text:
\section*{Introduction}
In any intelligent image processing system, there are essentially two main steps: the acquisition of the image and the recognition of the desired patterns. Image acquisition for any pattern recognition method can be performed in multiple ways. For instance, classical sources (incoherent light from thermal radiation or a coherent beam from a laser) or quantum sources (entangled photons obtained from down conversion or squeezed light) can be used to obtain the images. Classical bright field imaging techniques employing the former sources, have the disadvantage of high probe illumination requirement, especially while imaging sensitive samples. Additionally, they are also plagued by the shot noise inherent in the intensities, and the background noise from the environment. Quantum techniques such as quantum illumination, or ghost imaging or even interaction-free imaging, alleviates the problems of background noise, and the probe illumination by utilizing quantum correlations between photon pairs~\cite{shapiro2012physics}. Furthermore, quantum sub-shot noise imaging~\cite{brida2010experimental} and super resolution techniques~\cite{tsang2016quantum} enhance the noise sensitivity and resolution in any images beyond the classical limits.

As a second important step, pattern recognition in the acquired images is a prominent feature of any intelligent imaging system. Face recognition~\cite{Wright} is one of the branches of pattern recognition, with numerous applications such as face ID verification, passport checks, entrance control, computer access control, criminal investigations, crowd surveillance, and witness face reconstruction~\cite{2D1}, among several others. For face recognition, several classical machine learning algorithms  exist~\cite{hasan2021human}, generally requiring huge computational resources especially when faced with the problem of identification from a large database. Quantum machine learning algorithms employing quantum features such as superposition and entanglement~\cite{lloyd2014quantum,lloyd2014quantum,rebentrost2014quantum,lloyd2016quantum, biamonte2017quantum,Schuld,powell2013quantum,barrios2017role,cardenas2018multiqubit,paneru2020entanglement} promise enhancements in terms of the computing resources and the speed compared to the classical counterparts. Several experimental researches have been done to implement these algorithms~\cite{collins2000nmr,schuch2002implementation,wu2011experimental,takeuchi2000experimental,gulde2003implementation,barz2014two}. In this article, we present a quantum algorithm for face recognition as one of the potential applications of quantum algorithms in machine learning.

The problem of identification of faces from any images generally constitutes different steps (shown in Figure~\ref{Fig:fig1}): creating a database of faces consisting of training and test images, feature extraction using principal component analysis (PCA), linear discriminant analysis (LDA) or independent component analysis (ICA), feature matching using dissimilarity measures, and recognition~\cite{draper2003recognizing}. PCA extracts the eigenstates (or eigenfaces) of the covariance matrix of the images in the database, including information like average face, gender (male or female), face direction, brightness, shadows, etc. ICA, however, extracts the independent elements such as eyes, eyebrows, mouth, nose, etc. in a face. Quantum algorithms which provide speedup for PCA and ICA have already been proposed~\cite{lloyd2014quantum, harrow2009quantum}. Here, we focus on three main steps: (1) Quantum Principle Component Analysis (QPCA) ~\cite{lloyd2014quantum}, (2) Quantum Independent Component Analysis (QICA)~\cite{harrow2009quantum}, and (3) Dissimilarity measures (i.e., face matching), to develop a quantum algorithm for face recognition. In what follows, we present a quantum algorithm for dissimilarity measures for face matching with speedup. This is based on a quantum algorithm to compute the log determinant divergence using both the determinant and the trace of a matrix. Our algorithm combined with the inputs obtained from quantum imaging techniques provides a fully intelligent pattern identification system, with the joint benefit of the low-dose and higher resolution of quantum imaging methods, and the speedup and efficiency of the quantum algorithms.
Figure~\ref{Fig:fig1} shows the flowchart of the quantum algorithm for the pattern identification.

\begin{figure*}
\centering
\includegraphics[width=18cm]{Fig2.jpg}
\caption{\textbf{Intelligent pattern recognition in quantum imaging}. Data from quantum imaging methods such as (\textbf{a}) Interaction Free Imaging and (\textbf{b}) Ghost Imaging act as an input to (\textbf{c}) Quantum Pattern Recognition Processor (QPRP). The latter, i.e., QPRP, applies quantum machine learning to find the patterns in the database.}
\label{Fig:fig2}
\end{figure*}
\section*{Quantum Face Recognition}
Classical algorithms are unable to process quantum data directly. During the conversion of the quantum states (qudits) to classical data (bits), most of the information is lost in the measurement process, due to the ``collapse'' of the wavefunction. Although techniques such as quantum state tomography implemented on unlimited ensemble of the states can be used to fully reconstruct the quantum states from classical projections, these processes are generally complex and expensive. Therefore, the optimal input to our quantum algorithms, would be the quantum states directly obtained from quantum processes, for example, quantum imaging methods, or from a quantum memory, without performing a strong measurement on the wavefunction.

Photonic quantum memories~\cite{lvovsky2009optical}, allowing storage and on-demand retrieval of quantum states of light, is one of the key components for the realization of quantum optical pattern-recognition technology. Quantum memories essentially form a quantum database for the matching stage in the recognition process. With the state-of-art quantum memories, the possibility of storing hundreds of spatial modes has already been shown in experimental studies using atomic-cold gases~\cite{parniak2017wavevector,pu2017experimental}. Furthermore, using solid-state atomic memories, it is possible to simultaneously store hundreds of photonic quantum states in distinct temporal modes, thus allowing us to store patterns scanned at separate times~\cite{bonarota2011highly,tang2015storage}. In addition, optically accessible spin-states of certain atomic systems can reach several hours of coherence time~\cite{zhong2015optically}. A very recent experimental demonstration reports one-hour memory lifetime for light storage, showing the feasibility of long-lived photonic quantum memory devices~\cite{ma2021one}.
Atomic memory approaches have also been shown to reach high retrieval efficiencies up to $92\%$~\cite{hsiao2018highly} and high fidelities above $99\%$~\cite{liu2020reliable}.
However, an implementation with all of the aforementioned properties still  remains as a challenge in developing a practical quantum database memory.

Quantum techniques such as quantum ghost imaging~\cite{morris2015imaging}, quantum lithography~\cite{boto2000quantum}, or quantum sensing~\cite{israel2014supersensitive}, when appropriately interfaced with photonic quantum processors, for example an array of optical fibers connected to an integrated quantum photonic circuit, can also act as inputs to our algorithms (see Figure ~\ref{Fig:fig2}). Here for the case of our face recognition algorithm, we assume that the input images are acquired by quantum ghost imaging ~\cite{morris2015imaging}. Ghost imaging exploits the spatial correlations between photon pairs generated through a nonlinear process called spontaneous parametric down-conversion (SPDC). Since the images are obtained by triggering the shutter in order to capture only the ``coincident'' photon pairs, the level of background noise is significantly reduced, along with a reduction in probe illumination. In a variation of this technique using non degenerate photon pairs, the image detection and sample interaction can happen at different wavelengths, which can be useful when imaging sensitive tissues when limited in detection technologies~\cite{chan2009two}. Combining quantum detection techniques such as interaction-free measurement with ghost imaging, the illumination level required for the same levels of Signal to Noise ratio (SNR) in images~\cite{zhang2019interaction} is further reduced significantly. Figure~\ref{Fig:fig3} shows some of the images of human faces obtained in a quantum ghost imaging setup, where spatially correlated photon pairs (namely signal and idler), are generated by pumping a BiBO crystal with pump photons. Phase holograms placed in a Spatial Light Modulator, a liquid crystal device, created by superimposing the human faces with a diffraction grating acts as an object for the signal photon, while the idler photon passes to the Intensified Charged Coupled Devices (ICCD) camera via a delay line. The images are obtained by triggering the ICCD shutter with the signal photons detected through a Single Photon Avalanche Diode (SPAD) detector -- see Supplementary Information (SI) for the detail of the experimental setup.

\begin{figure*}
\centering
\includegraphics[width=18cm]{Fig3.jpg}
\caption{\textbf{Face recognition in ghost images.} (\textbf{a}) Images of the original human faces (top) and the corresponding experimental ghost images (bottom) obtained in a ghost imaging setup. A femtosecond laser is used to generate spatially entangled photon pairs. One of the photons illuminates a spatial light modulator, which imprints different images onto the photon, and can act as a trigger for the other photon that was detected by an intensified CCD camera. (\textbf{b}) Quantum Independent Component Analysis (QICA), and Quantum Principal Component Analysis (QPCA), of the faces to detect the independent components, and principal features in the faces. (\textbf{c}) Dissimilarity measure between the ghost images with the images in the database for their identification.}
\label{Fig:fig3}
\end{figure*}

\subsection{Quantum principal component analysis (QPCA)} We have now the input images either retrieved from a quantum memory or directly as outputs from a quantum imaging setup. The pattern recognition processor applies Quantum Principal Component Analysis (QPCA)~\cite{lloyd2014quantum,kopczyk2018quantum} to extract the principal eigenvectors of the covariance matrix $C_X$, formed by the set of the training images.

Let us consider a set of  $N$-dimensional training images (or faces), $\{\vert {x}^{(1)} \rangle,\ldots, \vert {x}^{(M)} \rangle\}$. Here, $\vert {x}^{(i)} \rangle$ is the i-th training image, which is given by,
\begin{equation}\label{eq:imagevectors}
    \vert {x}^{(i)} \rangle =\sum_{q =1}^{N} x_q^{(i)} \vert {\psi}^{(i)}_q \rangle,
\end{equation}
where $x_q^{(i)}$ are the components, and $\vert {\psi}^{(i)}_q \rangle$ are the basis kets. The covariance matrix ${C}_X$ can be formed as a sum over $M$ training faces~\cite{kopczyk2018quantum},
\begin{equation}\label{eq:covariancematrix}
    {C}_X = \frac{1}{M} \sum_{i=1}^{M} \vert { x}^{(i)} \rangle \langle { x}^{(i)} \vert.
\end{equation}
The next step is to exponentiate the covariance matrix ${C}_X$, so that we can use the Quantum Phase Estimation (QPE) subroutine for finding the eigenvectors and eigenvalues. It has been shown that the exponentiation of the covariance matrix, i.e., $e^{-i{C}_X\,t}$, can be performed in $\mathcal{O}(\log{}N)$ time~\cite{lloyd2014quantum}.\\

In QPCA algorithm, for the phase estimation subroutine, we apply the operator ${U} = e^{-i{C}_X\, t}$ on ${C}_X$ ~\cite{kopczyk2018quantum}. The action of ${ U}$ on one of the states $\vert  x^{(i)}\rangle $ in ${ C}_X$ is:
\begin{align}
    e^{-i{C}_X\,t} \vert x^{(i)} \rangle \rightarrow  \sum_{j=1}^{M} c^{(ij)} \vert {\phi}^j \rangle,
\end{align}
where $\vert {\phi}^{j} \rangle$'s are the eigenvectors of ${C}_X$, and $c^{ij}=e^{-i\tilde\lambda_c^{(j)}t}\langle \phi^j \vert {x}^{(i)} \rangle$ in which $\tilde\lambda_c^{(j)}={\left(2\pi\lambda_c^{(j)}t\right)}/{2^n}$ where $\lambda_c^{(j)}$'s are the corresponding estimated eigenvalues of ${C}_X$ with precision $n$~\cite{lloyd2014quantum, kopczyk2018quantum}.\\

In order to obtain the principal eigenfaces (the eigenvectors of the covariance matrix with larger eigenvalues), we define a score $s^{(ij)}$, which is the projection of an eigenvector $\vert \phi^{(j)} \rangle$ on a training vector $\vert x^{(i)} \rangle$,
\begin{eqnarray}
    s^{(ij)}=\langle x^i \vert \phi^j \rangle=\sum_{q=1}^{N}x^{(i)}_q \phi^{(j)}_q
    ,
\end{eqnarray}
where $\phi^{(j)}_n$ are the components of the eigenvector $\vert \phi^j \rangle$. The eigenvectors corresponding to the $r$ highest scores are the principal components (or eigenfaces). Each face can be expanded in terms of the $r$ eigenfaces (principal components) but with different weights $\omega_j'$s as follows
\begin{equation}
    \vert \text{Face}^{(i)} \rangle = \sum_{j=1}^{r}  \omega_j \vert {\mathbf \phi}^j \rangle.
\end{equation}
The ``mean image'' is the eigenface corresponding to the largest eigenvalue of $C_X$. The QPCA algorithm is efficient for the case $r\ll N$~\cite{kopczyk2018quantum}.\\

\subsection{Quantum independent component analysis (QICA)}
In classical machine learning, Independent Component Analysis (ICA) is performed to decompose an observed signal into a linear combination of unknown independent signals~\cite{draper2003recognizing}. Similar to the PCA, the ICA finds a new basis to represent the data, however with a different goal. We assume that there is a data set of faces $s \in {R}^d$ that is a collection of $d$ independent elements in the face such as nose, eye, eyebrow, mouth, etc. Each image observed through a camera can be expressed as ${x}={F}\cdot s$, where ${F}$ is a mixing matrix of the independent face elements. Repeated observation gives us a dataset ${x}$ as $\{x^{(i)},\ldots,x^{(M)}\}$, and ICA estimates the independent sources $s^{(i)}$ that had generated the face.
We let ${W}={F}^{-1}$ which is the unmixing matrix and solve the linear systems of equations $s^{(i)}={W}\,x^{(i)}$ for estimating the independent elements of the face. We should note here that $s^{(i)}$ is a d-dimensional vector and $s^{(i)}_j$ is the data of element $j$. Similarly, $x^{(i)}$ is an d-dimensional vector, and $x^{(i)}_j$ is the observed (or recorded) element $j$ by camera. The ICA can be exponentially speedup via a quantum algorithm for sparse matrices, with the Harrow-Hassidim-Lloyd (HHL) algorithm~\cite{harrow2009quantum}, which is used to solve linear systems of equations optimally with $O(\log{}N)$. For comparison, classically it takes a time $O(N^3)$ to be solved via the Gauss elimination, and approximately $O(N\sqrt{\kappa})$ via iterative methods~\cite{harrow2009quantum} for a sparse matrix of size $N\times N$, with $\kappa$ being the ratio between the greatest and the smallest eigenvalue.

\subsection{Pattern matching: Comparing Faces}
As important details of a face are obtained either by using QPCA or QICA, each face is represented in the form of a sparse matrix in which non-important elements are set to zero. The last and important step of the algorithm is comparing the face patterns to recognize the target face.
Pattern matching algorithms investigate exact matches in the input with pre-existing patterns in the database. In fact, the problem here is comparing matrices with each other. The evaluation of matching between matrices (or face patterns) can be done by using ``dissimilarity'' \cite{cichocki2015log} measures that calculate the ``distance'' between the matrices.  The lower the values of the dissimilarity/distance measures, more similar the matrices, with the fully matched matrices having a zero distance. One such distance measure used to compare two matrices $X$ and $Y$ is called the ``Log-determinant divergence''~\cite{cichocki2015log,dhillon2008matrix} defined as,
\begin{equation}
    D(X,Y)=\text{Tr}\left(X\cdot Y^{-1}\right)-\log \det\left(X\cdot Y^{-1}\right)-N,
\end{equation}
where $N$ is the dimension of the matrices. When $D=0$, the matrices $X$ and $Y$ are completely matched, and higher the distance value the more different are the matrices. The least value among the all distance values identifies the best match and consequently recognizes the face. As it is seen in the distance formula, it is a benefit to be able to calculate the trace and the determinants of matrices with speedup to expedite the distance calculation. In the following, we propose quantum algorithms for computation of the determinant and the trace of a sparse matrix.\newline

\noindent\textbf{Quantum Computation of Sparse Matrix Determinants and Trace:} To obtain a measure of dissimilarity between two matrices we need to calculate the determinant and the trace of the sparse matrix $A = X\cdot Y^{-1}$. First we calculate $Y^{-1}$ using the HHL algorithm~\cite{harrow2009quantum} and obtain $A$ by multiplying it with $X$.
We then apply the Quantum Phase Estimation (QPE) subroutine, which consists of a quantum Fourier transform (QFT) followed by a controlled Unitary ($\text{CU}$) operation, with $U = e^{-iA\,t}$, and a inverse quantum Fourier transform. We then apply a controlled Rotation operation followed by the inverse Quantum Phase Estimation (QPE) subroutine. At the end we have a multiplication operator $\Pi$ which finally gives us the product of the eigenvalues -- the algorithm steps are explained in more detail in the Supplementary Information. The running time of the algorithm up to the third step, i.e. applying the controlled-U operator, is $O(\log{}N(s^2 \kappa^2 /\epsilon))$~\cite{harrow2009quantum}, where
$s$ is the sparsity, $\kappa$ is the ratio of largest eigenvalue to the smallest eigenvalue of $A$, and $\epsilon$ is the acceptable error. Additionally, the multiplication operation in the last step can be performed in time $O(\log{} N)$ and the algorithm should run $N$ times. Therefore, the overall complexity of the algorithm is $O\left(N\,{\log{}}N(1+ s^2 \kappa^2 / \epsilon)\right) $, which is much faster than the classical ones (see Table~\ref{Table1}).
\begin{table}[h]
\caption{A Comparison of complexities between the classical approaches and our quantum approach, current work (CW), for the computation of determinant.}
\centering
\begin{tabular}{c c c c}
    \bf{Approach} & \bf{Method} & \bf{Complexity} & \bf{Ref.}\\
    \hline\hline
    Classic & Laplace & $N^3$ & \cite{bunch1974triangular}\\
    Classic & Gaussian & $N^3$ & \cite{bunch1974triangular}\\
    Classic & Coppersmith-Winogard & $N^{2.373}$ & \cite{williams2014multiplying} \\
    Classic & Wiedemann & $N^{2} \log N$ & \cite{wiedemann1986solving} \\
    Quantum & Our method & $N \log N$ & CW \\

\end{tabular}
\label{Table1}
\end{table}
\newline

In order to compute the trace of the  matrix $A$, an adder quantum algorithm~\cite{ruiz2017quantum} can speedup the computation. The adder operation between two diagonal elements is mainly based on the quantum Fourier transform (QFT), i.e. $\vert \Phi (a) \rangle :=\text{QFT}\,\vert a \rangle= \frac{1}{\sqrt N}\sum_{k=0}^{N-1}e^{i\frac{2\pi a k}{N}}\vert k \rangle$ and the inverse QFT, i.e., $\text{QFT}^{-1}\vert \Phi (a) \rangle=\vert a \rangle$. By continuation of this method sequentially for the all diagonal elements, one can obtain the trace of the matrix. The detail of the adder algorithm and the quantum circuit for the computation of trace is discussed in the Supplementary Information (Figure~\ref{fig:s2} shows the corresponding quantum circuit). The whole process which is based on QFT and QFT$^{-1}$ has a complexity of $O(\log{} N)$.

\begin{table}[h]
\caption {Summary of estimated complexities in quantum face recognition algorithm.}
\centering
\begin{tabular}{c c c c}
    \bf{Method} & \bf{Output} & \bf{Complexity} & \bf{Ref.}\\
    \hline
    \hline
    QPCA & Eigenfaces & $\log N$ & \cite{lloyd2014quantum}\\
    QICA $\&$ HHL & Face components & $\log N$ & \cite{harrow2009quantum}\\
    HHL & Matrix inversion & $\log N$ & \cite{harrow2009quantum} \\
    Our method & Determinant calculation & $N \log N$ & CW \\
    Our method & Trace calculation & $\log N$ & CW \\
    Log-det divergence & Face matching & $N \log N$ & CW \\
    Our method (General) & Face recognition & $N \log N$ & CW
\end{tabular}
\label{Table2}
\end{table}

QPCA and QICA both have logarithmic complexities, i.e., $O(\log{}N)$. For the calculation of the log determinant divergence, the computation of trace has a complexity of $O(\log{}N)$, while the determinant has complexity of $O(N\log{}N)$). Hence, the overall complexity of the whole algorithm is $O(N\log{} N)$. Table~\ref{Table2} shows a summary of estimated complexities along with the complexity of the general quantum face recognition algorithm.

\section*{Conclusion}
In summary, we propose a new concept of a quantum protocol for 2D face recognition, combining the benefits of quantum imaging in image acquisition with the speedup from the quantum machine learning algorithms. In this concept, we consider images to be obtained via a ghost imaging protocol either as inputs to the quantum memories or as a hardware encoding of quantum information for the photonic pattern recognition processor. Feeding the ``images'' directly from a quantum protocol also eliminates the need for the conversion of classical data to quantum inputs for the processor saving valuable computational resources. The quantum pattern recognition processor then runs an algorithm composed of three main subroutines: (1) quantum  principal components analysis (QPCA), (2) quantum independent component analysis (QICA), and (3) quantum dissimilarity measures for comparing faces. For the QPCA and QICA, we propose slight modifications in the existing algorithms, whereas for finding the dissimilarity measure, we propose a novel algorithm for obtaining the distance between two matrices based upon a metric called log-determinant divergence. Our algorithm obtains the determinant and the trace of the two matrices in $O(N\log{N})$ time -- N is the dimension of the matrix. Complexity analysis shows that all of the three parts have speedup as compared to their classical counterparts, with the overall complexity given by $O(N\log{N})$. Our conceptual protocol provides a framework for an intelligent and fully quantum image recognition system with quantum inputs and a quantum machine learning processor. The joint benefits of the quantum image acquisition and quantum machine learning promises exciting technological developments in the field of image recognition systems.

redpajama_set_name: RedPajamaArXiv


text:
Media freedom in Europe and the US has seriously declined
European Parliament is deeply concerned about the state of media freedom within the EU and denounces the violence, harassment and pressure faced by journalists.
European MEPs see a "growing pattern of intimidation aimed at silencing journalists", citing the murders of Daphne Caruana Galizia and Ján Kuciak and his fiancée as examples of the risks faced by investigative journalists. They call on public figures to refrain from denigrating journalists and insist on the legal obligation to investigate all attacks, highlighting that female journalists are especially vulnerable and should be given additional protection
Death threat towards the Editor-in-chief, October 2020, from Lebanese born, Norwegian citizen Michael Abdelnoor PhD
Abdelnoor: "You will be killed!"
Abdelnoor, 2020
The editor-in-chief's E mail to Abelnoor October 23, 2020
Harald Jan Dahle <haroldsworld@yahoo.com>
To: Epidemic
Fri, Oct 23 at 11:16 AM
I was in Oslo a week ago, we met and went to Aker pier. From there we walked towards the right side of City Hall before parting on the corner at Crown Princess Martha's Square. We also talked about the police harassment of me. I remember you saying I was going to be killed, and you said that seriously. I did not say anything about this. And in that connection, I want to hear from you if this statement is based on tips you have received, or if it was an assessment that you made yourself on your own basis, after an overall assessment. Can you please say something about this, thank you very much.
The has been no answer from Dr. Abdeloor
THE MURDER OF DAPHNE
Daphne Anne Caruana Galizia was a Maltese writer, blogger, and anti-corruption activist, who reported on political events in Malta. On 16 October 2017, Caruana Galizia was murdered close to her home when a car bomb was detonated inside her vehicle attracting widespread local and international condemnation of the attack.
In a resolution adopted with 553 votes to 54 and 89 abstentions, Parliament points to "attempts by governments of some member states to silence critical and independent media and undermine media freedom and pluralism". MEPs are particularly concerned about the state of public service media in some EU countries, where they have become an "example of pro-government propaganda".
They stress that media freedom, pluralism, independence, and the safety of journalists are crucial components of the right to freedom of expression and information and are essential to the democratic functioning of the EU.
"Freedom of the media has been deteriorating in recent years", warns the text, and the COVID-19 outbreak has exacerbated this deteriorat-ion.
THE MURDER OF MICHAEL
Who Killed Michael Hastings? Michael Hastings was driving a 2013 Mercedes C250 coupe when he crashed into a tree on Highland Ave. in Los Angeles at approximately 4:30 am on June 18. Video posted online showed the car in flames, and one neighbor told a local news crew she heard a sound like an explosion. Another eyewitness said the car's engine had been thrown 50 to 60 yards from the car. There were no other vehicles involved in the accident.
The fire was so all-consuming that it took the Los Angeles County coroner's office two days to identify Hastings' body, but Clarke said a cyber attack on the vehicle would have been nearly impossible to trace "even if the dozen or so computers on board hadn't melted." Hastings practiced a brand of no-holds-barred journalism that tended to anger powerful people. His 2010 profile of Gen. Stanley McChrystal, published in Rolling Stone, was so damaging that it ostensibly prompted President Barack Obama to fire the general (the president denied that the article had a role in his decision).
In the days before his death, Hastings was reportedly working on a story about a lawsuit filed by Jill Kelley, who was involved in the scandal that brought down Gen. David Petraeus, according to the LA Times. KTLA reported that Hastings told colleagues at the news site BuzzFeed that he feared the FBI was investigating him. On June 20, the FBI denied that any investigation was under way.

redpajama_set_name: RedPajamaCommonCrawl


Netflix Users Can Instantly Watch Movies And TV Episodes Via PS3 !
Computer Entertainment America Inc and Netflix Inc are going to join forces in order to make thousands of movies and TV episodes from Netflix, to be streamed instantly to TVs via the Playstation 3 Computer Entertainment System. Moreover, Netflix has decided to begin streaming via the PS3 system next month with no additional cost to Netflix members in the US who already have a PS3 system.
Till September 30, Neflix had 11.1 million U.S. subscribers and the PS3 system's installation reached close to 9 million units in the US.
As the next step the partnership has decided to let the consumers play high definition games, watch Blu-ray movies, listen to music, view photos, browse internet and download content from Playstation Network. At the same time, you can download exclusive blockbuster movies and popular TV shows and you can reserve a free instant streaming disc for PS3 systems by visiting Netflix site. If you want to have this stream immediately, pay $8.99 for a month and have an eternal fun.


What is Entrepreneurship? Tough question, everyone is an entrepreneur, whether in his personal or professional life. Entrepreneurship is given to everyone, people said.
Entrepreneurship is a decision impacting your lifestyle, a sacrifice somehow not really measured at the beginning. Ready to exclusively think about your project' success ? For my part, every night is another night thinking on how we can improve our business.
Entrepreneurship is about seeking taste for freedom while knowing that the road will be strewn with pitfalls. You know roller coasters? Same story. Entrepreneurial life is a constant emotional lift, where one must know how to take a step back while being inside day-t-day operations. Indeed, good news is your life will never be the same, a good day will be the national startup day in your head, and a bad one, a total misunderstanding of why I do that? Why am I doing this especially?
Entrepreneurship is about loving risk. Measured in some way, but whatever your decision it will be your fault if you have not taken the right one and vice versa of course.
Entrepreneurship is a challenge to surpass yourself, trying to give the world new horizons. It's a hectic life where every day is different, great people, strong opportunities opening up, new ideas emerging.
And you, how do you define Entrepreneurship ?
Dear Terry, thanks for your message ???? I definitely think entrepreneurship is a way of being convinced that we can make this world a better place by delivering useful and impactful solutions. What is your project about ? ????


Thanks so much for the rankings, Rudy! I'm in a 12 team h2h 6×6 R/HR/RBI/SB/OBP/SLG league. In an attempt to adjust your rankings, I've made 6×6 rankings using your dollar values for R/HR/RBI/SB/(2xOPS) Do you think this is a decent equation to be using? The results seem a bit wonky, ie, trout valued at 88$, top 50 players being extremely valuable and players after 100 being devalued, but maybe this is correct? Some things that I figured would happen, (billy hamilton type guys being devalued) came true, but overall, is my math right or should I perhaps be using 1.5xops? thanks a lot!
That should work….i just checked trout and he'd be $53 based on that formula. Did u subtract the AvG$ and then mult OPS by 2. Maybe u multiplied the sum by 2 instead?
(r)14.6 +	(hr)12.3 + (rbi)9.8 +	(sb)11.2 +(opsx2) 27.2 and I now got his value at 75.1 This seems slightly high, but a lot more reasonable. Perhaps this is correct, given that my league somewhat overvalues power hitters? Thanks again for your help!!
@Rudy Gamble: OK, thanks so much, Rudy!
Why is it that Miggy jumps up so much in an OPS despite Gian's tendency to rake?
What would be a better ranking took for a 5 x 5 using OPS? This list or the 5 x 5 OBP list?
Is the 6×6 OPS good to use for my 7×7 that adds hits as a category? Would you make any adjustments?
belt/corey seager (you) vs Rudy/steamer BIG time.
@Montezuma's Revenge… right now: oops, wrong post, Grey vs You/Steamer on those.
Yeah, similar projections but large ranking differences. I think he's being drafted way too high given his projections are Jhonny Peralta-like.
it certainly appears you lowered soler, but why is schwarbs still 24? this from OPS rater.
i've taken his PA total down to 491. i have him playing about 70-75% of games right now with him getting most starts in LF against RHP and some starts at catcher.
why when i input ARI it shows SEA and ARI hitters?
Because it includes the hyperlink in the filter and ARI is in mariners. Type ariz and u are fine. Worst is BAL does nothing because it is in Razzball.com….
@Rudy Gamble: wait then what do i put in for BAL?
@Mantis Toboggan MD: nevermind, there was a hint in the idea that "ARI" somehow had SEA show up, i can input all parts of the team name, so orio for BAL works.
Ding ding ding. I would also have accepted BALT.
What is the right math to convert 12-team auction values to 11-team? Thanks very much.
@hegotsgame: subtract $.90 from every player gets you very close.
Jose Ramirez is listed as SS-eligible in Yahoo, but he isn't actually. How much does that impact his ranking (auction value)? Thanks.
@Adam Goldfarb: Thanks for the catch. It doesn't change his value as only catchers get any positional value adjustment.
Hey, Rudy. Is there a way to select the custom stats categories (e.g., 6×6 OPS) and a number of teams other than 12? For example, can I get the ratings for a 6X6 OPS league with 10 teams? Thanks!
My league is a 10 team, Yahoo 6×5 including OPS league. If I want to take your OPS hitters and Grey's pitchers so I can make an accurate rankings for my league and sort by $ can I export your Yahoo 6×6 OPS rankings above, export Grey's Hit+Pitch rankings, cut out Grey's pitchers, and then paste them into my spreadsheet that has your OPS hitters from Yahoo 6×6 OPS rankings and then sort by $?
Will the $ amount for your hitters be equivalent to the $ amount Gray assigned to his pitchers? I believe this is what you instructed me last year, but I want to make sure this is still correct and after blending the two rankings I'm comparing apples to apples.
My fear is the $ value for your hitters won't be equivalent to Grey's pitchers. For instance if Grey's number one pitcher is Scherzer and he's rated at $70 and your number one hitter is Trout and he's rated at $64.5 then obviously the $ values aren't equivalent. I just want to make sure when I blend the two rankings together I'll be comparing apples to apples.
Do I need to inflate or deflate your hitters/Grey's pitchers $ amount? Or can I just blend them and they'll be of equal value?
my $ are 67/33 for hit/pitch. grey's right now is at 66/34 so they should match up pretty well after that adjustment.
I'm sorry, I didn't see a formula. Was the link you provided a spreadsheet of Grey's pitcher rankings with adjusted $ values?
Should I export those pitcher rankings and corresponding 5×5 $ amount and paste them into your 6×6 OPS hitter rankings?
Just want to make sure I do this correctly before I draft incorrectly using an inaccurate spreadsheet.
After review I see this (below). But I'm confused on what to do. My leagues is a 10 team, 6×5 (including ops) league. Based on your instructions, what should I do to Scherzer's value? He's listed at $33.9 in the link you provided me (non 5×5). What would I multiply Scherzer's 33.9 value by based on my league information and your below formula?
To calculate pitcher dollars for your league, add up all the categories used in your league, multiply by 5/(Categories in League), and then add 1. Use $K for both K and K/9. To convert to 10 team, multiply the sum of $ by 0.95 and subtract 1.33.
I have 10 hitters I start every day (c, 1, 2, 3, ss, of, of, of, util, util) and 8 pitchers (2 sp, 2 rp, 4 p).
Ok, so you think I should leave the pitchers alone and inflate the hitters. You think I should use the 10 team yahoo rankings (link below), delete the hitters, adjust the 6×6 ops hitters, and then add them back in to the yahoo 10 (only pitchers now) rankings?
How would you adjust the hitters then? For instance, Trout is $64.5 in the 6×6 ops rankings. What would you do to his $ to get it to align more evenly with pitchers?
For comparison, I looked at Scherzer in the Yahoo 10 rankings (where we'd be adding the adjusted hitters) and his value is $34.5.
Would you still add $1 to all the hitters and subtract 1.25 from all the pitchers?
What would your final numbers look like for Trout and Scherzer? Could you show me the math formulas and how you arrived at your numbers so I can use that on all the other hitters and pitchers?
yes. i just checked and the formula for turning my 12-team Yahoo hitter $ into 10-team is multiply $ by 0.94 and subtract 1.4.
it is true that Yahoo's use of 10 hitters vs 13/14 COMPLETELY changes the value of hitters. if you still go by 67/33, it is as if you increase hitters by 1.3x….and THEN you have the reality that the waivers are so deep for hitters that it is silly to spend money on anything but guys well-above waiver-value.
i view that format as really no different than ESPN/standard leagues as it isn't really 8 pitchers. you effectively have at least 9 pitchers with all that rotation.
it's not going to help much to share how the formulas work other than that the split is 67% of $260 for hitters and 33% of $260 for pitchers. So you can adjust those $ as you see fit (since effective 10 pitchers, 9 hitters…any adjustment to hitters should be multiplied by 10/9 for pitchers to even things out). Make sense?
Ok, so I should do that to all the pitchers in this link, and then cut them out and paste them into your 6×6 OPS rankings and the hitters and pitchers will be congruent value?
Are these pitcher rankings Grey's and follow his current pitcher rankings?
you said 10 team league. the 6×6 OPS offense is 12-team. so you are knocking down the offensive players to match 10-team. (just use this for 10 team pitching – https://razzball.com/playerrater-preseason-yahoomlb10/).
grey's are 12-team pitcher rankings. it might be best to start w/ mine and then use his to adjust pitchers up/down where you agree w/ him. the general pitcher allotments are similar (we are both near 67/33) but, typically, grey prefers pushing down early round pitchers and pushing up later pitchers vs my model.
I also just noticed that the Yahoo 10 rankings when you just look at the pitchers currently does not match up with Grey's pitcher rankings. Is the Yahoo 10 rankings of pitchers intended to match up with Grey's pitcher rankings?
Lol sorry to be a repetitive jerk, but I just want to match Grey's pitcher rankings with your 6×6 ops.
If Yahoo 10 pitchers are Grey's pitcher rankings then it'd be perfect to blend them with your 6×6 ops rankings. Just not sure if they are Grey's pitchers.
If you go to Grey's pitcher rankings he has them ranked Scherzer, DeGrom, Sale…but in Yahoo 10, the pitchers are ranked Scherzer, Sale, DeGrom. So not sure if they are the same?
grey's rankings are completely separate from those in the player raters which are based on steamer rates and my playing time + other adjustments.
so you can mash together grey's pitcher rankings and the 6×6 OPS hitter rankings. i just have no guidance to give on how you then order amongst the hitters and pitchers.
Ok, so to help with values and drafting guidelines, I'll use your 6×6 ops hitters and pair them with the yahoo 10 rankings (pitchers only).
You suggest I inflate the hitters due to my 6×5 league (including ops). You also say maybe deflate the pitchers? In your opinion, what is the best way to go here and what formulas should I use to compare apples to apples?
Below are Trout and Scherzer's values from the 6×6 ops rankings and yahoo 10 pitcher rankings. Can you show me the math I need to use on each of them and then I'll do the same thing to all the other hitters and pitchers? Thanks!
you combine the 12-team hitters with the 10 team pitchers. you deflate all the hitters down to 10 team $ values with the formula: multiply $ by 0.94 and subtract 1.4.
with that done, the sum of all $1+ hitters/pitchers should be in the $2600 range (10x$260).
from there, you can nudge hitters and pitchers up/down as you see fit. to keep the sum of $ equal, you just need to adjust pitchers more (since there are less than hitters). i'd adjust it by 1.11x the hitter adjustment so remove $1 from hitters = +$1.11 for pitchers.
i have no real recomomendation on how much/little to make adjustments. all personal preference.
When you say combine the 12 team hitters and the 10 team pitchers do you mean Yahoo 12 team and NOT 6×6 OPS? Why wouldn't I use the 6×6 OPS rankings since my league is OPS?
And based on your instructions above, you leave the Yahoo 10 pitchers alone and add them to the hitters (not sure if I should use Yahoo 12 or 6×6 OPS). Regardless of which hitters you say to use, I should use the same formula on the hitters?
Please let me know if this math is correct and which hitters I should use, either Yahoo 12 or 6×6 OPS. And don't touch pitcher $ from Yahoo 10?
use the yahoo 6×6 OPS rankings. those $ are based on 12-team though. that's what i'm saying.
trout's value is slightly higher in OPS since he is above average in OPS so those $ make sense.
initially you don't need to inflate the pitchers. just use the 10-team ones. after deflating the hitters, do a sanity check that the sum of $1+ hitters/pitchers = $2600. assuming so, you can make any macro-adjustment you want for hitters and pitchers as long as you adjust pitchers by 1.11x the adjustment of hitters.
When you say do a sanity check that the sum of +$1 hitters/pitchers = $2600, how do I do that? I don't understand.
sort the excel spreadsheet by $ in descending order. highlight all $ from the top down to $1. look on bottom right to see the sum.
or use the formula sumif(D:D,">=1″,D:D) with D:D including whatever column the $ are in within your spreadsheet.
So keep the Yahoo 10 pitchers value in tact. Add in 6×6 ops hitters but deflate their values by my Trout example below.
After I deflate all the hitters add them in to the Yahoo 10 pitchers. Total up all the $ and if it's $2600 roll with the $ values I have.
If it's more or less than $2600 then inflate/deflate either the hitters or pitchers to get to $2600.
Does it need to be exactly $2600 or can I expect it to be off by a little bit?
And how much off before I need to start inflating/deflating?
If I have to inflate/deflate, how do I know if I should be inflating or deflating the hitters or pitchers?
it won't be exactly $2600. as long as it's like +/- $50, it's fine. my SWAG is a little over $2600.
you don't need to inflate/deflate the hitters/pitchers at that point. you CAN if you feel the rankings are over/undervaluing hitters vs pitchers based on your draft preferences.
Perfect. Thank you for all of your help!
I'll just use this formula across all hitters and that should give me a good set of rankings for pitchers and hitters while taking into account OPS. Thanks, Rudy!


text:
Cebuano theater refers to the theater arts of the Cebuano people and those conducted in the Cebuano language.  It also refers to contemporary productions and adaptations produced in Cebu City.

Pre-19th century
Cebu has a long tradition of the theater arts. The arrival of Ferdinand Magellan in 1521 and Miguel López de Legazpi in 1565 both occasioned the performance of religious rituals that had the basic elements of theater. In 1598, a Spanish comedy written by the Jesuit Francisco Vicente Puche was performed in Cebu during the inauguration of a grammar school. In 1609 another Jesuit wrote a Cebuano play about the life of St. Barbara, which was performed in Bohol.

The next two centuries are sketchy in historical records.

Late 19th century
In 1880 a Spanish zarzuela was performed in Cebu by members of Compania de Navarro, a visiting troupe from Manila. The prompter of the Compania, Eduardo Lopez, stayed in Cebu and became an important personality in the local theater. Other theater personalities at this time include Sabas Veloso, Sebastian Lingatong and Balbino Abadia.

In 1894 a Spanish play entitled El Alcalde Interino was held during the feast of St. John the Baptist. Lopez, Veloso, Lingatong, Maximo Abadia, Leoncio Avila, and Simplicia Alcantara (a singer of the Compania) were included in the performers. The guest during this performance was the governor of Cebu at that time, Gen. Inocencio Junquera. He was impressed by the performance but realized that a bigger space than the convent of Parian Church (where the play was held) was required. This led to his construction of the Teatro Junquera (see Junquera's article for more information).

Outside Cebu, Cebuano theater was liveliest in the town of Valladolid (now Carcar), where a local form of the theater, called linambay became popular. Some of the more popular linambays were Gonzalo de Cordoba, Doce Pares, and Orondates.

Vicente Sotto's heyday: realistic Cebuano plays
In 1902, a young man named Vicente Sotto attacked the decadent forms of linambay in his newspaper Ang Suga. He was challenged by a friend to write his own play as he was always attacking the linambay form. Sotto wrote the Cebuano "Ang Paghigugma sa Yutang Nataohan" (Love of the Native Land) as a response. The play was successful; Sotto organized the Compania de Aficionados Filipinos. Within the year, two more plays were written by Sotto: Elena, which deals of a girl's love for an insurrecto; and Aurora, which deals with a scandal involving the priests and nuns of the Colegio de la Inmaculada Concepcion. Realism in Cebuano theater was stretched too much however; even Sotto himself was a victim of the movement he started, when prior to his running for mayor in 1907, a play entitled "Ang Taban" (1906, by Teodulfo V. Ylaya) was released. The play dealt with a kidnap allegation involving Sotto.

Contemporary Theater
Contemporary theater in Cebu ranges from street theater, balak-dula, expressionistic theater, and musical theater. The Cebuano's love for music brought on the staging of several Broadway plays performed by Cebuano talents leaving original Cebuano plays in the background. Several efforts from various organizations including the Arts Council of Cebu and LUDABI were made to bring back the love and respect for stage plays in Cebuano written by local artists. A good number of modern playwrights and directors emerged through the years to include the late Daisy Ba-ad, Al Evangelio, Raje Palanca and Allan Jayme Rabaya. Their original works have been staged by various schools and organizations.

One of the leading school-based theatre groups is the University of San Carlos Theatre Guild, who have been successfully producing at least two theatre runs per year for the past two decades.

Independent groups emerged such as Al Evangelio's Pasundayag Sugbo, Emmanuel Mante's Young Thespians of Cebu, Daisy Baad's Out of the Box Theatre Co., Raymond Ordoño's Crystal Cavalier Productions, Hendri Go's Little Boy Productions, and Sarah Mae Enclona-Henderson's 2TinCans Philippines, Inc.

Currently, Cebu Theatre runs up to 20 titles per year.

References

Resil Mojares, Introduksiyon sa Modernong Teatrong Cebuano, in Dulaang Cebuano (Manila: Ateneo de Manila University Press, 1997)
Vicente Sotto  Father  of Modern Cebuano Literature

Cebuano culture

redpajama_set_name: RedPajamaWikipedia


text:
'Nigerians'll Remain What They're Unless Evils Which Dominate Their Hearts Are Exorcised' —Awolowo
Pa Obafemi Awolowo
When on Friday, February 28, 1986, Samuel Cookey, a professor of political science, who had just been given an appointment by then military President, General Ibrahim Badamosi Babangida, IBB, wrote a letter requesting the Yoruba sage, Pa Obafemi Jeremiah Awolowo, to avail the Nigerian state the benefit of his wisdom, he (Cookey) did not bargain for what would be the response.
Obafemi Awolowo
Babangida had promised the Nigerian nation that he would enthrone a new social order by doing away with the politics of the past, bring in new breed politicians and change the political landscape.
And since he was not going to wave the magic wand, he sought and got Cookey to chair a Political Bureau. That Bureau was saddled with the responsibility of fashioning out, after due consultations with Nigerians via memoranda and public presentations, a new political order. It was in the pursuit of this mandate that the professor wrote to Pa Awolowo, requesting his participation and views.
To that request, Awolowo wrote the following:
"I received your letter of February 28, 1986, and sincerely thank you for doing me the honour of inviting me to contribute to the National Political Debate.
"The purpose of the debate is to clarify our thoughts in our search for a new social order.
"It is therefore meet and proper that all those who have something to contribute should do so.
"I do fervently and will continue fervently to pray that I may be proved wrong.
"For something within me tells me, loud and clear, that we have embarked on a fruitless search.
"At the end of the day, when we imagine that the new order is here, we would be terribly disappointed.
"In other words, at the threshold of our New Social Order, we would see for ourselves that, as long as Nigerians remain what they are, nothing clean, principled, ethical, and idealistic can work with them.
"And Nigerians will remain what they are, unless the evils which now dominate their hearts, at all levels and in all sectors of our political, business and governmental activities are exorcised.
"But I venture to assert that they will not be exorcised, and indeed they will be firmly entrenched, unless God Himself imbues a vast majority of us with a revolutionary change of attitude to life and politics or, unless the dialectic processes which have been at work for some twenty years now, perforce, make us perceive the abominable filth that abounds in our society, to the end that an inexorable abhorrence of it will be quickened in our hearts and impel us to make drastic changes for the better.
"There is, of course, an alternative option open to us.
"To succumb to permanent social instability and chaos.
"In the premises, I beg to decline your invitation.
"I am yours truly, Obafemi Awolowo"
It's 33 years now that Awo wrote this letter. It is clear in my mind that this deep thinker saw into the future as time continues to validate his thoughts. Nigeria may have inadvertently chosen the alternative – "permanent social instability and chaos".
Otherwise, how else does one explain what has been happening in Nigeria? I remember in 1982 or so when he returned from his annual spiritual retreat, scores of journalists gathered at the airport to interrogate him. One of the questions posted was to query his thoughts on how Nigeria can be redeemed and placed on the path of growth.
He replied emphatically that Nigeria is beyond redemption and there was no more hope. His political opponents dismiss his statement with ignominy and tagged him a prophet of doom.
Prior to that and before his departure for his spiritual retreat, he raised an alarm that Nigeria was sliding very fast in economic and social order metrics. He said there remained a slim chance to change the course of events and save Nigeria. He reeled our immediate steps to be taken by the government but he was labeled alarmist and his counsels were treated politically.
Today, only those who enjoy chaos thrive in Nigeria. It has been tragedies since then.
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April 19, 2019 Ike A. Offor	economic development, Ibrahim Babangida, national quagmire, Nigeria, Obafemi Awolowo, Samuel Cookey, Social chaos