for a property card, which indicates the amount of money earned through rent. If the number rolled is in the list of numbers next to the space that Darth Sidious is on, the territory immediately becomes controlled by a Separatist army. Spacecraft In addition to the regular troops fighting during battles, players can also add spacecraft to their territories. Bounties and bounty hunters were featured in all six films; the most notable was Jabba the Hut's bounty hunter Boba Fett, who captured Han Solo. Giovanniello holds a Bachelor of Arts in film theory and English literature from the University of Western Ontario.


Problems with the alignment of the teeth are a common reason why individuals choose to visit a cosmetic dentist. In some cases, a bad bite is actually a type of malocclusion, which affects the way the teeth in the dental arches line up.
In the majority of cases, the problem will be relatively mild. However, those who suffer from more severe malocclusion can also encounter a wide range of other health problems. Serious malocclusion can affect the shape of the face, which can in turn cause difficulties while speaking or chewing. Furthermore, people who suffer from serious malocclusion are more likely to also have pain caused by pressure on the jaw, as well as having a higher chance of experiencing tooth decay.
In order for the body to be able to digest food properly, it first needs to be chewed right. The mouth produces saliva, which actually contains various enzymes that are important to the digestive process. The enzymes found in saliva work together with stomach acid to produce chime, which is a substance that helps food make its way to the small intestine.
As digestion is a process that begins in the mouth, those who have problems that render them unable to chew food properly are more likely to suffer from a variety of digestive issues. Structural tooth problems and misalignment of the teeth have been linked with increased rates of GERD, stomach ulcers, heartburn and other digestive health concerns.
As TMJ has been shown to be linked with the onset of tinnitus, a misaligned jaw can lead to hearing problems. People whose teeth aren't aligned properly are more likely to complain of chronic headaches. Individuals with crowded teeth have a higher chance of experiencing gum disease, which has in turn been shown to be a risk factor for heart attack and stroke.
While dental problems are linked to a variety of physical health concerns, they also have the potential of negatively affecting a person's mental health. This is due to the fact that most people value their appearance and want to have a healthy smile. Individuals who aren't satisfied with the way their teeth look are more likely to stay on the sidelines of life instead of experiencing it to the fullest, which can have repercussions on their self esteem.
Houston Cosmetic Dentists offer a wide range of dental services that can help you improve the way your teeth look and help protect your oral and overall health.
If you are interested Houston Cosmetic Dentistry services, schedule a free consultation with the doctors at Sinada Dental – the place that Houston trusts for cosmetic dental services!


ZookaWare releases new RegZooka PC cleaner user interface to maximize user experience.
Zookaware has been offering high quality PC solutions and excellent tech support since 2004.
Zookaware releases new RegZooka PC cleaner user interface to maximize user experience. Users who access the updated version will first notice the visual changes, but will find that there are other updates to the newly released user interface as well.
1. Visual: First and foremost users will notice that there have been visual changes to the logo, background and accent colors throughout.
2. Navigation: Users notice after accessing the PC cleaner tools that the changes to the user interface have made RegZooka easier to navigate.
3. Comprehension: Updates to the instructions throughout the PC cleaner have increased consistency. This has been shown to increase the user's ability to comprehend the program and its features.
Zookaware has been offering high quality PC solutions and excellent tech support since 2004. Our products are designed specifically for the average PC user. At Zookaware they call it designing software for people, not geeks. The Zookaware product suite includes: CyberBackup, RegZooka, and SpyZooka. CyberBackup provides a safety net for users by protecting their data through remote storage. RegZooka offers PC error solutions through registry cleaning. SpyZooka finds and removes spyware from PCs and also acts as a preventive measure, keeping spyware from infecting the computer. The Zookaware suite of products was designed to work together to provide the average user with all the protection they need for their PC.


9 Top Motorcycle Movies for Winter 2018 | I JUST WANT 2 RIDE!!
Seen All Of Them Except For 21 Days Under The Sky!
Really glad to see Long Way Round mentioned. They also did another, Long Way Down, heading from the top tip of Scotland all the way to the bottom of Africa.
Long Way Round was pretty good. I found Long Way Down was more about "drama" than it was about motorcycles and the journey.
I happened to catch Harley and the Davidsons on Amazon Prime the other day. I was quite impressed.
Yep, H & the D was recommended to me by my son-in-law, hadn't even heard of it. Thoroughly enjoyed it.


Published 04/24/2019 08:17:40 am at 04/24/2019 08:17:40 am in Copper Colored Chargers.
copper colored chargers copper colored chargers.
silver colored plate chargers,copper colored plate chargers,gold colored plate chargers,copper colored chargers, bronze charger plates gold charger plates offsaveoncrafts bronze , chargers plates dinner plates bulk we can package twig charger plate, copper colored chargers ukescortdirectoryinfo copper colored chargers jr pure copper coil wireless charger plate for x galaxy note etc, mixing metallics and your wedding flowers j morris flowers a mixedmetal table settings and flowers at landsdowne resort and spa leesburg, charger plates dinnerware the home depot in, glass chargers archives party time rentals gold concentric charger with clear middle, charger plates and buffet plates crate and barrel .


Suffolk County Crime Stoppers and Riverhead Town police are seeking the public's help to identify the individuals who stole a forklift and other equipment from a Riverhead farm earlier this month.
A Datsun forklift, Husqvarna chainsaw, two Northstar pressure washers, a Schumacher battery charger, three Schumacher jump packs and a Remington pole saw were reportedly stolen from Anderson Farms at 375 Middle Road. The thefts occurred between Aug. 11 and Aug. 12.
Suffolk County Crime Stoppers offers a cash reward of up to $5,000 for information that leads to an arrest. Anyone with information can submit an anonymous tip by calling 1-800-220-TIPS or by texting SCPD and a message to "CRIMES" (274637). Emails can also be submitted to www.tipsubmit.com.
Photo caption: A similar forklift to the one reported stolen.


Katherine couldn't wait to grow up. For seventeen years she's been passed from orphanage to orphanage, yearning for the day when she can leave to discover her true home. With six months until her eighteenth birthday, she's sure this orphanage is going to be the worst. But she's about to discover that love always finds you just when you aren't looking for it. Live her life story in this heartwarming hidden object puzzle adventure!
Katherine couldn't wait to grow up. For seventeen years she's been passed from orphanage to orphanage, yearning for the day when she can leave to discover her true home. With six months until her eighteenth birthday, she's sure this orphanage is going to be the worst. But she's about to discover that love always finds you just when you aren't looking for it. Live her life story in this heartwarming hidden object puzzle adventure!
What's your user review score for Love Story: The Way Home?
Please login to add your score for Love Story: The Way Home Graphics played on the pc.
Please login to add your score for Love Story: The Way Home Lifespan played on the pc.
Please login to add your score for Love Story: The Way Home Value played on the pc.
Love Story: The Way Home has been added to Game-Debate but does not currently have the Love Story: The Way Home system requirements.


Sector views on national minimum wage ….
editorial … Feisty or not, Lumka Yengeni of the parliamentary portfolio committee on labour certainly knows how to advance her career. Whether all her recent parliamentary activity is in line with what Lithuli House wants one never knows but whatever she does seems to attract attention.
Reports on the consequent debates are with clients and will be posted on our website in a fortnight's time, since immediacy is the purpose of our reports to them.
The unusual thing about these meetings, as South Africa pivots on the edge of economic direction, is that Parliament should take it upon itself to debate the whole issue of the national minimum wage when the subject also has been scheduled by Deputy President, Cyril Ramaphosa for a national gathering.
Whether this is naiveté on the part of Lumka Yengeni or plain political upstaging only those close to her will know but the experience of the apple farmers in Elgin over labour broking will testify to the fact that she knows the art of political theatre.
Perhaps, on such a difficult subject, forewarned is forearmed.


text:
test_description='Test remote-helper import and export commands'

. ./test-lib.sh
. "$TEST_DIRECTORY"/lib-gpg.sh

compare_refs() {
	git --git-dir="$1/.git" rev-parse --verify $2 >expect &&
	git --git-dir="$3/.git" rev-parse --verify $4 >actual &&
	test_cmp expect actual
}

test_expect_success 'setup repository' '
	git init server &&
	(cd server &&
	 echo content >file &&
	 git add file &&
	 git commit -m one)
'

test_expect_success 'cloning from local repo' '
	git clone "testgit::${PWD}/server" local &&
	test_cmp server/file local/file
'

test_expect_success 'create new commit on remote' '
	(cd server &&
	 echo content >>file &&
	 git commit -a -m two)
'

test_expect_success 'pulling from local repo' '
	(cd local && git pull) &&
	test_cmp server/file local/file
'

test_expect_success 'pushing to local repo' '
	(cd local &&
	echo content >>file &&
	git commit -a -m three &&
	git push) &&
	compare_refs local HEAD server HEAD
'

test_expect_success 'fetch new branch' '
	(cd server &&
	 git reset --hard &&
	 git checkout -b new &&
	 echo content >>file &&
	 git commit -a -m five
	) &&
	(cd local &&
	 git fetch origin new
	) &&
	compare_refs server HEAD local FETCH_HEAD
'

test_expect_success 'fetch multiple branches' '
	(cd local &&
	 git fetch
	) &&
	compare_refs server master local refs/remotes/origin/master &&
	compare_refs server new local refs/remotes/origin/new
'

test_expect_success 'push when remote has extra refs' '
	(cd local &&
	 git reset --hard origin/master &&
	 echo content >>file &&
	 git commit -a -m six &&
	 git push
	) &&
	compare_refs local master server master
'

test_expect_success 'push new branch by name' '
	(cd local &&
	 git checkout -b new-name  &&
	 echo content >>file &&
	 git commit -a -m seven &&
	 git push origin new-name
	) &&
	compare_refs local HEAD server refs/heads/new-name
'

test_expect_success 'push new branch with old:new refspec' '
	(cd local &&
	 git push origin new-name:new-refspec
	) &&
	compare_refs local HEAD server refs/heads/new-refspec
'

test_expect_success 'push new branch with HEAD:new refspec' '
	(cd local &&
	 git checkout new-name
	 git push origin HEAD:new-refspec-2
	) &&
	compare_refs local HEAD server refs/heads/new-refspec-2
'

test_expect_success 'push delete branch' '
	(cd local &&
	 git push origin :new-name
	) &&
	test_must_fail git --git-dir="server/.git" \
	 rev-parse --verify refs/heads/new-name
'

test_expect_success 'forced push' '
	(cd local &&
	git checkout -b force-test &&
	echo content >> file &&
	git commit -a -m eight &&
	git push origin force-test &&
	echo content >> file &&
	git commit -a --amend -m eight-modified &&
	git push --force origin force-test
	) &&
	compare_refs local refs/heads/force-test server refs/heads/force-test
'

test_expect_success 'cloning without refspec' '
	GIT_REMOTE_TESTGIT_REFSPEC="" \
	git clone "testgit::${PWD}/server" local2 2>error &&
	grep "This remote helper should implement refspec capability" error &&
	compare_refs local2 HEAD server HEAD
'

test_expect_success 'pulling without refspecs' '
	(cd local2 &&
	git reset --hard &&
	GIT_REMOTE_TESTGIT_REFSPEC="" git pull 2>../error) &&
	grep "This remote helper should implement refspec capability" error &&
	compare_refs local2 HEAD server HEAD
'

test_expect_success 'pushing without refspecs' '
	test_when_finished "(cd local2 && git reset --hard origin)" &&
	(cd local2 &&
	echo content >>file &&
	git commit -a -m ten &&
	GIT_REMOTE_TESTGIT_REFSPEC="" &&
	export GIT_REMOTE_TESTGIT_REFSPEC &&
	test_must_fail git push 2>../error) &&
	grep "remote-helper doesn.t support push; refspec needed" error
'

test_expect_success 'pulling without marks' '
	(cd local2 &&
	GIT_REMOTE_TESTGIT_NO_MARKS=1 git pull) &&
	compare_refs local2 HEAD server HEAD
'

test_expect_failure 'pushing without marks' '
	test_when_finished "(cd local2 && git reset --hard origin)" &&
	(cd local2 &&
	echo content >>file &&
	git commit -a -m twelve &&
	GIT_REMOTE_TESTGIT_NO_MARKS=1 git push) &&
	compare_refs local2 HEAD server HEAD
'

test_expect_success 'push all with existing object' '
	(cd local &&
	git branch dup2 master &&
	git push origin --all
	) &&
	compare_refs local dup2 server dup2
'

test_expect_success 'push ref with existing object' '
	(cd local &&
	git branch dup master &&
	git push origin dup
	) &&
	compare_refs local dup server dup
'

test_expect_success GPG 'push signed tag' '
	(cd local &&
	git checkout master &&
	git tag -s -m signed-tag signed-tag &&
	git push origin signed-tag
	) &&
	compare_refs local signed-tag^{} server signed-tag^{} &&
	test_must_fail compare_refs local signed-tag server signed-tag
'

test_expect_success GPG 'push signed tag with signed-tags capability' '
	(cd local &&
	git checkout master &&
	git tag -s -m signed-tag signed-tag-2 &&
	GIT_REMOTE_TESTGIT_SIGNED_TAGS=1 git push origin signed-tag-2
	) &&
	compare_refs local signed-tag-2 server signed-tag-2
'

test_expect_success 'push update refs' '
	(cd local &&
	git checkout -b update master &&
	echo update >>file &&
	git commit -a -m update &&
	git push origin update &&
	git rev-parse --verify remotes/origin/update >expect &&
	git rev-parse --verify testgit/origin/heads/update >actual &&
	test_cmp expect actual
	)
'

test_expect_success 'push update refs disabled by no-private-update' '
	(cd local &&
	echo more-update >>file &&
	git commit -a -m more-update &&
	git rev-parse --verify testgit/origin/heads/update >expect &&
	GIT_REMOTE_TESTGIT_NO_PRIVATE_UPDATE=t git push origin update &&
	git rev-parse --verify testgit/origin/heads/update >actual &&
	test_cmp expect actual
	)
'

test_expect_success 'push update refs failure' '
	(cd local &&
	git checkout update &&
	echo "update fail" >>file &&
	git commit -a -m "update fail" &&
	git rev-parse --verify testgit/origin/heads/update >expect &&
	test_expect_code 1 env GIT_REMOTE_TESTGIT_FAILURE="non-fast forward" \
		git push origin update &&
	git rev-parse --verify testgit/origin/heads/update >actual &&
	test_cmp expect actual
	)
'

clean_mark () {
	cut -f 2 -d ' ' "$1" |
	git cat-file --batch-check |
	grep commit |
	sort >$(basename "$1")
}

cmp_marks () {
	test_when_finished "rm -rf git.marks testgit.marks" &&
	clean_mark ".git/testgit/$1/git.marks" &&
	clean_mark ".git/testgit/$1/testgit.marks" &&
	test_cmp git.marks testgit.marks
}

test_expect_success 'proper failure checks for fetching' '
	(cd local &&
	test_must_fail env GIT_REMOTE_TESTGIT_FAILURE=1 git fetch 2>error &&
	cat error &&
	grep -q "Error while running fast-import" error
	)
'

test_expect_success 'proper failure checks for pushing' '
	(cd local &&
	git checkout -b crash master &&
	echo crash >>file &&
	git commit -a -m crash &&
	test_must_fail env GIT_REMOTE_TESTGIT_FAILURE=1 git push --all &&
	cmp_marks origin
	)
'

test_expect_success 'push messages' '
	(cd local &&
	git checkout -b new_branch master &&
	echo new >>file &&
	git commit -a -m new &&
	git push origin new_branch &&
	git fetch origin &&
	echo new >>file &&
	git commit -a -m new &&
	git push origin new_branch 2> msg &&
	! grep "\[new branch\]" msg
	)
'

test_expect_success 'fetch HEAD' '
	(cd server &&
	git checkout master &&
	echo more >>file &&
	git commit -a -m more
	) &&
	(cd local &&
	git fetch origin HEAD
	) &&
	compare_refs server HEAD local FETCH_HEAD
'

test_expect_success 'fetch url' '
	(cd server &&
	git checkout master &&
	echo more >>file &&
	git commit -a -m more
	) &&
	(cd local &&
	git fetch "testgit::${PWD}/../server"
	) &&
	compare_refs server HEAD local FETCH_HEAD
'

test_done

redpajama_set_name: RedPajamaGithub


text:
You are here: Home ∼ SA Rugby boss on BLM: We have to own up
SA Rugby boss on BLM: We have to own up
Jurie Roux
SA Rugby CEO Jurie Roux says black players and coaches should be given a platform to tell their stories and everyone else should make greater effort to hear what is being said.
Last week, Proteas cricketer Lungi Ngidi, who was recently named SA Men's ODI and T20I Cricketer of the Year, told reporters that he will encourage his Proteas teammates to take a united stand against racism.
It served to ignite a closer look at this subject in a South African sporting context, with several other black cricketers sharing their experiences of racism and prejudice, while the likes of Faf du Plessis, Hashim Amla and Rassie van der Dussen have unequivocally thrown their support behind Ngidi.
The international movement has also progressed into rugby circles, with a host of players, including Springbok captain Siya Kolisi and several other former players and coaches, highlighting inequalities within the local rugby system and calling for an end to systemic racism within the game.
ALSO READ: 'SA has not been kicked out of Super Rugby'
In a media briefing via a digital news conference on Tuesday, Roux said while SA Rugby's transformation targets are being met, the organisation is willing to have open and honest conversations about past injustices levelled against people of colour and is open to suggestions for ways to make amends and for the way forward.
'As a federation, with no disrespect to other federations, we have been very good at acknowledging the sins and the wrongs of the past,' Roux said. 'We've been making good progress around our transformation, specifically with the new strategic transformation plan that we have up until 2030.Within that plan there are 36 dimensions for which we've agreed targets with all our unions and all of those targets are towards what government has asked us.
'Having said that, I think what we're dealing with here at the moment with Blacks Lives Matter, farm murders and gender-based violence and all the other social problems the president has asked us to look at currently within this country, transcends far beyond transformation and what a transformation plan is. Because we are now dealing with what is basically respect for others, respect for cultural differences and respect for human life and how we treat people on a daily basis.
'What we as sport can do for this county, and we have shown it all along in specifically rugby, is that we can materially influence certain of those elements in the way we operate and the way we react to those things.'
ALSO READ: No Test rugby in SA in 2020
'In terms of Blacks Lives Matter specifically, and towards the issues that are currently on the table, I think we need to own up in terms of those conversations and not steer away from those conversations. The first thing we need to do is let people tell their stories, but more importantly we've got to listen to those stories and then hear what they're saying. That's always been the issue, letting people voice what it is they believe is wrong. We've got to listen to those stories but more importantly we've got to hear what they are saying.
'We've got to continue dialogue and we've got to figure it out together and determine if we need to adapt things to help figure it out then absolutely we have to do that. What we should not do is steer away from the uncomfortable conversations and the things people refer to as the awkward truths, because it's the realities of our lives. These issues are very serious matters that transcends way beyond sport and are part of our daily lives.
'So, certainly from our point of view, there are people voicing their concerns and we've got to take those concerns, listen to it and look at where we stand in terms of those. Is our plan significant and robust enough to address those things? If they need to change then we'll go back to our executive and our unions and say listen:
"Yes, we have agreed on the following and, yes, we're happy with what we're doing and the progress we're making and we consistently said we don't celebrate progress, we'll celebrate when we've got success, so maybe some of these elements we need to change."'
Last week a group of 49 black coaches released a joint statement relating to SA Rugby's lack of black coaches at club and provincial levels.
On Tuesday, Roux singled out that matter and confirmed that SA Rugby is working on improvement in that department.
'Specifically around the coaches, Rassie [Erasmus, director of rugby] did present us with a plan around the fast tracking of black coaches and getting us to the targets quicker than where we are at the moment,' Roux explained.
'We will look at that plan a little more seriously now and will address the issues that are there. I believe we were the first [sport governing body] to have really stepped up and shown that we have intent and a direction that we want to go in and that we're consistently trying to achieve that.
'In terms of dialogue, a process will follow and we will listen to all of those things. Our executive will decide the way forward for us within that environment , but first foremost we need to return to what is respect for other people and human beings across the board.'
ALSO READ: 'We simply want fair opportunities'
Photo: Ashley Vlotman/Gallo Images
Posted in News, Top headlines Tagged black lives matter, Jurie Roux, NEWS, sa rugby
Post by Mariette Adams