text:
\section{Introduction}

\IEEEPARstart{T}{he} \emph{efficient market hypothesis} (EMH) asserts that financial market valuations incorporate all existing, new, and even hidden information, since investors act as rational agents who seek to maximize profits. Behavioral finance \cite{kahneman-tversky-1979} has challenged this notion by emphasizing the important role of behavioral and emotional factors, including \emph{social mood} \cite{Prechter99}, in financial decision-making. As a consequence, measuring investor and social mood has become a key research issue in financial prediction.

Traditionally, public and investor mood are measured by surveys. For example, the Gallup Life Evaluation Index measures the general well-being of the US public on a daily basis by conducting a survey across a representative sample of the US population. Investor mood is likewise assessed by surveys, in which investors or newsletter writers rate their current stance on the market, e.g. Daily Investor Sentiment \footnote{\url{www.trade-futures.com}} and Investor Intelligence \footnote{\url{http://www.investorsintelligence.com/x/us_advisors_sentiment.html}}. In spite of their popularity, surveys are, however, resource intensive and thus expensive to conduct, and can be subject to problems related to responder truthfulness \cite{Da2010,singer2002}, individual biases, social biases, and group think.

In recent years, researchers have explored a variety of methods to compute indicators of the public's sentiment and mood state from large-scale online data. This approach holds considerable promise. First, computational analysis of public sentiment or mood may be more rapid, accurate and cost-effective to conduct than large-scale surveys. Second, there now exists considerable support for the claim that the resulting public mood and sentiment indicators are indeed valid measurements of public sentiment and mood, even to the degree that they have been found to predict a variety of socio-economic phenomena, including presidential elections \cite{Tumasjan2010}, commercial sales \cite{Choi2009,Mishne2006}, and influenza epidemics \cite{Culotta:epidemics2010}. It is of considerable interest to behavioral finance that a respectable and growing amount of literature in this area has shown that computational indicators of public sentiment may also have predictive value with respect to financial market movements \cite{Antweiler2004,BollenMao2011,Bordino2011:websearch,Da2010,Da2011,Preis2010}.

To the best of our knowledge, three distinct classes of online data sources have been investigated for financial prediction. First, \emph{news media} content has been shown to be an important factor shaping investor sentiment. For instance, Tetlock found that high levels of pessimism in the Wall Street precede lower market returns the following day \cite{Tetlock2007}. This effect has also been observed at the level of individual firms, with high negative sentiment forecasting lower firm earnings \cite{Tetock08:language}. In \cite{Schumaker09:textmining} it was shown that adding textual features of news to a stock prediction system can improve the forecasting accuracy.

Second, \emph{web search (query) data} has been shown to be related to and even predictive of market fluctuations. Search volumes of stock names reveal investor attention and interest, and high search volumes thus predict higher stock prices in the short-term, and price reversals in the long-term \cite{Da2010}. Also, search volumes of stocks correlate highly with trading volumes of the corresponding stocks, with peaks of search volume anticipating peaks of trading volume by one day or more \cite{Bordino2011:websearch}. Similar phenomena have been found at the weekly level \cite{Preis2010}.

Third, \emph{social media feeds} are becoming an important source of data to support the measurement of investor and social mood extraction. In an early study, Internet stock message boards were studied to predict market volatility and trading volumes \cite{Antweiler2004}. In past couple of years, public mood indicators extracted from social networks such as Facebook \cite{Karabulut2011}, LiveJournal \cite{Gilbert2010} and Twitter \cite{BollenMao2011} have been used to predict stock market fluctuations.

Together these results are highly suggestive that a variety of web-scale data sources may provide predictive power in financial analytics. However, each of the mentioned investigations uses different types of web data to predict different financial indicators. It is not clear which mood indicators constructed from particular data sources most effectively capture investor mood-related signals and thereby provide the best predictive power.

In this paper, we therefore collect multiple data sources, i.e. surveys, news headlines, search engine data and Twitter feeds, from which we define a variety of sentiment indicators, i.e. Survey Investor Sentiment, Negative News Sentiment, Google search volumes of financial terms, Twitter Investor Sentiment and Tweet volumes of financial terms. Subsequently, we determine the predictive value of these sentiment indicators over a range of financial indicators, i.e. Dow Jones Industrial Average price, trading volumes, market volatility (VIX) and the price of gold.

\section{Data Collection and Sentiment Analysis}
In this section we outline our data collection methods, and how we computed investor sentiment indicators from Twitter, news, and search engine data.

\subsection{Survey Data}
Surveys are the most direct and common method for collecting investor sentiment. Investor Intelligence (II), published by an investment services company, determines whether opinion in over one hundred independent market newsletters points towards a bullish, bearish or correction market. II has been available at a weekly level dating to 1964. Daily Sentiment Index (DSI) provides daily market sentiment readings on all active US markets daily since 1987, and is one of the most popular short-term market sentiment indices for futures traders. High vs. low DSI values of respectively above 90\% or below 10\%, suggests that a short-term top or bottom is either developing, or has been achieved.

\subsection{News Media}
We chose eight news media outlets to collect our news data from: Wall Street Journal, Bloomberg, Forbes.com, Reuters Business\& Finance, BusinessWeek, Financial Times, CNNMoney and CNBC. These are the top news sources for financial traders and investors. In order to track recent and featured news from these sources, we followed their respective Twitter accounts (\emph{``wsjusnews"}, \emph{``wsjbreakingnews"}, \emph{``wsjmarkets"}, \emph{``bloombergnews"}, \emph{``bloombergnow"}, \emph{``bloomberg"}, \emph{``forbes"}, \emph{``BusinessWeek"}, \emph{``Reuters Business"}, \emph{``reuters\_biz"}, \emph{``financialtimes"}, \emph{``FinancialTimes"}, \emph{``CNNMoney"}, \emph{``CNBC"}). We then extracted and parsed the URLs from these tweets, saving the story headlines as our news corpus. This approach of using headlines is based on previous research that studied stock price reaction to news headlines \cite{chan2003:newsheadlines}.

Previous research has demonstrated that negative mood seems to be more predictive of financial market values than positive mood \cite{Tetlock2007}. There are two well-accepted financial lexicons for negative word identification. One is the Harvard IV-4 dictionary \footnote{\url{http://www.wjh.harvard.edu/~inquirer/}} as used in \cite{Tetlock2007, Tetock08:language}. The other \footnote{\url{http://www.nd.edu/~mcdonald/Word_Lists.html}} is developed by Loughran and McDonald in \cite{Loughran2011:liability}, which is shown to better reflect the tone of financial text than the Harvard IV-dictionary. In our paper, we apply the latter financial negative lexicon to our news headlines. We count the total number of words in a news headline and take the ratio of the number of \emph{negative sentiment} words to the total number of words in the headline. Then, we sum the emotional ratio and divide by the total number of news articles on the same day, yielding our \emph{Negative News Sentiment} score.  Fig. \ref{wf} shows the example of top negative financial terms of the news headlines from July 31st to August 9th 2011, when the DJIA dropped while market volatility increased. As a result words such as \emph{``downgrade"}, \emph{``cut"},\emph{``crisis"} and \emph{``losses"} frequently occur in news headlines in that period.

\begin{figure}
	\begin{center}
		\includegraphics[width=9cm,height=6cm]{wf.pdf}
		\caption{\label{wf} Frequency of negative terms in News headlines from July 31st to August 9th 2011.}
	\end{center}
\end{figure}

\subsection{Search Engine Data}
\label{sec:searchengine}
Previous research has shown that search volume itself can be a mood indicator for financial market \cite{Bordino2011:websearch,Da2010,Da2011,Preis2010}. In \cite{Da2010}, it has been shown that the more people search on economic negative terms such as \emph{``recession"} and \emph{``bankruptcy"}, the more pessimistic people feel about the economy. To create a search query-based indicator of financial mood, we took the following steps. First, we downloaded the weekly search volume data for a set of seed queries including \emph{``dow jones"}, \emph{``stock market"}, \emph{``stock to buy"}, \emph{``stock"}, \emph{``bullish"}, \emph{``bearish"}, \emph{``financial news"} and \emph{``wall street"} from Google Insights for Search (GIS)\footnote{\url{http://www.google.com/insights/search/}}. GIS is a Google service that provides search volume data from January 2004 to the present. Second, to more fully capture search activity related to the financial markets we expanded these seed keywords with those terms that are top relevant search terms as recommend by GIS. This procedure resulted in a lexicon of about 26 financial search terms  for which we again retrieved GIS search frequency indices, resulting in a time series of GIS frequencies for all searches containing those 26 terms as shown in Table \ref{st}.

\begin{table}[!htb]
		\caption{26 Search Terms\label{st} }
		\begin{center}
		\begin{tabular}{c}
		\hline
DJIA, Dow,Dow Jones, Dow Jones Industrial Average,
\\
bearish, bear market,best stock, bullish, bull market,
\\
finance, finance news, financial news, financial market, \\
long stock, SP500, stock, stock market, stock decline, stock fall,
\\
stock market crash, stock market news, stock market today, \\
stock price, stock to buy, wall street, wall street news today \\

\hline
	\end{tabular}
	\end{center}
	\end{table}

\subsection{Social Media Data}
The enormous amount of social media data that has become available in recent years has provided significant research opportunities for social scientists and computer scientists. In fact, Twitter, which is now one of the most popular microblogging services, has been extensively used for real time sentiment tracking and public mood modeling \cite{bollen10:mood,Golder11:nature}. And its financial predictive power has also been explored. In \cite{Huberman2010} , it has been shown that Twitter content and sentiment can be used to forecast box-office revenues of movies. In \cite{zhang:coins2010}, the correlation between emotional tweets and financial market indicators are studied, indicating that the percentage of emotional tweets is significantly negatively correlated with Dow Jones, NASDAQ and S\&P500 values, but positively correlated with VIX values. Moreover, in \cite{BollenMao2011} a six-dimensional model of public emotions is derived from Twitter  (Calm, Alert, Sure, Vital, Kind and Happy) and found to have significant predictive power with respect to DJIA fluctuations.

In this paper, we use a 15\%-30\% random sample of all public tweets posted every day from July 2010 to September 2011. From this collection, we define two Twitter-based financial mood indicators: Twitter Investor Sentiment (TIS) and Tweet volumes of financial search terms (TV-FST). These are discussed in greater detail below.

\subsubsection{Twitter Investor Sentiment}
We simply define a tweet as bullish if it contains the term \emph{``bullish"}, and bearish if it contains the \emph{``bearish"}. On the basis of the number of Bearish and Bullish tweets on a given day, we define the investor sentiment score, Twitter Investor Sentiment ($TIS$) on day $t$, denoted $TIS_{t}$ as follows:

\begin{equation}
TIS_{t}=\frac{N_{bull}}{N_{bull}+N_{bear}}
\label{tis}
\end{equation}

where $N_{bull}$ is the number of bullish tweets on day $t$ and $N_{bear}$ is the number of bearish tweets on day $t$ .

\subsubsection{Tweet Volumes of Financial Search Terms (TV-FST)}
\label{sec:tv-fst}
As mentioned in Section~\ref{sec:searchengine}, search query volume of stock names and various financial/economic terms has been used in previous research as proxies of public and investor mood. Our proposal is to apply a similar approach to define our Tweet Volumes of Financial Search Terms indicator (TV-FST). We want to compare Tweet volumes and Search volumes of the same search queries. To do so, we use the following procedure for data processing: First, we compute both the weekly Google search volumes (GIS) and daily Tweet volumes of those 26 financial search terms from July 2010 to September 2011. Second, we calculate the weekly mean over the daily volumes of tweets. This step is necessary to compare Twitter (daily) and GIS (weekly) at the same time scale. Third, and finally, we take the average of the separate, weekly time series generated for each individual term, which yields a GIS and Tweet volume time series over 66 weeks, for the combination of all the financial search terms. Fig. \ref{TwittervsGIS} shows these two time series.

\begin{figure}[!htb]
	\begin{center}
		\includegraphics[width=9cm,height=7cm]{Twitter_GIS_DJIA_ccf.pdf}
		\caption{\label{TwittervsGIS}Weekly TV-FST vs. GIS.}
	\end{center}
\end{figure}

A correlation analysis over all weekly values of the two time series reveals a statistically significant Pearson correlation coefficient of 0.62 ($p<0.01$). To see whether these two indicators signal notable movement in the financial market, we marked the time period from July 23rd to August 20th 2011 in a shaded rectangle as shown in Fig. \ref{TwittervsGIS}. During this period, the stock market had a huge decline (i.e., the DJIA declined 1864 points between July 22nd and August 19th 2011.) We can see that from June 4th, 2011 (at the first vertical line), TV-FST values started to increase, while 5 weeks later, on July 9th 2011 (at the second vertical line), GIS followed. This suggests that GIS may be less efficient than Twitter in revealing public/investor negative sentiment.

\subsection{Economic and Financial Market Data}
We collected daily and weekly Dow Jones Industrial Average, trading volume, Volatility (VIX) from Yahoo! Finance. In addition, we calculate the market log returns $R$ of stock prices $S(t)$ over a time interval $\Delta t$ as follows:
\begin{equation}
R_{\Delta t}=logS(t+\Delta t) - logS(t)
\label{logreturn}
\end{equation}

Here $\Delta t= 1$. Additionally, we also retrieved the price of gold \footnote{\url{http://www.gold.org/investment/statistics/gold_price_chart/}} over the same period of time. Table \ref{datanote} summarizes the corresponding time range and daily/weekly scale for all the data we obtained.

\begin{table}[!htb]
	\begin{center}
		\caption{\label{datanote}Time-range coverage of different data sources.}
		\begin{tabular}{ccc}
		\hline
		Data     		&  Daily(mm/dd/yy) 	&    Weekly(mm/yy)					 \\\hline
		DSI (Daily Sentiment Index)         & 07/01/10 -09/05/11 		&  /			\\ \hline
		II (Investor Intelligence)        	&   /  	& 01/08 - 09/11
\\ \hline
		TIS (Twitter Investor Sentiment)          &  07/01/10 – 09/29/11  		& /
\\ \hline
\pbox{20cm}{TV-FST (Tweet volumes \\of financial search terms)} & 07/01/10 – 09/29/11 & /
\\
\hline
		NNS(Negative News Sentiment)          & 07/01/10 – 09/29/11  	& /
\\ \hline
		GIS (Google Insight Search)       & /    	& 01/08 - 09/11
\\ \hline
DJIA/VIX/Volume/Gold & 07/01/10 – 09/29/11 & 01/08 - 09/11
		\\\hline

		\end{tabular}
	\end{center}
	\end{table}

\section{Search Volume (GIS)-based prediction of financial indicators}
\label{sec:weeklyAnalysis}
\subsection{Search Volume and Financial Indicator Correlations}
In this section, we compare the GIS time series (search query volume of 26 financial search terms) with the DJIA price, volume, and the price of gold from January 2008 to September 2011, roughly 196 weeks. This period was punctuated by significant market volatility, as well as significant bear and bull markets, thus allowing us to perform our analysis under a variety of market conditions.

We first compute the pair-wise correlation between our 26 time series of GIS search terms and the financial time series. All time series are transformed to log scale for analysis. The results are summarized in Table \ref{topGIS}. Due to the space limitations, we only list the correlations of 10 search terms.

\begin{table}[!htb]
	\begin{center}
		\caption{\label{topGIS}Pearson correlation coefficients between GIS and VIX, DJIA, Trading Volume.}
		\begin{tabular}{cccc}
		\hline
		Search Query     		& VIX   		&   DJIA     	&  Volume				 \\\hline
		\emph{DJIA}          & 0.88    		&  -0.76        	& 0.69    					\\
		\emph{Dow Jones}       	&   0.84   	& -0.69    		&   0.68     						\\
		\emph{Dow}        &  0.83   		& -0.67    		& 0.68
\\
		\emph{Dow Jones Industrial Average}           & 0.78     	& -0.77   		& 0.65   		 			\\
		\emph{Stock market news}      &  0.77     	& -0.37   		& 0.59  						\\
		\emph{Finance}        & 0.71    	& -0.50   		& 0.70   		\\
\emph{Stock market today} & 0.69 & -0.62 & 0.51 \\
\emph{Financial news} & 0.68 & -0.43 & 0.57 \\
\emph{Stock} & 0.66 & -0.38 & 0.57 \\
\emph{SP500} & 0.65 & -0.34 & 0.49

\\\hline
		\end{tabular}
	\end{center}
	\end{table}

We find relatively strong correlations in most cases, especially for what seem to be DJIA-relevant search terms such as \emph{``DJIA"}, \emph{``Dow Jones"}, etc. The GIS time series has a positive correlation with the VIX and trading volumes, but negative correlations with DJIA, which may indicate that as more people search on financial terms, the market will be more volatile (i.e. high VIX), and trading volumes will be higher, while DJIA prices will move lower.

For further testing, we keep the top search term whose search volume has the highest correlation with the corresponding financial index for each time series. In Fig. \ref{scatterplot}, we overlaid the resulting time series with the mentioned financial indicators to visually examine the occurrence of any particular trend.

The top panels of Fig. \ref{scatterplot} show the actual time series whereas the lower panels show the scatter plot of GIS values vs. financial indicator values in log-log scale. A simple visual inspection of the top panels reveal a clear correlation between GIS search term volumes and the financial indicator time series; peaks in GIS values generally co-occur with those of VIX and Volume values, and in some cases even precede the peaks of the various financial time series (DJIA, Gold). The scatter plots in Fig. \ref{scatterplot} show that search volumes exhibit a high positive correlation with VIX and trading volume ($\gamma=0.88$, $\gamma=0.70$), and a high negative correlation with DJIA price ($\gamma=-0.77$). The correlation between gold price and search volumes on \emph{``gold"} is also satisfactory ($\gamma=0.45$). This correlation value may in fact be an underestimation due to non-linear patterns in how the two variables relate. For $log$(gold prices) $>7.0$ we do observe a linear pattern of correlation. Below that value there seems to be little to no correlation. This pattern is confirmed by the trend plot at the upper right of Fig. \ref{scatterplot}: from mid-2010 to the end, at higher gold prices, we indeed observe a strong positive correlation, and in fact two spikes of search volumes appear before the gold price reached its peak in early September 2011.

\begin{figure}[!htb]
	\begin{center}
		\includegraphics[width=9cm,height=10cm]{scatterplot_trend.pdf}
		\caption{\label{scatterplot}Trend analysis and log scale scatter plots of GIS time series vs. financial indicators such as VIX, DJIA closing values, gold price and DJIA trading volume. (Search query terms are inside the brackets).}
	\end{center}
\end{figure}

VIX is a widely used measure of market risk and is often referred to as the ``investor fear gauge". Our results show that search volumes of financial terms reflect VIX fluctuations, implying that search volume for key financial terms may be a computational gauge of ``investor fear".

To evaluate time-lag correlations between search volume and financial time series, we compute their cross-correlation.  In order to compare the effectiveness of search volumes with the survey data with respect to how well they predict the financial markets, we also include the Investor Intelligence (II) time series in our analysis.

Consider two series $x=\{x_{1},...,x_{n}\}$ and $y=\{y_{1},...,y_{n}\}$, the cross correlation $\gamma$ at lag $k$ is then defined as:
\begin{equation}
\gamma=\frac{\sum_{i}(x_{i+k})-\bar{x})(y_{i}-\bar{y})}{\sqrt{\sum_{i}(x_{i+k})-\bar{x})^2}\sqrt{\sum_{i}(y_{i}-\bar{y})^2}}
\label{corr}
\end{equation}

where $\bar{x}$ and $\bar{y}$ are the sample mean values of the $x$ and $y$, respectively. We use the cross-correlation function provided in $ccf$, an R statistics package. For example, where   $ccf(x,y)$ estimates the correlation between $x[t+k]$ and $y[t]$, it means that we keep $y$ still, but move $x$ forward or backward in time by a lag of $k$. Where $k>0$, it means $y$ anticipates $x$, and vice versa.

As can be seen in Fig. \ref{ccfFinGIS}, DJIA values and GIS (search volume) exhibit the highest correlation and particularly so on the right side of the graph where lag values are positive, i.e. $k>0$, and, in other words, GIS values lead DJIA values. A similar effect can be observed for GIS vs. VIX values, especially where $k=[+1,+3]$ weeks. In contrast, as shown in Fig. \ref{ccfFinGIS}, the cross correlation between II and VIX seems to work in the opposite direction, indicating that VIX leads changes in II values. The correlation coefficients at both sides seem to be roughly balanced for trading volume. The search query time series for \emph{``gold"} exhibits the opposite effect of other search query time series: GIS search volumes on \emph{``gold"} do not lead gold prices. This runs counter to our earlier observation (in Fig. \ref{scatterplot}) that spikes of \emph{``gold"} search volumes precede spikes in gold prices, indicating that \emph{``gold"} GIS may yet have predictive value under certain conditions. We speculate this may be due to a non-linear interaction with absolute gold price levels, but we leave this for future exploration.

\begin{figure}[!htb]
	\begin{center}
		\includegraphics[width=9cm,height=6cm]{ccf_DJ_Vol_VIX_II_GIS.pdf}
		\caption{\label{ccfFinGIS} Cross correlation analysis between financial time series and search volume (GIS) time series.}
	\end{center}
\end{figure}

\subsection{Granger Causality Analysis}

We further refine the observations discussed above by a Granger causality test, a technique that is widely used to analyze the relations between economic time series. The Granger causality test is a statistical hypothesis test to determine whether a time series $X(t)$ is useful in forecasting another time series $Y(t)$ by attempting to reject the null hypothesis that $X(t)$ does not help predict, i.e. Granger-cause, $Y(t)$. The alternative hypothesis is that adding $X(t)$ does help predict $Y(t)$. An F-test is conducted to examine if the null hypothesis can be rejected.

We caution that Granger causality analysis might establish that the lagged value of $X(t)$ exhibits a statistically significant correlation with $Y(t)$. However, correlation does not prove causation. In other words, Granger causality testing does not establish actual \emph{causality}, merely a statistical pattern of lagged correlation. This is similar to the observation that cloud cover may precede rain and may thus be used to predict rain, but does not itself actually cause rain.

Table \ref{grangerpGIS} presents the results of applying the Granger causality test in two directions, i.e. with positive and negative lags, reflecting the hypothesis that each time series may Granger cause the other.

\begin{table}[!htb]
	\begin{center}
		\caption{\label{grangerpGIS}Statistical significance (p-values) of Granger causality analysis between search volumes/ II and financial indicators over lags of 1, 2, and 3 weeks.}
		\begin{tabular}{cccc}
		\hline
		    		& 1   		&   2     	&  3				 \\\hline
		VIX$\rightarrow$GIS          & 0.0051$\star\star\star$    		& 0.0004$\star\star\star$        	& 0.0010$\star\star\star$
    					\\
		GIS$\rightarrow$VIX      	&   0.0025$\star\star\star$   	& 0.0202$\star\star$    		&   0.0091$\star\star\star$       						\\
		  VIX$\rightarrow$II      &  8.04e-05$\star\star\star$    		& 3.63e-07$\star\star\star$      		& 9.98e-08$\star\star\star$
\\
		II$\rightarrow$VIX           & 0.398     	& 0.726   		& 0.849  		 			\\
		DJIA$\rightarrow$GIS     &  0.207     	& 0.040$\star\star$   		& 0.096$\star$  						\\
		GIS$\rightarrow$DJIA        & 7.85e-04$\star\star\star$     	& 1.48e-03$\star\star\star$    		& 9.31e-04$\star\star\star$   		\\
Volume$\rightarrow$GIS & 0.409 & 0.705 & 0.843 \\
GIS$\rightarrow$Volume& 0.020$\star\star$ & 0.028$\star\star$  & 0.101 \\
Gold$\rightarrow$GIS & 0.055$\star$ & 0.104 & 0.082$\star$ \\
GIS$\rightarrow$Gold & 0.139 & 0.00036$\star\star\star$  & 0.0013 $\star\star\star$

\\\hline
\multicolumn{4}{l}{ ($p-value < 0.01$: $\star\star\star$, $p-value< 0.05$: $\star\star$, $p-value< 0.1$: $\star$)}
		\end{tabular}
	\end{center}
	\end{table}

The values in the first column of Table \ref{grangerpGIS} represent the particular hypothesis under consideration. For example, ``VIX$\rightarrow$GIS" represents the null hypothesis that adding VIX does not help predict GIS. As can be seen from the listed $p$-values, this particular null-hypothesis is rejected with a high level of confidence. In the row below, we observe that adding GIS can also help predict VIX. However, the Granger causality between Investor Intelligence (II) and VIX runs in only one direction, i.e. VIX$\rightarrow$II: adding survey data (II) does not help predict VIX. In addition, the null hypothesis that adding GIS does not help predict DJIA, is strongly rejected at a high level of confidence level. Similarly, we find a very significant $p$-value for GIS$\rightarrow$Gold at lag 2 and 3 weeks. GIS of the previous 1 to 2 weeks significantly Granger-cause trading volume.

\subsection{Forecasting Analysis}
\label{sec:weeklyPred}
Can search volumes predict future values of financial indicators? As a further validation, we conduct a 1-step ahead prediction over 20 weeks based on a baseline model, denoted $M_{0}$, and an advanced model, denoted $M_{1}$. Here $Y$ represents the particular financial index (i.e. DJIA, trading volumes or VIX) and $X$ represents a sentiment indicator. In this section we will focus on GIS in particular.

\begin{equation}
M_{0}: Y_t=\alpha+\sum_{i=1}^{n}\beta_{i}Y_{t-i}+\epsilon_{t}
\label{M0}
\end{equation}

\begin{equation}
M_{1}: Y_t=\alpha+\sum_{i=1}^{n}\beta_{i}Y_{t-i}+\sum_{i=1}^{n}\gamma_{i}X_{t-i}+\epsilon_{t}
\label{M1}
\end{equation}

Forecasting accuracy is measured in terms of the Mean Absolute Percentage Error (MAPE) and the direction accuracy. The MAPE is defined as follows:

\begin{equation}
MAPE=\frac{\sum_{i}^{n}|\frac{y_{i}-\hat{y_{i}}}{y_{i}}|}  {n}\times{100}
\label{MAPE}
\end{equation}

where $\hat{y_{i}}$ is the predicted value and $y_{i}$ is the actual value.
Direction accuracy is measured simply in terms of whether $(\hat{y_{i,t+1}}- y_{i,t})\times(y_{i,t+1}-y_{i,t})>0$. In other words, if the difference between today's and yesterday's predicted value has the same sign as the difference between today's vs. yesterday's observed value, we conclude that the direction of the change was predicted accurately for that day.

Our search volume and financial indicator time series are available from January 2008 to September 2011. There are 196 weeks in total and we use the last 20 weeks, i.e. May 21st 2011 to October 1st 2011, as the predicting period. Each forecast uses only the information available up to the time the forecast is made. The raw data are transformed to log scale before prediction. For VIX and DJIA prediction, the lag $n$ is chosen to be 3 weeks. However, according to the Granger test analysis shown in Table \ref{grangerpGIS}, the $p-value$ is not significant for lags $>2$ weeks in the case of GIS vs. trading volume. We therefore chose $n=2$ in Eq. \ref{M0} and Eq. \ref{M1} for trading volume prediction. Fig. \ref{errorBar} shows the prediction errors for these 20 forecasting weeks. Table \ref{GISPrediction} shows the forecasting errors expressed as MAPE and direction accuracy.

\begin{table}[!htb]
	\begin{center}
		\caption{\label{GISPrediction}Forecasting accuracy of using weekly search volumes to predict financial indicators (DJIA, Volume and VIX).}
		\begin{tabular}{cccc}
		\hline
		    		& Model   		&   MAPE     	&  Direction				 \\\hline
		\multirow{2}{*}{DJIA}  &Model~0          & 0.253   		& 0.55 \\
                                                      &Model~1 & 0.244 & 0.70 \\ \hline
\multirow{2}{*}{Volume}  &Model~0          & 0.386  		& 0.55 \\
                                                      &Model~1 & 0.366 & 0.55 \\ \hline
\multirow{2}{*}{VIX}  &Model~0          & 4.560  		& 0.55 \\
                                                      &Model~1 & 4.148 & 0.65 \\ \hline
		\end{tabular}
	\end{center}
	\end{table}

From these results it appears that adding search volumes (1) reduces the MAPE prediction error for VIX, DJIA and trading volumes predictions, and (2) improves the direction accuracy for DJIA and VIX forecasting, but not for trading volumes.
\begin{figure}[!htb]
	\begin{center}
		\includegraphics[width=10cm,height=10cm]{Prediction_DJIAVolume.pdf}
		\caption{\label{errorBar}Prediction Error Plot.}
	\end{center}
\end{figure}

Fig. \ref{errorBar} furthermore shows that during several weeks the baseline model output outperformed the advanced model. This again highlights the difficulty of financial market prediction, even using data that has been shown to have statistically significant Granger causality with the particular financial indicators. We offer the observation that on August 15th 2011 (highlighted with a yellow bar), the prediction error of the advanced model (red) dropped well below that of the baseline model (blue). In that period (August 15th -19th) the weekly VIX reached a high value of 43.05, the DJIA decreased over 450 points, and trading volumes increased significantly compared to the previous week. This is suggestive that search volumes of financial terms may be particularly useful for prediction when the market experiences high degrees of volatility, significant changes in values and high trading volumes.

\section{Twitter, Search Engine, News Media and Survey-based prediction of financial indicators}
\subsection{Correlation Analysis}
In previous sections we focused on weekly analysis due to data availability. However, our Twitter data and the Daily Sentiment Index (DSI) were recorded \emph{daily} from July 1st 2010 to September 29th 2011, for a total of 456 days. Given the availability of daily data, in this section our analysis will focus on daily time series, rather than weekly.

Again, Google Insight Search (GIS) does not provide daily volume search data. We therefore do not use GIS search volumes in our daily analyses, and instead use the Tweet volumes of financial search terms (TV-FST), as introduced in Section \ref{sec:tv-fst}.

In total, we examine four daily sentiment indicators, i.e. Twitter Investor Sentiment (TIS), Tweet Volume of Financial Search Terms (TV-FST), Negative News Sentiment  (NNS) and Daily Sentiment Index (DSI). Using the same definition as shown in Section \ref{sec:tv-fst}, the TV-FST is calculated as the average of Tweet volumes of all these financial search terms. Table \ref{corrTIS_TVFST_DSI} displays the Pearson correlation values observed between these sentiment indicators.

\begin{table}[!htb]
	\begin{center}
		\caption{\label{corrTIS_TVFST_DSI}TIS, NNS, TV-FST, and DSI correlations.}
		\begin{tabular}{ccccc}
		\hline
		    		& TIS   		&   NNS     	&  TV-FST & DSI				 \\\hline
		TIS &   1          &    		&   &   \\
                   NNS & -0.237 & 1 & & \\
                   TV-FST &-0.304 & 0.225 & 1 &  \\
                   DSI  & 0.431 & -0.322 & -0.202 & 1 \\
                                                   \hline

		\end{tabular}
	\end{center}
	\end{table}

Survey data, DSI (percentage of bullish readings), has a positive correlation with TIS, but negative correlations with the other two sentiment indicators: TV-FST and NNS. TV-FST exhibits a negative correlation with DSI and TIS, but a positive correlation with NNS, which suggests that TV-FST may be a bearish/negative sentiment indicator. All listed correlations are statistically significant with $p-value < 0.01$.

After linearly extrapolating financial indicators values missing on weekends (because of markets closing), we analyze the correlation between these sentiment indicators and financial market indexes. The results are shown in Table \ref{sentiment_Fin_corr}.

\begin{table}[!htb]
	\begin{center}
		\caption{\label{sentiment_Fin_corr}Correlations between sentiment and financial indicators.}
		\begin{tabular}{ccccc}
		\hline
		    		& DJIA   		&   Log return     	&  Volume & VIX				 \\\hline
		TIS &   -0.071          &   0.267$\star$  		& -0.127$\star$  & -0.314$\star$   \\
                   NNS & 0.147$\star$ & -0.147$\star$ & 0.039 & 0.237$\star$ \\
                   TV-FST &0.449$\star$ & -0.091 & 0.096 & 0.183$\star$ \\
                   DSI  & 0.277$\star$ & 0.181$\star$ & -0.341$\star$ & -0.832$\star$ \\
                                                   \hline
\multicolumn{5}{l}{($\star$ indicates $p-value < 0.01$)}

		\end{tabular}
	\end{center}
	\end{table}

We observe that TIS is positively correlated with market log returns (cf. Eq. \ref{logreturn}) and negatively correlated with VIX. DSI is positively correlated with DJIA closing values, as well as log return, but negatively correlated with trading volume and VIX. VIX reflects perceived market risk, with higher VIX values potentially indicating greater levels of investor fear. Its negative correlation with DSI and TIS may therefore indicate that the latter correspond to positive sentiment, or a lower perception of risk or fear among investors. Conversely, the positive correlation between VIX vs. NNS and TV-FST may indicate that these are indeed indicators of fear or negative sentiment.

To better view the correlation between the sentiment indicators and financial market, we plot the time series of DJIA and four sentiment indicators, in Fig. \ref{dailyplot}.

\begin{figure}[!htb]
	\begin{center}
		\includegraphics[width=10cm,height=10cm]{DJIA_Twitter_News_TVFST_AllAdv.pdf}
		\caption{\label{dailyplot}Time series of DJIA and TIS, DSI, NNS as well as TV-FST.}
	\end{center}
\end{figure}

In Fig. \ref{dailyplot}, the time series in the top panel shows the daily DJIA closing value from July 1st 2010 to September 29th 2011. The four time series in the lower panel represent TIS, DSI, NNS and TV-FST during the same time period and they are smoothed over the past 30 days. We invert the TIS and DSI to make them consistent with the directionality of the other two negative market indicators (i.e. NNS and TV-FST). As such, ``up" means negative sentiment, while ``down" indicates positive sentiment.

We marked five time periods in the lower panel of Fig. \ref{dailyplot} with rectangle bars to indicate when DJIA prices fell in August and November 2010, and March, June and August 2011. Before DJIA prices fell in August 2010 (indicated by the first rectangle bar), it can be seen that the TIS and NNS graphs moved upwards (i.e. a rise in negative sentiment), while DSI dropped (i.e. positive). Before the second bar (November 2010), we see TIS and TV-FST trending upward. Before the fall in DJIA prices in March 2011 (third bar) we observe a clear and long-term increase of TV-FST, NNS and TIS values. TIS and TV-FST values are trending upwards before the fourth bar that marks June 2011. All four sentiment indicators trend upwards before the last bar  that makes August 2011, but the ``up" trend of DSI seem to lag the ``up" trend of NNS and TV-FST. In conclusion, though there is considerable noise in the daily data, the non-survey sentiment indicators, especially TIS and TV-FST, do show significant increases in negative sentiment that clearly precede periods of falling DJIA prices.

\subsection{Granger Causality Analysis}
The prediction of stock market returns is a matter of considerable interest. To determine whether any of our sentiment indicators are useful to predict daily DJIA log returns, we conduct a Granger causality analysis similar to Section \ref{sec:weeklyAnalysis}. According to Table \ref{sentiment_Fin_corr}, the correlation coefficient between TV-FST and log returns is statistically insignificant  ($\gamma=-0.09$). To determine which of our set of search terms are most effective to predict log returns, we conduct a correlation analysis between the search volumes of each financial term individually and log returns. Then, we select the search terms \footnote{\emph{DJIA, dow, Dow Jones, Dow Jones Industrial Average, SP500, stock(s) fall(s), stocks decline, financial market.}} whose search volumes exhibit the most significant correlations with log returns, and take the average of their time series to be the TV-FST$'$. The correlation coefficient between the resulting TV-FST$'$ and daily log returns is -0.30 with a $p$-value $<0.01$. Table \ref{grangerDaily} lists the $p$-values for a number of bi-directional Granger causality tests of log returns vs. our sentiment indicators. We find statistically significant Granger causation in both directions between log returns and TIS, NNS, and TV-FST$'$, with the exceptions of lag = 1, TV-FST$'\rightarrow$Return, and lag = 3, 5, Return$\rightarrow$TV-FST$'$. No statistically significant Granger causation was observed between DSI and log returns. These results indicate that sentiment indicators extracted from Twitter (TIS and TV-FST$'$) and News headlines (NNS) are predictive to the DJIA log return, but DSI is not predictive.

\begin{table*}
	\begin{center}
		\caption{\label{grangerDaily}Statistical significance (p-values) of  Granger causality analysis  between daily log return and TIS, NNS, DSI and TV-FST$'$.}
		\begin{tabular}{cccccc}
		\hline
		    		& 1   		&   2     	&  3	 & 4 & 5 			 \\\hline
		TIS$\rightarrow$Return          & \textless{0.001}$\star\star\star$    		& 0.0086$\star\star\star$        	& 0.035$\star\star$ & 0.028$\star\star$ & 0.021$\star\star$
    					\\
		Return$\rightarrow$GIS      	&   \textless{0.001}$\star\star\star$     	& \textless{0.001}$\star\star\star$  		&   \textless{0.001}$\star\star\star$ & \textless{0.001}$\star\star\star$ & \textless{0.001}$\star\star\star$     						\\
		  NNS$\rightarrow$Return      &  0.017$\star\star$    		& 0.030$\star\star$      		& 0.011$\star\star$  &0.005$\star\star\star$  	 & 0.004$\star\star\star$
\\
		Return$\rightarrow$NNS           & 0.014$\star\star$     	& 0.013$\star\star$   		& 0.031$\star\star$ & 0.030$\star\star$ & 0.055$\star\star$  		 			\\
		DSI$\rightarrow$Return     &  0.523     	& 0.138   		& 0.203 & 0.308 & 0.377 \\
		Return$\rightarrow$DSI        & 0.267     	& 0.174  		& 0.647 & 0.377 & 0.059 $\star$   		\\
TV-FST$'\rightarrow$Return & 0.413 & \textless{0.001}$\star\star\star$ & \textless{0.001}$\star\star\star$ & \textless{0.001}$\star\star\star$ & \textless{0.001}$\star\star\star$ \\
Return$\rightarrow$TV-FST$'$& 0.0025$\star\star\star$ & 0.019$\star\star$ & 0.151  & 0.071$\star$ & 0.140
\\\hline
\multicolumn{4}{l}{ ($p-value<0.01$: $\star\star\star$, $p-value< 0.05$: $\star\star$, $p-value<0.1$: $\star$)}
		\end{tabular}
	\end{center}
	\end{table*}

\subsection{Multiple Regression Analysis}

In this section, we conduct a multiple regression for daily log returns obtained according to Eq. \ref{logreturn}. The regression inputs are our four sentiment indicators and the past financial values of log return. As an additional control, we include VIX, since it is a well-accepted predictor for market return. The multiple regression model is shown in Eq. \ref{multipleRegressionEq}, where $n = 7$ days, and $Y$ represents the daily log return. In order to maintain a common scale, we normalized all data to standard scores.

\begin{equation}
\begin{split}
Y_{t}=\alpha+\sum_{i}^n\beta_{i}Y_{t-i} +\sum_{i}^n\chi_{i}TIS_{t-i}+\sum_{i}^n\delta_{i}NNS_{t-i}+ \\ \sum_{i}^n\phi_{i}TV-FST'_{t-i}+\sum_{i}^n\gamma_{i}DSI_{t-i}+\sum_{i}^n\eta_{i}VIX_{t-i}+\epsilon_{t}
\label{multipleRegressionEq}
\end{split}
\end{equation}

Table \ref{multiple_regression} provides the summary statistics of the multiple regression. Compared with the baseline model, the adjusted $R^2$ improves from 0.092 to 0.200. This means that an additional 11\% of the variation in log returns is accounted for by adding these sentiment indicators. After controlling for all other variables, we find that DSI is not a statistically significant predictor. The two sentiment indicators extracted from Twitter, i.e. TIS and TV-FST$'$, are however very significant predictors at a lag of 1 to 2 days. Here, we observe a reversal effect, namely that daily log returns are positively associated with TIS and TV-FST$'$ on the previous day, but negatively correlated with those on the lag of 2 days. VIX values at lags of 2 days are highly statistically significant predictors of log return. NNS is also a statistically significant predictor at lags ranging from 1 or 4 days, but with much less lower coefficients, e.g. at a lag$=1$ we find that the $p-value=0.08$, and the coefficient is -0.087, which means we expect to see a log return decrease of only 0.087 standard deviations for each one standard deviation increase of NNS.

\begin{table*}
	\begin{center}
		\caption{\label{multiple_regression}Summary statistics of multiple regression.}
		\begin{tabular}{c|cc|cc|cc|cc|cc|cc}
		\hline
		    		Lag & \multicolumn{2}{c|}{Return} & \multicolumn{2}{c|}{TIS} & \multicolumn{2}{c|}{NNS} & \multicolumn{2}{c|}{DSI} & \multicolumn{2}{c|}{VIX} & \multicolumn{2}{c}{TV-FST$'$}  \\ \hline
                                           & Coeff. & $p$-value & Coeff. & $p$-value & Coeff. & $p$-value & Coeff. & $p$-value & Coeff. & $p$-value & Coeff. & $p$-value  \\ \hline
1 & 0.282 & 0.004$\star\star\star$ & 0.170 & 0.008$\star\star\star$ &-0.087&0.080$\star$ & 0.389 & 0.385 &0.494 & 0.182 & 0.235 & 0.0007$\star\star\star$ \\
2 & -0.139 & 0.175 & -0.164 & 0.018$\star\star$ &-0.066 & 0.191 &-0.239 &0.709 & -1.161 & 0.029$\star\star$ & -0.324 &3.73e-05$\star\star\star$ \\
3 & 0.0006 & 0.995 & 0.069 & 0.316 & 0.048 & 0.349 & -0.265 & 0.678 & 0.641 & 0.235 & 0.059 & 0.486 \\
4 & -0.115 & 0.275 & -0.088 & 0.208 & -0.097 & 0.058 $\star$&0.730 & 0.251 & 0.384 & 0.479 & -0.059 & 0.490 \\
5 & -0.0212 & 0.837 & 0.152 & 0.031$\star\star$  & -0.017 & 0.740 & 0.177 &0.780 &0.502 &0.351 & 0.171 &0.045$\star\star$ \\
6 & 0.071 & 0.472 & -0.132 & 0.062 $\star$ & 0.057 & 0.257 & -0.514 &0.405 & -0.490 & 0.341 & -0.096 & 0.261 \\
7 & -0.117 & 0.040$\star\star$ & 0.005 & 0.935 & 0.008 & 0.874 & -0.115 & 0.789 & -0.204 & 0.559 & -0.024 & 0.743 \\
                                                   \hline
\multicolumn{12}{c}{($p-value<0.01$: $\star\star\star$, $p-value<0.05$: $\star\star$, $p-value<0.1$: $\star$)} \\
\multicolumn{12}{c}{Residual standard error: 0.893 on 406 degrees of freedom.} \\
\multicolumn{12}{c}{Multiple $R$-squared: 0.2841, Adjusted $R$-squared: 0.200 (baseline model (cf. Equation \ref{M0}, $Y$ is daily log return here): Adjusted $R$-squared 0.092).} \\
\multicolumn{12}{c}{$F$-statistic: 3.67 on 42 and 406 DF, $p$-value: 5.523e-12} \\
\hline
		\end{tabular}
	\end{center}
	\end{table*}

\subsection{Forecasting analysis}

To further test the hypothesis that adding sentiment indicators can help predict financial indicators such as the DJIA, trading volumes, and VIX, we conduct a 1-step forecasting test over 30 days, i.e. from August 31st 2011 to September 29th 2011. As with the weekly prediction in Section \ref{sec:weeklyPred}, the baseline model is based on its own historical financial values (cf. Model 0 in Eq. \ref{M0}) whereas the advanced model (cf. Model 1 in Eq.\ref{M1}) adds the historical values of the sentiment indicators TIS, NNS, TV-FST and DSI. Here we assume $n=7$. The forecasting accuracy is measured in terms of the Mean Absolute Percentage Error (MAPE) and the direction accuracy. Results are shown in Table \ref{dailyPrediction}.

\begin{table}[!htb]
	\begin{center}
		\caption{\label{dailyPrediction}Forecasting accuracy of using TIS, NNS, TV-FST and DSI to predict financial indicators (DJIA, trading volume and VIX).}
		\begin{tabular}{cccc}
		\hline
		    		& Model   		&   MAPE     	&  Direction				 \\\hline
		\multirow{2}{*}{DJIA}  &Model~0          & 1.00   		& 0.5 \\
                                                      &Model~1 & 0.97 & 0.63 \\ \hline
\multirow{2}{*}{Volume}  &Model~0          & 7.24  		& 0.47 \\
                                                      &Model~1 & 7.56 & 0.60 \\ \hline
\multirow{2}{*}{VIX}  &Model~0          & 4.00  		& 0.6 \\
                                                      &Model~1 & 3.88 & 0.67 \\ \hline
		\end{tabular}
	\end{center}
	\end{table}

 We find improvements in the direction accuracy and MAPE of the forecasting accuracy for DJIA, VIX and volume prediction, with the exception of the MAPE for volume prediction. However, the improvement is not highly significant. The extremely high volatility in the financial markets during our training and testing periods, especially in August and September 2011, may account for this. In addition, we used relatively simple linear models in this paper that may not be suited to model the complex interactions of factors involved in shaping financial market values. Further research will need to focus on the development of more accurate and more advanced linear/non-linear prediction models.

\section{Conclusion}
Behavioral finance challenges the Efficient Market Hypothesis by emphasizing the important role that human emotion, sentiment and mood play in financial decision-making. Thus places the accurate measurement of sentiment and mood at the heart of a discussion over how to best model and predict the behavior of the financial markets. Previous research in this domain has relied mainly on surveys or news analysis to obtain investor sentiment. Research has recently started to leverage very large-scale web data, including search engine and social media data, to assess public as well as investor sentiment. However, most existing work adopts only a single data source (survey, social media or search engine data) as a proxy to public and investor sentiment, and then uses it to computer a particular financial index. To the best of our knowledge, no work has been done to perform a detailed survey of a variety of different classes of mood indicators extracted from a variety of classes of data sources. Studying the relations between different mood indicators and their predictive relationships to different financial indexes is necessary to unravel the causal relations sentiment and mood relate to the financial markets, and thus crucial in improve financial forecasting models. Our paper is a first, preliminary contribution of such a comparison to the rapidly emerging domain of \emph{computational behavioral finance}.

In this paper, we collect six sentiment indicators from investor sentiment surveys (II and DSI), social media (Twitter), news media services, as well as search engine (Google). Those include DSI bullish percentage, Investor Intelligence (II), Twitter Investor Sentiment (TIS), Tweet volumes of financial search terms (TV-FST), Negative News Sentiment (NNS) and Google search volumes of financial search terms (GIS).

First, in a weekly analysis, we find a significant correlation between weekly GIS of financial terms with DJIA closing values, trading volume, and VIX values. Granger causality tests confirm that GIS is indeed predictive of financial indicators, but surveys of investor sentiment (i.e. II) are not. Weekly forecasting accuracy was improved by adding GIS search volumes of financial terms, notably so when the DJIA was trending downward and the VIX was indicating high volatility, such as in August 2011.

Second, in our daily analysis, all mood indicators exhibited a significant correlation with log returns and VIX values. Controlling for other mood indicators including the VIX, we find that TIS and the TV-FST values of the previous 1-2 days are very statistically significant predictors of daily market returns, while DSI is not. NNS is also found to be a statistically significant predictor, however, compared to the TIS and TV-FST, we find less significant predictability of log return. This finding indicates that the predictive power of Twitter's two sentiment indicators outperformed survey sentiment as well as news media analysis. Moreover, we found that before the highly downward movement of DJIA in the end of July and August 2011, Tweet volumes of financial terms started to increase several weeks earlier than Google volumes did. This indicates a potential efficiency gain of Twitter over GIS.

Studying the predictive power of the online web data is still in its infancy. The various correlations and limitations of these different data sources, different sentiment measures, and its general prediction applicability to different domains remain unclear. Our work is the first attempt to extract a range of sentiment indicators from several popular data sources (Twitter, search engine and news) and use various sentiment indicators to predict different financial indexes (DJIA, trading volumes, VIX and gold) in both daily and weekly scale. Continued research is needed to deepen our understanding of why and how these different mood indicators relate to and predict socio-economic phenomena such as the financial markets.

\section*{Acknowledgements}
We would like to express our gratitude to trade-futures and Investor Intelligence for making their sentiment data available to our research program. We also thank the Socionomics Institute for their kind advice on a range of research questions, and their assistance in obtaining research data that was crucial in conducting the research presented in this paper.

\bibliographystyle{abbrv}

redpajama_set_name: RedPajamaArXiv


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text:
Miengo is a municipality in Cantabria, Spain.

Geography
Miengo is situated in the central coast of Cantabria, between the Ría de San Martín de la Arena and the Ría de Mogro. Miengo has five beaches: Cuchía, Los Caballos, Usgo, Robayera and Usil; and two "calas": Las Monedas y El Huevo Frito.
The natural limits of Miengo are the river Pas (east), the Ria de San Martin de la Arena (east) and the Cantabric sea (north). Miengo has border with the municipalities of Polanco, Pielagos, Liencres and Suances.

Population
Miengo has 4.540 in six villages:

 Bárcena de Cudón, 302.
 Cuchía, 745.
 Cudón, 532.
 Gornazo, 136.
 Miengo (Capital), 1.329.
 Mogro, 1.396.

Government

Avelino Cuartas (People's Party) is the actual mayor of Miengo, since 1995.

Economy

6,1% of the population of Miengo works in agriculture, 38,5% works in industry and 55,4% works in services. The unemployment rate is 11,4%.

References

Municipalities in Cantabria

redpajama_set_name: RedPajamaWikipedia


/UCW/ TORONTO, ONTARIO, CANADA – Titan Medical Inc. (the "Company") (TSX: TMD)(OTCQX: TITXF: (%) ) takes this opportunity to provide an update.
As of today's date, Dr. Reiza Rayman has resigned as President of Titan Medical Inc. Dr. Rayman co-founded the Company in July 2008, with a view to developing a new robotic surgical platform. The Board of Directors takes this opportunity to thank Dr. Rayman for his clinical inspiration and years of service as President of the Company. The Board would also like to wish Dr. Rayman much success in his future endeavours.
"I have full confidence in Titan and the impact that SPORT™ Surgical System will have on patient care," said Dr. Reiza Rayman.
Effective immediately, David J. McNally, recently appointed CEO and Director, has also taken on the role of President.


text:
Abiodun Promises To Complete Projects Inherited From Amosun
Rockcity FM, June 3, 2019 June 3, 2019 , NEWS, POLITICS, Abeokuta, Ake, Aremo Olusegun Osoba, Awujale of Ijebuland, Deputy Governor, Diocesan Bishop of Egba, Former President, Governor Dapo Abiodun of Ogun State, Noimot Salako Oyedele, Oba Adedotun Aremu Gbadebo, Oba Babatunde Ajayi, Oba Kehinde Olugbenle, Oba Rasak of Ago Iwoye, Olusegun Obasanjo, Otunba Gbenga Daniel, Post Inauguration Thanksgiving Service at the Cathedral Church of Saint Peter, Reverend Emmanuel Adekunle, Senator Elect, Suraj Adekunbi, the Akarigbo of Remoland, the Alake of Egbaland, the Olu of Ilaro, the Speaker of the Ogun State House of Assembly, Tolu Odebiyi., 0
Governor Dapo Abiodun of Ogun State says his administration will complete all the ongoing road projects inherited from the Amosun's administration.
Abiodun, speaking at the Post Inauguration Thanksgiving Service at the Cathedral Church of Saint Peter, Ake, Abeokuta, said he did not believe that projects which were initiated before he assumed Office should be abandoned.
He explained that such projects, if abandoned, would amount to a wastage of the tax payers funds and detrimental to the growth of the State.
The Governor said he had informed each Local Governments to submit a list of rural roads, which is of priority to their economy, for immediate rehabilitation.
Abiodun also promised to drive the provision of social amenities through the public private partnership to improve the economy of the State.
The Diocesan Bishop of Egba, Reverend Emmanuel Adekunle in his sermon, urged Abiodun to remain humble as he was before becoming the Governor of the State.
He implored the Governor to seek for political guidance from party leaders and be wary of last minutes friends who are only interested in appointments.
At the event were Deputy Governor, Noimot Salako Oyedele, the Speaker of the Ogun State House of Assembly, Suraj Adekunbi, Former President, Olusegun Obasanjo, Aremo Olusegun Osoba and Otunba Gbenga Daniel as well as Senator Elect, Tolu Odebiyi.
Others were the Alake of Egbaland, Oba Adedotun Aremu Gbadebo, the Akarigbo of Remoland, Oba Babatunde Ajayi, the Olu of Ilaro, Oba Kehinde Olugbenle and the Awujale of Ijebuland represented by Oba Rasak of Ago Iwoye.
READ ALSO: Oyegun Says Oshiomhole Lacks Capacity To Manage Big Party Like APC
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Nigeria's prisons inmates population hits 68,000
Rockcity FM, March 13, 2017 March 13, 2017 , NEWS, 0
The Nigerian Prison Service (NPS) has put the total number of inmates in prison formations nationwide at over 68,000,...
IBB Says Military Coup No Longer Viable In Nigeria
Rockcity FM, January 14, 2020 January 14, 2020 , NEWS, Uncategorized, General Ibrahim Babangida, Nigerian, 0
A former military president, General Ibrahim Babangida has said that military take-over of power is no longer viable in...
Nigerian pastor arrested in Zambia for drug trafficking
Rockcity FM, January 26, 2018 January 25, 2018 , NEWS, 0
A Nigerian prophet has been arrested for drug trafficking by Zambian authorities. Isaac Julius Amata, 42, was arrested after...
Senate Passes Bill To Penalize Lecturers for Sexual Abuse
Rockcity FM, July 8, 2020 July 8, 2020 , NEWS, 0
The Senate has passed a bill seeking to penalize sexual abuse of students in the nation's tertiary institutions. The...
National Assembly threatens to invoke its powers against President Buhari
Rockcity FM, June 6, 2018 June 6, 2018 , NEWS, 0
National assembly has issued a 12 point resolutions which they asked President Muhammadu Buhari to address urgently. The lawmakers...
TSA: Ogun nurses, midwives seek concession for health sector
Rockcity FM, February 3, 2017 February 3, 2017 , NEWS, OTHERS, Ogun State, 0
Nurses and midwives in the federal hospitals in Ogun state have called on the presidency to remove the health...
Police couple, 2 others killed in fire outbreak in Adamawa
Police inspector, his wife who is a police sergeant, and their daughter have been burnt to death in a...
Emir advocates conversion of mosques into schools in the North
Rockcity FM, February 8, 2017 February 8, 2017 , NEWS, Kano state, 0
Emir of Kano, Alhaji Muhammadu Sanusi has asked state governors in the north to consider converting mosques in the...
Dogonyaro Okays Mikel's Return To Super EaglesOyegun Says Oshiomhole Lacks Capacity To Manage Big Party Like APC

redpajama_set_name: RedPajamaCommonCrawl


A 33-year-old company employee has been arrested on suspicion of attempting to kidnap a 12-year-old girl in Nagoya's Nishi Ward.
According to police, Ayumu Shibayama approached the girl in his vehicle and suggested to give her a lift back home just after midnight on Sunday, Sankei Shimbun reported. The girl had gone to a convenience store near her house.
Shibayama drove the girl around for around 10 minutes after picking her up, police said. Meanwhile, a passerby who had noticed that Shibayama was following the girl with his vehicle close to the convenience store contacted the police. Authorities found Shibayama's car nearby and took the girl home.
The description in the story does not make sense. So he drove her around, and then they found his vehicle. So what exactly happened? If he dropped her off near her house, there is no crime and no case. Or was he stopped while driving away with her? The article does not say so.
"..Is it a crime for one person to take another home?"
The next time you feel like helping girl walking alone at night, dont approach her just notify the police about her where abouts.
In many "third-world" countries, children routinely hitchhike to school with strangers, nobody there presumes anything perverted.
I wonder, who is more civilized, them or us?
The guy has not broken any laws, unless the girl says he has. The police will probably have to let him go.
In my daughter's case a man went a lot further than that but when I went to the police station, they said if he had not put his hand inside her clothing they could do nothing. Even though he had exposed himself to her on her way home from elementary school, and forcefully placed her hand on his thingy. They laughed to one another, commenting "Another young idiot."
It looks really suspicious, but as someone pointed out, it's up to the cops to prove guilt. But what what exactly was the crime? I suppose they could beat a confession out if him.
But as long as the guy sticks to his story, true or not, I don't see what the cops will manage to accomplish, aside from getting him fired.
I lived in the country side half of my life so I see what you mean. But as you say yourself, "in those days". But the world changes.
You can still do it though : just call the police and explain the situation.
A 12 year old to go the the combini before 10pm is OK in a local area, where somebody noticed. But 12am? When I was 12, 9:30pm was bedtime, or the wooden spoon! I wonder if there is any pachinko in Nagoya? They close at 10pm, so maybe the parents told her to turn off the video game and go and buy her combini dinner, and bring back a packet of cigs.
I agree his actions seem suspicious.
But if a 12 year old girl were walking alone a quiet forest road at the dead of night, would it be a crime to a 33 year old man to stop and offer her a lift home, especially if she seemed lost or if was rainy or cold.
Where I grew up, it's a plausible scenario, and in those days, people would have expected the adult man to offer help and then wouldn't have immediately assumed he was a perv.
I used to live in Nagoya, they don't call it little detroit for nothing.
Shibayama drove the girl around for around 10 minutes after picking her up, police said.
Either she lives quite far from that conbini or his satnav wasn't working.
Man, back in elementary school in the 80s, my class invited a police officer to speak to us as part of the "say no to strangers" campaign. It was like 1st or 2nd grade and forever shaped my mind. Do they do that here?
DisillusionedToday 08:46 am JSTIt's unfortunate that, in today's society we have to believe the worst. It is quite possible he only wanted to give her a lift home, but not plausible.
are you serious? in what century was it ok for some 33 year-old guy to "help" a child that wasn't lost? she just walked to the nearest conbini to get something.
and btw, what the heck were her parents thinking? one, letting her go by herself to conbini at midnight. and two, not teaching her any better than to never get into a car with a stranger. the parents are lucky she's still alive and not harmed.
Well really questionable what proper parent let their underage child roam into convenience store after midnight. Then again, he had been following he with a vehicle, probably could have driven away to indulge in his own lifestory, but well. There is no telling whether he is a hero or a villain unless someone reads his thoughts.
A 12 year old girl out alone at midnight!? Even if it was just to go to the combini near her home the parents still deserve a slap, they are potentially all very lucky a member of the public was alert.
It's unfortunate that, in today's society we have to believe the worst. It is quite possible he only wanted to give her a lift home, but not plausible.
Is it a crime for one person to take another home?
Maybe not, but following a child you don't know and taking him or her in you car is suspicious.
Creepy, but I doubt a conviction will stick.
Why is a 12 year old girl walking around alone at midnight anyway? Always best to not invite disaster if you can.
To be fair, in the old days, if a citizen were to find a child walking around at midnight they'd be obliged to accompany them home, especially if it were cold and rainy outside.
Time to blame the victim...Why in tha hell would she get into a creepy guy's car late at night in tha first place?


text:
A Fluid Life and an Open, Honest, Hurt Heart
by fangswandsfairy • Book News, Book Review, Book Review Fiction, Book Review Non-Fiction • Tags: family, Marriage, Memoir, personal development, relationship, self-help
THE BOOK OF HELP
A Memoir in Remedies
Written by: Megan Griswold
Read by: Megan Griswold
PRHA | Imprint: Random House Audio
Genre: Self-Help – Personal Growth – Happiness
A hilarious and heartbreaking memoir-in-remedies by a self-described "professional soul-searcher" that details a journey of self-discovery through over 160 tonics, seminars, regimens, and transformative therapies.
From an early age, Megan Griswold was set on a path of looking outside herself to learn about herself. At birth her parents found a Christian Science practitioner for her, at age seven she asked Santa for a mantra, and at 12 she started taking workshops on personal growth. Her over-the-top existential curiosity eventually led her to outdoor leadership training in Patagonia where she met her husband. Their marriage was not without challenges, and after he was arrested, Megan's world was shattered. Rather than let herself shatter, she embarked on a journey of self-discovery with renewed vigor.
The Book of Help is the story of the 15,000 hours and 160 different remedies, therapies, and modalities that Megan went through, poking and prodding herself to feel better and be better. She explores a past life family tree, gets trained in Five Element Acupuncture, becomes a doula, takes Ayahuasca, does a water fast, and sees psychics and a bevy of therapists. With a voice that is at once intimate and hilarious, Megan captures the openness and honesty necessary for people to take a new path in life. Readers will open the book with curiosity about all the different healing therapies that Megan tries, but leave with a new understanding of themselves. https://www.penguinrandomhouseaudio.com/book/592246/the-book-of-help/
A post about a memoir is often, maybe always, going to seem like a judgement on the life described as much as it is about the writing, editing or design of the book. And, so, this one resonated with me — I think most everyone has been betrayed by someone they love, endured difficult familial issues, and/or sought out different ways of understanding themselves will find something relevant here.
It's also funny with sections of irony so hard-edged and heart-breaking, but told with a sense of rear-view mirror humor that somehow makes it easier to bear. I went through a self-development phase and spent several years contemplating my navel. But, I admired how seriously Megan was in her quest.
In light off what her most difficult situations were, I think the programs pushed her towards acceptance and coping with things over which she had no control. That took time and a lot of thinking.
Megan's family were instrumental in self-examination born of Christian Science but after leaving the religion. You can take the way Christian Science practitioners on problems in a contemplative way as Megan describes their approach. She talks about overhearing her parents "discussing" things. And how she asked for a mantra when she was a young child. Partly she was motivated by wanting to be with her parents and bask in their attention. Her participation in the many programs and self-development/help books set up an approach to life and its issues. Sometimes she learned something, sometimes it helped, and sometimes it made things worse.
Megan is devoted to family and education as she searches for love and an occupation. She is no slouch and I really admired her serious survival skills. One thing her psycho-spritual quest did was create a fluidity in her living spaces and occupation. She is compassionate and works hard at her life and attitude. And, it affected how she handles hard situations. And, at the end she is not developing or improving, but she has a handle on it and a sense of what matters to her.
And as the narrator she really sells her story. She is funny and vulnerable. All those workshops and expeditions gave her self-confidence and a great sense of humor. I also think she's self-aware without rolling over other people. This is not a self-help book, it's a book about one woman's quest to find meaning in and understand life. I enjoyed it and found Megan's story relevant.
PUBLISHER: https://www.penguinrandomhouseaudio.com/book/592246/the-book-of-help/
AUDIBLE: https://www.audible.com/pd/The-Book-of-Help-Audiobook/B07LBZCF9Y?
AUTHOR: https://megangriswold.com/
« Digging Deep: UNDERGROUND	» An Enigma and a Road Trip: THE RUNAWAY
Trying to find meaning and understanding can take you in many directions. Not all of them in the right direction.
I have lived that for sure! Thanks for commenting.


The Georgia Rose is the newsletter for anyone who wants to learn more about roses and growing them in the Southeast. Written and published 3 times a year by Ryan Tilley, Rose Gardens by Ryan, it is a must-read rose publication. All issues are archived on site for easy reference and notifications are emailed as new editions are available. Full color web editions or black and white print editions are available.


September 12, 2017 - Fluke Process Instruments will showcase a range of sensors for various industry sectors and applications at the 2017 Advanced Engineering exhibition. The latest addition to the noncontact, infrared thermometer portfolio, the Endurance series covers measured temperatures from 50 °C to 3,200 °C. It comprises monochrome and ratio pyrometers with various spectral ranges. The sensors feature a stainless steel IP65 housing, galvanically insulated I/Os, a range of sighting options including a high-resolution onboard video camera, remote monitoring via Industrial Ethernet, and a built-in web server for process troubleshooting and traceability. Applications include the metals, glass, plastics, and semiconductor industries.
Additions to the manufacturer's temperature profiling portfolio include a profiler designed specifically for miniature wave selective soldering processes: the DATAPAQ SelectivePaq takes measurements from surface-mount assemblies as they pass through the preheating and dip soldering phases. The system combines a four-channel DATAPAQ Q18 micro data logger with micro-miniature thermocouple plugs, the DATAPAQ Insight Reflow software, and a low-mass thermal shield that is 20 mm high and 40 mm wide.
The DATAPAQ EasyTrack3 system is designed to make profiling oven cure of painted and powder-coated products easy. Loggers are now available for up to three consecutive profiling runs before data download to a PC. Upon leaving the oven, a traffic-light indicator on the logger casing shows whether process criteria have been met. The DATAPAQ EasyTrack Insight Professional software includes advanced analysis functions such as rise/fall, peak difference, area under curve calculation, marking of up to six oven zones in the temperature graph, a probe map for high repeatability, saved zooms for reviewing and sharing of data, and customized profile reports.


This document describes the purpose and functionality of Per VLAN Spanning Tree (PVST) simulation on Multiple Spanning Tree (MST) switches. It also addresses the basic rules that must be followed in order to avoid PVST simuation inconsistencies and the reason for these inconsistencies.
Cisco recommends that you have basic knowledge of MST concepts, such as Common and Internal Spanning Tree (CIST) and boundary ports.
Often, MST regions are connected to other domains - Per VLAN Spanning Tree Plus (PVST+) or rapid-PVST+ regions. These switches that run PVST+ (or rapid) cannot process MST-type Bridge Protocol Data Units (BPDUs). For this reason, there must be a backward-compatibility mechanism that runs so that these two domains can interact with each other seamlessly. This is what PVST simulation addresses and achieves.
This simulation must be run only on boundary ports - these are ports that are directly connected to the PVST+ domain switches. The receipt of a Shared Spanning Tree Protocol (SSTP) BPDU on the port of a switch that runs MST causes the PVST simulation mechanism to trigger.
In this topology, Switch 1 (SW1) runs PVST+, while switches SW2, SW3, and SW4 run MST and are all in the same region.
If the root bridge for CIST is within a non-MST region, the spanning-tree priority of VLANs 2 and above within that domain must be better (lesser) than that of VLAN 1.
If the root bridge for CIST is within a MST region, VLANs 2 and above defined in the non-MST domains must have their spanning-tree priorities worse (greater) than that of the CIST root.
If you do not adhere to these two rules, you encounter the PVST simulation failure. These two rules, in a way, are identical to the root-guard feature and are actually derived from it.
The next sections examine the rules (scenarios) individually in order explain how PVST simulation works.
SW1 does not hear any BPDUs that it can understand, so it elects itself as the root for all VLANs, and starts to send BPDUs toward the MST-region switches. When SW2 receives a SSTP BPDU on Fa0/1, it understands that the interface is connected to a PVST+ domain. It subsequently sets the flag in order to enable PVST simulation on this interface.
A critical concept to understand is that only the Institute of Electrical and Electronics Engineers (IEEE) BPDU for VLAN 1 is processed for the root bridge election. This is compared to only the instance 0 information from the MST region. No other instance information is used in order to elect the root bridge for CIST. No other VLAN information from the PVST+ domain other than VLAN 1 is used in order to elect the CIST root bridge.
SW1 generates one BPDU for every VLAN, and sends them to SW2. These BPDUs are simply used for consistency checks as part of the PVST simulation. However, their information is not copied anywhere.
When these BPDUs arrive on SW2, the VLAN 1 BPDU is processed, which is reflected in the outputs. The other BPDUs then go through the PVST simulations root-guard-based consistency check.
In this setup, the consistency check passes and there is no PVST simulation failure. In order to generate a failure, increase the priority of VLAN 2 to greater than 8192 on SW1.
Port path cost 200000, Port priority 128, Port Identifier 128.3.
The information that comes from SW1 is 12290:0022.0dba.9d00, and this is compared to 8193.0022.0dba.9d00. Since the port is a root port, and it has received an inferior BPDU, it enters into a PVST simulation failure state and displays the error message seen previously. This is because the boundary port cannot be in two different states at once - the receipt of the inferior BPDU dictates that the port should move to designated, whereas via VLAN 1 information dictates that the port should remain a root port. This confusion is prevented with PVST simulation. The port is also moved to a PVST simulation inconsistent state.
This situation reverses the roles from the previous scenario. The root bridge for the CIST is now in the MST region. SW2 is the root bridge.
Fa0/1 is still the boundary port, and the PVST simulation runs on this interface. This now plays a very important role again. The PVST+ domain expects one BPDU per VLAN, but MST does not do that. PVST simulation takes the bridge information of instance 0 (priority + MAC address), and it creates one BPDU for every VLAN that is allowed across its interface with this information. It simply tags each of these BPDUs with the appropriate VLAN IDs.
In order to generate a failure condition for this, change the priority for VLAN 2 on SW1 to a value lower than 12,288.
The information that comes from SW1 is 8192:0022.0dba.9d00, and this is compared to 12288:0022.916d.5380. Since the port is a designated port, and it received a superior BPDU, it enters into a PVST simulation failure state and displays the previous error message. The port is also moved into a PVST simulation inconsistent state.
If the MST region has the root bridge for CIST, PVST simulation is required in order to replicate instance 0 information, and create one BPDU for every VLAN that is allowed across the trunk and tag it with the appropriate VLAN information.
If the root bridge for CIST is outside of the MST region, then PVST simulation is required to process VLAN 1 information only. The other BPDUs (VLANs 2 and above) are used for consistency checks and information from these VLANs is never copied as root bridge information.
If these conditions are not met, the boundary port is put into a PVST simulation inconsistent state until the problem is corrected.


text:
ReviewsDVD, Blu-ray and TV
The Two Johns
Six classic Ford-Wayne collaborations, some new to DVD
By Uncut
Before proceeding with this six-movie set, you have to acknowledge that anything which calls itself John Wayne: The John Ford Collection yet fails to feature The Searchers or The Man Who Shot Liberty Valance is riding into trouble. Still, it's a fine place to start exploring Hollywood's most legendary actor-director partnership. Vitally, it sees the first UK DVD release for Stagecoach (1939) [5], his first with Wayne, who sauntered into stardom after a decade in movies as the Ringo Kid, a charming outlaw bent on revenge. Ford presents society as bickering strangers on a coach trundling through a hostile wilderness, loosely slamming dimebook thrills against the anti-bourgeois bite of his source story, Guy de Maupassant's caustic Boule De Suif, Orson Welles watched it 40 times.
Drawn from Eugene O'Neill, The Long Voyage Home (1940) [5], also making its DVD debut, was actually shot by Citizen Kane cinematographer Gregg Toland, whose stunning high-contrast etches bawdy and maudlin (Ford's favourite poles) vignettes of sailors far from home during WWII. It's more Ford than Wayne, with the actor cast as an innocent Swedish farm-boy.
The heart of the set, though, lies in their monumental "Cavalry Trilogy". The bitter complexities of The Searchers aside, these films display Wayne's finest performances for Ford. For anyone who buys into the old canard that he couldn't act, they're the movies to be enlightened by: as the captain at desolate Fort Apache (1948) [5], trying subtly to prevent commander Henry Fonda from bringing down massacre; as the ageing, aching widower reluctantly eying retirement in the peerless She Wore A Yellow Ribbon (1949) [5]; as the sensitive Colonel Yorke bitterly estranged from wife Maureen O'Hara during the divisive Civil War, now signing off on an illegal sortie into Mexico, to rescue children in the reflective and leisurely Rio Grande (1950) [5]. O'Hara returns as the flame-haired colleen who captures Wayne's heart in The Quiet Man (1952) [3]. He's the boxer who, having killed an opponent in the ring, has fled to his homeland and vowed never to fight again. Chipped straight from the Blarney Stone, it's Ford's dream of Oirland, pure fantasy, and a film to cherish if you have any Irish in you, without having a stick up your ass about it. Universal is also releasing boxes entitled Wayne Out West, Wayne At War and Wayne In Action, and a 33-film John Wayne Collection. Hell, pilgrim-imagine what they're going to do in 2007, when Wayne's centenary rolls around!
John Robinson -
Head Pretender Chrissie Hynde reveals the secrets of her long-lived success. "When I stopped getting on the radio I didn't like it."
The Chieftains – Chronicles : 60 Years Of The Chieftains
Neil Spencer -


Experience the desert in the best possible light on a camel ride through the Dubai Desert Conservation Reserve. Take to the trail on a 'ship of the desert' – the dromedary camel - to see the desert flora and fauna.
Two guests per camel (maximum).
Lone-travellers will not share a camel.
Children will ride on the same camel as their parent/guardian. Not recommended for children under 4 years of age.
Winter: Approximately 7am from Dubai but this will be confirmed at the time of booking based on your exact pickup location and the time of year.
Summer: Approximately 3:30pm from Dubai but this will be confirmed at the time of booking based on your exact pickup location and the time of year.


My husband and I have a new addiction — and that is to consistently get our blood pressure shooting up! Strike that. Our latest fancy is this small yet very in demand restaurant called Shrimp Shack. Of course, most of you has heard of it. We have been indulging ourselves with the jumbo shrimps, crabs and lobsters. We have dined in its King Khalid Branch Road store, the one in Althaghar Plaza? The one with Jarir – Saco-Panda and Chuck e Cheese, yeah, that one!
I have seen a store in Aldhabab Street beside KFC, although I am not certain whether it's operational or has it been operational all this time. Eversince Jahez and HungerStation became popular, I have shied away from the actual restaurant scenes so forgive my being semi-ignorant in this regard.
So the price, considering the servings Shrimp Shack has, I'd say it's quite affordable and economical. Comparing the cost vs. my regular dine out meal which is the likes of ribeye, one would totally feel so frugal having ordered their jumbo shrimps, hahaha! The gastronomical load is the same, even made me feel "fuller" and more satisfied — AND YES COSTING ME WAY LESS which matters a lot to me.
The place though, it's quite chaotic, the one in Althaghar Plaza did not have toilets and it's awkward given the nasty smell of shrimps, regardless of the plastic gloves. I mean, handwashing is more than a necessity. Why didn't they have a toilet? It's like a takeout place forcibly made a dine-in. This point though still did not keep the people out. It was still jampacked and a total box office because people come here for the food and it's quality, since yeah — it's topnotch!
Apart from the toilet, you'd find a slight issue that you're given a refillable drink cup ala-Burger King/Taco Bell but they wouldn't let you refill on your own. They had to do it for you. That's the norm in KSA actually (and I am aware) but the use of disposable cups makes the feel quite different, not in a negative way, but expectations-wise. I thought there was a dispenser accessible for me. That's it. It wasn't a disappointment, don't get me wrong. It was only a matter because my husband and I were eating voraciously and we kinda nearly choked haha and we needed a drink stat. Given the chaos and all the crew running around because of the previously mentioned box office scenario, it took a while before we got served. It was not something we held them liable for. It was us who were eating like there's no tomorrow.
I am pointing this out to be an issue though on HUNGERSTATION (NOT ON SHRIMPSHACK) – We have ordered 4x and the orders were all accepted but had to get declined some (significant) time later (after our high hopes to having sumptuous shrimp dinners) and later getting refunded because your drivers won't accept the order. I know the smell could be an issue but when Shrimp Shack signed up with you, it's an agreement that you consider that it would smell bigtime. If you won't provide drivers that are dedicated for this particular case, then the store loses its business because no one gets to order. It's something that I have noticed that all things boil down to "finding a driver"and voila, no one accepts.


text:
class Jeu;

class Fologun : public Arme
{
    static int _son1;
    static int _son2;
    static SDL_Surface* _surface;

public:
    Fologun();
    //virtual ~Fologun();

    virtual string name() { return "Fologun"; }

    virtual Arme* clone();

    virtual void fire1();
    virtual void fire2();

    // "virtual static" methods
    static void gameInit(Jeu* jeu);
    static void gameEnd(Jeu* jeu);
    virtual SDL_Surface* surface() { return _surface; }
};

#endif

redpajama_set_name: RedPajamaGithub


text:
O Vale de Hetch Hetchy () é um vale glaciar localizado no Parque Nacional de Yosemite na Califórnia, Estados Unidos.

Referências

Ligações externas

Hetch Hetchy
Parque Nacional de Yosemite
Sierra Nevada


This week, Forbes compiled a list of what they consider to be the best SUV deals for October.
The deals include some luxury models, such as the BMW X1 at $319/month with $4,244 due at signing. All of the deals are on brand new 2017 models. See the full list below.


You should not go through a divorce alone when you are facing the decision to seek a Texas divorce. People who are going through divorce are going through a very emotional time of their lives. You should not underestimate the importance of legal representation when filing for divorce. You should not simply want to get it over with and just agree to what your spouse desires. Your first step should be to find a Texas divorce lawyer who will secure the best deal for you and your future.
Compared to other states there are some things that differ from divorce laws in Texas. There is no one who ever gets married who plans to someday get a divorce. Divorce is a difficult time for every member of the family. You will need a Texas divorce lawyer especially if there are going to be arguments over obligations and disagreements over division of the marital assets. During this stressful time, a good Texas divorce lawyer can help find common ground.
Knowing which district court has jurisdiction over your case is important if you are filing for divorce in Texas. The requirements of law are that you have lived in the states for at least 6 consecutive months and have resided in a single county for at least 3 months.
You should make known the reason for your divorce. Grounds for divorce can be no-fault grounds or fault-based grounds. If neither party is at fault for the marriage break-up, then this is a no-fault divorce. IF both parties agree that their bond is irretrievably broken, then divorce is the only solution. A fault-based divorce means that either one or both spouses have caused the marriage to dissolve through reasons such as adultery, abandonment, or cruelty.
Divorce involving children requires that both spouses work out an agreement before the case goes to the judge. Both parents have to sign a written agreement involving the welfare of their minor children. The final decision still belongs to the judge who decides if the agreement is fair to all concerned parties.
There are certain laws and restrictions that are bound to Texas and divorce lawyers are trained to handle divorces in the state. In the discovery process, each spouse learns from the other all relevant information concerning the divorce. A decision is then made as to the couple's property values and division.


Costuming 101 - Historic Interpretations, Inc.
Don't have your own Garb?
Building a set of historically appropriate garb, or clothing, is something Historic Interpretations will guide and teach each volunteer. For those volunteers new to our organization and/or do not have attire appropriate for a specific area, Historic Interpretations Costume Guilde will loan clothing on the day of an event for a small fee for cleaning.
Many of our volunteers create or purchase costumes appropriate for reenactment.
The following are resources vetted by our Historic Interpretations' costuming experts. Have questions, please let us know!
Click on the buttons below for links to downloadable content.


text:
Q: What's the difference between world and view for purpose of camera? I'm a beginner so I'm probably confused on some fundamental level, but I seem to get the same result with the following two Draw() routines and I'm wondering is there any difference and is one more correct than the other?
In one I apply transformation to the world matrix and in the other I apply transformation to the veiw matrix.  I think the result is exactly the same but I'm not 100% sure.
    protected override void Draw(GameTime gameTime)
    {
        GraphicsDevice.Clear(Color.CornflowerBlue);

        Matrix worldMatrix =
            Matrix.CreateTranslation(view.Position.X, view.Position.Y, view.Position.Z) *
            Matrix.CreateRotationX(view.Rotation.X) *
            Matrix.CreateRotationY(view.Rotation.Y) *
            Matrix.CreateRotationZ(view.Rotation.Z);

        Matrix viewMatrix = Matrix.CreateLookAt(view.CameraPosition, view.CameraTarget, view.CameraUp);

        Matrix projectionMatrix = Matrix.CreatePerspectiveFieldOfView(view.FieldOfView,
                                                                      GraphicsDevice.Viewport.AspectRatio,
                                                                      view.NearPlane,
                                                                      view.FarPlane);

        DrawModels(worldMatrix, viewMatrix, projectionMatrix);

        base.Draw(gameTime);
    }

How's that different from:
    protected override void Draw(GameTime gameTime)
    {
        GraphicsDevice.Clear(Color.CornflowerBlue);

        Matrix worldMatrix = Matrix.Identity;

        Matrix viewMatrix =
            Matrix.CreateTranslation(view.Position.X, view.Position.Y, view.Position.Z) *
            Matrix.CreateRotationX(view.Rotation.X) *
            Matrix.CreateRotationY(view.Rotation.Y) *
            Matrix.CreateRotationZ(view.Rotation.Z) *
            Matrix.CreateLookAt(view.CameraPosition, view.CameraTarget, view.CameraUp);

        Matrix projectionMatrix = Matrix.CreatePerspectiveFieldOfView(view.FieldOfView,
                                                                      GraphicsDevice.Viewport.AspectRatio,
                                                                      view.NearPlane,
                                                                      view.FarPlane);

        DrawModels(worldMatrix, viewMatrix, projectionMatrix);

        base.Draw(gameTime);
    }

A: The three matrices have semantic meaning:

*

*First, the World matrix moves models around the world (you have one for each object)

*Then, the View matrix moves the entire world around - effectively this is the same as moving the camera around within the world

*Finally, the Projection matrix takes the transformed vertices of the world and projects them into the raster space of the screen

These three matrices are multiplied in that order.
So if you multiply myWorld * myView and assigned the result into the view matrix, leaving the world as the identity matrix, the result is the exactly the same as if you had assigned myWorld into world and myView to view and let the next layer down handle the multiplication.
While the result is the same - it is generally best to stick with the semantic meanings where they are available. It makes your code more readable.

A: Matrix multiplication is associative: (A*B)C = A(B*C),
but not commutative: AB != BA for every matrix. Thus, if DrawModels multiplies the matrices world, view and projection from left to right, then the two methods give exactly the same results (+- rounding errors...).
It's purely a design issue, how the matrices are decomposed or concatenated. E.g. premultiplying viewMatrix and projectionMatrix can make sense -- IIRC viewMatrix * symmetric_projectionMatrix introduces just 4 or 5 extra multiplications to the generation of the viewMatrix from 3 Euler angles or quaternion camera representation. Doing the multiplication for generic 4x4 matrices takes 64 multiplications...
One thing to consider is how to cull bounding boxes -- either axis aligned, or generic. This can be done in world space, camera space or clip space. Which ever makes most sense should dictate how the matrices are premultiplied.

redpajama_set_name: RedPajamaStackExchange