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

Word embeddings and pre-trained language models allow to build rich representations of text and have enabled improvements across most NLP tasks. The most successful models include word embeddings like FastText \cite{fasttext1_bojanowski2017enriching}, and, more recently, pre-trained language models which can be used to produce contextual embeddings or directly fine-tuned for each task. Good examples of the later are character-based models like Flair \cite{akbik2018coling} and masked language models like BERT \cite{devlin2019bert}. In many cases the teams that developed the algorithms also release their models, which facilitates both reproducibility and their application in downstream tasks. Given that the models are expensive to train, many companies and research groups tend to use those models, rather than building their own. This could be suboptimal as, for many languages, the models have been trained on easily obtained public corpora, which tends to be smaller and/or of lower quality that other existing corpora. In addition, pre-trained language models for non-English languages are not always available. In that case, only multilingual versions are available, where each language shares the quota of substrings and parameters with the rest of the languages, leading to a decrease in performance \cite{devlin2019bert}.  The chances for smaller languages, as for instance Basque, seem even direr, as easily available public corpora is very limited, and the quota of substrings depends on corpus size.

As an illustration of the issues mentioned above, the multilingual BERT which Basque shares with other 103 languages is based on Wikipedia corpora, where English amounts to 2.5 thousand million tokens whereas for Basque it contains around 35 million tokens. Our corpus uses, in addition to the Basque Wikipedia, corpora crawled from news outlets (191M tokens). Another important issue is subword tokenization. Thus, for some common Basque words such as \texttt{etxerantz} (to the house) or \texttt{medikuarenera} (to the doctor), the subword tokenization generated by the monolingual BERT we trained will substantially differ from the output produced by the multilingual BERT:

\begin{verbatim}
 mBERT: Et #xer #ant #z
 ours:  Etxera #ntz

 mBERT: Medi #kua #rene #ra
 ours:  Mediku #aren #era
\end{verbatim}

More specifically, mBERT's subwords tend to be shorter and less interpretable, while our subwords are closer to linguistically interpretable strings, like \texttt{mediku} (doctor) \texttt{aren} ('s) and \texttt{era} (to the).

Furthermore, most of the time the released models have been thoroughly tested only in English. Alternatively, multilingual versions have been tested in transfer learning scenarios for other languages, where they have not been compared to monolingual versions \cite{devlin2019bert}.
The goal of this paper is to compare publicly available models for Basque with analogous models which have been trained with a larger, better quality corpus. This has been possible for the FastText and Flair models. In the case of BERT, only the multilingual version for 104 languages is available for Basque. We focus on four downstream NLP tasks, namely, topic classification, sentiment classification, Part-of-Speech (POS) tagging and Named Entity Recognition (NER).

The main contribution of our work is threefold: (1) We show that, in the case of Basque, publicly available models can be outperformed by analogous models trained with appropriate corpora. (2) We improve the state of the art in four tasks for Basque. (3) We make all our models publicly available\footnote{\scriptsize \url{http://ixa2.si.ehu.es/text-representation-models/basque}}. Our results show that this is indeed the case for FastText and Flair models. In the case of BERT, we show that the monolingual Basque BERTeus model\footnote{Also available from Hugginface in \scriptsize{\url{https://huggingface.co/ixa-ehu/berteus-base-cased}}} outperforms the official multilingual BERT by a large margin, with up to 10 absolute point improvement. The increases are consistent for the three models in all four tasks. Our experiments have been performed training models which are analogous to the corresponding existing pre-trained models. This allows to make a head-to-head comparison, and to conclude that the training corpus is key. In the case of BERT, training a monolingual model instead of a multilingual one shared with 103 other languages is also a key factor, so we can rule out that it is not any change in the models that cause the better performance.

Although our results set a new state of the art for the four tasks, our goal was not to train and use the systems in the most optimal manner. Instead, and as mentioned before, we have focused on head-to-head comparisons. In this sense, better results could be expected using variations of pre-trained models that have reported better results for English \cite{liu2019roberta}, or making an effort in developing better adaptations to each of the tasks.

\section{Related work}\label{sec:related-work}

Deep learning methods in NLP rely on the ability to represent words as continuous vectors on a low dimensional space, called word embeddings. Word2vec~\cite{mikolov2013distributed} or GloVe~\cite{Pennington14glove:global} are among the best models that build word embeddings by analyzing co-occurrence patterns extracted from large corpora. FastText~\cite{fasttext1_bojanowski2017enriching} proposes an improvement over those models, consisting on embedding subword units, thereby attempting to introduce morphological information. Rich morphology languages such as Basque should especially profit from such word representations. FastText distributes embeddings for more than 150 languages trained on Common Crawl and  Wikipedia. In this paper we build FastText embeddings using a carefully collected corpus in Basque and show that it performs better than the officially distributed embeddings in all NLP we tested, which stresses the importance of a following a carefully designed method when building and collecting the corpus.

The aforementioned methods generate static word embeddings, that is, they provide a unique vector-based representation for a given word independently of the context in which the word occurs. Thus, if we consider the Basque word \emph{banku}\footnote{In English: bank.}, static word embedding approaches will calculate one vector irrespective of the fact that the same word \emph{banku} may convey different senses when used in different contexts, namely, ``financial institution'',``bench'', ``supply or stock'', among others. In order to address this problem, contextual word embeddings are proposed; the idea is to be able to generate different word representations according to the context in which the word appears. Examples of such contextual representations are ELMO \cite{Peters:2018} and Flair \cite{akbik2018coling}, which are built upon LSTM-based architectures and trained as language models. More recently, \newcite{devlin2019bert} introduced BERT, a model based on the transformer architecture trained as a masked language model, which has obtained very good results on a variety of NLP tasks. The multilingual counterpart of BERT, called mBERT, is a single language model pre-trained from corpora in more than 100 languages. mBERT enables to perform transfer knowledge techniques among languages, so that systems can be trained on datasets in languages different to the one used to fine tune them~\cite{heinzerling-strube-2019-sequence,DBLP:conf/acl/PiresSG19}.

When pre-training mBERT the corpora sizes in different languages are very diverse, with English corpora being order of magnitudes larger than that of the minority languages. The authors alleviate this issue by oversampling examples of lower resource languages. However, as the parameters and vocabulary is shared across the languages, this means that each language has to share the quota of substrings and parameters with the rest of the languages. As shown in the introduction, this causes tokenization problems for low resource languages.

CamemBERT~\cite{martin2019camembert} is a recent effort to build custom RoBERTa \cite{liu2019roberta} pre-trained language models for French, using 138GB of text from a monolingual French corpus extracted from Common Crawl. The authors show that CamemBERT obtains significant improvements on many French tasks compared to the publicly available multilingual BERT. However, the architecture used in CamemBERT is different to BERT, which makes the comparison among the models less straightforward. In contrast, we try to mimic the BERT architecture and parameters when building our BERTeus models, thus allowing a head-to-head comparison among them. In addition we also report results for FastText and Flair models.

\section{Building Basque models}\label{sec:build-basq-models}

In order to train accurately word embeddings and language models we need a corpus as large as possible, but also meeting certain criteria. Those criteria included clean and correctly structured text (having paragraph and document limits, specially relevant for training BERT language models). With that aim, we collected a corpus in Basque comprising crawled news articles from online newspapers and the Basque Wikipedia\footnote{The dump from 2019/10/01 was used.}. In total the collected corpus contains 224.6 million tokens, of which 35 million come from the Wikipedia. Table \ref{tab:corpus} shows the composition of the corpus. Henceforth, we will refer to this corpus as the \emph{Basque Media Corpus} (BMC).

\begin{table}[!ht]\small
\centering
\begin{tabular}{@{\hspace{0.3cm}}lcc} \hline
 {\textbf{}} & {\textbf{Text type}} & {\textbf{Million tokens}}  \\ \hline
Wikipedia & enciclopedia & 35M \\
Berria newspaper & news & 81M  \\
EiTB & news & 28M \\
Argia magazine & news & 16M \\
Local news sites & news &  64.6M \\ \hline
\hline
\textbf{BMC} & & 224.6M\\
\hline
\end{tabular}
\caption{Composition of the Basque Media Corpus (BMC).}\label{tab:corpus}
\end{table}

\subsection{Static Embeddings: FastText}\label{sec:build-basq-models:static}

To the best of our knowledge, the only publicly available static word embeddings for Basque are those distributed by Facebook and learned using an approach based on the skipgram model, where  each  word  is  represented as a bag of character n-grams \cite{fasttext1_bojanowski2017enriching}. The approach is implemented in the FastText library\footnote{\scriptsize \url{https://fasttext.cc/}}, and two types of pre-trained word vectors are available (based on 300 dimensions):

\textbf{Wiki word vectors} (FastText-official-wikipedia) were trained on Wikipedia using the skip-gram model described in \cite{fasttext1_bojanowski2017enriching} with default parameters (a window size of 5, 3-6 length character n-grams and 5 negatives).

\textbf{Common Crawl word vectors} (FastText-official-common-crawl) were trained on Common Crawl and Wikipedia using CBOW with position-weights, with character n-grams of length 5, a window size 5 and 10 negatives\cite{fasttext2_grave2018learning}.

In this work, we trained \textbf{BMC word vectors} (FastText-BMC) of 300 dimensions on the BMC corpus described above, using CBOW with position-weights and the default parameters of the original paper. \cite{fasttext1_bojanowski2017enriching}.

\subsection{Contextual String Embeddings: Flair}\label{sec:build-basq-models:flair}

Flair refers to both a deep learning system and to a specific type of character-based contextual word embeddings. Flair (embeddings and system) have been successfully applied to sequence labeling tasks obtaining state-of-the-art results for a number of English Named Entity Recognition (NER) and Part-of-Speech tagging benchmarks \cite{akbik2018coling}, outperforming other well-known approaches such as BERT and ELMO \cite{devlin2019bert,Peters:2018}. In any case, Flair is of interest to us because they distribute their own Basque pre-trained embedding models obtained from a corpus of 36M tokens (combining OPUS and Wikipedia).

\paragraph{Flair-BMC models:} We train our own Flair embeddings using the BMC corpus with the following parameters: Hidden size 2048, sequence length of 250, and a mini-batch size of 100. The rest of the parameters are left in their default setting. Training was done for 5 epochs over the full training corpus. The training of each model took 48h on a Nvidia Titan V GPU.

\paragraph{Flair Embeddings:} Flair's embeddings model words as sequences of characters. Moreover, the vector-based representation of a word will depend on its surrounding context. More specifically, to generate word embeddings they feed sentences as sequences of characters into a character-level Long short-term memory (LSTM) model which at each point in the sequence is trained to predict the next character. Given a sentence, a forward LSTM language model processes the sequence from the beginning of the sentence to the last character of the word we are modeling extracting its output hidden state. A backward LSTM performs the same operation going from the end of the sentence up to the first character of the word. In this case, the extracted hidden state contains information propagated from the end of the sentence to this point. Both hidden states are concatenated to generate the final embedding.

\paragraph{Pooled Contextualized Embeddings:} Flair embeddings, however, struggle to generate an appropriate word representation for words in underspecified contexts, namely, in sentences in which, for example, local information is not sufficient to know the named entity type of a given word. In order to address this issue a variant of the original Flair embeddings is proposed: ``Pooled Contextualized Embeddings'' \cite{akbik2019naacl}. In this approach, every contextualized embedding is kept into a \emph{memory} which is later used in a pooling operation to obtain a global word representation consisting of the concatenation of all the local contextualized embeddings obtained for a given word. They reported significant improvements for NER by using this new version of Flair embeddings. Note that this does not affect to the way the Flair pre-trained embedding models are calculated. The pooling operation is involved in the process of using such pre-trained models in order to obtain word representations for a given task such as NER or POS using the Flair system.

\paragraph{Flair System:} For sequence labeling tasks, the calculated character-based embeddings are passed into a BiLSTM-CRF system based on the architecture proposed by \cite{huang2015bidirectional}. For text classification tasks, the computed Flair embeddings are fed into a BILSTM\footnote{A single layer of size 128 was used, with word reprojection and a dropout value of 0.3068.} to produce a document level embedding which is then used in a linear layer to make the class prediction. Although for best results they recommend to stack their own Flair embeddings with additional static embeddings such as FastText, in this paper our objective is to compare the official pre-trained Flair embeddings for Basque with our own Flair-BMC embeddings.

\subsection{BERT language models}\label{sec:build-basq-models:bert}

We have trained a BERT \cite{devlin2019bert} model for Basque Language using the BMC corpus motivated by the rather low representation this language has in the original multilingual BERT model. In this section we describe the methods used for creating the vocabulary, the model architecture, the pre-training objective and procedure.

The main differences between our model and the original implementation are the corpus used for the pre-training, the algorithm for sub-word vocabulary creation and the usage of a different masking strategy that is not available for the BERT\textsubscript{BASE} model yet.

\paragraph{Sub-word vocabulary}

We create a cased sub-word vocabulary containing 50,000 tokens using the unigram language model based sub-word segmentation algorithm proposed by \newcite{kudo2018subword}. We do not use the same algorithm as BERT because the WordPiece \cite{wu2016google} implementation they originally used is not publicly available. We have increased the vocabulary size from 30,000 sub-word units up to 50,000 expecting to be beneficial for the Basque language due to its agglutinative nature. Our vocabulary is learned from the whole training corpus but we do not cover all the characters in order to avoid very rare ones. We set the coverage percentage to $99.95$.

\paragraph{Model Architecture}

In the same way as the original BERT architecture proposed by \newcite{devlin2019bert} our model is composed by stacked layers of Transformer encoders \cite{vaswani2017attention}. Our approach follows the BERT\textsubscript{BASE} configuration containing 12 Transformer encoder layers, a hidden size of 768 and 12 self-attention heads for a total of 110M parameters.

\paragraph{Pre-training objective}

Following BERT original implementation, we train our model on the Masked Language Model (MLM) and Next Sentence Prediction (NSP) tasks. Even if the necessity of the NSP task has been questioned by some recent works \cite{yang2019xlnet,liu2019roberta,lample2019cross} we have decided to keep it as in the original paper to allow for head-to-head comparison. For the MLM, given an input sequence composed of N tokens $x_1, x_2, ..., x_n$ we select a $15\%$ of them as masking candidates. Then, $80\%$ of these selected tokens are masked by replacing them with the [MASK] token, $10\%$ are replaced with a random word of the vocabulary and the remaining $10\%$ are left unchanged. In order to create input examples for the NSP task, we take two segments, $A$ and $B$, from the training corpus, where $B$ is the true next segment for $A$ only for $50\%$ of the cases. For the rest, $B$ is just a random segment from the corpus. At the end, the model is trained to optimize the sum of the means of the MLM and NSP likelihoods.

As our vocabulary consists of sub-word units, we use whole-word masking (WWM), that applies the masking to whole words instead of sub-word units. This new masking strategy makes the MLM task more difficult for the system as it has to predict the whole word instead of predicting just part of it. An upgraded version of BERT\textsubscript{LARGE}\footnote{\scriptsize{\url{https://github.com/google-research/bert}}} has proven that WWM has substantial benefits in comparison with previous masking that was done after the sub-word tokenization.

\paragraph{Pre-training procedure}

Similar to \cite{devlin2019bert} we use Adam with learning rate of $1e-4$, $\beta_1=0.9$, $\beta_2=0.999$, L2 weight decay of $0.01$, learning rate warmup over the first $10,0000$ steps, and linear decay of the learning rate. The dropout probability is fixed to $0.1$ on all the layers.

As the attentions are quadratic to the sequence length, making longer sequences much more expensive, we pre-train the model with sequence length of $128$ for $90\%$ of the steps and sequence length of $512$ for $10\%$ of the steps. In total we train for $1,000,000$ steps and a batch size of $256$. The first $90\%$ steps are trained using Cloud v2 TPUs and for the rest of the steps we use Cloud v3 TPUs \footnote{\scriptsize{\url{https://cloud.google.com/tpu/pricing}}}.

\section{Evaluation and Results}\label{sec:eval-results}

We conduct an extensive evaluation on four well known NLP tasks: Topic Classification, Sentiment Classification, Part-of-Speech (POS) tagging and Named Entity Recognition (NER). The datasets used for each task are described in their respective sections.

We train our systems to perform the following comparisons: (i) FastText official models (Wikipedia and Common Crawl) vs FastText-BMC model; (ii) the official Flair embedding models vs our Flair-BMC model and, (iii) BERTeus with respect to multilingual BERT.

To train the Flair system we use the parameters specified in \cite{akbik2019naacl} for Pooled Contextual Embeddings. Flair is tuned on the development data using the test only for the final evaluation. We do not use the development set for training.

For comparison between BERT models we fine-tune on the training data provided for each of the four tasks with both the official multilingual BERT \cite{devlin2019bert} model and with our BERTeus model (trained as described in Section \ref{sec:build-basq-models:bert}).

Every reported result for every system is the average of five randomly initialized runs. The POS and NER experiments using mBERT and BERTeus are performed using the transformers library \cite{Wolf2019HuggingFacesTS} where it is recommended to remove the seed for random initialization.

\subsection{Topic Classification}\label{sec:topic}

For the task of topic classification a dataset containing 12k news headlines (brief article descriptions) was compiled from the Basque weekly newspaper Argia\footnote{\scriptsize \url{https://www.argia.eus}}. News are classified uniquely according to twelve thematic categories. The dataset was divided into train (70\%), development (15\%) and test datasets (15\%). The Basque Headlines Topic Classification (BHTC) dataset is publicly available under CC license.\footnote{\scriptsize \url{https://hizkuntzateknologiak.elhuyar.eus/assets/files/BHTC.tgz}}

The FastText and Flair related experiments were conducted using the Flair text classifier with the same hyperparameter settings\footnote{max-epoch 50, learning rate 0.1, minibatch size 64, and  patience 3 }. BERTeus and mBERT fine-tuning was performed over 3 epochs with a learning rate of 2e-5 and a batch size of 16.

\begin{table}[!ht]\small
\centering
\begin{tabular}{@{\hspace{0.3cm}}lcc} \hline
 {\textbf{}} & {\textbf{Micro F1}} &  {\textbf{Macro F1}} \\ \hline
\textbf{Static Embeddings} & & \\
FastText-Wikipedia & 65.00 & 54.98 \\
FastText-Common-Crawl & 28.82 & 3.73  \\
FastText-BMC  & 69.45 & 60.14 \\
\hlin
\textbf{Flair Embeddings}\\
Flair-official & 65.25 & 55.83 \\
Flair-BMC  & 68.61 & 59.38  \\ \hline
\textbf{BERT Language Models}\\
mBERT-official  & 68.42 & 48.38  \\
BERTeus  & \textbf{76.77}	& \textbf{63.46}  \\
\hline
\textbf{Baseline} \\
TF-IDF Logistic Regression & 63.00 & 49.00 \\
\hline
\end{tabular}
\caption{Basque topic classification results on Argia corpus.}\label{tab:topic}
\end{table}

Table \ref{tab:topic} shows the results obtained by the different models. Firstly, it can be seen that every BMC-trained model outperforms its official counterpart for all the three settings, by at least 4\% in all cases, for both micro and macro F1-scores. The best results are obtained by BERTeus. For each type of embedding or language models, the results show the effectiveness of developing your own models for your own language.

\subsection{Sentiment Classification}\label{sec:polarity}

Sentiment classification is evaluated using a corpus of tweets containing messages related to the cultural domain\footnote{\scriptsize{\url{https://hizkuntzateknologiak.elhuyar.eus}}}. The corpus contains annotations for three classes (positive, negative and neutral), and a total of 2,936 examples. For the experiments in this paper the corpus was divided into train (80\%), test (10\%) and development (10\%) sets. Class distribution corresponds to 32\% positive, 54\% neutral and 14\% negative.

For FastText and Flair experiments we used the Flair text classifier with the same hyperparameter settings as those used for Topic Detection in Section \ref{sec:topic}. This is also the case for BERTeus and mBERT: fine-tuning over 3 epochs with a learning rate of 2e-5 and a batch size of 16.

\begin{table}[!ht]\small
\centering
\begin{tabular}{@{\hspace{0.3cm}}lcc} \hline
 {\textbf{}} & {\textbf{micro F1}} &  {\textbf{Macro F1}} \\ \hline
\textbf{Static Embeddings} & & \\
FastText-Wikipedia & 71.10 &	66.72 \\
FastText-Common-Crawl & 66.16 & 58.89  \\
FastText-BMC  & 72.19 &	68.14 \\
\hlin
\textbf{FlairEmbeddings}\\
Flair-official & 72.74 & 67.73 \\
Flair-BMC  & 72.95	& 69.05 \\ \hline
\textbf{BERT Language Models}\\
mBERT-official  & 71.02 & 66.02 \\
BERTeus  & \textbf{78.10}	& \textbf{76.14} \\
\hline
\textbf{Baseline} \\
SVM \cite{san2019multilingual} & 74.02 & 69.87\\ \hline
\end{tabular}
\caption{Basque sentiment classification task on Behagune tweet corpus.}\label{tab:sentiment}
\end{table}

Table \ref{tab:sentiment} shows that every BMC-trained model outperforms its official counterpart for all the three settings. The difference between Flair-official and Flair-BMC is rather small in terms of micro F1-score, but Flair-BMC obtains a higher macro F1. Furthermore, BERTeus establishes a new state-of-the-art on this particular dataset, improving previous results \cite{san2019multilingual} by 4.08 points in micro F1 score, and 6.27 in macro F1 score. BERTeus specially improves the classification of the positive and negative classes, which provides a boost in terms of macro F1.

\subsection{POS Tagging}\label{sec:pos-tagging}

In order to facilitate comparison with previous state-of-the-art methods, we experiment with the Universal Dependencies 1.2 data, which provides train, development and test partitions. The Basque UD treebank \cite{aranzabe2015automatic} is based on a conversion from part of the Basque Dependency Treebank (BDT) \cite{aduriz2003construction}. The treebank consists of 5274 sentences (60563 tokens) and covers mainly literary and journalistic texts. Previous best result for this dataset in Basque has been reported by \cite{heinzerling-strube-2019-sequence}. They use Byte Pair Embeddings (BPEmb) which are trained on subword-segmented text. The segmentation of the text is obtained by using the Byte Pair Encoding (BPE) unsupervised algorithm, which iteratively merges frequent pairs of adjacent symbols into new ones. The BPEmbeddings are then fed into a LSTM sequence tagging architecture.

\begin{table}[!ht]\footnotesize
\centering
\begin{tabular}{@{\hspace{0.3cm}}lccc} \hline
 \textbf{} & \textbf{Word Accuracy} \\ \hline
\textbf{Static Embeddings} & \\
FastText-Wikipedia & 94.09 \\
FastText-Common-Crawl & 91.95 \\
FastText-BMC  & 96.14 \\ \hline
\textbf{FlairEmbeddings} \\
Flair-official & 97.50 \\
Flair-BMC  &  97.58 \\ \hline
\textbf{BERT Language Models}\\
mBERT-official &  96.37 \\
BERTeus & \textbf{97.76} \\ \hline
\textbf{Baseline} \\
\cite{heinzerling-strube-2019-sequence} & 96.10 \\ \hline
\end{tabular}
\caption{Basque POS tagging results on UD 1.2.}\label{tab:pos}
\end{table}

Table \ref{tab:pos} shows the results obtained by the different models. Firstly, it can be seen that every BMC-trained model outperforms its official counterpart for all the three settings, albeit the difference between Flair-official and Flair-BMC is rather small. Secondly, our BMC-models (both BERTeus and Flair) establish a new state-of-the-art on this particular dataset significantly improving over the result reported by \cite{heinzerling-strube-2019-sequence}. In any case, the results show the effectiveness of developing your own models for your own language. This is especially supported by the difference in performance obtained by BERTeus with respect to mBERT-official.

\begin{table}[!ht]\footnotesize
\centering
\begin{tabular}{@{\hspace{0.3cm}}lccc} \hline
 \textbf{} &\textbf{Precision} & \textbf{Recall} & \textbf{F1} \\ \hline
\textbf{Static Embeddings} & & &  \\
FastText-Wikipedia & 72.42 & 50.28 & 59.23 \\
FastText-Common-Crawl & 72.09 & 45.31 & 55.53 \\
FastText-BMC  & 74.12 & 67.33 & 70.56 \\
\hlin
\textbf{Flair embeddings}\\
Flair-official & 81.86 & 79.89 & 80.82 \\
Flair-BMC & 84.32 & 82.66 & 83.48 \\ \hline
\textbf{BERT Language Models} \\
mBERT-official  & 81.24 & 81.80 & 81.52 \\
BERTeus  & 87.95 & 86.11 & \textbf{87.06} \\ \hline
\textbf{Baseline} \\
\cite{agerri2016robust} & 80.66 & 73.14 & 76.72 \\ \hline
\end{tabular}
\caption{Basque NER results on EIEC corpus.}\label{tab:ner}
\end{table}

\begin{table*}[!t]
\centering
\begin{tabular}{@{\hspace{0.3cm}}lcccc} \hline
\textbf{} & \multicolumn{4}{c}{\textbf{Task}} \\
 & {\textbf{Topic Classification}} & {\textbf{Sentiment}} &  {\textbf{POS}} & {\textbf{NER}}\\ \hline
\multicolumn{5}{@{}l}{\textbf{Static Embeddings}} \\
FastText-Wikipedia & 65.00 & 71.10 & 94.09 & 59.23 \\
FastText-Common-Crawl & 28.82 & 66.16 & 91.95 & 55.53 \\
FastText-BMC  & 69.45 & 72.19 & 96.14 & 70.56 \\
\hlin
\multicolumn{5}{@{}l}{\textbf{Flair Embeddings}}\\
Flair-official & 65.25 & 72.74 & 97.50 & 80.82 \\
Flair-BMC  & 68.61 & 72.95 & 97.58 & 83.48 \\ \hline
\multicolumn{5}{@{}l}{\textbf{BERT Language Models}} \\
mBERT-official  & 68.42 & 71.02 & 96.37 & 81.52 \\
BERTeus  & \textbf{76.77} & \textbf{78.10} & \textbf{97.76} & \textbf{87.06} \\ \hline
\textbf{Baselines} & 63.00 & 74.02 & 96.10 & 76.72 \\ \hline
\end{tabular}
\caption{Summary table across all tasks. Micro F1 scores are reported.}\label{sec:results-discussion:table}
\end{table*}

\subsection{Named Entity Recognition}\label{sec:named-entity-recogn}

EIEC\footnote{\scriptsize{\url{http://ixa2.si.ehu.eus/eiec/eiec_v1.0.tgz}}} is a gold standard corpus for NER in Basque \cite{alegria2006lessons}. The corpus contains 44K tokens for training (3817 unique entities) and 15K for testing (931 entities). Although EIEC is annotated with four entity types (Location, Person, Organization and Miscellaneous), the \emph{Miscellaneous} class is rather sparse, occurring only in a proportion of 1 to 10 with respect to the other three classes. Thus, in the training data there are 156 entities annotated as \emph{Miscellaneous} whereas for each of the other three classes it contains around 1200 entities.

As for the previous tasks above, we compare our BMC-trained models (FastText, Flair and BERT) with respect to the official releases. We also compare with the previous published baseline on this dataset, which trains a Perceptron model with a simple and shallow feature set combined with clustering features based on unigram matching \cite{agerri2016robust}.

Table \ref{tab:ner} reports the results of our comparison for NER using the usual conlleval script from the CoNLL NER shared tasks \footnote{\scriptsize{\url{https://www.clips.uantwerpen.be/conll2002/ner/}}}. We can see that the BMC-trained models improve over the official models distributed by FastText, Flair and mBERT, with a much larger margin than for POS tagging. It is also worth mentioning that there is a nice balance between the precision and the recall obtained by both Flair-BMC and BERTeus. Finally, we think that the larger differences in performance between NER and POS tagging might be partially due to the small size of the EIEC NER corpus. In any case, more experimentation is required to clarify the issue.

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

As illustrated by the overview of results provided by Table \ref{sec:results-discussion:table}, our BMC-trained models improve over previous work for all four tasks evaluated. This is true when we compare each model with their official counterpart, but also in absolute terms, where BERTeus outperforms every other model by a wide margin.

We believe that the POS tagging results on UD 1.2 are especially interesting, given that previous work had already established a very competitive baseline \cite{heinzerling-strube-2019-sequence} and that our aim was to provide a head-to-head comparison between analogous models. For example, best performance with Flair is usually the result of combining Flair string contextual embeddings with static embeddings such as FastText \cite{akbik2018coling}, but our objective was not to optimize the experiments for best results but, rather, to compare with the Flair-official model for Basque. In the same way, when training BERTeus we strived to comply with the BERT architecture and parameters thus allowing a
head-to-head comparison between them.

Another interesting issue that arises by looking at the scores across tasks is the robust performance of the FastText-BMC embeddings. In fact, the FastText-BMC embeddings used to train the Flair document classifier outperform multilingual BERT for the Topic Detection and Sentiment Analysis tasks. Moreover, and unlike the FastText-official models, the results of FastText-BMC do not fluctuate as much, performing quite robustly across tasks. Finally, our Flair-BMC model outperforms both Flair-official and multilingual BERT in every setting, although it remains far from the performance displayed by BERTeus.

All these issues show the importance of carefully collecting a corpus to generate pre-trained language models specific to each language, especially for less-resourced ones which are usually under-represented in large multilingual models.

In the case of BERT, we see an additional factor which, for sure, influences the better results of the in-house BERTeus model, that of training a monolingual model instead of a highly multilingual one. In this sense, we think that the good performance of BERTeus is due to the combination of two factors: larger and better corpus, as well as the fact of using language-specific subword tokenization. These improvements provide a huge boost in performance which, for example, help to correctly classify difficult NER examples (such as those of metonymy), as displayed in Table \ref{tab:ner-examples}. In this example, ``United States'' and ``Western Europe'' are correctly classified as ORGANIZATION entities whereas mBERT predicts them to be LOCATION, which is incorrect in this particular context.

\begin{table*}[!t]
\centering
\resizebox{\textwidth}{!}{
\begin{tabular}{lllllllllllllll}
      & Chechenya &  &   &  &  &  & United & States &  & Western & Europe &  &  &  \\
      & Txetxeniako & gai & horretan & agerian & geratu & da & Estatu & Batuek & eta & Mendebaldeko & Europak & eman & dioten & sustengua. \\
\textbf{Gold: }      & B-LOC & O & O & O & O & O & \textbf{B-ORG} & \textbf{I-ORG} & O & \textbf{B-ORG} & \textbf{I-ORG }& O & O & O. \\
\textbf{mBERT} & B-LOC & O & O & O & O & O & \color{red} B-LOC & \color{red} I-LOC & O & \color{red} B-LOC & \color{red} I-LOC & O & O & O. \\
\textbf{BERTeus} & B-LOC & O & O & O & O & O & \textbf{B-ORG} & \textbf{I-ORG} & O & \textbf{B-ORG} & \textbf{I-ORG} & O & O & O. \\
\end{tabular}}
\caption{An example from the EIEC corpus where the mBERT model misses to disambiguate the cases of metonymy. English translation of the sentence: \textit{``On that issue about Chechenya the support given by the United States and Western Europe has been visible.''}}
\label{tab:ner-examples}
\end{table*}

\section{Conclusions and future work}\label{sec:concl-future-work}

In this paper we show that a number of Basque monolingual models trained on our own corpora produce much better results than publicly available versions in downstream NLP tasks. More specifically, we build monolingual word embeddings using FastText, and pre-trained language models using Flair and BERT. All of them have been trained with the Basque Media Corpus (BMC), a 226M token corpus comprising Wikipedia and news outlets. The results on a broad range of NLP tasks, including topic classification, sentiment classification, PoS tagging and NER, show improvements in all tasks with respect to third-party models. The best results in all cases are obtained with our BERTeus model, with up to 10 absolute point increases with regard to the use of the official multilingual BERT, setting a new state-of-the-art in all four tasks.

It is important to note that our experiments have been performed training models which are analogous to the corresponding existing pre-trained models, instead of optimizing for best results in each of the tasks. This strategy allows to make a head-to-head comparison, and to conclude that the training corpus is key. In the case of BERT, training a monolingual model instead of a multilingual one shared with 103 other languages is also a key factor, so we can rule out that it is not changes in the models that cause the increased performance. In this sense, new variations of those models have reported better results \cite{liu2019roberta}. In the future, we plan to release models which try to keep up with the state-of-the-art results of the time.

Our results for Basque seem to indicate that, specially in the case of small languages, it is crucial to devote the necessary resources to gather large high-quality corpora and to train monolingual models on those corpora.

In order to facilitate reproducibility all benchmarks and models used in this work are publicly available. We also hope that these models will serve to improve the results of NLP applications for Basque, both at research centres and industry.

\section{Acknowledgments}

This work has been partially funded by the Basque Government (IXA excellence research group and Elesight, Hazitek grant no. ZL-2018/00787), the~Spanish Ministry of Science, Innovation and Universities (DeepReading RTI2018-096846-B-C21, MCIU/AEI/FEDER, UE) and by \textit{Ayudas Fundación BBVA a Equipos de Investigación Científica 2018} (BigKnowledge).  Jon Ander Campos enjoys a doctoral grant from the Spanish MECD. Ander Barrena enjoys a post-doctoral grant ESPDOC18/101 from the UPV/EHU. Rodrigo Agerri is funded by the Ramon y Cajal Fellowship RYC-2017-23647 and acknowledges the~support of the NVIDIA Corporation with the~donation of a Titan V GPU used for this research. We also acknowledge the support of Google Cloud.

\vspace{0.5cm}

\noindent \textbf{Author Contributions:} The first four authors (marked with $^{*}$) contributed equally to this work.

\section{Bibliographical References}

\bibliographystyle{lrec}

redpajama_set_name: RedPajamaArXiv


text:
Chico Torino, es una localidad uruguaya del departamento de Colonia.

Ubicación
La localidad se encuentra situada en la zona sureste del departamento de Colonia, entre los arroyos Sarandí Grande y Sarandí Chico. Se accede por camino vecinal desde las rutas 51 y 52. Dista 4 km de Colonia Valdense.

Población
Según el censo del año 2011 la localidad contaba con una población de 61 habitantes.

Referencias

Enlaces externos
Instituto Nacional de Estadística: Plano de la localidad de Chico Torino

Localidades de Colonia

redpajama_set_name: RedPajamaWikipedia


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text:
Q: Can I add message to the tqdm progressbar? When using the tqdm progress bar: can I add a message to the same line as the progress bar in a loop?
I tried using the "tqdm.write" option, but it adds a new line on every write. I would like each iteration to show a short message next to the bar, that will disappear in the next iteration. Is this possible?

A: You can use set_postfix to add values directly to the bar.
Example:
from tqdm import tqdm
pbar = tqdm(["a", "b", "c", "d"])
num_vowels = 0
for ichar in pbar:
    if ichar in ['a','e','i','o','u']:
        num_vowels += 1
    pbar.set_postfix({'num_vowels': num_vowels})

The postfix dictionary is integrated into the progress bar:
100%|███████████| 4/4 [00:11<00:00,  2.93s/it, num_vowels=1]

Instead of a dictionary, you can use set_postfix_str to just add a string to the end of the progress bar.

A: I personally find it much cleaner to use the with statement:
from tqdm import tqdm

with tqdm(['a','b','c']) as t:
  for c in t:
    t.set_description(f'{c}')

A: The example shown in Usage of tqdm works well for me.
pbar = tqdm(["a", "b", "c", "d"])
for char in pbar:
    pbar.set_description("Processing %s" % char)

Or alternatively, starting Python 3.8 which supports the walrus operator :=:
for char in (pbar := tqdm(["a", "b", "c", "d"])):
    pbar.set_description(f"Processing {char}")

A: Although all the answers here are correct, tqdm also provides a set_postfix_str method. The advantage over set_postfix is that you can pass your own formatted string in place of key value pairs. Also set_postfix sorts the key value pairs alphabetically. Here is an MWE.
from tqdm import tqdm
import numpy as np

loop_obj = tqdm(np.arange(10))

for i in loop_obj:
    loop_obj.set_description(f"Count: {i}")  # Adds text before progessbar
    loop_obj.set_postfix_str(f"Next count: {i+1}")  # Adds text after progressbar

A: You can change the description to show a small message before the progress bar, like this:
from tqdm import trange
from time import sleep
t = trange(100, desc='Bar desc', leave=True)
for i in t:
    t.set_description("Bar desc (file %i)" % i)
    t.refresh() # to show immediately the update
    sleep(0.01)

/EDIT: in the latest releases of tqdm, you can use t.set_description("text", refresh=True) (which is the default) and remove t.refresh() (thanks to Daniel for the tip).

A: Other answers focus on dynamic description, but for a static description you can add a desc argument into the tqdm function.
from tqdm import tqdm

x = [5]*1000
for _ in tqdm(x, desc="Example"):
    pass

Example: 100%|██████████████████████████████████| 1000/1000 [00:00<00:00, 1838800.53it/s]

A: I personally use .set_description() and a progression_bar assignment statement before the for loop:
from tqdm import tqdm

progression_bar = tqdm(["An", "iterable", "object"])
for element in (progression_bar):
    progression_bar.set_description("Processing « %s »" % str(element))

A: When using the tqdm progress bar in Python, there are different ways to define it, either by using the with statement or by assigning the progress bar to a variable with tqdm() function. In some cases, you may want to update the progress bar inside the loop without adding an extra indentation layer.
To achieve this, you can use the set_postfix() method to update the progress bar with the latest information. To avoid adding an extra indentation layer, you can define the progress bar outside of the loop and use it inside the loop. For example, you can use the enumerate() function to iterate over a list while keeping track of the index, and then pass this to the progress bar like this:
from tqdm import tqdm

object_list = ["a", "b", "c"]

pbar = tqdm(enumerate(object_list), descr="...")
for i, obj in pbar:
    ...
    pbar.set_postfix("foo")
    ...
    pbar.set_postfix("bar")

In this way, you define the tqdm progress bar before the loop, and then you can use the set_postfix() method to update the progress bar inside the loop without adding an extra indentation layer. By using the enumerate() function, you also have access to the current index, which you can pass to the set_postfix() method to display it in the progress bar.
This approach provides the flexibility to update the progress bar inside the loop while keeping the code clean and readable. This answer is based on the comments from @cbrnr and @tim-mb.

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text:
Outlawing New Houses in California
UCLA's most recent Anderson Forecast indicates that there has been a significant shift in demand in California toward condominiums and apartments. The Anderson Forecast concludes that this will cause problems, such as slower growth in construction employment because building multi-unit dwellings creates less employment than building the detached houses that predominate throughout California and most of the nation. The Anderson Forecast says that this will hurt inland areas (such as the Riverside-San Bernardino area and the San Joaquin Valley) because their economies are more dependent on construction than coastal areas, such as Los Angeles, the San Francisco Bay Area and San Diego.
Detached Housing Permits Remain Strong in the Historic Context: The Anderson Forecast reports that multi-unit building permits have recovered more quickly than building permits for detached housing. However, any such shift is likely to be highly volatile. Since the peak of the bubble, the distribution of building permits between detached and multi-unit in California has been on a roller coaster. Indeed the Anderson Forecast characterizes the "2010 US Census" as "showing a significant shift in demand toward condominiums and apartments." Actually, the 2010 US Census asked no question from which such a conclusion about housing types or any question from which such a conclusion could be drawn.
The trends in the building permit data are not completely clear. In 2005, the year before prices started to collapse, 75 percent of building permits in California were for detached housing. This trended downward, reaching a low of 52 percent in 2008. In 2009, the detached housing recovered to account for 73 percent of all housing building permits. Then the figure fell back to 59 percent in 2010.
With these erratic trends, it is tricky to forecast longer term market trends and consumer demand. Economic projections in 1934 would have suffered from a similar problem, as the Great Depression was continuing and no one could really tell when it would end. Today's continuing housing depression may be similar.
Moreover, as the Anderson Forecast notes, detached housing construction declined in the early 1980s, dropping to 42 percent in 1985. In fact, over the 25 years between 1960 and 1985, detached houses accounted for an average of only 54 percent of new housing construction in California, well below the 2010 figure of 59 percent (Figure 1).
Equally important, the condominium market remains in a deep depression. In 2010, less than four percent of houses built for sale in the United States were multi-unit buildings, including condominiums (Figure 2), as an increasing majority of multi-unit buildings have been built as rentals (Figure 3). Comparable California data is not available, but from the peak of the bubble (2006/7) to 2009, there was a loss of more than 3,000 owner occupied multi-unit dwellings with 10 or more units, while owner occupied detached houses increased by nearly 100,000 (Note 1).
If there is an intrinsic pent-up preference for condominium living, it is not evident in the poor performance of high-density developments even in such theoretically desirable places as Santa Monica, San Francisco, Oakland, San Jose and North Hollywood. Condominium prices, for example, have fallen 52 percent in the major California metropolitan areas, compared to 48 percent for single-family houses (Figure 4). Naïve developers, relying too much on the much promoted notion that suburban empty-nesters were chomping at the bit to move to new housing in the core area, often watched their empty units liquidated at $0.50 or less on the dollar or turned into rentals. Further, if people are moving to apartments, it's not for love of density but more likely due weakening economic circumstances.
Inland California Continues to Grow Faster: The Anderson Forecast also suggests that growth in interior California will suffer because "workers are less likely to move inland into an apartment and commute toward the coast." This assumption of slower inland growth reflects the conventional wisdom that areas outside the large coastal metropolitan areas have stopped growing since the burst of the housing bubble as people flock towards the coastal urban core (Note 2). The reality is different, as interior California and the peripheral metropolitan areas of the larger metropolitan regions (Note 3) continue to grow more strongly even in bad economic times. After the burst of the bubble, from 2008 to 2010 (Figure 5):
In the Los Angeles area, the adjacent Riverside-San Bernardino ("Inland Empire") and Oxnard metropolitan areas, combined, have grown at seven times the rate of the core Los Angeles metropolitan area.
In the San Francisco Bay area, the adjacent Napa, Santa Cruz, Santa Rosa and Vallejo metropolitan areas, combined, have grown nearly twice as quickly as the core San Francisco and San Jose metropolitan areas.
California's deep interior, the San Joaquin Valley has grown even faster than the exurban areas of Los Angeles and San Francisco.
One key reason: most people who move to interior areas do not commute toward the core. For example, less than 10 percent of workers in the Riverside-San Bernardino metropolitan area commute into Los Angeles County, a market share that declined 15 percent between 2000 and 2007. Many also simply cannot afford the higher cost of living in the coastal metropolitan areas, which likely will continue to retard growth in the core metropolitan areas.
The Policy Threat to New Houses : A survey by the Public Policy Institute of California suggests a vast preference (70%) for detached housing among the state's consumers. This continuing preference is demonstrated by detached housing prices that are generally two times historic norms relative to incomes in the coastal metropolitan areas (Los Angeles, San Francisco, San Diego and San Jose).
Yet now, this choice is under a concerted assault by both the state and many local governments, cheered on by most media and the academic community. For years, planning regulations have driven land prices so high that house prices have risen to well above the rest of the nation (Figure 5) under regulations referred to by terms such as "smart growth" and "urban containment." The regulations and the inevitably resulting speculation propelled a disproportionate rise (nearly $2 trillion) in California house prices compared to national norm. If California house prices had risen at the same rate relative to incomes as in more liberally regulated areas, the loss to financial markets could have been hundreds of billions of dollars less when the bubble burst (Figure 6).
Planning for Crowding and Density: California's assault on detached housing is taking on a distinctly religious fervor. The state's global warming law (Assembly Bill 32) and urban planning law (Senate Bill 375) is providing a new basis to impose draconian limits on the construction of detached housing. For example, in the San Francisco Bay area, it has been proposed that 97 percent of new housing be built within the existing urban footprint. That would mean an emphasis on multi-unit housing and little or no new housing on the urban fringe. The option of a single family home will be all but non-existent for even solidly middle income Californians.
Planning authorities in the Bay Area seem oblivious to the fact that destroying affordability also destroys growth, already evident by the state's poor economic performance and ebbing demographic vigor. Planners rosily project 2 million more people between 2010 and 2035 in the San Francisco Bay area. The growth rate over the past 10 years suggests a number less than half that (Figure 7) and given the rapid aging of the area, even this estimate may be too high. The planners also project more than 1.2 million new jobs, something difficult to believe given the more than 300,000 job loss (Note 4) that occurred in the Bay Area between 2000 and 2010 (Figure 8).
The Environmental "Fig Leaf:" The environmental justification for these policies is fragile . Research supporting higher density housing has routinely excluded the greater emissions from construction material extraction and production, building construction itself and common greenhouse gas emissions from energy consumption that does not appear on consumer bills. Further, higher densities are associated slower and more erratic speeds, which retards fuel efficiency and increases greenhouse gas emissions, a factor not sufficiently considered.
The report seems to ignore any other options besides rapid densification, which as McKinsey Global Institute has pointed out is not at all necessary to reduce GHG emission reductions. They point to other factors as more fuel efficient cars.
Oddly, the San Francisco Bay Area proposal does not even mention working at home (much of it telecommuting), the most environmentally friendly way of accessing employment. Working at home has grown six times the rate of transit since 2000 in the Bay Area.
Outlawing New Houses Detached housing remains the overwhelming choice of Californians. There is no indication that this preference is about to be replaced by a preference for high-density housing. Current and future middle class Californians could be corralled into more crowded conditions, because questionable planning doctrines mandate that detached housing should be outlawed.
1. Calculated from 2006, 2007 and 2009 American Community Survey data. The over ten unit category is used because is more generally reflective of the dense condominium development generally favored by densification advocates (Latest data available).
2. Another questionable tenet of conventional wisdom is that the price declines in the outer suburbs were greater than in the cores. When the price declines reached their nadir, core California markets were generally at least as depressed from their peak prices as suburban markets.
3. Metropolitan region refers to combined statistical areas, which have a core metropolitan area, such as the Los Angeles MSA and include surrounding metropolitan areas, such as the Riverside-San Bernardino MSA and the Oxnard MSA.
4. Annual, 2000 to 2010, calculated from California Economic Development Department data.
Lead photo: Houses in Los Angeles. Photograph by author.
Wendell Cox is a Visiting Professor, Conservatoire National des Arts et Metiers, Paris and the author of "War on the Dream: How Anti-Sprawl Policy Threatens the Quality of Life"
Largest World Cities: 2014
Largest Cities in the World: 2016
World Urban Areas Population and Density: A 2012 Update
Largest 1,000 Cities on Earth: World Urban Areas: 2015 Edition
The Costs of Smart Growth Revisited: A 40 Year Perspective

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Child Development Associates (CDAs) are skilled caregivers with demonstrated ability to work with newborn through five-year-old children, infant and toddler or preschool children and their families. Some are center-based caregivers, while others are family day care providers. If you are interested in becoming a CDA, Eastern Iowa Community Colleges can provide you with the required training. For more information, please contact Gale Roeder, Associate Director, Family & Consumer Sciences, Continuing Education, 101 West Third Street, Davenport, IA 52801 or call (563) 336-3444 or toll-free 1-800-850-5440. Essentials 2.0 for Child Development Associates: Working With Young Children (approximately $66) and CDA Assessment Packet ($34) are the textbooks used for all components of the CDA program.
College Credit is available through an articulation agreement with Scott Community College, Clinton Community College and Muscatine Community College. Students who have successfully completed the Child Development Associate Credential by completing their course work through the Eastern Iowa Community Colleges, Continuing Education Division, successfully completed the portfolio, work experience and Credentialing Examination may be eligible to receive up to 17 hours of college credit towards a degree at Scott Community College, Clinton Community College or Muscatine Community College.
A Child Development Associate (CDA) is a person who is able to meet the specific needs of children and who, with parents and other adults, works to nurture children's physical, social, emotional, and intellectual growth in a child development framework.
Eastern Iowa Community Colleges CDA program is for experienced child caregivers or those who want to enter the field. The program includes 120 hours of class work plus 480 hours of field experience, all designed to give you the background to provide quality, professional child care. When you complete the program, you will be eligible to take the CDA national credentialing assessment.
Eligibility: You must be at least 18 years old and have earned your high school diploma or GED/HSET.
Early childhood development is an emerging profession. Find out if you have what it takes to be part of it. Assess your aptitudes and interests and the career opportunities the early childhood profession provides.
10 hours. 1.0 CEUs for child care providers.
Class work covers basic development concepts, normal developmental characteristics of children, appropriate activities and expectations for children at various ages, observation and recording children's behavior.
20 hours. 2.0 CEUs for child care providers.
It is important to establish and maintain a safe, healthy learning environment for children in their early years. This class will show you how.
Acquire the knowledge and skills to support social and emotional development and provide positive guidance to children.
This class will help you acquire knowledge and skills you need to advance the physical, cognitive, communicative and creative competence of young children.
Acquire the knowledge and skills you need to establish positive and productive relationships with families of program participants.
This class will teach you how to maintain an effective, well-run, purposeful program which is responsive to participant needs.
Preparation for final assessment: parent questionnaires, resource files, personal interview with a council representative and review of materials for final assessment.
After you have completed the CDA training program, you are eligible to take the CDA national credentialing assessment, given by the Council for Early Childhood Professional Recognition.


Medicare lost a billion dollars a year over the last decade due to overbilling by doctors and other medical professionals, according to an investigation by The Center for Public Integrity.
Medical groups used higher rates that added at least $11 billion for treating elderly Medicare patients over the 10-year period under examination. The biggest culprit in the problem, accounting for $6.6 billion, is a practice known as "upcoding." Doctors and hospitals bill Medicare by applying a billing code to an office visit or a procedure. For example, a 99211 is a problem that requires five minutes or less of a doctor's time and can be billed at about $20. A 99215, on the other hand, which is billed at about $140, refers to a problem that demands a complex decision and takes 40 minutes or more of doctor's time. In upcoding, the doctors and hospitals bill for a visit or procedure that is higher than the one they performed, a violation that is difficult to monitor considering that Medicare deals with an average of one million billings a day.
In their defense, doctors said the fee hikes were justified because of the complexity of caring for seniors. They also told the Center for Public Integrity that some increases may have been a result of corrections to years of under-billing for these patients.
Some counties have particularly bad records. Santa Rosa County in western Florida led the country with 60% of its billings being for the two most expensive Medicare codes. In second place, at 57%, was neighboring Escambia County, which includes the city of Pensacola.
The biggest issue is the disparity of payments to different physicians. I am a primary care physician who spends time with his patients and have to borrow money every month to make payroll. While some physicians may get paid princely ammounts for endoscopies that take them 15 minutes, I get paid peanuts to treat a complex patient that may easily take an hour of work just with the patient. Not even taken into account the fact that I get phone calls from my patients at all hours of day and night including weekends and holidays. The payment to physicians should NOT be based on the number of minutes spent face to face with the patient but should include all the countless hours that one spends conferring with consultants, reviewing records and arguing with insurance companies. Physicians who have high paying specialties can hire any number of staff to deal with all the paperwork and myriad of administrative issues. Those of us who actually spend time with our patients and actually care for them, get paid next to nothing for the work and effort we put into our profession.
I just received a $3,000 bill for 2 visits to a new doctor. He did a couple of Ultrasounds, in the office, and a few other tests. I was SHOCKED at the $900 charges for an ultrasound that took only 10-15 minutes and had the equipment in his office. He did another on my corataid artery -- for what reason I do not know!! I came in for Stomach problems. He annouces he will do a FULL Workup!! NOT Necessary and I feel he has OVERBILLED! I am not going back and see that another doctor had billed $125 for a simple office visit that took less than 10 minutes!! It's Outrageous!! I will not be seeig a doctor for a long time -- May die of whatever, but they are sucking the life out of Medicare -- and we DO pay 20% -- of $3,000 - that more than I can afford a MOnth!!
OverBilling by Ohio Valley Medical Center, 2000 eoff street, wheeling, wv 26003-3823, Dr Feder, Billed my insurance company 3 times for the exact same test 3 times within a 1 month period. 3 Bloodtests for testosterone, the exact same test ordered me to take it three times, now i am stuck with a huge BILL my insurance company will not pay. Just imagine what they are doing to the Government if this is happening to me.
Good news! Only a billion dollars per annum? That's the amount Bush wasted in Iraq every, what, two days? Another way to think of it: One billion dollars averaged over a total of 350 million claims per year = $3 overage per bill, correct? Less than the price of a hospital aspirin. Pretty remarkable record, I'd say.


Christopher Batcheldor recalls how he couldn't tell Zainab Deen that firefighters would not be able to reach her and her son.
A firefighter sent to the Grenfell fire has recalled how he could not bring himself to tell a trapped woman that she and her son would not be rescued.
In a written statement submitted to the inquiry into the disaster, Christopher Batcheldor acknowledged that he lied to Zainab Deen and toddler Jeremiah, who were trapped on the 14th floor of the west London tower block.
Mr Batcheldor, a crew manager at Fulham fire station, spoke to Ms Deen, known as Zenay, after being passed a phone by her brother, Francis.
In his statement he relayed the pair's harrowing final moments, recalling hearing "ear-splitting" screams that confirmed to him "that was it".
Mr Batcheldor said he heard a little boy crying from the start of the call and said he was "gagging" to hear the door kicked down by firefighters.
Around 30 minutes into the conversation, the toddler stopped coughing and crying.
In his statement, Mr Batcheldor said: "Zenay was crying 'my boy's dead'. She said 'I want to be with my son'.
Mr Batcheldor learned half-way through the call, which went on for more than an hour, that his colleagues in the tower were not able to get above the 12th floor.
He said: "I knew I couldn't tell her this. I just couldn't tell her that so I basically lied to her and continued to tell her that we were coming for her.
"It got to a point where she wasn't talking much. I could hear a bit of coughing and spluttering. I could hear that she was still there but she wasn't responding.
"I kept on chatting to her. For Francis, I had to keep up the pretence that she was OK.
"When she stopped responding I could hear a little whimpering but I kept talking to her in case she could hear my voice."
Mr Batcheldor said there was then five to 10 minutes of silence, before he heard "ear-splitting" screams for around a minute as the flames "got to her".
He knew Ms Deen was dead, but could not face telling her brother.
He said: "I told him that we had got disconnected and perhaps her battery had gone. He asked me if she was out.
"I told him we (the fire brigade) were right there, trying to get to her."
He added: "Francis hugged me and thanked me. He said that Zenay would be really grateful.
"I knew that I had just lied to him."
Mr Batcheldor told the unit collating information from emergency calls made by trapped residents that Ms Deen and her son were both dead.
More than 70 people died in the June 2017 blaze.
The inquiry into the fire has heard its last day of evidence from firefighters and will now pause for a month.
Sir Martin Moore-Bick, head of the inquiry, said it would next meet on 3 September for a brief procedural hearing.


"International medical graduate" is used by Dr. Levey  as an attempt to decrease the negative stigmata associated with the term "foreign medical graduate." However, Dr. Levey's recommendation of evaluation and training solely for international medical graduates suggests a negative attitude.
Program directors must maintain the highest standards for internal medicine residents, and it would be more appropriate to re-evaluate the internal medicine postgraduate entrance process for all prospective residents. My experience as a foreign medical graduate in an internal medicine residency program, together with other studies , leads me to conclude that Dr. Levey's "pre"-postgraduate training evaluation would help to maintain the desired standards if it were applied to all entering residents.
Published: Ann Intern Med. 1993;118(5):396-398.


text:
Maisha Glover
Meet Maisha
Maisha Scott Glover is a senior vice president in our National Security business. She develops strategies that help clients drive transformation through the integration of advanced technology, mission resiliency support, and consultative expertise.
Maisha is an expert advisor on topics such as digital and analytics strategy, automation and the future of work, and diversity and workforce readiness. She has more than 25 years of experience providing the intelligence community, Department of Defense (DoD), and private sector clients with integrated, leading-edge solutions that drive transformation. A self-described lifelong learner, she applies the best technologies and talent innovation strategies from the private sector to rapidly evolving national security challenges and is passionate about mentoring future generations of diverse and inclusive national security leaders.
A proven public sector executive and advisor, Maisha previously held leadership roles at McKinsey & Company, SAIC, and Leidos. She also served in multiple roles as a policy and counterterrorism analyst for the U.S. government.
Maisha serves on the board of directors for the Intelligence and National Security Alliance, a nonpartisan organization that brings together public, private, and academic sectors of the intelligence and national security communities to develop practical and creative solutions to national security problems.
She holds bachelor's degrees in public administration and liberal arts from Kentucky State University and a J.D. from American University's Washington College of Law.
Meet Maisha Glover, Booz Allen's newest national security leader Washington Business Journal
Intelligence Industry Government Industry Public Service Industry
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https://www.boozallen.com/our-people/leadership/maisha-glover.html


text:
Kevin Wright
BFA '12
Writing for Film + TV
What I appreciate the most about my time in the Writing for Film + Television major is the fundamental understanding of storytelling the staff instilled in me. Not only was the writing process fully immersive and the class size intimate, but breaking down screenplays, novels and even graphic novels on a line-level prepared me for the industry in invaluable and unexpected ways. Being able to speak with authority and confidence in a story meetings is largely the byproduct of my UArts education and the foundation laid by the instructors."
Kevin R. Wright '12 is a creative producer of Production & Development at Marvel Studios located on the Disney Lot in Burbank California, where alongside his colleagues in the feature film division, he is responsible for creative oversight of projects on the studio's slate. Kevin is currently Co-Producing the recently announced Loki series for Disney's upcoming streaming service Disney+. He was Associate Producer on Ant-Man and The Wasp and previously served as the Sr. Manager of Production & Development on Doctor Strange, Black Panther and Avengers: Infinity War.
Explore Writing for Film + TV


Dermalogica Soothing Shave Cream will help you calm down and get close. For a super-close shave that won't leave skin feeling sore, apply this ultra-lubricating cream. It helps skin recover from damage and helps prevent aggravation brought on by shaving. Ideal for those who prefer to shave with a cream.
An ultra calming medium weight lubricating shave cream that reinforces skin's natural protective barrier and defends against razor burn.
Aloe Barbadensis Leaf Juice, Allantoin, Symphytum Officinale (Comfrey) Leaf Extract and Panthenol (Pro-Vitamin B5): soothing ingredients that aid in skin's post shave recovery process.
Tocopheryl Acetate (Vitamin E) and Sambucus Nigra (Elderberry) Flower Extract: skin smoothing antioxidants help defend against skin aging free radicals.
Simmondsia (Jojoba) Seed Oil and Butyropermum Parkii (Shea Butter) provide superior softening and conditioning.
Triticum Vulgare (Wheat) Germ Extract and Saccharomyces Cerevisiae (Yeast) Extract: soothing ingredients that combat irritation, reddening and mechanical peeling associated with shaving. Helps protect skin's natural barrier layer that is so often compromised during shaving.
Hyaluronic Acid: hydrates even the driest of skin conditions.
Menthol: imparts a cool, refreshing feeling on skin white calming.
Apply Dermalogica Soothing Shave Cream to clean, damp skin.
Can be applied alone or over Dermalogica Pre-Shave Guard.


text:
Amber Alert for 16-year-old Northeast Texas teen has been canceled
Amber Alert issued for 16-year-old Northeast Texas teen(Amber Alert issued for 16-year-old Northeast Texas teen)
By Brennon Gurley
Royse City, Texas (KLTV) - UPDATE: An Amber Alert issued for a missing teen from Northeast Texas Sunday morning has been canceled, according to Royse City Police.
Police confirmed that the juvenile was found unharmed in Somerville, TX. She is now in the custody of her father according to Royse City Police Department's Facebook page.
Further details have not been released.
An Amber Alert has been issued this morning for a missing teen from Northeast Texas believed to be in danger.
Authorities in Rockwall County are searching for 16-year-old Kylee Ann White in connection to her abduction.
According to officials, White was last seen at 500 block of Love Lane in Royse City, which is northeast of Dallas, around 7 a.m. on Saturday June 13th.
White is described as 5 feet, eight inches tall. She weighs about 120 pounds and has blonde hair with hazel eyes and pink braces.
Authorities in Rockwall County are searching for 16-year-old Kylee Ann White in connection to her abduction.(Texas Dept. of Public Safety)
Authorities believe White is in grave danger or immediate danger.
At this time the suspect in connection to her disappearance is unknown.
Anyone with information is asked to call the Rockwall County Sheriff's Office at (972) 204-7001.
Copyright 2020 KLTV. All rights reserved.
WebXtra: Tyler man competes on Wheel of Fortune
Vietnam War reflects on his service during VFW event Friday


In almost any geographic area with a dramatically sized metropolitan city, you will find dozens and maybe countless plastic surgeons. But what type is right for your surgery? Like any specialized kind of health care, you want someone who's both well qualified and has a fair quantity of experience in body contouring surgery.
Sometimes it is easy…you know someone who has had the plastic surgery that you want and has received a good experience…so you go to their plastic surgeon for a preliminary evaluation. Person to person is more often than not a good referral method. More often, however, you're much less fortunate and you have to take up a search on your own own. This is how to accomplish it.
First, only go to a board-certified plastic surgeon. This could sound territorial to some, but as it pertains to major body contouring surgery it's not. For something similar to Botox or laser light treatments and other minor cosmetic procedures, a number of so-called'cosmetic surgeons'may suffice and the outcomes may be only marginally different. But body contouring surgery is not trivial and requires plenty of training and experience from doing reconstructive procedures of the body. There is a huge difference between managing inadequate Botox and dealing with a postoperative wound separation of a leg lift. Only someone trained in the entire scope of cirujanos plasticos en bogota and reconstructive surgery has the information to cope with major surgery….and their potential complications. Seek out plastic surgeons in your area online. the state site of the American Society of Plastic Surgeons. Plastic surgeons listed there have to truly have the right training and board-certification to be a member.
Once you have found a handful of potential surgeons to see, visit their websites and view what they've to say and show about bariatric plastic surgery procedures. Although not having any info or results showing does not mean they do not have adequate experience, those cosmetic surgeons that certainly have a pastime in performing these kind of plastic surgery. Cross check their names at the internet site of the American Society of Bariatric Plastic Surgery, a pastime number of plastic surgeons who've or are sharing expertise in body contouring surgery. Today additionally there are a handful of young plastic surgeons that have received special training by completing a fellowship in bariatric plastic surgery beyond their basic plastic surgery residency.
Do as many consultations with different cosmetic surgeons as you are feeling necessary. Yes it will definitely cost to possess consultations but that is peanuts compared to the actual cost of surgery and the risks to that you simply is going to be exposed. The best plastic surgeon is often the one which makes you are feeling the absolute most comfortable and takes the full time and energy to spend time with you. Many patients are told to ask their prospective surgeon what their experience is and exactly how many they do monthly, year, etc. While there is nothing wrong with these questions, I don't discover how helpful these answers often are. Exactly how many operations do you have to accomplish to be proficient at it? For many plastic surgeons it could be just a couple every now and then, for others it many take many more. A more pertinent question is ask to see photos of results or talk to patients of the plastic surgeon who've had the process before several months. This is a more tangible question that can be quite informative. Lack of photos or an unwillingness to share them or put you into connection with former patients is more revealing when compared to a numbers question.


Don't have a cow dude! It's time to do The Bartman all over your set of keys with the new The Simpsons CRAP-TACULAR! Keychain Series. This collection has something for any fan of the residents of Springfield. Each keychain figure features a lobster clasp on a detachable ring and each add more than just a perfect piece of flare!
Collect all 18 to your wardrobe and show off your Springfield!
This is a single blind box. Please add 24 units to your cart for a full case of 24.


I have seen so many bloggers write on their pictures and I always wondered how they did it. Well I finally tried it! As you can see, I still need some practice on my handwriting on my tablet. This took me the longest time to do! I did it around 5 times because I kept trying to save it, and then I couldn't find it once it was saved. Lets just say I'm not very tech savvy. Well after 5 mini heart attacks here is a picture with my writing on it.
I wrote one of my favorite lyrics on this picture. It comes from "Walk Away" by Ben Harper. Great song.
Anyway, I hope you all are having a great weekend! I am in the process of moving so that is what my weekend and coming week will consist of. We all know the joys of moving. Wish me luck!
This is really cool. I think I will try it out on the iPad. By the way, thank you so much for your sweet comment on my blog. That's how I was able to find your amazing blog here. I love it! Your style awesome. I will now follow you on bloglovin'.


text:
Boris Johnson to become prime minister with 0.2% of the nation's vote
Mia Jankowicz
Published: 10:07 AM July 23, 2019 Updated: 6:15 PM September 17, 2020
Boris Johnson has been voted leader of the Conservative party and stands to take office as prime minister. Picture: Yui Mok/PA Wire - Credit: PA
Boris Johnson has been elected as leader of the Conservative party and is due to enter number 10 as Prime Minister.
He saw off 11 other candidates via a round of MPs' ballots before entering into a contest with Jeremy Hunt for party members' votes. Around 160,000 people were eligible to vote, which amounts to about 0.2% of the country.
He gathered 92,153 votes out of around 160,000 eligible votes.
On Brexit, Johnson has promised that he will ensure the UK leaves the EU by October 31 - and without a withdrawal agreement if it comes to it. Despite Johnson's calls for optimism about the matter, no EU leader has expressed any intention of renegotiating the withdrawal agreement without a serious alternative proposal.
Several ministers are expected to resign in response to Johnson's premiership.
Foreign office minister Sir Alan Duncan has already resigned, saying that the UK should be leading in Europe and focusing on domestic issues rather than toiling away under "the dark cloud of Brexit".
Justice secretary David Gauke, chancellor Philip Hammond, former leadership candidate and international development secretary Rory Stewart are all likely to follow suit. Meanwhile, vocally Brexit sceptic backbenchers Dominic Grieve and Justine Greening have indicated they are among those who would vote against the government if Johnson attempts to force through a no-deal Brexit.
READ: Sir Alan Duncan resignation letter bemoans 'the dark cloud of Brexit'Boris Johnson has long been enormously popular with his party membership, and confirmed this by gaining 66.4% of the memberships' votes. But is a divisive figure outside of that group, and his leadership campaign has been embroiled in controversy. Soon after he began the contest, an old column of his emerged where he decried the "arrogance" and"contempt" of Gordon Brown for taking over from Tony Blair in 2007 without a general election.
He then faced a private prosecution from Marcus Ball, who tried to take him to court for making misleading statements while in public office, over his endorsement of the "£350 million to the NHS" promise on the side of the Vote Leave campaign bus. The attempt was quashed after a judicial review.
He remained notoriously shy of scrutiny during the campaign period and was branded a "coward" by rival Hunt for refusing to take part in all but two televised debates.
Later, police were called to the home he shares with his partner, Carrie Symonds, after neighbours overheard a loud row, fearing for Symonds' safety. After initially denying they had been called out, police told the press that they were satisfied that all was well at the house.
Johnson also drew criticism for refusing to back up former ambassador to the USA, Sir Kim Darroch, when confidential diplomatic cables critical of Donald Trump were leaked to the Mail Online, enraging the thin-skinned president. Sir Kim said his subsequent resignation was motivated, in large part, by a lack of support from the Tory leadership favourite Johnson.
WATCH: Boris Johnson heckled by angry Conservative member at latest hustingsLabour MP David Lammy, who supports the campaign for a People's Vote on Brexit, said: "One theme runs through Boris Johnson's career like letters through a stick of rock: that he will say anything to get what he wants and cares little or nothing about the consequences when he lets people down."
On Wednesday, Theresa May will hold her final Prime Minister's Questions before formally offering her resignation to the Queen. Johnson will then visit the palace for the monarch's assent.
He is expected to appoint his cabinet on Thursday, which is also when the House of Commons will rise for a summer break. MPs are not currently expected to return until September 3.


text:
The Demand
Cal Lutheran biologist on 'Science Friday'
Monkey studies to fill part of live broadcast from KCLU
Anita Stone (center) has taken Cal Lutheran students, including recent graduates Jacob Jasper and Stephanie Straw, with her to study monkeys in Brazil since she joined the university's faculty two years ago.
(THOUSAND OAKS, Calif. – Sept. 25, 2018) A California Lutheran University biologist will discuss her field studies of squirrel monkeys in her native Brazil during an episode of the nationally distributed public radio show "Science Friday" to be broadcast live from KCLU in Thousand Oaks on Friday, Oct. 12.
"Science Friday" staff selected assistant professor of biology Anita Stone to be one of the scientists on the show, which airs from 11 a.m. to 1 p.m. Fridays on KCLU. Host Ira Flatow will interview her for about 20 minutes, during which listeners can call in or tweet their questions.
Stone has been studying the same population of the tiny Saimiri collinsi species of squirrel monkey living in the eastern Amazonia forests since 2002, when she spent a year in the field collecting data for her doctorate in ecology and evolutionary biology. She has returned for about two months every summer to monitor their behavior, taking Cal Lutheran students with her since she joined the university's faculty two years ago.
Distinguishing the monkeys from each other can be difficult because they weigh no more than two pounds, move quickly and hang out in social groups of about 50. Stone's team developed a new method of trapping the smart monkeys a few years ago so she could mark some of them with collars and microchips and get blood samples. She has discovered many intriguing behaviors, such as the males remaining on the outer corners of the group except during mating season. Females dominate the males, and even juveniles bully males.
"Science Friday" draws more than 2 million listeners throughout the world each week with its lively and informative discussions about science, technology, health, space and the environment. It started as a program within "Talk of the Nation" in 1991 and spun off as its own series in 2013. Flatow's many honors include the 2012 Isaac Asimov Award, the 2010 Nierenberg Prize and the 2007 National Science Teachers Association Faraday Science Communicator Award.
KCLU is presenting a special performance featuring Flatow and Southern California scientists on Oct. 13 at the Civic Arts Plaza in Thousand Oaks. Students and other representatives from Cal Lutheran and other area schools will present science demonstrations and information at tables outside the event. All of the tickets have already been distributed to KCLU donors, Cal Lutheran students and faculty, and other representatives from the schools and science community in the area, but the public can hear portions of the event during the Oct. 19 episode of "Science Friday."
A community service of Cal Lutheran, KCLU provides NPR and local news programming to 130,000 weekly listeners in Ventura County at 88.3 FM, southern Santa Barbara County at 1340 AM and 102.3 FM, Santa Maria at 89.7 FM, San Luis Obispo at 92.1 and online at KCLU.org.
Biographer of the stars
Austin Truong
Derek Helton, '96 M.D., F.A.C.P.
Karen M. Olsen '83
Generous Cal Lutheran donor dies
Cal Lutheran students to present research
Cal Lutheran students research at CERN
Kacey Egusa '18
Kelly R. Culwell '95, MD MPH
With your help, we can make the Swenson Science Center a reality for Cal Lutheran students and send well-prepared graduates out into the world.
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Discussion in 'Gunsmithing' started by zgardner, Jan 15, 2013.
Has anyone replaced a stock on either of these two guns?
I was looking into buying on of these to rifles stock and then replacing the stock with something like a Mcmillan A3 of A# sporter.
Please let me know what possiblities there are?
I just replace the original stock on my 110 (22-250) with a Boyds thumbhole. I pillar bedded it and used Devcon plastic steel. It took my loads that shot 1/2moa or better and cut em in half. With the original stock I always had muzzle jump that would take my sights completely off target, the new stock put a stop to that, at 300yrds my cross hairs never leave the target, and now felt recoil is more along the lines of my 10-22, not that an 11lb 22-250 has much recoil to begin with. But it is a completely different rifle now.
So, it was no problem replacing a stock in model 110.
I have a stock from stockade. It's the Predator/Tactical stock. Sort of a Mcmillan A5 Clone. I got the money saver and did the finish work myself and got the Detachable mag bottum metal and mag for like 550 bucks to my door. Bit of a wait. I bedded it with Devcon myself. Really happy with it.
Perfect. I like the Stockade stocks.
I think I'm settling in on 111 Long Range Hunter and putting it in the tactical stock.
Do they cut the stock to fit the exact model? Making sure the barrel channel isn't to small over extremely to big.
If you have them paint the stock does the webbing and texture come out like a Weatherby finish?
I would call stockade. Be advised the wait time was around four months when I ordered mine. MY rifle is a franken rifle so I called and had numerous conversations with the owner. Fit and finish was very good. The money safer saves you around $150 bucks and it only took me a couple hours to sand and puddy the stock.
Also if you can get over the way they look (I actually like the look of the chassis) a Mcree or XLR chassis is a good option. Again you are limited by your imagination and wallet not aftermarket offering for savage rifles.
To be clear do you already own the rifle or are you about to buy the rifle? If you dont already have it I would check out the classifieds you can safe time in money by buying a rifle that's already been moded.
I don't own the rifle. Have a guy that i can get one bought brand new for around $600 to $700.
I like the ballistics of this caliber and figured this was a decent buy that I could just swap the stocks and go.


We are delighted to have Brymore Productions in the Museum.
In sock drawers, notebooks, and the minds of people just like you, are tales of significant importance, waiting for discovery. The inspired inspectors who uncover these fables are members of an elite unit known as the Bureau of Untold Stories, and these are the stories you've shared.
Unravel the hidden stories of the audience with our inspectors as they utilise live sound effects, improvisation and their unconventional storytelling skills to immerse you into the excitement of an old radio mystery.
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Free event, No need to Book - Just Drop-in.


John Brown Media has been selected by Total South Africa, the multinational oil and gas company, as the publisher of their staff magazine, @Total, for southern Africa.
Ravi Bhaya, ex MD of Starcom MediaVest Group (SMG) Indonesia, has been appointed as the new MD for SMG SA.
It was first-to-market when it launched in July 2011, when the financial group opened up smartphone sales to its customers.
The station have put together a winning team of well-known DJs.
Early next year, the company plans to introduce Reuters.TV, an ad-supported digital service that allows subscribers to receive personalised video content created solely for the platform.


Sophomores Katie Brown and Ziad Kamil attend a protest against gun violence as part of the new Youth DIrect Club. The club offers many opportunities for students to get involved.
The Youth Direct Club aims to increase student activism at the school and educate students on important social and political issues. While it was created partway through the school year, the club has already been involved in several demonstrations and offers students a way to find out about events and issues that interest them. It was formed earlier this year by sophomore Ziad Kamil, who wanted to help students at the school become more outspoken and active in politics.
"I created the club because I feel that many students at W-L aren't politically active, and I wanted to introduce them to political actions," Kamil said.
Club members decide on events during meetings, then go to protests or demonstrations on these issues. Students in Youth Direct can then decide whether or not issue interests them enough to participate. "The club itself is a way for students to be informed on actions from the political left or right that are happening in the DMV area," Kamil said.
It can be difficult for students to initially get involved in political activism. Some want to participate with a group, and others have difficulty finding an event to start with. The Youth Direct Club offers students a way to get involved and become an activist for what they believe in. "I feel the most difficult part of starting to go to these actions is finding people to go with, or not being comfortable being alone while at the actions," Kamil said.
Students get involved for a variety of reasons. Some feel strongly about being politically active and trying to make a difference. "I decided to become involved mostly based on my personal beliefs, but also because I felt that as an Arlington resident, I need to utilize the proximity to the U.S. government that we are so lucky to have," Trudo said.
Two students were arrested for unlawful protesting during the event outside Senator McConnell's office. "No charges have been dealt yet, though, as [the students] were put in a delinquency program, then reintroduced into the court system with the recommendation of not being prosecuted," Kamil said.
Despite punishments or setbacks during their protesting and other work, the school's student activists are still working towards their goals. Even sophomore Katie Brown, who was one of the students arrested during the protest at Senator McConnell's office, is still determined to help create change. "[Being arrested] was temporarily discouraging, but overall I'm still active in trying to fight gun violence, so it didn't hold me back too long," Brown said.
As the country's political divide has increased, activism from young people and students has become more and more common. The survivors of February's school shooting in Parkland, have become fixtures in the national debate over gun control. Students across the country staged walkouts to support the cause, and the March for Our Lives saw more than 200,000 people descend on Washington, D.C. to call for stricter gun control and efforts to put an end to American gun violence.


text:
import itertools
import numpy
import renju

POS_TO_LETTER = 'abcdefghjklmnop'
LETTER_TO_POS = {letter: pos for pos, letter in enumerate(POS_TO_LETTER)}

def to_move(pos):
    return POS_TO_LETTER[pos[1]] + str(pos[0] + 1)

def to_pos(move):
    return int(move[1:]) - 1, LETTER_TO_POS[move[0]]

def list_positions(board, player):
    return numpy.vstack(numpy.nonzero(board == player)).T

def sequence_length(board, I, J, value):
    length = 0

    for i, j in zip(I, J):
        if board[i, j] != value:
            break
        length += 1

    return length

def check_horizontal(board, pos):
    player = board[pos]
    if not player:
        return False

    i, j = pos
    length = 1

    length += sequence_length(
        board,
        itertools.repeat(i),
        range(j + 1, min(j + renju.Game.line_length, renju.Game.height)),
        player
    )

    length += sequence_length(
        board,
        itertools.repeat(i),
        range(j - 1, max(j - renju.Game.line_length, -1), -1),
        player
    )

    return length > renju.Game.line_length

def check_vertical(board, pos):
    player = board[pos]
    if not player:
        return False

    i, j = pos
    length = 1

    length += sequence_length(
        board,
        range(i + 1, min(i + renju.Game.line_length, renju.Game.width)),
        itertools.repeat(j),
        player
    )

    length += sequence_length(
        board,
        range(i - 1, max(i - renju.Game.line_length, -1), -1),
        itertools.repeat(j),
        player
    )

    return length >= renju.Game.line_length

def check_main_diagonal(board, pos):
    player = board[pos]
    if not player:
        return False

    i, j = pos
    length = 1

    length += sequence_length(
        board,
        range(i + 1, min(i + renju.Game.line_length, renju.Game.width)),
        range(j + 1, min(j + renju.Game.line_length, renju.Game.height)),
        player
    )

    length += sequence_length(
        board,
        range(i - 1, max(i - renju.Game.line_length, -1), -1),
        range(j - 1, max(j - renju.Game.line_length, -1), -1),
        player
    )

    return length >= renju.Game.line_length

def check_side_diagonal(board, pos):
    player = board[pos]
    if not player:
        return False

    i, j = pos
    length = 1

    length += sequence_length(
        board,
        range(i - 1, max(i - renju.Game.line_length, -1), -1),
        range(j + 1, min(j + renju.Game.line_length, renju.Game.height)),
        player
    )

    length += sequence_length(
        board,
        range(i + 1, min(i + renju.Game.line_length, renju.Game.width)),
        range(j - 1, max(j - renju.Game.line_length, -1), -1),
        player
    )

    return length >= renju.Game.line_length

def check(board, pos):
    if not board[pos]:
        return False

    return check_vertical(board, pos) \
        or check_horizontal(board, pos) \
        or check_main_diagonal(board, pos) \
        or check_side_diagonal(board, pos)

redpajama_set_name: RedPajamaGithub


Calling all cheese cake lovers!! Finally the long awaited and highly anticipated Pablo Cheese Tart has opened in Jakarta, Indonesia on 17th November 2016 in Gandaria City Mall, 8th March 2017 in NEO Soho Mall, and 8th December 2017 in Mall Kelapa Gading 5.
Originated from Osaka, Japan, the popularity of Pablo Cheese tart has reached South Korea, Taiwan, Thailand, Philippine and finally Indonesia. AnakJajan recently wrote a review about Pablo Cheese Tart in Osaka and Tokyo https://anakjajan.com/2016/09/14/pablo-cheese-tart/, and judging from the positive feedbacks, we can tell that many of you are very excited about it.
Luckily AnakJajan had the privilege to try it before the opening day so we can share it a complete info about the menu for you. The 1st outlet in Gandaria City has a several seating area so besides taking out, they also can enjoy the freshly baked products there.
AnakJajan are glad that Pablo Cheese Tart Indonesia immediately offers many variety of products on its first outlet in Gandaria City, other than the signature Cheese Tart you can also find Matcha Cheese Tart and Chocolate Cheese Tart as well as Hokkaido Mille Crepe, Mini Pablo and Frute Smoothies.
Unlike any other cheese tart or cake out there, freshly baked Pablo's Cheese Tart has a very unique smooth yet delicate texture, the baked cheese custard itself has a very light taste, not overly cheesy and not overly sweet, and that makes it special.
This is the signature and best selling item in Pablo, crunchy flaky crust filled with cheese custard then baked till perfection and delicately glazed with apricot jam.
In Japan, you can choose how well cooked you want your cheese tart to be from rare to medium, but for all oversea outlets, Pablo only serve medium rare version. Well that explains why it tasted somehow different from our experience in Japan, because we always have rare version with more molten filling.
The matcha version is quite lovely with great aroma of green tea and inside the tart there are azuki or red bean and shiratama or glutinous rice mochi for element of surprise.
Inside the soft delicate chocolate cheese custard, you will get a nice bitter sweet chocolate chips.
You can also find Pablo Mini available in 3 flavors. The mini bite size tart has a more dense yet still soft texture and stronger cheese taste, me and Mr. Jajan quite like it.
Soft serve ice cream is available in two different flavors, Matcha or Cheese. It has a nice smooth soft creamy texture but the taste of cheese was too subtle, more likely to milk soft cream compared to the one we had in Osaka.
Frute is the signature smoothie drink here, there are 5 flavors available and AnakJajan opted for the two of the most recommended flavors, Classic Cheese Tart and Matcha Shiratama.
Classic Cheese Tart Frute basically is the drink version of Pablo's original cheese tart with the blend of apricot and fresh cream cheese. It tasted pretty nice, not overly sweet with the right level of cheesiness and creaminess thanks to the tangy flavor from apricot.
The matcha or green tea version is also quite delightful with matcha jelly and shiratama topped with unique red bean/ ogura whipped cream.
Overall, AnakJajan are glad that finally one of our favorite brands from Japan has opened a branch in Indonesia, now we don't need to fly to Japan anymore whenever we crave for the heavenly cheese tart by Pablo. Let's Jajan!!
Sudah halal kah pablo cheese tart?
kalau disimpan di kulkas semalem (mau dibawa keluar kota soalnya), kira2 masih lumayan bagus enggak ya?
Hallo, ini diameternya brp ya ukurannya?