text:
\section{Introduction}

    Cosmical radio emissions received by radio telescopes are  noise-like signals  which are characterized  by a {\it normal}  (Gaussian) probability distribution function ${\cal N}(0,\sigma_{sig})$, i.e., with zero mean and  variance $\sigma_{sig}^2$. Background radio emission and radio receivers also produce   normal  noise  ${\cal N}(0,\sigma_{sys})$. Given a large number of n samples $x_i, i=0..n$ of  normally distributed data with  zero mean, a classical statistical  procedure for the estimation of  variance $\sigma^2$ is
\begin{equation}
\widehat{\sigma^2_{n}}=\frac{1}{n}\sum_{i=1}^{n}x_{i}^2
\end{equation}
The time-honoured estimate (1) may be obtained by using the Maximum
Likelihood method \citep{warden69} and is a {\it minimum variance
unbiased estimate}. Traditional radiometrical techniques implement
this procedure in analogue or digital form in conventional
radiometers: the estimate of $\widehat{\sigma_{sig}^2
+\sigma_{sys}^2}$ is obtained after   square-law detection and
low-pass filtering (averaging) \citep{rohlfs06}. This statistical
inference is based  upon an essential assumption about the
probability distribution of the data  ``signal noise + systen
noise'': the distribution must be normal. As in many cases in
applied statistics this assumption is not always valid for radio
astronomy. Most of the data   may correspond to the model  with an
assumed  normal distribution, but there are also a number of {\it
outliers}, atypical data which  stand out from the bulk of the data.
As a result there is an  ``approximately''  normal distribution that
gives rise to  outliers, i.e., the distribution has a normal shape
in the central region  but  has tails that are heavier  than those
of a normal distribution. If such approximate normality were to
hold, the results of using a normal distribution theory will
nevertheless not hold approximately. In the presence of heavy tails
the estimate (1) based on the maximum likelihood principle  is no
longer the best and may have unacceptably low statistical efficiency
(large variance) and  very large bias. These phenomena in applied
statistics have been well known from the time of Gauss, Newcomb and
Eddington.

Radio frequency interference (RFI) creates a situation in radio
astronomy where such outliers arise \citep{fridman01, kest07}.
Industry, ground and satellite communications, ground and airborne
radar, power lines, radio and TV stations, etc. produce all kinds of
additional noise which penetrate into  extremely sensitive radio
astronomy receivers. Radio observatory  computers are themselves
often the sources of RFI. Many authors demonstrated the
effectiveness of different RFI mitigation methods
 \citep{elli03, baan04, fisher05, mitch05, poul05, Zhang05, kest07} in observations with single dishes and radio interferometers.  Adaptive noise cancellation (ANC)  with reference antenna was described in \citep{barn98}. Post detector and post correlation processing using  reference antennas was suggested in \citep{briggs01}. Higher order statistics (HOS) analysis was proposed in \citep{frid01a}.
Principal results were achieved with  blanking and  reference
antennas.  The high expectations of new projects (LOFAR, ATA, SKA)
are based on the implementation of spatial processing - nulling in
the direction of RFI.  But very often RFI do not obey the model
assumptions which guarantee the success of this kind of processing.
They are variable, sporadic and are not  fully coherent at the sites
of  multi-element systems \citep{thom03, jeffs05}.

In this paper  a new view on blanking is proposed based on
statistical methods granting stable estimates of variance in the
presence of outliers. These methods would be useful in radio
astronomy RFI mitigation, including  {\it real-time} processing. The
aim of this paper is to  compile  a selection of  methods of stable
variance estimates suitable for RFI mitigation   and to study their
properties via computer simulation. Some examples obtained during
real observations are also given.

The typical appearances of RFI  is of strong impulse-like bursts  in
both temporal and spectral domains.  They behave as outliers in the
data with normal distribution and  the   data  received may be
characterized by the {\it contaminated} normal distribution
\begin{equation}
F(x)=(1-\epsilon){\cal N}(0,\sigma_{sys})+\epsilon F_{RFI}(x),
\end{equation}

where ${\cal N}(0,\sigma_{sys})$ is the ``clean'' probability
distribution, $F_{RFI}(x)$ is the usually unknown distribution of
RFI, $\epsilon$ characterizes the fraction of  $F_{RFI}(x)$ in the
total $F(x), 0<\epsilon<1$. The theory of robustness   pioneered by
\citep{tukey60, huber64, hampel71} gives recommendations on
obtaining  statistically stable estimates of parameters in the
situation similar to (2).   The estimate is called statistically
stable (robust) if slight changes in distribution have a relatively
small effect on its value, or, in other words, robustness means
insensitivity of a statitical procedure  to small deviations from
assumptions about the normality of data's  distribution.

Several approaches to developing robust estimates exist:
$M$-estimates based on a generalized maximum likelihood \citep
{huber04}, $L$-estimates based on order statistics \citep{david03},
$R$-estimates based on  ranks \citep{hett84} and estimates based on
nonparametrical statistics \citep{sheskin00}.

\section{Stable estimates of variance}
A selection of stable (robust) estimates of variance will be
described in this section. There are different ways of judging the
stability of an estimate. Given a distribution $F(x)$ and a
population $X$ representing  this distribution, an estimate $T(X,F)$
is the functional depending on $F(x)$. If this estimate is
relatively unaffected by small changes in $F$, i. e., the functional
is continuous  with respect to $F$, then the estimate is said to
have  {\it qualitative robustness}.  For a distribution $F(x)$
contaminated by a distribution $G(x)$ with probability $\epsilon$
 \begin{equation}
F_{\epsilon}(x)=(1-\epsilon)F(x)+\epsilon G(x)
\end{equation}
the  {\it influence  function} \citep{hampel86}  $IF(T,F)$ is widely
used in theoretical analysis
\begin{equation}
IF(x)=\lim_{\epsilon \rightarrow 0}\frac{T(F_{x,\epsilon
})-T(F_{x})}{\epsilon }
\end{equation}
The $IF(T,F)$ is the relative influence of x on $T(F)$  having the
value $x$ with probability $\epsilon$. The estimate $T(F)$ is said
to have {\it infinitesimal robustness}  if $IF(x)$  is bounded. The
$IF$  of classical  both arithmetical mean and variance are not
bounded. A simple finite-sample version of  $IF$ called {\it
empirical influence function} (or sensitivity function) exists. It
is constructed in the following way. Suppose there is an estimate
$T_{n-1}$ of a sample $(x_{1},...,x_{n-1})$. A new $n-th$ sample $x$
is added and $T_{n}(x_{1},...,x_{n-1},x)$  is considered as a
function of x.  For the estimate (1) the sensitivity function is
\begin{equation}
IF(x)=\frac{x^{2}-\widehat{\sigma ^{2}_{n}}}{n+1}
\end{equation}
Another useful characteristic of robustness is the {\it breakdown
point}: the minimum value  of $\epsilon$ for which an estimate goes
to infinity as  $x$ grows. The breakdown point of conventional mean
and variance is 0. The breakdown point of median is 0.5, i.e., up to
50\% of data may be outliers and they do not alter the correct value
of the median.

Taking into consideration our practical goal to use  estimates of
variance in observations,  two  measures of stability will be
exploited for the  following  statistical procedures.

1. The relative empirical influence function with  the following
modification: for a sample length $N$ the number of additional
outliers with an amplitude $X_{RFI}$ will be $M=[\epsilon N]$, where
$[q]$ means  the greatest integer part of $q$. The estimate
calculated for a contaminated sample is compared with the estimate
correspondimg to a ``clean'' sample:
 \begin{equation}
REIF(\epsilon x)=\frac{T_{N+M}(F_{x,\epsilon
})-T_{N+M}(F_{x,\epsilon =0})}{T_{N+M}(F_{x,\epsilon =0})}.
\end{equation}
The relative empirical influence function will be calculated during
computer simulation for the estimates described below.

2. Stability against outliers is achieved  at the expense  of the
effectiveness of an estimate. In the absence of outliers the
standard deviation of a robustly estimated variance is, as a rule,
larger  than that of a simple estimate (1). To characterize this
loss the parameter $LOSS$ will be used:
\begin{eqnarray}
LOSS=SNR_{T_{N}}/SNR0,\\
SNR_{T_{N}}=\frac{\widehat{T_{N}(\sigma +\Delta \sigma )}-\widehat{T_{N}(\sigma )}}{rms[\widehat{T_{N}(\sigma )}]},\nonumber\\
SNR0=\frac{\widehat{T0_{N}(\sigma +\Delta \sigma
)}-\widehat{T0_{N}(\sigma )}}{rms[\widehat{T0_{N}(\sigma
)}]}.\nonumber
\end{eqnarray}

For  a small increase of $\Delta \sigma << \sigma$ two
signal-to-noise ratios $SNR$ are compared: $SNR_{T_{N}}$ of a given
estimate  of variance $\widehat{T_{N}(\sigma )}$ and $SNR0$ of the
estimate (1)  with the best potential effectiveness.

The relative empirical influence function and loss are calculated
via computer simulations ($\sigma=1, n=10^{5}$)  for the estimates
presented below.

\subsection{ Variance of the trimmed data}
Let  $x_{1},...., x_{n}$ be a random sample and let $x_{(1)} \leq
x_{(2)}\leq...\leq x_{(n)}$ be the observations sorted in ascending
order. The $ith$ largest value $x_{(i)}$ is called the $ith$ {\it
order statistic}. Let $\gamma $ denote  the chosen amount of
trimming, $0 \leq \gamma  \leq 0.5$ and $k=[\gamma  n]$.
 The sample trimmed variance is computed by removing the $k$  largest and  $k$ smallest data
and using the values that remain:
\begin{eqnarray}
T_{1}=\frac{K_{trim}}{N-2k}\sum_{n=k}^{N-k}(x_{n}-\widehat{\mu }_{trim})^{2}\\
\widehat{\mu }_{trim}=\frac{1}{N-2k}\sum_{n=k}^{N-k}x_{n}, \nonumber
\end{eqnarray}
where $\mu_{trim}$ is the sample mean of the trimmed data. Trimming
lessens the variance of  data and the coefficient $K_{trim}$ makes
$T_{1}$ the consistent estimator for  data with  normal
distribution. Table  1 gives the values of $K_{trim}$  for different
$\gamma$.

\begin{table}[h]
\caption{Consistency factor and $LOSS$ as functions of $\gamma$
for trimming}
\begin{tabular}{llllll}
\tableline \tableline
$\gamma$ & 0.005 & 0.01 & 0.025 & 0.05 & 0.1 \\
$K_{trim}$ & 1.085 & 1.147 & 1.32 & 1.6 & 2.28 \\
$LOSS$ & 0.98 & 0.95 & 0.90 & 0.86 & 0.83 \\
\tableline
\end{tabular}
\end{table}
The third row in this table gives the values of $LOSS$ for different $\gamma$.\\
Fig. 1 shows the relative empirical influence functions for
untrimmed data (Fig. 1a) and for the variances computed for trimmed
data with $\gamma=0.05$ (Fig. 1b). The parameter $\epsilon$
(``eps'') indicates  the percentage of outliers in the total volume
of data.

\subsection{Winsorized sample variance}
A sample $x_{1},...., x_{n}$ is sorted in ascending order.  For the
chosen $0 \leq \gamma  \leq 0.5$ and $k=[\gamma  n]$ winsorization
of the sorted data consists of setting
\begin{equation}
W_{i}=\left\{ \begin{array}{lcl}x_{(k+1)}, & if & x_{(i)}\leq x_{(k+1)}\\
                                x_{(i)},   & if  & x_{(k+1)}<x_{(i)}<x_{(n-k)}\\
                    x_{(n-k)}, & if & x_{(i)}\geq x_{(n-k})\\

  \end{array} \right.
\end{equation}

The winsorized sample mean is $\widehat{
\mu_{w}}=\frac{1}{n}\sum_{i=1}^{n}W_{i}$ and the winsorized sample
variance is
\begin{equation}
T_{2}=\frac{1}{n-1}\sum_{i=1}^{n}(W_{i}-\widehat{\mu _{w}})^{2}
\end{equation}

Table 2 gives values of the consistency factor $K_{winsor}$  for
different $\gamma$.

\begin{table}[h]
\caption{Consistency factor and $LOSS$ as functions of $\gamma$
for winsorization}
\begin{tabular}{llllll}
\tableline \tableline
$\gamma$ & 0.005 & 0.01 & 0.025 & 0.05 & 0.1 \\
$K_{winsor}$ & 1.019 & 1.041 & 1.103 & 1.227 & 1.54 \\
$LOSS$ & 0.99 & 0.98 & 0.96 & 0.88 & 0.82 \\
\tableline
\end{tabular}
\end{table}
The third row of this table  gives the values of $LOSS$ for different $\gamma$.\\
Fig. 1c shows the relative empirical influence functions  for the
variances computed for winsorized data with $\gamma=0.1$. The
parameter $\epsilon$ (``eps'') indicates the percentage of outliers
in the total volume  of data.

\subsection{ Median absolute deviation}

This estimate for  sorted data $x_{(1)} \leq x_{(2)}\leq...\leq
x_{(n)}$ is defined by
\begin{equation}
T_{3}=1.483\times med_{1\leq i\leq n}\{\left|
x_{i}-med(x_{i})\right| \},
\end{equation}
where
\begin{displaymath}
\left. \begin{array}{lll}
med= & 0.5(x_{(m)}+x_{(m+1)}), & n=2m,\\
med= & x_{(m+1)},& n=2m+1\\

  \end{array} \right.
\end{displaymath}
The breakdown point for this estimate is 0.5, i.e.,  almost half
the data may be contaminated by outliers. But the effectiveness of
$T_{3}$ is much lower than for the $T_{1}$ and $T_{2}$: the
$LOSS=0.6$.

\subsection{Interquartile range}
This estimate is defined by
\begin{equation}
T_{4}=[(x_{1-q}-x_{q})/1.35]^2,
\end{equation}
where $q=0.25$ and for  distribution  $\cal{P}$  $x_{q}$ satisfies
$\cal{P}$$(x \leq x_{q})=q$. The breakdown point for $T_{4}$ is 0.25
and the $LOSS=0.6$. Fig. 1i shows the relative empirical influence
functions  for the variances computed with  $T_{4}$.

\subsection{Median  of pairwise averaged  squares}
This estimate is motivated by the Hodges-Lehmann estimate of
location
\begin{displaymath}
\widehat{\mu _{n}}=med(\frac{x_{i}+x_{j}}{2}, 1\leq i\leq j\leq n),
\end{displaymath}
which for variance is
\begin{equation}
T_{5}=1.483\times med_{1\leq i\leq jn}[(x_{i}^{2}+x_{j}^{2})/2]
\end{equation}

Fig. 1d shows the relative empirical influence functions  for the
variances computed with $T_{5}$  . The parameter $\epsilon$
(``eps'') indicates the percentage of outliers in the total volume
of data. The $LOSS$ for $T_{5}$ is 0.78.

\subsection{ $Qn$  estimate}
This estimate is proposed in \citep{rous93} and requires fewer
operations than $T_{5}$, but is quite effective. It combines the
ideas of the  Hodges-Lehmann estimate and the Gini estimate
\citep{kendal67}:
\begin{equation}
T_{6}=2.2219\{\left| x_{i}-x_{j}\right| ,i\leq j\}_{(k)},
\end{equation}
where  $k={h \choose 2}$ and $h=[n/2]+1, k \approx {n \choose 2}/4$.
This  estimate  is the $kth$ order statistic of the ${n \choose 2}$
interpoint distances. Fig. 1e shows the relative empirical influence
functions  for  variances computed with $T_{6}$. The $LOSS=0.88$.

\subsection{Biweight variance}
First the following auxiliary values are calculated
\begin{displaymath}
Y_{i}=\frac{x_{i}-M}{9\times MAD},
\end{displaymath}
where $M$ and $MAD$ are the median mean and median absolute
deviation, respectively. Then the coefficients are defined as
\begin{displaymath}
a_{i}=\left\{\begin{array}{ll}
 1, & if \left| Y_{i}\right| <1\\
 0, & if \left| Y_{i}\right| \geq 1
\end{array}\right.
\end{displaymath}
in which case the estimate is
\begin{equation}
T_{7}=\frac{\sqrt{\sum_{i=1}^{n}a_{i}(x_{i}-M)^{2}(1-Y_{i})^{4}}}{\left|
\sum_{i=1}^{n}a_{i}(1-Y_{i}^{2})(1-5Y_{i}^{2})\right| }
\end{equation}
Fig. 1f shows the relative empirical influence functions   for
variances computed with $T_{7}$. The $LOSS=0.92$.

\subsection{Bend Midvariance}
This estimate of variance is described in \citep{wilcox04}. Set
$\beta =0.1$ and  $m=[(1-\beta )n+0.5]$. Let $W_{i}= \left|x_{i}-M
\right|,i=1,...n$, and $W_{(i)}$ are these numbers sorted in
ascending order, M is the median mean of data $x$.
 Then the estimate of  $1-\beta$ quantile of the distribution of $W_{(i)}$ is $\widehat{\omega _{\beta}}=W_{(m)}$. Now set
\begin{displaymath}
Y_{i}=\frac{x_{i}-M}{\widehat{\omega _{\beta }}}
\end{displaymath}
and
\begin{displaymath}
a_{i}=\left\{\begin{array}{ll}
 1, & if \left| Y_{i}\right| <1\\
 0, & if \left| Y_{i}\right| \geq 1
\end{array}\right.
\end{displaymath}
in which case the bend midvariance is
\begin{equation}
T_{8}=\frac{\omega _{\beta }^{2}\sum_{i=1}^{n}\{(\psi
(Y_{i})\}^{2}}{(\sum_{i=1}^{n}a_{i})^{2}}
\end{equation}
where
\begin{displaymath}
\psi (t)=\max [-1,\min (1,t)].
\end{displaymath}
Fig. 1g shows the relative empirical influence functions  for
variances computed with $T_{8}$. The $LOSS=0.96$.

\subsection{Estimate with exponential weighting}
This estimate is described in \citep{shur01}. Mean is estimated as a
solution of the  equation
\begin{equation}
\widehat{\mu _{r}}: \hspace{0.5cm} \sum_{i=1}^{n}(x_{i}-\widehat{\mu
_{r}})e^{-q_{i}/4}=0
\end{equation}
and variance is defined as a solution of the equation
\begin{equation}
T_{9}=\sigma
_{r}^{2}:\hspace{0.5cm}\sum_{i=1}^{n}[(x_{i}-\widehat{\mu
_{r}})^{2}/\sigma _{r}^{2}-2/3]e^{-q_{i}/4}=0,
\end{equation}
where $q_{i}=(x_{i}-\widehat{\mu_{r}})^{2}/\widehat{\sigma ^{2}}$.
Fig. 1h shows the relative empirical influence functions     for
variances computed with $T_{9}$. The $LOSS=0.98$.

D. A. Lax performed a Monte Carlo study of more than 150 variance
estimators. Seventeen of these estimators were selected as being
either promising or commonly used \citep {lax85}. The results of our
computer simulation are in agreement with this test \citep {lax85},
especially concerning the high efficiency of  estimate $T_{1}$ and
$T_{7}$ and the low efficiency of  $T_{3}$.

The choice of a particular estimate depends on the type and
intensity of RFI, the type of observations and the method of
implementation (hardware or software).  $T_{8}$ and $T_{9}$  are the
best estimates from the point of view of  $LOSS$.  $T_{3}$ and
$T_{5}$ remove outliers in a most effective way (high  value of  the
breakdown point). Number sorting   and permutations of pairwise
measurements which are necessary in several algorithms require  more
computational time and computer memory.

\section{Computer simulations}

This section presents computer simulations of RFI mitigation using
algorithms which were employed in observations (Section 4).  Two
estimators from the the previous section have been chosen:
winsorization and exponential weighting. The reasons for this choice
are as follows.

All algorithms described in Section 2 give estimates of variance,
i.e., they work as  total power detectors (TPD) in radiotechnical
terminology. They can be applied in  single dish observations both
in continuum and in spectral observations. In the same manner  they
can substitute for TPDs   which are already  installed in  existing
radio telescopes.  Nowadays it is practically impossible for
technical and organizational reasons.In future  radio relescopes may
be equipped  with some RFI mitigation techniques and these variance
estimators could then be implemented in hardware or software shape.
But at the present time any experiment with RFI mitigation at
existing ratio telescopes must take  the
 technical constraints of implementation into account. Therefore only those estimators from Section 2 were chosen which could provide  not only  estimates of variance but also  ``clean'' data which could be applied further to TPD or to the correlator already in use in a radio telescope backend.

\subsection {Winsorization followed by total power detectors}
  Winsorization  with the parameter $\gamma=0.05$  was chosen
mainly because  impulse-like RFI in the temporal domain was
predominant during observations made at Effelsberg  presented in
Fig. 8. It was strong and sparse.  The percentage of  RFI in the
whole volume of data was not large $(<5\%)$.  Both  winsorization
and trimming can provide raw data without outliers but trimming
inconveniently reduces the number of samples   because of this
implementation problem: ``timing'' is distorted, i. e., there are
gaps (not zeros) in the presumed unbroken flow of data and the radio
telescope backend has no information about the location of these
gaps. The problem is easily solved in software during simulations
but may be critical in real observations. Winsorization  unlike
trimming preserves the total number of samples.

The block diagram  of computer simulation presented in Fig. 2  shows
the structure  of the algorithm using winsorization. The input
signal is the mixture of three signals: system  noise with normal
distribution ($\sigma=0.5$),  signal noise, also with normal
distribution ($\sigma=0.05$)  and impulse-like interference
imitating RFI. The signal noise is switched ``on'' and ``off''
emulating ``on-source'' or ``off-source'' position of the antenna
main beam, respectively. Poisson distribution ($\lambda=0.004$)
governs the appearance of RFI  and the lognormal distribution
(mean=12, standard deviation=6) determines the random amplitudes of
impulses. Input signal samples are stored in the buffer and sorted
to create  order statistics. The length of these statistics is equal
to $N=100$. The winsorization block provides both  variance
estimates for each of the $N$ samples and also the winsorized
samples which are applied to the external TPD (as in observations
presented  in subsection 4. 1). The loop with $M=100$ cycles
corresponds to  post-detector averaging: the  estimates of variance
after the winsorization block  are accumulated during $M$  cycles
and the mean calculated for these $M$  cycles represents the
simulation result.

Fig. 3    shows the results of computer simulations :\\
a) input noise with  normal distribution, $\mu=0.0, \sigma=0.5$, no
interference;
b) total power detector output, each point in this figure corresponds to squaring and averaging of  $N \times M=10^4$ samples in figure a); there are two steps, ``up'' at point $\#100$ and  ``down'' at point $\#200$  corresponding to the increase of $\sigma$  from value $0.5$ to the value $\sigma+\Delta \sigma, \Delta \sigma=0.05$ ( ``on-source'' and ``off-source'' position of antenna beam);\\
c) interferences in the form of random impulses are added to the noise a);\\
d) total power detector output with the input signal  c);\\
e) total power detector output with the input signal c) and
preliminary winsorization.  The difference between the scales in the
vertical axes  in d) and e)  demonstrates the effect of interference
mitigation.

 \subsection{Exponential weighting}
Exponential weighting  also provides not only  estimates of variance
but  data with suppressed outliers. This estimator is also of
superior  effectiveness. And unlike winsorization, it does not sort
input data. The influence of sorting on  phase information can be
detrimental to radiointerferometic  and pulsar (de-dispersion
procedure) observations. Impulse-like RFI in the frequency domain
was prevalent during the pulsar and image synthesis observations at
WSRT (subsection  4. 2 and 4. 3).  It is to  demonstrate
exponential weighting in the frequency domain which is the aim of
this computer simulation.

The block diagram  presented in Fig. 4 shows the structure of the
algorithm using exponential weighting in the spectral domain. The
input signal is a mixture of four signals: system  noise with normal
distribution ($\sigma=1.0$) filtered to emulate the WSRT backend
low-pass filters; two noise-like signals with normal distribution
imitating emission and absorption spectral lines, respectively, and
interference waveforms. A randomly phase-modulated  sinusoidal
carrier  is used as the RFI signal, the index of modulation is equal
to $\pi /2$ (binary phase modulation). Poisson distribution
($\lambda=0.3$) determines  the moments of phase jumps.

There are two loops in the algorithm: the inner loop ($m=1,..M$)
which is responisble for the storage of $M$ instantaneous spectra of
the input signal for RFI mitigation processing, and the outer loop
($l=1,..L$) which repeats  consecutively $L$ identical stages of
processing inside the inner loop,  showing the  dynamics of the
running spectrum.  Interference is intermittently switched  ``on''
and ``off'' inside the inner loop, the percentage in the total
averaging interval is less than $7\%$.

$N$ samples of the input signal are Fourier-transformed with
forward FFT, $N=512$. The inner loop with the counter ($m=1,..M$)
provides $M$ complex spectra (each having a length equal to $N$)
which are stored in the buffer.  So there are two sets (real and
imaginary) of data in the buffer numbered  as $m=1,..M$ for each of
the $N=512$ spectral channels. $L$ power spectra of the input signal
averaged on $M$ instantaneous spectra are calculated  in the outer
loop ($l=1,..L$). Fig. 5a shows the three-dimensional time-frequency
presentation of  the sequence of $L=50$  averaged ``dirty'' spectra.

 Equation (18) is solved separately for each of $2 \times N$  real and imaginary sets of data providing estimates of $\widehat{\sigma^2_{M,real}(i)}$ and  $\widehat{\sigma^2_{M,imag}(i)}$  - variances in $i=1,..N$ spectral channels calculated using $M$ samples of  real and imaginary components of the instantaneous spectra. Random values in the real and imaginary part of the instantaneous spectrum are independent, so the sum of these  estimates in each spectral channel gives the total estimated power spectrum for each spectral channel.  This ``clean'' power spectrum is represented in Fig. 5b for $L=50$ time intervals.
In real observations each of  $l-th$ time interval is equal to $N \times M \times \Delta t$ seconds, where $\Delta t=1/2\Delta f$ is the input signal sample interval,  $\Delta f$ is the bandwidth of the input signal.  \\
 In  applications  it is practical  to find  the solution to  (18)  when $\mu_{r}=0$ using the  approach of stochastic approximation:
\begin{equation}
\widehat{\sigma _{m}}=\widehat{\sigma
_{m-1}}+\frac{1}{m}\sum_{k=1}^{m}(\frac{x_{k}^{2}}{\widehat{\sigma
_{m-1}}}-\frac{3}{5})\exp (-\frac{x_{k}^{2}}{3\widehat{\sigma
_{m-1}}}),
\end{equation}

$m=1,..M$. Fig. 5c and 5d show the result of averaging   $L$
spectra  in Fig. 5a and 5b for the ``dirty'' and ``clean''power
spectra, respectively. The frequencies of interferences do not
coincide with the frequencies of spectral lines . It can therefore
be seen that the components with  normal distribution (system noise
and spectral lines)  are untouched by this RFI mitigation procedure.
The sequence of pictures in Fig. 6 similar to Fig. 5 demonstrate the
result of computer simulation when the frequencies of interferences
coincide with the frequencies of spectral lines .  In this case  we
again see   that the restoration of the spectra is  satisfactory.

Figures 5 and 6  demonstrate  how the algorithm works with  spectra.
However  experimental constraints did not require the provision of
estimates of variance or power spectrum but did required the
``clean'' signal in  the temporal domain similar to the input signal
(the level of the signal, the bandwidth). Therefore  running
estimates of   ``clean'' and ``dirty'' power spectra are used for
exponential weighting of the running complex instantaneous spectra:
each complex value of an instantaneous spectrum being multiplied by
$exp(-P(i)/3\widehat{\sigma^2(i)})$, where $P(i), i=1,..N$ is the
input spectral variance (power spectrum) in the channel  $i$,
$\widehat{\sigma^2(i)}$ is the estimate of the ``quiescent'' power
spectrum in the channel  $i$ found from Eq. (18). A corresponding
delay must be introduced because of the time required  to calculate
all $\widehat{\sigma^2(i)}$. Then, after the backward FFT, the
signals in the temporal domain can be applied to TPDs or
correlators.  This auxiliary output was used to supply the ``clean''
raw data in the observations described in Sections 4. 2  and 4. 3.

Figure 7 shows the impact of RFI mitigation on cross-correlation
(radiointerferometric observations). The algorithm in the block
diagram  shown in Fig. 4 was applied to two signals with  the
additional coherent Gaussian component ($\sigma_{s}=0.1$) emulating
the noise from a radio source  received at both sites of  the radio
interferometer. Sporadic interference with the duty cycle equal to
$0.2$ was generated as  frequency-modulated carrier and was
identical at both sites which is the worst case scenario: $100\%$
correlated RFI. In reality the impact of  RFI in radio
interferometers is considerably reduced  due to fringe stopping and
delay tracking procedures \citep{thom82}.

 Figures 7a and 7b demonstrate  three-dimensional time-frequency presentations of the input ``dirty''
and ``clean'' power spectra on one site, respectively. Deep
``troughs''   can appear in the spectrum in Fig. 7b  at the places
of interferences due to the exponential effect of weighting: when
interference is strong the algorithm works similarly to the
``thresholding and blanking'' algorithm but more smoothly and
without  the {\it a priori} knowledge  necessary for the positioning
of the threshold level. Figures 7c and 7d give the averaged
normalized cross-correlation functions corresponding to Fig. 7a and
7b, without and with RFI mitigation, respectively. The central parts
of the cross-correlation functions between channels $\# 400$ and
$\#600$ are shown. The scale of the vertical axe in Fig. 7c is
significantly larger   than that in Fig. 7d thus showing strong
excessive cross-correlation due to RFI.

It is necessary to add two further comments here for the purposes of
discussion.

1. The  estimators of  variance described in Section 2 and those
tested in Section 3  are essentially nonlinear procedures.  For
example, one can, at least theoretically, imagine a  huge  outlier
which  renders  the conventional estimator (1) completely
non-functional whereas these estimators work perfectly.
Radioastronomers  often ask what is the value of RFI suppression
(usually  in dB) when an RFI mitigation algorithm is applied?
Example in Fig. 3 demonstrates that it is not always possible to
answer this question correctly. The larger  the outlier's amplitudes
the more effectively they are deleted. If the amplitudes of impulses
in Fig. 3c are 10 times larger the result in  Fig. 3e will be the
same or even slightly improved. This is also valid for the
``thresholding and blanking'' algorithm which has been succesfully
used in several articles on RFI mitigation. That is why  it is
better to judge an RFI mitigation procedure in each paticular case
in a combined manner, that is looking at the ``signal-of-interest''
distortions by both the RFI residuals and the procedure itself.

2. The estimators of Section 2 are intended for  applications when
sporadic, impulse-like interference disturbs observations. However
interference
 is often continuous, persistent and practically constant during the averaging interval.
 For example, for $M=100, N=512, \Delta f=20MHz$ this interval is equal to $1.28 \times 10^{-3}$ sec which is rather a short  interval
for several RFI to change their amplitudes. The estimators of
Section 2 will not yield any benefits in this case. But the same
fact that RFI is quasi-constant at a reasonably  chosen averaging
interval  can help    to decouple the Gaussian component with a
``quiescent'' value of $\sigma$ and RFI with ease. A simple
procedure can do this. The power spectrum after FFT is usually
calculated as

 \begin{equation}
P(f)=1/M\sum_{m=1}^{M}\{Re[s(f)_m]^{2}+Im[s(f)_m]^{2}\}
 \end{equation}
The sums of RFI and ``useful'' Gaussian noise in each spectral
channel are squared and averaged. Strong RFI dominates in this case
over the noise. Another algorithm is proposed here: to  separately
estimate the variances  of real and imaginary parts of the spectrum
and then add them to obtain  the total variance in each spectral
channel, i.e., the power spectrum sought. The variances of real and
imaginary parts of the complex spectrum must be calculated taking
into consideration that the mean value now is no longer  equal to
zero because of the presence of RFI.  So the general formula for
sample  variance must be applied: for a random value $x$, the sample
variance is $var(x)=$$\widehat{\sigma
_{x}^{2}}=1/M\sum_{m=1}^{M}(x_{m}-\widehat{x})^{2}$ which for the
power spectrum at frequency $f$ is

\begin{eqnarray}
P(f)=var[Re(s(f)]+var[Im(s(f)]=\\
1/M\sum_{m=1}^{M}Re[s(f)]_{m}-\widehat{Re[s(f)}]\}^{2}+\nonumber\\
1/M\sum_{m=1}^{M}Im[s(f)]_{m}-\widehat{Im[s(f)}]\}^{2},\nonumber\\
\widehat{Re[s(f)}]=1/M\sum_{m=1}^{M}Re[s(f)]_{m},\nonumber\\
\widehat{Im[s(f)}]=1/M\sum_{m=1}^{M}Im[s(f)]_{m}\nonumber
\end{eqnarray}

In this case  RFI is eliminated due to its  constant value  for all
$M$ samples of the instantaneous spectra $s(f)]_{m}$.  It may also
be useful for the mitigation of  weak but persistent RFI whose
detrimental impact is revealed only after lengthy averaging. This is
a conjecture which has been proved in computer simulations but
which, of course, requires
 experimental confirmation.

\section{Examples from observations}

\subsection{ Observations in continuum at Effelsberg  radio telescope}

Radio source 1448+762 was observed in continuum at the Effelsberg
radio telescope. The receiver  output  was split on two channels.
The signals from one channel were processed  by  a FPGA processor
(Altera Stratix S80) and then  sent  to the total power detector.
The signals from the second channel  were applied straight to the
total power detector. The bandwidth of the signals applied to the
total power detector was equal to 20 MHz. These  channels provided
two radio telescope outputs: one  output with RFI mitigation and
another  without. Pairs of radio source scans from these outputs
were made simultaneously.  Because the  total power detector  was an
integral part of the radio telescope backend equipment,  the
algorithm implemented in FPGA  only processed the IF (intermediate
frequency) signal with the aim of ``cleaning" it of  RFI. The
analogue input signal was digitized in 12-bit ADC with  40Msamples
speed, then processed in FPGA,  transformed back into  analogue form
and applied to the total power detector. Winsorization of the signal
in temporal domain ($\gamma=0.05$) was implemented.

Fig. 8 shows  eight scans of the  radio source, each scan
represented by two panels: the top panel - scan with RFI mitigation,
the lower panel - without RFI mitigation. Fig. 9  displays radio
images  of the source  built using  scans similar to those in Fig.
8:  the left panel - without RFI mitigation, the right panel - with
RFI mitigation.

\subsection{Pulsar observations  at WSRT}

A new pulsar machine PUMA-2 has been installed at the Westerbork
synthesis radio telescope (WSRT). The radio telescope works in
tied-array mode in which all 14 signals from antennas are added in
phase , i.e., there is one   output  (in reality with  two
polarizations) as for a single dish. The 20 MHz-baseband signals
from each of the eight  frequency channels of WSRT  are digitized (8
bit) and stored in the mass storage system which has sufficient
hard disk capacity to support at least 24 hours of continuous
observations. Signal processing can  therefore be undertaken  {\it
off-line}.

In our experiment a block of  data  recorded during 10 sec, $40
\times 10^6$  eight-bit samples/sec, was used. All RFI mitigation
processing and the total power detector (TPD) were realized entirely
in software during {\it off-line} processing.  The estimate of
variance with exponential weighting was used. Fig. 10  displays the
results of processing. Upper row, left panel: TPD outputs for  two
polarizations calculated from the  raw data with RFI, right panel:
TPD outputs, RFI removed. Middle row, left panel: example of a time
fragment  of the power spectrum with RFI;  right panel : the same
time fragment, RFI removed. Lower row shows pulsar profiles after
de-dispersion and  folding at the pulsar period, left panel: pulsar
profile  averaged  over 10 sec from both polarizations of raw data,
middle panel: similar pulsar profile, RFI removed, right panel:
pulsar profile restored with observational data obtained at 1420 MHz
without any RFI which is put here for comparison with the profile in
the middle panel.

\subsection{Radio image synthesis at WSRT}

Radio source DA240 was observed at WSRT at  a central  frequency of
357 MHz with the  bandwidth  equal to 20 MHz in  the presence of
strong RFI. The RFI mitigation system (RFIMS) was used for {\it
real-time} processing \citep{baan04}. Analogue baseband signals
were digitized (12bit ADC, 40 Msamples/sec), processed in FPGA
(Altera StratixS80) and  transformed back to analogue form for
subsequent processing in the WSRT correlator. The algorithm used for
the removal of RFI  was similar to the algorithm with exponential
weighting, except that the variance was not used as an output,
instead   ``cleaned" , exponentially weighted  signals were applied
to the correlator.

Radio images of the source DA240 are shown in Fig. 11. Upper row,
left panel: image without RFI mitigation; right panel: image with
RFI mitigation. Lower row:  central parts of  the image presented in
the same order.  The stretched form of the synthesized images is
explained by the fact that the observations lasted  8 hours, instead
of a full 12 hour  aperture synthesis cycle.

RFI mitigation was implemented on each of 14 radio telescopes at
WSRT. This may give rise to some distortions because of the
difference of equipment  characteristics at the different antennas.
Other observations were made specifically to judge the  ``toxicity"
of the RFI mitigation procedure. Two pairs of radio images of  radio
source 4C34.47 were synthesized: one was observed at 1420 MHz with
the RFI mitigation system and without it and another at 345 MHz,
also with the RFI mitigation system and without it. The first pair
of images served as a test for ``toxicity" while the second pair
demonstrated the effectiveness of RFI mitigation. Both pairwise
observational data  were stored during  simultaneous observations.

Fig. 12 displays the results of the image synthesis.
 Upper row, left panel: central  frequency 345 MHz, without the RFI mitigation system; right panel: central  frequency 345 MHz, with the RFI mitigation system. Notice the difference of  intensity levels in the figures.
Lower row, left panel: central  frequency 1420 MHz without the RFI
mitigation system.
 right panel: central  frequency 1420 MHz with the RFI mitigation system. After subtracting one image from the other the {\it rms} noise  is  less than 0.7 mJy/beam which signifies a good similarity  in the images.

\section{ Conclusions}

1. Statistical analysis of raw data with the finest available time
and frequency resolution can help during observations in an RFI
contaminated envinronment. Estimates of variance are an  important
part of both  classical  and robust  statistics. Growing concern
about RFI pollution  should persuade  the  radio astronomy community
to pay more attention to a variety of algorithms developed in the
realm of robust  statistics. Tradional radiometers with simple
square-law detectors, spectrum analyzers and correlators must be
equipped with these tools. This framework  of robust estimates puts
the successfully tested  blanking of RFI on a more stable
foundation.

2. Statistically faithful, robust  estimates of variance are
especially appropriate for application in an  impulse-like strong
RFI  environment. RFI is effectively  suppressed and the
accompanying  loss in the signal-to-noise ratio is  tolerable. Table
3 gives  a  summary  of available RFI mitigation methods which have
been proposed during the last years. Some of these have been tested
in real observations.   Blanking showed truly good results in the
case of    impulse-like strong RFI. There are slots in the Table 3
where  ``removing or blanking""  is referred to. The aforementioned
robust  algorithms can be  applied in these particular situations.

3. The results produced from the  observations at the Effelsberg
radio telescope (image synthesis by beam scanning) and at WSRT
(Earth rotation aperture synthesis) included  in this article
demonstrate the usefulness of this kind of RFI mitigation both in
{\it real-time} and  {\it off-line}  (pulsars).

4. The choice of a particular algorithm depends on the type and
intensity of RFI.The proportion of RFI  presence in data is also
important. The type of implementation may determine the choice:
{\it off-line}  or {\it real-time}.  All options are open now. Mass
storage systems allow data  to be processed {\it off-line} on
powerful computers. Nowadays both existing radio telesopes  future
projects (LOFAR, ATA, SKA)  will generate such huge amounts of data
that  {\it real-time} processing is  vital: DSP, FPGA or
supercomputers are possible solutions. Therefore  the creation of
`` robust" radiometers, spectrum analyzers and correlators  is an
urgent necessity.

\acknowledgments I am  grateful to Juergen  Neidhoefer  and  Ernst
Fuerst for their help during observations and data processing at the
Effelsberg radio telescope, to Ben Stappers and  Ramesh Karuppusamy
for providing the pulsar data and  Subhashis Roy  for AIPS
processing of 4C34.47 data.

\clearpage \onecolumn
 \centerline{Table 3: The application of RFI mitigation methods}

\hspace{1cm}

\begin{tabular}{|l |l |l||l|l|}
\tableline
  Type of radio  & Type of RFI, & Type of RFI,   & Observations  in continuum & Spectral observations \\
  telescope & intensity  & structure  &   &   \\
\tableline \tableline

  &  & impulse-like &   tolerable & tolerable\\
\cline{3-5}
  & weak &  narrow-band & Removing (blanking)  & ANC with a reference channel \\
  & RFI &  &              in spectral domain & and post-TPD subtraction; \\
& & & & HOS analysis\\
\cline{3-5}

  & & wideband & ANC with a reference channel   &ANC with a reference channel \\
Single &  &  &        and post-TPD estimation & and post-TPD estimation \\
 & & &  \& subtraction  &  \& subtraction\\
 \cline{2-5}
dish &   & impulse-like & Removing (blanking) & Removing (blanking)\\
 & & &                   in temporal domain & in temporal domain\\
\cline{3-5}
  & strong  & narrow-band & Removing (blanking)& ANC with a reference\\
 & RFI& &                   in spectral domain &  channel; HOS analysis \\
\cline{3-5}
 & & wideband &  ANC with &  ANC with\\
& & &           a reference channel & a reference channel\\
\tableline
 &  &  impulse-like & tolerable & tolerable\\
\cline{3-5}
 & weak  & narrow-band &  tolerable & Post-correlation ANC with\\
  & RFI   &   & & reference channels; HOS analysis\\
\cline{3-5}
 & &wideband & tolerable & Post correlation ANC with\\
Connected   & & & & reference channels\\
\cline{2-5}
radio &strong & impulse-like & Removing (blanking)  & Removing (blanking) \\
interferometer  & RFI &  & in temporal domain & in temporal domain\\
\cline{3-5}
 & &  narrow-band & Removing (blanking) & ANC with a reference\\
& & & in spectral domain & channel. Spatial filtering\\
\cline{3-5}
& & wideband & ANC with a reference & ANC with a reference\\
& & & channel. Spatial filtering& channel.  Spatial filtering\\
\tableline
& weak & impulse-like & tolerable& tolerable\\
\cline{3-5}
 & RFI & narrow-band& tolerable& tolerable\\
\cline{3-5}
 &  & wideband &   tolerable& tolerable\\
\cline{2-5}
VLBI & strong & impulse-like & Removing (blanking) & Removing (blanking)\\
   & RFI  & &      in temporal domain &in temporal domain\\
\cline{3-5}
 & & narrow-band& Removing (blanking)& ANC with a reference\\
  & &  & in spectral domain & channel\\
\cline{3-5}
 & & wideband & ANC with a reference & ANC with a reference\\
& & &  channel & channel\\
\tableline
\end{tabular}

RFI -  radio frequency interference.\\
TPD - total power detector.\\
ANC - adaptive noise cancellation.\\
HOS - higher order statistics.\\

\clearpage

redpajama_set_name: RedPajamaArXiv


text:
Trident University Health Science Professor Honored By National Communication Association.
Dec 28, 2018 | News 2018 |
December 28, 2018 – Cypress, CA – Trident University International (Trident) is proud to announce that Dr. Leandra Hernández, professor in the College of Health and Human Services, received two awards at the National Communication Association (NCA) 104th Annual Conference last month in Salt Lake City.
She was the recipient of the NCA Feminist and Women's Studies Division's 2018 Bonnie Ritter Book Award for the book, Challenging Reproductive Control and Gendered Violence in the Américas: Intersectionality, Power, and Struggles for Rights.
Available though Lexington Books, this book examines reproductive and gendered violence against women in Texas and Latin America. Dr. Hernández and co-author Dr. Sarah de Los Santos Upton, University of Texas at El Paso, analyze contemporary issues including reproductive justice, feminicides, and anti-feminicidal activism.
Additionally, Dr. Hernández received the 2018 NCA Applied Communication Division Bill Eadie Distinguished Article Award for the peer-reviewed work, "Telling global public health stories: Narrative message design for issues management." This article, which was published in Communication Research, was co-authored with Dr. Marissa J. Doshi and Dr. Joshua B. Barbour.
Dr. Hernández is an instructor for the courses Health Communication & Advocacy, Cross-Cultural Health Perspectives, and Cultural Diversity in Health Sciences. She holds a Ph.D. in Health Communication from Texas A&M University, a Master of Arts in Mass Communication from University of Houston, and a Bachelor of Arts in Mass Communication from University of St. Thomas.
Founded in 1998, Trident University International (Trident) is a leading online postsecondary university serving adult learners. Trident uses the EdActive™ learning approach, which employs case-based learning in an online setting to teach real-world relevant critical thinking skills to enhance the lives and careers of students. Trident offers high-quality bachelors, masters, and doctoral degree programs and certificates, led by a qualified faculty team, over 80% of whom have doctoral degrees. Trident is regionally accredited by the WASC Senior College and University Commission (WSCUC) and has over 32,000 alumni, including more than 27,000 with a military affiliation. The University has received multiple acknowledgements from Military Times, Victory Media's Military Friendly Schools, and Military Advanced Education & Transition for their dedication to military-affiliated students. Visit www.trident.edu, Trident's Facebook page, or call at to learn more about Trident's wide range of educational options.

redpajama_set_name: RedPajamaCommonCrawl


text:
import React, { Component } from 'react';
import PropTypes from 'prop-types';

class ScrollToTop extends Component {

	componentDidMount(){
		this.props.element().scrollTop = 0;
	}

	render(){
		return null;
	}
}

ScrollToTop.propTypes = {
	element: PropTypes.func.isRequired
};

export default ScrollToTop;

redpajama_set_name: RedPajamaGithub


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The kitchen was on fire, and it wasn't my fault.
Quite frankly, I did just about every reasonable thing I could to prevent the situation, and when it happened, I struggled to ease it, but ultimate control was beyond me. The kitchen was still on fire, and it still wasn't my fault. While The Sims is a game that gives the player a phenomenal amount of power and control over life, but I still couldn't seem to suppress the flames in the kitchen.
My sim was a young adult just starting his career as a poor novelist. He made eight simoleons in a good week of writing, and spent his idle time reading worse fiction than his and learning to cook. The cook books were just barely in his budgetary reach, and I made certain I kept him very well stocked with cooking knowledge before I ever loosed him on a stove. He was cooking level six when I set him loose on Goopy Carbonara, a difficulty two dish. Over the months he poured over the cook books, I'd even gotten him enough money to upgrade the stove, so he'd have even less opportunity to bungle what should've been a rote practice for his now well-read and well-learned hands.
He set the kitchen on fire.
While I tried to suppress his panic and get him to actually start easing the flame – all while the fire alarm blared in shrill rage at the foolishness inferno – I marveled at how ridiculous the entire scenario was. Here I was, an omnipresent God with the ability to selectively make and unmake reality at my whims, paying special attention to an idiot and the house fire he was inevitably nurturing. Yet, I was still having fun.
There's a weird amount of joy hidden in the passive hand of The Sims. This strange sense of comfort with playing in a digital dollhouse while a bad writer and inexplicably worse chef tries to burn his §10,000 house down over middling italian food. That there's a world where reading cook books is of personal importance and writing books with titles like "Love, Sweet Throbbing Gondola" is of any significance help make the things of actual significance outside of the digital world feel less suffocating.
Games are great at escapism, giving the opportunity to get away from the crush of daily life, but they're also great ways to illustrate things to ourselves that would otherwise be invisible to our own introspection. They give us outlets for our hopes and aspirations that we may not have consciously acknowledged ourselves. Although I took up cooking in 2015, my sims had been learning cooking in addition to writing since the late 2000s. Likewise, my sims had modded-in typewriters in the original The Sims before writing was even a career possibility, but it was one that I had gravitated to even before I knew that I personally loved the craft of writing.
My foolish, panicking sim was a good guideline for the person I wanted to become. A teenager making a guy with decent hair, business casual dress, and the extroversion that made him sociable enough at parties was what I ended up finding myself trying to become as I went from goofy teen to goofy adult.
Likewise, having the digital outlet be able to solve problems in fractions of the time helped build the confidence that actual reality could be less strangling as well. Although I couldn't make the inevitable reality of homework deadlines and the need to become responsible any less pressing, The Sims showed me that any problem could be solved with a few days' or weeks' efforts, and that the steps to resolving my sim's problems weren't dissimilar to my own, just that mine seemed to take so much longer, and that the things I could do today would ripple out and resolve the problem that seemed never-ending to a more impatient, younger mind. I found joy in the solace that my stupid sims brought me.
Much like owning a pet, having the sim around made me feel like even when I wasn't in direct control of the things that happened, I could still have control over the end goal. I could set an accomplishment as a goal, take steps to get there, and with enough patience and perseverance, it could be achieved. My younger self really needed that advice, and getting to see it play out over the space of an afternoon was something that really brought happiness along with the escapism. I found comfort in that.
As I get older, and a little bit wiser for the aging, I find the advising aspect of The Sims stops being at all significant in my life, but I still find joy in the passive experience. Getting to be a part of the sim's life is a joyful thing for me, even if it is somewhat pointless and more than a little silly. Getting to go back and spend an afternoon with a ridiculous little creature that reads and writes books, spends his afternoons still in his pajamas clacking away at a computer, and occasionally sets appliances on fire brings a calmness to my life that is no less appreciable today than it was years ago when I needed the sense of scale as well.
It's weird to say "It's a joy to catch the kitchen on fire" in a completely earnest way, and hopefully doesn't also paint me as an arsonist in the process, but I still quite like the joy I find when I'm able to spend some time with my sims. Even when they spark up new and exciting foolishness infernos.
Which still aren't my fault, by the way.
Written for Critical Distance's Blogs of the Round Table under the "Joy" theme. If this is up your alley, go give the other pieces a read.
Taylor Hidalgo is a freelance writer, editor, and lifelong appreciator of fire. Although he doesn't consider himself an arsonist, nor has he ever intentionally set anything on fire, he does crash and burn occasionally on Twitter.


text:
Abdulláh abú Ma'arúf, ,  (* 30. prosince 1957 Jirka), je izraelský politik arabské národnosti a člen komunity Drúzů; poslanec Knesetu za Sjednocenou kandidátku.

Biografie
Je členem komunity izraelských Drúzů. Patřil mezi první odpírače vojenské služby v jejich řadách (izraelští Drúzové na rozdíl od muslimů a křesťanů většinově slouží v izraelské armádě). Studoval lékařství v Sovětském svazu. Specializuje se na urologii. Působil jako jeden z prvních lékařů v rodné obci Jirka a byl jedním z předáků výboru arabsko-drúzské iniciativy. Je členem politické strany Chadaš.

Ve volbách v roce 2015 byl zvolen do Knesetu za Sjednocenou kandidátku (aliance čtyř politických stran arabské menšiny). Při nástupu do Knesetu se musel vzdát ruského občanství (stát Izrael umožňuje dvojí občanství, ale poslanci parlamentu smí být držiteli pouze izraelského občanství).

Odkazy

Reference

Externí odkazy
  Profil na portálu ynetnews.com

Izraelští Arabové
Izraelští Drúzové
Izraelští lékaři
Urologové
Členové Knesetu
Členové Chadaš
Členové Sjednocené kandidátky
Narození v roce 1957
Žijící lidé
Muži

redpajama_set_name: RedPajamaWikipedia


As we enter the 4th quarter of 2013 PFM is starting to gain traction. We have multiple "conceptual" products under contract with local designers. These products are both revolutionary and evolutionary in nature. Provisional patents are in the process of being applied for on some of the products and preliminary prototypes are being built. Although all of the actual machining and finishing of the foam is currently being sourced to outside vendors we can still handle virtually any project as long as it is not too large in scope. If you have a need or project please feel free to contact us; should the scope of the project be too large for us to handle at this time we will be honest with you and direct you to some of our affiliated vendors to help you with your need.
As the volume of business ramps up we will enter into Stage 1 of PFM's evolution; during this stage we intend to move into a small facility that will allow us to do most of the artistic finishing of our projects in-house. All of the machining of the foam will continue to be outsourced during this stage due to the lack of both floor space and funds to purchase the FROGMill 4 axis CNC milling machine that will be the backbone of our operation. Nearly all R&D projects will also be handled in our own facility under Ken's direction. We anticipate employing 2-3 disabled veterans on a part time basis.
With the launch of "Stage 2" PFM will need to meet its increased demands by moving into a slightly larger facility; PFM will have also make its first purchase of a FROGMill 4 axis milling machine and our own FROGSkin spray finishing system! A large portion of the machining can now be done in-house as well as the majority of the finishing. New product R&D will be ramping up; new industries will be sought and serviced aggressively. We hope by this time to be actively employing between 5 and 10 disabled veterans.
WooHoo! This is what we have been working so hard to accomplish; Stage 3 will be the culmination of all our dreams…a fully robust and fully functional Phoenix Foam Manufacturing facility. In order to enter this phase of operation we will increase the size of our facility to over 5,000 square feet; this will be needed to house the purchase of our second FROGMill 4-axis milling machine and our first FROGWire CNC hot wire foam cutting machine. All scanning operations will also now be brought in-house with the long awaited purchase of the FROGScan laser and FROGScanFlash white light scanning tools. We anticipate during the fully functional Stage 3 operation to have between 10 and 20 disabled veterans employed both full and part-time as volume requires.


What are the 10 Common SEO Mistakes to Avoid?
Search Engine Optimisation or SEO has witnessed an enormous change over the past few years than it were 5 years back. Within these few years, Google has updated its algorithm many times. The more recent ones have been Penguin and Panda and Pigeon algorithms.
While the Penguin and Panda were set up to fight back link spam and poor content quality, Pigeon was updated to focus on local results. There was also an update made on machine learning technology like RankBrain to strengthen the main algorithm's accuracy. There have been mobile updates from Google to reward websites that optimize for smartphone users.
All these updates have been pointing to the fact that there are some common SEO mistakes that every marketer needs to avoid.
One of the common mistakes that most websites consider less is not keeping up relevance in content. Relevancy in the sense, giving your users/visitors a pleasant experience with a well-written information that keeps up with modern styling and provides an easy navigation. Such type of websites fall into authority category of websites and this is the category that every other websites should strive to fall in.
Provides a unique and in-depth content in the website that delivers what your users are looking for.
Some of the Search Engine Optimisers or companies who wants immediate gains will look out for tactics to cheat on search engines. They'll use some spam tactics and methodologies to automatically gain backlinks, traffic and more exposure. All these immediate gains can give you temporary gains which can lead to long-term penalties from search engines.
Everyone wants quick gains in a highly competitive world, so many are lured by shorts cuts and immediate gains which leads to mal-practices as these, which can risk the website from getting de-indexed from search engines entirely.
The web users/visitors are mostly attracted towards photographs, great banners, videos and other graphics used on the website. While these things may look pleasing to your users, it is also essential that you reduce the page loading time of your website. If you have a poorly loaded website, your customers are more likely to leave quickly. Since more users are likely to visit a website from mobile devices, loading time is very important as these devices lack large amounts of memory or strong Internet connections.
This is also a detrimental factor for search engines, as they pay close attention to a visitor's actions. What they search, how long they remain in a page and what actions they take, all are accountable for search engines to deliver more relevant results to them. If a visitor quickly leaves a page, search engines think that the particular page is not relevant to them. Therefore, a page that loads quickly will have better/higher rankings.
There are a number of factors that affect a website's page loading time. Sometimes a badly written code or any conflict issues with plugins can be the causing factors.
Web tools like GTmetrix and Pingdom can help analyse these types of problems.
Sometimes, if you look at a website, it might seem confusing to many of us. It will have many navigational options which are often unrelated. Page navigation is an important consideration to both search engines and consumers alike. For a consumer, they will always prefer for an easily understandable navigational options that'll get necessary information in just one or two clicks. Therefore, the important information should be always be presented at the centre location. Each page should be clearly labelled and properly formatted to make the content easy to digest.
Often, there are misconceptions among SEO regarding the backlink building process that sometimes go wrong. While links from external websites are a necessity to have credibility in search engines, it is important that the backlinks are acquired naturally. Very often natural links from other outside sources are not possible and this does not mean that SEO companies should not try to acquire backlinks. Bulk purchasing of backlinks are heavily frowned upon by all the search engines, especially Google.
One of the best possible way to acquire natural linking is to create highly engaging or entertaining contents or posts in your website or in the blog.
With the emergence of smartphones, there have been a steep rise in the number of consumers conducting local searches before they actually visited a real store. Therefore, if you value your local consumers, you need to update your details to avoid any devastating impact on your business.
Update the details (Name, Address and phone numbers) in the local directories relevant to your industry.
Update your local details with search engines like Google (My Business) and Bing (Places).
Once search engines verify your business, your local customers can easily find you.
If you have moved a page content to a different URL, search engines will consider it as a new page with no ranking history. Whereas, if you have done a 301 redirect or a 302 redirect, the page will be considered as old. When 301's permanently transfer url authority, 302's are considered temporary and signal that the content will return to its original location at some point in the future. There is also a 404 status code available for times when content is permanently removed and not available anywhere else online.
Although, 301/302 redirects are simple task, when you move large number of pages/urls to another, this needs to be taken special care. Because, not redirecting properly can lead to a scenario where the search engines penalise for vanishing (404) pages. Since that would also mean forfeiting any established backlinks and authority to the old URLs, it is one of the most costly mistakes in the SEO world.
There is a misconception that once a website is created with solid content that converts at a favourable rate, then it needs no update. This is wrong, because search engines always prefer fresh and updated content. This will help search engines to decide the website/business is active so that these pages are crawled easily for searches.
This will only result in people visiting/revisiting your domain or spend more time on the site to visit additional pages. It will give good signal to search engines for their engagement.
Many businesses ignore the fact that using keywords as anchor text is preferable for inbound links. They tend to use words like "click here" on contact and lead pages, which semantically does not make any impact on search engines. "Learn More" or "Read More" are similar disaffecting anchor texts.
Try alternatives with Keywords. For example: "Click to speak with a Web Design professional!" or "Read more on Search Engine Optimisation".
It's essential to realise that search engine optimisation is an ever-evolving process that requires a large time investment and plenty of hard work. Quick results or expecting the site's to rank from 10th page to 1st is almost impossible to achieve naturally without an enormous advertising budget and some serious influencers pushing the campaign.
One thing that every company who wants to appear their websites high in searches must keep in mind that search engine optimisation is never truly completed, since algorithms change and new competitors appear every time. The important fact to take into consideration is that customers also keep changing their search habits to find great products and services. So if you want to be a successful online business, you'll have to adapt to these changes and change your business/SEO strategies accordingly.


All apartments have washer and dryer hookups, a microwave, dishwasher and air conditioning. Griggs Village includes a private patio, off-street parking and an on-site laundry facility. It also offers a range of social services for residents. Building maintenance, water and sewer, trash pick up and lawn care are included. It is located in the Columbus City School District.


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The Sad Reason Hollywood Royalty Isn't Capable Of a Summer 2013 Blockbuster
By Nick Recktenwald
We mortals may be reveling in the summer sun, but things aren't so sunny in the Hollywood hills.
For many elite actors and actresses, this has been an atypically meager summer blockbuster season. Movies premiering from May to Labor Day used to mean A-listers could show up on set, phone in a performance in front of a green screen, and rake in Whedon-sized stacks of cash.
However, this summer, with the notable exception of human goldmine Robert Downey, Jr., Hollywood's most bankable stars have largely failed to be, well, bankable. Instead, with six weeks left in the traditional summer release season, movies based on existing IPs and featuring relative unknowns have reigned at the domestic box office. I define "relative unknown" by rigorous scientific standards: if your grandmother could not pick the actor or actress out of a lineup, then you know he or she has yet to ascend to pop culture's highest (or lowest, depending on your grandma's taste in film) echelon.
Let's examine the numbers: after Downey's Iron Man 3, the second-highest grossing film in America this year has been The Man of Steel, starring walking Men's Health ad Henry Cavill. Hitherto, the only people aware of Cavill were devotees of The Tudors, a tepid Showtime drama that took history's worst husband, Henry VIII, asked, "but what if he had a six-pack?" and milked that concept for four seasons. Also, fans returned en masse for the Star Trek sequel, but I'm not sure we can really call Benedict Cumberbatch Hollywood "royalty." Yet.
The three and four ranked films on the domestic chart, Despicable Me and Monsters University respectively, feature widely recognizable talent. But while everyone's grandmother knows Billy Crystal, the marketing for animated films rarely relies on the voice actors to push tickets because no one's 5-year old knows who Billy Crystal is.
Conversely, older millennials were likely shocked when our generation's most iconic movie star, Will Smith, found himself skunked at the box office. Sure, After Earth featured derivative visuals and a none-too-subtle repackaging of Scientology's core tenets and beliefs, but take a moment to remember the '90s: Will Smith movies simply did not under-perform at the box office (OK, except that one). Yet for all Smith's status as bona fide Hollywood royalty, After Earth opened to a disappointing $27.5 million, and has yet to crack $60 million in the United States. However, Smith's high-profile failure has been familiar this summer. Johnny Depp managed to be both racially insensitive and party to a box office bomb with his portrayal of Tonto in The Lone Ranger. To date, that film has earned $80 million, but cost around $215 million to produce. Bruce Willis, Channing Tatum, Vince Vaughn, and Owen Wilson have all seen their expensive summer blockbusters fail to return on their investment. Most recently, R.I.P.D., starring Ryan Reynolds and the venerable Jeff Bridges, ripped off the basic premise of Men in Black, but forgot to copy the latter film's massive ticket sales: it opened to $12.8, which is the only hilarious thing about that so-called "action comedy."
So, what's rotten in Tinseltown? Are our largest stars fading, threatening to implode in on themselves some Jerry Bruckheimer disaster movie? Or are audiences simply becoming savvier consumers, wising up when someone like Will Smith is playing salesman to After Earth's obvious snake oil?
I'm tempted argue for the latter phenomenon, but that would entail having faith in my fellow man, and that has not been rewarded of late. Certainly, the the top 10 earners this summer average a 60 Metacritic score, while the next 10 notably anemic earners average a dismal 43.7. Of course, you do not have to be enrolled in summer school to know neither is a passing grade, so critical praise cannot really be the catalyst. Audiences and film critics rarely agree with each other, and this is especially true in the summer. How else can we account for staggering earnings of the Transformers franchise, or even the continued employment of the awful Shia Lebeouf? That brings us back to the first option: does star power actually qualify as power anymore? Are we doomed to movies based on board games featuring milquetoast, no-name actors?
As it often does, hope comes from afar. As I suggested last week, the international box office has not only gained in importance in recent years, but also this summer it has kept the "bankable" on many an actor's résumé. For example, although After Earth proved uninteresting to American audiences, it has grossed a healthy $175 million internationally, and when added to its $59 domestic take, it allows Smith to wipe some of the mud from his face – with 50, if not 100, dollar bills. This year alone, Brad Pitt's World War Z, James Franco's Oz the Great and Powerful, and Bruce Willis' A Good Day to Die Hard have seen their domestic misfortunes reversed into legitimate box office success almost entirely by international ticket sales.
The shimmering patina of our favorite stars may not beckon to American audiences as it once did, but foreign audiences appear to be gleaning the same delight from their performances that we once foolishly thought we alone owned.
Entertainment writers and critics love to bemoan the dearth of fresh, genuine movie stars, but shifting market trends suggest that for the first time, international — not American — may bestow that distinction on the next generation of Julia Roberts and Johnny Depps. Like so much else in this country, the stars of the "Hollywood Walk of Fame" may be made in China.


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All Categories Emergency Alerts Port Alerts & Advisories Port Security and Operations
Port Alerts & Advisories
Port Security and Operations
The Port of Palm Beach is working closely with United States Customs and Border Protection, the United States Coast Guard and other federal and local partners to monitor the developments of the Coronavirus (COVID-19).
Right now our cargo operations remain open.
Our priority is always the safety and well-being of our employees, tenants, port users, visitors and residents.
We continue to take a proactive approach to combat the spread of the coronavirus. Our cleaning personnel are routinely sanitizing and sterilizing surfaces, seating areas, escalator handrails, elevator buttons and equipment in the port buildings and other areas of the Port.
All Port of Palm Beach employees have been instructed to routinely wipe down working areas when working in the office.
Hand sanitizing stations have been set up in areas around the Port.
All port tours and presentations are temporarily suspended. Instead, we have created a virtual presentation, which can be found on our website under the "Community" section.
Regular monthly Board of Commissioners meetings have been moved from the Board Room to the cruise terminal locate don the 2nd floor of the Marine Office Complex. We will be accepting public comments/questions online or by mail. A submission form will be published on our website in the "Agenda and Minutes" page a week before the meeting.
The port reception desk and lobby on 6th floor of the Marine Office Building is open to the public from 8:30 AM AM to 5:00 PM.
In compliance with Palm Beach County's mask mandate, everyone entering Port property is required to wear a face covering.
Port security gates remain open for regular hours, except for gate 5, near the South Gate facility, which is closed on Saturdays only.
The Port continues to disseminate important information to port tenants. All port tenants continue to take safety measures to keep their employees safe while at work to prevent the spread of the virus. It's important to note maritime workers have been deemed "essential" under the governor's stay-at-home order.
Tropical Shipping recently posted a safety update on its website. The company says, "We want our customers to know that as part as of our ongoing operations, Tropical thoroughly cleans each container after each sailing. We will continue that process and will follow all CDC requirements. Tropical cares about the health and well-being of its employees and customers, and we recommend the following CDC guidelines at work and with your families."
Several tenants have implemented procedures that requires all crew members to remain on board while docked at Port of Palm Beach.
U.S. Customs & Border Protection has reopened its Global Entry Trusted Traveler enrollment center located on the 3rd floor of the Port's Marine Office Complex. Services are by scheduled appointments only. CBP is also extending the renewal time to 18 months for those Global Entry members who apply for renewal prior to the expiration date. For more information call 561-844-1703 ext. 244.
Pleasure boaters needing CBP clearance have to self report at the CBP office at Palm Beach International Airport located at 1000 Palm Beach International Airport, West Palm Beach, FL. 33406. CBP has implemented temporary procedures to facilitate an Automated Vessel Entrance and Clearance AVEC) process. AVEC requires vessel agents and masters to conduct vessel/entrance clearance procedures through a combination of email and telephonic communications.
As an international seaport, we require all vessels to be vetted at sea before entering the Port of Palm Beach. U.S. regulations require the master of a ship destined for a U.S. port of entry to immediately report any death or illness among the ship's passengers or crew. Below you will see a link to the most recent United States Coast Guard Marine Safety Information Bulletin and a document that details vessel reporting requirements for sick or deceased persons.
Our cruise terminal remains closed. In accordance with the CDC No-Sail Order, Bahamas Paradise Cruise Line has suspended operations until at least late December.
For more information about the Coronavirus, please visit the links below:
Centers for Disease Control & Prevention
Florida Department of Health
Coronavirus and the workplace
Essential maritime critical infrastructure workers
Marine Safety Information Bulletin 3-16-20
Vessel Reporting Requirements for sick or deceased
COVID-19 Updates for truck operators
Emergency Declaration
Berthing Calendar
Receive Port Updates
IT Support Request
Port Security Training


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What Is the Difference Between Sucrose and Sugar?
An allergy to food products is a topic that we should have some awareness of—because many people suffer from this medical condition. Perhaps we know of a friend or family member who has a sugar or wheat allergy. With some knowledge, we can be sensitive toward their needs, give them support, and help in times of medical emergency.
A food allergy is the result of the immune system's negative response to certain food products. Up to 2 percent of adults and 8 percent of children, especially children under the age of six, are affected by allergies. Wheat allergies are a reaction to wheat proteins (various components in wheat) while a sugar allergy is a reaction to different types of sugar. Sugar and wheat allergies may be difficult to navigate since many food products contain one or both products.
The symptoms of a sugar allergy are various—including the intolerance to certain types of sugar. One person may have an intolerance for table sugar while someone else will be allergic to brown sugar or powdered sugar, while someone else may be allergic to all sugars. Symptoms of sugar allergy will include vomiting, rashes or hives (outbreaks), heartburn, headaches (migraines), fatigue, restlessness, and anxiety. A person may have one of these symptoms or may have all of them, depending on their type of allergy sensitivity.
Wheat allergies are triggered by different proteins contained in the wheat. One person's allergies can be triggered by glutelins (cereal and seed protein), while another person can be allergic to just prolamins (plant storage protein). Wheat allergy symptoms include: depression, chest pain, eczema, fast heart rate, skin hives, muscle and joint pain, vomiting, dizziness, irritable bowel syndrome, fatigue, itchy eyes, runny nose, and diarrhea. This is by no means a comprehensive list. Again, a combination of symptoms or just a singular symptom will be experienced. Diagnosis depends upon allergy tests conducted by a doctor.
Food allergy symptoms may develop into anaphylactic shock if left untreated. Even though this is a rare reaction to sugar and wheat allergies, it should not be taken lightly. A person suffering from anaphylactic shock could succumb and die in the span of several minutes due to restricted airways. An immediate treatment for this medical emergency is an adrenaline shot—which immediately opens up the airways in the body and keeps the heart pumping blood. If you witness what seems to be anaphylactic shock, call for emergency medical care immediately.
Preventive measures are best in dealing with sugar and wheat allergies. Developing a diet that does not include either wheat or sugar products may sound difficult, but it's necessary if you want to avoid negative reactions from food. Consulting a dietician is best in developing this specific diet and finding food substitutes.
Jason Candanedo is currently a student at Empire State College, pursuing a Bachelor of Arts in social theory, social structure and change. He has been writing since August 2008 on his political blog and since September 2009 as a freelance writer for various websites.