text:
\section{Introduction}
Stock market prediction is the act of trying to determine the future value of a company stock~\cite{malkiel1985random}. Apparently, the successful prediction of a stock's future price can yield significant profit, making the prediction problem an area of strong appeal for both academic researchers and industry practitioners. However, Stock market prediction is usually considered as one of the most challenging issues among time series predictions due to its noise and volatile features~\cite{wang2012novel}. During the past decades, machine learning models, such as Support Vector Regression (SVR)~\cite{basak2007support} and Support Vector Machines(SVM)~\cite{hearst1998support}, have been widely used to predict financial time series and gain high predictive accuracy ~\cite{refenes1994stock,das2012support,lu2009financial}.

How to accurately predict stock movement is still an open question with respect to the economic and social organization of modern society. The well-known efficient-market hypothesis (EMH)~\cite{malkiel2003efficient} suggests that stock prices reflect all currently available information and any price changes based on the newly revealed relevant information. However, due to the implicit correlations between daily events and their effect on the stock prices, finding relevant information that contribute to the change of price for each individual stock are difficult. Besides, the influences of events to stock prices can occur in indirect ways and act in chain reactions, which sets obstacles for precise market prediction. There are three approaches related to the information required to make a prediction. The first approach, technical analysis, is based on the premise that the future behavior of a financial time series is conditioned to its own past. Secondly, fundamental analysis, is based on external information as political and economic factors. A major source of information are text from the internet, these information are taken from unstructured data as news articles, financial reports or even microblogs. Nofsinger et al~\cite{nofsinger2001impact} shows that in some cases, investors tend to buy after positive news resulting in a stress of buying and higher stock prices, they sell after negative news resulting in a decrease of prices. Finally the third approach considers as all relevant information coming from both, financial time series and textual data.

In this work, our goal is to leverage public released financial news and train a model named Attention-based LSTM (At-LSTM) to make prediction on directional changes for both Standard \& Poor's 500 index and individual companies stock price. Our model consists a Recurrent Neural network(RNN) to encode the news text and capture the context information, self attention mechanism is applied to distribute attention on most relative words, news and days . The model input are financial news titles extracted from Reuters and Bloomberg. Our model take advantages from the rapid development of deep neural networks and we show that our model is competitive with other state-of-the-art approaches, demonstrating the effectiveness of recent NLP technology advances for computational finance.

The rest of the paper are organized as follows. We introduce related work in Section 2. Followed by, we present some background and the methodologies for our proposed prediction model in Section 3. Experimental setup and results are demonstrated in Section 4. The paper finally concludes and point out future directions in Section 5.
\section{Related work}
Stock market prediction is an intriguing time-series learning problem in finance and economics, which has attracted a considerable amount of research. Efforts on predicting stock market have been carried out based on different resources and approaches. For example, one of the most widely studied approach relies on analyzing recent prices and volumes on the market~\cite{schoneburg1990stock,akgiray1989conditional,goccken2016integrating,adebiyi2014comparison,kim2012simultaneous}.

Analyzing stock market using relevant text is complicated but intriguing~\cite{wu2009stock,xie2013semantic,ding2014using,ding2015deep,ding2016knowledge,chang2016measuring,peng2015leverage,luss2015predicting,skuza2015sentiment,sehgal2007sops}. For instance, a model with the name Enalyst was introduced in Lavrenko et al. Their goal is to predict stock intraday price trends by analyzing news articles published in the homepage of YAHOO finance. Mittermayer and Knolmayer implemented several prototypes for predicting the short-term market reaction to news based on text mining techniques. Their model forecast 1-day trend of the five major companies indices. Wu et al.~\cite{wu2009stock} predicted stock trends by selecting a representative set of bursty features (keywords) that have impact on individual stocks. Vivek Sehgal et al.~\cite{sehgal2007sops} introduced a method to predict stock market using sentiment. Similarly, Michał Skuza et al.~\cite{skuza2015sentiment} used sentiment from postings on twitter to predict future stock prices. However, these methods have many limitations including unveiling the rules that may govern the dynamics of the market which makes the prediction model incapable to catch the impact of recent trends.

More recently, neural networks have been leveraged to further improve the accuracy of prediction. In general, neural networks are able to learn dense representations of text, which have been shown to be effective on a wide range of NLP problems, given enough training sample. This is the case in stock market prediction where the prices of stocks are available together with a great collection of relevant text data. This provides a good setting for exploring deep learning-based models for stock price prediction. More specifically, dense representations can represent related sentiment, events, and factual information effectively, which can then be extremely challenging to represent using sparse indicator features.

Advance of deep learning models has inspired increasing efforts on stock market prediction by analyzing stock-related text. For example, Ding et al.~\cite{ding2015deep} showed that deep learning representation of event structures yields better accuracy compared to discrete event features.
They further augmented their approach ~\cite{ding2016knowledge} to incorporating an outside knowledge graph into the learning process for event embedding. As another example, Chang et al.~\cite{chang2016measuring} used neural networks to directly learn representations for news abstracts, showing that it is effective for predicting the cumulative abnormal returns of public companies. Other work,e.g.,~\cite{kim2012simultaneous,goccken2016integrating,adebiyi2014comparison}, has proposed different models of neural network that improve the prediction accuracy.

\section{Methodology}
In this section, we first introduce some relative background on our prediction models. Followed by, we introduce the design of our proposed model to predict the directional movements of Standard \& Poor's 500 index and individual companies stock price using financial news titles. The model is named as Attention-based LSTM (At-LSTM) and shown in Fig~\ref{fig:traditional-convolutional-network}.
\subsection{Background}
\subsubsection{Bag of Words Model and Word Embedding}
The Bag-of-Words model is a simplifying representation often used in Natural Language Processing and Information Retrieval~\cite{Bag}. Also known as the vector space model. In this model, a text (such as a sentence or a document) is represented as a bag of its words, disregarding grammar and even word order but keeping multiplicity. The bag-of-words model is commonly used in document classification where the occurrence of each word represents a feature for training a classifier.

Word Embedding is the collective name for a set of language modeling and feature learning techniques in Natural Language Processing (NLP) where words or phrases from the vocabulary are mapped to vectors of real numbers~\cite{Word}. Conceptually it involves a mathematical embedding from a space with one dimension per word to a continuous vector space with much lower dimension. Neural networks can be used to generate this mapping and the most common algorithm used are continuous bag of words(CBOW) and skip-gram algorithm~\cite{mikolov2013efficient}.

Word and phrase embeddings, when used as the underlying input representation, have been shown to boost the performance in NLP tasks such as syntactic parsing and sentiment analysis.

\subsubsection{Convolutional Neural Network}
As shown in figure~\ref{fig:traditional-convolutional-network}, Convolutional Neural Network (CNN) is a class of deep, feed-forward artificial neural networks that has successfully been applied to analyzing visual imagery and NLP related tasks. Same as neural network, CNN are made up of neurons that have learnable weights and biases. Each neuron receives some inputs, performs a dot product and optionally follows it with a non-linearity. A Convolutional layer is composed by four consecutive operations: Convolution, subsampling(pooling), activation and dropout. The convolution layer can help select features so that CNN requires minimal pre-processing compared to other deep learning models. The core part of the Convolutional neural network is the convolution filter.

Convolution filters leverage two important ideas that can help improve a machine learning system: sparse interaction and parameter sharing. Sparse interaction contrasts with traditional neural networks where each output is interactive with each input. As a result, the output is only interactive with a narrow window of the input. Parameter sharing refers to reusing the filter parameters in the convolution operations, while the element in the weight matrix of traditional neural networks are used only once to calculate the output.

\begin{figure}[t!]
	\centering
	\resizebox{\columnwidth}{!}{
	\begin{tikzpicture}
		\node at (0.5,-1){\begin{tabular}{c}input image\\layer $l = 0$\end{tabular}};

		\draw (0,0) -- (1,0) -- (1,1) -- (0,1) -- (0,0);

		\node at (3,3.5){\begin{tabular}{c}convolutional layer\\with non-linearities\\layer $l = 1$\end{tabular}};

		\draw[fill=black,opacity=0.2,draw=black] (2.75,1.25) -- (3.75,1.25) -- (3.75,2.25) -- (2.75,2.25) -- (2.75,1.25);
		\draw[fill=black,opacity=0.2,draw=black] (2.5,1) -- (3.5,1) -- (3.5,2) -- (2.5,2) -- (2.5,1);
		\draw[fill=black,opacity=0.2,draw=black] (2.25,0.75) -- (3.25,0.75) -- (3.25,1.75) -- (2.25,1.75) -- (2.25,0.75);
		\draw[fill=black,opacity=0.2,draw=black] (2,0.5) -- (3,0.5) -- (3,1.5) -- (2,1.5) -- (2,0.5);
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		\draw[fill=black,opacity=0.2,draw=black] (1.5,0) -- (2.5,0) -- (2.5,1) -- (1.5,1) -- (1.5,0);

		\node at (4.5,-1){\begin{tabular}{c}subsampling layer\\layer $l = 3$\end{tabular}};

		\draw[fill=black,opacity=0.2,draw=black] (5,1.25) -- (5.75,1.25) -- (5.75,2) -- (5,2) -- (5,1.25);
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		\draw[fill=black,opacity=0.2,draw=black] (4.25,0.5) -- (5,0.5) -- (5,1.25) -- (4.25,1.25) -- (4.25,0.5);
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		\draw[fill=black,opacity=0.2,draw=black] (3.75,0) -- (4.5,0) -- (4.5,0.75) -- (3.75,0.75) -- (3.75,0);

		\node at (7,3.5){\begin{tabular}{c}convolutional layer\\with non-linearities\\layer $l = 4$\end{tabular}};

		\draw[fill=black,opacity=0.2,draw=black] (7.5,1.75) -- (8.25,1.75) -- (8.25,2.5) -- (7.5,2.5) -- (7.5,1.75);
		\draw[fill=black,opacity=0.2,draw=black] (7.25,1.5) -- (8,1.5) -- (8,2.25) -- (7.25,2.25) -- (7.25,1.5);
		\draw[fill=black,opacity=0.2,draw=black] (7,1.25) -- (7.75,1.25) -- (7.75,2) -- (7,2) -- (7,1.25);
		\draw[fill=black,opacity=0.2,draw=black] (6.75,1) -- (7.5,1) -- (7.5,1.75) -- (6.75,1.75) -- (6.75,1);
		\draw[fill=black,opacity=0.2,draw=black] (6.5,0.75) -- (7.25,0.75) -- (7.25,1.5) -- (6.5,1.5) -- (6.5,0.75);
		\draw[fill=black,opacity=0.2,draw=black] (6.25,0.5) -- (7,0.5) -- (7,1.25) -- (6.25,1.25) -- (6.25,0.5);
		\draw[fill=black,opacity=0.2,draw=black] (6,0.25) -- (6.75,0.25) -- (6.75,1) -- (6,1) -- (6,0.25);
		\draw[fill=black,opacity=0.2,draw=black] (5.75,0) -- (6.5,0) -- (6.5,0.75) -- (5.75,0.75) -- (5.75,0);

		\node at (9.5,-1){\begin{tabular}{c}subsampling layer\\layer $l = 6$\end{tabular}};

		\draw[fill=black,opacity=0.2,draw=black] (10,1.75) -- (10.5,1.75) -- (10.5,2.25) -- (10,2.25) -- (10,1.75);
		\draw[fill=black,opacity=0.2,draw=black] (9.75,1.5) -- (10.25,1.5) -- (10.25,2) -- (9.75,2) -- (9.75,1.5);
		\draw[fill=black,opacity=0.2,draw=black] (9.5,1.25) -- (10,1.25) -- (10,1.75) -- (9.5,1.75) -- (9.5,1.25);
		\draw[fill=black,opacity=0.2,draw=black] (9.25,1) -- (9.75,1) -- (9.75,1.5) -- (9.25,1.5) -- (9.25,1);
		\draw[fill=black,opacity=0.2,draw=black] (9,0.75) -- (9.5,0.75) -- (9.5,1.25) -- (9,1.25) -- (9,0.75);
		\draw[fill=black,opacity=0.2,draw=black] (8.75,0.5) -- (9.25,0.5) -- (9.25,1) -- (8.75,1) -- (8.75,0.5);
		\draw[fill=black,opacity=0.2,draw=black] (8.5,0.25) -- (9,0.25) -- (9,0.75) -- (8.5,0.75) -- (8.5,0.25);
		\draw[fill=black,opacity=0.2,draw=black] (8.25,0) -- (8.75,0) -- (8.75,0.5) -- (8.25,0.5) -- (8.25,0);

		\node at (12,3.5){\begin{tabular}{c}fully connected layer\\layer $l = 7$\end{tabular}};

		\draw[fill=black,draw=black,opacity=0.5] (10.5,0) -- (11,0) -- (12.5,1.75) -- (12,1.75) -- (10.5,0);

		\node at (13,-1){\begin{tabular}{c}fully connected layer\\output layer $l = 8$\end{tabular}};

		\draw[fill=black,draw=black,opacity=0.5] (12.5,0.5) -- (13,0.5) -- (13.65,1.25) -- (13.15,1.25) -- (12.5,0.5);
	\end{tikzpicture}
	}
	\caption[Architecture of a traditional convolutional neural network.]{The architecture of the original convolutional neural network, as introduced by LeCun et al. (1989), alternates between convolutional layers including hyperbolic tangent non-linearities and subsampling layers. In this illustration, the convolutional layers already include non-linearities and, thus, a convolutional layer actually represents two layers. The feature maps of the final subsampling layer are then fed into the actual classifier consisting of an arbitrary number of fully connected layers. The output layer usually uses softmax activation functions.}
	\label{fig:traditional-convolutional-network}
\end{figure}

\subsubsection{Long-Short Term Memory}
Long Short Term Memory (LSTM) networks are a special kind of RNN, proposed by Hochreiter in 1997~\cite{hochreiter1997long}. LSTM units are a building unit for layers of a Recurrent Neural Network(RNN)~\cite{LSTM}. A RNN composed of LSTM units are often called an LSTM network. LSTM are explicitly designed to avoid the long-term dependency problem. A common LSTM unit is composed of a cell, an input gate, an output gate and a forget gate. The cell is responsible for "remembering" values over arbitrary time intervals; hence LSTM is capable of learning long-term dependencies.
\begin{figure}[H]
    \centering

\begin{tikzpicture}[
    prod/.style={circle, draw, inner sep=0pt},
    ct/.style={circle, draw, inner sep=5pt, ultra thick, minimum width=10mm},
    ft/.style={circle, draw, minimum width=8mm, inner sep=1pt},
    filter/.style={circle, draw, minimum width=7mm, inner sep=1pt, path picture={\draw[thick, rounded corners] (path picture bounding box.center)--++(65:2mm)--++(0:1mm);
    \draw[thick, rounded corners] (path picture bounding box.center)--++(245:2mm)--++(180:1mm);}},
    mylabel/.style={font=\scriptsize\sffamily},
    >=LaTeX
    ]

\node[ct, label={[mylabel]Cell}] (ct) {$c_t$};
\node[filter, right=of ct] (int1) {};
\node[prod, right=of int1] (x1) {$\times$};
\node[right=of x1] (ht) {$h_t$};
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\node[filter, left=of x2] (int2) {};
\node[prod, below=5mm of ct] (x3) {$\times$};
\node[ft, below=5mm of x3, label={[mylabel]right:Forget Gate}] (ft) {$f_t$};
\node[ft, above=of x2, label={[mylabel]left:Input Gate}] (it) {$i_t$};
\node[ft, above=of x1, label={[mylabel]left:Output Gate}] (ot) {$o_t$};

\foreach \i/\j in {int2/x2, x2/ct, ct/int1, int1/x1,
            x1/ht, it/x2, ct/it, ct/ot, ot/x1, ft/x3}
    \draw[->] (\i)--(\j);

\draw[->] (ct) to[bend right=45] (ft);

\draw[->] (ct) to[bend right=30] (x3);
\draw[->] (x3) to[bend right=30] (ct);

\node[fit=(int2) (it) (ot) (ft), draw, inner sep=0pt] (fit) {};

\draw[<-] (fit.west|-int2) coordinate (aux)--++(180:7mm) node[left]{$x_t$};
\draw[<-] ([yshift=1mm]aux)--++(135:7mm);
\draw[<-] ([yshift=-1mm]aux)--++(-135:7mm);

\draw[<-] (fit.north-|it) coordinate (aux)--++(90:7mm) node[above]{$x_t$};
\draw[<-] ([xshift=1mm]aux)--++(45:7mm);
\draw[<-] ([xshift=-1mm]aux)--++(135:7mm);

\draw[<-] (fit.north-|ot) coordinate (aux)--++(90:7mm) node[above]{$x_t$};
\draw[<-] ([xshift=1mm]aux)--++(45:7mm);
\draw[<-] ([xshift=-1mm]aux)--++(135:7mm);

\draw[<-] (fit.south-|ft) coordinate (aux)--++(-90:7mm) node[below]{$x_t$};
\draw[<-] ([xshift=1mm]aux)--++(-45:7mm);
\draw[<-] ([xshift=-1mm]aux)--++(-135:7mm);
\end{tikzpicture}
    \caption[Architecture of a traditional Long short term memory neural network.]{The architecture of the original Long short term memory neural network, as introduced by Hochreiter et al. ~\cite{hochreiter1997long},  Here, $f_t$ is the forget gate's activation vector, $i_t$ is the input gate's activation vector, $o_t$ is the output gate's activation vector, $x_t$ is the input vector to LSTM unit, $h_t$ is the output vector of the LSTM unit and $C_t$ is the cell state vector}
    \label{fig:traditional LSTM neural network}
\end{figure}
The expression long-short term refers to the fact that LSTM is a model for the short-term memory which can last for a long period of time. An LSTM is well-suited to classify, process and predict time series given time lags of unknown size and duration between important events. Besides, LSTM shows the promising result in sentence encoding in many NLP applications ~\cite{sutskever2014sequence,gers2002learning}. The LSTM archticture are shown in figure~\ref{fig:traditional LSTM neural network}, the computations of LSTM cells are:
\begin{spacing}{1.0}
\begin{align}
	f_t = \sigma(W_f[h_{t-1},x_t] + b_f)
\end{align}
\begin{align}
	 i_t = \sigma(W_i[h_{t-1},x_t] + b_i)
\end{align}
\begin{align}
	\tilde{C}_t = tanh(W_C[h_{t-1}, x_t] + b_C)
\end{align}
\begin{align}
	C_t = f_t \otimes C_{t-1} + i_t \otimes \tilde{C}_t
\end{align}
\begin{align}
	 o_t = \sigma(W_o[h_{t-1},x_t] + b_o)
\end{align}
\begin{align}
	h_t = o_t \otimes tanh(C_t)
\end{align}
\end{spacing}

The forget gate is described in Equation (1) and is used to decide whether a value should be reserved. The Input gate in Equation (2) is used to control which values should be updated through a certain time step. Here, a new value $\tilde{C}_t$ is created as per Equation (3) using the $tanh$ function. Next, with Equation (4), the cell state will be updated from $C_{t-1}$ to $C_t$, $f_t$ and $i_t$ are used here to decide whether an information should be discarded. in Equation (5), an output gate is used to filter out redundant values. The final output is calculated by Equation (6). $W$ is the weight matrix and $b$ refers to the bias.

\subsubsection{Optimization Algorithm and Loss Function}
In this section, we will briefly introduce the Optimization algorithm and loss function we used in our prediction model.

\begin{itemize}
  \item \textit{\textbf{Adadelta Optimization Algorithm}}: Apart from Stochastic Gradient Descent, Adaptive Gradient Algorithm or the famous Adam algorithm~\cite{kingma2014adam}, we chose Adadelta~\cite{zeiler2012adadelta} as our optimization algorithm. Adadelta is an optimization algorithm that can be used to update network weights iterative based on training data. Adadelta combines the advantages of two other extensions of stochastic gradient descent, Adaptive Gradient Algorithm and Root Mean Square Propagation Algorithm. The method dynamically adapts over time using only first order information and has minimal computational overhead beyond vanilla Stochastic Gradient Descent~\cite{zeiler2012adadelta}. The method requires no manual tuning of a learning rate and appears robust to noisy gradient information, different model architecture choices, various data modalities and selection of hyperparameters.
 \item \textit{\textbf{Cross Entropy Loss}}: In information theory, the cross entropy between two probability distributions $p$ and $q$ over the same underlying set of events measures the average number of bits needed to identify an event drawn from the set~\cite{Cross}. Cross entropy is the most suitable loss function since we want to measure the output from our prediction model with it's true output. A binary cross entropy loss formula are shown in Equation (7). In which, $J(w)$ refers to the loss, $N$ refers to the number of examples, $y_n$ is the expected output and $\tilde{y}_n$ is true output. We use this formula as our loss function in the model.
\begin{align}
    J(w)=-\frac{1}{N}H(p_n,q_n)=-\frac{1}{N}\displaystyle\sum_{i=1}^{N} [y_n*\log\tilde{y}_n+(1-y_n)*\log(1-\tilde{y}_n)]
\end{align}
\end{itemize}
\subsection{Model design}
In this subsection, we introduce the design of our Attention-based LSTM model (At-LSTM). The design has four stages: Input and Embedding layer, news-level Bi-LSTM and self-attention layer, day-level Bi-LSTM and self-attention layer and the final output and prediction layer. These stages will be described below and shown in figure ~\ref{fig:model}.
\begin{figure}[H]
\centering
\includegraphics[width=1.0\textwidth,height=3.5in]{Model.png}
\caption{\label{fig:model}Model Structure}
\end{figure}

\subsubsection{Input and Embedding Layer}
\textit{\textbf{Model Input}}: The same world event can be described in different expression ways. This variety makes the representation space for events very sparse. Several approaches~\cite{ding2015deep,ding2016knowledge} represent the event as tuple $< S; V; O >$ to gain generalization. However, we argue that this representation is oversimplified and might lose lots of valuable information. Instead, we use the entire news title content as our models input and use LSTM-based encoder to encode it to a distributional representation to tackle with sparsity problem.
\\\textit{\textbf{Word and Character Embedding}}: For each input news, we remove the punctuation and then use a word embedding layer to calculate the embedded vectors for each word. The embedding layer takes a sequence of sentences as input, this sequence corresponds to a set of titles of news articles.
These embedding are unique vectors of continuous values with length $w = (w_1,...,w_l)$ and \begin{math}w_i\in R^{m}\end{math} for each word in the training corpus, $m$ is the word level embedding dimension.

Existing pre-trained word embedding such as Glove~\cite{pennington2014glove} and Word2Vec~\cite{mikolov2013efficient} typically comes from general domains( Google News or Wikipedia, etc). However, these word embeddings often fail to capture rich domain specific vocabularies. We therefore train our own word embedding with financial domain news text consisting of news articles from Reuters an Bloomberg. The data are further described in Section 4.

In addition, we leverage character composition from Chen et al.~\cite{chen2017recurrent} and concatenate the character level composition with the original word embedding to gain rich represent for each word. The character composition feeds all characters of each word into a Convolutional Neural Network (CNN) with max-pooling~\cite{kim2014convolutional} to obtain representations $c = (c_1,...,c_l)$ and \begin{math}c_n\in R^{n}\end{math} for each word in the training corpus, $n$ is the character composition dimension. Finally, each word is represented as a concatenation of word-level embedding and character-composition vector $e_i = [w_i;c_i]$. A matrices \begin{math}e^s\in R^{k*(m+n)}\end{math} can be used to represent a news after the embedding layer, where $k$ is the length of the news.

\subsubsection{News Level Bi-LSTM and Self-Attention Layer}
\textit{\textbf{Bi-LSTM Encoding}}: After the embedding layer, we fed the words and their context in the news title into a Bi-LSTM based sentence encoder to perform distributional representation.  Bidirectional LSTM (Bi-LSTM) is a variant of LSTM which shows better result then uni-direction LSTM in recent NLP tasks as they can understand context better. A bidirectional LSTM runs a forward and backward LSTM on a sequence starting from the left and the right end, respectively~\cite{chen2017recurrent}. In this case, Bi-LSTM can not only preserve information from the past, but also catch the information from the future. We obtain the hidden vectors($\overrightarrow{h_i}$ and $\overleftarrow{h_i}$ shown in Equation (8) and (9)) from the sentence encoders and concatenate them to $H^t=[\overrightarrow{h_1};\overleftarrow{h_1},....,\overrightarrow{h_m};\overleftarrow{h_m}]$, \begin{math}H_i^t\in R^{2u}\end{math} represents the $ith$ news title after encoding in date $t$ and $m$ refers to the sequence number.

\begin{spacing}{1.0}
\begin{align}
	\overrightarrow{h_n} = \overrightarrow{LSTM}(e^s)
\end{align}
\begin{align}
	\overleftarrow{h_n} = \overleftarrow{LSTM}(e^s)
\end{align}
\end{spacing}

\textit{\textbf{Word level Self-attention layer}}: Instead of taking the average of the hidden vector after the sentence encoding, we leverage multi-hop self-attention mechanism~\cite{lin2017structured} on top of the Bi-LSTM layer. The attention mechanism takes the whole LSTM hidden states $H^t_i$ as input, and outputs a vector of weights $A$:

\begin{align}
    A = softmax(W_2tanh(W_1H_i^{t\intercal}))
\end{align}

Shown in figure~\ref{fig:attention}, here $W_1$ is a weight matrix with a shape of \begin{math}W_1\in R^{d_a*2u}\end{math} which u refers to the hidden unit of the news level Bi-LSTM. and $W_2$ is a vector of parameters with size \begin{math}W_2\in R^{r*d_a}\end{math}, $d_a$ and $r$ are hyper parameter that can be set arbitrarily. $H_i^t$ are sized \begin{math}H_i^t\in R^{n*2u}\end{math}, and the annotation vector $A$ will have a size \begin{math}A\in R^{r*n}\end{math}, the $softmax()$ ensures all the computed weights sum up to 1. Then we sum up the LSTM hidden states $H_i^t$ according to the weight provided by $A$ to get a vector representation $N_i^t$ for the input sentence. We can deem Equation (10) as a 2-layer MLP without bias, whose hidden units numbers are $d_a$ and parameters are ${W_1,W_2}$. We compute the $r$ weighted sums by multiplying the annotation matrix $A$ and LSTM hidden states $H_i^t$:

\begin{figure}[H]
\centering
\includegraphics[width=1.0\textwidth,height=4.0in]{attention.PNG}
\caption{\label{fig:attention}Self attention mechanism}
\end{figure}

\begin{align}
    N_i^t = AH_i^t
\end{align}

Eventually, the sentence encoding vector $H_i^t$ then becomes a matrix \begin{math}N_i^t\in R^{r*2u}\end{math} and we use $N_i^t$ to represent the $ith$ news title in date $t$ after encoding. By using multi-hop attention mechanism, the vector representation usually focuses on a specific component of the sentence, like a special set of related words or phrases. Therefore, it is expected to reflect an aspect, or component of the semantics in a sentence instead of adding attention on a specific word.

Shown in equation (12), we apply another MLP layer on top of our self-attention layer to learn which attention group should be rewarded with the highest assign value. We name this as attention-over-attention, the weight matrix have a shape of \begin{math}W_3\in R^r\end{math}, and the final representation of the sentence encoding vector $N_i^t$ are shaped as \begin{math}N_i^t\in R^{2u}\end{math}. At last, we use $N_i^t$to represent a news title encoded from the input.

\begin{align}
    N_i^t = tanh(W_3N_i^t+b_1)
\end{align}

\textit{\textbf{News level Self-attention layer}}:
Not all news contributes equally to predicting the stock trend. Hence, in order to reward the news that offers critical information, we apply the same structure multi-hop self-attention on top of the encoding layer to aggregate the news weighted by an assigned attention value. Specifically:
\begin{spacing}{1.0}
\begin{align}
    A = softmax(W_5tanh(W_4N^{t\intercal}))
\end{align}
\begin{align}
   D_t = AN^t
\end{align}
\begin{align}
    D_t = tanh(W_6D_t+b_2)
\end{align}
\end{spacing}

Here, $N^t=(N_1^t,....,N_m^t)$ and \begin{math}N^t\in R^{m*2u}\end{math}, in which $m$ refers to the number of news in date $t$. Note that the weights $[{W_4,W_5,W_6,b_2}]$ in the news level attention layer are different from the word level attention layer weight $[{W_1,W_2,W_3,b_1}]$. A vector $D_t$ represents the temporal sequence for all the news proposed in the date $t$. The merit of using multi-hop self-attention mechanism is that it learns and assign different groups of attention value to the news encoding. Formally, the first group of attention reward the news that contains positive sentiments to the stock market("raise", "growth" or "decrease", "down" etc). Whereas the second group of attention assign there reward to the news that mentions the major companies in the S\&P 500 ("Microsoft","Google" instead of a small company outside of the S\&P 500).

Obviously, the attention layer can be trained end-to-end and thus gradually learn to assign more attention to the reliable and informative news based on its content.
\subsubsection{Day level Bi-LSTM and self-attention layer}
\begin{spacing}{1.0}
\begin{align}
	\overrightarrow{h_i} = \overrightarrow{LSTM}(D_t)
\end{align}
\begin{align}
	\overleftarrow{h_i} = \overleftarrow{LSTM}(D_t)
\end{align}
\end{spacing}
We adopt day level Bi-LSTM to encode the temporal sequence of corpus vectors $D_i$, \begin{math}t\in [1,N]\end{math}. Shown in Equation (16) and (17), We obtain the hidden vectors($\overrightarrow{h_i}$ and $\overleftarrow{h_i}$) from the day-level Bi-LSTM and concatenate them to $H_i=[\overrightarrow{h_1};\overleftarrow{h_1},....,\overrightarrow{h_N};\overleftarrow{h_N}]$, $H_i$ represents a vector that encodes the temporal sequence $D_t$ where $N$ refers to the sequence number.
Since the news published at different dates contribute to the stock trend unequally, we adopt self-attention mechanism again to reward the dates that contribute most to the stock trend prediction, Shown in Equation below:
\begin{spacing}{1.0}
\begin{align}
    A = softmax(W_8tanh(W_7H_i^\intercal))
\end{align}
\begin{align}
    V = AD
\end{align}
\begin{align}
    V = tanh(W_9V+b_3)
\end{align}
\end{spacing}

In the formula, $D=(D_1,....,D_t)$ and \begin{math}D\in R^{N*2v}\end{math} , \begin{math}V\in R^{2v}\end{math} represents the final vector for all news proposed before the prediction date $t+1$ in a delay window with size $N$, where $v$ is the hidden unit number in the day level Bi-LSTM. Note that the weight matrix $[{W_7,W_8,W_9,b_3}]$ in the day level attention layer are different from the weight matrices mentioned in the previous section.

\subsubsection{Output and Prediction Layer}
The last stage of our At-LSTM model is a traditional fully connected layer with softmax as activation function whose output is the probability distribution over labels. In this work, the objective is to forecast the direction of daily price movements of the stock price, this direction are used to create a binary class label where a label [1,0] represents that the stock price will increase and label [0,1] represents that the stock price will decrease.

\section{Experiments}
\subsection{Experimental Setup}
\subsubsection{Data}

We evaluated our model on a data set of financial news collected from Reuters and Bloomberg over the time period from October 2006 to November 2013. This data set was made publicly available by Ding et al.~\cite{ding2014using} and shown in table~\ref{tab:Data set}. We further collected data from Reuters for 473 companies listed in the Standard \& Poor's 500 over the time period started from November 2013 to march 2018. Meanwhile, the historical stock price data from October 2006 to March 2018 for all individual shares in Standard \& Poor's 500 are collected from Yahoo Finance. The second part of the data are used for individual stock price prediction and shown in table ~\ref{tab:individual}. Due to the page limit, We only show the major companies listed in the S\&P 500, this will also be applied in the result section.

\begin{table}[H]
\centering
\begin{tabular}{ |p{3cm}||p{3cm}|p{3cm}|p{3cm}| }
 \hline
 \multicolumn{4}{|c|}{Data for S\&P 500 index prediction} \\
 \hline
 Data set & Training & Development & Testing\\
 \hline
Time interval & 20/10/2006-27/06/2012 & 28/06/2012-13/03/2013&   14/03/2013-20/11/2013\\
\hline
News&  445,262 & 55,658 & 55,658\\
\hline
\end{tabular}\caption{\label{tab:Data set}Data for S\&P 500 index prediction}
\end{table}

\begin{table}
\centering
\begin{tabular}{ |p{3cm}||p{3cm}|p{3cm}|p{3cm}| }
 \hline
 \multicolumn{4}{|c|}{Data for individual stock prediction} \\
 \hline
 Company Symbol & Training news& Development news& Testing news\\
 \hline
GOOG & 5744 & 718 & 718\\
\hline
AMZN & 3245 & 406 & 405\\
\hline
CSCO & 2471 & 309 & 308\\
 \hline
MSFT & 4056 & 406 & 405\\
\hline
AAPL & 9720 & 1215 & 1214\\
 \hline
INTC & 4355 & 545 & 544\\
\hline
IBM & 2016 & 252 & 252\\
 \hline
AMD & 1224 & 153 & 153\\
\hline
NVDA & 1440 & 168 & 167\\
\hline
QCOM & 924 & 116 & 115\\
\hline
WMT& 2793 & 350 & 349\\
\hline
T& 1228 & 154 & 153\\
\hline
\end{tabular}\caption{\label{tab:individual}Data for individual stock prediction: The time interval of the news crawled from Reuters various between different companies. Hence, here we only present the number of articles in the training, development and testing set respectively.}
\end{table}

Following Ding et al.~\cite{ding2014using} we focus on the news headlines instead of the full content of the news articles for prediction since they found it produced better results. We use news articles on each day and predict if the S\&P 500 index closing price movement (increase or decrease) in the day $t+1$ compared with the closing price on day $t$.

\subsubsection{Implementation Details}
As mentioned in the previous section, We pre-trained 100 dimentional word embedding~\cite{mikolov2013efficient} with skip-gram algorithm on the data set shown in table~\ref{tab:Data set}, the size of the trained vocabulary is 153,214. In addition, firm names and an UNK token to represent any words out of the vocabulary are added to the vocabulary set, having an initial embedding initialized randomly with Gaussian samples. The word embedding are fine-tuned during model training. The character embedding has 15 dimensions, and CNN filters length are [1,3,5] respectively, each of those are 32 dimensions. The news level Bi-LSTM and day-level Bi-LSTM both have 300 hidden units, the day level LSTM window size $N$ has been set to 7. Hyper-parameters $d_a$ and $r$ in the self attention layer are set to 600 and 10, respectively. Mentioned in section 3, we use Adadelta for our optimization algorithm, the initial learning rate has been set to 0.04. Our model are trained for 200 Epoch.

\subsection{Base Lines and Proposed Model}
In this subsection, we propose a few baselines to compare with our proposed model. For the sake of simplicity, the following notation identifies each model:
\begin{itemize}
    \item \textit{\textbf{SVM}}: Luss and d'Aspremont et al. ~\cite{luss2015predicting} propose using Bags-of-Words to represent news documents, and constructed the prediction model using Support Vector Machines (SVMs).
    \item \textit{\textbf{Bag-At-LSTM}}: At-LSTM without sentence encoder. We take the average of the word embedding inputs instead of using Bi-LSTM to encode the news title.
    \item \textit{\textbf{WEB-At-LSTM}}: Same as our proposed model but without the character level composition.
    \item \textit{\textbf{Ab-At-LSTM}}: Instead of the news title, we use the news abstract as input for our model, the model structure remains the same.
    \item \textit{\textbf{Doc-At-LSTM}}: We further leverage a Hierarchical Attention Networks proposed by Yang et al.~\cite{yang2016hierarchical} to represent the entire news Document, this model adds a sentence level attention layer into our proposed model to differentiate more and less important content when constructing the document representation.
    \item \textit{\textbf{Tech-At-LSTM}}: We concatenate seven Technical indicator leveraged from Zhai et al.~\cite{zhai2007combining} shown in figure ~\ref{fig:tech} with the vector $V$ after the day level LSTM layer and fed together into the prediction layer.

    \begin{figure}[H]
    \centering
    \includegraphics[width=1.0\textwidth,height=3.0in]{tech.PNG}
    \caption{\label{fig:tech}Self attention mechanism}
    \end{figure}

    \item \textit{\textbf{CNN-LSTM}}: We use CNN instead of the news level self-attention layer. Note that the word and day level self-attention layer still remains the same. We want to see how well the self-attention layer works compared to CNN which is good at capturing local and semantic information from texts.

    \item \textit{\textbf{E-NN}}: Ding et al.~\cite{ding2014using} reported a system that uses structure event tuples input and standard neural network prediction model.
    \item \textit{\textbf{EB-CNN}}: Ding et al.~\cite{ding2015deep} proposed a model using event embedding input and Convolutional Neural Network as prediction model.
    \item \textit{\textbf{KGEB-CNN}}: Ding et al.~\cite{ding2016knowledge} further incorporated an outside knowledge graph into the learning process for event embedding. The model structure is the same as EB-CNN.
\end{itemize}

\subsection{Result and Discussion}
Shown in table~\ref{tab:Result}, the results on the comparison between the models SVM and the rest of the models indicates that deep neural network model achieves better performance than the SVM model. Comparison between Bag-At-LSTM and At-LSTM demonstrates that sentence encoding with LSTM have slightly better result than Bag-of-words model. Furthermore, WEB-At-LSTM and At-LSTM indicates that the character level composition helps improved the models accuracy. The technical Indicator leveraged from Zhai et al.~\cite{zhai2007combining} doesn't show any performance improvement to our model. In contrast, it results in a decline on the accuracy and it might be caused by adding noise to the dense representation vector $V$ before the output and prediction layer. The comparison between CNN-LSTM and At-LSTM shows that the news level self-attention layer can help capture more relevant news titles and their temporal features. As Ding et al. concluded in~\cite{ding2014using}, the news titles might contain more useful information where as the abstract or article might cause some negative effect to the model. The Ab-At-LSTM and Doc-At-LSTM confirmed this viewpoint since their accuracy are lower than the proposed model that only used the information from the title.

Our proposed model has a 65.53\% max accuracy and a average accuracy of 63.06\% which is lower than the KGEB-CNN proposed by Ding et al., this is likely due to Knowledge graph event embedding (KGEB) is a more powerful method for model the content in news titles than the sequence embedding shown in this work.
\begin{table}[H]
\centering
\begin{tabular}{|p{3cm}||p{3cm}|p{3cm}|}
 \hline
 \multicolumn{3}{|c|}{S\&P 500 index prediction Experimental Results} \\
 \hline
 Model & Average Accuracy & Max Accuracy\\
 \hline
SVM & 56.38\% & --\\
\hline
Bag-At-LSTM & 61.93\% & 63.06\%\\
 \hline
WEB-At-LSTM & 62.51\% & 64.42\%\\
 \hline
Ab-At-LSTM & 60.6\% & 61.93\%\\
 \hline
Doc-At-LSTM & 59.96\% & 60.6\%\\
\hline
Tech-At-LSTM & 62.51\% & 64.42\%\\
\hline
CNN-LSTM & 61.36\% & 63.06\%\\
\hline
E-NN & 58.83\% & -- \\
\hline
EB-CNN & 64.21\% & -- \\
\hline
KGEB-CNN & \textbf{66.93}\% & -- \\
\hline
At-LSTM & 63.06\% & \textbf{65.53}\%\\
\hline
\end{tabular}\caption{\label{tab:Result}Experimental Results: We don't know the max accuracy in~\cite{ding2014using,ding2015deep,ding2016knowledge}, so here we assume the accuracy presented in those papers are the average accuracy and use -- to represent the max accuracy.}
\end{table}

We use the At-LSTM model for individual stock price prediction after confirmed that it out performs other approaches. The results are shown in table~\ref{tab:indiResult}, each stock shown in the table has more than 66\% accuracy and the company WALMART has an average accuracy of 70.36\% and max accuracy of 72.06\%. Apparently, predicting individual stock price leads to higher accuracy than predicting S\&P 500 index, and this is mainly due to the news article we used for input. In terms of the individual stock prediction, the news article we used are more relative to it's corresponding company. In contrast, we used the full corpus as input for S\&P 500 index prediction and certainly this adds noise to our model and hence effects the accuracy.
\begin{table
\centering
\begin{tabular}{|p{3cm}||p{3cm}|p{3cm}|}
 \hline
 \multicolumn{3}{|c|}{Individual stock prediction Experimental Results} \\
 \hline
 Company & Average Accuracy & Max Accuracy\\
 \hline
GOOG & 68.75\% & 71.25\%\\
\hline
AMZN & 67.32\% & 69.46\% \\
\hline
CSCO & 66.82\% & 67.62\%\\
 \hline
MSFT & 67.92\% & 69.89\%\\
\hline
AAPL & 67.52\% & 69.42\%\\
 \hline
INTC & 67.12\% & 67.63\%\\
\hline
IBM & 69.49\% & 71.41\%\\
 \hline
AMD & 66.12\% & 69.10\%\\
\hline
NVDA & 69.35\% & 70.51\%\\
\hline
QCOM & 68.53\% & 69.70\%\\
\hline
WMT& \textbf{70.36}\% & \textbf{72.06}\%\\
\hline
T& 68.53\% & 69.70\%\\
\hline
\end{tabular}\caption{\label{tab:indiResult}Experimental Results for individual stock prediction: We only list the major companies that are in the S\&P 500.}
\end{table}
\section{Conclusion}
This paper has been motivated by the successes of Deep learning methods in Natural Language Processing task. we proposed a Attention-based LSTM model(At-LSTM) to predict the directional movements of Standard \& Poor's 500 index and individual companies stock price using financial news titles. Experimental results suggests that our model is promising and competitive with the state-of-the-art model which incorporate knowledge graph into the learning process of event embeddings~\cite{ding2016knowledge}.

There are some directions in our future work. While previous work and our result has found that including the body text of the news performs worse than just the headline, there may be useful information to extract from the body text, other directions include looking at predicting price movements at a range of time horizons, in order to gauge empirically how quickly information are absorbed in the market, and relate this to the finance literature on the topic. The financial time series are known by its volatility, in many cases small changes in the series that can be interpreted as noise. Moreover, the elimination of small variations makes the model focus only on news with significant variation on prices which might lead to accuracy increase.

\bibliographystyle{elsarticle-num}


text:
Q: Android bluetooth get the heart rate measurement I want to get the value of the HRM of an "A&D UA-651BLE" device.
this is what's written in the datasheet of this device to get the HRM value:

*

*Set the application to pairing mode to start scanning.

*Start pairing of A&D BLE device following each instruction manual.

*At pairing mode, the application should set time and date and any other device settings
  to A&D BLE device. After successful pairing, A&D BLE device shows "End" on the screen.

*Take a measurement and finish the measurement, then A&D BLE device start BLE
  connection with advertising. The application starts scanning with suitable interval so that
  the application catches the advertising of A&D BLE device as soon as it can.

*At initial connection or pairing, the Application set "2" to CCCD (Client Characteristic
  Configuration Descriptor) so that A&D BLE device sends a measurement data with
  Indication.

*After A&D device recognizes to be set "2" to CCCD and to be synchronized time and date
  within 5 seconds after connected, send the data with Indication.

*If the timeout set CCCD and time and date is expired, A&D BLE device will not send data
  and store the data in memory. The stored data in A&D BLE device can send next
  successful connection.

this is my service code:
public void setCharacteristicNotification(BluetoothGattCharacteristic characteristic,
                                              boolean enabled) {
        if (mBluetoothAdapter == null || mBluetoothGatt == null) {
            Log.w(TAG, "BluetoothAdapter not initialized");
            return;
        }

        mBluetoothGatt.setCharacteristicNotification(characteristic, enabled);

        // This is specific to Heart Rate Measurement.
        if (UUID_HEART_RATE_MEASUREMENT.equals(characteristic.getUuid())) {
            BluetoothGattDescriptor descriptor = characteristic.getDescriptor(
                    UUID.fromString(SampleGattAttributes.CLIENT_CHARACTERISTIC_CONFIG));
            descriptor.setValue(BluetoothGattDescriptor.ENABLE_NOTIFICATION_VALUE);
            mBluetoothGatt.writeDescriptor(descriptor);
        }
    }

and this is the method that read data:
final byte[] data = characteristic.getValue();
            if (data != null && data.length > 0) {
                final StringBuilder stringBuilder = new StringBuilder(data.length);
                for(byte byteChar : data)
                    stringBuilder.append(String.format("%02X ", byteChar));
                Log.e("HRM value",stringBuilder.toString());
                dataComposition.put(characteristic.getUuid().toString(),stringBuilder.toString());
                intent.putExtra(EXTRA_DATA,dataComposition);
            }

the problem is that this code doesn't return any data !!

A: There's an Android Open Source Project example that does precisely this, easiest option would be to clone the android-BluetoothLeGatt code, build and compare it to your own. If you can't spot the difference / issue simply deploy both app's and step through both sets of code. Having some known working code will also help to rule out the possibility that the HRM is not functioning properly.

A: Do you have and example , i try this with equal device and i cant obtain the information y try with
public String response() {
if (mConnected) {
    mBluetoothLeService.readCharacteristic(characteristica);
    byte response[] = characteristica.getValue();
    String respuesta = ReadBytes(response);
    mBluetoothLeService.disconnect();
    return respuesta;
} else {
    return null;
}
}

redpajama_set_name: RedPajamaStackExchange


text:
Q: Parameters to helpers I did this custom helper:
 public static System.Web.Mvc.MvcHtmlString RzTextBoxForCNPJ<TModel, TValue>(this System.Web.Mvc.HtmlHelper<TModel> html,
            System.Linq.Expressions.Expression<System.Func<TModel, TValue>> expression)
        {
            System.Collections.Generic.Dictionary<string, object> htmlAttributes = new System.Collections.Generic.Dictionary<string, object>();

            htmlAttributes.Add("class", "form-control cnpj");

            return (html.TextBoxFor(expression, htmlAttributes));
        }

To avoid need to add class attributes to field of specific type.
But. When using this helper:
@RzHelpers.RzTextBoxForCNPJ(model => model.Cnpj)

I received this error:
CS7036: There is no argument given that corresponds to the required formal parameter 'expression' of 'RzHelpers.RzTextBoxForCNPJ(HtmlHelper, Expression>)'
As if I was passed wrong type or number of parameters.
It is not my first helper and never must pass the first parameter, only from the 2nd onwards.
What am I forgotting?

A: You are calling this method as a static method of RzHelpers class. So the method is not used as an extension and requires the first parameter. Hence the error.
You should be calling it as a method of an instance you are extending, HtmlHelper:
@Html.RzTextBoxForCNPJ(model => model.Cnpj)


President Obama said Wednesday that the Ukrainian government would face "consequences" if it crosses the line in cracking down on protesters in Kiev.
Obama said the government is "primarily responsible" for ensuring the protests remain peaceful, but also warned that protesters need to demonstrate without resorting to violence.
"I want to be very clear, as we work through these next several days in Ukraine, that we're going to be watching closely, and we expect the Ukrainian government to show restraint, to not resort to violence in dealing with peaceful protesters," Obama said. "We've also said we expect peaceful protesters to remain peaceful, and we'll be monitoring very closely the situation, recognizing that with our European partners and the international community there will be consequences if people step over the line."
Obama added of the consequences: "And that includes making sure the Ukrainian military does not step into what should be a set of issues that can be resolved by civilians."
It's not the first time Obama has referenced a "line" when it comes to civil unrest in a foreign country. Obama infamously set the so-called "red line" in Syria, but once the Syrian government crossed that line, Obama found little support in Congress for military action.
Update 5:22 p.m.: A number of senators are now calling for sanctions.
"The United States condemns in the strongest terms the violence that's taking place. And we have been deeply engaged with our European partners as well as the Ukrainian government and the opposition to try to ensure that that violence ends. But we hold the Ukrainian government primarily responsible for making sure that it is dealing with peaceful protesters in an appropriate way, that the Ukrainian people are able to assemble and speak freely about their interests without fear of repression. And I want to be very clear as we work through these next several days in Ukraine that we're going to be watching closely and we expect the Ukrainian government to show restraint, to not resort to violence in dealing with peaceful protestors. We've also said we expect peaceful protestors to remain peaceful and we'll be monitoring very closely the situation, recognizing that with our European partners and the international community there will be consequences if people step over the line. And that includes making sure the Ukrainian military does not step into what should be a set of issues that can be resolved by civilians. So the United States will continue to engage with all sides in the dispute in Ukraine and ultimately our interest is to make sure the Ukrainian people can express their own desires and we believe a large majority of Ukrainians are interested in an integration with Europe and the commerce and cultural exchanges that are possible for them to expand opportunity and prosperity. But regardless of how the Ukrainian people determine their own future it is important the people themseves make those decisions and that's what the United States will continue to strive to achieve."


Job retraining programs have become an economic lifeline for millions of Americans who were hard hit hard during the Great Recession.
At 60 years old, the last thing Barbara Stillwell wanted to do was to look for a new job. But when the economy tanked and the housing bubble burst in 2008, the Bellingham, Wash., resident had no choice but to hit the job market again after being laid off from her real estate position [source: Heeringa].
With no money coming in and the bills piling up, Stillwell decided to undergo a career makeover. She walked into the office of WorkSource Northwest, which provides unemployed workers with new job skills, and asked for help. WorkSource helped Stillwell obtain a scholarship to the local community college where she embarked on a new career in the medical billing field. She graduated in 2009 [source: Heeringa].
Then reality set in. No one wanted to hire an older woman since there were many younger, more qualified people around. Undaunted, Stillwell went back to WorkSource and updated her computer skills, which ultimately landed her a part-time job at the local American Red Cross chapter. Stillwell hopes a full-time position will open up [source: Heeringa].
With the country slowly recovering from the greatest economic upheaval since the Great Depression, job retraining programs, such as those provided by WorkSource, have become an economic lifeline for millions of Americans. Jobs are so tight that some experts say retraining is the only option that people have, for both white and blue collar workers, who were hard hit equally hard during the Great Recession [source: Semuels].
Yet, training for a new career is extremely daunting and downright scary, especially for those who have spent years, sometimes decades, working in one industry. Job training programs are designed to give workers the skills they need to become more competitive in the job market. Some of those skills are generic, such as learning new computer programs, while other programs provide more detailed instruction.
Before you sign up for job retraining and change careers, we'll tell you what to consider.


Wittgenstein? He drags in Wittgenstein? Probably drags in other malcontents and roughnecks, too. Why doesn't he cite someone who knows, like La Wall or Jessup? But no, he's a hoity-toity New Yorker, probably from uptown who hangs out at MOMA and has long hair in a pony tail and a beard. Jeez, for the price of coupla beers at Stinky's Place ol' Moony Stevens would have given him more BS than you can fit into a Ford Expedition.
From the NEw York Times, HARK!!!! Bull has now become MAINSTREAM!!! AcaDEMic!!
Harry G. Frankfurt, emeritus professor of philosophy at Princeton, whose essay on the art of hokum, first written in 1986, has now become a small book published by Princeton University Press.
arry G. Frankfurt, 76, is a moral philosopher of international reputation and a professor emeritus at Princeton. He is also the author of a book recently published by the Princeton University Press that is the first in the publishing house's distinguished history to carry a title most newspapers, including this one, would find unfit to print. The work is called "On Bull - - - - ."
"One of the most salient features of our culture is that there is so much [bull]. Everyone knows this. Each of us contributes his share. But we tend to take the situation for granted. Most people are rather confident of their ability to recognize [bull] and to avoid being taken in by it. So the phenomenon has not aroused much deliberate concern, nor attracted much sustained inquiry."
The essay goes on to lament that lack of inquiry, despite the universality of the phenomenon. "Even the most basic and preliminary questions about [bull] remain, after all," Mr. Frankfurt writes, "not only unanswered but unasked."
The balance of the work tries, with the help of Wittgenstein, Pound, St. Augustine and the spy novelist Eric Ambler, among others, to ask some of the preliminary questions - to define the nature of a thing recognized by all but understood by none.
What is [bull], after all? Mr. Frankfurt points out it is neither fish nor fowl. Those who produce it certainly aren't honest, but neither are they liars, given that the liar and the honest man are linked in their common, if not identical, regard for the truth.
"It is impossible for someone to lie unless he thinks he knows the truth," Mr. Frankfurt writes. "A person who lies is thereby responding to the truth, and he is to that extent respectful of it."
The bull artist, on the other hand, cares nothing for truth or falsehood. The only thing that matters to him is "getting away with what he says," Mr. Frankfurt writes. An advertiser or a politician or talk show host given to [bull] "does not reject the authority of the truth, as the liar does, and oppose himself to it," he writes. "He pays no attention to it at all."
And this makes him, Mr. Frankfurt says, potentially more harmful than any liar, because any culture and he means this culture rife with [bull] is one in danger of rejecting "the possibility of knowing how things truly are." It follows that any form of political argument or intellectual analysis or commercial appeal is only as legitimate, and true, as it is persuasive. There is no other court of appeal.
The reader is left to imagine a culture in which institutions, leaders, events, ethics feel improvised and lacking in substance. "All that is solid," as Marx once wrote, "melts into air."
That was no I -- that was someone scared shitless by confronting Death. See the little reflection of the skull?
A true I is a whispy sorta mammalucca indeed, conquering nations and hearts while occupying no more space than a dream about sunlight.
Did somebody want an "I"? CLICK!
Many happy returns, Mom....over and over!!
As Amos says, FT doesn't get it. What he doesn't get is that hundredth posts have no special significance here on the MOAB. Sure, there are significant numbers, in fact there's one in roughly every hundred posts. But, only MOABites know the formula for determining MOAB-significant numbers. It involves calculations based on the orbital velocities of certain small third-world countries and the gross national product of a wandering herd of wilderbests wyldabeets gnus at a secret indisclosed location somewhere within one of those same third-world countries. We're certainly not gonna share that information with the likes of Teddy.
Flaming O'Teddy just doesn't get it.
Even when he gets what he wants, he doesn't get it.
It's mine. Eat my shorts!
so has MOAB received any Valentines? Hey! Is MOAB single? looking? playing the field? Where's FOAB Whatever happenned to him?
Inquiring minds want to know - or at least be pointed to pertinent posts if I somehow missed this vital information on the first go round.
There have been sixty-two hundred and ninety-five posts to the MOAB.
It is February 14th, 2005.
OK so now I've posted here.
I am still discovering the bells and whistles of mudcat.
Yesterday I went through the midi songs and talked to myself on Mudcat chat.
and I was so busy showing B. the back door I forgot - we're closing in on 6300!
use with caution. Please turn the lights out if you are the last to leave.
Churchy comes closest, but Grundoon is the master.
Concur. Grundoon is a transcended Zen master avatar, an inhabited identity created and operated by one who has reached enlightenment.
Pogo and Albert are mere students in comparison.
I have answered Bunn's question via the Secret Mudcat PM network.
Now, along the same lines, I've been re-re-etc.-reading Pogo, and have decided that my favorite character therein is Grundoon. Grundoon is sooooooooo wise for his or her years, and his or her remarks are always wise and dead on point.
In object-oriented programming, the ability of different objects to respond, each in its own way, to the same message.
a difference in DNA sequence among individuals. Genetic variations occurring in more than 1% of a population can be used for genetic linkage analysis.
The ability of different object to respond to the same message in specific ways: Objects can have very different implementations of the same message. In Smalltalk polymorphic behaviour in responce to messages is independent of inheritance.
What has Polly morphed into now? Is she one of those anime characters?
Sheeshe -- ya wanna be careful they don't mess with your inheritance, ya know, coming out about polymorphism and all.
Say it long and say it loud.
I'm polymorphic and I'm proud.
and I'm proud ! ! ! !
Gee, that's not the way she looked when I came in. She was dressed in her old jeans and a sweat shirt, old sneakers and a red bandana around her hair, and she was out back turning the compost pile with a bucket and a spade fork. She was muttering something about never getting any time any more to do stuff on her own, because she's called to the computer so often.
That's not why she was cryin', Rapaire. It's cuz I tole her about you getting so you were gonna announce yourself as being anthropomorphic. Reckon she preferred you'da kept that part to yourself.
Sunday. I come in through the back door 'cause the front door is locked. Poor ol' Mom's all along, sitting in her rockin' chair, jist a-rockin' while she strums her banjo, smokin' her ol' corncob pipe, takin' a pull now an' then from the jug of corn squeezins thats a-sittin' on the porch next to her, an' jist cryin' her heart out cuz none of her chilluns have come 'round ta see her yet.
Misspellings, inadvertent and otherwise, are also required (Section 17.4.5.23). In fact, everything this is not implied is required.
Sure, as long as the "vice-versa" is understood I can live with what Amos said.
No, not with a GUT like that!
Many words, some exalted, some with sizzle, some adulatory, some derogatory, can be used to try to define the mystery that is Rapaire; but I am fairly certain that "cute" ain't one.
Why destroy elegance just for the sake of a trivial clarification?
"Harmony through BS, and vice-verse."
Besides, GUBS requires it (section 43.22.0.6).
I think the problem is one of disparately designed resonant surfaces resulting in disjoint reality constructs. What we need to establish here is a Hutchins Consort Effect. This will do away with obsessive individualism and acheive Harmony through BS, which should also be our slogan, I think.
For maximum effect, please imagine the following post being read by Billy Bob Thornton in character as Karl from "Slingblade".
My GUT was my birthday gift to MOAB. I'm changing the name, by the way, from BSGUT to Grand Unified BS Law, or GUBS Law, or just plain GUBS when you're speaking among us plain folks. It'll be easier that way, and you can actually pronounce it.
I have also forwarded the change to the Nobel Prize Committee.
And HAAAAPPPY BIRTHDAY to all the Rapaire Wanna-be's out there in Bibliolandia!!
Sorry SRS, I was out in the wild rain at the most unusual concert, I will have to start a thread about it, most unusual and delightful. A family of violins ranging from teeny soprano down to a super-large contrabass all built at the same period by the same artist, tuned to the same intervals as a violin.


Brent crude oil prices fell more than 1 percent on Monday after Washington said it may grant waivers to sanctions against Iran's oil exports next month, and as Saudi Arabia was said to be replacing any potential shortfall from Iran.
International benchmark Brent crude oil futures LCOc1 were at $83.26 per barrel at 0352 GMT, down 90 cents, or 1.1 percent, from their last close.
In a sign that Iran oil exports won't fall to nothing from November, India will buy 9 million barrels of Iranian crude next month, Reuters reported on Friday.
Traders said ongoing concerns that the U.S.-Chinese trade war could slow down economic growth also weighed on crude on Monday.
China's stocks fell sharply on Monday despite an announcement from Beijing over the weekend that it would slash the level of cash that banks must hold as reserves, a sign of underlying investor anxiety over the heated Sino-U.S. trade war.
Further weighing on oil prices was "chatter that Saudi Arabia has replaced all of Iran's lost oil", said Stephen Innes, head of trading for Asia-Pacific at futures brokerage Oanda in Singapore.


New Dehli, Oct 14 (IANS) Ben Edlund, who was an executive producer and a writer of "Supernatural" from season two to season eight, is amazed at the long run of the American fantasy horror television series, which features Jared Padalecki and Jenson Ackles.
"I love those guys – Jared and Jenson. It was a big part of my life to work on 'Supernatural'. It (working with them again) would be a lot of fun. I ended up working with Eric Kripke (creator of 'Supernatural') on 'Revolution', and that was really cool," Edlund told IANS on the phone from Sydney.
"I occasionally go to events to see Jared and Jenson, and we always reminisce fondly. To work with those guys again on 'Supernatural' would be cool." he added.
Along with writing and being executive producer/co-executive producer/consulting producer on "Supernatural", Edlund also directed some episodes of the show, which narrates the Winchester brothers' monster-hunting adventures along with dealing with personal turbulances. The show airs in India on AXN.
In fact, Edlund got a place in the narrative as a character named Chuck Shurley, who was a writer and a prophet and went by the pen name "Carver Edlund". It was an amalgamation of Edlund's surname with that of producer/writer Jeremy Carver.
What about taking the story on to the big screen?
"That would be epic and pretty huge. I wonder what it will be. I haven't really thought about it. I think it will be interesting, but then they are still going strong, which is amazing. They are working on the 13th season," he said.
Edlund became famous with his satirical superhero The Tick which he created in 1986 as a mascot for a newsletter for a chain of comic book stores. he infused life into his creation with an animated show in the 1990s, and then brought it back in 2001 in a live action version. It was short-lived as the show was cancelled due to low ratings but attained cult status soon after.
After writing and producing for shows like "Angel", "Supernatural", "Firefly" and "Revolution", Edlund revisited his creation for a live-action series of "The Tick" for Amazon Prime Video.
"The Tick" is about bue-clad title superhero (played by Peter Serafinowicz) and how he finds his side-kick in a reluctant accountant, Arthur (essayed by Griffin Newman). it is about their adventures and run-ins with villains. Edlund is attached to the Amazon Prime Video project as an executive producer.
Talking about his journey, he said: "Being a part of this showbiz has been a learning (experience). I enjoy the fact we really never stop learning. It has been a process of trying to find out what makes people care about things and how you get people's attention.
Apart from TV shows, Edlund also works on his music.
Editors note: I can find nothing to suggest that there is an Supernatural movie in the works, right now. I believe this was speculation that got a little lost in translation.


Have games of chance with the Devil ever worked out well?
We caught a glimpse of it at last year's E3, but we finally got an extended look at the 1930's style platform shooter, Cuphead, at Microsoft's press conference. The video game lovechild of brothers Chad and Jared Moldenhauer follows the exploits of Cuphead and Mugman, a duo tricked into doing the Devil's bidding after a dice game gone awry.
Seriously, though, who takes bets from Beelzebub anymore? You'd think at this point our concave-headed heroes would have laughed The Hellbeast out of the room once he made his offer. I suppose having liquid receptacles for heads may affect their decision-making skills slightly, though.
The game features two player local co-op and will be headed to both Xbox One and PC sometime next year. Check out the E3 trailer below.
Cuphead looks like an absolute blast and is part of Microsoft's initiative to flood their library with as many innovative indie games as humanly possible. Given the relative dearth of couch co-op offerings available on the Xbox One (but not PC, oddly enough), I'm sure co-op enthusiasts the world round will be pleased to have as many options as possible, especially if they're as unique as this.
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Minnesota Named One Of The Happiest States Again
Lauren Wells Published: September 22, 2022
vistoff
Here we go again! Minnesota was just named one of the happiest states in the country and it's not the first time we've taken that title, either.
In fact, a few years back, we were also named one of the happiest states in the country in a study by WalletHub. At the time in 2019, we were ranked at number three, making us the third happiest state in the entire country.
The year prior, we were also named one of the happiest states, along with the neighboring state of Wisconsin. At the time, Minnesota was the third happiest state and Wisconsin made the cut at number eleven. Not too shabby if you ask me!
We've taken many titles over the years, including being named one of the best states to live in just a few short months ago. We were also labeled some of the hardest-working people, as Minnesota was named one of the hardest-working states.
Now, Minnesota has been named one of the happiest states yet again, once again by WalletHub. They released a study of the happiest states in the country Tuesday (September 20th).
So where did Minnesota rank this time? Once again, Minnesota came in at number three. The study looked at things like those that get enough sleep, of which Minnesota came in at number two. Minnesota also has a high volunteer rate, which helped our high ranking. Minnesota also placed high on the list of safest states.
Part of me was surprised by this study, given the fact that Minnesota is known for its long and cold winters as opposed to warm and tropical states. However, there is just something about midwesterners. We enjoy the simple things in life and a quiet way of life.
It should be noted that other factors in this study include divorce rate, income growth, unemployment rates, work hours and more. As for Wisconsin, they came in at number twenty-one on the study.
So what state was named the happiest in the country? Hawaii! That one is sort of a no-brainer and doesn't need an explanation. Maryland, New Jersey and Utah also round out the top five.
If you are thinking of a move, it may be best to avoid places like Louisiana, Arkansas and West Virginia. Only time will tell which study we top next!
Ways A Minnesota Summer Is Much Like Winter
It might not seem like it on the surface, but a Minnesota summer has a lot in common with a Minnesota winter. From a strictly meteorological standpoint, they are the polar opposites of each other. However, many common themes, elements, and principals thread their way through each of the seasons.
Some of the similarities involve the way we prep for each of the seasons. Other shared items involve rituals, chores, finances, and yes - even weather. (I know, you're thinking - how can the weather be the same in the summer and the winter; trust me - it can be - read on to find out how.)
One thing is for certain, though, as to how Minnesotans deal with summer and winter: True to our nature, we confront and embrace each of the seasons all at the same time.
Phrases Minnesotans Use That Outsiders Might Not Understand
It's safe to say that probably every part of the country has their own slang words, terms, or language that they use that's commonly understood among members of the community. But - if someone from outside of the area were to hear them, they would either not understand them, or they would misinterpret the meaning that the words have in that particular geographic region. It's no different in Minnesota. Minnesotans have some 'vague' terms that might be open to interpretation from others outside of the state, but have very definitive meanings to natives.
Source: Minnesota Named One Of The Happiest States Again
Filed Under: lifestyle, Minnesota, study, wallethub
Categories: Featured, Lifestyle, Minnesota News
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redpajama_set_name: RedPajamaCommonCrawl


KNOXVILLE, Tenn. (WVLT) -- Chocolate is always a good remedy when you feel stressed or sad, but it doesn't seem like the best idea if you're trying to live a healthy life.
"People think chocolate is bad for you," said Dr. Pete Sulack, America's Leading Stress Expert.
Would you believe it if he said chocolate is actually healthy?
"Because of the antioxidants, its going to help with your heart, with your brain," said Dr. Pete. "It has good, healthy fats."
Dr. Pete Sulack shopped around Earth Fare in Turkey Creek. He told Local 8 News dark chocolate is full of antioxidants. It's even a super food.
"Some say its almost 20 times more powerful as an antioxidant than even blueberries and strawberries," said Dr. Pete.
Cocoa makes dark chocolate bitter, but that's also what makes it healthy.
"The cocoa is an evergreen tree," said Dr. Pete. "The coco beans or the cocoa seeds are where we get all the nutrients."
So the more cocoa, the better.
"So if you want the benefits of chocolate, you wanna make sure you're consuming it with at least 70% cocoa because it's rich in antioxidants, its anti-inflammatory in nature and something you can be very comfortable consuming on a regular basis," said Dr. Pete.
Eating dark chocolate is all about moderation. A chocolate bar should last you a week, not an hour.
That's about ten grams a day. That will help fight cardiovascular disease by increasing blood flow and decreasing blood pressure.
It's even good for your skin.
"If you consume dark chocolate 30 minutes before going out into the sun, it will bring blood flow to the skin and prevent you from burning," said Dr. Pete.
You can even increase the benefits -- Eat a piece of dark chocolate with an organic apple. That combination boosts the antioxidants.


Many of my family members were killed in Auschwitz and Dachau. One of my family members, Romani Rose, runs the Sinti and Roma Documentation Center in Manheim Germany that tells the histsory. He is one of many members of my family who have been activists for Sinti and Roma even building the first memorial site.
My surviving family members like my grandparents and Great Aunts/Uncles still have the tattoos. for them, tattoos had a very different meaning.
My grandmother and brother comparing tattoos.
My Grandmother was in Auschwitz. Her tattoo used to have a "Z" on it which was how Gypsies were identified in the camps. (Z for Zigeuner which means Gypsy in German) But for fear of discrimination she had it changed to an "A" which is how the Jews were identified.
This is my Papu who recently passed away. He was a victim of Mengela at Auschwitz and underwent the water tests.
This is how we honor those who have fallen by saying NEVER AGAIN!


After twelve grueling weeks of training it was finally time for the Elite squad to travel up to Sheffield to compete in the Four Nations Karate Championships. The competition was set over two days with the under 14's competing on the Saturday. Carla Rudkin was first up to represent the SEKF and did the squad proud with a great Passai, unfortunately she was up against the eventual winner. However, the girl that managed silver, Carla had beaten in the WKU at the beginning of the month. This was very encouraging. Alessio Sebis was up next in his category and performed two good kata's. On this occasion he was narrowly beaten in the 2nd round which was a shame especially as he had won both a gold and silver in his previous competitions.
In the kumite, again Carla was first up and beat her opponent with a great head kick, unfortunately she narrowly lost in the following round. Harrison West was up next in the under 12's and fought well, but on this occasion he did not succeed to the next round. One of our gold medal hopes, Alessio, was suffering from a migraine and I was forced to take the decision to withdraw him from the competition. It then become the job of Elliot Lowman to take control of the under 12's category, which he did by winning several rounds and managed a well respected quarter final placing, just missing out on a bronze medal. The category had four pages of competitors. The last competitor up on the Saturday was James Arnold in the under 14's, this was a difficult category for James as he was only twelve last month. This did not stop him demolishing his first opponent, unfortunately he lost in the next round but gave a good performance.
On the Sunday, Patchouli Philips was first up in the Kata, unfortunately, after performing a good kata she did not manage to advance further. Zak Smyth, on the other hand was in a zone during his Kata category and performed several great kata's just missing out on a bronze medal.
In the Kumite, Damla Kaya fought well winning her first round fight with a determined attitude and went on to narrowly miss out on a bronze medal. Zak was up next and won his first round fight with ease. However in the 2nd round, Zak was dwarfed by his opponent and even though he fought very well, he could not find a way through. Louis Naraine our other gold medal hopeful was last to compete and he did not disappoint. After watching his first fight it was clear to me that it was Louis's time to shine and no one was going to stand in his way of the Four Nations under 16's title. After many rounds of great fighting, movement and mental strength, I am proud to announce that Louis was crowned champion.
Thanks to Danny and all the parents who supported our squad.


Rusty is a young housecat; barely more than a kitten. But the scent of adventure, the thrill of the hunt, has always remained in his nostrils, and he dreams of freedom. One day, he brazenly ventures into the woods, where the wildcats live, and finds an entire new life. He is grafted into the ThunderClan, one of four clans of wild cats which live in the woods behind the TwoLegPlace, as Firepaw. Thus begins Into the Wild, the first book in the Warriors series.
Into the Wild is written in a delightful, sort of modern-day Redwall type of book. The characters are all cats, which think, speak and act as humans. Whenever I come across books like this, I think of the protagonist as a man trapped inside of an animal's body. Indeed, the story of Firepaw, formerly Rusty, is far beyond that of a cat, but rather that of a complex character, which struggles with loyalty, questioning his beliefs, and fitting in with others. That being said, this book is written for younger audiences, around middle school, thus some of the themes are not-so-subtly woven in.
The plot is simple: Rusty, a former housecat, wanders into the woods, and into the territory of the ThunderClan. Ridiculed as a kittypet, he is nonetheless accepted as an apprentice among the ranks of the ThunderClan warriors. ThunderClan is in a precarious struggle for survival against the other three wildcat clans, all of whom are running short on food with the encroaching of the TwoLegs (humans). And when the ShadowClan drives out the WindClan and dominates the RiverClan, it is up to ThunderClan to stand against the wildcats of the north.
I found little fault with this book. My biggest problem would be the cats' belief in a StarClan, or the stars, which are the 'souls' of all the deceased cats of the clan. Cats pray to this StarClan, and rely on it for guidance. Worship of ancestors, even in fiction, isn't right; and of course there is no mention of the One True God. A second, smaller gripe would be that the Clan leaders each have nine lives, and can reincarnate when seriously wounded. Other than that, there is little in this book to be worried about.
This series is written for a middle-school audience, and I think that for these grades this book is excellent. I highly recommend it to fans of Brian Jacques as well, who will find the talking animals and interesting depiction of events from an animal's perspective refreshingly similar to the Redwall series. Still; these aren't just for younger audiences – I am a senior in high school and still enjoyed reading this book.
Great review. I enjoyed this series except for the StarClan thing that you mentioned, I found that a little over the top.
I really like this review. I have read many of these books and at first found them great(Except for what you mentioned) but after going into the third series it became a bit annoying. The Starclan thing becomes bigger. And I stopped reading these books. But the first series is really good(a series consist of six books)!
@Beckyelsie: I have been a huge Redwall fan and I found these similar to it.
Yeah there are lots of books by Erin Hunter. Well the three authors who make up Erin hunter. They also have another series about a Polar bear I believe. But I haven't read it.


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Prince William 'wept' watching BBC's Diana interview as 'something inside him snapped'
The interview is said to have had an 'extraordinary' impact on Prince William, then just 13, as he reportedly 'hated the idea of everything being on television'
Prince William welcomes probe into Diana interview
Prince William 'wept' as he watched Princess Diana's famous Panorama interview and 'something inside him snapped,' a royal author claims.
The Princess sent shockwaves through the monarchy with the Panorama interview, which included candid details about her marriage and Prince Charles's relationship with Camilla Parker Bowles, his now wife.
Diana admitted her relationship with James Hewitt, a former British cavalry officer, was "more than friendship" and confessed that she loved him and also questioned Charles's suitability as King.
The 1995 interview is said to have had an 'extraordinary' impact on Prince William, then just 13.
"Before the 58 minutes ended, William was weeping," Robert Lacey writes in Battle Of Brothers: William, Harry And The Inside Story Of A Family In Tumult.
The Princess of Wales with her sons Prince William and Prince Harry in 1995 (
Princess Diana's brother Earl Spencer says it's 'duty' to protect her reputation
Prince Harry criticised for silence over BBC's Diana interview as William backs inquiry
He claimed William's Eton housemaster, Dr Andrew Gailey, found him visibly upset.
"Gailey told Diana that he found her son slumped on the sofa, his eyes red with tears," Mr Lacey wrote in the Daily Mail.
Simone Simmons, the Princess's confidante and faith-healer, said the Prince had 'hated everything being on TV,' according to Lacey.
Martin Bashir's interview with Princess Diana (
Corbis via Getty Images)
Prince William has welcomed an inquiry (
Diana's brother Earl Spencer 'not at all satisfied' with BBC Panorama inquiry
Prince William has welcomed an inquiry into allegations that BBC journalist Martin Bashir forged documents to convince Princess Diana to speak with him for a Panorama interview in 1995.
The Duke of Cambridge is said to have been in talks with the BBC over the past two weeks after the allegation once again reemerged.
Diana's brother Earl Spencer alleged that he was shown "false bank statements" by Bashir and they were used to help the reporter gain access to the princess.
According to the BBC, Diana wrote a note saying she did not see the false bank statements and they played no part in her decision to speak with Bashir.
Martin Bashir in the interview with the Princess
Diana's brother Earl Spencer alleged that he was shown "false bank statements" by Bashir
The BBC has said the investigation will begin straight away and seek to discover what steps the broadcaster and Bashir took to land the interview.
Prince William said on Wednesday: "The independent investigation is a step in the right direction.
"It should help establish the truth behind the actions that led to the Panorama interview and subsequent decisions taken by those in the BBC at the time."
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The god King Belus was in Greek mythology a king of Egypt and father of Aegyptus and Danaus and (usually) the brother to Agenor an ancient mystery. The wife of the god King Belus has been named as Achiroe, or Side (eponym of the Phoenician city of Sidon) It also has a phonic connection to the mythology of the Celtic Sidhe and the Foe of man the Foemorian Giants. Diodorus Siculus claims that the god King Belus founded a colony on the river Euphrates and appointed the priests-astrologers whom the Babylonians call Chaldeans who like the priests of Egypt are exempt from taxation and other service to the state. The god King Belus was the son of Poseidon and Libya. He may also be Busiris, son of Libya, ruler of Egypt, killed by Heracles, although Heracles was born many generations after the god King Belus since he was a grandchild of Perseus. According to Pausanias, Belus founded a temple of Heracles in Babylon. The Bibliotheca also claims that Agenor was Belus' twin brother. The god King Belus ruled in Egypt, and Agenor ruled over Sidon and Tyre in Phoenicia. The wife of the god King Belus has been named as Achiroe, allegedly daughter of the river-god Nilus. Her sons Aegyptus and Danaus were twins. Later Aegyptus ruled over Egypt and Arabia, and Danaus ruled over Libya. Pseudo-Apollodorus says that it was Euripides who added Cepheus and Phineus as additional sons of the god King Belus of Egyptian mythology. In the Hesiodic Catalogue of Women, the god King Belus was also the father of a daughter named Thronia on whom Hermaon that is Hermes, fathered Arabus, presumably the eponym of Arabia.
A final source of the ancient Egyptian myth is the writings of ancient Greek and Roman writers like Herodotus and Diodorus Siculus, who described Egyptian mythology and religion in the last centuries of its existence. Prominent among these writers is Plutarch, whose work De Iside et Osiride contains, among other things, the longest ancient account of the myth and mystery of Osiris in ancient Egyptian mythology. These authors' knowledge of Egyptian religion was limited because they were excluded from many ancient religious practices, and their statements about Egyptian mythology beliefs are affected by their biases about Egypt's culture watch video for more from Ancient Mystery on Youtube.


Antibes Bistro, 112 Suffolk Street.
And it's the middlebrow restaurants ($30-$60 per person for an entree, wine, an appetizer and/or dessert) that tend to make me hyper-critical. I don't expect a trained chef to cook my supper, but I assume the menu was put together by one. The food has to be better than what I bang out in my own kitchen most nights or I'm leaving dissatisfied. I don't expect a sommelier, white tablecloths or uniformed servers, but I want a pleasant vibe, proper service and decent wine choices. I want a place where the dining experience is lovely, and the food impressive. Antibes bistro is such a place.
It's a perfect spot for a romantic dinner, with candlelight, exposed brick and lots of dark wood. But I also bring guests from out of town or other neighborhoods here, just to show it off. I love hearing, "I wish this place were in my neighborhood!" — from Montreal friends, Long Island family, Washington Heights band-mates and Los Angeles associates. Every neighborhood ought to have a casual French joint with great service, jazz playing in the background (some nights it's live jazz) and homemade ice cream in flavors like salty caramel or white fig. Of course the food has to be special if I'm going to walk out feeling like I got my money's worth. At Antibes, it is.
The theme running through the menu: little twists on typical bistro dishes. The salmon comes with cauliflower puree and ratatouille, but the addition of harissa oil is unexpected. The tiramisu is topped with halva shavings. In lesser hands, such flourishes could be annoying, even disastrous. But Chef David's restraint is such that the exotic touches don't seem the least bit out of place; they just add interest to the small, carefully chosen menu.
There is no deep fryer, so you won't be having steak frites at Antibes. But there is a NY Strip on the menu, and they frequently run a steak au poivre special. They also have trout, salmon, a vegetarian pasta dish and a decent brick chicken. If you're looking for "wow factor" the Guiness-braised short rib is the way to go. The tender meat is served in a rich reduction of the braising liquid, accompanied by a rectangle of au gratin potatoes and sauteed spinach. I've worked through most of the menu, and keep coming back to the short rib. It's intense, but balanced in flavor. The portion size is more French than American, leaving you satisfied instead of overstuffed with rich food.
On the appetizer list, the truffle-roasted asparagus is a good way to go. The other night they had an arctic char with broiled fennel (an appetizer special) that blew our table of four away. I'm a fan of starting with a fish course, and this was an excellent choice. The modest piece of broiled fish didn't stand a chance.
The wine list is very French (by the glass offerings start at $7). They have a full bar, so if you must have a glass of Armagnac to end your meal (goes well with the homemade ice cream) it's not a problem. Antibes is open for dinner every night and brunch on weekends. I don't do brunch, but I hear theirs is popular. I prefer to go slightly early on a weeknight, when Cynthia and I can pretty much have the place to ourselves. It's more romantic that way.
Last year I was very enthusiastic about Massaya Rosé, from the Bekaa Valley in Lebanon. With the season for drinking pink wine upon us I was very curious about how the 2011 vintage had shaped up. It's in at Seward Park Liquors for $13 a bottle, and it's very good. Though the wine is from Lebanon the style is pure Provence: some fruit on the nose, very dry in the mouth with a refreshing lingering acidity. It's a little simple, but can easily go toe to toe with similarly priced examples from France. I expect this will be my rosé of choice this season. It's also an exceptional value.


Long dead hero comes back to life in 'Detective Comics' #996 – AiPT!
This character inspired one of the biggest DCU characters of 2018.
The latest story arc in Detective Comics has been filled with deaths, near deaths, and mysterious twists. Someone is going after Batman who also knows he's Bruce Wayne. In the latest issue out today Batman spans the globe to figure out who is messing with him.
Much of this issue has action and adventure, though it's near the end that the big reveal of a long dead DCU character makes his appearance. After scaling snowy mountains and dueling with his old sensei from the Temple Kirigi Batman carries on his journey.
He's off to New Mexico where seemingly out of nowhere Mister Miracle rears his head. But not just any Mister Miracle, Thaddeus Brown, the first ever Mister Miracle.
This character not only mentored Scott Free aka the current Mister Miracle, but Scott took up the name Mister Miracle in honor of Thaddeus' circus stage name he used as an escape artist. And you thought you knew everything about Mister Miracle! Thaddeus has also been long dead with his death appearing Mister Miracle #1 back in 1971.
In the final moments, Batman and Thaddeus get flipped under the Earth. To where we won't know till issue #997.
This is only part of the story and I recommend reading the entire issue. It's a good story arc! You can read it digitally today.


Are You Fully Maximising The Use Of Your CRM System?
Are You Getting the Most Out of Your CRM System?
My mother had had it with Android phones and decided to switch to Apple!
So, she purchased the iPhone 5 several years back. She bought it purely because it was 1) premium (and she could afford it) 2) it stood out from android phones because of its craftsmanship and finally, 3) ease of use.
But what she probably didn't realise is that she was paying a premium price for much more than just the above. In actuality, she was also getting a phone with high quality hardware and software, better security, a smooth operating system (iOS), and better quality applications, just to name a few.
Okay, this is not an article promoting iPhones. What I'm trying to get at is that she probably isn't fully maximising the function of her iPhone and is therefore not reaping its full benefits. Which is a waste of RM4K, in my books.
This applies to any technology we use. Let's take the example of a CRM system like Salesforce. Salesforce is a premium and mature CRM system with a full suite of customer relationship management functions. If you're not maximising all of its features, you're not getting a high ROI.
A question you should ask is are you maximising the use of your CRM system?
Sales, marketing, and customer service are all interconnected. If you're using a CRM system merely for sales automation, then you're missing out. Why? The sales data that currently sits in your system is incredibly valuable to the marketing department. Based on which stage of the buying cycle the customer is in, marketing can use that data to send out targeted campaigns.
As for customer service, getting access to customer history, transactions, etc. are key to providing high quality support. If you're a Salesforce user, you may want to consider looking at its other products. The beauty of Salesforce is that each product is connected to the other, and everything sits on one single platform. That way, data updated by a sales rep, can also be viewed by a customer service rep in real-time. Amazing, isn't it?
Wouldn't you want to know what your customers are saying about you or a competitor? Listen to what they are saying on their preferred channel of communication.
Have regular chats with employees who are using the CRM system. Ask them what they like and dislike about the system, and what are the gaps they observe in the system.
Alan Baker, president and chief consultant of consulting firm Spitfire Innovations said: "The design of [a] CRM application can always be improved.
One common issue with Malaysian companies using CRM systems is they don't spend as much time and money on change management.
It's an accepted fact that some people don't like, or even, reject change. This is why it's crucial to facilitate their adoption process. Understanding the use and functionality of the system is one thing, but seeing how it fits into their daily activities, how it simplifies their job, and making them understand the long-term ROI, are key to getting their buy-in.
Come up with SMART milestones and ensure that this is communicated to employees and teams who are using the system. The last thing you need is a CRM implementation failure.
There a host of things you can do internally to continuously educate employees about your CRM system. Ambassadors are key for this. Appoint employees or leaders who get on well with others to turn employees' learning experience into a fun and engaging one.
They can regularly run mini activities together with the marketing department to test employees on their CRM knowledge, and to reward them as they progress along their learning and adoption journey.
Continuous learning is key. Even if you have been an Oracle, SAP, or Salesforce user for years, there are always updates and new features being added to the system. If you are looking to upskill as an admin, developer, or business analyst, look for good learning modules which provide online courses and projects for you to complete at your own pace.
Salesforce Trailhead is an example of a fun and engaging way to learn, upskill, and get certified.
"The challenges faced was mostly experienced when the team was trying to figure out what data we wanted to incorporate from the previous tool and mapping it accordingly. It was a lot of strategising and trying to understand how differently Pardot functioned. The good thing about Pardot and on a more general level is that, Salesforce has a very extensive knowledge base. From video tutorials, to articles, to guided learning modules, webinars and community groups, the team and I made sure to soak up as much as we could and work our way through the tool with these information at hand.
Sometimes all you need is some expert advice. Get a certified Salesforce expert from Lava Protocols who is able to not just listen but to truly understand your business issues and needs.
At Lava Protocols, we get a lot of customers asking us to advise them on which technologies best suit their needs, how to integrate them into their business processes, and how to get everyone on board with the solution.
Our experience spans across various industries. Our top industries include aviation, tourism, education, IT, service, and government.
Some of the top companies we have worked with include Taylor's University, MAVCOM, MAB, Maxis, and Tourism Malaysia. See more of our clients here.
Email us at asklava@lavaprotocols.com or call +603 7885 9720 to speak to one of our consultants if you'd like to get the most out of your CRM.
Lava is a cloud service provider and an authorised Salesforce Partner in Malaysia. We have more than a decade of experience in cloud solutions which includes marketing automation, CRM implementation, change management, and consultation. We pride ourselves in not just being a CRM partner but in also understanding the needs of our customers and taking their business to the next level.


Once again Sidney Crosby was named the most valuable player of the playoffs.
The Pittsburgh Penguins captain finished the postseason with eight goals and 19 assists in 25 games.
Crosby has become the third player to win back-to-back Conn Smythes, he won the award a year ago when his team defeated the San Jose Sharks to win the Stanley Cup. The other two players to have earned that honour two consecutive years were goaltender Bernie Parent of the Philadelphia Flyers in 1974 and 1975 and Mario Lemieux in 1991 and 1992.
"There's so many guys who could have easily won that, and I think we just had a group of guys that understood big moments and knew how to find ways to win," said Crosby.
When asked who he would have voted for, Crosby listed a group of teammates he believed were worthy of the award.
"I think [Evgeni Malkin] comes to mind right away," said Crosby. "If you could somehow put our two goaltenders on there — you know, there were games that they stole for us throughout the playoffs.
Last night the Pittsburgh Penguins defeated the Nashville Predators to capture their fifth championship in franchise history.