text:
\section{Introduction}
Fake news represents the press that is used to spread false information and hoaxes through conventional platforms as well as online ones, mainly social media. There has been an increasing interest in fake news on social media due to the political climate prevailing in the modern world \cite{Tucker et al.,Calvillo et al.,Monti et al.}, as well as several other factors. Detecting misinformation on social media is as important as it is technically challenging. The difficulty is partly due to the fact that even humans cannot accurately distinguish false from true news, mainly because it involves tedious evidence collection as well as careful fact checking. With the advent of technology and ever-increasing propagation of fake articles in social media, it has become really important to come up with automated frameworks for fake news identification. In this paper, we describe our system which performs a binary classification on tweets from social media and classifies it into "real" or "fake". We have used transfer learning in our approach as it has proven to be extremely effective in text classification tasks, with a reduced training time as we do not need to train each model from scratch. The primary steps for our approach initially include text preprocessing, tokenization, model prediction, and ensemble creation using a soft voting schema. Post evaluation, we have drastically improved our fake news detection framework with a heuristic post-processing technique that takes into account the effect of important aspects of tweets like username handles and URL domains. This approach has allowed us to produce much superior results when compared to the top entry in the official leaderboard. We have performed an ablation study of the various attributes used in our post-processing approach. We have also provided examples of tweets where the post-processing approach has predicted correctly when compared to the initial classification output.

\section{Related Work}
Traditional machine learning approaches have been quite successful in fake news identification problems. Reis et al. \cite{Reis et al.} has used feature engineering to generate hand-crafted features like syntactic features, semantic features etc.
The problem was then approached as a binary classification problem where these features were fed into conventional Machine Learning classifiers like K-Nearest Neighbor (KNN), Random Forest (RF), Naive Bayes, Support Vector Machine (SVM) and XGBOOST (XGB), where RF and XGB yielded results that were quite favourable.
Shu et al. \cite{Shu et al.} have proposed a novel framework TriFN, which provides a principled way to model tri-relationship among publishers, news pieces, and users simultaneously. This framework significantly outperformed the baseline Machine Learning models as well as erstwhile state-of-the-art frameworks. With the advent of deep learning, there has been a significant revolution in the field of text classification, and thereby in fake news detection. Karimi et al. \cite{Karimi et al.} has proposed a Multi-Source Multi-class Fake News Detection framework that can do automatic feature extraction using Convolution Neural Network (CNN) based models and combine these features coming from multiple sources using an attention mechanism, which has produced much better results than previous approaches that involved hand-crafted features. Zhang et al. \cite{Zhang et al.} introduced a new diffusive unit model, namely Gated Diffusive Unit (GDU), that has been used to build a deep diffusive network model to learn the representations of news articles, creators and subjects simultaneously.
Ruchansky et al. \cite{Ruchansky et al.} has proposed a novel CSI(Capture-Score-Integrate) framework that uses an Long Short-term Memory (LSTM) network to capture the temporal spacing of user activity and a doc2vec \cite{doc2vec} representation of a tweet, along with a neural network based user scoring module to classify the tweet as real or fake. It emphasizes the value of incorporating all three powerful characteristics in the detection of fake news: the tweet content, user source, and article response.
Monti et al. \cite{Monti et al.} has shown that social network structure and propagation are important features for fake news detection by implementing  a geometric deep learning framework using Graph Convolutional Networks.

\textbf{Language models:} Most of the current state-of-the-art language models are based on Transformer\cite{Vaswani et al.} and they have proven to be highly effective in text classification problems. They provide superior results when compared to previous state-of-the-art approaches using techniques like Bi-directional LSTM, Gated Recurrent Unit (GRU) based models etc. The models are trained on a huge corpus of data. The introduction of the BERT \cite{Devlin et al.} architecture has transformed the capability of transfer learning in Natural Language Processing. It has been able to achieve state-of-the art results on downstream tasks like text classification. RoBERTa \cite{Liu et al.} is an improved version of the BERT model. It is derived from BERT's language-masking strategy, modifying its key hyperparameters, including removing BERT's next-sentence pre-training objective, and training with much larger mini-batches and learning rates, leading to improved performance on downstream tasks. XLNet \cite{Yang et al.} is a generalized auto-regressive language method. It calculates the joint probability of a sequence of tokens based on the transformer architecture having recurrence. Its training objective is to calculate the probability of a word token conditioned on all permutations of word tokens in a sentence, hence capturing a bidirectional context. XLM-RoBERTa \cite{Conneau et al.} is a transformer \cite{Vaswani et al.} based language model relying on Masked Language Model Objective. DeBERTa \cite{He et al.} provides an improvement over the BERT and RoBERTa models using two novel techniques; first, the disentangled attention mechanism, where each word is represented using two vectors that encode its content and position, respectively, and the attention weights among words are computed using disentangled matrices on their contents and relative positions, and second, the output softmax layer is replaced by an enhanced mask decoder to predict the masked tokens pre-training the model. ELECTRA \cite{Clark et al.} is used for self-supervised language representation learning. It can be used to pre-train transformer networks using very low compute, and is trained to distinguish "real" input tokens vs "fake" input tokens, such as tokens produced by artificial neural networks. ERNIE 2.0 \cite{Sun et al.} is a continual pre-training framework to continuously gain improvement on knowledge integration through multi-task learning, enabling it to learn various lexical, syntactic and semantic information through massive data much better.

\section{Dataset Description}
The dataset \cite{Patwa et al.} for CONSTRAINT COVID-19 Fake News Detection in English challenge was provided by the organizers on the competition website\footnote{https://competitions.codalab.org/competitions/26655}. It consists of data that have been collected from various social media and fact checking websites, and the veracity of each post has been verified manually. The "real" news items were collected from verified sources which give useful information about COVID-19, while the "fake" ones were collected from tweets, posts and articles which make speculations about COVID-19 that are verified to be false. The original dataset contains 10,700 social media news items, the vocabulary size (i.e., unique words) of which is 37,505 with 5141 words in common to both fake and real news. It is class-wise balanced with 52.34\% of the samples consisting of real news, and 47.66\% of fake samples. These are 880 unique username handle and 210 unique URL domains in the data.

\section{Methodology}
We have approached this task as a text classification problem. Each news item needs to be classified into two distinct categories: "real" or "fake". Our proposed method consists of five main parts: (a) Text Preprocessing, (b) Tokenization, (c) Backbone Model Architectures, (d) Ensemble, and (e) Heuristic Post Processing. The overall architecture of our system is shown in Figure-\ref{initial_process_diag_fig}. More detailed description is given in the following subsections.

\subsection{Text Preprocessing}
Some social media items, like tweets, are mostly written in colloquial language. Also, they contain various other information like usernames, URLs, emojis, etc. We have filtered out such attributes from the given data as a basic preprocessing step, before feeding it into the ensemble model. We have used the tweet-preprocessor\footnote{pypi.org/project/tweet-preprocessor/} library from Python to filter out such noisy information from tweets.

\subsection{Tokenization}
During tokenization, each sentence is broken down into tokens before being fed into a model. We have used a variety of tokenization approaches\footnote{huggingface.co/docs/tokenizers/python/latest/} depending upon the pre-trained model that we have used, as each model expects tokens to be structured in a particular manner, including the presence of model-specific special tokens. Each model also has its corresponding vocabulary associated with its tokenizer, trained on a large corpus data like GLUE, wikitext-103, CommonCrawl data etc. During training, each model applies the tokenization technique with its corresponding vocabulary on our tweets data.
We have used a combination of XLNet \cite{Yang et al.}, RoBERTa \cite{Liu et al.}, XLM-RoBERTa \cite{Conneau et al.}, DeBERTa \cite{He et al.}, ERNIE 2.0 \cite{Sun et al.} and ELECTRA \cite{Clark et al.} models and have accordingly used the corresponding tokenizers from the base version of their pre-trained models.

\subsection{Backbone Model Architectures}
We have used a variety of pre-trained language models\footnote{huggingface.co/models} as backbone models for text classification. For each model, an additional fully connected layer is added to its respective encoder sub-network to obtain prediction probabilities for each class- ``real" and ``fake" as a prediction vector. We have used transfer learning in our approach in this problem. Each model has used some pre-trained model weights as initial weights. Thereafter, it fine-tunes the model weights using the tokenized training data. The same tokenizer is used to tokenize the test data and the fine-tuned model checkpoint is used to obtain predictions during inference.

\iffalse
\subsubsection{XLM-RoBERTa : }
XLM-RoBERTa \cite{Conneau et al.} is a transformer \cite{Vaswani et al.} based language model relying on Masked Language Model Objective, and is capable of processing text from 100 separate languages.

\subsubsection{RoBERTa : }
RoBERTa \cite{Liu et al.} is an improved version of the BERT \cite{Devlin et al.} model. It is derived from BERT's language-masking strategy, modifying its key hyperparameters, including removing BERT's next-sentence pre-training objective, and training with much larger mini-batches and learning rates, leading to improved performance on downstream tasks.

\subsubsection{XLNet : }
XLNet \cite{Yang et al.} is a generalized auto-regressive language method. It calculates the joint probability of a sequence of tokens based on the transformer architecture having recurrence. Its training objective is to calculate the probability of a word token conditioned on all permutations of word tokens in a sentence, hence capturing a bidirectional context.

\subsubsection{DeBERTa : }
DeBERTa \cite{He et al.} provides an improvement over the BERT and RoBERTa models using two novel techniques; first, the disentangled attention mechanism, where each word is represented using two vectors that encode its content and position, respectively, and the attention weights among words are computed using disentangled matrices on their contents and relative positions, and second, the output softmax layer is replaced by an enhanced mask decoder to predict the masked tokens pre-training the model.

\subsubsection{ELECTRA : }
ELECTRA \cite{Clark et al.} is used for self-supervised language representation learning. It can be used to pre-train transformer networks using very low compute, and is trained to distinguish ``real" input tokens vs "fake" input tokens, such as tokens produced by artificial neural networks.

\subsubsection{ERNIE 2.0 : }
ERNIE 2.0 \cite{Sun et al.} is a continual pre-training framework to continuously gain improvement on knowledge integration through multi-task learning, enabling it to learn various lexical, syntactic and semantic information through massive data much better.
\fi

\subsection{Ensemble}
In this method, we use the model prediction vectors from the different models to obtain our final classification result, i.e. "real" or "fake". To balance an individual model's limitations, an ensemble method can be useful for a collection of similarly well-performing models. We have experimented with two approaches: soft voting and hard voting, that are described in the following figure:

\begin{figure}
\centering
\includegraphics[width=\textwidth]{System_Architecture.png}
\caption{Fake News Identification Initial Process Block Diagram}
\label{initial_process_diag_fig}
\end{figure}

\subsubsection{Soft Voting : }
In this approach, we calculate a "soft probability score" for each class by averaging out the prediction probabilities of various models for that class. The class that has a higher average probability value is selected as the final prediction class.
Probability for ``real" class, $P^r(x)$ and probability for ``fake" class , $P^f(x)$ for a tweet $x$ is given by,
\iffalse
\begin{equation}\label{equ1}
   P(r) = \sum_{i=1}^{n}\frac{P(model^{real}_i)}{n}
\end{equation}
\begin{equation}\label{equ2}
   P(f) = \sum_{i=1}^{n}\frac{P(model^{fake}_i)}{n}
\end{equation}
\fi
\begin{equation}\label{equ1}
    P^r(x) = \sum_{i=1}^{n}\frac{P^r_i(x)}{n}
\end{equation}
\begin{equation}\label{equ2}
   P^f(x) = \sum_{i=1}^{n}\frac{P^f_i(x)}{n}
\end{equation}
where $P^r_i(x)$ and $P^f_i(x)$ are ``real" and ``fake" probabilities by the $i$-th model and $n$ is the total number of models.
\subsubsection{Hard Voting : }
In this approach, the predicted class label for a news item is the class label that represents the majority of the class labels predicted by each individual model. In other words, the class with the most number of votes is selected as the final prediction class. Votes for ``real" class, $V^r(x)$ and Votes for ``fake" class , $V^f(x)$ for a tweet $x$ is given by,
\begin{equation}\label{equ3}
   V^r(x) = \sum_{i=1}^{n}{I(P^r_i(x) \geq P^f_i(x))}
\end{equation}
\begin{equation}\label{equ4}
   V^f(x) = \sum_{i=1}^{n}{I(P^r_i(x) < P^f_i(x))}
\end{equation}
where the value of $I(a)$ is $1$ if condition $a$ is satisfied and $0$ otherwise.
\subsection{Heuristic Post-Processing}
In this approach, we have augmented our original framework with a heuristic approach that can take into account the effect of username handles and URL domains present in some data, like tweets. This approach works well for data having URL domains and username handles; we rely only on ensemble model predictions for texts lacking these attributes. We create a new feature-set using these attributes. Our basic intuition is that username handles and URL domains are very important aspects of a tweet and they can convey reliable information regarding the genuineness of tweets. We have tried to incorporate the effect of these attributes along with our original ensemble model predictions by calculating probability vectors corresponding to both of them. We have used information about the frequency of each class for each of these attributes in the training set to compute these vectors. In our experiments, we observed that Soft-voting works better than Hard-voting. Hence our post-processing step takes Soft-voting prediction vectors into account. The steps taken in this approach are described as follows:

\begin{itemize}
    \item   First, we obtain the class-wise probability from the best performing ensemble model. These probability values form two features of our new feature-set.

    \item We collect username handles from all the news items in our training data, and calculate how many times the ground truth is "real" or "fake" for each username.

    \item   We calculate the conditional probability of a particular username indicating a real news item, which is represented as follows:

        \begin{equation}\label{equ3}
            P^r(x|username) = \frac{n(A)}{n(A) + n(B)}
        \end{equation}

    where n(A) = number of "real" news items containing the username and  n(B) = number of "fake" news items containing the username. Similarly, the conditional probability of a particular username indicating a fake news item is given by,

        \begin{equation}\label{equ4}
            P^f(x|username) = \frac{n(B)}{n(A) + n(B)}
        \end{equation}

        We obtain two probability vectors that form four additional features of our new dataset.

\begin{figure}[b]
\centering
\includegraphics[width=\textwidth]{Post_Processing_System_Architecture.png}
\caption{Fake News Identification Post Process Block Diagram}
\label{post_process_diag_fig}
\end{figure}

\item  We collect URL domains from all the news items in our training data, obtained by expanding the shorthand URLs associated with the tweets, and calculate how many times the ground truth is "real" or "fake" for each domain.

\item  We calculate the conditional probability of a particular URL domain indicating a real news item, which is represented as follows:

        \begin{equation}\label{equ3}
            P^r(x|domain) = \frac{n(P)}{n(P) + n(Q)}
        \end{equation}

    where n(P) = number of "real" news items containing the domain and  n(Q) = number of "fake" news items containing the domain. Similarly, the conditional probability of a particular domain indicating a fake news item is given by,
        \begin{equation}\label{equ4}
            P^f(x|domain) = \frac{n(Q)}{n(P) + n(Q)}
        \end{equation}
            We obtain two probability vectors that form the final two additional features of our new dataset.

\item  In case there are multiple username handles and URL domains in a sentence, the final probability vectors are obtained by averaging out the vectors of the individual attributes.

\item   At this point, we have new training, validation and test feature-sets obtained using class-wise probability vectors from ensemble model outputs as well as probability values obtained using username handles and URLs from the training data. We use a novel heuristic algorithm on this resulting feature set to obtain our final class predictions.

\end{itemize}
Table \ref{username domain examples} shows some samples of the conditional probability values of each label class given each of the two attributes, URL domain and username handle. We have also shown the frequency of those attributes in the training data.
\begin{table}
\centering
\caption{Few Examples on URL Domain-name and Username attribute distribution data }
\label{username domain examples}
\resizebox{\textwidth}{!}{%
\begin{tabular}{|c|c|c|c|l|c|c|c|c|}
\cline{1-4}\cline{6-9}
\multicolumn{4}{|c|}{Example of URL Domain Name Prob. Dist.} & ~ ~ & \multicolumn{4}{c|}{Example of UserName Prob. Dist.} \\
\cline{1-4}\cline{6-9}
\begin{tabular}[c]{@{}c@{}}URL Domain \\Name \end{tabular} & \begin{tabular}[c]{@{}c@{}}$P^r(x|domain)$ \end{tabular} & \begin{tabular}[c]{@{}c@{}}$P^f(x|domain)$ \end{tabular} & \begin{tabular}[c]{@{}c@{}}Frequency \end{tabular} & \multirow{6}{*}{} & UserName & \begin{tabular}[c]{@{}c@{}}$P^r(x|username)$ \end{tabular} & \begin{tabular}[c]{@{}c@{}}$P^f(x|username)$ \end{tabular} & \begin{tabular}[c]{@{}c@{}}Frequency \end{tabular} \\
\cline{1-4}\cline{6-9}
news.sky & 1.0 & 0.0 & 274 &  & MoHFW\_NDIA & 0.963 & 0.037 & 162 \\
\cline{1-4}\cline{6-9}
medscape.com & 1.0 & 0.0 & 258 &  & DrTedros & 1.0 & 0.0 & 110 \\
\cline{1-4}\cline{6-9}
thespoof.com & 0.0 & 1.0 & 253 &  & ICMRDELHI & 0.9903 & 0.0097 & 103 \\
\cline{1-4}\cline{6-9}
newsthump.com & 0.0 & 1.0 & 68 &  & PIB\_ndia & 1.0 & 0.0 & 83 \\
\cline{1-4}\cline{6-9}
theguardian.com & 0.167 & 0.833 & 6 &  & CDCMMWR & 1.0 & 0.0 & 34 \\
\cline{1-4}\cline{6-9}
\end{tabular}
}
\end{table}
The details of the heuristic algorithm is explained in the following pseudocode (Algorithm-\ref{h_algo}). In our experiment, the value of threshold used is 0.88. The post-processing architecture is shown in Figure-\ref{post_process_diag_fig}.
\begin{algorithm}
	\caption{Heuristic Algorithm}
	\label{h_algo}
	{\textbf{Result: }label ( ``real" or ``fake") }
\begin{algorithmic}[1]
  \If {$P^r(x|username) > threshold $ AND $P^r(x|username) > P^f(x|username)$}
    \State {label = ``real"}
  \ElsIf{$P^f(x|username) > threshold $ AND $P^r(x|username) < P^f(x|username)$  }
    \State {label = ``fake"}
  \ElsIf {$P^r(x|domain) > threshold $ AND $P^r(x|domain) > P^f(x|domain)$}
    \State {label = ``real"}
  \ElsIf{$P^f(x|domain) > threshold $ AND $P^r(x|domain) < P^f(x|domain)$  }
    \State {label = ``fake"}
  \ElsIf{$P^r(x) > P^f(x)$}
    \State {label = ``real"}
    \Else
    \State {label = ``fake"}
  \EndIf
\end{algorithmic}
\end{algorithm}
\section{Experiments and Results}
\subsection{System Description}
We have fine-tuned our pre-trained models using AdamW\cite{adamW} optimizer and cross-entropy loss after doing label encoding on the target values. We have applied softmax on the logits produced by each model in order to obtain the prediction probability vectors. The experiments were performed on a system with 16GB RAM and 2.2 GHz Quad-Core Intel Core i7 Processor, along with a Tesla T4 GPU, with batch size of 32. The maximum input sequence length was fixed at 128. Initial learning rate was set to 2e-5. The number of epochs varied from 6 to 15 depending on the model.

\subsection{Performance of Individual Models}
We have used each fine-tuned model individually to perform "real" vs "fake" classification. Quantitative results are tabulated in Table-\ref{single_base_model}. We can see that XLM-RoBERTa, RoBERTa, XLNet and ERNIE 2.0 perform really well on the validation set. However, RoBERTa has been able to produce the best classification results when evaluated on the test set.

\begin{table}[h]
\centering
\caption{Individual model performance on validation and test set}
\label{single_base_model}
\resizebox{\textwidth}{!}{%
\begin{tabular}{|c|c|c|c|c|c|c|c|c|}
\hline
\multicolumn{1}{|c|}{\multirow{2}{*}{\textbf{Model Name}}} & \multicolumn{4}{c|}{\textbf{Validation Set}} & \multicolumn{4}{c|}{\textbf{Test set}} \\ \cline{2-9}
\multicolumn{1}{|c|}{} & \multicolumn{1}{c|}{\textbf{Accuracy}} & \multicolumn{1}{c|}{\textbf{Precision}} & \multicolumn{1}{c|}{\textbf{Recall}} & \multicolumn{1}{c|}{\textbf{F1 Score}} & \multicolumn{1}{c|}{\textbf{Accuracy}} & \multicolumn{1}{c|}{\textbf{Precision}} & \multicolumn{1}{c|}{\textbf{Recall}} & \multicolumn{1}{c|}{\textbf{F1 Score}} \\ \hline
XLM-RoBERTa (base) & 0.968 & 0.968 & 0.968 & 0.968 & 0.970 & 0.970 & 0.970 & 0.970 \\ \hline
RoBERTa (base) & 0.970 & 0.970 & 0.970 & 0.970 & \textbf{0.972} & \textbf{0.972} & \textbf{0.972} & \textbf{0.972} \\ \hline
XLNet (base, cased) & 0.975 & 0.975 & 0.975 & 0.975 & 0.966 & 0.966 & 0.966 & 0.966 \\ \hline
DeBERTa (base) & 0.964 & 0.964 & 0.964 & 0.964 & 0.964 & 0.964 & 0.964 & 0.964 \\ \hline
ELECTRA (base) & 0.948 & 0.948 & 0.948 & 0.948 & 0.953 & 0.953 & 0.953 & 0.953 \\ \hline
ERNIE 2.0 & \textbf{0.976} & \textbf{0.976} & \textbf{0.976} & \textbf{0.976} & 0.969 & 0.969 & 0.969 & 0.969 \\ \hline
\end{tabular}%
}
\end{table}
\vspace{-10px}
\subsection{Performance of Ensemble Models}
We tried out different combinations of pre-trained models with both the ensemble techniques: Soft Voting and Hard Voting. Performance for different ensembles are shown in Table-\ref{soft_voting_model} and \ref{hard_voting_model}.
From the results, we can infer that the ensemble models significantly outperform the individual models, and Soft-voting ensemble method performed better overall than Hard-voting ensemble method. Hard-voting Ensemble model consisting of RoBERTa, XLM-RoBERTa, XLNet, ERNIE 2.0 and DeBERTa models performed the best among other hard voting ensembles on both validation and test set. Among the Soft Voting Ensembles, the ensemble consisting of RoBERTa, XLM-RoBERTa, XLNet, ERNIE 2.0 and Electra models achieved best accuracy overall on the validation set and
a combination of XLNet, RoBERTa, XLM-RoBERTa and DeBERTa models produces the best classification result overall on the test set. Our system has been able to achieve an overall F1-score of 0.9831 and secure a joint 8\textsuperscript{th} rank in the leaderboard, against a top score of 0.9869.

\begin{table}[h]
\centering
\caption{Performance of Soft Voting for different ensemble models on validation and test set}
\label{soft_voting_model}
\resizebox{\textwidth}{!}{%
\begin{tabular}{|c|c|c|c|c|c|c|c|c|}
\hline
\multirow{2}{*}{\begin{tabular}[c]{@{}c@{}}\textbf{ Ensemble Model}\\\textbf{ Combination} \end{tabular}} & \multicolumn{4}{c|}{\textbf{Validation Set} } & \multicolumn{4}{c|}{\textbf{Test set} } \\
\cline{2-9}
 & \textbf{Accuracy}  & \textbf{Precision}  & \textbf{Recall}  & \textbf{F1 Score}  & \textbf{Accuracy}  & \textbf{Precision}  & \textbf{Recall}  & \textbf{F1 Score}  \\
\hline
\begin{tabular}[c]{@{}c@{}} RoBERTa+XLM-RoBERTa\\ +XLNet\\ \end{tabular} & 0.9827 & 0.9827 & 0.9827 & 0.9827 & 0.9808 & 0.9808 & 0.9808 & 0.9808 \\
\hline
\begin{tabular}[c]{@{}c@{}} RoBERTa+XLM-RoBERTa\\ +XLNet+DeBERT\\ \end{tabular} & 0.9832 & 0.9832 & 0.9832 & 0.9832 & \textbf{0.9831} & \textbf{0.9831} & \textbf{0.9831} & \textbf{0.9831} \\
\hline
\begin{tabular}[c]{@{}c@{}} RoBERTa+XLM-RoBERTa\\ +XLNet+ERNIE 2.0\\ +DeBERTa\\ \end{tabular} & 0.9836 & 0.9836 & 0.9836 & 0.9836 & 0.9822 & 0.9822 & 0.9822 & 0.9822 \\
\hline
\begin{tabular}[c]{@{}c@{}} RoBERTa+XLM-RoBERTa\\ +XLNet+ERNIE 2.0\\ +Electra\\ \end{tabular} & \textbf{0.9841} & \textbf{0.9841} & \textbf{0.9841} & \textbf{0.9841} & 0.9808 & 0.9808 & 0.9808 & 0.9808 \\
\hline
\end{tabular}
}
\end{table}
\vspace{-20px}
\begin{table}[h]
\centering
\caption{Performance of Hard Voting for different ensemble models on validation and test set}
\label{hard_voting_model}
\resizebox{\textwidth}{!}{%
\begin{tabular}{|c|c|c|c|c|c|c|c|c|}
\hline
\multirow{2}{*}{\begin{tabular}[c]{@{}c@{}}\textbf{ Ensemble Model}\\\textbf{ Combination} \end{tabular}} & \multicolumn{4}{c|}{\textbf{Validation Set} } & \multicolumn{4}{c|}{\textbf{Test set} } \\
\cline{2-9}
 & \textbf{Accuracy}  & \textbf{Precision}  & \textbf{Recall}  & \textbf{F1 Score}  & \textbf{Accuracy}  & \textbf{Precision}  & \textbf{Recall}  & \textbf{F1 Score}  \\
\hline
\begin{tabular}[c]{@{}c@{}} RoBERTa+XLM-RoBERTa\\ +XLNet\\ \end{tabular} & 0.9818 & 0.9818 & 0.9818 & 0.9818 & 0.9804 & 0.9804 & 0.9804 & 0.9804 \\
\hline
\begin{tabular}[c]{@{}c@{}} RoBERTa+XLM-RoBERTa\\ +XLNet+DeBERT\\ \end{tabular} & 0.9748 & 0.9748 & 0.9748 & 0.9748 & 0.9743 & 0.9743 & 0.9743 & 0.9743 \\
\hline
\begin{tabular}[c]{@{}c@{}} RoBERTa+XLM-RoBERTa\\ +XLNet+ERNIE 2.0\\ +DeBERTa\\ \end{tabular} & \textbf{0.9832} & \textbf{0.9832} & \textbf{0.9832} & \textbf{0.9832} & \textbf{0.9813} & \textbf{0.9813} & \textbf{0.9813} & \textbf{0.9813} \\
\hline
\begin{tabular}[c]{@{}c@{}} RoBERTa+XLM-RoBERTa\\ +XLNet+ERNIE 2.0\\ +Electra\\ \end{tabular} & 0.9822 & 0.9822 & 0.9822 & 0.9822 & 0.9766 & 0.9766 & 0.9766 & 0.9766 \\
\hline
\end{tabular}
}
\end{table}
\vspace{-17px}
\subsection{Performance of Our Final Approach}
We augmented our Fake News Detection System with an additional heuristic algorithm and achieved an overall F1-score of 0.9883, making this approach state-of-the-art on the given fake news dataset \cite{Patwa et al.}. We have used the best performing ensemble model consisting of RoBERTa, XLM-RoBERTa, XLNet and DeBERTa for this approach. We have shown the comparison of the results on the test set obtained by our model before and after applying the post-processing technique against the top 3 teams in the leaderboard in Table \ref{post proc Comp}. Table \ref{result_tab} shows a few examples where the post-processing algorithm corrects the initial prediction. The first example is corrected due to extracted domain which is \textit{``news.sky"} and the second one is corrected because of presence of the username handle, \textit{``@drsanjaygupta"}.
\begin{table}
\centering
\caption{Performance comparison on test set}
\label{post proc Comp}
\begin{tabular}{|c|c|c|c|c|}
\hline
 \textbf{Method}  & \textbf{Accuracy}  & \textbf{Precision}  & \textbf{Recall}  & \textbf{F1 Score}  \\
\hline
Team g2tmn (\textit{Rank 1}) & 0.9869 & 0.9869 & 0.9869 & 0.9869 \\
\hline
Team saradhix (\textit{Rank 2}) & 0.9864 & 0.9865 & 0.9864 & 0.9864 \\
\hline
Team xiangyangli (\textit{Rank 3}) & 0.9860 & 0.9860 & 0.9860 & 0.9860 \\
\hline
\begin{tabular}[c]{@{}c@{}}Ensemble Model\\ \end{tabular} & 0.9831 & 0.9831 & 0.9831 & 0.9831 \\
\hline
\begin{tabular}[c]{@{}c@{}}Ensemble Model +\\Heuristic Post-Processing \end{tabular} & \textbf{0.9883} & \textbf{0.9883} & \textbf{0.9883} & \textbf{0.9883} \\
\hline
\end{tabular}
\end{table}
\vspace{-17px}
\begin{table}
\caption{Qualitative comparison between our initial and final approach.}
\centering
\label{result_tab}
\resizebox{\textwidth}{!}{%
\footnotesize
\begin{tabular}{lccc}
\multicolumn{1}{c}{\uline{Tweet}} & \begin{tabular}[c]{@{}c@{}}\uline{Initial }\\\uline{Classification }\\\uline{Output}\end{tabular} & \begin{tabular}[c]{@{}c@{}}\uline{Final }\\\uline{Classification }\\\uline{Output}\end{tabular} & \uline{Ground Truth} \\
\hline
\begin{tabular}[c]{@{}l@{}}Coronavirus: Donald Trump ignores COVID-19 rules \\with 'reckless and selfish' indoor rally https://t.co/JsiHGLMwfO\end{tabular} & fake & real & real \\
\hline
\begin{tabular}[c]{@{}l@{}}We're LIVE talking about COVID-19 (a vaccine transmission)\\ with @drsanjaygupta. Join us and ask some questions\\ of your own: https://t.co/e16G2RGdkA https://t.co/Js7lemT1Z6\end{tabular} & real & fake & fake \\
\hline
\end{tabular}
}
\label{example_pred}
\end{table}
\vspace{-22px}
\subsection{Ablation Study}
We have performed an ablation study by assigning various levels of priority to each of the features (username and domain) and then checking which class's probability value for that feature is maximum for a particular tweet, so that we can assign the corresponding "real" or "fake" class label to that particular tweet. For example, in one iteration, we have given URL domains a higher priority than username handles to select the label class. We have also experimented with only one attribute mentioned above in our study. Results for different priority and feature set is shown in Table \ref{ablation_tab}.

Another important parameter that we have introduced for our experiment is a threshold on the class-wise probability values for the features. For example, if the probability that a particular username that exists in a tweet belongs to "real" class  is greater than that of it belonging to "fake" class, and the probability of it belonging to the "real" class is greater than a specific threshold, we assign a "real" label to the tweet. The value of this threshold is a hyperparameter that has been tuned based on the classification accuracy on the validation set. We have summarized the results from our study with and without the threshold parameter in Table \ref{ablation_tab}.\newline
As we can observe from the results, domain plays a significant role for ensuring a better classification result when the threshold parameter is taken into account. The best results are obtained when we consider the threshold parameter and both the username and domain attributes, with a higher importance given to the username.

\begin{table}[h]
\centering
\caption{Ablation Study on Heuristic algorithm}
\label{ablation_tab}
\resizebox{\textwidth}{!}{%
\begin{tabular}{|c|c|c|c|c|}
\hline
\multirow{2}{*}{\begin{tabular}[c]{@{}c@{}}\textbf{Combination of Attributes~}\\(\textbf{in descending order of Attribute Priority}) \end{tabular}} & \multicolumn{2}{c|}{\textbf{with Threshold} } & \multicolumn{2}{c|}{\textbf{without Threshold} } \\
\cline{2-5}
 & \begin{tabular}[c]{@{}c@{}}\textbf{F1 Score on}\\\textbf{Validation Set}\end{tabular} & \begin{tabular}[c]{@{}c@{}}\textbf{F1 Score on}\\\textbf{Test Set}\\ \end{tabular} & \begin{tabular}[c]{@{}c@{}}\textbf{F1 Score on}\\\textbf{Validation Set}\\ \end{tabular} & \begin{tabular}[c]{@{}c@{}}\textbf{F1 Score on}\\\textbf{Test Set}\\ \end{tabular} \\
\hline
\{\textit{username, ensemble model pred}~\}  & 0.9831 & 0.9836 & \textbf{0.9822} & \textbf{0.9804} \\
\hline
\{\textit{domain, ensemble model pred~}\}  & \textbf{0.9917} & 0.9878 & 0.9635 & 0.9523 \\
\hline
\{\textit{domain, username, ensemble model pred~}\}  & 0.9911 & 0.9878 & 0.9635 & 0.9519 \\
\hline
\{\textit{username, domain, ensemble model pred~}\}  & 0.9906 & \textbf{0.9883} & 0.9645 & 0.9528 \\
\hline
\end{tabular}
}
\label{Post_Proc_scores}
\end{table}

\vspace{-19px}

\section{Conclusion}
In this paper, we have proposed a robust framework for identification of fake tweets related to COVID-19, which can go a long way in eliminating the spread of misinformation on such a sensitive topic. In our initial approach, we have tried out various pre-trained language models. Our results have significantly improved when we implemented an ensemble mechanism with Soft-voting by using the prediction vectors from various combinations of these models. Furthermore, we have been able to augment our system with a novel heuristics-based post-processing algorithm that has drastically improved the fake tweet detection accuracy, making it state-of-the-art on the given dataset. Our novel heuristic approach shows that username handles and URL domains form very important features of tweets and analyzing them accurately can go a long way in creating a robust framework for fake news detection.
Finally, we would like to pursue more research into how other pre-trained models and their combinations perform on the given dataset. It would be really interesting to evaluate how our system performs on other generic Fake News datasets and also if different values of the threshold parameter for our post-processing system would impact its overall performance.

redpajama_set_name: RedPajamaArXiv


text:
Trump Just Killed a Study Looking Into the Health Effects of Coal in Central Appalachia
Understanding the risks of living near mountaintop removal mining sites just stalled.
Nathalie Baptiste
ReporterBio | Follow
Jay Mallin/Zuma
The Trump administration has told the National Academy of Sciences to stop working on a study about the potential health risks for people living near mountaintop coal-removing sites in Central Appalachia.
"Everyone knows there are major health risks living near mountaintop removal coal mining sites," Bill Price, the senior Appalachia organizer at the Sierra Club said in a statement. "It's infuriating that Trump would halt this study on the health effects of mountaintop removal coal mining, research that people in Appalachia have been demanding for years."
In 2014, a West Virginia University study found that dust from mountaintop removal coal-mining sites was linked to increased incidences of lung cancer. The following year, the state formally asked the Obama administration for help in studying these health effects, and in 2016, the Office of Surface Mining Reclamation and Enforcement (OSMRE) gave the NAS $1 million to determine the human health effects for people living near coal mine operations.
But last week, in an August 18 letter to the National Academy of Sciences (NAS), the OSMRE said that the Department of the Interior has begun an agency-wide review of all its grants and cooperative agreements that exceed $100,000, as part of a the Department's "changing budget situation" and that the agency should halt all work on the study. The NAS says that it is ready to resume work on the study when the review is complete.
"Communities living with daily health threats were counting on finally getting the full story from the professionals at the National Academies of Science," Price said. "To take that away without warning or adequate reason is beyond heartless."
Not only can coal have an impact on public health, burning it releases carbon dioxide, a greenhouse gas that contributes to global warming. Nonetheless, the Trump administration has been aligned with climate skeptics, and throughout his campaign and presidencyTrump expressed support for the coal industry.
When he announced a new agenda for the Environmental Protection Agency, Administrator Scott Pruitt told a group of coal miners that "the coal industry was nearly devastated by years of regulatory overreach, but with new direction from President Trump, we are helping to turn things around for these miners." At an Iowa rally in June, Trump promised to put coal miners back to work. "We've ended the war on clean, beautiful coal," he said. "And we're putting our miners back to work."
Bill Price, from the Sierra Club, says that revoking this study demonstrates the administration's real priorities when it comes to coal. "It appears that the only people Trump cares about in Appalachia are coal executives," he wrote, "not the people who've lived and worked here for generations."
Climate Desk
Scott Pruitt Has Been Mining Some Bad Coal Stats
Jonathan Thompson
5 Great News Stories That Prove Trump's Bet on Coal is Wrong
Karen Hao
Lots of Activists Are Really Not Happy that Tom Steyer Has Entered the 2020 Race
Alexander C. Kaufman
If Politics Doesn't Shape Your Beliefs About the Climate Crisis, What Does?
Kate Yoder

redpajama_set_name: RedPajamaCommonCrawl


text:
package org.jabref.logic.journals;

import org.junit.jupiter.api.Test;

import static org.junit.jupiter.api.Assertions.assertEquals;
import static org.junit.jupiter.api.Assertions.assertFalse;
import static org.junit.jupiter.api.Assertions.assertTrue;

class AbbreviationTest {

    @Test
    void testAbbreviationsWithTrailingSpaces() {
        Abbreviation abbreviation = new Abbreviation("Long Name", "L. N.");

        assertEquals("Long Name", abbreviation.getName());
        assertEquals("L. N.", abbreviation.getAbbreviation());
        assertEquals("L N", abbreviation.getMedlineAbbreviation());
        assertEquals("L. N.", abbreviation.getShortestUniqueAbbreviation());
    }

    @Test
    void testAbbreviationsWithTrailingSpacesWithShortestUniqueAbbreviation() {
        Abbreviation abbreviation = new Abbreviation("Long Name", "L. N.", "LN");

        assertEquals("Long Name", abbreviation.getName());
        assertEquals("L. N.", abbreviation.getAbbreviation());
        assertEquals("L N", abbreviation.getMedlineAbbreviation());
        assertEquals("LN", abbreviation.getShortestUniqueAbbreviation());
    }

    @Test
    void testAbbreviationsWithSemicolons() {
        Abbreviation abbreviation = new Abbreviation("Long Name", "L. N.;LN;M");

        assertEquals("Long Name", abbreviation.getName());
        assertEquals("L. N.;LN;M", abbreviation.getAbbreviation());
        assertEquals("L N ;LN;M", abbreviation.getMedlineAbbreviation());
        assertEquals("L. N.;LN;M", abbreviation.getShortestUniqueAbbreviation());
    }

    @Test
    void testAbbreviationsWithSemicolonsWithShortestUniqueAbbreviation() {
        Abbreviation abbreviation = new Abbreviation("Long Name", "L. N.;LN;M", "LNLNM");

        assertEquals("Long Name", abbreviation.getName());
        assertEquals("L. N.;LN;M", abbreviation.getAbbreviation());
        assertEquals("L N ;LN;M", abbreviation.getMedlineAbbreviation());
        assertEquals("LNLNM", abbreviation.getShortestUniqueAbbreviation());
    }

    @Test
    void testGetNextElement() {
        Abbreviation abbreviation = new Abbreviation("Long Name", "L. N.");

        assertEquals("L. N.", abbreviation.getNext("Long Name"));
        assertEquals("L N", abbreviation.getNext("L. N."));
        assertEquals("Long Name", abbreviation.getNext("L N"));
    }

    @Test
    void testGetNextElementWithShortestUniqueAbbreviation() {
        Abbreviation abbreviation = new Abbreviation("Long Name", "L. N.", "LN");

        assertEquals("L. N.", abbreviation.getNext("Long Name"));
        assertEquals("L N", abbreviation.getNext("L. N."));
        assertEquals("LN", abbreviation.getNext("L N"));
        assertEquals("Long Name", abbreviation.getNext("LN"));
    }

    @Test
    void testGetNextElementWithTrailingSpaces() {
        Abbreviation abbreviation = new Abbreviation("Long Name", "L. N.");

        assertEquals("L. N.", abbreviation.getNext("Long Name"));
        assertEquals("L N", abbreviation.getNext("L. N."));
        assertEquals("Long Name", abbreviation.getNext("L N"));
    }

    @Test
    void testGetNextElementWithTrailingSpacesWithShortestUniqueAbbreviation() {
        Abbreviation abbreviation = new Abbreviation("Long Name", "L. N.", "LN");

        assertEquals("L. N.", abbreviation.getNext("Long Name"));
        assertEquals("L N", abbreviation.getNext("L. N."));
        assertEquals("LN", abbreviation.getNext("L N"));
        assertEquals("Long Name", abbreviation.getNext("LN"));
    }

    @Test
    void testDefaultAndMedlineAbbreviationsAreSame() {
        Abbreviation abbreviation = new Abbreviation("Long Name", "L N");
        assertEquals(abbreviation.getAbbreviation(), abbreviation.getMedlineAbbreviation());
    }

    @Test
    void testDefaultAndMedlineAbbreviationsAreSameWithShortestUniqueAbbreviation() {
        Abbreviation abbreviation = new Abbreviation("Long Name", "L N", "LN");
        assertEquals(abbreviation.getAbbreviation(), abbreviation.getMedlineAbbreviation());
    }

    @Test
    void testDefaultAndShortestUniqueAbbreviationsAreSame() {
        Abbreviation abbreviation = new Abbreviation("Long Name", "L N");
        assertEquals(abbreviation.getAbbreviation(), abbreviation.getShortestUniqueAbbreviation());
    }

    @Test
    void testEquals() {
      Abbreviation abbreviation = new Abbreviation("Long Name", "L N", "LN");
      Abbreviation otherAbbreviation = new Abbreviation("Long Name", "L N", "LN");
      assertTrue(abbreviation.equals(otherAbbreviation));
      assertFalse(abbreviation.equals("String"));
    }
}

redpajama_set_name: RedPajamaGithub


text:
Old tapes renew fall passion
Published 12:00 am Tuesday, August 31, 2004
There's something about this time of year that just makes you long for football. Last week's slightly cooler temperatures had me dusting off my crimson and white pon poms and preparing for yet another season.
Like many people in the South, I have my favorite college football team (Alabama, of course) and follow them with a passion reserved for few things. But, I like the sport in general and can get just as much pleasure watching an exciting high school game (it helps that Hartselle wears crimson and white, too) as any professional game.
So you can imagine my excitement Saturday when I found a replay of last year's Florida/Miami game on the television. I knew Miami won the game, but sat down and watched it anyway. It was followed by a replay of last year's Kentucky/Arkansas game. I watched that, too.
With my anticipation at a peak, I pulled out some tapes of old Alabama games. In the pile was the 1992 Sugar Bowl, the 1999 SEC championship and the 2001 Iron Bowl. I opted for the Sugar Bowl and popped in the tape.
Within minutes, I was transported back to the game. There was Jay Barker and Gene Stallings, the awesome defense and the amazing plays. I quickly got caught up in the game and felt the old pit-in-my stomach feeling.
&uot;Come on defense, tighten up,&uot; I yelled at one point before I realized that I was indeed watching a tape.
&uot;Calm down,&uot; I said to myself. &uot;We win this one, though we sure could have looked better right there.&uot;
Shortly after the game ended, the phone rang. My dad was on the other end telling me, ironically enough, a football story.
Dad had gone to football practice with my nephew Adam, who's in his first year of playing the sport. Adam's a sweet kid, the kind who always says &uot;thank you&uot; and &uot;yes m'am,&uot; and part of me had a hard time picturing him smacking people on the gridiron. Apparently, Adam has some trouble with that, too.
He was walking to the field with his dad when he learned he would be playing a scrimmage game against some older boys.
&uot;You better go ahead and call the ambulance now,&uot; he told his dad.
My dad, who coached football for years, just laughed.
&uot;It reminded me of your brother,&uot; he said. &uot;He used to tell me he could't go in the game against the big kids.&uot;
I just laughed. I didn't tell him I was watching old football games, much less that I was getting nervous about the outcomes.
&uot;Well, I think people put way too much emphasis on football anyway,&uot; I said, trying to keep a straight face as I uttered the words.
It didn't work.
Leada DeVaney serves as publisher of the Hartselle Enquirer and the Madison County Record. She can be reached at mailto:leada.devaney@hartselleenquirer.com


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Returns a list describing what indexes to generate for this part. This list is a list of lists of subpart kinds. Each item in the list generates one index and the index will include all of these part kinds.


We are excited to announce a new ClearCorrect workshop series in January and March of 2013! These workshops will be held in Houston, Texas by our founder Dr. Willis Pumphrey and one of ClearCorrect's top providers, Dr. Peter Rivoli. This series will also cover both basic and advanced clear aligner therapy and marketing techniques.
In celebration of ClearCorrect's six-year anniversary, we are offering special discounted prices on all four workshops through the month of November. Availability is limited, so choose your workshop today!
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5 CE credit hours will be awarded for the completion of the advanced workshop.


dear gods help me, what have I done.
No clue what's up but HUGS!!!!!!
cortneyofeden and I are here if you need to chat, vent. talk. cry. etc.
it sounds like you have gone thru something hard like many people around me of late. If you need an ear, sister... it is warmly hear for you as you have been for me.


Feeling overwhelmed? Everyone I know has more on their plate than they know how to accomplish -- often to the point of being unable to get anything productive done. Some people are not only stressed, they are feeling depressed -- unable to complete basic tasks; not to mention, make progress toward their dreams or ways of making their lives more enjoyable.


Group Health, Safety & Environment Manager required to join a multidisaplined construction company based from Farnborough and covering a number of sites across surrounding counties.
This is a fantastic opportunity for a Group Health, Safety & Environment Manager to join a multidisciplined construction organisation who have an excellent reputation within their field. You will be responsible for manging a Health and Safety Advisor, monitoring safety performance and creating core new policies and procedures, attending project meetings as well as being involved in creating and developing strategic plans for the major project.
This role will offer the successful candidate the chance to work within a strategic and hands on based capacity. The successful applicant will be leading multiple projects working closely with sub-contractors.
Please apply now for your chance to be considered for this excellent opportunity.
Please note that due to a high volume of applications only successful candidates will be contacted for this role.


"Green trail-blazer Costa Rica is drawing up plans to cut its net greenhouse gas emissions to zero before 2030, the government said on Thursday, and aims to be the first nation to offset all its carbon.
"Environment Minister Roberto Dobles said the tiny, jungle-cloaked Central American nation would clean up its fossil fuel-fired power plants, promote hybrid vehicles and increase tree planting to balance its emissions.
"The goal is to be carbon neutral," Dobles told Reuters. "We'd like to do it in the next 20 years." He said Costa Rica would also eliminate net emissions of other greenhouse gases."


text:
Become What You Are
An interview by John Loudon from from Parabola, Fall 1982
I met Brother David Steindl-Rast, of the Roman Catholic Benedictine Order, at the San Francisco Zen Center's Edward Conze guest house where he was staying briefly on his way to a monastery in Big Sur. The setting was apt: Conze was a Westerner who became one of the century's great authorities on Buddhism, and the Victorian house has an inviting spaciousness, an unpretentious elegance and absence of clutter, yet real warmth—all of which fit the monk with whom I was to speak. To those who encounter Brother David now and again, he seems very much a man on the move, remarkably mobile for a monk. Yet despite all this travelling and speaking, he always appears a calm eye at the center of any storm of activity. To a passing observer, he might look disturbingly gaunt and ascetic, confirming popular prejudices about monks being world-haters. But as soon as he greets you, the illusion of severity vanishes: he is so warm and effervescent that you really want to learn how he packs so much alertness and delight into his life.
Originally from Vienna, Austria, Brother David has a doctorate in psychology and has been a monk for twenty-six years now; he currently lives in a small community, called the Grange, in Connecticut. He says that he is as much at home in a Zen monastery as in a Catholic one, and it's hard to think that he would not be at home anywhere. For he has a remarkable ability to be joyfully and wholly present: when he listens, he does nothing else; when the phone interrupts, he takes the call with full attention and delight; when he answers questions he does so with the kind of care and élan that make an interviewer's task a joy. More than many teachers I've met, the man is his message, and it is hard to imagine a more persuasive and attractive advocate for the Catholic monastic tradition.
Many people ask him whether the spirituality he embodies and presents is really the Catholicism that they've found so difficult to appreciate in other forms which they've encountered. But it may be that few people have so appropriated that tradition that they can express it with such simple grace.
—John Loudon
John Loudon: What does "holy warfare" mean to you?
Brother David Steindl-Rast: Today the notion of warfare is inseparable from that of alienation, whereas the very essence of spiritual warfare in the monastic tradition is the overcoming of alienation — what we call nowadays pulling or getting yourself together. And the monastic symbol for pulling yourself together is the belt, which monks wear in many different traditions. The aim is to overcome alienation from yourself, from others, and from God.
JL: What forces need to be overcome in this struggle against alienation?
BD: Well, in the classical discussion of holy warfare in the writings of the Eastern Elders of the early Church, these forces are personified as demons. Even in the New Testament Paul says that it is not against "flesh and blood" that we are struggling, but against principalities and powers of evil. But it's not necessary to take these powers literally, in a fundamentalist way, and in fact to do so we probably would do an injustice to the early Fathers who wrote in those terms. They were no doubt as alert to the metaphorical nature of this imagery as we are, just as Buddhists have long known that the different hells in their tradition are best understood as mental or psychological states, not actual places.
JL: Can you give examples of some of these personified forces and some indication of how you might express them today?
These three elements — anger, lust, and laziness — are precisely the three ways that we can fail to be present where we are, and the whole idea of getting yourself together is to be present where you are.
BD: The three great forces that the Christian Elders in the Egyptian desert identified as the enemies against which we're battling are anger, lust, and laziness. The third one is called the noonday devil. It is in the middle of everything — of a day, of a life — that you can lose your resolve, that torpor can set in. When you're in the middle of swimming across a river, it's too far to go back and seems too far to reach the other side, and you are tempted to give up. Well, these three elements—anger, lust, and laziness—are precisely the three ways that we can fail to be present where we are, and the whole idea of getting yourself together is to be present where you are and, in the Christian context, to respond to the presence of God.
Anger really means impatience (as opposed to the righteous anger that is desirable in many circumstances). Impatience makes us get ahead of ourselves, reaching out for something in the future and not really being content with where we are, here and now.
Lust extends much wider than the sexual sphere, and essentially means attachment to something that is not present, or is not the appropriate thing right now.
And one by-product of laziness, of being victimized by the noonday devil, is sadness — not the genuine sorrow of compassion, but the lifeless ennui of never really being involved in the present, with what's happening.
If you would like another contemporary interpretation of the idea of spiritual warfare, there is C.S. Lewis' The Screwtape Letters , in which he translates the tradition with great wit and insight into a modern idiom. It's all about struggling with the forces that are all around us in the world and within us and that distract us from being really unified, in one piece.
The sun rises and the bell rings, and you are to be there: Your impatience can't make it happen before the right time; your attachment to staying in bed can't delay it; and you'll miss it if you're up but not really present, alert, attentive.
JL: When I was thinking about the theme of holy warfare, it occurred to me that there are military virtues — such as discipline, strength, courage, resolve, fidelity, and so on — which are also vital to spiritual growth. And especially the aspect of discipline, involving training and regular practice. What are the disciplines that have been developed that can be used against these devils today?
BD: The word discipline is very significant in this context, since it is not primarily a military term. The corresponding military term is regimentation. Discipline is a school term: the discipulus is the disciple, the pupil. Even the word pupil is apt here, because it is related to the pupil in our eye, the pupilla — the little doll, the little image of oneself that one sees in another's eye. This eye-to-eye contact is the essence of discipline: discipline is the attitude that you have when you see eye-to-eye with your teacher. Today especially people reject external regimentation, and are looking for a teacher that gives discipline eye-to-eye. The drill sergeant doesn't care if you are eye-to-eye with him or anybody else, just that you do what you are told. But discipline involves bringing out what is already within you. That's what the true teacher does. And the other virtues you mentioned have similar parallels. Fortitude or courage, for instance, is simply the resolve to overcome obstacles. Spiritual warfare involves the acquiring and implementation of the strengths and virtues needed to overcome obstacles.
JL: Discipline suggests to me habits of behavior and regular practices that the teacher would presumably teach. How does this dimension relate to overcoming anger, lust, laziness?
BD: Within the monastery, which is my background and the essential environment that I feel comfortable with and know well, there is a particularly highly developed tradition of such training. In fact, the monastery can be understood precisely as a setting in which this discipline is cultivated. It is a place to which people go in order to get themselves together, again in the sense of uniting with themselves, with others, with God.
The two realms in which this discipline is cultivated are space and time, and the aim is that the whole of life should be brought together from alienation to fullness. With regard to time, for instance, there are in monasteries all sorts of bells, gongs, clappers, drums, and so on — all kinds of signals that tell you what it is time for. The struggle is within yourself to overcome your laziness, your attachments, your impatience in order to be truly wherever you need to be at any particular time. T.S. Eliot speaks of "Time, not our time," and he explicitly says this in relation to the Angelus bell that, in monastic life, rings three times a day — at sunrise, at sunset, and at high noon. The sun doesn't rise again or wait for you if you oversleep and don't get up when the bell rings. The sun rises and the bell rings, and you are to be there: Your impatience can't make it happen before the right time; your attachment to staying in bed can't delay it; and you'll miss it if you're up but not really present, alert, attentive. If this sort of timeliness appeals to you, as it does to me, these signals are not a torturing regimentation but musical invitations, celebrations of particular moments.
The difficult aspect, of course, is the one expressed by St. Benedict in his Rule: "When the bell rings, stop everything. Don't even cross your t's or dot your i's, but go quickly." The challenge is to learn to respond immediately to whatever it is time for. Not to wonder whether you have time for it or whether you like it, but simply to respond when it is time. And the truth of this discipline is universal. For instance, in Taoism, the flow goes on and you can either be in tune with the flow or not. All these signals are simply means to get you into the flow, and the less you are in tune the more difficult the immediate responding is, the more obstacles you have to overcome to get with it.
With regard to space, the monastery is organized in such a way that there is a place for everything, and relatedly that everything is there, the monastery is self-contained. The ideal is wonderfully expressed in the Benedictine tradition by the famous plan of St. Gall, which is reflected more or less in many medieval monasteries. With everything there and a place for everything, you can be at home in your world, in the place where you belong. And belonging and getting yourself together are closely related. This sufficient world, which St. Benedict calls a workshop for the spiritual life, affords the spaces and the tools for working on yourself, transforming yourself, and in turn the world around you.
Novices always have difficulties with both aspects — time and space. When it is time for something, they often want to do something else; when this is the place to be, they often want to be somewhere else. And isn't this how it is for most people? The monastery also emphasizes neatness and orderliness; most visitors notice this immediately. There is a close relation between the struggle to put things in order within your self, within your life, and the ordering of the space around you. But novices find this hard to understand. They say, "We came here to learn spiritual matters, and what I'm told to do is how to put my shoes on, when to put them on and take them off, to put them down with the right one on the right side, the left on the left, and parallel, not toed in. What does that have to do with the spiritual life?" It has everything to do with it. That is the spirituality; it isn't something that you do just as a novice, and then graduate to spirituality. But it takes a long time to see that orderliness and cleanliness is not just cleaning the room but it is getting your life in order.
So bringing things into order is the goal. Order is the disposition of things in which each gives to the other its room, its own proper place. That's the external aspect. The other is that order that springs from love: there's no other way of establishing order except through love. So spiritual warfare is radically unlike what we know as warfare, which is rooted in hate and alienation and leads to chaos.
JL: Besides the imagery of warfare, some people have compared spiritual discipline to athletic training. There is the talk, for instance, about becoming an athlete of Christ.
BD: Both the athletic imagery and that of spiritual weaponry occur in St. Paul, but the weapons he speaks of are faith, hope, and love. I am convinced that in the present world, in which peace and order are no longer possible through arms, it is best to change our spiritual vocabulary, because misunderstandings do arise on the popular level. I am much more comfortable with speaking about spiritual struggle, since that does not necessarily involve struggling against someone else. You can struggle up a mountain, or struggle to get your body in shape. It even applies to animals: a chick struggling to get out of the eggshell. Plants struggle to break through cracks in the concrete, and amazingly they manage to. And similarly, I prefer to speak of obstacles rather than enemies. The struggle against obstacles, I think, puts the essentials of the tradition of spiritual warfare into contemporary language that is proper and helpful.
JL: Do you think the spiritual path demands a special way of life?
BD: If by a special way of life, you mean a special place like a monastery, I would say no. But if the question implies making an effort, having to struggle, I would say yes. The difference between other animals around us and ourselves seems to be that dogs and cats and birds and other animals don't have to struggle to be good at what they are. But we human beings somehow have to struggle to become what we are.
JL: Our being is to become.
BD: Yes. We experience ourselves as unfinished, and we have to struggle to become a finished product. Actually, we're never completely finished; that's our glory and our agony. We remain open-ended.
JL: In contemporary Catholicism, and in the past as well, there seem to be two divergent paths: there seem to be two divergent paths: there is that of those who emphasize spirituality, spiritual disciplines and growth, and then there is the more general, popular path in which salvation is available through regular participation in the sacraments and the life of the church generally. The former way sees becoming a Christian as a lifelong task; the latter stresses fidelity to being a good Catholic. Can you say something about this?
BD: You speak of participating in the sacraments. At the heart of all the sacraments, especially the eucharist and baptism, is the celebration of the struggle of Christ through death to resurrection. If you really participate in the sacrament, it is impossible not to enter into that struggle. The whole idea of the sacrament is to go through that struggle yourself in communion with the struggle of Christ, to participate day by day and hour by hour in the struggle of dying into greater fullness of life. And the real issue is not whether there is one kind of life that allows for this acceptance of death that leads to fuller life, and so is a spiritual life rather than a run-of-the-mill life. No, the real question is to what extent within ordinary life we can wake up to the essential inner struggle of realizing the fullness of life. Going to church, sending your kids to Catholic schools, and so on, by themselves don't do anything; they're worthless, unless they lead you into, wake you up to that struggle.
JL: Since you participate in both the Christian and the Zen communities, do you think there is an ultimate difference between Christianity and Buddhism, and what kinds of differences do you see between the two?
BD: The point is, how ultimate is "ultimate"? There are many different levels. On one level there are great cultural differences: the two traditions grew up in entirely different settings, and so are dissimilar in many respects. But the moment that you penetrate through the accidental cultural differences, you find a remarkable similarity. Sometimes now I cannot remember if I'm in a Christian or a Buddhist monastery. The atmosphere is very similar. Then you go deeper still, and you discover profound differences in approach, although it's difficult to put them into words. Basically, the Biblical tradition centers on the Word in the widest sense: the divine speaks to us, approaches us, and we have to respond; we're burdened with responsibility.
JL: The Bible also emphasizes hearing over seeing.
BD: And the reason for the emphasis on hearing is the call to live by the word of God, being nourished by it, responding to it. In Zen the stress is not on the word, but on the silence—the silence that is so profound that you can go down into it forever and ever. Openness, emptiness, void—all this permeates Zen. Of course, in the Christian tradition, the Word comes out of the silence and returns to the silence. But despite the teaching of the dark night of the soul and the like, the Christian tradition still stays very close to the Word. Though there are lots of words in Buddhism, they aim at silence. After everything is said and done, the Zen teacher will say, "Ah yes, but what a pity that we have to say anything at all." The saying doesn't really effect anything; what I think might well be the deepest level, you can experience communion and unity between the traditions, the complementarity of the Word and the silence.
By changing yourself, you're beginning to change the world. In fact, you're changing the world much more by changing yourself than if you're running around blindly, involved in one cause after another.
JL: What is the connection between the life of contemplation and the call to social action in the world?
BD: You can't really be a contemplative, unless you also want to change the world. You want to change yourself, and that's where the struggle comes in. By changing yourself, you're beginning to change the world. In fact, you're changing the world much more by changing yourself than if you're running around blindly, involved in one cause after another. But the difference between what we call the apostolic and the contemplative orders, or vocations, is that the apostolic approach says, "We live in this world, we're responsible for it, and we have to do something to change the world for the better." The monastic answer is, "We are not strong enough to change the world in general. Let's change that little spot where we are. And let's put a wall around it and say this is as far as we go, as far as our strength reaches. And now within that narrow confine, let's change the world, make it more what it's supposed to be." That approach has its drawbacks, too, because it can become ingrown, its own private little affair. And the apostolic approach has limitations, because it can become so watered down that nothing spiritual remains. So we need the two; they are the poles of one continuum. People who are now engaged in apostolically changing the world need to come back periodically to a monastic environment where what they are trying to achieve everywhere is to a certain extent achieved already. And if the world could gradually become what a good monastery or Zen center is, that would be fine. The monastic communities can provide the strength, the encouragement to realize that true order can be achieved.
JL: Traditionally, Catholicism has emphasized that the contemplative life is valuable in and of itself, even if the effect on the outside world is not very immediate or direct, but with the faith that spiritual service of God would redound ultimately to the benefit of all of humankind. How would you translate that idea into contemporary terms?
BD: The problem is that all too easily you can think of the spiritual as the opposite of the material. But in authentic Christianity, the material is completely integrated with the spiritual. The essence of Christianity is incarnation. Spiritual is not opposed to material, but to the unspiritual. It's better to speak of alive and dead. Spirit, "breath," means life. The unspiritual or "the flesh," as the New Testament puts it, does not mean the material, the bodily. Flesh stands for that which is dead and in the process of decay. So it's best to think of death not in the sense of negating life, denying life. Life-affirming and life-denying are what spiritual and unspiritual mean. So from that viewpoint, there is a struggle for more and more spirituality, but this spirituality does not deny the world and material things, but expresses itself in more and more beautiful transformation of the material world. Now and then you see a place where every roof tile and every door knob speaks of spirituality, and it reminds you that material things can be completely transformed.
JL: I asked you earlier if a spiritual life demanded a special way of life, and in the light of the distinctions that you've made, I'm beginning to think that what it actually comes down to concretely is how you spend your day. Of course, monks spend their day differently than people who drive trucks or work in offices and so on. How do you spend your day? And what principles that the monastic life has taught you might apply to people who live in the "ordinary world"?
BD: One doesn't go to the monastery to lead a different kind of life from the rest of people. The challenge of living according to certain principles is the same for everyone, and we all need to lead a special kind of life if we want to come truly alive. The monastic day starts with getting up earlier than most of us would like to get up. So the struggle is right there at the start.
What one should and can take out of the monastic life is its very essence, and that is the grateful approach to life moment by moment, being grateful in everything you do.
JL: Do you get up earlier because it is difficult, or because it's good to be up when the sun comes up?
BD: You never do anything, theoretically or ideally, just because it's more difficult. You do it in spite of it's being difficult, but for a good reason. The reason for getting up early is that these early morning hours provide a setting, a quiet, a silence that never comes again later in the day; there is something special going on in those early hours. And you're also there for the sunrise, dawn, which is very important: you celebrate the dawning of each new day. But it's struggle to get up and to remain alert.
Then during the day, there are several times for prayer and times when we get together to celebrate important points in the day — high noon, sunset, night prayers at the end of the day. The rest of the time is spent studying or in manual labor. Manual labor is significant and everybody in the monastery takes part in it, including the Abbot. It's simply a part of life. It keeps you humble, down to earth (humus—the word that also gives us humor and human). Essentially, then, monastic life is dedicated to prayer, manual labor, and study.
JL: How much of this regimen can you take with you when you travel?
BD: It's very difficult, and that's why monks don't usually travel. The kind of prayer that I find most helpful, in place of the divine office that is chanted seven times a day in the monastery, is the prayer of the heart from the Eastern Christian tradition, which involves a kind of mantric repetition of the name of Jesus. But I try to restrict my travel, because it's so hard to take much of the monastery with you, although it's fine if I can stay in another monastery, such as Zen or Camaldolese [one of the Benedictine orders in the Roman Catholic church with a monastery in southern California].
JL: And how would you suggest that the values of that sort of structure be translated to people who live their whole lives in the situation you find yourself in when you're not in the monastery?
BD: There's no point in just imitating the externals. What one should and can take out of the monastic life is its very essence, and that is the grateful approach to life moment by moment, being grateful in everything you do. That means, for instance, an alertness to the character of every moment demands a response, and the basic Christian response is trust in the giver.
JL: But you can't have awareness just by wanting it, can you? There are people here at the Zen Center who have spent years and years of their lives trying to be more awake.
BD: That's true. But there are degrees of wakefulness. And people who have practiced for years and years may not realize that they have made great steps toward greater wakefulness. The difficulty in speaking about wakefulness is that when you are asleep you can't just wake yourself up. But if you focus on thankfulness, it is easier, since being grateful is within your power. If you do it again and again, you remind yourself that every moment is a given moment. Gratefulness is an experience that everyone has, and seems very natural when cultivated. Actually, it is emphasized more explicitly in Buddhist monasteries, where there are so many formal bows. It is a form of teaching us to receive everything — a cup of tea, another person — with gratitude.
JL: So this rhythm of gift and response is a spiritual practice, or at least a way that anybody can practice in any circumstances.
BD: Yes, and I don't think spiritual practice is too grandiose a term for it. If you really explore its larger implications, it is at the core of every spiritual practice, although it may be expressed in quite different ways.
JL: What is the importance of the dialogue between Christianity and Zen?
BD: These are traditions that seem to me to have a lot of future and that complement one another well. And what really interested me in Buddhist-Christian dialogue was the monastic dimension. I wanted to know in what sense Buddhists are monks like I am. And ultimately I've come to see that monastic life isn't something that is especially connected to Buddhism or to Christianity, but is related to one's frame of mind, one's own inner bent.
JL: So it's an essential human vocation or option; in any culture or society there are going to be people who want to live this way?
BD: Right, and you could even think of it as an externalization of a dimension that is in every human being and is sometimes very strong in people who do not externalize it because of their life circumstances.
JL: You spoke about our always becoming and never reaching the end. What is it that one is supposed to become? What's the struggle for?
BD: As the Christian tradition sees it, each one of us is a unique word that is spoken, or a unique way of saying the one eternal Word of God. Each one of us is a word, and we become the word that we are by our response to all the other words around us, human or otherwise. Thus we become the word that we are meant to be. If the word is in the process of being spoken, you can never really say it's finished. In a certain sense, the word is completed with my death, when all that I have made of my life is rounded off. But even then, the Cappadocian Fathers in the early church taught that heavens is not a static state, but a dynamic experience of moving deeper and deeper into the ultimate, and the ultimate can never be completely discovered.
Both eastern and western monastic traditions have stories of the spiritual master who is very accomplished and is having trouble finding a teacher of his own. And he is directed, in a dream or a vision or in some other way, to someone who is more advanced than he is, but is the last person you would have expected.
JL: If you're playing tennis, I suppose that one person eventually wins in the end, but the joy of playing is not just getting to the end.
BD: That's a good point. The spiritual struggle is like learning to play tennis, with the muscle pain, the awkwardness, the frustration, and so on at the beginning. The element of playing is very important in spirituality, because otherwise you begin to wonder what all this struggling is for. The goal is partly the enjoyment; it doesn't come later, but within the very process of the struggle.
JL: What about the people who aren't even playing the game?
BD: I tend to be very trusting and to believe that even in people in whom we least see it, deep down there is that aliveness, that longing, that struggle, and it's just well covered over. My world view is not that there are a few people who really struggle and that the masses haven't awakened to their real calling. My view is that in some the process is more obvious and in others the process is more hidden. And that is a common view in monastic traditions, East and West. Both have stories of the spiritual master who is very accomplished and is having trouble finding a teacher of his own. And he is directed, in a dream or a vision or in some other way, to someone who is more advanced than he is, but is the last person you would have expected. In Buddhism it's a butcher for example, someone way down the spiritual line, who you'd expect to have no spiritual consciousness at all. And in the Christian tradition it's often a merchant with a big family and no time to pray, just buying and selling all day. And all of a sudden the searching teacher discovers this is it, this is the one.
And the most urgent spiritual task today is one being waged by just such "ordinary people" — the struggle against nuclear arms, the struggle for peace, which means harmony among all things.
JL: What do you regard as your special vocation?
BD: Strangely enough, I really joined the monastery to spend the rest of my life there, and I am perfectly happy to stay there without going out at all. But I do accept invitations to speak or participate in events when there are not that many people available who are interested and experienced in an area, such as the Buddhist-Christian dialogue. And these days I'm more and more involved in working with people who are quite alienated from the Christian tradition, even though many of them were raised as Christians. I very much enjoy, for instance, workshops with New Age people, many of whom come out of a Christian background but have been away from it for a long time and are now ready to give Christianity a new look. They have a real need and longing to be reconciled with their roots. Much has to be thrown out and forgotten for good, but there also is a lot in the Christian tradition, if you grew up in it, that cannot readily be replaced by anything else. So you have to come to terms with it. Essentially, my vocation is simply to be a monk, but part of that is this sort of healing mission that not too many others are involved in.
JL: So your vocation is to live the Christian monastic life, and then to communicate what you discover in it?
BD: Really the latter part is more a matter of exposing myself to other people who have the monk within them, and haven't discovered it. One doesn't need to say much; it seems to be a help to find a monk who can be a catalyst for the monastic bent of mind that is in all of us.
Reprinted from Parabola, Fall 1982
(Vol. VII, #4, pp. 60-67)
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It's stunning how much camera technology has evolved to the point where a device that costs less than $500 can be easily interchanged with another one that holds a price tag of nearly $8,000 or sometimes even more.
Thanks to recent innovations from companies like Sony, Canon, and Panasonic, there are already low to medium priced mirrorless cameras providing the ability to shoot with a high degree of quality that could be hardly distinguished from the images captured by some higher-end models.
Tech guru Max Yuryev recently explored this thought-provoking phenomenon by producing a video that blindly compares the entry-level Panasonic G7 and the Canon EOS C200 Cinema Camera.
Apparently, both the Panasonic G7 and Canon C200 can shoot in 4K (up to 30fps and 60fps respectively). While each camera does have its own unique features in terms of form factor, sensor size, dynamic range, slow-motion recording, codec options, ISO range, etc., they both produce high-quality video that would satisfy most, if not all of your production needs.
For the comparison, Yuryev took several shots with both cameras in the same environment and lighting conditions, trying to match the camera settings as close as possible in order to give each rival a fair fight. That being said, I encourage you to observe this video as the results may surprise you.
Looking at all of the clips from the comparison, both cameras produce stellar results providing crisp and highly detailed footage across the board. This is especially true for the shots concerning buildings and other exteriors as you could see the edges in both images are sharp and detailed.
While the footage from the G7 and C200 could easily be intercut with each other, if you examine the footage more closely, you will start to notice the differences between the cameras. For shots that zoomed into both images, for instance, the C200 has a bit more sharpness than the G7 in the edges, plus the former has a wider dynamic range, although to most this factor may be considered as a negligible difference, especially when you think of the price.
From a color perspective, Canon's color science has been the industry standard for many years, and it's arguably the biggest selling point for Canon cameras to date. Nevertheless, the colors from the G7 are still very natural and life-like regarding this particular test (except for the yellow tint that becomes quite apparent in some of the shots). In fact, there may be some situations where the G7's color rendition may appeal more to you. All in all, when it comes to color fidelity, it's just a matter of personal preferences and subjective assessment.
The final test in the video showed of each camera's autofocus ability. It's clear that the C200 performed the best hands down with its ability to quickly detect a subject and adjust focus accordingly. This victory is attributed to the Canon's Dual-Pixel Autofocus Technology which currently dwarfs the autofocus performance from any other camera manufacturer.
While the C200 technically outperforms the G7, the big takeaway here is realizing how far we've come in this post-DSLR revolution era. The fact that you can get a camera with such incredible image quality for just a few hundred bucks is mind-blowing as this was never possible during the dawn of DSLRs. We are currently living a filmmakers' dream that was unimaginable less than a decade ago, and we can only hope that technology will keep evolving to provide better for less.


Beautifully remodeled home has fresh paint, new wood laminate floors, cedar plank ceiling, completely updated kitchen with tile backsplash, new stainless refrigerator, stove and dishwasher, white cabinets, pantry closet, black granite countertops and recessed lighting. Baths offer new fixtures, mirrors, shower/tub in second bath and tile shower plus double sinks in master bath. Separate laundry area. This one won't last long!


The community has spoken, and the Marion County Board of Education and Superintendent Taylora Schlosser have listened.
As a result, the school district is going back to the drawing board, and the process of addressing school facilities is starting over.
During the Marion County Board of Education's meeting Thursday evening, June 14, Superintendent Taylora Schlosser recommended the board "slow down" and not move forward with a plan to build a new 8-9 center.
The school board unanimously agreed, and the district will now start over and create an entirely new district facilities plan. The previous plan, which was recommended by the Local Planning Committee, was to construct a new 8-9 center adjacent to the Marion County High School campus and renovate MCHS. Under that plan, Lebanon Elementary would eventually relocate to the current Marion County Knight Academy. However, the board rescinded that plan Thursday evening, and it's completely off the table.
Now, a new Local Planning Committee will convene, RossTarrant Architects will begin the process of evaluating the MCPS facilities and the process will start over.
Schlosser said if the committee goes through the entire process and believes the best thing is to renovate current buildings, she would support that plan.
"If the committee comes together and we want and we believe that the best thing is to renovate Calvary, to renovate Lebanon Elementary, to renovate Marion County High School… I will support that 100 percent," she said.
Schlosser said she's listened to feedback from the community, and that's why she is recommending the district start the entire process over.
School board members even seemed surprised by her recommendation at Thursday's meeting.
"We didn't expect that," Board Chairman Butch Cecil said after Schlosser made her recommendation.
Chairman Cecil proceeded to voice his support for Schlosser's recommendation, and point out that he hasn't forgotten what he said back in 2016 during the nickel campaign regarding the needs of Calvary Elementary and Lebanon Elementary schools.
Vice Chairman Kaelin Reed and Board Member Carrie Truitt both expressed their support of Schlosser's recommendation to slow down and start over.
Truitt pointed out that she made that exact recommendation during a previous school board work session.
During Thursday's meeting, the school board gave community members in attendance a chance to speak.
Magistrate Larry Caldwell thanked the board for its decision to start over.
Caldwell then used the school district's slogan of "Dream, Believe, Achieve" to make his next point.
Melissa Lee Knight spoke not only as a parent, but also as a member of the previous LPC and a member of the state advisory council for exceptional children.
Knight said if the district had all the money in the world, she believes an 8-9 center would be "remarkable," but she believes the district must be very efficient in how it uses its current facilities.
But, efficiency can be pushed to the limit, as well, according to Knight.
Knight said she thought it was a very smart move to step back and look at the total impact of the district facilities plan, not just on the children, but also on the community as a whole.


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Credit Counseling (PDF) - What to do when you think credit counseling is right for you.
Credit Repair (PDF) - Having a plan in place to repair your credit can eliminate unnecessary worry. Here are a few steps to get you started.
Tips for Choosing a Credit Counselor (YouTube Video) - This video explains the things you need to know before contacting anyone about credit counseling.
Debt Collection (PDF) - With consumer debt at all-time highs, more people are being contacted by "debt collectors". Know your rights.
Debt Collection Scam (YouTube Video) - SCDCA warns consumers to be on guard of a telephone scam that plays on consumer fears by threatening incarceration for a past debt.
Harassed by Debt Collectors? (YouTube Video) - There are limits to what debt collectors can and can't do when trying to recover an owed debt. Check out this advice on what to do if a debt collector is harassing you.
Wage Garnishment (YouTube Video) - Understand wage garnishment and learn when your money can and can't be taken.


Android First Responder is an OS update designed to save lives. With First Responder turned on, Android will briefly analyze the user's facial features every time they unlock their phone, scanning for any sign of facial droop. If a stroke is detected, the user will receive a notification, prompting them to seek immediate medical attention. With Android First Responder, strokes can be detected earlier than ever before, saving millions of lives and preventing long-term disability for even more.


text:
Wall Street Journal, February 19, 2013
By HEIDI WALESON
Wagner: Parsifal, Metropolitan Opera, 15. Februar 2013
New Approaches to Themes Sacred and Sexual
In an imaginative new production of Richard Wagner's "Parsifal" that is perfectly suited to the music, François Girard successfully transforms the opera, which opened at the Metropolitan Opera on Friday, from a faux-Christian rite into a timeless story about a beleaguered community that is held together—barely—by a sacred ritual that is itself under threat. Arresting, consistently absorbing stage pictures expertly follow the mournful flow of this slow-moving epic, while a powerhouse cast of singers and the Met Orchestra under the sure direction of Daniele Gatti ensure that the evening has both gravity and momentum.
In Wagner's libretto, the Holy Grail is protected by an order of knights. Their leader, Amfortas, suffers horribly from a wound that will not heal, and can be cured only by a holy fool who is "enlightened by compassion." Mr. Girard moves "Parsifal" into a postapocalyptic time. In Acts I and III, Michael Levine's striking set is a parched, treeless landscape bisected by a stream which flows with blood, a symbol of the wound that divides the community. The knights, in modern white shirts and black trousers (the costumes are by Thibault Vancraenenbroeck), huddle in a circle on one side of it. On the other is a silent, excluded group of women, an indication that Mr. Girard isn't going along with the libretto's premise that forbidden sexual desire is the root of all evil; rather, it is a symbol of a fractured society.
In Act II Parsifal, the "holy fool," descends into the wound itself—the Met stage is covered with a pool of "blood." Ghostly flower maidens with long black hair and white dresses tempt him in Carolyn Choa's creepy, seductive choreography. He resists them and the seductress Kundry, whose white dress and bed grow red with the blood as she splashes around in it. He recovers the lost Grail spear, kills the sorcerer Klingsor, and returns to the knights to heal Amfortas's wound and become their leader.
David Finn's sensitive lighting dramatizes the deterioration of the knights' home between Acts I and Act III, and Peter Flaherty's video designs are eloquent, stylized abstractions—clouds, planets, landscape and even women's bodies—that enhance the drama of the transformation scenes and the Grail ritual.
Ritual remains a central feature of this production. Yet Mr. Girard also builds a poignantly human story through the principal singers. As Gurnemanz, the éminence grise of the grail knights, bass René Pape was magisterial and warm, with a penetrating delivery that enlivened his long monologues. Baritone Peter Mattei seemed to be living the agony of Amfortas, both in the fierceness of his singing and his halting, excruciating attempts to walk. Jonas Kaufmann made Parsifal complicated and vivid, from the adolescent shrug with which he conveyed his initial lack of understanding to the pure, messianic authority of his final transformation. Evgeny Nikitin was a properly brutal, slashing Klingsor, and Katarina Dalayman brought controlled passion to Kundry, expertly crafting the seduction scene. Mr. Girard has her lift the Grail for the final ritual, as the women and the men mix together onstage for the first time. Wagner might not have approved, but the gesture of reconciliation, overriding the libretto's misogyny and obsession with male purity, fit the music and completed Mr. Girard's moving, modern vision.


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Like in my previous post, a lot of IT professionals are also realizing the global network threat that is upon us.
Crackers (like everybody else) are now able to hookup to a decent Internet-connection with "broadband"-sufficient bandwidth, which also means; crackers are now globally able to take down multi-national corporation networks without too much hassle (provided they're not secured too much, not that it can't be bypassed, it just takes a lot longer to do it).
Steven J. Vaughan-Nichols (Cyber Critic) advises people to jump over from traditional DNS systems, to OpenDNS. After switching my static DNS adresses in my router(s), I found some interesting features in OpenDNS, like the ability to stop phishing-attack attempts, or advanced adress-filtering. They've gone through the works and secured the OpenDNS system to be as secure as can be, without crippling day-to-day performance.
For example, you have to be a registered OpenDNS user with a/multiple registered and confirmed IP-adress(es) to use name resolving through OpenDNS.


Dr. Orna Fisher is a double board-certified plastic surgeon serving Silicon Valley in the San Francisco Bay Area. She is known for her surgical precision and for her personalized approach with patients. Dr. Fisher's mission is to provide you with top quality plastic surgery and the latest cosmetic procedures. Dr. Fisher's practice focuses on aesthetic precision, the highest safety standards, and utmost integrity.
We are conveniently located inside Premier Plastic Surgery in Palo Alto, CA across from Stanford University. We feature a private on-site AAAASF-certified surgical center.
Face Procedures Find the right solution to fit your desired results.
Cosmetic Breast Surgery Gain confidence with an enhanced, and natural appearance.
Body Sculpting A variety of services to help you secure your best shape.
Men's Services Discover the tools to get you to your best self.
Dr. Orna Fisher has been recognized for her excellence in plastic surgery, including numerous awards and peer-reviewed publications. Dr. Fisher has earned multiple Top Doctor designations for plastic surgery, including being named a RealSelf Top Doctor. She was also voted Best Cosmetic Surgeon in the Best of Silver State Awards. Dr. Fisher is a member of multiple prestigious societies including the American Society of Plastic Surgeons and the International Society of Aesthetic Plastic Surgery.
Dr. Fisher has been recognized for her excellence, including numerous awards and peer-reviewed publications. Most recently, Dr. Fisher was voted Best Cosmetic Surgeon in the Best of Silver State Awards and has earned multiple Top Doctor designations for plastic surgery.
Real results from real patients.
To gain a true visual understanding of Dr. Orna Fisher's level of skill and experience, please visit our before and after gallery. Here you will see real photo cases of actual patient results from a variety of different procedures.
I gave Dr. Orna Fisher a rating of six out of five stars! She's double board certified in plastic surgery, and a true artist when it comes to achieving excellent surgical results. Five months ago, after several years of contemplation and due diligence, I underwent a LipoAbdominoplasty to repair loose, dimply skin from a botched liposuction procedure done years earlier. I felt confident in Dr. Fisher's expertise as she was highly recommended by several physicians in Silicon Valley. Dr. Fisher prepared me well for surgery, set realistic expectations, and took care in answering my questions. On the day of surgery and after waking from anesthesia, I was delighted to discover Dr. Fisher had accomplished far more than we previously discussed. While I did experience pain for the initial couple days following surgery, it was well worth it! My tummy looks great, and I'm looking forward to wearing a bikini again this summer.
The office is comfortable and Doctor Fisher is amazing!
Dr. Fisher has been voted as a Top Doctor in multiple patient-choice as well as peer-nominated award and publications. She is a RealSelf Top Doctor and has been a featured guest plastic surgeon in television, radio, and newspaper segments.
We invite you into this section to browse through a sample of articles, awards, and segments featuring Dr. Fisher.


As I prepare to head back home from my business trip I was thinking about this song. I can't physically fly, but I can climb into an airplane and let them fly me home. I guess I am getting a little technical - that's what happens when given free time in a hotel room.
Weather willing I WILL fly and be home once again. Wish me success!