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

While most NLP resources are English-specific,
there have been several recent efforts to build \textbf{multilingual benchmarks}.
One possibility is to collect and annotate data in multiple languages separately \citep{clark2020tydiqa}, but most existing datasets have been created through translation \citep{conneau2018xnli,artetxe2019xquad}.
This approach has two desirable properties: it relies on existing professional translation services rather than requiring expertise in multiple languages, and it results in parallel evaluation sets that offer a meaningful measure of the cross-lingual transfer gap of different models. The resulting multilingual datasets are generally used for evaluation only, relying on existing English datasets for training.

Closely related to that,
\textbf{cross-lingual transfer learning} aims to leverage large datasets available in one language---typically English---to build multilingual models that can generalize to other languages.
Previous work has explored 3 main approaches to that end: machine translating the test set into English and using a monolingual English model ({\small \textsc{Translate-Test}}\xspace), machine translating the training set into each target language and training the models on their respective languages ({\small \textsc{Translate-Train}}\xspace),
or using English data to fine-tune a multilingual model that is then transferred to the rest of languages ({\small \textsc{Zero-Shot}}\xspace).

The dataset creation and transfer procedures described above result in a \textbf{mixture of original,\footnote{We use the term \textit{original} to refer to non-translated text.} human translated and machine translated data} when dealing with cross-lingual models. In fact, the type of text a system is trained on does not typically match the type of text it is exposed to at test time: {\small \textsc{Translate-Test}}\xspace systems are trained on original data and evaluated on machine translated test sets, {\small \textsc{Zero-Shot}}\xspace systems are trained on original data and evaluated on human translated test sets, and {\small \textsc{Translate-Train}}\xspace systems are trained on machine translated data and evaluated on human translated test sets.

Despite overlooked to date, we show that \textbf{such mismatch has a notable impact} in the performance of existing cross-lingual models. By using back-translation \citep{sennrich2016improving} to paraphrase each training instance, we obtain another English version of the training set
that better resembles the test set, obtaining substantial improvements for the {\small \textsc{Translate-Test}}\xspace and {\small \textsc{Zero-Shot}}\xspace approaches in cross-lingual Natural Language Inference (NLI). While improvements brought by machine translation have previously been attributed to data augmentation \citep{singh2019xlda}, we reject this hypothesis and show that the phenomenon is only present in translated test sets, but not in original ones.
Instead, our analysis reveals that this behavior is caused by subtle \textbf{artifacts arising from the translation} process itself. In particular, we show that translating different parts of each instance separately (e.g., the premise and the hypothesis in NLI) can alter superficial patterns in the data (e.g., the degree of lexical overlap between them), which severely affects the generalization ability of current models.
Based on the gained insights, we improve the state-of-the-art in XNLI, and show that some previous findings need to be reconsidered in the light of this phenomenon.

\section{Related work}
\label{sec:related_work}

\paragraph{Cross-lingual transfer learning.}
Current cross-lingual models work by
pre-training multilingual representations using some form of language modeling, which are then fine-tuned on the relevant task and transferred to different languages.
Some authors leverage parallel data to that end \citep{conneau2019xlm,huang2019unicoder}, but
training a model akin to BERT \citep{devlin2019bert} on the combination of monolingual corpora in multiple languages is also effective \citep{conneau2019xlmr}. Closely related to our work, \citet{singh2019xlda} showed that replacing segments of the training data with their translation during fine-tuning is helpful.
However, they attribute this behavior to a data augmentation effect, which we believe should be reconsidered given the new evidence we provide. %

\paragraph{Multilingual benchmarks.} Most benchmarks covering a wide set of languages have been created through translation, as it is the case of XNLI \citep{conneau2018xnli} for NLI, PAWS-X \citep{yang2019pawsx} for adversarial paraphrase identification, and XQuAD \citep{artetxe2019xquad} and MLQA \citep{lewis2019mlqa} for Question Answering (QA). A notable exception is TyDi QA \citep{clark2020tydiqa}, a contemporaneous QA dataset that was separately annotated in 11 languages.
Other cross-lingual datasets leverage existing multilingual resources, as it is the case of MLDoc \citep{schwenk2018corpus} for document classification and Wikiann \citep{pan2017wikiann} for named entity recognition.
Concurrent to our work, \citet{hu2020xtreme} combine some of these datasets into a single multilingual benchmark, and evaluate some well-known methods on it.

\paragraph{Annotation artifacts.} Several studies have shown that NLI datasets like SNLI \citep{bowman2015snli} and MultiNLI  \citep{williams2018broad} contain spurious patterns that can be exploited to obtain strong results without making real inferential decisions. For instance, \citet{gururangan2018annotation} and \citet{poliak2018hypothesis} showed that a hypothesis-only baseline performs better than chance due to cues on their lexical choice and sentence length.
Similarly, \citet{mccoy2019right} showed that NLI models tend to predict \textit{entailment} for sentence pairs with a high lexical overlap.
Several authors have worked on adversarial datasets to diagnose these issues and provide a more challenging benchmark \citep{naik2018stress,glockner2018breaking,nie2019adversarial}. Besides NLI,
other tasks like QA have also been found to be susceptible to annotation artifacts \citep{jia2017adversarial,kaushik2018reading}.
While previous work has focused on the monolingual scenario, we show that translation can interfere with these artifacts in multilingual settings.

\paragraph{Translationese.} Translated texts are known to have unique features like simplification, explicitation, normalization and interference, which are refer to as \textit{translationese} \citep{volansky2013translationese}.
This phenomenon has been reported to have a notable impact in machine translation evaluation \citep{zhang2019effect,graham2019translationese}.
For instance, back-translation brings large BLEU gains for reversed test sets (i.e., when translationese is on the source side and original text is used as reference), but its effect diminishes in the natural direction \citep{edunov2019evaluation}.
While connected, the phenomenon we analyze is different in that it arises from translation inconsistencies due to the lack of context, and affects cross-lingual transfer learning rather than machine translation.

\section{Experimental design}
\label{sec:design}

Our goal is to analyze the effect of both human and machine translation in cross-lingual models. For that purpose, the core idea of our work is to (i) use machine translation to either translate the training set into other languages, or generate English paraphrases of it through back-translation, and
(ii) evaluate the resulting systems on original, human translated and machine translated test sets in comparison with systems trained on original data.
We next describe the models used in our experiments (\S \ref{subsec:models}), the specific training variants explored (\S \ref{subsec:variants}), and the evaluation procedure followed (\S \ref{subsec:tasks}).

\subsection{Models and transfer methods} \label{subsec:models}

We experiment with two models that are representative of the state-of-the-art in monolingual and cross-lingual pre-training: (i) {\small \textsc{Roberta}}\xspace \citep{liu2019roberta}, which is an improved version of BERT that uses masked language modeling to pre-train an English Transformer model, and (ii) {\small \textsc{XLM-R}}\xspace \citep{conneau2019xlmr}, which is a multilingual extension of the former pre-trained on 100 languages.
In both cases, we use the large models released by the authors under the fairseq repository.\footnote{\url{https://github.com/pytorch/fairseq}} As discussed next, we explore different variants of the training set to fine-tune each model on different tasks. At test time, we try both machine translating the test set into English ({\small \textsc{Translate-Test}}\xspace) and, in the case of {\small \textsc{XLM-R}}\xspace, using the actual test set in the target language ({\small \textsc{Zero-Shot}}\xspace).

\subsection{Training variants} \label{subsec:variants}

We try 3 variants of each training set to fine-tune our models: (i) the original one in English ({\small \textsc{Orig}}\xspace), (ii) an English paraphrase of it generated through back-translation using Spanish or Finnish as pivot ({\small \textsc{BT-ES}}\xspace and {\small \textsc{BT-FI}}\xspace), and (iii) a machine translated version in Spanish or Finnish ({\small \textsc{MT-ES}}\xspace and {\small \textsc{MT-FI}}\xspace).
For sentences occurring multiple times in the training set (e.g., premises repeated for multiple hypotheses), we use the exact same translation for all occurrences, as our goal is to understand the inherent effect of translation rather than its potential application as a data augmentation method.

In order to train the machine translation systems for {\small \textsc{MT-XX}}\xspace and {\small \textsc{BT-XX}}\xspace, we use the big Transformer model \citep{vaswani2017attention} with the same settings as \citet{ott2018scaling} and SentencePiece tokenization \citep{kudo2018sentencepiece} with a joint vocabulary of 32k subwords. For English-Spanish, we train for 10 epochs on all parallel data from WMT 2013 \citep{bojar2013findings} and ParaCrawl v5.0 \citep{espla2019paracrawl}. For English-Finnish, we train for 40 epochs on Europarl and Wiki Titles from WMT 2019 \citep{barrault2019findings}, ParaCrawl v5.0, and DGT, EUbookshop and TildeMODEL from OPUS \citep{tiedemann2012opus}. In both cases, we remove sentences longer than 250 tokens, with a source/target ratio exceeding 1.5, or for which \texttt{langid.py} \citep{lui2012langid} predicts a different language, resulting in a final corpus size of 48M and 7M sentence pairs, respectively. We use sampling decoding with a temperature of 0.5 for inference, which produces more diverse translations than beam search \citep{edunov2018understanding} and performed better in our preliminary experiments.

\begin{table*}[ht!]
\begin{center}
\addtolength{\tabcolsep}{-1.5pt}
\resizebox{1\linewidth}{!}{
\insertXnliDevTable
}
\caption{\textbf{XNLI dev results (acc).} {\small \textsc{BT-XX}}\xspace and {\small \textsc{MT-XX}}\xspace consistently outperform {\small \textsc{Orig}}\xspace in all cases.
\label{tab:xnli_dev}}
\end{center}
\end{table*}

\subsection{Tasks and evaluation procedure} \label{subsec:tasks}

We use the following tasks for our experiments:

\paragraph{Natural Language Inference (NLI).} Given a premise and a hypothesis, the task is to determine whether there is an \textit{entailment}, \textit{neutral} or \textit{contradiction} relation between them. We fine-tune our models on MultiNLI \citep{williams2018broad} for 10 epochs using the same settings as \citet{liu2019roberta}. In most of our experiments, we evaluate on XNLI \citep{conneau2018xnli}, which comprises 2490 development and 5010 test instances in 15 languages. These were originally annotated in English,
and the resulting premises and hypotheses were independently translated into the rest of the languages by professional translators. For the {\small \textsc{Translate-Test}}\xspace approach, we use the machine translated versions from the authors. Following \citet{conneau2019xlmr}, we select the best epoch checkpoint according to the average accuracy in the development set.  %

\paragraph{Question Answering (QA).} Given a context paragraph and a question, the task is to identify the span answering the question in the context. We fine-tune our models on SQuAD v1.1 \citep{rajpurkar2016squad} for 2 epochs using the same settings as \citet{liu2019roberta}, and report test results for the last epoch.
We use two datasets for evaluation: XQuAD \citep{artetxe2019xquad}, a subset of the SQuAD development set translated into 10 other languages, and MLQA \citep{lewis2019mlqa} a dataset consisting of parallel context paragraphs plus the corresponding questions annotated in English and translated into 6 other languages. In both cases, the translation was done by professional translators at the document level (i.e., when translating a question, the text answering it was also shown).
For our {\small \textsc{BT-XX}}\xspace and {\small \textsc{MT-XX}}\xspace variants, we translate the context paragraph and the questions independently, and map the answer spans using the same procedure as \citet{carrino2019automatic}.\footnote{We use FastAlign \citep{dyer2013simple} for word alignment, and discard the few questions for which the mapping method fails (when none of the tokens in the answer span are aligned).}
For the {\small \textsc{Translate-Test}}\xspace approach, we use the official machine translated versions of MLQA, run inference over them, and map the predicted answer spans back to the target language.\footnote{We use the same procedure as for the training set except that (i) given the small size of the test set, we combine it with WikiMatrix \citep{schwenk2019wikimatrix} to aid word alignment, (ii) we use Jieba for Chinese segmentation instead of the Moses tokenizer, and (iii) for the few unaligned spans, we return the English answer.}

Both for NLI and QA, we run each system 5 times with different random seeds and report the average results. Space permitting, we also report the standard deviation across the 5 runs. In our result tables, we use an underline to highlight the best result within each block, and boldface to highlight the best overall result.

\section{NLI experiments}
\label{sec:nli}

We next discuss our main results in the XNLI development set (\S \ref{subsec:translatetest}, \S \ref{subsec:zeroshot}), run additional experiments to better understand the behavior of our different variants (\S \ref{subsec:translationese}, \S \ref{subsec:stress}, \S \ref{subsec:distribution}), and compare our results to previous work in the XNLI test set (\S \ref{subsec:sota}).

\subsection{{\small \textsc{Translate-Test}}\xspace results} \label{subsec:translatetest}

We start by analyzing XNLI development results for {\small \textsc{Translate-Test}}\xspace. Recall that, in this approach, the test set is machine translated into English, but training is typically done on original English data. Our {\small \textsc{BT-ES}}\xspace and {\small \textsc{BT-FI}}\xspace variants close this gap by training on a machine translated English version of the training set generated through back-translation. As shown in Table \ref{tab:xnli_dev}, this brings substantial gains for both {\small \textsc{Roberta}}\xspace and {\small \textsc{XLM-R}}\xspace, with an average improvement of 4.6 points in the best case. %
Quite remarkably, {\small \textsc{MT-ES}}\xspace and {\small \textsc{MT-FI}}\xspace also outperform {\small \textsc{Orig}}\xspace by a substantial margin, and are only 0.8 points below their {\small \textsc{BT-ES}}\xspace and {\small \textsc{BT-FI}}\xspace counterparts. Recall that, for these two systems, training is done in machine translated Spanish or Finnish, while inference is done in machine translated English. This shows that the loss of performance when generalizing from original data to machine translated data is substantially larger than the loss of performance when generalizing from one language to another.

\subsection{{\small \textsc{Zero-Shot}}\xspace results}  \label{subsec:zeroshot}

We next analyze the results for the {\small \textsc{Zero-Shot}}\xspace approach. In this case, inference is done in the test set in each target language which, in the case of XNLI, was human translated from English. As such, different from the {\small \textsc{Translate-Test}}\xspace approach, neither training on original data ({\small \textsc{Orig}}\xspace) nor training on machine translated data ({\small \textsc{BT-XX}}\xspace and {\small \textsc{MT-XX}}\xspace) makes use of the exact same type of text that the system is exposed to at test time.
However, as shown in Table \ref{tab:xnli_dev}, both {\small \textsc{BT-XX}}\xspace and {\small \textsc{MT-XX}}\xspace outperform {\small \textsc{Orig}}\xspace by approximately 2 points, which suggests that our (back-)translated versions of the training set are more similar to the human translated test sets than the original one. %
This also provides a new perspective on the {\small \textsc{Translate-Train}}\xspace approach, which was reported to outperform {\small \textsc{Orig}}\xspace in previous work \citep{conneau2019xlm}: while the original motivation was to train the model on the same language that it is tested on, our results show that machine translating the training set is beneficial even when the target language is different.

\subsection{Original vs. translated test sets}  \label{subsec:translationese}

\begin{table}
\begin{center}
\addtolength{\tabcolsep}{-1.5pt}
\resizebox{0.84\linewidth}{!}{
\insertTranslateTable
}
\end{center}
\caption{\textbf{NLI results on original (OR), human translated (HT) and machine translated (MT) sets (acc).} {\small \textsc{BT-XX}}\xspace and {\small \textsc{MT-XX}}\xspace outperform {\small \textsc{Orig}}\xspace in translated sets, but do not get any clear improvement in original ones.
\label{tab:translationese}}
\end{table}

So as to understand whether the improvements observed so far are limited to translated test sets or apply more generally, we conduct additional experiments comparing translated test sets to original ones.
However, to the best of our knowledge, all existing non-English NLI benchmarks were created through translation. For that reason, we build a new test set that mimics XNLI, but is annotated in Spanish rather than English. We first collect the premises from a filtered version of CommonCrawl \citep{buck2014ngram}, taking a subset of 5 websites that represent a diverse set of genres: a newspaper, an economy forum, a celebrity magazine, a literature blog, and a consumer magazine. We then ask native Spanish annotators to generate an \textit{entailment}, a \textit{neutral} and a \textit{contradiction} hypothesis for each premise.\footnote{Unlike XNLI, we do not collect 4 additional labels for each example. Note, however, that XNLI kept the original label as the gold standard, so the additional labels are irrelevant for the actual evaluation. This is not entirely clear in \citet{conneau2018xnli}, but can be verified by inspecting the dataset.} We collect a total of 2490 examples using this procedure, which is the same size as the XNLI development set. Finally, we create
a human translated and a machine translated English version of the dataset using professional translators from Gengo and our machine translation system described in \S \ref{subsec:variants},\footnote{We use beam search instead of sampling decoding.} respectively.
We report results for the best epoch checkpoint on each set.

As shown in Table \ref{tab:translationese}, both {\small \textsc{BT-XX}}\xspace and {\small \textsc{MT-XX}}\xspace clearly outperform {\small \textsc{Orig}}\xspace in all test sets created through translation, which is consistent with our previous results. In contrast, the best results on the original English set are obtained by {\small \textsc{Orig}}\xspace, and neither {\small \textsc{BT-XX}}\xspace nor {\small \textsc{MT-XX}}\xspace obtain any clear improvement on the one in Spanish either.\footnote{Note that the standard deviations are around 0.3.}
This confirms that the underlying phenomenon is limited to translated test sets.
In addition, it is worth mentioning that the results for the machine translated test set in English are slightly better than those for the human translated one,
which suggests that the difficulty of the task does not only depend on the translation quality.
Finally, it is also interesting that {\small \textsc{MT-ES}}\xspace is only marginally better than {\small \textsc{MT-FI}}\xspace in both Spanish test sets, even if it corresponds to the {\small \textsc{Translate-Train}}\xspace approach, whereas {\small \textsc{MT-FI}}\xspace needs to {\small \textsc{Zero-Shot}}\xspace transfer from Finnish into Spanish.
This reinforces the idea that it is training on translated data rather than training on the target language that is key in {\small \textsc{Translate-Train}}\xspace.

\subsection{Stress tests}  \label{subsec:stress}

\begin{table}
\begin{center}
\addtolength{\tabcolsep}{-1.5pt}
\resizebox{1\linewidth}{!}{
\insertStressTestTable
}
\end{center}
\caption{\textbf{NLI Stress Test results (combined matched \& mismatched acc).} AT = antonymy, NR = numerical reasoning, WO = word overlap, NG = negation, LN = length mismatch, SE = spelling error. {\small \textsc{BT-FI}}\xspace and {\small \textsc{MT-FI}}\xspace are considerably weaker than {\small \textsc{Orig}}\xspace in the competence test, but substantially stronger in the distraction test.
\label{tab:stress}}
\end{table}

In order to better understand how systems trained on original and translated data differ, we run additional experiments on the NLI Stress Tests \citep{naik2018stress}, which were designed to test the robustness of NLI models to specific linguistic phenomena in English. The benchmark consists of a competence test, which evaluates the ability to understand antonymy relation and perform numerical reasoning, a distraction test, which evaluates the robustness to shallow patterns like lexical overlap and the presence of negation words, and a noise test, which evaluates robustness to spelling errors. Just as with previous experiments, we report results for the best epoch checkpoint in each test set.

As shown in Table \ref{tab:stress}, {\small \textsc{Orig}}\xspace outperforms {\small \textsc{BT-FI}}\xspace and {\small \textsc{MT-FI}}\xspace on the competence test by a large margin, but the opposite is true on the distraction test.\footnote{We observe similar trends for {\small \textsc{BT-ES}}\xspace and {\small \textsc{MT-ES}}\xspace, but omit these results for conciseness.}
In particular, our results show that {\small \textsc{BT-FI}}\xspace and {\small \textsc{MT-FI}}\xspace are less reliant on lexical overlap and the presence of negative words. This feels intuitive, as translating the premise and hypothesis independently---as {\small \textsc{BT-FI}}\xspace and {\small \textsc{MT-FI}}\xspace do---is likely to reduce the lexical overlap between them.
More generally, the translation process can alter similar superficial patterns in the data, which NLI models are sensitive to (\S \ref{sec:related_work}). This would explain why the resulting models have a different behavior on different stress tests.

\subsection{Output class distribution}  \label{subsec:distribution}

With the aim to understand the effect of the previous phenomenon in cross-lingual settings, we look at the output class distribution of our different models in the XNLI development set.
As shown in Table \ref{tab:distribution}, the predictions of all systems are close to the true class distribution in the case of English. Nevertheless, {\small \textsc{Orig}}\xspace is strongly biased for the rest of languages, and tends to underpredict \textit{entailment} and overpredict \textit{neutral}. This can again be attributed to the fact that the English test set is original, whereas the rest are human translated. In particular, it is well-known that NLI models tend to predict \textit{entailment} when there is a high lexical overlap between the premise and the hypothesis (\S \ref{sec:related_work}).
However, the degree of overlap will be smaller in the human translated test sets given that the premise and the hypothesis were translated independently, which explains why \textit{entailment} is underpredicted.
In contrast, {\small \textsc{BT-FI}}\xspace and {\small \textsc{MT-FI}}\xspace are exposed to the exact same phenomenon during training, which explains why they are not that heavily affected.

So as to measure the impact of this phenomenon, we explore a simple approach to correct this bias: having fine-tuned each model, we adjust the bias term added to the logit of each class so the model predictions match the true class distribution for each language.\footnote{We achieve this using an iterative procedure where, at each step, we select one class and set its bias term so the class is selected for the right percentage of examples.} As shown in Table \ref{tab:bias}, this brings large improvements for {\small \textsc{Orig}}\xspace, but is less effective for {\small \textsc{BT-FI}}\xspace and {\small \textsc{MT-FI}}\xspace.\footnote{Note that we are adjusting the bias term in the evaluation set itself, which requires knowing its class distribution and is thus a form of cheating. While useful for analysis, a fair comparison would require adjusting the bias term in a separate validation set. This is what we do for our final results in \S \ref{subsec:sota}, where we adjust the bias term in the XNLI development set and report results on the XNLI test set.} This shows that the performance of {\small \textsc{Orig}}\xspace was considerably hindered by this bias, which {\small \textsc{BT-FI}}\xspace and {\small \textsc{MT-FI}}\xspace effectively mitigate.

\begin{table}
\begin{center}
\addtolength{\tabcolsep}{-2.5pt}
\resizebox{1\linewidth}{!}{
\insertClassDistributionTable
}
\end{center}
\caption{\textbf{Output class distribution on XNLI dev.} All systems are close to the true distribution in English, but {\small \textsc{Orig}}\xspace is biased toward \textit{neu} and \textit{con} in the transfer languages. {\small \textsc{BT-FI}}\xspace and {\small \textsc{MT-FI}}\xspace alleviate this issue.
\label{tab:distribution}}
\end{table}

\begin{table}
\begin{center}
\addtolength{\tabcolsep}{-1.5pt}
\resizebox{0.83\linewidth}{!}{
\insertBiasTable
}
\end{center}
\caption{\textbf{XNLI dev results with class distribution unbiasing (average acc across all languages).} Adjusting the bias term of the classifier to match the true class distribution brings large improvements for {\small \textsc{Orig}}\xspace, but is less effective for {\small \textsc{BT-FI}}\xspace and {\small \textsc{MT-FI}}\xspace.
\label{tab:bias}}
\end{table}

\subsection{Comparison with the state-of-the-art}  \label{subsec:sota}

\begin{table*}
\begin{center}
\addtolength{\tabcolsep}{-1.5pt}
\resizebox{1\linewidth}{!}{
\insertXnliSotaTable
}
\caption{\textbf{XNLI test results (acc).}
Results for other methods are taken from their respective papers or, if not provided, from \citet{conneau2019xlmr}. For those with multiple variants, we select the one with the best results.
\label{tab:xnli_test}}
\end{center}
\end{table*}

So as to put our results into perspective, we compare our best variant to previous work on the XNLI test set. As shown in Table \ref{tab:xnli_test}, our method improves the state-of-the-art for both the {\small \textsc{Translate-Test}}\xspace and the {\small \textsc{Zero-Shot}}\xspace approaches by 4.3 and 2.8 points, respectively. It also obtains the best overall results published to date, with the additional advantage that the previous state-of-the-art required a machine translation system between English and each of the 14 target languages, whereas our method uses a single machine translation system between English and Finnish (which is not one of the target languages). While the main goal of our work is not to design better cross-lingual models, but to analyze their behavior in connection to translation, this shows that the phenomenon under study is highly relevant, to the extent that it can be exploited to improve the state-of-the-art.

\section{QA experiments}
\label{sec:qa}

So as to understand whether our previous findings apply to other tasks besides NLI, we run additional experiments on QA. As shown in Table \ref{tab:mlqa}, {\small \textsc{BT-FI}}\xspace and {\small \textsc{BT-ES}}\xspace do indeed outperform {\small \textsc{Orig}}\xspace for the {\small \textsc{Translate-Test}}\xspace approach on MLQA. The improvement is modest, but very consistent across different languages, models and runs. The results for {\small \textsc{MT-ES}}\xspace and {\small \textsc{MT-FI}}\xspace are less conclusive, presumably because mapping the answer spans across languages might introduce some noise. In contrast, we do not observe any clear improvement for the {\small \textsc{Zero-Shot}}\xspace approach on this dataset. Our XQuAD results in Table \ref{tab:xquad_f1} are more positive, but still inconclusive.

These results can partly be explained by the translation procedure used to create the different benchmarks: the premises and hypotheses of XNLI were translated independently, whereas the questions and context paragraphs of XQuAD were translated together. Similarly, MLQA made use of parallel contexts, and translators were shown the sentence containing each answer when translating the corresponding question. As a result, one can expect both QA benchmarks to have more consistent translations than XNLI, which would in turn diminish this phenomenon. In contrast, the questions and context paragraphs are independently translated when using machine translation, which explains why {\small \textsc{BT-ES}}\xspace and {\small \textsc{BT-FI}}\xspace outperform {\small \textsc{Orig}}\xspace for the {\small \textsc{Translate-Test}}\xspace approach. %
We conclude that the translation artifacts revealed by our analysis are not exclusive to NLI, as they also show up on QA for the {\small \textsc{Translate-Test}}\xspace approach, but their actual impact can be highly dependent on the translation procedure used and the nature of the task.

\begin{table*}
\begin{center}
\addtolength{\tabcolsep}{-1.5pt}
\resizebox{0.93\linewidth}{!}{
\insertMlqaTable
}
\end{center}
\caption{\textbf{MLQA test results (F1 / exact match).}
\label{tab:mlqa}}
\end{table*}

\begin{table*}
\begin{center}
\addtolength{\tabcolsep}{-1.5pt}
\resizebox{0.72\linewidth}{!}{
\insertXquadFTable
}
\end{center}
\caption{\textbf{XQuAD results (F1).} Results for the exact match metric are similar.
\label{tab:xquad_f1}}
\end{table*}

\section{Discussion}
\label{sec:discussion}

Our analysis prompts to reconsider previous findings in cross-lingual transfer learning as follows:

\paragraph{The cross-lingual transfer gap on XNLI was overestimated.} Given the parallel nature of XNLI,
accuracy differences across languages are commonly interpreted as the loss of performance when generalizing from English to the rest of languages.
However, our work shows that there is another factor that can have a much larger impact: the loss of performance when generalizing from original to translated data. Our results suggest that the real cross-lingual generalization ability of {\small \textsc{XLM-R}}\xspace is considerably better than what the accuracy numbers in XNLI reflect.

\paragraph{Overcoming the cross-lingual gap is not what makes {\small \textsc{Translate-Train}}\xspace work.} The original motivation for {\small \textsc{Translate-Train}}\xspace was to train the model on the same language it is tested on. However, we show that it is training on translated data, rather than training on the target language, that is key for this approach to outperform {\small \textsc{Zero-Shot}}\xspace as reported by previous authors.

\paragraph{Improvements previously attributed to data augmentation should be reconsidered.} The method by \citet{singh2019xlda} combines machine translated premises and hypotheses in different languages (\S \ref{sec:related_work}), resulting in an effect similar to {\small \textsc{BT-XX}}\xspace and {\small \textsc{MT-XX}}\xspace.
As such, we believe that this method should be analyzed from the point of view of dataset artifacts rather than data augmentation, as the authors do.\footnote{Recall that our experimental design prevents a data augmentation effect, in that the number of unique sentences and examples used for training is always the same (\S \ref{subsec:variants}).}
From this perspective, having the premise and the hypotheses in different languages can reduce the superficial patterns between them, which would explain why this approach is better than using examples in a single language.

\paragraph{The potential of {\small \textsc{Translate-Test}}\xspace was underestimated.}
The previous best results for {\small \textsc{Translate-Test}}\xspace on XNLI lagged behind the state-of-the-art by 4.6 points. Our work reduces this gap to only 0.8 points by addressing the underlying translation artifacts. The reason why {\small \textsc{Translate-Test}}\xspace is more severely affected by this phenomenon is twofold: (i) the effect is doubled by first using human translation to create the test set and then machine translation to translate it back to English, and (ii) {\small \textsc{Translate-Train}}\xspace was inadvertently mitigating this issue (see above), but equivalent techniques were never applied to {\small \textsc{Translate-Test}}\xspace.

\paragraph{Future evaluation should better account for translation artifacts.} The evaluation issues raised by our analysis do not have a simple solution. In fact, while we use the term \textit{translation artifacts} to highlight that they are an unintended effect of translation that impacts final evaluation, one could also argue that it is the original datasets that contain the artifacts, which translation simply alters or even mitigates.\footnote{For instance, the high lexical overlap observed for the \textit{entailment} class is usually regarded a spurious pattern, so reducing it could be considered a positive effect of translation.} In any case, this is a more general issue that falls beyond the scope of cross-lingual transfer learning, so we argue that it should be carefully controlled when evaluating cross-lingual models. In the absence of more robust datasets, we recommend that future multilingual benchmarks should at least provide consistent test sets for English and the rest of languages. This can be achieved by (i) using original annotations in all languages, (ii) using original annotations in a non-English language and translating them into English and other languages, or (iii) if translating from English, doing so at the document level to minimize translation inconsistencies.

\section{Conclusions}
\label{sec:conclusions}

In this paper, we have shown that both human and machine translation can alter superficial patterns in data, which requires reconsidering previous findings in cross-lingual transfer learning. Based on the gained insights, we have improved the state-of-the-art in XNLI for the {\small \textsc{Translate-Test}}\xspace and {\small \textsc{Zero-Shot}}\xspace approaches by a substantial margin.
Finally, we have shown that the phenomenon is not specific to NLI but also affects QA, although
it is less pronounced there thanks to the translation procedure used in the corresponding benchmarks.
So as to facilitate similar studies in the future, we release our NLI dataset,\footnote{\url{https://github.com/artetxem/esxnli}} which, unlike previous benchmarks, was annotated in a non-English language and human translated into English.

\section*{Acknowledgments}

We thank Nora Aranberri and Uxoa Iñurrieta for helpful discussion during the development of this work, as well as the rest of our colleagues from the IXA group that worked as annotators for our NLI dataset.

This research was partially funded by a Facebook Fellowship, the Basque Government excellence research group (IT1343-19), the Spanish MINECO (UnsupMT TIN2017‐91692‐EXP MCIU/AEI/FEDER, UE), Project BigKnowledge (Ayudas Fundación BBVA a equipos de investigación científica 2018), and the NVIDIA GPU grant program.

This research is supported via the IARPA BETTER Program contract No. 2019-19051600006 (ODNI, IARPA activity). The views and conclusions contained herein are those of the authors and should not be interpreted as necessarily representing the official policies, either expressed or implied, of ODNI, IARPA, or the U.S. Government. The U.S. Government is authorized to reproduce and distribute reprints for governmental purposes notwithstanding any copyright annotation therein.

redpajama_set_name: RedPajamaArXiv


Melissa Monroe and Eric Spoerl race towards the finish line during the Rescue Race at the 94th Maverick Club Rodeo in historical Cimarron, New Mexico.
Sunset over California wind farm on the Pacific Crest Trail; 2018.
Manzana wind farm, Twin Lakes, California, Pacific Crest Trail.
Mount Whitney sunrise summit, Inyo, California, Pacific Crest Trail; 2018.
"Shocks" hikes up Mount Whitney for the sunrise along the Pacific Crest Trail.
Mountain man Carter Smith surveys his surrounding area in a dense grove of aspen trees.
David O'Neill, Associate Director of Camping at Philmont Scout Ranch, leads a group of Camp Directors in a flag retirement ceremony.
Adam looks out over Palisade Basin from Thunderbolt Pass in Bishop, California.
Head of Dean fire lookout, Philmont Scout Ranch, Cimarron, New Mexico; 2017.
A scout enjoys the sunset from Head of Dean's fire lookout.
Head of Dean foggy trail, Philmont Scout Ranch, Cimarron, New Mexico; 2017.
Jackson Graydon walks down Head of Dean's meadow on a foggy afternoon at Philmont Scout Ranch.
Mount Tallac sunset, Tahoe, California, Pacific Crest Trail; 2018.
"Trash Bath" takes photos on top of Mount Tallac, Tahoe, California.
Kite-flying on the summit of Mount Whitney, Inyo, California; 2018.
"Cloudripper" celebrates his sunrise summit of Mount Whitney and flies his kite to celebrate.
Hiking in southern California, Whitewater Preserve, Pacific Crest Trail; 2018.
Ryan walks through some desert section near Whitewater Preserve, California.


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Microsoft President: Governments Must Cooperate on Cybersecurity
Microsoft's Brad Smith calls on nations and businesses to work toward "digital peace" and acknowledge the effects of cybercrime.
It's an exciting time to be in technology, according to Microsoft president Brad Smith. It's also a dangerous time.
Smith took the stage at this year's Web Summit, a tech conference held in Lisbon, Portugal, to emphasize the need for global cooperation on cybersecurity as technology continues to evolve. The benefits that technology has created are as dangerous as they are awe-inspiring, he said.
"It's an exciting time to be at a place like this," he said. "But that's not the only thing that's happening. We also live in a time when new threats are emerging … new threats that involve technology itself" and culminate in attacks on electrical grids and elections alike.
Addressing an audience of tech professionals, Smith explained: "The tools that we've created — the tools, oftentimes, that you've created — have been turned by others into weapons." It's something Microsoft sees in 6.5 trillion signals and data points it receives daily, he added.
Smith said often when he speaks to people in government about these attacks, they sometimes say "we don't really need to worry" because cyberattacks involve machines targeting machines, not machines targeting people. He disagrees.
"That is a problem. Because people are being victimized by these attacks," he explained. He called 2017 "a wake-up call" in terms of the way people in nation-states and governments are using technological tools as weapons. WannaCry and NotPetya were the prime examples.
We can't expect people to recognize the problems of cybercrime if we don't recognize how people are suffering. Hospitals were paralyzed when WannaCry hit the UK. At England's National Health Service, 19,000 appointments were canceled. Surgeries didn't happen. Shortly after WannaCry hit 300,000 machines in 150 countries, he added, NotPetya struck.
"What NotPetya represents is not just the evolution of the attack in terms of methodologies involved, but also the evolution of intent," said Smith. Last year, almost 1 billion people were victims of a cyberattack. "These issues and these threats are going to continue to grow … because everything is connected," he warned. It's time to have a conversation around security.
"In a world where everything is connected, everything can be disrupted," he continued.
Governments around the world must play a role in protecting civilians and civilian infrastructure, he said, and protect people while they're using devices on which their lives exist. However, governments can't do this alone, and so he also called on businesses to step up.
"Businesses need to do better as well, and there is no part of the business community, across Europe or in the US or around the world, that has a higher responsibility than one part of the business community — and that is the tech sector," Smith noted. IT has the greatest responsibility to be "first responders" in keeping people safe when there are cyberattacks.
The same week he gave this talk at Web Summit, Smith explained in an interview with CNBC how Microsoft wants to connect with Congress and work together to create cybersecurity guidelines for civilians. Key issues range from threats on democracy to artificial intelligence in the workplace.
We have reached a point at which people are enthusiastic about the evolution of technology; however, their eagerness is matched with growing worry about what this technology can do.
"The big shift has been [that] the era where everyone was just excited about technology has become an era where people are excited and concerned at the same time — and that's not unreasonable," he explained in a conversation with CNBC.
Smith says Microsoft wants to work with President Trump, as it worked with President Obama, to address the risk of technology. The concern isn't only for America, but for all countries.
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redpajama_set_name: RedPajamaCommonCrawl


text:
module Haslo.InstalmentPlan
where
import Haslo.BasicType
import Text.PrettyShow

-- | Single instalment details.
--   Single instalment is always balanced - see @instalmentCheck@.
data Instalment = I { iAmt       :: Amount   -- ^ installment amount
                    , iRepayment :: Amount   -- ^ repayment
                    , iInterest  :: Interest -- ^ interest calculated over the full period
                    , iIntPaid   :: Amount   -- ^ interest paid with installment
                    }
                    deriving (Eq,Ord)

instance Show Instalment where
         show (I i c int iP) = showAmtWithLen 10 i ++ "="
                            ++ showAmtWithLen 10 c ++ "+"
                            ++ showAmtWithLen 8 iP ++ "; "
                            ++ show (int / 100)

-- | Single element of 'InstalmentPlan'.
--   One element of instlment plan contains following ideas:
--   - details of instalment (see above)
--   - principal after instalment has matured
--   - late not yet paid interest (which are base for interest accrual)
--   - interest rate of current instalment
data InstalmentPlanLine = IPL { iplInst      :: Instalment  -- ^ Instalment itself
                              , iplPrincipal :: Amount      -- ^ Principal after
                              , iplIntLate   :: Interest    -- ^ Sum of not paid late interest after current instalment
                              , iplRate      :: Rate        -- ^ Nominal interest rate
                              }
                              deriving (Eq,Ord)

instance Show InstalmentPlanLine where
         show (IPL i c iL r) = "(" ++ show i ++ ") "
                             ++ showAmtWithLen 11 c ++ " "
                             ++ show (iL / 100) ++ " "
                             ++ show r

-- | Plan of instalments reflects whole life of loan. Following rules are implemented:
--
--   * All instalments are regular duration. Duration is not stored within instalment plan.
--
--   * Each instalment can have its own interest rate which is stored within each row separately.
--
--   * Any payments postponements like first intalment postponement are implemented as
--   usual instalment with instalment amount equal 0.
type InstalmentPlan = [InstalmentPlanLine]

-- | Unwraps list of instalment amounts from InstalmentPlan
instList :: InstalmentPlan -> [Amount]
instList = map (iAmt . iplInst)

-- | Initial principal of a loan
initPrincipal :: InstalmentPlan -> Amount
initPrincipal ip =  iplPrincipal ipl + (iRepayment . iplInst) ipl
        where ipl = head ip

-- | Folded instalment plan is another view of the loan.
--   It folds all instalments of same amount and interest rate into one entry of given duration.
type FoldedInstalmentPlan = [FoldedInstalmentPlanLine]

-- | One entry of @FoldedInstalmentPlan@.
data FoldedInstalmentPlanLine = FIPL { fiplAmt  :: !Amount    -- ^ Instalment amount.
                                     , fiplDur  :: !Duration  -- ^ Number of such instalments in this series.
                                     , fiplRate :: !Rate      -- ^ Interest rate of series.
                                     }
     deriving (Eq, Ord, Show)

-- | Folds @InstalmentPlan@ to @FoldedInstalmentPlan@
foldIP :: InstalmentPlan -> FoldedInstalmentPlan
foldIP [] = []
foldIP (ipl : ip) = foldIP' ip [iplToFipl ipl]
    where iplToFipl (IPL (I a _ _ _) _ _ r) = FIPL a 1 r
          incDuration (FIPL a n r) = FIPL a (n+1) r
          foldIP' [] result = reverse result
          foldIP' (ipl : ip) (x:xs) | a == fiplAmt x && r == fiplRate x = foldIP' ip (incDuration x : xs)
                                    | otherwise                         = foldIP' ip (iplToFipl ipl : x : xs)
              where a = (iAmt $ iplInst ipl)
                    r = (iplRate ipl)

redpajama_set_name: RedPajamaGithub


text:
Selecting an Infusion Center: 5 Key Factors to Consider
An increasing number of medicines for psoriasis, Crohn's disease, multiple sclerosis (MS), rheumatoid arthritis, asthma, and other chronic conditions are being delivered by intravenous infusion. These next-generation therapies provide patients with effective new treatment options, but also require them to make an important decision: where should I go for my infusion?
In many cases, the dermatologists, gastroenterologists, neurologists, rheumatologists and other specialists prescribing those medications recommend that patients receive their infusions at community-based cancer centers. To better understand why those physicians would send patients who don't have cancer to an oncology practice, Advance Media recently interviewed three hematologists-oncologists with Regional Cancer Care Associates LLC (RCCA), one of the nation's largest networks of oncology specialists. With more than 125 cancer specialists practicing at 31 care centers located throughout New Jersey, Connecticut, Maryland, Pennsylvania, and the Washington, DC areas, RCCA provides care to roughly 24,500 new cancer patients and 245,000 established patients each year. However, thousands of people with other conditions, such as MS or Crohn's disease, also regularly visit RCCA offices for infusions. The RCCA physicians interviewed say that five factors shape those patients' decisions:
1. BALANCING SAFETY AND CONVENIENCE
"For many years, infused therapies were delivered only in the hospital. Over the past 20 years or more, however, infusions have been given in community-based clinics, as well, and in some cases they now are even being administered by a visiting nurse in a patient's home," explains Denis Fitzgerald, MD, Board Chairperson of RCCA.
"While in-home administration represents the most convenient approach, only a few therapies are suitable for infusion at home. Even in those cases, there is always a concern about responding to an infusion reaction. Adverse events are uncommon, and visiting nurses are trained to provide initial care for them, but managing an infusion reaction in the home setting just doesn't compare with responding to a medical issue in a fully staffed and equipped community clinic or hospital. At RCCA, there is always a physician on site when infusions are given. Doctors, as well as nurse practitioners, are just a few steps away should there be a problem or question," notes Dr. Fitzgerald, a board-certified medical oncologist and hematologist who practices in RCCA's Little Silver, NJ office.
At the same time, Charanjeev Kapoor, MD says that receiving an infusion at community-based clinics such as RCCA's offices is more convenient than going to a hospital. "In terms of everything from parking to processing, the hospital entails much more of a hassle factor," says Dr. Kapoor, who practices at RCCA's Manchester, CT office. "At our offices, patients sign in, are assessed and counseled, and then receive their infusion in a very efficient manner, so that they can get on with their day and their lives," says Dr. Kapoor, who is board-certified in internal medicine, as well as hematology and medical oncology. "I think community-based offices such as ours offer the best of both worlds in terms of convenience, accompanied by ready access to medical personnel and resources," he adds.
That convenience extends to making life easier for patients who see specialists at academic medical centers in New York, Philadelphia, or other major cities, but who don't want to travel to those centers for regular infusions, notes Dr. Fitzgerald. "We have many people who go to Manhattan two or three times a year to see their neurologist or gastroenterologist, but who don't want to travel to the city every month – or even more often – for routine care, so they come to us for their infusions," he explains.
2. EXPERTISE AND EXPERIENCE
By virtue of providing chemotherapy and other infused treatments to tens of thousands of patients each year, RCCA healthcare professionals have developed deep expertise with administering intravenous medications, says Dr. Fitzgerald. "Research across a number of professions shows that there is a clear relationship between volume and skill. That's true of pilots and surgeons, and it's also true of the members of our infusion teams," he notes.
Dr. Kapoor adds that this expertise translates into very effective patient counseling that addresses people's spoken and often-unspoken concerns. "Our nurses review why the medication has been prescribed, how it works, the data on its efficacy, and what people need to know about potential side effects, including symptoms and how we will address them should they arise," he explains.
"Receiving an infusion in a hospital may cost a patient twice as much, or even more, than receiving an infusion of the same medication at the same dose in a community-based clinic," says Dr. Fitzgerald. He explains that hospitals generally have contracts with insurers that allow them to charge higher rates than community clinics due to the institutions' considerable overhead costs. Hospitals also charge administration fees for giving medications that are far higher than those of community-based practices, with those expenses hitting patients in the form of steep co-pays and other out-of-pocket costs. "Because of this cost differential, many private insurers have begun mandating that patients receive infusions in community-based centers rather than hospitals whenever possible," he notes.
4. SETTING
RCCA's offices are designed expressly to provide a comfortable environment for people receiving chemotherapy and other types of infusions.
"Our West Hartford, CT office has 20 treatment chairs in an infusion room designed so that everyone can look out into an open area and not feel confined," says Dr. Kapoor.
Dr. Fitzgerald explains that the RCCA offices also have specially designed "mixing areas" where medications are prepared for infusion in a manner that prevents introduction of airborne contaminants. "The regulations regarding preparation of infused therapies are quite rigorous, as they should be, and they actually are becoming even more stringent. We closely follow the evolving regulatory landscape so as to remain fully compliant with all new requirements," he says.
The physical environment is only one aspect of the welcoming atmosphere for infusion patients, Dr. Fitzgerald adds. "One advantage of going to a community-based office rather than a large hospital is that our receptionists, nurses, pharmacists, pharmacy technicians, and office personnel all come to know our patients very quickly and develop an ongoing rapport with them."
But are people who don't have cancer uncomfortable coming to an oncology practice and sitting next to chemotherapy patients who, in some cases, clearly have late-stage disease? "Frankly, some people express that concern initially, but we find that their discomfort typically is gone by the end of their first session. In large part, that's because of the upbeat atmosphere created by our staff and due to the incredible resilience, grace and good humor of our patients and their families. Non-oncology patients have even told me that the experience has given them a new perspective on their own situation, however serious it may be," Dr. Fitzgerald explains.
5. COMMUNICATION WITH REFERRING PHYSICIANS
One of the benefits of practicing in the community is that we have excellent working relationships with our colleagues from other specialties. We see them at the hospital, we know one another by name, we speak frequently. This is a tremendous advantage for our patients," says Dr. Kapoor, noting that if, for instance, there is a question about the treatment of a patient who comes to RCCA's offices for infusion of an MS therapy, he can quickly contact the referring neurologist to clarify matters.
Dr. Fitzgerald adds that there will be even greater use of infusion-administered medications in the years ahead, as more and more therapies being developed to treat chronic conditions will be delivered intravenously. He notes, "Given this trend, more patients will have to make a decision about where to receive their infusion. By considering all of the factors involved – such as safety, setting, cost, convenience, and experience – patients and their families can arrive at the choice that is right for them."
With more than 80 oncology physicians and nurse clinicians practicing at 20+ care centers throughout Connecticut, Maryland, New Jersey and the Washington, DC area, RCCA provides care to roughly 22,000 new cancer patients and 225,000 established patients each year. RCCA offers those patients immunotherapy, targeted treatment, cell-based therapy and other cutting-edge treatments and diagnostic modalities, as well as access to clinical trials.
Infusion Therapy: Not Just for Chemotherapy
Share on facebook Share on twitter Share on linkedin Share on email Share on print If you know someone who has undergone chemotherapy or have
How Immunotherapy Targets Cancer Cells
Immunotherapy is an advanced type of cancer treatment. Instead of surgical procedures or radiation therapy, it works alongside the body's natural immune system to fight
Starting Chemotherapy at the Height of the COVID-19 Outbreak: An RCCA Patient's Story – and Message to Others with Cancer
Share on facebook Share on twitter Share on linkedin Share on email Share on print Elaine Meehan's life changed drastically not once but twice in


text:
Girls Sold into Forced Labour Largest Group of Trafficking Victims Identified by IOM in Bangladesh Refugee Camps - Bangladesh
16 October 2018ReliefWeb
Refugees & IDPs
Young girls sold into forced labour are the largest group of trafficking victims identified by the UN Migration Agency (IOM) in Bangladesh's Rohingya refugee camps. IOM counter-trafficking experts warn that more than a year into a crisis that has seen the number of Rohingya refugees in Cox's Bazar soar to almost a million, more desperate families are sending their young daughters off into dangerous work situations because most households have no other way to earn money in the camps.
As Rohingya refugee crisis escalates, women emerge as front-line responders
6 December 2017UNFPA
The border between Bangladesh and Myanmar is the site of the world's fastest growing refugee crisis. Over 620,000 Rohingya refugees from neighbouring Rakhine State in Myanmar have arrived in Cox's Bazar District in just three months. With more refugees arriving every day, settlements are overflowing.
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text:
22 May 2020 - 3 January 2021
Discover where the real and wizarding worlds intertwine, and how the wonders of the natural world have inspired myths, legends and magical creatures for generations.
Embark on a journey where not all is as it seems, where magical beasts and fantastic animals share abilities and behaviours that allow them to dazzle, attract and escape.
Celebrate real-world explorers who, like the character Newt Scamander™, help us to understand our planet's extraordinary creatures so we can protect them for the future.
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From Timna to Mars is a mind blowing read.
Wow, this book is a lot more than I expected. It is a gripping and thrilling book that makes you open your mind and do some deep soul searching. I really enjoyed it and reality of the plot really hit home. The intrigue and mystery are intense and held me from the start. Making me realizes we need to stop and rethink many of the daily things we do or we will all suffer. We only have one earth and we are killing it slowly. The well defined characters are perfect for the story and really worked well together. The detailing is outstanding, and although it is very scientific, it is easy to comprehend. I felt like I was right there. It is written in a smooth and easy flow, impossible to put down. It is one of the best I have read. I love the depth of the author's mind and I would like to see more. He is truly gifted. I highly recommend this book, it is awesome.
"FROM TIMNA TO MARS": Searching for Rare Earth Metals is a unique and exciting book to read. The author does an excellent job taking real world issues and creating a suspense novel around possibilities. Rare earth metals are being used in much of modern devices we enjoy, yet by definition there are a finite amount of these metals available. He has taken this little heard of crisis and presenteda fascinating novel around the importance these metals have in our lives. He has excellent character development and is able to make you feel like you are having the experiences of the main novel characters. The other thing the author does exceptionally well is keep the book moving. There is continual suspense and once you start reading you do not want to put the book down. The book can be considered a science fiction or a suspense novel. The book covers the story of professor and archeologists Avner Amram and his Wife Daphna who discover a rare earth metal, rhenium, in pure form in the Timna Caves in Israel. As they contemplate what to do with this metal and who they should tell of their discovery, the plot thickens quickly. Every country uses the metal to build everything from turbine blades to jet engines. It has the advantage of a very high melting point making it the perfect metal for high temperature applications. The author does an amazing job at articulating real life global concerns over rare earth metals. What would happen if one country hoarded the metals or used them to gain international power? While these metals are difficult to find and extract, our modern day technology relies greatly on their existence. What happens if we run out of these metals on earth, what then? As he explores the "what if's" of these important economic questions, the reader is taken on a journey that includes global consequences and worldwide government espionage and intrigue. The main characters entertain the idea of finding rare metals in outer space, particularly on the planet mars and nearby asteroids. The archeologists have invented a machine that can detect the presence rare earth metals at these far away locations.
As secrets are leaked and governments race to gain control, for the purpose of more world power, the reader is carried along for a gripping ride. The book is filled with suspense and page turning events with unexpected twists and turns all along the way. It gives a whole new view on what can be accomplished with space travel.
For the mass of readers who want nothing more than to be swept up in a super fast, thrilling ride with spies, counterintelligence agents, masterminds of science, and countries that rule everything from the world's finances to industries across the board - THIS is the book for you!
An F-16 falls from the sky as the pilot parachutes out and is saved…An F-16, with an Ace pilot at the helm mysteriously crashes in Oahu…A KGB officer is told to find the answer to a mysterious 1908 explosion in order to discover a special substance that the Kremlin wants before anyone else has a chance to get it… And all this in just the first two pages.
Professor Daphna Amran is a brilliant woman who has a slight obsession when it comes to King Solomon, and finding the location of anything that he left behind from his reign. When she is excavating and stumbles over the opening to an unexplored cave, she is beyond excited. However, she gets even more so when the samples she brings back to her lab show a very rare metal embedded in the sediment. The presence of this metal will open up a huge race between superpowers, as well as the 'junior,' Israel, as they struggle to find a way to get this rare-earth metal before it's too late.
Daphna's husband, Avner, has his own long resume, and he becomes more than a bit interested with his wife's discovery. He and his company soon land a contract to build a certain piece of technology that will be able to find these rare-earth metals in space - whether that be on asteroids, Mars, or anywhere else in the big dark solar system.
President to the Prime Minister, that rare-earth metals disappearing is not an issue to ignore; if they are lost completely, before no new source is found, industries would go bankrupt and the entire economic state of the world would basically fall apart. Unfortunately, as with a great deal of important matters, the scientists are ignored.
Things change, of course, when agents and spies get involved, all doing their best, playing their greatest roles, using code words, and meeting up with people who may or may not be on their side, to steal technology and information. In this case, ignorance is no longer bliss.
technological marvel that will locate these rare-earth metals, each and every country vies to steal the data in order to claim success for themselves.
The oddest part? The source that must be found may be far closer than anyone thinks; and the realm of an ancient King may just be the link they need to find it.
Quill says: Fast-paced, exhilarating and extremely intelligent, there is not a moment of downtime in this incredible novel!


Artist and "No Album" in the web interface and on the SB1 itself.
Part. No actual Coltrane albums were listed under his name.
thought that this was now in the "fixed, download the latest nightly"
System is Mandrake 10.1 on a 2.8GHz pentium using 6.1.0 from 04-06.
>thought that this was now in the "fixed, download the latest nightly"
>System is Mandrake 10.1 on a 2.8GHz pentium using 6.1.0 from 04-06.
Dave - have you tried restarting your server?
>> System is Mandrake 10.1 on a 2.8GHz pentium using 6.1.0 from 04-06.
> Dave - have you tried restarting your server?
to blow the db away first?
>to blow the db away first?
trouble tracking down, and the symptoms are like what you've seen.
Can I ask your reason for running Wipe Cache in the first place?
>> need to blow the db away first?
> trouble tracking down, and the symptoms are like what you've seen.
> Can I ask your reason for running Wipe Cache in the first place?
there was no need to delete the .db file as a wipe cache did the trick.
Wipe Cache was actually the cause of the mess up in the fist place?
which started this whole thing for me.
>there was no need to delete the .db file as a wipe cache did the trick.
>Wipe Cache was actually the cause of the mess up in the fist place?
>which started this whole thing for me.
Yes - I am right now. Until I fix the bug.
I'm assuming you had a blank entry under "No Artist" ?
> I'm assuming you had a blank entry under "No Artist" ?
this has not been tagged so appears under "No Artist".
that probably saved me from several hours of cursing.
>this has not been tagged so appears under "No Artist".
been changed or removed, and mark it to clean up in the database.
>that probably saved me from several hours of cursing.


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During the week of Feb. 16-20, Northwestern Health Unit staff will be promoting the next phase of its "Do One Thing" campaign: positive ticketing for active families.
Staff will "ticket" families they catch doing physical activity together.
The Northwestern Health Unit invites you to attend a "Play Date" this Wednesday (Nov. 27) from 4:30-7 p.m. at J.W. Walker School.
This event is open to parents and families with children of all ages, however activities will be targeted to those under eight years of age.
There is no cost to attend, and dinner will be provided to children and their families.
The Northwestern Health Unit would like to advise clients, suppliers, and community partners that its Sioux Narrows-Nestor Falls office will be closed Aug. 22-24 while it relocates.
Effective Monday (Aug. 27), the office will be located at 5736 Highway 71, which previously was the Ontario Tourist Information Centre building in Sioux Narrows.
Effective Monday, July 30, Northwestern Health Unit services no longer will be offered at 21 Wolsley St. in Kenora.
CEO Mark Perrault is looking forward to welcoming clients at the new address (210 First St. N.) on Tuesday, Aug. 7.
"This move puts us closer to the heart of downtown Kenora, in a more visible and convenient location," he noted.
During the month of June, the Northwestern Health Unit is promoting the message that being a dad is one of the greatest jobs and is like no other.
Dads play an important role in their children's lives by being involved and role-modelling positive behaviours for their children.
The Northwestern Health Unit is pleased to offer prenatal education in your community.
Attending prenatal classes will help those expecting a new baby to prepare for the birthing experience and parenting.
April 21-28, 2012 is National Immunization Awareness Week and the Northwestern Health Unit would like to remind everyone of the importance of immunization.
Thanks to immunization, many diseases are effectively and safely controlled, thus reducing the burden of illness in our communities.
No matter what our age, we never outgrow our need for immunizations.


The aim is to explore how MacDonald's theories of the imagination and of storytelling might transfer to a modern context, and the project will combine visual image and reflective blogging to encourage new appreciations of MacDonald's text.
There are two lectures on GM by Larry Fink on the iTunes University. Christian Mythmaker: His Works and Themes and Pictorial Biography. The easiest way to find them is to search for Summit 2010 – the ones you are looking for are numbers 60 and 125. Thanks to Caleb Morris on the Wingfold Email List for this.
This blog is for everyone who values the ideas and writings of George MacDonald. Through it we intend to give notice of upcoming events, reviews and general ruminations on this fascinating, but often misunderstood writer. George MacDonald appeals to a wide spectrum of people and that diversity will be reflected here. Please feel free to add your own comments to the posts.
Published under the auspices of the George MacDonald Society it will also include 'notices' from the George MacDonald WWW Page (www.george-macdonald.com) and will hopefully become the the first port of call for news in the 'macdonaldsphere'.


It's just amazing, the arrangement of the spectrum scripts on to the canvas. One of its kind the due artists work together to give the best outcome on the canvas from their creative imagination. Click on the link to know this special art.
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Does Walmart have sushi grade tuna?
Asked: Ben, Last Updated: June 27, 2022, 11:14
Sam's Choice Premium Wild Caught Sesame Crusted Seared Ahi Tuna, Sushi Grade, 4 oz - Walmart.com - Walmart.com.
Do squids have multiple brains?
Despite having a diffuse central nervous system with multiple brain-like hubs, octopi and cuttlefish make split-second decisions. When hunting or hiding, they quickly perceive their environment and coordinate different regions of their body independently to change colours.
Can clams make pearls?
Natural pearls are made by certain types of bi-valve mollusc, such as clams or oysters. A bi-valve mollusc has a hard outer shell, made from calcium carbonate, which is joined by a hinge. Its soft body is protected from predators inside of this hard shell.
How many shrimp can you eat in a day?
In this regard, how much shrimp can you eat in a day? The current guidelines from both the USDA and American Heart Association state that dietary cholesterol intake should be limited to no more than 300 mg per day, and three ounces of shrimp provides about half that amount.
What is the best water for shrimp?
RODI water is 99% pure H20 for your aquarium and ideal for sensitive species of shrimp. If you use tap water with Caridina shrimp, it's possible to get quick die off of your shrimp. If you are using RODI water, you will need to remineralize the water when making water changes.
Which fishing hook is larger size 1 or size 32?
Fishing hook sizes are generally referred to by a number from the smallest (size 32) to the largest (size 19/0). For hook sizes from 32 to 1, the larger the number, the smaller the hook.
How many oysters do I need for 10 people?
Oysters: Appetizer Engagements: 3-4 oysters per person. As a second or third course (where other items are being served with the oysters): 5-6 oysters per person. For a main course (including an Oyster Roast or a Bull & Oyster Roast), figure 6-8 oysters per person. Clams: Many people serve clams along with oysters.
Where does Keta salmon come from?
Keta is one of the best values in wild Alaska salmon and its mild character keeps it approachable to consumers who are a tad shy of full-flavored seafood. All of our fish is harvested in Alaska – some of it gets frozen and then shipped over to China where it's portioned and packed.
Which vein do you remove on shrimp?
The first "vein" is the alimentary canal, or the "sand vein," and is where body wastes like sand pass through. You remove it, partly because it's unappetizing, but also so you don't bite down on the sand and grit. The "white vein" on the inner crescent side of the shrimp is the blood vessel.
What's the best fish to dehydrate?
Cod, halibut, bass and sole are some of the types of lean fish to choose from....Dehydrating these types of fish is inadvisable:
Catfish (5.2% fat)
Lake Trout (11% fat)
Mackerel (9.9% fat)
Mullet (6.0% fat)
Rainbow Trout (6.8% fat)
Salmon (9.3% fat)
Whitefish (7.2% fat)
Is it OK to marinate salmon overnight?
Is It Safe To Marinade Salmon Overnight? Yes! It is safe to marinade salmon in the fridge overnight. The USDA food safety information suggests that you can keep raw fish in the fridge for 1 to 2 days.
Why does smoked salmon taste bad?
'So some smoked salmon still has it on — and it is revolting. ... Other smoked salmon contains brown meat, which comes from blood vessels. It has a more fishy taste and tends to go off more quickly.
Is my fish dying if it's laying on its side?
They will float uncontrollably to the top of the aquarium, turned upside down, while still being alive. In some cases the fish will lay upside down or sideways on the bottom, unable to swim upwards. ... The swim bladder disease in fish is not lethal on its own and it is rather a symptom than an independent condition.
Are shrimp dirty animals?
Imported shrimp, more than any other seafood, has been found to be contaminated with banned chemicals, pesticides, and even cockroaches, and it skirts food-safety authorities only to wind up on your plate. The number one reason for all that: the dirty conditions in which farmed shrimp are raised.
What is obokun fish in English?
Silver catfish Obokun is a local freshwater catfish also known as silver catfish. It is a prized component of the Nigerian seafood cuisine.
What is the best wild salmon to buy?
King salmon King (Chinook) There's a reason this species is at the top of the list and earned itself the royal moniker: King salmon is considered by many to be the best salmon money can buy. It's rich, high in fat, and big.
Is it safe to swim with jellyfish?
Although they've got a bad reputation, it's perfectly safe to swim with jellyfish at a few places in the world. Kakaban Island in Indonesia's Derawan Archipelago holds one of these marine lakes, populated by thousands of stingless jellies. Visitors don snorkel gear and slide into an ethereal scene.
Do you take the skin off mackerel?
Either way, you will be able to very easily pull the flesh off the bones in large chunks. The skin of mackerel is very thin and can be eaten but if desired, you can remove it simply by rubbing it gently with the ball of your thumb. Do be sure to remove and discard the fins.
Can frozen cooked shrimp make you sick?
While there are no reports of anyone getting sick from the shrimp, anyone who bought an affected bag should throw it away or return it for a refund. ... The FDA confirms it found salmonella in cooked shrimp from Kader Exports during a test.
Does raw salmon have parasites?
Yet, it's important to be aware that raw salmon may contain parasites, bacteria, and other toxins that can be harmful even in small doses. Only eat raw salmon that's been stored and prepared properly. If you have a compromised immune system, don't risk eating raw salmon.
What is long sliced smoked salmon?
Long Sliced Side This technique is the more modern method of slicing and involves slicing the salmon lengthways (from head to tail) so as to create a long thin slice of smoked salmon – without the brown meat. It is sometimes referred to as a 'lateral' slice.
Should I remove crayfish molt?
Definitely DO NOT REMOVE THE SHELL. He'll eat it to help build his new shell faster. This is doubly important if he's in a tank with other crayfish or larger fish... a newly molted crayfish is an easy meal for a lot of things.
How do you cook frozen whole clams?
To cook frozen clams, thaw the clams in the refrigerator, scrub the shells and wash the clam meat under cool water. Steam or use the clams in recipes such as chowders, soups and pasta. Remove frozen clams from the freezer 24 hours before cooking, and let them thaw in the refrigerator.
Can a giant squid sink a ship?
In truth, the giant squid can't sink a ship, and it's not likely you'll be snatched off the deck by one.
What clams are OK to eat?
Look for clams that are open, not closed The primary indicator as to whether or not clams are good is if they are open or closed. Clams should be slightly open, and if you tap on them, the shells should close rather quickly. If the clam shells are closed or do not close when you tap on them, then the clam is dead.
What fish is not good for dogs?
Avoid at all costs!
Tuna. Most tuna is very dense meat. If you must feed tuna choose yellow fin tuna. It is usually found in parts of the world that doesn't have as much heavy metals. ...
Other contaminated fish to avoid: King mackerel, sword fish, eels, sea bass, trout, flounder and cod.
Is it OK to eat small fish bones?
If you've swallowed a fishbone and feel fine, you don't need to see a doctor. If the bone didn't scratch your throat on the way down, you shouldn't have any further problems. It will eventually be eliminated and removed from your body by the natural digestive process.
How many years do squids live?
It's believed that giant squid live about five years and, in that time, reproduce only once.
Does vacuum packed fish go bad?
Vacuum packaging does not significantly prolong the shelf life of unfrozen fish. Vacuum packaging does have a good effect on the eating quality of frozen fish, where it slows down oxidzation. At 4 degrees Celsius (normal fridge temperature) expected shelf life is around 7-8 days.
Can you eat sockeye salmon raw?
Sockeye Salmon Sashimi. Pacific salmon and tuna which have never come into contact with fresh water are generally safe to eat raw straight out of the ocean. But on rare occasion, they can be infected with a parasitic worm. ... For fish to be called sushi grade, or sashimi grade, it must be frozen.


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The members of our executive committee are responsible for the day-to-day management of the company's business operations and key group functions.
Pat Ward
Pat spent 20 years with Aggregate Industries in various roles across their UK and US businesses. He joined them in 1995 and in 1999 was given the opportunity to relocate to Denver as Vice President of the Colorado business. At the time of leaving the USA, Pat had responsibilities for the businesses in Nevada, Colorado, Texas, Oklahoma and the Mid-Atlantic region. He was appointed CEO of Aggregate Industries Europe in April 2014. Pat joined Breedon in January 2016, and was appointed to the Board in March 2016.
Rob Wood
Group Finance Director
Rob has over 15 years' experience in the international building materials industry. He qualified as a Chartered Accountant with Ernst & Young and subsequently joined Hanson PLC where he held a number of senior positions including Finance Director Brick Continental Europe, Finance Director Building Products UK and Chief Financial Officer Australia and Asia Pacific. Following the acquisition of Hanson PLC by HeidelbergCement AG, Rob returned to the UK and joined Drax Group plc as Group Financial Controller. During his time at Drax he also spent a period of time as Head of M&A. Rob joined Breedon and was appointed to the Board in 2014.
Alan Mackenzie
Managing Director - Breedon Northern
Alan is Managing Director of our Northern operations, which extend from Stornoway in the Hebrides to northern England. He has spent more than 30 years in the aggregates industry, initially with Wimpey Minerals and then with Tarmac, where he was latterly Contracting Director of the company's largest region in central England. Alan joined Ennstone in 2001 as Regional Director for North and W est Scotland and in 2002 became Chief Executive of BEAR Scotland (in which Ennstone had a significant shareholding). He rejoined Ennstone in 2006 as Chief Executive of its Scottish business and when Ennstone was acquired by Breedon in 2010 he became Chief Executive of Breedon Aggregates Scotland, renamed Breedon Northern following Breedon's acquisition of Hope Construction Materials in 2016. Alan is a Past President of the European Asphalt Pavement Association and is currently Chairman of the UK Mineral Products Association Asphalt Committee and the Asphalt Industry Alliance. He is also immediate past Chairman of the Mineral Products Association in Scotland and a member of the MPA Council.
Mike Pearce
Managing Director - Breedon Southern
Mike joined Breedon in 2018 as Managing Director of the Group's southern division, with operations extending from Barrow-in-Furness in Cumbria to Greenhithe in Kent. He joined the Group from Aggregate Industries, where he was most recently Managing Director of the aggregates division, prior to which he served successively as head of concrete products (including the high-profile Bradstone brand); ready-mixed concrete (including London Concrete); asphalt & contracting; and major projects; before being appointed Commercial Director of Aggregate Industries Europe. Mike is a member of the Council of the Mineral Products Association and also sits on the West Midlands Regional Council of the CBI.
Jude Lagan
Managing Director - Breedon Cement
Jude has over 30 years' experience in the construction materials industry. Following the acquisition of the Lagan Group in 2018 Jude joined Breedon Group as Managing Director of Breedon Cement, responsible for its activities in the UK & Ireland.
The early part of his career was spent in southern England in the asphalt and specialist contracting division of Whitemountain, part of the Lagan Group, eventually becoming Managing Director. Jude returned to Ireland in 2000 to head up the in-house construction of Lagan Cement's new €120 million greenfield cement plant 80 kilometres west of Dublin. Following successful commissioning in 2002, Jude was operational director of the cement business, eventually becoming Managing Director of Lagan Cement Group in 2008. He is a member of the Institute of Quarrying and Vice Chairman of the Cement Manufacturers of Ireland.
Terry Lagan
Managing Director - Breedon Republic of Ireland
Terry has worked in the Irish and GB construction industries for more than 30 years. Following the acquisition of the Lagan Group in 2018 he joined Breedon Group as Managing Director of Lagan, Breedon's business in RoI.
Terry set up the original Lagan Group business in RoI in 1989, which originally comprised an asphalt production & contracting division along with a bitumen emulsion production plant. The business developed from there to cover the entire island, with operations from Cork to Donegal and Galway to Dublin, becoming the largest independent producer of construction materials in RoI.
Terry previously worked at Tarmac PLC, initially in their R&D laboratory in the West Midlands, before moving into production in the north-west and later joining the contracting division, working on a variety of large-scale projects.
Aidan Mullan
Managing Director - Breedon Northern Ireland
Aidan has over 28 years' experience in the civil engineering and construction materials industry, having worked in operational and senior management roles on large highway infrastructure and airfield projects across the UK and Republic of Ireland. Aidan joined Breedon in 2018 following Breedon's acquisition of the Lagan Group, as Regional Director of Whitemountain, Breedon's business in Northern Ireland and in 2019 was appointed as Managing Director.
Aidan joined Lagan in 1991 as a Graduate Civil Engineer in the highway and civil engineering division and progressed in his career in a number of operational and management roles, before being appointed to the position of Operations Director and member of Whitemountain's leadership team in 2008. In this role, Aidan was responsible for quarry and asphalt materials, civil engineering works, highway infrastructure and maintenance, airfield construction and aggregate export. He is a member of the Council for the CEF NI and also served on the MPA NI Executive.
Stephen Tagg
Group Human Resources Director
Stephen joined Breedon Group in May 2016 as Group Human Resources Director, with a remit to establish and develop a HR function in order to support the Group's growth. He currently has additional responsibility for overseeing group safety. He was previously Organisation & Human Resources Director at Aggregate Industries, having spent 21 years with the company in a variety of senior HR roles. Prior to AI, he worked in Employee Relations and HR roles in the mining industry with British Coal. Stephen is a member of the CBI Employment and Skills Board, is a Fellow of the Chartered Institute of Personnel and Development and a director of the Mineral Products Qualification Council.
Ross McDonald
Group Services Director
Ross joined Ennstone plc as Company Secretary in 2006 and became Chief Compliance Officer of Breedon Aggregates on its formation in 2010. He was appointed Group Services Director in 2016. Ross is responsible for, amongst other things, legal and regulatory compliance, legal affairs, corporate governance, insurance and risk management, minerals and planning, and he has a leading role in our M&A team. Before joining Ennstone he was Company Secretary of Melrose plc (now Melrose Industries plc), McKechnie plc and Assistant Company Secretary of BICC plc (now Balfour Beatty PLC). Ross has a Bachelor of Laws degree from Edinburgh University and is a fellow of the Institute of Chartered Secretaries and Administrators.
Stephen Jacobs
Head of Communications & Marketing
Steve has overseen Breedon's communications and marketing since the group's creation in 2010. Prior to joining Breedon, Steve was closely involved in the housebuilding and building materials industries as a communications consultant for more than 20 years, most of which he spent as a partner in FTI Strategic Communications (formerly Financial Dynamics), one of the UK's leading international corporate and financial communications consultancies. He has responsibility for all Breedon Group's internal and external communications and marketing activities.


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17-Year-Old Swimmer Michael Andrew Wins First World Title
By USA Swimming | Dec. 09, 2016, 10:52 p.m. (ET)
Michael Andrew celebrates his silver medal in the 100-meter individual medley at the 13th FINA World Swimming Championships (25m) at the WFCU Centre on Dec. 9, 2016 in Windsor, Ontario.
WINDSOR, Canada – Coming off a victory last night in the 800-meter freestyle, Leah Smith (Pittsburgh, Pa./Cavalier Swimming) claimed her second world title of the meet in the 400m free on day four of the FINA World Championships (25m) at Windsor, Canada's WFCU Centre.
Through four nights of competition, Team USA has earned 18 medals – seven gold, eight silver and three bronze.
In a personal best time, Smith fought off a fast field of competitors to win the 400m freestyle in 3-minutes, 57.78 seconds.
"It was hard to get up and go again after last night, but I knew that having a good 800 free would bode well for my 400 free," said Smith. "Tonight I wanted to be confident in my race strategy, that's something I forgot about last night, I knew if I did that then that I could do great."
Michael Andrew (Lawrence, Kan./Race Pace Club) also took home gold, earning his first individual world championship title in the men's 100m IM. Andrews finished in a personal best time of 51.84.
Other highlights for the United States on Friday:
Kelsi Worrell (Evansville, Ind./Indiana University) – 50m Butterfly, Silver, 25.27
Tom Shields (Huntington Beach, Calif./California Aquatics) – Semifinal, 50m Butterfly American Record: 22.38
Men's 4x50m Free Relay - Silver, 1:24.47
o Paul Powers (Gainesville, Ga./University of Michigan)
o Blake Pieroni (Valparaiso, Ind./Indiana University)
o Michael Chadwick (Charlotte, N.C./University of Michigan)
o Tom Shields
Men's 4x200 Free Relay – Silver, 6:53.34
o Blake Pieroni
o Jacob Pebley (Corvallis, Ore./California Aquatics)
o Pace Clark (Memphis, Tenn./Athens Bulldog Swim Club)
o Zane Grothe (Boulder City, Nev./Badger Swim Club)
Team USA Breaks Second Relay Record In As Many Days At Swimming Worlds Dec. 08, 2016
U.S. Women Break World And American Records At Short-Course Swimming World Championships Dec. 07, 2016
First-Ever Competition Circuit Announced For Para Swimming, Including Stop In Indianapolis Dec. 07, 2016
Team USA Opens Short-Course Swimming World Championships With Five-Medal Haul Dec. 07, 2016
Whirlwind 2016 Continues For Swimmer Josh Prenot At Short-Course Worlds Dec. 06, 2016
Kelsi Worrell
Tom Shields
Blake Pieroni
Jacob Pebley