text:
The almanac
Today is Wednesday, March 12, the 71st day of 2014 with 294 to follow.
The moon is waxing. The morning stars are Mars, Mercury, Neptune, Saturn and Venus. The evening stars are Jupiter and Uranus.
Those born on this date are under the sign of Pisces. They include pioneer automaker Clement Studebaker in 1831; New York Times publisher Adolph Ochs in 1858; artist Elaine de Kooning in 1918; actor/singer Gordon MacRae in 1921; novelist Jack Kerouac in 1922; Mercury astronaut Wally Schirra in 1923; writer Harry Harrison in 1925; playwright Edward Albee in 1928 (age 86); actor Billie "Buckwheat" Thomas in 1931; Southwest Airlines co-founder Herb Kelleher in 1931 (age 83) former U.N. Ambassador and Atlanta Mayor Andrew Young in 1932 (age 82); actor Barbara Feldon in 1933 (age 81); Hall of Fame basketball Coach Eddie Sutton in 1936 (age 78); singer/songwriter Al Jarreau in 1940 (age 74); singer/actor Liza Minnelli in 1946 (age 68); former Massachusetts Gov. Mitt Romney in 1947 (age 67); Rock and Roll Hall of Fame member James Taylor in 1948 (age 66); Jackson 5 member Marlon Jackson in 1957 (age 57); former baseball player Darryl Strawberry in 1962 (age 52); actor Aaron Eckhart in 1968 (age 46); and musician Pete Doherty in 1979 (age 35).
On this date in history:
In 1912, Juliette Gordon Low organized the first Girl Scouts of America troop in Savannah, Ga.
In 1930, Mahatma Gandhi began a campaign of civil disobedience against British rule in India.
In 1933, U.S. President Franklin D. Roosevelt made the first of his Sunday evening "fireside chats" -- informal radio addresses from the White House to the American people.
In 1938, Nazi Germany invaded and occupied Austria.
In 1947, in a speech to Congress, U.S. President Harry Truman outlined what became known as the Truman Doctrine, calling for U.S. aid to countries threatened by communist revolution.
In 1963, the U.S. House of Representatives voted to grant former British Prime Minister Winston Churchill honorary U.S. citizenship.
In 1993, more than 250 people were killed in a wave of bombings in Mumbai.
In 1994, the Church of England ordained its first women priests.
In 1999, former Soviet allies Hungary, Poland and the Czech Republic joined NATO.
In 2002, U.N. Secretary-General Kofi Annan, speaking after Israeli raids killed 31 Palestinians, declared that Israel must end its "illegal occupation" of Palestinian land.
In 2003, Elizabeth Smart, 15, who had been kidnapped from her Salt Lake City home in June 2002, was found in the custody of a panhandler and his wife in nearby Sandy, Utah.
In 2008, New York Gov. Eliot Spitzer resigned after being caught in a high-priced prostitution scandal.
In 2009, a 17-year-old boy, who felt "no one recognized my potential," killed 16 people, including nine students, at his former school in Winnenden, Germany. He died in a shootout with police.
In 2012, after days of violence, Palestinian and Israeli authorities agreed to a truce, an agreement reached with the help of Egyptian mediators.
In 2013, Steven Ray Thacker, 42, who killed three people during a crime spree in three states was executed in Oklahoma.
A thought for the day: "Influence is like a savings account. The less you use it the more you've got." -- Andrew Young
Adolph Ochs
Clement Studebaker
Gordon MacRae
Nicolas Cage, Kesha attend the 'Color Out of Space' premiere in LA


Hi, we're Bushwacker Eco Tours and since the 1990s, we've been sharing our favourite holiday spots with visitors to Australia on our small group adventure tours. We grew up here and have been exploring the country since we were kids - and now we've bottled a lifetime of Aussie holiday memories into our tours. From the Greatest Sand Island in the world- Fraser Island, Moreton Island and North Straddie Island to the Outback of Carnarvon Gorge to the Ancient Rainforest of Springbrook and Gold Coast Hinterland, you won't miss a thing - Lake Mckenzie (top ten best lakes in the world), Snorkel or Kayaking the Shipwrecks of Moreton Island to explore and escape the city with Waterfalls and rainforest hikes - plus a few of our secret spots you may not know so well, but many people tell us they were their favourites.
Travelling in a small group really is the only way to go! You'll never take another large coach tour again.
One of the most important aspects of your experience is the guide. An experienced and friendly guide with exceptional attention to detail can make your Queensland tour unforgettable. All of our Bushwackers guides are highly knowledgeable and passionate about Queensland and Australia. They're 'quintessential Aussies' with a great sense of humour, plenty of experience and a warm personality. Whether you're interested in history, flora, fauna or our colourful culture, Bushwacker's expert guides promise to bring Queensland to life.
Touring with like-minded people from all over the world results in plenty of good times and new friendships. When you're travelling in a group of twenty or less, you're bound to meet people with whom you have lots in common, and yet still find it possible to mix and mingle with everybody during meal times. Activities like feeding the dolphins, wildlife encounters with dingos and stories around the campfire are so much more enjoyable when you're sharing the moment with new friends.
A Flexible Schedule on large-group coach tours, itineraries are often confined to a tight and rigid schedule, leaving no opportunities for flexibility or surprises. Enjoying your time soaking in your very own lake without the crowd? Let's stay a little while longer! Are there plenty of cheeky kangaroos and emus on our way through Carnarvon Gorge National Park? Take your time with photographs to ensure you get the perfect shot. With a small group, we don't like to hurry – after all, you are on vacation.


FOSTER CITY, Calif.--(BUSINESS WIRE)--Gilead Sciences, Inc. (Nasdaq: GILD) today announced that John F. Milligan, PhD, Gilead's President and Chief Executive Officer; Robin L. Washington, Gilead's Executive Vice President and Chief Financial Officer; and John McHutchison, AO, MD, Gilead's Chief Scientific Officer and Head of Research & Development, will participate in a fireside chat at the Morgan Stanley Global Healthcare Conference in New York on Wednesday, September 12 at 11:05 a.m. Eastern Time.
The audio portion of the fireside chat will be accessible live through the company's Investors page at www.gilead.com/investors. Please connect to the company's website at least 15 minutes prior to the start of the presentation to ensure adequate time for any software download that may be required to listen to the webcast. The replay will be available for 14 days following the presentation.


text:
Describe a famous person whom you admire - Cue Card # 636
Last Updated: Thursday, 02 May 2019 14:22
Written by IELTS Mentor
IELTS Speaking Part 2: IELTS Cue Card/ Candidate Task Card.
Describe a famous person whom you admire.
You should say:
who this person is
what makes him/her famous
where does he/she live
and explain why you admire him/her.
[You will have to talk about the topic for one to two minutes. You have one minute to think about what you are going to say. You can make some notes to help you if you wish.]
Model Answer 1:
Having born and raised in one of the New York suburbs at the house of a dentist father in 1984, Mark Elliot Zuckerberg made his mark at an elementary school by getting interested in computer programming. But, little did anybody know at that time that the little "computer whiz" would revolutionize the concept of social media networking by creating a thing called "facebook.com" in 2004. Whatever followed after that is just 'history'.
I would like to thank you for this nice topic and in my opinion, Zuckerberg is one of the most prolific and successful programmers and entrepreneurs ever, and I admire him so much. In fact, Mark Zuckerberg became so famous that Time Magazine declared this American technology entrepreneur and philanthropist as 'person of the year" in 2010.
Having managed to connect more than 1 billion people among themselves one way or another, today, the social media "Mughal" has every right to claim his place in history as the "greatest web entrepreneur" of all time, some would say. Living in a house of more than 5000 square feet, with a price tag of about $7.3 million, in a town called Palo Alto, California, Mark Zuckerberg became the youngest self-made billionaire with a net worth of more than $65.4 billion today.
Admiring a person for offering the world a new horizon of virtual reality by inventing a thing called "Facebook" in order to turn this world into a small place is easy. I could also easily admire his clear way of thinking which motivated him to wear the same T-shirt every day so that he didn't have to waste his time in deciding small things such as what to wear, or what to eat for breakfast every day. But what I admire the most about this person is his risk-taking abilities (dropping out from Harvard University or turning down very lucrative offers from companies like Microsoft and AOL, just to name a few) and innovative ideas with a clear sense of vision in his mind so that he could finally deliver something really, really amazing to this world.
Finally, one couldn't help admiring a "philanthropist" like Mark Zuckerberg, who is already a living legend in his mid 30's, when he decided to donate at least 50% of his business shares- worth about $65 billion now – to promote equality and human development in the world. Now, how many people can do that!
Sample Answer 2:
We all have our favourite personalities whom we respect and love from the bottom of our heart. While some of them are friends and family whom we meet almost every single day, a few of them are great sportsmen or famous silver screen stars whom we have never met. Our admiration for a famous person makes us follow him/her and it's a dream for many to meet a well-known celebrity. Interestingly, I have a list of my favourite personalities from different arenas whom I follow on social networking platforms and I admire them for their talents, skills, contributions, philanthropic activities, and success stories.
One such famous person whom I greatly admire is Lionel Messi, one of the greatest football players of all time. He is a well-respected sportsman whom I have been following in news and Twitter for more than eight years. I scarcely miss a football match in which he plays. He is a wonder-boy in football and the favourite sportsman of millions of football fans around the world.
Messi is an Argentine professional footballer who plays as a forward both for the Spanish club Barcelona and the Argentina national team. Messi has won five Ballon d'Or awards, two FIFA Club World Cup golden ball, a record five European Golden Shoes, nine La Liga titles, four UEFA Champions League titles, six Copas del Rey and he holds many other records and success stories. Both a prolific goalscorer and a creative playmaker, Messi holds many enviable records in the professional football. He has scored over 680 senior career goals for his club and country. Often considered the best player in the world and regarded by many as one of the greatest players of all time, he is world renowned and inspiration to many aspiring football players. Messi is his country's all-time leading goalscorer and he currently lives in Castelldefels, a village near Barcelona.
Messi has earned my deep admiration primarily for his amiable character, and formidable skills. Besides, he is the best football player alive and I really enjoy his performance and skills. Throughout his career, he has been involved in charitable efforts aimed at vulnerable children, a commitment that stems in part from the medical difficulties he faced in his own childhood. Since 2004, he has contributed his time and finances to the United Nations Children's Fund (UNICEF). One of the highest paid sportsmen of all time, he is a legend in football history and deserved our respect and admiration. I admire him more than any other football player I know about.
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redpajama_set_name: RedPajamaCommonCrawl


Order in the next 9 hours 30 minutes and your package will dispatch on Tuesday, May 7.
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text:
Q: iTextRendered - Russian Characters in PDF I'm using iText2.0.8 to generate PDF files.
In brief here's what the code looks like:
FileOutputStream fOut = new FileOutputStream(fileFullPath);
ITextRenderer renderer = new ITextRenderer();
renderer.setDocumentFromString(str); //str is HTML fetched from a velocity template
renderer.layout();
renderer.createPDF(fOut);
fOut.close();

This works fine. However, when I have Russian characters, they simply show up 'blank' in the PDF. I tried to convert the fetched HTML string to UTF-8 but then the Russian Characters started to show up gibberish.
Does anyone know a way around this?

redpajama_set_name: RedPajamaStackExchange


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H E Dr Ahmed bin Mohammed bin Obaid al Sa'eedi, Minister of Health, led Oman's delegation to the 64th Session of the Regional Committee for Eastern Mediterranean Region of World Health Organization (WHO) in Pakistan.
The conference was held from October 9 to 12 in Islamabad.
The plenary session was inaugurated by Mamnoon Hussain, President of Pakistan, stated a press release.
Representatives of 22 countries in WHO's Eastern Mediterranean Region, including national, regional and international organisations took part in the event.
On the sidelines of the event, H E Dr Sa'eedi also met Saira Afzal Tarar, Minister for National Health Services Regulation and Coordination in Pakistan and also called on the director general of WHO.
The Ambassador of Pakistan in Muscat H E Ali Javed congratulated Oman for the achievement of Dr Yasmin bint Ahmed bin Jaafar, adviser to DG of Primary Health Care, who was awarded 'Dr Ali Tawfiq Shousha Foundation Award and Scholarship during the conference.
H E Dr Sa'eedi was exclusively interviewed by Pakistan Television (PTV). He praised the bilateral bonds of affinity and kinship terming them as 'historical in all aspects'.


text:
\section{Introduction}
The presence of long-range dependence and the estimation of its characteristic Hurst exponent in financial time series have been widely discussed in several recent research papers, e.g. \citep{alvaetal2008,carboneetal2004,czarnecki2008,dimatteo2007,dimatteoetal2005,grech2004,matos2008,peters1994,Vandewalle1997}. Even though there are several studies of the finite sample properties of different methods \citep{couillarddavidson2005,ellis2007,grech2005,weron2002}, most authors still interpret the results only on the basis of a simple comparison of the estimated Hurst exponent $H$ with the theoretical value for an independent process of 0.5. In more detail, Hurst exponent of 0.5 indicates two possible processes -- either an independent one \citep{beran} or a short-range dependent one \citep{lillofarmer2004}. If $H > 0.5$, the process has significantly positive and non-summable auto-covariance coefficients at all lags and it is called persistent \citep{mandelbrot1968}. When $H < 0.5$, the process has significantly negative summable auto-covariances at all lags and the process is said to be anti-persistent \citep{barkoulasetal2000}. For the persistent process, an increment (decrement) is statistically more likely to be followed by another increment (decrement). Reversely, the anti-persistent process reverts more frequently than the independent process. However, the estimates for a random process can strongly deviate from the limit of $0.5$ \citep{couillarddavidson2005,weron2002}.

Most authors estimate expected values and confidence intervals based on simulations of a standard normal process $N(0,1)$. The returns of the financial markets, however, are not normally distributed \citep{cont2001} and are  heavy-tailed. The validity of such approach can thus be questioned. This paper tries to fill the gap with original results for independent processes drawn from the family of stable distributions. Stable distributions are peaked and fat-tailed for different parameters, which fits the financial market data more closely. Thus, we want to find out how the sampling properties of the Hurst exponent estimates change with fat tails. For this purpose, expected values and confidence intervals are estimated for rescaled range analysis ($R/S$), multifractal detrended fluctuation analysis ($MF-DFA$), detrending moving average ($DMA$) and generalized Hurst exponent approach ($GHE$) for time series of lengths ranging from 512 to 65,536 observations.

The paper is organized as follows. In Section 2, rescaled range analysis, detrended fluctuation analysis, detrending moving average and generalized Hurst exponent are described in detail. Section 3 focuses on the finite sample properties of the different Hurst exponent estimators. Simulation methodology, stable distributions and research design are presented as well as the results of the Monte Carlo study. After examining the finite sample properties of the different Hurst exponent estimation methods, we use these results and we estimate daily and monthly Hurst exponents on high frequency data.

\section{Hurst exponent estimation}
Many methods of the Hurst exponent estimation have been proposed and used in literature and each of these methods has specific advantages and disadvantages. In our research, we focus on classical methods of rescaled range analysis \citep{hurst1951} and detrended fluctuation analysis \citep{pengetal1994} as well as quite novel methods of multifractal detrended fluctuation analysis \citep{Kantelhardt2002}, detrending moving average \citep{Alessio2002} and generalized Hurst exponent approach \citep{dimatteo2003}. In this section, we shortly describe each method and we follow with a brief review of papers dealing with the finite sample properties of the estimators.

\subsection{Rescaled Range Analysis}
Rescaled range analysis $(R/S)$ was developed by Harold E. Hurst while working as a water engineer in Egypt \citep{hurst1951} and further augmented by \citep{mandelbrotwallis1968}. In the procedure, one takes the returns of the time series of length $T$ and divides them into $N$ adjacent sub-periods of length $\upsilon$, where $N\upsilon=T$. For each sub-period, one calculates the average value and constructs new series of accumulated deviations from the arithmetic mean values (the profile). Then the range, which is defined as the difference between the maximum and the minimum value of the profile, and the standard deviation of the original time series for each sub-period, is calculated. Each range is standardized by the corresponding standard deviation and forms a rescaled range so that the average rescaled range for a given sub-period of length $(R/S)_{\upsilon}$ is calculated. The rescaled range scales as $(R/S)_{\upsilon}\approx c\upsilon^{H}$, where $c$ is a finite constant independent of $\upsilon$ \citep{dimatteo2007,taqquetal1995}. To uncover the scaling law, simple ordinary least squares regression on logarithms of each side is used so that $\log{(R/S)}_{\upsilon}\approx\log{c}+H\log{\upsilon}$, where $H$ is the Hurst exponent.

$R/S$ was shown to be biased for small $\upsilon$ (e.g. \citep{couillarddavidson2005}). The expected values of rescaled ranges for finite samples are given in \cite{Anis1976}. For a discussion of $R/S$ variance, see a recent paper of \cite{Mielniczuk2007}. In our research, we use the original version described in this section.

 \subsection{Multifractal Detrended Fluctuation Analysis}
\label{sec2.2}
Multifractal detrended fluctuation analysis ($MF-DFA$), proposed by \citep{Kantelhardt2002}, is a generalization of detrended fluctuation analysis ($DFA$), proposed by \citep{pengetal1994}. The main advantage of this technique is that it can be used for non-stationary multifractal data. Basic notion is the examination of deviations from polynomial fit of different moments $q$. For $q=2$, $MF-DFA$ turns into standard $DFA$.

In the procedure, the time series is divided into sub-periods and the profile is constructed in the same way as for rescaled range analysis. The polynomial fit of order $l$, labeled as $X_{\upsilon,l}$, of the profile is estimated for each sub-period. In our analysis, we set $l=1$ and thus we use the linear detrending. Therefore, we omit the $l$ labeling in the rest of the text. A detrended signal $Y(t,i)$, for sub-period $i=1,\ldots,N$ is then constructed as $Y(t,i)=X(t,i)-X_{\upsilon}(t,i)$. Fluctuation $F^2_{DFA,q}(\upsilon,i)$, which is defined for each sub-period of length $\upsilon$ as $F^2_{DFA,q}(i)=(1/\upsilon\sum_{i=1}^{\upsilon}{Y(t,i)^{2})^{q/2}}$, is then averaged over $N$ sub-periods of length $\upsilon$ for different $q$ and forms $F_{DFA,q}(\upsilon)=(1/N\sum_{i=1}^{N}{F^2_{DFA,q}(i)})^{1/q}$ which then scales as $F_{DFA,q}(\upsilon)\approx c\upsilon^{H(q)}$, where again $c$ is a constant independent of $\upsilon$ and $H(q)$ is the generalized Hurst exponent. The Hurst exponent is again estimated through ordinary least squares regression on logarithms of both sides so that $\log{F_{DFA,q}(\upsilon)}\approx\log{c}+H(q)\log{\upsilon}$ \citep{Kantelhardt2002}.

Moreover, $MF-DFA$ has some characteristics which are important for the examination of processes with heavy tails. \citep{Kantelhardt2002} showed that $H(q) \approx 1/q$ for $q>\alpha$ and $H(q) \approx 1/\alpha$ for $q\leq\alpha$, where $\alpha$ is a parameter of stable distributions defined later in the text.

\subsection{Detrending Moving Average}

Detrending moving average ($DMA$) is quite a new technique proposed by \citep{Alessio2002} and further discussed in \citep{carboneetal2004}. In the method, one does not need to divide the time series into sub-periods. The method is based on deviations from the moving average of the whole series. Fluctuation $F^2_{DMA, \lambda}$ is defined as $F^2_{DMA, \lambda}=\sum_{t=\lambda}^{T}{(X(t)-\bar{X}_{\lambda}(t))^2}$ where $\bar{X}_{\lambda}(t)=\sum_{k=0}^{\lambda-1}{X(t-\lambda)}$ is the moving average with a time window $\lambda$. The fluctuation then scales as $F^2_{DMA, \lambda}\approx c\lambda^{2H}$.

Moving average $\bar{X}_{\lambda}(t)$ can take various forms in centering (e.g. backward, forward, centered) and weighting (e.g. weighted, not weighted, exponential). For our purposes, we use simple backward moving average. For discussion, see \citep{Alessio2002}.

\subsection{Generalized Hurst Exponent Approach}

Last method used in this study, generalized Hurst exponent approach ($GHE$), is another method suitable for detection of multifractality. The method, recently re-explored for the financial time series by \citep{dimatteo2003}, is based on scaling of $q$-th order moments of the increments of the process $X(t)$. The scaling is characterized on the basis of the statistic $K_q(\tau)$, which is defined as $K_q(\tau)=\sum_{t=0}^{T-\tau}|X(t+\tau)-X(t)|^q/(T-\tau+1)$ for time series of length $T$. The statistic scales as $K_q(\tau)\approx c\tau^{qH(q)}$.

For the purposes of long-range dependence detection, the case of $q=2$ is important as $K_2(\tau)$ is connected to the scaling of the autocorrelation function of the increments. Therefore, we can estimate the Hurst exponent $H(2)$ from relationship $K_q(\tau)\approx c\tau^{2H(2)}$, which is comparable with estimates of $H$ of $R/S$ and $DMA$ and $H(2)$ of $MF-DFA$. For $q=1$, $H(1)$ characterizes the scaling of the absolute deviations of the process \citep{dimatteo2007}.

\subsection{Literature review}

Number of applied papers strongly outnumbers the studies dealing with finite sample properties of the Hurst exponent estimators. However, majority of these studies show that the estimators can differ significantly from the expected Hurst exponent even for independent process. Confidence intervals for testing of the null hypothesis of no long-range dependence are then quite wide. Short chronological review of the studies follows.

\citep{taqquetal1995} presented results of quite wide range of the Hurst exponent estimators (9 in total) on the simulated series of 10,000 observations and 50 realizations. We mention only the results for $R/S$ and $DFA$ concerned by our study. \citep{taqquetal1995} finds that $R/S$ overestimates the true Hurst exponent while $DFA$ underestimates it. Nevertheless, the study is quite limited as it provides small number of simulations for only one set of observations, which is considered to be high for financial applications as 10,000 daily observations equals to approximately 40 years of data.

Extending \citep{taqquetal1995} findings, \citep{Taqqu1998} tested 8 different estimators under different heavy-tailed distributions as well as FARIMA models with heavy-tailed innovations. The authors came to the conclusion that $R/S$ and $DFA$ are quite robust to different distributions whereas both are sensitive to the presence of short-range dependence in the process. However, there are several questions about the methods used and most importantly, the results for $DFA$ need more focus. Firstly, the method is called "variance of residuals method" as is the case for \citep{taqquetal1995}. Secondly, the method is defined in a different way than in \citep{pengetal1994} and the authors claim that for an independent process with stable innovations, the expected value of Hurst exponent is not equal to 0.5 but to $1/\alpha$. However, \citep{Kantelhardt2002} showed that this is the case for $MF-DFA$ with $q=1$ for $1\le\alpha\le2$. \citep{taqquetal1995} is the case as well. Finally, the results are again based on 50 simulations of the length of 10,000.

\citep{weron2002} compares the efficiency of $R/S$ and $DFA$ for an independent underlying process. The author finds that $DFA$ outperforms $R/S$ and also discusses the influence of minimal scales $\upsilon$. The simulations are based on the standard normal distribution. However, the author, similarly to the previous two references, defines the algorithm of $DFA$ as $MF-DFA$ with $q=1$ hence the results must be taken with caution again.

As a generalization of $DFA$, \citep{Kantelhardt2002} proposed its multifractal version ($MF-DFA$) and showed some finite samples results. Most importantly, the authors showed the theoretical relationship between $q$, $\alpha$ and $H$, which is described in Section \ref{sec2.2}. Moreover, the deviations from the true values of generalized Hurst exponent $H(q)$ are shown for both unifractal ($H(q)=H$) and multifractal time series ($H(q)$ varies with $q$).

\citep{couillarddavidson2005} focus on $R/S$ solely and show that the estimates based on \citep{Anis1976} are better than the ones of \citep{peters1994}. The authors show the results on both the rescaled ranges and the Hurst exponent estimates based on the standard normal distribution. Moreover, the authors show that the confidence intervals should not be based on the Central Limit Theorem as the standard deviations of the estimates do not obey the square root law. Such result was already discussed in \citep{weron2002} where the confidence intervals are based on percentiles rather than standard deviations.

\citep{grech2005} compare the finite sample properties of $DFA$ and $DMA$ for the standard normal distribution process. The authors show that $DFA$ outperforms $DMA$ in both bias and variance for the time series of up to 30,000 observations. However, the results are quite questionable due to several issues. Firstly, the estimates are taken into consideration only in the case of $R^2>0.98$ for the Hurst exponent estimation regression without any discussion of a ratio of omitted estimates. Secondly, the choice of lags $\lambda$ is not discussed at all for $DMA$.

The other aspect of $R/S$ is discussed in \citep{ellis2007} who examines the efficiency of $R/S$ method with the use of either contiguous or overlapping sub-series and concludes that the methods do not differ significantly while the confidence intervals show the similar deviation from the Central Limit Theorem as shown in \citep{weron2002}.

Finally, a further research on $R/S$ versus $DFA$ are discussed in \citep{Mielniczuk2007}. The authors show that if the bias of $R/S$ is taken into consideration, the root mean square errors are smaller for $R/S$ than for $DFA$ for all time series lengths from $2^9$ to $2^{15}$ as well as for Hurst exponents from 0.5 to 0.9.

In the following section, we challenge these studies and run the extensive Monte Carlo study to find out how rescaled range analysis ($R/S$), multi-fractal detrended fluctuation analysis ($MF-DFA$), detrending moving average ($DMA$) and generalized Hurst exponent approach ($GHE$) behave when estimating Hurst exponent on independent series with different heavy tails and different time series lengths ranging from 512 to 65,536 observations.

\section{Finite sample properties of different Hurst exponent estimators}
The primary objective of this paper is to examine the robustness of the various Hurst exponent estimation methods to heavy-tailed data and contribute to the previous discussions.

The majority of the research papers dealing with the finite sample properties of Hurst exponent estimation methods base their results on simulations of the standardized normal distribution $N(0,1)$. However, a wide spectrum of real-world data is non-normal so that we cannot really use these results for statistical inference when estimating Hurst exponent on these data. To examine the behavior of the estimators of Hurst exponent for an $i.i.d.$ non-normal process with heavy tails, we take advantage of changing the parameter $\alpha$ in the stable distributions. Before we continue in describing the research design, we briefly introduce the stable distributions.

\subsection{Stable distributions}
Stable distributions form a class of probability laws with appealing theoretical properties which fit  well-known stylized facts about financial markets such as skewness and excess kurtosis. Such distributions are described by four parameters -- $\alpha,\beta,\gamma,\delta$. $\alpha$ is the characteristic exponent and $0<\alpha \leq2\ $. $\beta$ is the skewness parameter and $-1 \leq \beta \leq 1$. For $\beta = 0$, the distribution is symmetric; for $\beta > 0$, it is skewed to the right; and for $\beta < 0$, it is skewed to the left. While the parameters $\alpha$ and $\beta$ determine the shape of the distribution, $\gamma$ and $\delta$ are scale and location parameters, respectively.

Due to a lack of closed-form formulas for the probability density functions (except for three stable distributions -- Gaussian, Cauchy, Levy), the $\alpha$-stable distribution can be described by a characteristic function which is the inverse Fourier transform of the probability density function, i.e., $\phi(u)=E\exp(iuX)$. In our paper, we use the following characteristic function:
\begin{equation}
 \footnotesize
\label{eq2}
\phi(u)=
\left\{
\begin{array}{lr}
\exp(-\gamma^{\alpha}|u|^{\alpha}[1+i\beta(\tan\frac{\pi\alpha}{2})($sign$ u)(|\gamma u|^{1-\alpha}-1)]+i\delta u) & \alpha \ne 1 \\
\exp(-\gamma |u| [1+i\beta\frac{2}{\pi}($sign$ u) \ln (\gamma |u|)]+i\delta u) & \alpha = 1
\end{array}
\right.\end{equation}
In order to simulate random stable variables, we use a method of \cite{Chambers1976} which is more efficient than inverse Fourier Transforms method \citep{Menn2006} and was proved to be accurate by \cite{Weron1996}.

For all values of the parameters $\alpha<2$ and $-1<\beta<1$, the stable distributions have two tails that are asymptotically power laws. If the data come from stable distribution, the empirical distribution function should be approximately a straight line with slope $-\alpha$ in a log-log plot.

For the financial market analysis, the characteristic exponent $\alpha$ gives additional important information. When $\alpha < 2$, extreme events are more probable than for the Gaussian distribution ($\alpha=2$). From an economic point of view, $0 < \alpha < 1$ implies infinite expected returns. To avoid this, we examine only the cases when $1 < \alpha \le 2$.

For more details about stable distributions, we refer readers to \citep{Zoltarev, SamorodintskyTaquu1994,Borak2005}.

\subsection{Monte Carlo design}
The simulations are constructed in order to find out how $R/S$, $DFA$, $MF-DFA$, $DMA$ and $GHE$  methods react to non-normal data with heavy tails. We use $i.i.d.$ $\alpha$-stable distributed random variables from $S(\alpha,0,\sqrt{2}/2,0)$, where $1.1 \le \alpha \le 2$ with a step of 0.1. For each parameter $\alpha$, 1,000 time series with lengths from $2^{9}$ to $2^{16}$ are simulated and Hurst exponents for $R/S$, $DFA$ (eqivalent to $MF-DFA$ with $q=2$), $MF-DFA$ with $q=1$, $DMA$, $GHE$ with $q=1$ and $GHE$ with $q=2$ are estimated.

In other words, we simulate the grid of independent identically distributed stable increments with the different $\alpha$ for various series lengths. For each position in the grid, we estimate Hurst exponents with the use of the different methods. This allows us to see how the finite sample properties of the estimation methods change with the varying coefficient $\alpha$. The first set, with $\alpha=1.1$, has the heaviest tails, and with increasing $\alpha$, the tails approach the Gaussian normal distribution with $\alpha=2$. Moreover, each set shows how the estimates behave with changing time series lengths.

Table \ref{tab1} summarizes the expected values of the estimated Hurst exponents for the grid. Table \ref{tab2} and Table \ref{tab3} show the 2.5\% and the 97.5\% quantiles of the Hurst exponent estimates. Figure \ref{fig:ex2} illustrates all the results and serves best for the quick orientation. It shows the estimated expected values with the 2.5\% and the 97.5\% quantiles for all time series lengths and for all methods considered by our Monte Carlo simulation. We discuss the results for each method separately.

\begin{figure}
   \centering
   \includegraphics[width=5in]{CI}
  \caption{Expected values of $H$ with 2.5\% and 97.5\% quantiles for the different $\alpha$s and methods of estimation. Figures show the results for (a) $R/S$, (b) $DMA$, (c) $MF-DFA$ with $q=1$, (d) $DFA$,(e) $GHE$ for $q=1$ and (f) $GHE$ for $q=2$. The horizontal axis goes from simulations with the heaviest tails ($\alpha=1.1$) to the simulations with the normal distribution ($\alpha=2$). Figures also show results for different simulated time series lengths from $2^{9}$ to $2^{16}$ with the longest time series of $2^{16}$ plotted in the boldest black. The lower the time series length, the greyer the plots. The mean values are plotted as full lines while the quantiles are dashed lines. Figures (c) and (e) also contain the theoretical expected values of $H(q) \approx 1/\alpha$ for $q\leq\alpha$ in bold red. (The theoretical expected value of the other methods is 0.5.)}
   \label{fig:ex2}
\end{figure}

\subsection{Results for $R/S$}
We can see that the expected value of the $R/S$ method\footnote{For the estimations of $R/S$ and $MF-DFA$, we use a sub-length $\upsilon$ equal to the power of a set integer value \citep{weron2002}. Thus, we set a basis $b$, a minimum power $pmin$, and a maximum power $pmax$ so that we get $\upsilon=b^{pmin},\ldots,b^{pmax}$, where $b^{pmin}\ge b$ is the minimum scale and $b^{pmax}\le T$ is the maximum scale. The minimum scale is set to 16 observations and the maximum one is set as a quarter of the time series length according to \citep{alvarezetal2005,einstein2001,grech2004,matos2008,peters1994}. Such choice avoids biases of low scales, where standard deviations and trends can be estimated inefficiently, and high scales, where the extreme values need not be averaged out. Moreover, the used intervals are contingent and non-overlapping -- see \citep{ellis2007} for discussion.} is converging to 0.5 with heavier tails. For length $2^{9}$, the expected value of $R/S$ (with quantiles in brackets) for the simulations with heaviest tails ($\alpha=1.1$) is 0.5318 (0.4172, 0.6502) while for length $2^{16}$, the expected value (quantiles) of $R/S$ with the same $\alpha=1.1$ is 0.5082 (0.4904, 0.5291). With growing $\alpha$, the expected value of $R/S$ also grows slowly. For the time series of length $2^{9}$, the expected value of $R/S$ for the simulations with the Gaussian normal distribution ($\alpha=2$) is 0.5627 (0.4088, 0.7179) while for length $2^{16}$, the mean of $R/S$ with the same $\alpha=2$ is 0.5235 (0.4956, 0.5489). Tables \ref{tab1}, \ref{tab2}, and \ref{tab3} together with Figure \ref{fig:ex2} (a) give the full picture. We can see that the length of the simulated time series improves the estimates through all $\alpha$s. Moreover, the expected value of the estimated $H$ is slowly converging to 0.5 with $\alpha$ changing from 2 to 1.1.

$R/S$ method is thus quite robust to the presence of heavy tails in the underlying data. It is interesting that the expected value of $H$ is converging to 0.5 with decreasing value of $\alpha$. In other words, the heavier the tails in the data are, the closer the expected estimate of $H$ to the value of 0.5 is. Moreover, the expected value of $H$ is slowly converging to 0.5 with increasing length of the sample.

\subsection{Results for $DMA$}
$DMA$ has very similar finite sample properties to $R/S$. The difference is in wider confidence intervals for small samples for DMA. On the contrary, $DMA$ has narrower confidence intervals for longer time series which makes it more precise. For the series with the heaviest tails ($\alpha=1.1$) and length $2^{9}$, the expected value (quantiles) of $DMA$ is 0.5036 (0.0877, 0.6792 ) while for length $2^{16}$, the mean (quantiles) of $DMA$ with the same $\alpha=1.1$ is 0.5276 (0.5192, 0.5358). With $\alpha$ approaching the value of 2, which represents the Gaussian normal distribution, the expected value of $DMA$ does not significantly change. More precisely, for the time series of length $2^{9}$, the expected value of $DMA$ for the simulations with the lightest tails ($\alpha=2$) is 0.5147 (0.3054, 0.6959) while for length $2^{16}$, the mean of $DMA$ with the same $\alpha=2$ is 0.5272 (0.5092, 0.5436). Again, Tables \ref{tab1}, \ref{tab2}, and \ref{tab3} together with Figure \ref{fig:ex2} (b) give the full picture. We have to make a note that we tried to estimate DMA with different window lengths $\lambda$ and minimums $\lambda_{min}$ as the method is very sensitive to these parameters. Finally, we set $\lambda_{min}=20$ and $\lambda_{max}=40$, which may produce bias when being used on larger datasets. But in order to maintain the comparative power, we choose to set the parameters for all simulations.

\subsection{Results for $DFA$}
Let's move to $DFA$ method, which seems to be even less precise than the previous methods of estimation. For the series with the heaviest tails ($\alpha=1.1$) and length $2^{9}$, the expected value (quantiles) of $DFA$ is 0.5027 (0.2234, 0.8513) while for length $2^{16}$, the mean (quantiles) of $DFA$ with the same $\alpha=1.1$ is 0.4984 (0.4301, 0.5659 ). With $\alpha$ approaching the value of 2, which represents the Gaussian normal distribution, the expected value of $DFA$ is still very close to 0.5 and it has slightly narrower confidence intervals. More precisely, for the time series of length $2^{9}$, the expected value of $DFA$ for the Gaussian normal distribution ($\alpha=2$) is 0.4940 (0.2910, 0.7280) while for length $2^{16}$, the mean of $DFA$ with the same $\alpha=2$ is 0.4981 (0.4630, 0.5361). See Tables \ref{tab1}, \ref{tab2}, and \ref{tab3} together with Figure \ref{fig:ex2} (d) for the full picture. Altogether, $DFA$ yields the closest expected estimate of $H$ for all different $\alpha$ exponents and for all lengths. Unfortunately, much wider confidence intervals make any statistical conclusions, especially for small samples, hard to state.

\subsection{Results for $MF-DFA$}
We simulated more general $MF-DFA$ method with $q=1$ so that the expected value of $H$ is $1/\alpha$ according to \citep{Kantelhardt2002} (In fact, the previously discussed $DFA$ is the special case of $MF-DFA$ with $q=2$). Results show that its expected value is diverging from 0.5 with heavier tails as expected but it underestimates the true value. For the series with the heaviest tails ($\alpha=1.1$) and length $2^{9}$, the expected value (quantiles) of $MF-DFA(q=1)$ is 0.7132 (0.4533, 1.0230 ) while for length $2^{16}$, the mean (quantiles) of $MF-DFA(q=1)$ with the same $\alpha=1.1$ is 0.7915 (0.6752, 0.9499). With $\alpha$ approaching the value of 2, which represents the Gaussian normal distribution, the expected value of $MF-DFA(q=1)$ approaches 0.5. More precisely, for the time series of length $2^{9}$, the expected value of $MF-DFA(q=1)$  for the simulations with the lightest tails ($\alpha=2$) is 0.4973 (0.3059, 0.7280) while for length $2^{16}$, the mean of $MF-DFA(q=1)$  with the same $\alpha=2$ is 0.4996 (0.4659, 0.5330). See Tables \ref{tab1}, \ref{tab2}, and \ref{tab3} together with Figure \ref{fig:ex2} (c) for the full picture. We can see that even  $MF-DFA(q=1)$ is quite inaccurate method for all $\alpha$ exponents and all series lengths (comparable to $MF-DFA(q=2)$ which is equivalent to $DFA$).

\subsection{Results for $GHE$}
Finally, we turn to $GHE$ methods. Expected value of $GHE(q=1)$ is again $1/\alpha$ as in the case of $MF-DFA(q=1)$. For length $2^{9}$, the expected value of $GHE(q=1)$ (with quantiles in brackets) for the simulations with the heaviest tails ($\alpha=1.1$) is 0.7832 (0.6072, 0.9761) while for length $2^{16}$, the mean (quantiles) of $GHE(q=1)$ with the same $\alpha=1.1$ is 0.8607 (0.8134, 0.9488). Thus $GHE(q=1)$ yields considerably better results than $MF-DFA(q=1)$ as it provides much narrower confidence intervals and it does not underestimate the expected value so strongly under the presence of heavier tails. For the time series of length $2^{9}$, the expected value of $GHE(q=1)$ for the simulations with the lightest tails ($\alpha=2$) is 0.4963 (0.3850, 0.5993) while for length $2^{16}$, the mean of $GHE(q=1)$ with the same $\alpha=2$ is 0.5001 (0.4910, 0.5085). Tables \ref{tab1}, \ref{tab2}, and \ref{tab3} together with Figure \ref{fig:ex2} (e) give the full picture. We can see that $GHE(q=1)$ yields much better results that $MF-DFA(q=1)$.

For length $2^{9}$, the expected value of $GHE(q=2)$ (with quantiles in brackets) for the simulations with the heaviest tails ($\alpha=1.1$) is 0.4955 (0.3657, 0.5777) while for length $2^{16}$, the mean (quantiles) of $GHE(q=2)$ with the same $\alpha=1.1$ is 0.4999 (.4963, 0.5036). Thus $GHE(q=2)$ yields considerably better results than other methods as it provides much narrower confidence intervals. For the time series of length $2^{9}$, the expected value of $GHE(q=2)$ for the simulations with the Gaussian normal distribution ($\alpha=2$) is 0.4946 (0.3920, 0.5926) while for length $2^{16}$, the mean of $GHE(q=2)$ with the same $\alpha=2$ is 0.5000 (0.4915, 0.5082). Tables \ref{tab1}, \ref{tab2}, and \ref{tab3} together with Figure \ref{fig:ex2} (f) give the full picture. We can see that $GHE$ with $q=2$ provides the best finite sample behavior among all the methods in the means of the lowest variance and bias.

\subsection{Comments on Results}
We only discussed the results of our simulations with the limiting values of $\alpha$ equal to  1.1 and 2 representing the simulations of time series with the heaviest tails and time series with the tails of the normal distribution, respectively. Tables \ref{tab1}, \ref{tab2}, and \ref{tab3} as well as Figures \ref{fig:ex2} and \ref{fig:ex0} show the whole grid of the simulated results so that the reader can see how the values change for different tail values and can also obtain precise quantile values. Figure \ref{fig:ex0} summarizes all the simulations as it shows the estimated probability distribution functions of all the estimated Hurst exponents using the different methods of estimation for the different values of $\alpha$. For each part of the grid, Figure \ref{fig:ex0} also shows the probability distribution functions for different lengths of simulated time series from $2^{9}$ up to $2^{16}$.

We can see that $R/S$ and $GHE$ with $q=2$ methods are robust to heavy tails. In fact, it is quite interesting that the confidence intervals of the $R/S$ estimate are narrowing with heavier tails. $GHE$ provides the most narrow confidence intervals. Even if we consider the $GHE$ with $q=1$ which has expected value of $H$ equal $1/\alpha$, it holds very good finite sample properties as well. On the other hand, all the other methods ($DMA$, $DFA$, $MF-DFA(q=1)$) deteriorate with heavier tails in the underlying distributions. While all the methods hold the expected value for all time series lengths and thus seem to be better for $H$ estimation than $R/S$ for the normal data with $\alpha=2$, the situation changes considerably for the non-normal simulations. The heavier the tails of the underlying data are, the wider the confidence intervals of estimate are. $MF-DFA(q=1)$ also tends to underestimate the expected $1/\alpha$ value.

In fact, we can see that $MF-DFA$ methods as well as $DMA$ are not appropriate for data with heavier tails and for small sample size. On the other hand, both $GHE$ tested methods proved to be very useful as they outperform all other methods quite strongly.

Note that our results are quite contrary to the ones of \cite{weron2002} who showed that $DFA$ outperforms $R/S$ in both bias and variance. However, the author used $\upsilon_{max}=T$ whereas we use $\upsilon_{max}=T/4$ for both methods. Hence, the choice of $\upsilon_{min}$ and $\upsilon_{max}$ strongly influences the final results of simulations. However, such choice has not been rigorously discussed in the literature yet and it should be of an interest for future research.

 \section{Empirical study: Application to HF data}
We showed that generalized Hurst exponent approach ($GHE$) can be used regardless the presence of heavy tails in the data and it has by far the best finite sample properties for short sample data. Thus we utilize this result and apply $GHE$ to estimate the intraday time-dependent Hurst exponent of S\&P 500 index on 1-minute data from the period beginning with 2.1.1983 and ending 30.11.2009. In other words, we estimate 6,764 values of the local Hurst exponents while each value is estimated on the information from one day. Thus the Hurst exponents for different days can be compared statistically as the final time series of intraday time-dependent Hurst exponents is not dependent as in the case of time-dependent Hurst exponent using a moving window \citep{grech2004,czarnecki2008}. Each value of the Hurst exponent is estimated for a single day on the intraday data so that we expect all the values to be close to $0.5$. Generalized Hurst exponent approach with its narrow confidence intervals allows us to detect possible periods with significantly higher or lower persistence.

Moreover, we also estimate local Hurst exponents from the same data for each month, providing a different view. The length of the time period allows to study how the persistence changed over  323 months. We have up to 8,000 one minute realizations in each month so that these estimates are very accurate.

\begin{figure}[h]
   \centering
   \includegraphics[width=4.5in]{HF}
  \caption{(a) Hurst exponent estimates using $GHE$ with $q=2$ for each day of 1983 - 2009 period using 1-minute data   (b)  Hurst exponent estimates using $GHE$ with $q=2$ for each month of 1983 - 2009 period using 1-minute data}
   \label{fig:ex1}
\end{figure}

Figure \ref{fig:ex1} shows the results of the estimation for both daily and monthly intraday time-dependent Hurst exponents. It is interesting how Hurst exponent decreases during the period from the level of above $0.8$ to the level of $0.5$. While Hurst exponents on daily data are still noisy, the estimates on monthly series are very strong as confidence intervals of $GHE$ for such long periods of intraday data are very narrow\footnote{For exact confidence intervals, see Tables \ref{tab1}, \ref{tab2}, and \ref{tab3}.}. The Hurst exponents from the last period of 2005-2009 can not be statistically distinguished from 0.5. For the previous periods, H=0.5 can be strongly rejected and we can conclude that the Hurst exponents are statistically different from its theoretical expected value for all the previous periods.

The result indicates how stock market structure changed from 1980s until now. Probably the introduction of algorithmic trading, exponentially rising volume of trades, is the main reason behind this change. Still, the result is quite surprising as we expected the Hurst exponent estimates to show only slight difference from its expected value of 0.5.

\section{Conclusion}
In this paper, we question the sampling properties of the Hurst exponent estimation methods under heavy-tailed underlying processes. Most existing studies focus on estimating expected values and confidence intervals based on simulations of a standard normal process. However, the returns of the financial markets are not normally distributed, which makes these results impracticable. This paper tries to fill the gap with the original results for independent processes drawn from the family of stable distributions, allowing for heavy tails.

The Monte Carlo study shows that rescaled range analysis ($R/S$) together with generalized Hurst exponent approach ($GHE$) are robust to heavy tails in the underlying process. Detrended moving average ($DMA$) together with detrended fluctuation analysis ($DFA$) and its multifractal generalization ($MF-DFA$) deteriorate with increasing heavy tails in the underlying distributions. While on normal data with $\alpha=2$, all the methods hold the expected value for all time series lengths and thus they seem to be better for $H$ estimation than $R/S$, the situation changes considerably on non-normal simulations. The heavier the tails of the underlying data are, the wider the confidence intervals of the estimates are. $MF-DFA(q=1)$ tends to underestimate the expected $1/\alpha$ value. We can conclude that $MF-DFA$ methods as well as $DMA$ are not appropriate for data with heavier tails and small sample size. On the other hand, both $GHE$ tested methods proved to be very useful as they show the best properties.

Finally, we utilize the results from our simulations on the high frequency data. On the 1-minute S\&P 500 index data from the period of 1983 to 2009, we estimate the generalized Hurst exponent based on $GHE$ for each day as well as for each month. This way, we estimate the Hurst exponents on separate information sets while the finite sample properties of $GHE$ allow us to use it regardless the presence of heavy tails even on smaller samples. The final result is quite surprising as it shows how the U.S. stock market changed from the strongly persistent in 1980s to independent one in the period 2005-2009.

\section*{Acknowledgements}
The authors would like to thank the anonymous referees whose suggestions helped to improve this paper substantially. The support from the Czech Science Foundation under grant 402/09/0965, support from the Grant Agency of Charles University (GAUK) under projects 118310 and 46108 and Institutional Support from the Department of Education MSMT 0021620841 are gratefully acknowledged.

redpajama_set_name: RedPajamaArXiv


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Ransomware infections and .PEDANT ransomware aim to encrypt your files using an encryption algorithm which may be very difficult to decrypt. This is why we have suggested several alternative methods that may help you go around direct decryption and try to restore your files. Bear in mind that these methods may not be 100% effective but may also help you a little or a lot in different situations.
Note! Your computer system may be affected by .PEDANT ransomware or other threats.
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Step 5: In the Applications menu, look for any suspicious app or an app with a name, similar or identical to .PEDANT ransomware. If you find it, right-click on the app and select "Move to Trash".
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Hey !, I found this information for you: ".PEDANT Ransomware — How to Remove Virus Infections". Here is the website link: https://sensorstechforum.com/pedant-ransomware-remove/. Thank you.


Recent advances in large area charge coupled device (CCD) imaging detectors and high speed microprocessors have prompted the development of a new class of electro-optical tracking instruments at the Jet Propulsion Laboratory (JPL). These instruments are designed for standardized NASA-wide usage and are characterized by their extremely high pointing accuracy and stability and performance capabilities which are largely software defined and thus easily adapted to a variety of mission requirements. This paper presents an examination of the methods by which CCD detectors are being incorporated in star tracker instruments and the performance capa-bilities that can be expected from currently available devices. The multi-function sensor concept, in which a single sensor can function in a variety of guidance applications, is described. Software algorithms designed to provide efficient extraction of guidance information from both point and extended images are also presented. CCD star tracker implementations currently underway at JPL are described, and performance data obtained during laboratory testing is presented and discussed.


text:
Throbbing Gristle Part Two: The Endless Not
Mute https://www.tinymixtapes.com/sites/default/files/arton3360_0.jpg
[Mute; 2007]
By Urban Guerilla https://www.tinymixtapes.com//writer/urban+guerilla 2007-04-30T05:40:46-04:00
Styles: industrial, experimental
Others: Coil, Psychic TV, CTI
The ripple effect of Throbbing Gristle felt to this day really cannot be understated. From post-punk and early electronic pop to the current dominion of noise, their work -- a confrontational, non-compromised assault on the senses -- has echoed far and wide. They referenced the bleak in an unflinching manner, forcing acknowledgment of perversions and the unexplainable. While this artistic concept certainly didn't make them an easy listen or media darlings, for the initiated they were an unmatched inspiration that resonates in their minds to this day. This has all been said a million times before though, hasn't it? Moving on...
It would have been naïve to expect Throbbing Gristle to replicate their initial stage of development. Judging from statements Peter made prior to their first Re-TG gig at the London Astoria, however (which was staged in lieu of the canceled ATP festival), that would have been an unlikely lure for him anyway. They needed to proceed in their explorations because he was not going to have any part in a waltz down memory lane, a sentiment that was likely shared by all. Sadly, upon release of Part Two some critics have chastised them for not retaining their aggressive sensibility. But what exactly did the media expect? I am quite certain that if this record had presented itself as Heathen Earth The Second, they would have just as quickly depreciated the work with accusations of no advancement, reliving past glories, etc. So, really, the question I pose is: Could they have pleased everyone with this record? I am afraid the answer would be an absolute and unequivocal 'no.'
With The Endless Not, Throbbing Girstle have thankfully moved forward and expanded upon their established identity, retaining inherent qualities that informed their past work while neoteric images and gestures birth in the existent frame. Shades of their work as the fractions that were/are Coil, Psychic TV and CTI filter into the equation as well, resulting in a document that stands as four individuals united addressing their 'now' as opposed to their past. I would suggest this is more of a rebirthing than a reunion. Their grey, claustrophobic sonic debris of yore have been replaced with a more expansive and colorful assessment of space, and while repetition remains a focus, as with earlier recordings, their manifestations are presented in a more prodigious manner.
The hypnotic opener "Vow of Silence" is a case in point; the focal point loop shifts ever downwards, while layers of voice and atmospheric noise scatter outward and pull back into the building, a cyclic swell suggesting a constant state of expansion and contraction. As a first statement, it is a powerful one and demonstrates the clearer textural focus assumed on this recording. "Rabbit Snare" follows with braying horns, piano flourishes, brush sprays, and Genesis P'Orridge's incomparable vocals, invoking shadows of a soundtrack to a Derek Jarman film never made, with Simon Fisher Turner swaying most agreeably in the filigree. The intoxicating lament of "Almost a Kiss" arches, swells, and collapses manifold upon the listener, while disembodied choirs reach out and evaporate just short of the point where they might become tangible. Strangely, it reminds me a bit vocally of one of my favorite Psychic TV moments, "I.C. Water." There is an undeniable dramaturgic overtone to the entire record that presents itself even in the gut-tugging collapse and roll of a track like "Lyre Liar"; it breathes an expanse that reaches beyond its auditory limits. However, the auditory sky-scratch is never more apparent than on "The Endless Not," as P'Orridge's voice circles overhead in response to cloudbursts and groundswells that bleed into the horizon.
Each member contributes a solo piece, which present a fragment of Throbbing Gristle's whole. Chris Carter and Cosey Tanni Tutti's respective pieces, "Separated" and "Above the Below," share a similar ambience, which is not surprising considering their history with one another. It is not to say that they don't each speak in their own voice, but there is a stasis connecting the track filaments, which reference each other as only a couple would. P'Orridge's track, "The Worm Waits Its Turn," is undeniably him, with rattle, screech, and cadence dancing alongside his ever suggestive voice. Peter's composition, "After the Fall," aches, lurches, and diffuses into the abyss placed before him; it holds within its strains the most delicate four minutes on the release.
So, at this point, I have written the many virtues of Part Two: The Endless Not and placed them before you, but the decision is ultimately yours. I can suggest this record until I go blue in the face, but do understand that this is a work of maturation. I feel it is highly successful for that; they don't sound dated or to mire themselves in themes that would betray their years. The Endless Not is ultimately a testament to getting it right, even after a lengthy separation, and proves that getting old doesn't mean that you have to suffer loss of potency. I look forward to the continuation of this leg of the mission.
1. Vow of Silence2. Rabbit Snare 3. Separated 4. Almost A Kiss 5. Greasy Spoon 6. Lyre Liar7. Above The Below 8. Endless Not9. The Worm Waits Its Turn10. After The Fall
More about: Throbbing Gristle
Throbbing Gristle highlight their latter albums with next phase of reissues
Genesis Breyer P-Orridge (Throbbing Gristle, Psychic TV)
The world's most famous pandrogyne sheds some wisdom
Throbbing Gristle's Chris Carter returns with new solo album after 17-year sabbatical
"thank YOU TMT for cheapening internet." - Third Man Records


text:
Home » Americas » Congress's sergeants-at-arms face questions about security failures leading to the assault on the Capitol.
Congress's sergeants-at-arms face questions about security failures leading to the assault on the Capitol.
The sergeants-at-arms of the Senate and House, Michael C. Stenger and Paul D. Irving, have resigned from their posts and are now facing intense scrutiny over the security failure last week that led to the deadly siege of the Capitol, its first occupation since the War of 1812.
The former chief of the Capitol Police, Steven Sund, told The Washington Post that they refused to grant his requests to put the National Guard on standby leading up to Congress's Electoral College certification, which Trump supporters ultimately disrupted, because they were too concerned about the "optics" of such a move.
The jobs forced both men to balance an array of often-conflicting forces, according to interviews with former colleagues, law enforcement experts and former sergeants-at-arms. Attempts to reach Mr. Stenger and Mr. Irving were unsuccessful.
Both positions derive their power directly from lawmakers. The Senate elects its sergeant-at-arms and the speaker of the House picks that chamber's. Mr. Stenger, who worked for the Senate, and Mr. Irving, his House counterpart, deftly tried to satisfy the 535 lawmakers who often had competing demands that made even mundane decisions like replacing locks on windows into fraught issues, according to interviews.
But the accusations by Mr. Sund, who had reported to both men, ignited criticisms that Mr. Stenger and Mr. Irving had placed politics over the safety of lawmakers, staff members and journalists assembled for the count of the Electoral College vote.
Terrance W. Gainer, who previously served as both the chief of the Capitol Police and the Senate's sergeant-at-arms, said that emergencies on Capitol Hill typically exposed problems with the chain of command that had festered during quieter times. He said that during the 2013 shooting at the Washington Navy Yard and the 2011 earthquake on the East Coast, security officials on Capitol Hill had differing views on how to react, complicating their response.
Tagged Congress's, F, sergeants-at-arms
Twin baby girl among 21 injured by Texas gunman who shot victims 'at random'
13 Philadelphia cops fired over racist posts
Calgary Firefighters Toy Association hosts Christmas party, gives toys to 4,300 kids
The Korean War Emptied the Town. Those Who Rebuilt It May Now Be Rewarded
Another 1,243 people die with Covid as UK cases start to slow


I dont need an intro or a conclusion (ill write those? what i need are facts and detailed accounts on the different opinions on the meaning of life among renowned greek philosophers.
Basically i need you to do the research part and Ill do the philosophizing/opinion part.
please make sure to talk about what some philosphers had to say in regards to the balance between good and evil and the universe just running its course. i talk alot about that in my part and i need comparisons to greek philosophers.
thank you very much, i know this is alot of instructions.. if you have ANY questions just email me, ill be checking my email constantly.. once again thank you so much.
Oes Liberalism have a future in International Relations theory? Oes left realist criminology develop a new theoretical perspective? Oes learning how to properly express oneself help Oes Lighting affect how people pay attention Oes living with someone before marriage increase your chances of divorce after marriage? Oes London Heathrow Airport need a third runway?


Have you ever wanted to chat with your friends in Steam outside of the client on Linux? If so, we've got the perfect thing for you: Open Steamworks. It's a simple bit of code that takes the Steam mobile chat API and wraps it up into a Pidgin plugin for you to use on your Linux desktop.
If you are an Arch Linux user interested in using Open Steamworks on your machine, someone has taken it upon themselves to make this awesome plugin available in the Arch Linux User Repository.
If you want to use it, all you have to do is point your favorite Arch User Repository client at this package to install it. Do you not have an AUR client? You might want to check out some of these.
Download the 64 bit plugin here or the 32 bit plugin here, then extract it to your Downloads folder. You'll also need to download the icon package for Pidgin to use. You can get it here.
When you have everything downloaded and extracted, it's time to install the plugin to the Pidgin plugin directory. Open up a terminal window and enter the commands below.
Doing this will install the Steam chat plugin into Pidgin's user plugin directory. This means that the Open Steamworks plugin will only be accessible by your username on the system. After doing this, we'll need to install the icon package.
Once the plugin and icon package has been installed, restart Pidgin. It needs to reload the plugins directory. When it's opened again, you'll be able to log in to your Steam account in Pidgin.
Note: once you've entered your login details, you'll need to check your email and input your Steam guard code (if you use it).
Do you want to use Steam chat on Linux but are not a fan of Pidgin? Don't worry! You can easily get all of your favorite Pidgin chat plugins working in Empathy by installing one simple package. Using your Linux distributions' package manager of choice, search for the package known as "telepathy-haze". After that, just install it and restart Empathy. Everything should be there after that.
From there you'll be able to go to the accounts section of Empathy and then add your Steam account to it. Please note: if you use Steam guard, you'll need to go to your account settings in the Empathy client and apply the security code that was emailed to you.
Steam is a great service, but it's kind of a pain to have it open all the time, especially since Steam can sometimes be CPU intensive. That's why I think this plugin, which allows you to chat with your Steam friends in Pidgin, is awesome. It takes one of the best parts of Valve's service and allows you to use it in your favorite instant messaging client.
I was able to get this to work with Pidgin but Empathy kept having an authentication error.
Then you can download the source .tz file, run make, then copy the libsteam.so to /usr/lib/pidgin on my particular distro. I was able to find out by going to the plugins list within pidgin and see the details for where the plugins were located.
In the end I just used the Steam Client.


text:
VINTAGE BASEBALL, FOOTBALL, and HOCKEY FT
Thread starter avalanche2006
I'm new here, but have almost 500 positive feedback on other sites.
I have these ft for other vintage. Pms work best.
I can email pics if requested. More cards to be added.
Goudy 225 Bill Jurges vg
National Chicle Diamond Stars 6 Max Bishop vg
National Chicle Diamond Stars 83 Paul Waner vg
64 Dark,82,90,143,154,158,173,185,193,200
223,227,241,242,243,244,249,251,252
8,44,45,57,71,83,88,91,95,96,101,114,121,123,130,138,139,150,154,161,164
166,168,169,173,174,175,180,191,227,231,239,240,249,250
2,23 Lemon,26,42,58,59,61,68,83,88,91,92,95,99,103,104,107,109,111,113,118,127,
130,134,135,136,138,139,140,143,149,153,157,
136,139,140,143,149,153,157,163,165,170,171,175,182,185,194,195,201,210,221,225,239
39 Trout vg
49 Sain ex
66 Preacher Roe ex
91 Schoendienst ex
112 Majesky g
168 Silvera Rc nrmt
222 Evers vg
245 Robertson ex
1953 Bowman color
1 vg
7 nrmt
15 nrmt
69 Grimm nrmt
109 poor
77 Mize nrmt
36 ex
95 Roberts poor
130 nrmt
15 Rosen g
42 Mueller ex
45 Ashburn
107 vg-ex
114 ex
147 vg
170 Erskine ex
46 vg-ex
70 Rosen nrmt
125 Boyer Rc
155 Mathews
50 Rhodes exmt
117 exmt
CL1 Checklist 1/3 ex
CL2 Checklist 2/4 exmt
25 Ford g
165 Kluzewski vg
376 vg creased
134 Phillies nrmt
312 Red Sox ex
327 Cubs nrmt
341 Pirates ex
375 Reese ex
377 Braves vg
464 Flood RC vg
476 Musial AS vg
438 Herzog ex
495 Score AS ex
3,18,22,23,24,28,48 Orioles,53,55,69 Giants,75,78,79,82,85,88,95,96,98,101,112,113,122,126
126,127,128,130,134,135,137,142,141,145,148,150Musial,154,161,170,171,174,175
178,193,205,228,229,261,266,275,307,317 Mays/Ashburn,322,324,342,343,347,356,358,368
372,375,375,381,386,390 Cepeda vg,395 Howard vg,406,409,411,413,416,418,422
424,425,426,434,438,440,446,457 Dodgers,489,497,544
1 Wynn,3,8,13,20,16,22,25,36,44,47,48,49,53,55 Mazeroski,60,65,70,75,80.83 Kubek
86,88,92,96,98,105,107,109,118,129,130,133,137,139,144,146,147,154,157,165,166
167,170,173 Martin,176,179,181,185,188,198,210 Killebrew,213,219,224,227 Stengel,244,247
250 Musial, 253,264 Roberts,270,271,285,298,304,308,309,310 Aparicio,311,335 Schoendienst,344,
348,356,369,374,398,407,414,422,423,424,432,434,449,446,467,470,472,509 Davis Rc
Bridges 31 exmt
5,8,10 B. Robinson,13,19,25,26,30 Fox,34,37,45,46,50,53,63,65,69,75,83,89 Martin
94,108,111,119,145,152,157,169,177,178,182,185,195,223,247,250,257,259
271,278,321,356,409,455,483,490,506,521
78 Sisler ex
9,26,43 Dodgers,73 Fox,74,85 Hodges,90,98,158 Braves,166,171,188
205,226 Giants,237,245,294 Phillies,301,305,311,384 Athletics,403,582
1 NL Batting Leaders exmt
6 AL ERA Leaders nrmt
25 Kaline ex
43 Stengel/Woodling vg
68 Snider/Hodges ex
79 Checklist nrmt
154 Alston nrmt
191 Checklist nrmt
417 Giants team nrmt
524 Cardinals team ex
1963 Fleer all nrmt-mt
1963 Post
20 Terry ex
3,11, 23,35 Mathews, 53,55 Banks,61,100 Howard,117,119,130,133,150 Mays
202,213,241,308,336,445,579
116,128,187 Stengel,206,220 B. Williams
234,257,260 Drysdale,282,379
90 Aparicio nrmt
99 Stargell/Clendenon nrmt
109 Slowron/Romano/Robinson nrmt
215 NL Batting Leaders Clemente/Aaron/Mays ex
254 Jenkins RC vg
1,6,7,9,24,27,29,34,38,39,40,41,57,61,67,68,77,78,79,80,84,86,93,101,102,104,107
111,117,119,129,136,137,143.143,152,153,156,157,158,159,162,163,164,165,168,172,175,179,181,193
195,196,207,209,212,218,219,226,232,243,249,251,254,257,258,268,269,270,274,275,276,279
281,286,288,298,304,307,310,316,319,322,323,324,335,338,343,367,374,379,381,383,384,387,389,390
82 Oliver Rc,85 Brock,99 Nettles Rc,216 Sutton,222,223,224,228,235 Hunter,242,248,251
252,271,272,279,290 Kaat,301,317,324,333,337,339,341,342,345,350 Allen
352,356,358,362,370,374,380,382,384,386,388,392,395,396,397,403,405,407,410,414
415,428 Brock AS,430 Bench AS,436,439,440 McCovey variation,441,444,445
448,449,450 Williams,451,452,455,456,457,459,462,464,467,469,473
474,477,478,484,489,494,497,501,502,505,544,573 Palmer,592,594,595,596,599,603,607
1,7,19,24,37,44,46,76,81,87,102,108,109,120
4,6,19,32,127,154,155,176,185,228,271,279,294,313,378,391
431,475,486,520,555,607,640 F. Robinson,647,668,681
1971 OPC
12,14,20,32,33,38,40,54,60,70,82,86,87,90,95,125,126,131,146,152,177
203,207,211,212,216,248,275,323,367,389,392,414,432,458,459
3,6,11,16,36,37,38,49,69,75,80,82,90 B. Robinson,92,98,114,122,148,193 fisk
228,238,259,284,311,353,358,411,425,429,443,469,501,502,503,506
507,508,509,510,512,515,516,518,522,525,527,528,530,532,533,534,535,539,541
12,14,47,71,131,140,146,155,190,190,195,198,214,296
408,422,499,503,525,534,562,590,591,606,628
Team Checklists unmarked
Red Sox, Tigers, Astros, Yankees, White Sox, Giants, Expos, Pirates
23,63,139,262,269,428,485,496,516,534,538,544,616,618,630,648,649
15,29,35,47,53,59,69,83,84,86,94,108,109,113,121,128,138,153,160
301,308,318,321,326,328,332,339,341,350,378,396,408,413,418,423,430
3,7,16,18,23,24,25,26,28,32,39,41,44,46,51,52,54,56,60,62,66,70,73,78,80
82,83,85,88,91,95,96,99,102,106,108,109,110,115,122,124,125,126,129
133,134,136,137,138,144,146,149,153,164,166,170,175,181,183,187,189,190
574,577,578,579,581,582,583,584,586,587,,592,602,603,607,609,611,612,613
652,653,654,655,658,660
28,44,58,74,99,127,146,158,208,292,296,380,383
720 Yastremski nrmt
48 Bowman # 97 Muha
48 Bowman # 76 Hefti
48 Bowman # 82 Kannady
50 Bowman # 32 Poole
50 Bowman $ 46 Baldwin
50 Bowman # 98 Morrison
50 Bowman # 125 Kalmanir
54 Bowman # 48 Stonsifer
54 Bowman # 58 Hunter
54 Bowman # 59 McColl
54 Bowman # 68 Paul
54 Bowman # 69 Snyder
54 Bowman # 72 Keane
54 Bowman # 75 Neal
54 Bowman # 77 Yelvington
54 Bowman # 85 Chapman
1957 Topps all nrmt
1960 Fleer exmt+
10,26,42,50,53,59,70,92,94,95,110,128
1964 Philadelphia all nrmt
1965 Philadelphia all nrmt except checklist
18,52,109,131
Checklist No. 2 198 unmarked vg
16,51,94,101
16, 34, 42,46,49,50,58,62,94,103,105,108,127,232 blanda
1970 Topps all ex
60,71,72,82,83,84,99,103,109,111,121, 253
1975 Topps Dierdorf 35 nrmt
70-71 Topps
16,31,38,42,61,62,83
28 Bruins leaders,46,145,170 Esposito T.,228
1,2,15,45,46,57,64,66,68,85,87,89,90,91,92,93,95,99,106,116,123,130,132,148
76-77 Topps nrmt
14,16,27,33,40 vachon,77,115 Trottier Rc,117,118.126
132 flames,140 north stars,160,175,182,192,212
GLOSSY INSERTS
1 Clarke
3 Esoposito T.
5 Dryden
7 Esposito P.
14 Bucyk
18 Gilbert
76-77 OPC
245 Esposito P. nrmt
77-78 OPC nrmt
21,22,70,92,251 Orr,371
10 Trottier,77,140 cheevers,165,168 Gilmore Rc,182,236
4 LL, 7 LL nrmt
185 Hull mt
249 Redwings mt
2 bourque,10 lafleur,24,25 bossy,55 Clarke,204,250 Gretzky,279,344 langway rc,359 ogrodnick rc
81-82 OPC exmt-nrmt
1 bourque,4 kasper rc,49,peplinski rc,65 sutter rc,,108 Anderson rc ,117 lowe rc,125 gretzky super action
148 murphy rc,159 beaupre rc,198 bossy,219,251 kerr rc,290 babych ip auto,347 gartner,383 gretzky,386
Bourque 5 nrmt
51 Gretzky nrmt
123 Sandstrom RC nrmt
37 MacLean RC nrmt
174 Courtnall RC nrmt
r_rich_r
I need these baseball:
80,117,119,137,143,175,195,196,218,232,304,307,335,367,399,418,448,449,
GotMelky
Found some as well.
83 Kubek
100 Howard
LEONOFNJ
Hi, Sent you a PM.
I'll start sending out pms but I'm looking for baseball before 1970 in ex or better condition.
1955 Bowman Tradelist:
62 exmt/nrmt, 73 Eddie Yost ex, 188 ex+, 220 ex
#382 exmt $6
#21 ex $6
1959 Tradelist:
289 exmt $3
424 ex/exmt,
468 Snider Baseball Thrill nrmt $20
30 Mathews exmt+
1963 Fleer Tradelist:
39 nrmt slightly out of square $12
43 Wills Vg/ex
1966 Tradelist: 445 exmt
1967 Tradelist: 103 Mantle Checklist ex, 124 exmt, 374 exmt, but oc, 376 exmt, 388 exmt, 408 exmt, 425 nrmt but oc, 435 exmt+, 447 ex/exmt
1969 Tradelist: 4 exmt corners, has ink in corner of Williams pic, 62 exmt, 441 ex, 461 ex, 474 exmt, 503 ex, 638 nrmt, 638 nrmt,
1970 Tradelist: 33 ex+, 35 ex, 37 ex/mt, 37 ex/mt but ink "out" on back, 38 ex, 57 ex, 84 ex+, 89 exmt, 89 ex, 91 ex+, 93 exmt, 98 exmt, 107 exmt, 109 exmt, 110 exmt, 120 exmt, 121 ex/exmt, 129 exmt, 129 ex+, 354 exmt, 377 exmt, 378 exmt/nrmt, 405 exmt, 406 nrmt, 408 ex+, 416 exmt, 423 exmt, 441 exmt, 452 Millan AS ex+
chieftazmisty
Looking at 1970 topps football # 246 what are you looking for in return
BucCollector
PM sent. Welcome to The Bench!
1958 Topps Baseball FS
Football FT/FS
Baseball FT/FS