text:
\section{Introduction }

Some properties of our well tuned probability density functions (PDFs) were described in ref.~\cite{landi05} and
were tested on fits of simulated straight tracks.
The limitation to straight tracks was mainly due to the
complexity of the method we applied for the first time. Hence, working with
two parameters, the debugging and testing of the application
can be followed on a surface. Furthermore, the data, elaborated in our approach, were collected
in a CERN test  beam~\cite{vannu} in the absence of a magnetic field.
The detectors we used, were few samples of double-sided silicon microstrip
detector~\cite{aleph,L3} as those composing the PAMELA tracker~\cite{PAMELA}.
The results of ref.~\cite{landi05} showed a drastic improvement of
the fitted track parameters respect to the results of the least squares methods.   We
observed excellent reconstructions even in presence of very noisy hits, often called {\em outliers}, that generally
produce worse fits.
This achievement is almost natural for our non gaussian PDFs. In fact, an outlier hit is an
event that is incompatible with the gaussian distribution that is always assumed as the error distribution of the
hit positions.
Thus, being the least squares a method strictly
optimum for a gaussian PDF, it must be modified in a somewhat arbitrary way to handle these pathological hits.
On the contrary, our PDFs are essentially different from a
gaussian, and those non standard cases are allowed (with low probability) by the non-gaussian tails of the PDFs.

We have to recall that the perception of a rough handling of the hit properties is well present
in literature, and Kalman filter modifications are often studied to accept extended deviations from a pure gaussian model.
These extensions imply the use of linear
combinations of different gaussians~\cite{fru}, but in a small number to avoid the intractability of the equations.
The unknown parameters are optimized from an improvements of the fits.
In late sense, our schematic approximations, used to initialize the maximum likelihood search, could be reconnected to those
extensions. In fact, to speed the convergence, we calculate a different gaussian PDF (or more precisely an effective variance)
for each hit.
Our comparisons take as references the least squares methods. The Gauss-Markov theorem states
their optimality among the linear methods, thus no other linear method can be better.

The confidence gained in ref.~\cite{landi05} with straight tracks allow us to deal with more complex tasks,
i.e. to reconstruct the tracks in a magnetic field and determine their momenta.
To face this extension with the minimum modifications of our previous work, we will  utilize the
simulated data and parameters
used in ref.~\cite{landi05}, adapting them to the geometry and average magnetic field of the PAMELA tracker.
In fact, the relatively low magnetic field introduces small modifications of the parameters
obtained in its absence. The average signal distribution of a minimum ionizing particle (MIP) is
slightly distorted by a Lorentz angle, that for the average magnetic field of the PAMELA tracker ( $0.43\, T$) is about
$0.7^\circ$. Around this incidence angle, the average signal distribution of a MIP is practically identical to that at
orthogonal incidence in the absence of a magnetic field, thus without further notice we will assume
a rotation of the detectors of their Lorentz angle. With these assumptions, we simulate high momentum tracks for
each side of the double sided detectors. In the PAMELA tracker, the low noise side of the detector
(the junction side) is used for momentum measurements. This side has an excellent resolution, a
strip each two is connected to the read out system and
the unconnected strip distributes the incident signal on the nearby ones optimizing the detector resolution.
The other side (the ohmic side) has the strips oriented perpendicularly to the junction side.
Each strip is connected to the readout system and has, by the complexity of construction, an higher noise
and a small, if any, signal spread to nearby strips. For those characteristics, this side responds in a way similar to the types
of microstrip arrays used in the large trackers of the CERN LHC~\cite{ALICE,ATLAS,CMSa}.
Thus, the simulations on this side, as bending side, can give a glimpse of our approach for other type of trackers even for
the (small angle stereo) double-sided detectors of the ALICE~\cite{ALICE} experiment.

\section{A compact derivation of the PDF}

As always, the MIP incidence of our geometry imposes the use of the minimum number of signal strips
to reduce the noise, hence only two signal strips will be used.
In ref.~\cite{landi05} we indicated the principal steps required to obtain the PDF for the center of gravity (COG) with two-strips, those steps followed the standard method described in the books about probability.
That procedure uses the essential tools of the calculus: integrals and derivatives.
In our cases, many pieces of integrals over geometrical domains must be
calculated to build the cumulative probability, and its derivative gives our final PDF. It is evident the length
and the complexity of this development.
Here we follow a very different approach (Quantum Mechanics style) that reaches identical
results in few steps.
The two strip COG (COG$_2$ in the following) is calculated with the origin in the center of the maximum-signal strip.
The strip signals are indicated with: $x_1$, $x_2$, and $x_3$, respectively the signal of the right strip,
central strip (with the maximum signal) and left strip. If $x_1>x_3$ the COG$_2$ is $x=x_1/(x_1+x_2)$,
if $x_3>x_1$ it is $x=-x_3/(x_3+x_2)$, other minor details will not be discussed here. Thus:
\begin{equation}\label{eq:equation_1}
\begin{aligned}
    P_{x_{g2}}(x)=&\int_{-\infty}^{+\infty}\mathrm{d}\,x_1\int_{-\infty}^{+\infty}\mathrm{d}\,x_2\int_{-\infty}^{+\infty}
    \mathrm{d}\,x_3\,P(x_1,x_2,x_3)\\
    &\,\Big[\theta(x_1-x_3)\delta(x-\frac{x_1}{x_1+x_2})+\theta(x_3-x_1)\delta(x+\frac{x_3}{x_3+x_2})\Big]
\end{aligned}
\end{equation}
where $P(x_1,x_2,x_3)$ is the probability to have the signals $x_1,x_2,x_3$ from the strips $1,2,3$. The signals
$x_i$ are at their final elaboration stage and ready to be used for
position reconstruction of the hit. The function
$\theta(x_j)$ is the Heaviside $\theta$-function: $\theta(x_j)=1$ for $x_j>0$, $\theta(x_j)=0$ for $x_j\leq 0$,
and $\delta(x)$ is the Dirac $\delta$-function.
It is immediate to verify the normalization of $P_{x_{g2}}(x)$ by direct integration.
Splitting the sum of eq.~\ref{eq:equation_1} in two independent
integrals and transforming the variables $x_1=\xi$, $x_1+x_2=z_1$ and $x_3=\beta$,
$x_3+x_2=z_2$, the jacobian of the transformation is one and the integrals in $z_1$ and $z_2$ can be
performed with the rule:
\begin{equation}\label{eq:equation_1a}
    \int_{-\infty}^{+\infty}\mathrm{d}\,z\, F(z-\nu)\,\delta(x\mp\frac{\nu}{z})=F(\frac{\pm\nu}{x}-\nu)\,\frac{|\nu|}{x^2}\, .
\end{equation}
Applying eq.~\ref{eq:equation_1a} to eq.~\ref{eq:equation_1} and using the limitations of the two
$\theta$-functions, eq.~\ref{eq:equation_1} becomes:
\begin{equation}\label{eq:equation_1b}
\begin{aligned}
    P_{x_{g2}}(x)=\frac{1}{x^2}\Big[&\int_{-\infty}^{+\infty}\mathrm{d}\,\xi\int_{-\infty}^\xi\,\mathrm{d}\beta\,
    P\big(\xi,\,\xi\frac{1-x}{x},\beta\big)\,|\xi|\,+\\
    &\int_{-\infty}^{+\infty}\mathrm{d}\,\beta\,\int_{-\infty}^\beta\,\mathrm{d}\xi\,P\big(\xi,\,
    \beta\frac{-1-x}{x},\beta\big)\,|\beta|\Big]\,.
\end{aligned}
\end{equation}
This form underlines very well the similarity with the Cauchy PDF; in the limit of $x\rightarrow\infty$ the $x$-part
of the $P$ arguments are $-1$ and $P_{x_{g2}}(x)\propto 1/x^2$ for large $x$

\subsection{The probability $P_{x_{g2}}(x)$ for small $x$}

The probability $P(x_1,x_2,x_3)$ can handle a strict correlation among the arguments, we will release this strict correlation in a
weakest one: the mean values of the strip signals are correlated, but the fluctuations around the mean values are independent.
Thus the probability $P(x_1,x_2,x_3)$ becomes the product of three functions $\{P_i(x_i),i=1,2,3\}$.
Each $P_i(x_i)$ is assumed to be a gaussian PDF with mean
values $a_i$ and standard deviation $\sigma_i$. To simplify, the constants $a_i,i=1,2,3$ are the noiseless signals
released by a MIP with impact point $\varepsilon$:
\begin{equation}\label{eq:equation_1d}
    P_i(x_i)=\exp\big[-\frac{(x_i-a_i)^2}{2\sigma_i^2}\big]\frac{1}{\sqrt{2\pi}\sigma_i}.
\end{equation}
Even with the gaussian functions, the integrals of eq.~\ref{eq:equation_1b} have no analytical expressions and effective
approximations must be constructed. We will not report our final forms that are very long, instead we will illustrate a limiting
case which gives a simple approximation and eliminates a disturbing singularity for the numerical integrations.
It easy to show that for $|x|\rightarrow 0$, $x^{-1}P_2\big(\xi(1-x)/x\big)$ and
$x^{-1}P_2\big(\beta(-1-x)/x\big)$ converge to two Dirac $\delta$-functions. Hence, for small $|x|$,
the integrals of eq.~\ref{eq:equation_1b} can be expressed as:
\begin{equation}\label{eq:equation_1c}
\begin{aligned}
    P_{x_{g2}}(x)=&\frac{|a_2|}{\sqrt{2\pi}}\Big\{
    \frac{\exp\big[-(x-\frac{a_1}{a_1+a_2})^2\frac{(a_1+a_2)^2}{2\sigma_1^2(1-x)^2}\big]
    \big[1-\mathrm{erf}\big((\frac{a_3}{a_2+a_3}-x)\frac{a_2+a_3}{\sqrt{2}\sigma_3(1-x)}\big)\big]}{2\sigma_1(1-x)^2}+\\
    &\frac{\exp\big[-(x+\frac{a_3}{a_3+a_2})^2\frac{(a_3+a_2)^2}{2\sigma_3^2(1+x)^2}\big]
    \big[1-\mathrm{erf}\big((\frac{a_1}{a_2+a_1}+x)\frac{a_2+a_1}{\sqrt{2}\sigma_1(1+x)}\big)\big]}{2\sigma_3(1+x)^2}\Big\}\,.
\end{aligned}
\end{equation}
Equation~\ref{eq:equation_1c} is correct only for $|x|\rightarrow 0$, but it is useful beyond this limit and contains many ingredients
of more complex expressions. An example can be seen in fig.2 of ref.~\cite{landi05}, the essential elements
are the two bumps (gaussian-like)  centered in the possible noiseless two-strip
COG: $a_1/(a_1+a_2)$ and $-a_3/(a_3+a_2)$.
Their effective standard deviations are modulated by the signal-to-noise ratio $\sigma_1/(a_2+a_1)$ and $\sigma_3/(a_2+a_3)$
and by the $1\pm x$ factor.
More complete expressions contain terms very similar to Cauchy PDFs  that are $\propto 1/x^2$ for large $x$.
The dimensions of the constants $a_j$ must be those of the $\sigma_j$, for both of them we take directly the
ADC counts. The $x$-variable (the COG$_2$) is a pure number expressed as a fraction of the strip size, or more precisely, the strip
size is the scale of lengths.

The form of the COG$_2$ inserted in eq.~\ref{eq:equation_1} synthesizes well our positioning
algorithm: each strip signal around
the strip with the maximum signal is used for position reconstruction.
The strategy of cluster detection is
supposed to be optimized to suppress the false hits and its results are used only for the
maximum-signal-strip selection, and the signals of the two lateral strips are used in any case
even for small positive or not too negative values. It must be underlined that the tiny amount of
information is relevant for positioning or to maintain the noise distribution.
In fact, the track reconstruction algorithms reach their optimum with
realistic probability distributions, and any unaccounted distortion induces a loss of resolution.

For construction, $P_{x_{g2}}(x)$ gives to the probability of an $x$ value with constant $\{a_j\}$.
For the track fitting this simple probability is useless, we need a PDF that, for any $x$-value, gives the probabilities
of the impact points $\{\varepsilon\}$. To insert this functional dependence on $P_{x_{g2}}(x)$, we must know the average variations
of the energies $\{a_1,a_2,a_3\}$ with $\varepsilon$. For this task, it is essential
a theorem illustrated in ref.~\cite{landi05} and the details of refs.~\cite{landi01,landi03,landi04}, this theorem  allows
the extraction of these functional dependencies from the data. Once extended in $\varepsilon$, the PDF can be rewritten as:
\begin{equation}\label{eq:equation_2}
    P_{x_{g2}}(x,E_t,\varepsilon)=\frac{F(a_1(\varepsilon),a_2(\varepsilon),a_3(\varepsilon),E_t,\sigma_1,\sigma_2,\sigma_3,x)}{x^2}\,.
\end{equation}
Where $F(a_1(\varepsilon),a_2(\varepsilon),a_3(\varepsilon),E_t,\sigma_1,\sigma_2,\sigma_3,x)$ is
the term in square brackets
of eq.~\ref{eq:equation_1b}. The functions $a_1(\varepsilon),a_2(\varepsilon),a_3(\varepsilon)$ are the
average noiseless fractions of signal collected by each strip and $E_t$ is the total signal
of the three strips: the central one with the maximum signal and the two lateral. We would need
the noiseless $E_t$, but the measured one is the only possibility.
The extraction of the functions $\{a_i(\varepsilon)\}$ from
real data of ref.~\cite{landi05} necessitates further refinements and numerical recipes.
But, once defined and checked their results, the procedure can be
standardized. In any case, slight variations of the $\{a_i(\varepsilon)\}$ around the best one give almost
identical track parameter distributions, thus their selection is very important but non critical.
The functions $\{a_i(\varepsilon)\}$ have a definition similar to the "templates" of ref.~\cite{CMS}.
The parameters $\sigma_1,\sigma_2,\sigma_3$
are the noise (eq.~\ref{eq:equation_1d}) of the three strips considered.
Our PDF can easily accomodate strips with different $\sigma$, but,
in the simulations, we will use a single $\sigma$ for all the strips of same detector side and these parameters will not be
reported in the future expressions. Equation~\ref{eq:equation_2} is normalized as function of $x$ but
is not normalized in $\varepsilon$, due to its irrelevance for the fit, this normalization will be neglected here.
A set of normalized PDFs from eq.~\ref{eq:equation_2} are illustrated in ref.~\cite{landi05} as $\varepsilon$ functions.

\subsection{Track definition}

Given our exploration of a new approach, our track definition must be the easiest allowed.
The tracks are circles with a large radius to simulate the high momentum MIPs
where the multiple scattering is negligible. The relation of the track parameters to the momenta is $p=0.3\, \mathrm{B}\, R$,
the form, adapted to our needs, from ref.~\cite{particle_group} where $p$ is in $\mathrm{GeV/c}$, $\mathrm{B}$ in Tesla and $R$, the track radius, in meters.\newline
The tracker model is formed by six parallel equidistant ($89\, mm$) detector layers as for the PAMELA tracker, with constant
magnetic field of $0.43\,T$ perpendicular to the track plane ($\xi\,,z$). The center of the tracker has coordinates
$\xi=0\,,z=0$, the $\xi$ axis is parallel to the layer planes and the $z$ axis is perpendicular.
The tracks are circles with center in $\xi=-R$ and $z=0$, and the magnetic field is parallel to the analyzing strips.
To simplify the geometry, the overall small rotation of the Lorentz angle ($0.7^\circ$)  is neglected now, but it will
be introduced in the following.
The simulated hits are generated (ref.~\cite{landi05}) with a uniform random distribution on a strip,
they are collected in groups of six to produce the tracks. For the least squares, the exact impact position $\varepsilon$ of each hit is subtracted from
its reconstructed $\eta_2(x_{g2})$ position (as defined in refs.~\cite{landi05,landi03,belau}) and it is added the value of the
fiducial track for the corresponding detector layer. In this way each group of six hits
defines a track with our geometry and the error distribution of $\eta_2$-algorithm. Identically for the
COG$_2$ positioning algorithm.
This hit collection
simulates a set of tracks populating a large portion
of the tracker system with slightly non parallel strips on different layers (as it is always  in real detectors).
All these tracks are possible realizations of our model track.
At our high momenta the track bending is very small and the track sagitta is smaller than the strip width.
Thus, the bunch of tracks has a transversal section around a strip size, on this width we have to
consider the Lorentz angle but its effect is clearly negligible.
Our preferred position reconstruction is the $\eta_2$ algorithm as in ref.~\cite{landi05}  because it
gives parameter distributions better than those obtained with the simplest COG$_2$
positions, but even the results for the COG$_2$ will be reported in the following.\newline
In the $\{{\xi},{z}\}$-plane, the circular tracks are approximated, as usual,  with parabolas linear in the
track parameters:
\begin{equation}\label{eq:equation_3}
\begin{aligned}
    &\xi=\beta+\gamma {z}-\alpha {z}^2=\varphi(z)\,\\
    &\xi_n=\beta_n+\gamma_n {z}-\alpha_n {z}^2=\varphi_n(z)\,.\\
\end{aligned}
\end{equation}
The first line of eq.~\ref{eq:equation_3} is
the model track, the second line is the fitted one.
The circular track is the osculating circle of the parabola $\varphi(z)$,
at our high momenta and tracker size the differences are negligible.
Our model track has $\gamma=0,\,\beta=0$, and $1/\alpha$ is proportional to the
track momentum. Due to the noise, the reconstructed track has equation $\varphi_n(z)$ and
the fitted parameters $\{\alpha_n,\beta_n,\gamma_n\}$ are distributed around the model values $\alpha,\beta,\gamma$.
For their non gaussian forms, our PDFs must be used with the non-linear search of the likelihood
maxima, but, as always, the search will be transformed in a minimization. The momentum
and the other parameters of the track $n$ are obtained minimizing, respect to $\alpha_n$, $\beta_n$ and $\gamma_n$ the function
$L(\alpha_n,\beta_n,\gamma_n)$ defined as the negative logarithm of the likelihood with the
PDFs of eq.~\ref{eq:equation_2}:
\begin{equation}\label{eq:equation_4}
\begin{aligned}
    &L(\alpha_n,\beta_n,\gamma_n)=-\sum_{j=6n+1}^{6n+6}\,\ln[P_{x_{g2}}(x(j),E_t(j),\psi_j(\alpha_n, \beta_n,\gamma_n)]\\
    &\psi_j(\alpha_n,\beta_n,\gamma_n)=\varepsilon(j)-\varphi(z_j)+\varphi_n(z_j)\,.
\end{aligned}
\end{equation}
The parameters $x(j)$, $E_t(j)$ (introduced in eq.~\ref{eq:equation_2}) are respectively:
the COG$_2(j)$ position, the sum of signal in the three strips for the hit $j$ and the track $n$,
${z}_j$ is the position of the
detector plane $j$ of the $n$th track.
The $\varepsilon$-dependence in the $\{a_i(\varepsilon)\}$ is modified to $\psi_j(\alpha_n,\beta_n,\gamma_n)$,
to place the impact points on the track. In real data, $\varepsilon(j)-\varphi(\alpha,\beta,\gamma,z_j)$ is
absent (and unknown) but the data are supposed to be on a track. We can easily use  a non linear form for the
function $\psi_j(\alpha_n,\beta_n,\gamma_n)$, but in this case is of scarce meaning. In more complex cases,
non linearities of various origin can be easily implemented.

We will reserve the definition of maximum likelihood evaluation (MLE) to the results of eq.~\ref{eq:equation_4} even
if the least squares method can be derived by a maximum likelihood.
The minimum search routine for the MLE  is initialized as in ref.~\cite{landi05}. The initial track parameters are
given by a weighted least squares with weights given by an effective variance ($\sigma_{eff}(i)^2$)
for each hit $(i)$ and $\eta_2(i)$ as hit position. The $\sigma_{eff}(i)^2$ is obtained from our PDF, but, for the form of
eq.~\ref{eq:equation_2}, the variance is an hill defined parameter even in
$\varepsilon$, and this hill definition must be eliminated with cuts on the integration ranges that
suppress the PDF tails. We use two sizes of cuts,
one for the low noise side and one for the high noise side of our double side detector.
The cuts are optimized to obtain gaussian distributions, with standard deviation $\sigma_{eff}(i)$, reproducing
our PDF for a good hit.
For a set of hits (good hits), eq.~\ref{eq:equation_2} has the form of a narrow high peak
and a gaussian (centered in
$\eta_2(i)$) with standard deviation $\sigma_{eff}(i)$ is built to reproduce well the $P_{x_{g2}}(x(i),E_t(i),\varepsilon)$ on
few of them.
These gaussian approximations look good in linear plots, the logarithmic plots show  marked differences even
in these happy cases, the tails are non-gaussian. The cuts,
so defined, are used for the $\sigma_{eff}(i)$ extraction in all the other hits, even where $P_{x_{g2}}(x(i),E_t(i),\varepsilon)$ is
poorly reproduced by a gaussian.

The $\{\alpha_n,\beta_n,\gamma_n\}$ given by the weighted least squares are almost always near those given by the minimization of eq.~\ref{eq:equation_4}, thus rendering less heavy the minimum search to the MATLAB~\cite{MATLAB}
{$fminsearch$} routine.
The closeness of these approximations to the MLE supports the
non criticality of the extraction of the
functions $\{a_i(\varepsilon)\}$. In fact, the approximate gaussian distributions
are often very different from the $P_{x_{g2}}(x(i),E_t(i),\varepsilon) $, but the realistic pieces of information
about the hits are sufficient to produce near optimal results. When the tails of the PDFs are important the MLE results are better.
The strong variations of the set $\{\sigma_{eff}\}$ on the strip are illustrated in figs. 6 and 11 of ref.~\cite{landi05}, and the patterns of those variations support a complex interplay among the physical properties of
the strips and the reconstruction algorithm. The time-consuming extraction of $\sigma_{eff}(i)$ (as indicated in ref.~\cite{landi05})
can be reduced building the surface $\sigma_{eff}(x,E_t)$ and calculating $\sigma_{eff}(i)$ with an interpolation.

\section{Low noise, high resolution, floating strip side}

The floating strip side is the best of the two sides of this type of strip detector. It is just this
side that measures the track bending in the PAMELA magnetic spectrometer.
In the test beam~\cite{vannu}, the noise of the "average" strips is well reproduced by a gaussian with a standard
deviation of 4 ADC counts, and the PDF for the sum of three strip signals has its maximum at 142 ADC counts with the
most probable signal-to-noise ratio of $35.5$ for a three strip cluster  ($SNR(n)=\sum_{i=1}^3 x_i(n)/\sigma_i$).
The functions $a_j(\varepsilon)$ are those of
ref.~\cite{landi05} for this strip type.
In the simulations we will use a high momentum of $350 \, GeV/c$. For this momentum and similar geometry,
we have a report with some histograms of a CERN test beam of the PAMELA tracker before its installation in the satellite.
The tracks were reconstructed with the $\eta_2$ algorithm, and this allows a positive check of our simulations.

At $350 \, GeV/c$, the hits of the tracks are largely contained
\begin{figure}[ht]
\begin{center}
\includegraphics[scale=0.5]{figure_1_sagitta.eps}
\includegraphics[scale=0.5]{figure_2_residual_a.eps}
\caption{\em Left plot. Blue line: distribution of the differences of hit $\#\,3$ minus  hit $\#\,1$ of a track for the $\eta_2$-algorithm; magenta line: the same for the COG$_2$. Right plot. True residuals of the reconstructed tracks: from MLE (red), from $\sigma_{eff}(i)$
(black), from $\eta_2$ (blue), from COG$_2$ (magenta), and the position errors of the $\eta_2$ (cyan)
and COG$_2$ (green).
}\label{fig:figure01}
\end{center}
\end{figure}
in a strip width ($51\, \mu m$), the left side of fig.~\ref{fig:figure01} shows the distributions of the
differences between two hits on a track
(the $\#\,3$ minus the $\#\,1$) for two different hit reconstruction algorithm.
They peak around 8 $\mu m$, but, as any COG reconstruction algorithm, the COG$_2$
algorithm produces a wider distribution due to a systematic error of refs.~\cite{landi01}.
The $\eta_2$-algorithm is  built to be free of this systematic error, here we corrected even the
asymmetry errors discussed in ref.~\cite{landi03,landi04}. Even if they are now very small,
the clear symmetry of the plots  is the product of this accuracy. For the absence of the
COG systematic error, the PDFs of the track parameters
given by the $\eta_2$ algorithm are  better than those given by the COG$_2$.
The results of our MLE are compared with those obtained by three different least squares.
Having to plot the results of four type of reconstructions we will use the following color convention:
\begin{itemize}
  \item {\bf{red lines}} refer to our MLE  (eq.~\ref{eq:equation_4}),
  \item {\bf black lines} are the weighted least squares with weight $1/\sigma_{eff}(i)^{2}$ and $\eta_2$ position,
  \item {\bf blue lines} for the least squares with the $\eta_2$ position algorithm
  \item {\bf magenta lines} for the least squares with the COG$_2$ position algorithm.
\end{itemize}
In the right plot
of fig.~\ref{fig:figure01} the distributions (or more precisely the histogram values divided by the number of entries and the step size) of the differences of the fitted positions respect to the exact ones are reported with the above color convention.
In the following we will call these differences as {\em true residuals} (allowed only in simulations) to distinguish from the
{\em residuals} generally defined as the
differences of the fitted positions from the reconstructed positions.
For further tests, two other lines are added, the cyan and green lines are the error distributions of the $\eta_2$ and COG$_2$
hit reconstruction algorithms. The PDFs of the residuals are not reported, but, those for the COG$_2$ and $\eta_2$ are
almost identical to the reported PDFs of the true residual. The residuals for other two approaches are very different
from the true residuals of fig.~\ref{fig:figure01}, they have very high peaks around zero. Our PDF of eq.~\ref{eq:equation_2}
and $\sigma_{eff}(i)$ allow the recognition of the good hits and the fit optimization selects to pass near to them, giving
an high frequency of small residuals.

The general expectancy of an improvement of the position reconstruction by redundant data is evidently disappointed
for the $\eta_2$ least squares: the cyan distribution is higher than the blue one. This mismatch is very similar to the case of a least squares on data from a Cauchy distribution~\cite{landi06}.
Instead, the  true residuals of COG$_2$ least squares look much better than their error distribution,
that is very large. The least squares looks able to round the error distribution.
But this effort consumes all its power, in fact if the
statistical noise is suppressed, the COG systematic error does not allow any modification of the true residuals.
On the contrary,  the true residual PDF of the $\eta_2$ least squares grows toward a Dirac $\delta$ function in the absence of
the statistical noise. We have to signal a light inconsistency among the histograms reported here and those of ref.~\cite{landi05}.
There we worked always with the strip pitch as scale for the length (as defined for the probability of eq.~\ref{eq:equation_1c}),
in the plots the horizontal scale was turned to the appropriate scales ($\mu m$). Here, the conversion to the correct dimensions
was performed before building the histograms, thus the vertical scales turn out different respect to those of ref.~\cite{landi05}.

\begin{figure}[h]
\begin{center}
\includegraphics[scale=0.53]{figure_3_GeV_m_1.eps}
\includegraphics[scale=0.53]{figure_4_GeV.eps}
\caption{\em Left plot. Distributions of the curvature in $(GeV/c)^{-1}$ (the $\alpha$ parameters) for the four types of fit. Right plot: distributions of the reconstructed momenta.
}\label{fig:figure02}
\end{center}
\end{figure}
As illustrated in fig.~\ref{fig:figure02}, the MLEs  give the best results for the momentum reconstruction,
and the weighted least squares, are very near to them. The fits of the standard
least squares with $\eta_2$
or COG$_2$ positioning algorithms show a drastic decrease in resolution. The use of the simple
COG$_2$ algorithm is the worst one.
Often the distributions of the left side of fig.~\ref{fig:figure02} are reported as resolution of the momentum reconstruction,
the k-value of ref.~\cite{particle_group}. For the $\eta_2$ and COG$_2$ least squares, the plots of the momentum distributions have appreciable shifts of the maxima
(most probable value) respect to the fiducial value of 350 $GeV/c$, the shifts are negligible for the other two
fits. These shifts are mathematical consequences of the change of variable from curvature to momentum.

\subsection{Other track parameters}

The complete track reconstruction must consider even the other two parameters of a track, the $\beta_n$
and $\gamma_n$. Their fits give very similar distributions to those plotted in ref.~\cite{landi05}.
The maxima of the distributions are now a little lower, in particular for the $\beta$ parameter. This is not unexpected,
in ref.~\cite{landi05} we had 3 degree of freedom for two parameters, here we have 3 degree of freedom for three
parameters, an effective reduction  of the redundancy that has a slight effect on the results.

\section{High noise, low resolution, normal strip side}

The other side of the double sided silicon microstrip detector has very different properties respect to the floating strip
side (the junction side). We will not recall the special treatments required to transform a ohmic side
in strip detector  and all the other particular setups necessary to its functioning. From the point of view of their data,
this side produces data very similar to a normal strip with a gaussian noise of 8 ADC counts,
twice of the other side (most probable signal-to-noise ratio $SNR(n)=18.2$). The absence of the floating strips gives to the histograms of the COG$_2$ the normal aspect with a high central density and a drop around $x_{g2}=0$.
No additional rises around
$x_{g2}=\pm1/2$ are present, they are typical of the charge spreads given by the floating strips. This absence reduces the
efficiency of the positioning algorithms that gain substantially from the charge spread. Now, the functions
$\{a_i(\varepsilon)\}$ of ref.~\cite{landi05} are similar to those of an interval function with a weak rounding to the borders,
this rounding is mainly due to the convolution of the strip response with
the charge spread produced by the drift in the collecting field. If a residual capacitive coupling is present, it
is very small. In any case, it is just this rounding that renders very good the resolution of the hits on the strip borders
with a small $\sigma_{eff}(i)$. Due to the differences respect to the other side, we have to implement the simulations
with a lower momentum of $150\, GeV/c$.

\begin{figure}[ht]
\begin{center}
\includegraphics[scale=0.53]{figure_1_sagitta_noflo.eps}
\includegraphics[scale=0.53]{figure_2_residual_a_noflo.eps}
\caption{\em Left plot. Blue line: distribution of the differences of hit $\#\,3$ minus  hit $\#\,1$ of a track for the $\eta_2$-algorithm, magenta line: the same for the COG$_2$. Right plot. True residuals of the reconstructed tracks. Color cades
defined above for the fits, the cyan and green PDFs are the positioning errors of the $\eta_2$
and COG$_2$ algorithms.
}\label{fig:figure03}
\end{center}
\end{figure}

The left side of fig.~\ref{fig:figure03} shows the distributions of the hit differences ($\#\,3$ minus $\#\,1$) to test the containment of the track on a strip width, the largest noise has evident effects on their forms. As usual the COG$_2$ distribution is larger than
the $\eta_2$ distribution. The true residuals of the fits are reported in the right side of fig.~\ref{fig:figure03} with the cyan and green lines for the error distributions of the $\eta_2$
and COG$_2$ hit reconstruction algorithms. The other color codes are the standard ones. Even here the data
redundancy for the $\eta_2$-fit does not improve the position reconstruction. The $\eta_2$ hit error
distribution is drastically better than that of the true residuals for the fit. The COG$_2$ position
error distribution has two maxima due to the
systematic error that is positive for the first half of the strip and negative in the second half,
this difference of sign combines with the noise random error to round the two maxima. The fit,
based on the COG$_2$ positions, has a good probability to partially average these sign differences
and produce a single wide maximum. The momentum distributions given by the four fits are reported
in fig.~\ref{fig:figure04}.

\begin{figure}[h]
\begin{center}
\includegraphics[scale=0.53]{figure_3_GeV_m_1_noflo.eps}
\includegraphics[scale=0.53]{figure_4_GeV_noflo.eps}
\caption{\em Left plot. Distributions of the curvature in $(GeV/c)^{-1}$ (the $\alpha$ parameters) for the four
different fits. Right plot: distributions of the reconstructed momenta.
}\label{fig:figure04}
\end{center}
\end{figure}

The results of our MLE and the weighted least squares are drastically better than the standard least squares.
Now the shift of the maxima for the momentum distributions of two least squares are more evident than in
fig.~\ref{fig:figure02}.

\section{Discussion}

A meaningful comparison among the different fits is somewhat difficult. Often the standard deviation of the PDF
is extracted interpolating a gaussian
function on non gaussian distributions, or two (or more) gaussian PDF are fitted hoping to describe two (or more) independent
gaussian processes. In ref.~\cite{landi05} we used the full width at half maximum, but, apart from its complex extraction,
it does not characterize very well the
distributions. In any case those differences are not of easy interpretation. Here we will use
two very precious resources contributing to the momentum measurements: the magnetic field and the signal-to-noise ratio.
The simulated magnetic field intensity and the signal-to-noise ratio are increased in the $\eta_2$ and COG$_2$ least squares
reconstructions to reach our best distributions (the red lines).

\subsection{Increasing the magnetic field}

\begin{figure}[h]
\begin{center}
\includegraphics[scale=0.45]{figure_12_GeV_m_1.eps}
\includegraphics[scale=0.45]{figure_13_GeV.eps}
\includegraphics[scale=0.45]{figure_10_GeV_m_1_noflo.eps}
\includegraphics[scale=0.45]{figure_11_GeV_noflo.eps}
\caption{\em Top plots. Low noise case, Curvature in $(GeV/c)^{-1}$ and momentum distributions for our MLE (red lines)
and $\eta_2$ least squares with a magnetic field increased by a factor 1.5. Bottom plots. Higher noise case, here the $\eta_2$
least square has a magnetic field 1.8 times greater than the MLE.
}\label{fig:figure05}
\end{center}
\end{figure}

With fixed momentum, the magnetic field is increased in the fit for the $\eta_2$ least squares.
The upper sector of fig.~\ref{fig:figure05} illustrates these results for the low noise side and
the overlaps with our
MLE. The red lines
are identical to those of fig.~\ref{fig:figure02}, the blue line are the
$\eta_2$ least squares for a magnetic field 1.5 times higher. The plots for the COG$_2$ least squares are not
reported to render easily legible the figures, in this case the magnetic field must be increased of  factor
1.8  to overlap our red lines.
The lowest sector of fig.~\ref{fig:figure05} reports the higher noise side, the red lines are identical to those of fig.~\ref{fig:figure04}, the blue lines ($\eta_2$ least squares) overlap the red lines with a magnetic field increased of 1.8 times.
Even here, the overlap with the COG$_2$ least squares is not reported, now, to obtain a reasonable
overlap, the magnetic field must be doubled. Clearly, the increment of the magnetic field produces other slight
differences in the tails of the distributions, but the main results are well evidenced.

\subsection{Increase of the signal-to-noise ratio}

Let us see now the effects of the increase of the signal-to-noise ratio.
These modifications reproduce in part the results
of the magnetic increase, but it is different in other parts. In fact, higher values  of
the magnetic field do not modify the form of the distributions of the curvature $\alpha$
(with dimension ${legth}^{-1}$),
the curvature distributions translate toward
higher values for a reduction of the track radius.
The dimensional transformations to $(\mathrm{GeV/c})^{-1}$ and $\mathrm{GeV/c}$ have the magnetic field intensity as scaling factor,
that can rise the distributions to the forms of fig.~\ref{fig:figure05} even for the COG$_2$ case.
But, as for the $\alpha$  parameters (with dimension ${length}^{-1}$),
the distributions of the true residuals of fig.~\ref{fig:figure01} and fig.~\ref{fig:figure03} are not
influenced by the rising of the magnetic, a part small effects on the Lorentz angle.

Keeping the magnetic field to $0.43\, T$, the raise of the signal-to-noise ratio modifies the
distributions of the $\alpha$ parameters and those of the true residuals and can bring them to overlap our red distributions.
In our simulations, this is accomplished scaling the amplitude of the random noise,
added to the strip signals, and rerunning the least squares fits. The plots of these results for the momentum (in $\mathrm{GeV/c}$)
and curvature (in $(\mathrm{GeV/c})^{-1}$) are not reported because they are practically identical to those
of fig.~\ref{fig:figure05}.
For the low noise side the  gaussian strip noise of $\sigma=4$ ADC counts must be reduced to 2.5 ADC counts,
raising the signal-to-noise ratio by a factor 1.6 to a huge most probable value of $SNR(n)=56.8$.
Now, even the blue line in fig.~\ref{fig:figure01} of the true
residuals reaches the red line, but the redundancy continues to be unable to move the true residual
distribution beyond that of the new $\eta_2$ errors.
A special mention must be devoted to the COG$_2$ distributions, the magenta lines.
They remain essentially unchanged in any plot for a (any) reduction of the strip noise. The COG$_2$ error distribution
of fig.~\ref{fig:figure01} (the green line) shows a slight modification becoming less rounded for the hard presence
of the systematic error, unaffected by the random noise.

For the higher noise side of the detector, a reduction to less of one half of the
noise (from $8$ to $3.6\ \ \mathrm{ADC}$-counts) is required to reproduce the low parts of fig.~\ref{fig:figure05}.
With this doubling (2.2) of the signal-to-noise
ratio the blue line (the $\eta_2$ best fit) overlaps the red line in fig.~\ref{fig:figure03} rising the most probable
signal-to-noise ratio to 40.5.
As for the other side, no noise reduction moves the distributions given by the COG$_2$ least-squares. The magenta lines remain practically unchanged a part a reduction of the fluctuations and a sharpening of the green line of fig.~\ref{fig:figure03}.

As just said above, the insensitiveness to the noise  reduction is due to the COG systematic error
of ref.~\cite{landi01}. This fact renders of weak relevance the efforts to
improve the signal-to-noise ratio in the detectors if the positioning algorithm
remains the COG. On the other side, this method is stable respect to
a decrease of the signal-to-noise ratio due to ageing or radiation damages,
as far as the COG systematic error dominates.

A last point remains to be analyzed, i.e. the huge difference between the curvature PDF
for the $\eta_2$ least squares in the low part fig.~\ref{fig:figure05} and the blue line of fig.~\ref{fig:figure02}.
Now the two detector types have very similar signal-to-noise ratio (3.6 ADC counts in the first and
4 ADC counts in the second), but, to reach the overlap of the two PDFs, another factor of around 2.4 is
needed. This factor is too large to be due to the $20\,\%$ of differences between
the sizes of their strips. The main difference must be due to the beneficial charge sharing of the floating strip
and the nearing of its detector architecture to the ideal detector defined in ref.~\cite{landi01}.

\section{Conclusions}

We extended the simulated track reconstructions of ref.~\cite{landi05} with the study of curved tracks in a
constant magnetic field of $0.43\, T$. All the other effects, $\delta$-rays,
multiple scattering (negligible at our high momenta), energy loss,
etc., are explicitly excluded by the simulation, focusing on the differences among various fit methods.
Each type of detectors of our double sided detector are examined as momentum analyzer.
The higher noise side has  signal properties very similar to single sided
detectors of large use in running trackers.
We use our well tuned PDFs in two form: complete in the MLE or schematic with weight parameters $1/\sigma_{eff}(i)^2$.
Each one of these two form is very effective for this task
respect to the results of two other type of track fitting explored i.e. the least squares with $\eta_2$ as position
algorithm and the least squares with the COG$_2$ positioning.
To establish a comparison among these fitting methods, we skip the usual ways of comparing standard deviations or
full width at half maximum. Instead, we modify two different tracker properties to reach
an overlap among the fit outputs: the magnetic field and the signal-to-noise ratio.
For the overlap of the two standard fits with our best distributions,
the magnetic field must be increased by a factor 1.5 for the $\eta_2$ fit and 1.8 for  the COG$_2$ in the low
noise side and 1.8 and 2 for the higher noise side.
The increase of signal-to-noise ratio is effective only for the $\eta_2$ based least squares, the overlaps are obtained
with  factors 1.6 and 2.2  for the two detector sides.
Any increase of the signal-to-noise ratio is unable to improve the curvature and momentum
distributions in the case of COG$_2$ positioning, the COG intrinsic
systematic error survives untouched to any reduction of the detector random noise.
In any case, the drastic improvement
of our well tuned PDFs in the momentum reconstructions  is evident. We have to remind that these are simulations and come
with all their
uncertainties. Assuming an optimistic view, this increase in resolution can be spent in different ways
either for better results on running experiments or in reducing the complexity of future experiments if the
baseline fits (almost always based on the COG positioning) are estimated sufficient.
With eq.~\ref{eq:equation_1c}, we start to give a glimpse of the analytical forms of our PDFs.
Even if this expression is of limited validity, its combination with the appropriate $\{a_j(\varepsilon)\}$
allows a faithful reproduction of the green and cyan distributions of figs.~\ref{fig:figure01} and~\ref{fig:figure03}
from the data. Those distributions are exclusive products of simulations, but, with slight redefinitions,
the simplified PDF is able to reproduce them. The complex task of MLE requires
more advanced expressions, future papers will be devoted to their discussion.

redpajama_set_name: RedPajamaArXiv


ANN ARBOR, Mich. -- The University of Michigan, University of California Los Angeles, University of Pennsylvania and Yale University have teamed up to launch a new initiative to educate nurses and physicians together to serve as leaders, researchers, and change agents in health care, community health and public policy.
Called the National Clinician Scholars Program, it will train scholars to lead policy-relevant research and catalyze partnerships to improve health and health care. At U-M, the program will be based in the Institute for Healthcare Policy and Innovation, and will be called the IHPI-CSP Program.
The program fills the gap left after the Robert Wood Johnson Foundation (RWJF) announced in 2014 that it will end its Clinical Scholars program for physicians. U-M has trained RWJF Clinical Scholars since 1995, and awards a master's degree in health and health care research to those who complete the program.
This new program, however, expands on the RWJF program by including nurse scientists in addition to physicians to address new and emerging issues related to health care delivery and improving the health of communities. .
The VA Center for Clinical Management Research, based at the VA Ann Arbor Healthcare System, is also a partner in the U-M program.
The new National Clinician Scholars Program will partner with organizations sponsoring a number of individual scholar positions and inter-professional training between nurses and physicians.
Consistent with the recommendations from the Institute of Medicine's "Future of Nursing" report, this new program will help to transform the health care system by providing opportunities for nurses and physicians to serve as full partners in health care redesign, improvement, and research. Fundamental elements from the RWJF program such as high-quality mentoring, a tailored curriculum, clinical work, and a national annual meeting will be retained in the new program.
At the heart of the program, partners at each institution work directly with each Scholar to identify and develop projects to solve current real world problems and to cultivate the Scholar's unique research and leadership skills. Integration of physician and nurse researchers in training to advance the field of healthcare from different perspectives will shape the future of health and healthcare in ways not seen before.
The program will include those who have completed their medical degree and residency training, Doctor of Nursing Practice training, or Ph.D. in nursing. Individuals selected for Cohort 2016-18 will begin the program on July 1, 2016. The new national program website at www.nationalcsp.org includes information for interested applicants. Applications will be accepted via www.ncspapp.org/login.php .
Additionally, the program will distribute a call for applications for new sites to join the consortium after this initial year.


text:
5 Things I Would Change About LG's G Flex
Evan Selleck
| February 18, 2014
LG made a few waves at the end of 2013, starting with one of the most talked about devices of the year, the G2. The handset introduced LG to a whole new level of desire from consumers, thanks to the high-end handset, and set the company forward on plenty of momentum. There's a good chance that the company could continue to win consumers over in 2014.
But that's not the only device that saw plenty of attention near the end of the year. LG unveiled their first smartphone with a curved display with their G Flex, another high-end device with plenty going for it, especially one main feature right there for everyone to stare at. Thankfully for anyone who wanted to get their hands on the device here in the States, LG unveiled back at the beginning of January that the curved handset would land on local store shelves in due time.
It's here now. And while my 30-day challenge with the handset will start tomorrow, these are five things I'd like to change about the handset right now. I have a (small) hope that some of these changes make it to the second-generation G Flex.
A Smaller Curved Display
The G Flex is like any other big phone from 2013: it has a huge display. At six inches, you can see the G Flex from a ways away, and it stands out in the crowd because of that curve. But this is one thing I want to change in any successor to the G Flex. While the industry likes to make displays bigger from one handset to the next, I want the G Flex 2 to boast a smaller display. 5.2 inches, maybe. Right now, it's just uncomfortable to use with one hand, and that's unfortunate.
Move the Buttons
Before the G Flex launched, and right around the time LG officially unveiled the G2, I wrote about how I thought it was great that LG was thinking outside the box when it came to the physical design for their phone(s). Putting the Power button, along with the volume rockers, on the back of the handset instead of the side seems like a small move, but it just gave the devices one more bullet point to stand out. I like the button placement on the G2, but for some reason it just doesn't feel comfortable at all on the G Flex. I'd like the buttons moved back to the sides.
Drop the Themes
The G Flex comes with two Themes right out of the box, and I can tell you right now that I'm just not a fan of either one of them at all. Just like any of the other points, this is a personal preference thing, but the themes are just too heavy-handed. I don't like the icons, and this is one device that I can honestly say that I'd just prefer to use stock Android. Right out of the box.
A Higher Resolution Display
When the G Flex was being introduced, LG went on and on about the display and how great it is. There's no denying that a 720p HD display is more than good enough, because it is. And the the handset's screen is great, with a pixel per inch count around 245. But, it could be better. And if LG really wants to promote that "IMAX experience," then the next G Flex should have a 1080p HD display.
Bring the Software Current
The G Flex was announced just a few days before the unveiling of Android 4.4, so I can't really fault it for not launching with the updated platform. However, that doesn't mean it shouldn't already be getting an update, especially the models that launched in the United States. Right now, the G Flex in hand is running Android 4.2, and I just want some KitKat goodness. Is that too much to ask?
As I said, my 30-day challenge for the G Flex starts tomorrow, and I'm sure my attitude towards the device will change, one way or another. Maybe I'll fall in love with the display with more time in front of it, or maybe I'll get used to the buttons. We'll find out soon enough. For now, though, at first use, those are the things I'd like to see changed.
Have you used the G Flex, or played around with one? If so, what would you change about it?
Previous ArticleLG uses G Pro 2 to demonstrate Knock Code feature
Next ArticleNokia teases Android-powered 'X' ahead of MWC as report claims no new Windows Phones at show
LG uses G Pro 2 to demonstrate Knock Code feature
New Samsung Galaxy Gear rumor claims several models inbound
HTC M8 rumored to be coming to market as 'The All New One'
How much work do you put into finding a new phone?

redpajama_set_name: RedPajamaCommonCrawl


Mark your Calendars!!! On Thursday, November 19th, at 6:30 pm, We will host a Cultural Dance Performance in the Gym.
Inis Cairde, an award winning Irish Dance School here in Raleigh will be performing and will talk about Irish Dance and the steps they perform.
Our 4th graders will perform the Mexican Hat Dance and we are also hoping to have a group of Chinese Dancers perform for us.
Come on out and enjoy the show Thursday night, Nov. 19th at 6:30 pm in the gym.


news Australian firm NextDC has announced it plans to raise equity to fund the building of two new data centres, including a second facility in Melbourne.
The firm announced on 23 November that it will raise new capital to the tune of $120 million by issuing around 50 million new shares.
Fifty million dollars'-worth of those shares will be offered to institutional investors at $2.55 per share.
There will also be a $70 million "entitlement offer" to existing shareholders, who will be offered a new share at $2.25 for every 6.23 shares already held – representing a 10.4% discount from TERP, NextDC said.
The possibility of a second MelbourneNextDC data centre had been aired back in August in the company's Full Year Results, but this is the first concrete announcement of the intention to actually build it.
NextDC currently five data centre facilities across Australia: Sydney (S1), Perth (P1), Brisbane (B1), Melbourne (M1) and Canberra (C1).
However, there has been "strong demand" for its data centre services and Brisbane (B1) and Melbourne (M1) are "rapidly reaching capacity constraints" – currently at 90% and 77% respectively, the firm said.
The raised equity will allow it to construct new data centres at Melbourne (M2) and Brisbane (B2).
M2 will cover an area of 10,000 square metres with an initial capacity of 2 megawatts (MW), eventually expanding to 25MW. This makes it bigger than M1, which covers 17,500 square metres data and has a maximum capacity of 15MW.
The planned B2 centre in Brisbane is smaller and will span only 3,000 square metres and have an initial capacity of 1MW, with an eventual capacity of 6MW.
Both new centres are expected to be completed and ready for business in the second half of 2017. The company is currently undertaking due diligence on a number of potential sites, it said.
Last week, NextDC issued a senior unsecured notes offering which was expected to raise $70 million to fund the B2 facility. The funding round was oversubscribed by $30 million, however, increasing the amount raised to AU$100 million.
The initial investment in the two new facilities will total between $175 million and $200 million over the next 12 to 18 months, including initial capacity and land acquisition, NextDC said.
The firm "expects" the new facilities to more than cover the cost of setting them up and that it "will deliver additional value" to shareholders over the long term, it added.


PCP is unusual in that it was written by George Kelly as one complete two volume work . The Psychology of Personal Constructs is over 1500 pages long. The first volume is the theory and the second volume is how the theory can be applied in a clinical context.
A psychology sets out to describe how each of us functions. Indeed when Kelly was asked to describe his theory he said that all he had done was to produce a way of describing the understandings that people were using in their everyday lives.
Constructs are the frameworks we use to understand the world. Ugly------Beautiful is a construct, as is Stingy---------Reasonable. Once `I have a clear idea of what constructs you use I can begin to understand how you function in your world.
Kelly was always very clear that he had written a Psychology, not a therapy. A therapy comes from within the psychology. If I understand how I construe normally, then my difficulties can be understood as ways of construing which are not working for me. Although Kelly himself only elaborated his work in psychotherapy PCP is applicable to every setting that people function within. It has therefore been used to understand education, organisations, forensics, counseling, coaching, architecture, residential organisations and many others.
So we always have a choice,, a key Kellian idea. We make that choice with some anticipation of what the likely consequences will be. PCP is a psychology of Anticipation, at its heart is the metaphor of people formulating their ways of meaning making, both verbal and non verbal (i.e. their constructs) which govern their future behaviour.
So PCP interventions are always aimed at understanding why the person is struggling to anticipate, what is not working in their construing system. The client is seen as a co creator of their own well being.
There are many introductions to PCP. A good basic one is available here.
There are also introductions available on many of the sites identified in the links section of this website.


Sands shift fast and far online. In this long-read Lori Goldberg looks at the big changing themes that all digital marketers must understand.
The term "Paradigm Shift" is overused particularly in high-growth sectors such as advertising and digital marketing, where paradigms are constantly shifting but not uncharacteristically so.
It's vital to separate evolving strategies from true paradigm shifts; evolving strategies are like ripples in an ocean that are quickly absorbed by the paradigm, whereas shifts are tidal waves that create massive changes requiring adaptation by the marketplace. Adaptive marketers – being opportunistic – crave the shifts for the opportunities they bring.
The image below is a common example of how we may disagree on shifting paradigms. Is it a duck or a rabbit?
Armed with this understanding of paradigm shifts, this series will examine 5 areas within digital marketing where shifts are occurring and offer prescriptive advice on how adaptive marketers can take advantage of new opportunities.
Today's smartphones are replacing the desktop and laptop computer with equally powerful processors, web-access, and speed that afford user freedom and spontaneity. There was a consensus years ago that a "mobile advertising strategy" was needed by advertisers given what we knew of the mounting smartphone adoption and shifts to mobile browsing, responsive websites, and mobile wallet capabilities.
Today, we have evolved - or perhaps shifted - to a mobile-first strategy. Statistics show for the first time that mobile phones are out-pacing desktops in accessing the web. As a result, the Internet Advertising Bureau announced at the end of 2016 that, "Mobile ad revenues account for more than half (51 percent) of all digital ad revenues for the first time." Mobile-specific content and apps including Instagram, WhatsApp and Snapchat untether users from their desktop.
What's more, mobile devices are being used to complete with brick and mortar stores at the point of purchase. For example, consumers seeking the latest hiking shoes are able to experience the product in a local store, then spontaneously purchase online from a low-priced competitor during their in-store visit.
Desktops will not fade quietly. Their relevance remains high with increased Web usage in streaming video and for making large web-based transactions. Consumers find desktops more user-friendly here for research-heavy purchases such as a new mattress, automobile, and home. However, research for these larger items may also occur on mobile before shifting to the desktop as a more trusted device for purchases.
· Don't stop at simply being responsive. Marketers need to push forward with mobile-first campaigns, content and design.
· Prioritize the mobile experience for users and where applicable, drive them back to their desktop if larger transactions are a goal.
· Understand consumer sentiment and adjust search terms for consumers that may be wanting to try a product at a competing local store, then buy from your website for a lower price.
· Google is penalising non-responsive sites by down-grading their rank in search results by a small margin. If you're not mobile-ready, your search rankings may be suffering.
8 in 10 of consumers would stop engaging with content that doesn't display well on their device – Adobe.
In summary, user data and advertiser spend indicates a paradigm shift away from desktop computers. Smartphones are no longer a secondary option in terms of technology, capabilities and speed. In fact, the mobile user delights in the spontaneity of Instagram, Snapchat, WhatsApp and more.
While desktop has seen gains in video consumption and trust in as a transaction tool for large purchases, advertisers are wise to think mobile first in designing campaigns, technical responsiveness, speed, and the sentiment of point-of-purchase online shoppers seeking competitive prices.
Banner ads, or display, have been the bread and butter of ad publishers for a long time. Today, they tend to clutter the web as a tool for branding, not clicks nor conversion. They have fallen in price, becoming easier to buy, and inventory is plentiful as the ever-expanding web keeps finding new ad real estate for banners to populate.
In part two of Paradigms at War, we look at the plight of the banner ad and ponder its future as advertisers respond to a paradigm shift that demands a reduction in ad clutter, improved performance, and mobile friendly viewability.
Previously, banner ads were an ideal format for desktop web browsers. Their variety of shapes and sizes were tailored to advertisers wanting high visibility, branded design, and of course - clicks.
Today, with advanced ad tracking and analytics, we know that up to 50% of banners ads are never viewed by the consumers (comScore 2016), introducing us to the term "Viewability". Advertising standards have rushed to redefine what a viewable ad is while advertisers fume over the lack of transparency.
Digital advertising, like radio, TV and print is somewhat based on the ads "opportunity to be seen" by consumers. Just as a radio listener may turn down the volume during an ad or a television viewer may run to the kitchen during a Superbowl commercial, there is no truly scientific way to count each individual's presence and attentiveness for the duration of an ad.
Advertisers are buying the ads' opportunity to be seen or heard by an estimated audience. Impressions are similar. Some ads may appear below the fold - near the bottom of the Web page - and never seen by the consumer, much less be properly digested, read or understood. Previously, impressions indicated that the ad loaded into the page – nothing more.
IAB considers an ad viewable on a desktop computer if 50% of the ads' pixels are in view for a minimum of 1 second. For video, 50% is required for 2 seconds. For larger display ad units 30% is required for at least 1 second.
But with lack of enforcement, defining an ad as viewable is just that: a definition. We have yet to enter the age of enforcement.
As a result, browsers such as Firefox were among the early creators of ad blocking tools which offer users a respite from the popping and pulsating banner ads that clutter pages, slow browsers, and create an otherwise poor user experience. Today, every browser, including Chrome, has a popup blocker.
In many cases they are turned on by default once the browser is installed. As a result, the price of banner ads has dropped along with their value to advertisers. Despite this, inventory of ads remains high, spending on banners ads continues to increase and real estate to host these ads is expanding, even into the dark edges of the Web where tremendous fraud can occur.
Technology such as trading desks and programmatic markets make buying and selling banner ads easier than ever, further driving prices down but layering in better targeting capabilities to maintain value. Viewability is not just a concern for digital advertising. It happens across all forms of digital and traditional/print, television and radio media. Regardless, spend continues to rise and the banner market remains hot.
A paradigm shift in banner advertising is occurring as consumers are becoming so overwhelmed by banner ads that the valuable consumer experience is suffering. This is a fundamental threat to consumers reacting to those ads. Browsers backed by Mozilla and Google are responding to defend the consumer experience. In turn, consumers are wanting original compelling content instead of blatant ads.
In summary, consumers are wanting quality ads over vague and plentiful. Ad-lag and intrusive ad behaviors that slow browser performance create a negative user experience and erode consumer trust. Browsers backed by Google and Mozilla are responding to block ads and protect consumers, ultimately wasting money spent and rendering 50% of banners and not viewable (but paid for).
The occurring shift has visual storytellers including Instagram and Snapchat engaging with consumers for longer periods of time and in more compelling ways. Native advertising is welcomed by consumers willing to exchange their time and attention in exchange for valuable information.
What sets advertising publishers apart is their ability to target ads at specific consumers and earn a measurable result, then adjust, retarget and advertise again. Google, with its algorithmic ability to translate consumer sentiment, context, and intent into the right ad, right time, right place skyrocketed the search giant into the advertising space, claiming market share from television, print and radio in the early days of the new millennium.
Facebook later took advertising intel to a new level with ad targeting based directly on consumers likes and dislikes within their user profile. This has all evolved significantly as you know and today the two are positioned and a duopoly in digital media, with no single competitor owning more than 5% of the digital ad market. Like any paradigm shift, this duopoly can be viewed in many ways with many benefactors and detractors.
With any duopoly comes a fear the service will erode and prices rise, leading to a stickier product for advertisers with few competitive options. In Spring of 2017, General Motors, Walmart, Verizon, AT&T and Johnson & Johnson cut spending on Google and YouTube when a lone journalist makes it public that their ads were appearing alongside neo-Nazi groups, videos promoting terrorism, fake news and the like.
In March 2017, AdAge wrote, "The resulting press coverage has sparked a full-fledged advertiser revolt." The issue of "brand safety" is now a real thing. Brands are wanting greater transparency into the content adjacent to their ads. In July, YouTube issued new brand safety rules and removed ads from some offensive content.
Matt Brittin, Google's president of business and operations in Europe, the Middle East and Africa, reportedly apologized for the brand safety issues affecting advertisers and acknowledged that the problem is a global issue.
The underlying sentiment is this: When Google alone dominates search, what are its obligations for full transparency? Competition can play a key role in ensuring that deeper awareness and transparency tools are provided to make advertisers aware of where their ads are appearing. In the case of Brand Safety, it was discovered by a journalist who saw the ads and took screenshots, not by any means germane to the ad network.
Recently, Verizon has taken on the task of being the transparent ad network, vowing to protect brand safety within its "Oath" network, which includes Verizon properties AOL, Yahoo!, Tumblr, MSN, Huffington Post, Flickr, BrightRoll and more. Alternate networks and rising stars include Snapchat are offering quality video and storytelling popular with users. Oracle (who recently acquired YouTube competitor TubeMogul), MediaMath, Rubicon Project and Adobe are all committed to providing alternatives, as is Amazon who is developing an ad supported video streaming channel.
With any paradigm shift comes new problems and new opportunities, and this is no different. Verizon's Oath is taking advantage of what they perceive to be a weakness on Google's part: brand safety. Slowly, a third ad network may be forming. But unlike many paradigm shifts, this one still favors Google-Facebook in many ways.
In summary, the paradigm shift to the Google-Facebook duopoly is nothing new in the realm of digital marketing. However, the stakes are much higher today than in the early days when Google and Yahoo! were dominant.
With few ad publishing choices, companies may look for work directly with these publishers instead of utilizing third parties and agencies to manage the relationships, however schooling themselves on media buying and management may be unlikely given bandwidth to manage not only creative but media, too.
The opportunity inherent in any monopoly (or duopoly) is for new brands to enter the space that can fill the perceived weaknesses in the market and there are plenty examples here.
The market has exploded with advertising opportunity and artificial intelligence-based technologies, including programmatic and trading desks, that have allowed media strategists to tame the marketplace. However, greater efficiency coupled with expanding inventory and demand has proved to be a double-edged sword.
The technology also stands accused of lowering ad quality, creating opportunity for fraud, and increased margins by third-party managers. Despite this, the value of this artificial intelligence-driven technology is growing while banner ad prices are falling.
Previously, programmatic became a popular artificial intelligence for efficiently selling remnant ads on public exchanges. Programmatic exchanges soon offered more than just remnants, even selling offline traditional ads.
Demand for ads continues to grow and inventory of ads increases thanks to the ever-expanding Internet that never fails to find more Website pages to advertise on – including some nefarious locations such as click farms. The banner ad basically became traded as a commodity – cheap and plentiful. Ad prices remain healthy given the value-added benefits of superior ability to track performance.
While the tech remains strong, the paradigm shift is occurring in the exchanges. Ad buyers are using programmatic technology to buy from public ad exchanges and private ad exchanges, each offering worthwhile benefits. Public exchanges include publishers such as Google, Yahoo, and OpenX offering low cost and lower-quality ads.
These public exchanges are believed to be sources of fraud, ranging from click farms to bots trolling the Web, responsible for grabbing over $7 billion in click payments. This is not to say all public exchanges are bad. Advertisers need to calculate the ROI they want from any campaign and determine if they are getting the desired results. Google, for example sells low cost ads on public exchanges but also uses ad blockers in its Chrome browser to block similar ads.
Private exchanges – recently including 100% of The New York Times' ads – offer higher quality exchanges run by major publishers. Ads are carefully selected using trading desks or demand-side platforms providing greater transparency and higher prices.
The paradigm shift here is less about changes in technology and more about how and where that technology is used. The use of programmatic in public exchanges is falling rapidly. Brands are taking programmatic in-house to shave margins and offer what they feel is better quality control.
Likewise, agencies are utilizing black listing to effectively remove poor quality publishers and targeting white lists of pre-approved publishers such as Yahoo!, New York Times, and Buzzfeed for the assurances they bring. In some cases, agencies cultivate their own white lists or use public ones.
However, use of programmatic is climbing rapidly in private exchanges, perhaps as a response to viewability and fraud issues. Meanwhile Google, Facebook, Instagram and Snapchat are pushing for better quality ads driven by native storytelling, video and stickier concepts that hold and retain viewers. Snapchat will soon be making it's "Stories" concept available outside the app for greater distribution and will certainly invite more competition.
The opportunities within technology-driven bulk media buying are to focus on the contributions of artificial intelligence that lead us to making smarter and more efficient buying decisions. The rise of programmatic will forever be linked with poor ad quality and clutter the it amplified, however that's a bit unfair. An analogy would be that powerful engines led to more speeding tickets. True, but that's not the point of the technology.
In summary, the shifting paradigm here is about artificial intelligence influencing bulk advertising technology for the benefit of greater efficiency and transparency. While Google and others benefit from lower-cost public ad exchanges, they join social platforms such as a Facebook and Instagram in building more influential and publically accepted forms of advertising, including video and native.
Bulk ad buying makes all oceans rise, even for the fraud it produces. Don't misunderstand poor ad quality to be endemic of programmatic, it's just one of the considerations in your choice of exchange networks.
Digital advertising has come under attack with a variety of labels and headlines, including click fraud, viewability, pixel stuffing, ad stacking, spoofing and many more. This fraud comes from a variety of human and non-human threats, such as bots trolling the Web and click farms of low wage workers in poverty-stricken countries clicking on ads. Horrible.
Despite this, digital advertising is stronger than ever, reaching new heights in 2017 by outspending television advertising for the first time with $209 billion worldwide or 41% of the market (IPG Mediabrands). In terms of viewability, digital media is not alone, here. Traditional media commonly buys advertising never viewed, magazine ads never read, radio ads never heard, and so on.
In documenting the demise of the 30-second television commercial, a study by metrics firm Aris indicated 84% of viewers commonly fast forward through ads. Yet, advertising on television means your ad dollars are spent even when your viewer runs to the kitchen during the commercials.
Today, click farms, bots and other nefarious schemes are estimated to take in as much as $7 billion in ad revenue. How did we get here? During the great dawn of digital advertising before fraud was ever imagined, popular pay-per-click ads were sold based on their ability to bring traffic to an advertiser's website.
Competing businesses clicked the ads, triggering a wave of click fraud that had Google scrambling to make refunds and improve tracking. The product evolved to reduce fraud by focusing on a proven sales conversion, where analytics proved purchases and ROI was easily interpreted; a remedy to click fraud.
A major component of this shift is analytics and our ability to track and measure each action. While this tracking ability sets digital apart from traditional forms of advertising, it's also become the metric by which ad publishers make money. Fooling the analytic means fraudsters can make a lot of money.
Viewability means as much as 50% of digital ads may never be seen (comScore 2016) yet they are paid for by the advertiser. This occurs when a website is loaded into a browser but the ad, which loads a fraction of a second slower, is never seen by the quick scrolling user. Advertisers are buying the ads' opportunity to be seen by an estimated audience. IAB considers an ad viewable on a desktop computer if 50% of the ads' pixels are in view for a minimum of 1 second. For video, 50% is required for 2 seconds. For larger display ad units 30% is required for at least 1 second.
Use of programmatic technology has flooded the market with banner ads never seen and perhaps exacerbated the ability for fraud to occur. This is not to say the programmatic and similar artificial intelligence is based on fraud. The fraudsters simply capitalize on the technology to propagate itself. In an effort to filter out the fraudsters, reputable agencies are cultivating white lists of "safe" publishers such as Buzzfeed, Yahoo!, and Weather.com and USA Today.
This provides greater assurances against fraud and enables the agency to market their own safe lists to clients or employ public lists. Ultimately, advertisers want a return on their investment regardless of media type and digital is no different. This is where digital succeeds in measurable ways with its robust target and analytics profiles. At the end of the day, digital advertising boasts greater transparency than any other ad media type.
Paradigm shifts in digital advertising have attempted to outpace fraud since the beginning and today the same is true. The invention of native advertising, video and content marketing is helping brands spread their message in a lasting, more meaningful way.
In summary, Viewability is nothing new to advertising but is the most recent buzzword by which digital is scrutinized. Traditional media suffers too when viewers step away from the television screen or fast-forward through a commercial.
These issues of quality don't equate to a paradigm shift on their own; however, when combined with enabling technologies such as programmatic, there is an opportunity to degrade the quality of ads, particularly banner ads, in mass numbers as consumers bypass the clutter with slow loading browsers, adblockers, and quick scrolling fingers.
The shift towards higher quality advertising experience means consumers are preferring ads that complement the viewing experience, including video ads, storytelling, content marketing, and native advertising.


Comprehensive list of East Haven, Connecticut physicians, doctors & medical clinics, centers and hospitals.
At the age of 46, with a graduate degree from the Yale University School of Medicine and over six years of medical knowledge in cardiothoracic surgery and cardiology, I suffered a heart attack. I was a single dad, obese with a 42 waist, I had high blood pressure, Type II diabetes, acid reflux disease and sleep apnea. It was the obesity that caused all of these problems, including the heart attack.


This 2-in-1 Bamboo Cradle for iWatch & iPhone is perfect for your Apple Watch and mobile device as it gives your bedside or desk a natural look.
The natural 2-in-1 Bamboo Cradle for iWatch & iPhone stand is as solid build for better stabilization and a natural look for home or office. It supports any iPhone and has a perfect charging slot for Apple Watch. Its minimalist design will surely give you a clean and organized look.


Mini Flames (Tiny Pom) - 2nd Place!
Sparklers (Mini Pom) - 4th Place!
Heatwave (Youth Pom) - 2nd Place!
Inferno (Junior Pom) - 2nd Place!
Dynamites (Senior Pom) - 4th Place!
And to add to the excitement, our youth pom Heatwave were also awarded the SPORTSMANSHIP AWARD for their super energetic and smile-filled performance!
Well done teams! Now it's time to work hard and perfect our routines even more ready for our next competition!


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Stewart Copeland on The Police, drum solos, Rush, double bass pedals and more
By Joe Bosso 28 May 2012
Copeland looks back at his career in our extensive interview
For Stewart Copeland, playing "outside your instrument" is what makes music really sing. © Peter Foley/epa/Corbis
Over the course of his storied career, Stewart Copeland has tangled and tussled with band members, music execs, drum sets, film directors, polo players, and just about anybody and everything necessary for the advancement of his myriad passions and pursuits.
And the day that we speak with the legendary drummer and composer, he's engaged in another battle, albeit a more personal one: "I'm getting over a flu," he says, which is surprising because he sounds both chipper and robust. "I'm not so bad now, but my head is still full of phlegm. Hopefully, I won't make too many coughing and retching sounds."
Timing to a drummer is, of course, everything, and if one simply must get the flu, Copeland found just the right pocket: He recently wrapped a residency at Southern Methodist University's Meadows School of the Arts in Dallas, and in early July he heads to London for the UK premiere of his three-movement composition Gameien D'Drum. After that, he'll hook up with his onetime Animal Logic bandmate Stanley Clarke for a European tour.
With a resume and history as full as Copeland's, there's much to talk about. "If the subject is me, I'll be endlessly fascinated by our conversation," he jokes, and indeed, during our lengthy chat, one which covers topics such as his time in The Police, how he approached his drum solo on the David Letterman show last year (and what he thinks of solos in general), his friendship with Neil Peart (whose band, Rush, he doesn't listen to), double bass drum pedals, the concept of "playing outside the instrument," and why keeping a simple beat offers infinite possibilities, Copeland proves to be a gripping raconteur.
And this is just Part 1. Keep your eyes peeled for the conclusion of our interview with Stewart Copeland.
Last year, your good friend Neil Peart told us about a term you came up with, the 'Eric Clapton Factor,' stemming from the fact that Clapton hated not being able to go out and play the guitar casually - he felt the pressure to be brilliant all the time. Neil said you stopped playing the drums for a while for the very same reason.
"Well, it wasn't so much that. For about 10 years, I was a film composer, and I was quite consumed with that. Bashing on the drums just receded into the distance. But then, during those times when I was called upon to play something, and the chops had vanished - they were long gone - I felt a responsibility to be Eric Clapton, as in 'Eric Clapton is God,' and I didn't feel like I could deliver, and so I became very reluctant to play drums in public. Until Les Claypool called and said that it was my civic duty."
Which was the start of Oysterhead.
"Yeah. That's right."
And Oysterhead, like The Police, is a trio. What do you find so fascinating about the three-piece approach?
"It leaves a lot of room to do stuff. In fact, the trio is a good format for making a band. But actually, playing with 10 guys on stage, or 50 or 90 guys on stage, is just as much fun and can be even more fun. The trio thing is really about forming a band and having a unit, and when you have a creative collaboration, the fewer cooks the better. Just make sure they're the right cooks." [laughs]
But whatever the number of people - or cooks - it's still a matter of finding ways to fill the space.
"That's what I've been saying for years. But I have discovered that when you play with lots of guys and they're filling up the spaces, it allows you to be more economical in your own contribution. Collaboration is what it's all about. The joy of playing music is the joy of collaborating. For instance, if there are a lot of percussionists on stage, it gladdens my heart - all those 16th notes that some other guy is playing are things that I don't have to play, and I can look for other places to put the pocket."
Last year, you participated in Solos Week on the Late Show With David Letterman, as did Neil Peart. We talked with him right as he was preparing for his segment -
"Did he give you his sorry tale?"
A sorry tale? Well, he was trying to shorten the live solo he had been playing on tour to fit the time constraints for TV. In fact, he was doing some serious math…
"See, I was having the exact opposite problem: I was trying to figure out how to drag out my three chops to last long enough to be called a drum solo, as opposed to a drum break. I don't do drum solos. In fact, I've only done two in my life: one time was on Letterman, and the other was in Africa when I was performing for lions. [In a scene in his 1985 film The Rhythmatist, Copeland plays drums in a steel cage while surrounded by savage, hungry beasts.] That second time, hitting the drums as loudly as possible was the only thing that saved my skin."
So the Letterman crowd was a better audience?
"Than the lions? Yes, they were much more appreciative. The lions were a very cold audience. They ran away - which was pretty much the desired result."
It's interesting that you did the solo on Letterman; Neil said you were very "anti-drum solo."
"Mmm… not so much. I pull Neil's chain on this at every possible opportunity, of course. You know, when I was a kid, I dreamed of playing a 17-hour drum solo. Then I grew up."
Because you had to stretch the solo you played on TV, did that mean it was more improvised?
"It was composed. I figured out 'First, I'll do this bit here, then I'll go to that and do that little bit here, and then I'll go over here and do that.' And I remembered that the last time I'd composed a drum solo was in college, which entailed me stringing together my coolest chops.
"Then, as I say, I grew up and became a professional musician. Years went by, decades went by, and I was called upon to actually do a drum solo in front of an audience. It was pretty much the same process, and it took me back to that water tower on the grounds of the college."
By the way, just curious: What did you think when Rush added some Police-type elements to their music in the early '80s?
"I had only heard of it. I didn't actually listen to Rush albums, and I couldn't tell you one from the other - although Neil is a good buddy. [laughs] We agreed to disagree on certain things, and it is a measure of his greatness as a human being that he is completely over whatever my feelings might be about Rush.
"This truly is the case: He's never heard me say a wonderful thing about his band. He's read a few comments that I wish he hadn't read. In unguarded moments, things tend to slip out. Musicians tend to be snippy about one another, and I'm generally not. I generally love all music and all players.
"There was a time when bands like Rush were the epitome of what The Police were theoretically against, which was an overemphasis on musicality. Our ethos in the early days was about the primal scream and that musical technique was a distraction from that mission. There may have been a few comments that I might have made regarding Rush's position on that debate, and it is really, really to Neil's credit that he's over that debate. And we get along great."
Yet, at the same time, it's no secret that The Police packed a lot of musical sophistication into some pretty tight songs.
"Yeah, but that was by accident."
Some years back at a drum clinic, you startled the crowd when you played nothing but a simple beat for two minutes straight. Do you feel that drummers still miss the point you were trying to put across?
"The point was that there is so much expression in the execution of a mantra-like rhythm, that when you strip it right down, the tiniest things, the most minute fluctuations of the 16th note hi-hat, have great effect. And you don't get that effect unless everything else is extremely streamlined. The point I was trying to make is that within the confines of a very strict, repetitive loop, you can really have a world of expression."
You proved as much with Every Breath You Take. For the most part, you keep the beat simple and steady, but you throw in crashes out of the blue. So many drummers can't get those moves down.
"Nor can I. I think at the last Police show, we were still arguing how to get from the chorus back into the verse."
Are you considered a lefty? You write with your left hand…
"I'm left-handed, but I play instruments right-handed. It's just more convenient that way. And because most instruments require both hands to be working real hand, I find that you have to be ambidextrous. Like most left-handed people, I am close to being fully ambidextrous."
So on the drums, you're kind of a Ringo.
"I guess. I wasn't aware that he's left-handed."
He's a lefty and plays with his kit set up for a righty.
"But see, it's a right-handed world, and for lefties, our right hand is better than the left hand on righties."
Ringo would always say that the reason his playing was so unique was that, unlike other drummers, he played with his kit set for a righty, but he would still lead with his left.
"Mmm… I think that's an interesting thing for him to say, but I don't know if it's true. He leads from his heart, which is what makes him such a good drummer. Which hand does what follows where in his solar plexus he feels the rhythm is. All hands and feet are on the same mission.
"Certainly, sometimes you lead with the left and sometimes you lead with the right, depending on what you're doing - and also which hand is more tired at any particular moment. When practicing, which is different from playing shows or playing music, you work on making both hands just as strong."
Performing at the La Mar de Musicas Festival, Spain, 2011. © JOSE ALBALALEJO/epa/Corbis
You once talked about "playing outside your instrument." When did you come up with this idea, and can you speak about what it means to you?
"It came to me when I was playing polo - you 'play outside your horse.' If you're thinking about your horse and your equestrian skills, and things like proper riding and hitting the ball, let alone playing the game and putting your horse in the right place on the field…
"See, you shouldn't even be thinking about the horse. You have to be outside the horse. Your body and horse are one. You shouldn't be thinking about riding. You have to think, 'Here's the ball. I need to get it there. I need to stop that guy from getting to the ball. Uh-oh, there's a pass and that's where I gotta be.' When you do that, you're thinking outside your horse. You're playing the game.
"Put this to music: The mechanics of playing an instrument should be furthest from your mind. You've got to think outside your instrument, play outside your instrument. You've got to think about the music: 'What is the music? Where are the other players are? What's going on? Where's the groove?' - things like that. What drum you're hitting, what your technique is - that should be completely subliminal."
When you sat down at the kit with this concept in your head, did you feel as though you played differently?
"I had always thought that way. It was only when I came up with the polo analogy that it crystallized into something that I could make into a riff when talking to people such as yourself."
A while back, you started using a double bass drum pedals.
"Yes! That little bastard in Slipknot…"
Although you seem to have gotten by just fine with a single pedal. Did you ever think of going all-out rock and playing a double bass drum kit?
"Well, I did in Curved Air, much to own personal and musical ruination. [laughs] I don't know why - maybe it was maturity as a musician or something like that - but later on, after seeing what Joey Jordison can do without losing that throb… and they also invented the double bass drum pedal using only one bass drum, which meant that the ergonomics were extremely improved… I don't know, it worked out better when I came back to that.
"I don't use the double bass pedal a lot - just for the occasional flash and effect - but there is one particular thing that it can do; it works like a paradiddle: If you've landed on your foot but you need to get back up to your snare drum, but you need two beats to get your there, you can do go, 'ba-da-bahh!' It's ergonomics.
"Now, John Bonham would have just done that with one foot because he was John Bonham. Because he was... the mountain."
Copeland (seen here in 1980) is Tama Drums' first and longest-term artist endorser. © Lynn Goldsmith/Corbis
Let's talk about The Police a bit. In the early days of the band, you had to make records fast and, one would assume, cheap. Did that youthful hunger, and the limitations you worked with, lead to a certain creativity that was different from when you had lots of time and money in the studio?
"There was certainly hunger, and the hunger was the driving force behind getting up in the morning, figuring out a studio, making that call, calling the other studio and seeing if you can haggle the price down - the mechanics of getting a band going and working and just being were driven by hunger and fear. Once we had slain those dragons and had gotten into the sacred grove of a studio, then it was all the opposite; it was joy, it was creativity, it was the reward, it was the upside.
"However, if our hunger had gotten into the sacred grove, then we would have been more commercial. Basically, we were trying to do something that was really cool. Once our rent was paid, we weren't thinking that we had to make money. In the Police room, we were doing what we thought was the coolest thing that we could do. We weren't thinking about the marketplace at all.
"Back to the hunger thing, yes, outside the band room, I was thinking, This is what we've got to do. We've got to wear this uniform. We've got to fly this flag. We have to spout this ethos to get us into a niche in the music market called punk. You know, we can get in there, we'll cut our hair, and we're jussssst about young enough to squeeze into that demographic. All that calculation - certainly. But in the band room, we just did what we thought was cool."
Andy Summers' embrace of guitar pedal effects predated The Edge by a couple of years. As a drummer, how did you work with the different spaces and sounds he created?
"He created such an orchestral effect that I just wanted to listen to it. Shut me up. What Sting played on the bass pumped me up and made me want to blaze away; it got me all excited. But then I'd hear Andy play one big fat chord that would ring out with those sounds he had... and I was just listening."


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Me, Myself and Irene(2000)
Original Print Info Genre User Reviews Other Reviews
FOR Me, Myself and Irene (2000) YOU CAN
MISCELLANEOUS NOTES
Released in United States March 2000
Released in United States on Video January 9, 2001
Released in United States Summer June 23, 2000
Jim Carrey reportedly received $20 million to star.
Began shooting May 11, 1999.
Completed shooting July 27, 1999.
Conundrum Entertainment is the production company of Peter and Bob Farrelly.
Released in United States March 2000 (Shown in-part as "promotional clip" at at ShoWest 2000 in Las Vegas, Nevada March 6-9, 2000.)


PH-24 Myocardial Phantom | Capintec, Inc.
This is the cardiac muscle phantom for PECT in Nuclear Medicine. This provides the experiment how the high integrated IR in the liver effects the SPECT image of the cardiac muscle. The cold defect can be set in the left cardiac muscle. This structure allows the phantom to create the background in each the lung field, the mediastinum and the cardiac right ventricle.


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April 10, 2016 by Tina Williams
**Blog Tour** ~ Sins Against the Sea by Nina Mason
#paranormal #mythology #romance
Nina Mason's new release, Sins Against the Sea, looks like a fabulous read!
"If the merfolk were real, imagine how they might feel about the way we humans treat the ocean. That was my premise for Sins Against the Sea, a story deeply steeped in the mythology and folklore of the Scottish isles."
–Nina Mason, author
Title: Sins Against the Sea
Genre: Paranormal Romance/Mythology
Heat Level: Moderately steamy
Format: Kindle and Paperback
Cordelia Parker, an oil company spokeswoman, has never believed in magic, despite her late mother's fanciful stories. Then, during an oil spill in Scotland's mysterious Minch, her reality is shattered when she stumbles upon a merman in a hidden cave. Even more shocking, she feels the same soul-level connection to him she felt for the ocean before it claimed both her parents. A fish out of water in his sexist culture, Cuan feels a powerful attraction to Cordelia, but, because she knows what he is, he is duty-bound to kill her. If humankind discovers the Blue Men of the Minch are real, they will destroy them the way they destroy the ocean, whose abundance belongs at once to all and none. Thus, the only way he can have a life with her is to prove she is more than she appears, trust her with his secrets, and give up his undersea world. With so many obstacles to overcome, they will need a miracle to be together…or maybe just a splash of ancient island magic.
Here's more about the author of Sins Against the Sea:
Nina Mason is an incurable romantic who strives to write the same kind of books she loves to read: those that entertain, edify, educate, and enlighten. She has one series out at present, Royal Pains, which follows a Scottish duke and duchess through the events of the Restoration period. Born and raised in Southern California, Ms. Mason currently lives with her family in Woodstock, Georgia. When she isn't writing, she works as a consultant for Pure Romance, a line of relationship-enhancement products. Nina combines author readings with in-home Pure Romance parties. She also does Pure Romance parties on Facebook. For more information, email her at ninamasonauthor@gmail.com.
Nina is the author of two other series: Royal Pains, an erotic historical romp set in the Restoration era, and Knights of the Tarot, a paranormal romance/urban fantasy featuring shapeshifting faery heroes and various forms of divination.
Here's where you can find Nina on social media:
Visit Nina's website: http://ninamasonauthor.com Follow Nina on Facebook: http://facebook.com/ninamasonromance Follow Nina on Twitter: http://twitter.com/ninamasonauthor
Find Nina on Goodreads: http://goodreads.com/ninamasonauthor Subscribe to Nina's newsletter: https://vr2.verticalresponse.com/s/authorninamasonsnewsletter
This entry was posted in Blog Tour, Fantasy, Paranormal, Romantic Suspense and tagged mythology, nina-mason, paranormal-romance, romance, scotland, sensual-romance, sins-against-the-sea.
One thought on "**Blog Tour** ~ Sins Against the Sea by Nina Mason"
Nina Mason says:
Thanks so much for featuring Sins Against the Sea on your blog. You rock!