text:
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Musharraf Full Interview Series- Sab Se Pehle Pakistan- 25 June 2017
In this exclusive interview series on BOL TV, the former president of Pakistan, the former chief of Pakistan Army and the chairman APML General (R) Pervez Musharraf greets nation Eid-ul-Fitr Mubarak and speaks in detail on topic of current shortage of electricity ...
Musharraf Full Interview - Dunya TV - 22 February 2017
Watch Mr. Kamran Shahid of 'Dunya TV' program 'On The Front' in conversation with the former president of Pakistan, the former chief of Pakistan Army and the chairman APML General (R) Pervez Musharraf recounting the vital events that are shaping ...
Musharraf Full Interview - Khara Sach - 20 June 2017
Watch Mr. Mubashir Luqman of '24 hours News TV' program 'Khara Sach' in conversation with the former president of Pakistan, the former chief of Pakistan Army and the chairman APML General (R) Pervez Musharraf recounting the vital events that are ...
In this exclusive interview series on BOL TV, the former President of Pakistan Pervez Musharraf speaks in detail on the topic of his vision of "Pakistani culture and heritage". https://www.youtube.com/watch?v=0rssAYLF3Ao
President Musharraf's Message On Kashmir Day - 5 February 2017
Watch the former president of Pakistan, the former chief of Pakistan Army and the chairman APML General (R) Pervez Musharraf's heartfelt message on Kashmir day, its significance and what it means to people of Kashmir and people of Pakistan by recounting ...
« Pervez Musharraf's special Interview in 'Islamabad Say' with Fareeha Adress (00:70:09)
Council on Foreign Relations video: A Conversation with Pervez Musharraf (00:58:03) »
Sharing a heart-felt email…
Under your leadership Pakistan's foreign reserves went more than 16 Billion USA Dollars & with your great efforts Pakistan came out of these Defense & Economical sanctioned by UN
Below is an email that I just received yesterday and thought of sharing it with you all.
His Excellency Pervez Musharraf,
Former President of Islamic Republic of Pakistan.
the day when you took charge of Pakistan that day was a great day for Pakistan & the people of Pakistan. Everybody in Pakistan & outside Pakistan knew that present Government on that time did not allow your plane to land on any Airport of Pakistan, they were playing with your life & as well as other hundreds of people who was also on board with you on that flight. With Allah's Blessings Pakistan Army took charge of Karachi Airport & Allah has safe the life of all the people who were on board with you & your plane was safely landed on Karachi Airport.
When you took charge of Pakistan the Economical situation of Pakistan was very bad, thr was no foreign reserves & Pakistan was among the sanctioned by UN.
Under your leadership Pakistan's foreign reserves went more than 16 Billion USA Dollars & with your great efforts Pakistan came out of these Defense & Economical sanctioned by UN.Unfortunately, leaders who claimed themselves as a leaders of the people of Pakistan & they were playing song of Democracy all the time but they did not bring this foreign reserve before you in their ruling period.
Sir, everybody knows inside & outside of Pakistan that the day you left the Government of Pakistan, every department of Pakistan including Railways, Airways, Steel mill, etc was running in profit but after few months these departments has gone in Billions of rupees of loss, it means the people who controlled all of these departments were King of corruption & they took all the money from all these departments & showed losses to the people of Pakistan.
At present no one is sincere with Pakistan & with the poor people of Pakistan, the Pakistan is going under the loans from IMF day by day. Poor people of Pakistan are committing suicides because they cannot live with these expenses & no proper source of income & shelter. Present Ministers & their young children in any department of Ministry, either Federal or Provincial Ministry they are just taking the money of people of Pakistan by corruption & also taking loans of Billions of rupees from Pakistani Banks & not returning this money to Banks & getting themselves free from these loans, reason is that why they are doing this because now today the Pakistan is fully in control of these corrupt people & there is no check & balance on these people.
Poor people of Pakistan don't know that these people who declare themselves in front of Pakistani people as good & sincere politicians, they are lying just to get vote from them & people do believe whatever they say.
Sir, I know to remove your government Pakistani media has played a major role against you & their duty was all the time to do propaganda against you & your government. It is understood for money that these private media can do propaganda for any person who is sincere with the Government of Pakistan & poor people of Pakistan. It was you who always told that "sab say pehley Pakistan".
I am definitely sure the people who were behind this media are present in Pakistan & ruling in Pakistan either in Federal or Provincial Government.
Sir, in your ruling period Imran Khan was against you & has given many interviews against you but now when now you are not in Government I heard Imran Khan on television interview on the corruption matter of Steel Mill of Pakistan saying that when Pervez Musharraf left Government the Pakistan Steel Mill was in profit & about 28 Million rupees including cash & raw material was in Steel Mill but in few months all money & material was disappeared from the Steel Mill & the Steel Mill showed under the loss of 6 Billion of rupees, which is altogether 34 Billion rupees which was disappeared from Pakistan Steel Mill after you left the Government. The present Government has took loans from IMF or World Bank in 2 & half years time which the previously Government has not taken in past 60 years.
Sir, in your ruling period media was doing propaganda against you that you did not respect the decisions of Higher Courts in Pakistan & all the Law movement they prepared against you. Today when Election Commission of Pakistan has declared many Qualification Degrees as false of our elected Ministers & members of Parliament than where is the media & also why our respectable Higher Courts are not giving them punishment according to Constitution of Pakistan to the people who has done this shameful act. They are sitting in Pakistan but they don't know the whole world is looking to these people & laughing on them. Why today the present rulers are not respecting the decisions of Higher Courts & why these Lawyers are not coming on the streets against the present ruler's their corruptions & for that they are not obeying the orders of Higher Courts of Pakistan.
Sir, I & poor people of Pakistan need people like you for Pakistan as a Leader of Pakistani Nation & Leader for whole Islamic World as Late King Faisal, Late Zulfikar Ali Bhutto Shaheed & Dr Mahatir Mohammed because we strongly believe that Pakistan will be safe in your hands. You are a brave Retired General of Islamic Republic of Pakistan who has seen the scene of death from very close on many incidents & many times, the enemies of Pakistan has done assassination attack on you but always Allah has saved your life & His Blessings were always with you.
We are proud of u & we will pray that Allah will bring you back as a Leader for Pakistani Nation. Long Live Pakistan!!!
Aysha Nawab
(London UK)
Tags: Asia, Government, Government Of Pakistan, Imran Khan, Islamabad, Jinnah International Airport, Pakistan, Pervez Musharraf
This entry was posted on November 29, 2010, 2:03 PM and is filed under Others, Twitter. You can follow any responses to this entry through RSS 2.0. Responses are currently closed, but you can trackback from your own site.
pervaiz naviede
We are a bunch of volunteers and starting a new scheme in our community.
Your site provided us with helpful info to work on. You have performed a formidable
job and our entire group will probably be thankful to you.
A. Khan replied:
Thank you Pervaiz for the kindest words.
Ch Abdul Latif Gujjar
PAKISTAN FIRST. Musharraf is brave and good decision maker leader after ZA Bhutto in Pakistan, 23 Mar 2012 is the golden day for peoples of Pak when our faithful and honest leader come back home,
shahid khan
u r the man ov justice n the symbol ov pakistaniz power i salute u pride ov pakistan God Bless u.
Dr Tahir
View March 30, 2011
Sir Pervaiz Musharaff sb ithink you did a marvellous job and iam a big fan of classic music and yr interest in classic music alongwith ustad hamid ali khan is breathtaking i salute you sir


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text:
\section{Introduction}
\label{SEC:Intro}

In recent years, researchers have spent significant effort on appearance based face recognition.
In particular, (sparse) representation based face classification has shown success in the
literature \cite{Wright09,CRCzhanglei2011}.
Given a test face image, the classification rule is based on the minimum representation error
over a set of training facial images. In this category, one of the well-known methods might be the sparse
representation based classifier (SRC) \cite{Wright09}. SRC  linearly represents a
probe image by all the training images under the sparsity constraint/regularisation using the $L_1$ norm. The
success of SRC has induced a few sparse representation based algorithms
\cite{RSC11,ESRC2012,yang2010gabor,dengdefense2013}.
Competitive results have been observed using non-sparse $ L_2 $ norm regularised representations
\cite{CRCzhanglei2011,LRC10,Javen2011}. The main advantage of these approaches is its computational
efficiency due to the closed-form solution.
Zhang et al.\ argued that it is  the collaborative representation but not the $L_1$  sparsity  that
boosts the face recognition performance \cite{CRCzhanglei2011}.
Their method is dubbed as Collaborative Representation Classification (CRC).

   Instead of focusing on the encoding strategy and directly using the training samples
   as the dictionary,  another research topic is  to learn a dictionary using improved variants of sparse coding.
   Yang et al.\ \cite{yang2011fisher} proposed a dictionary learning method using the Fisher discrimination criterion.
   Dictionary learning was formulated as an extended K-SVD problem in
   \cite{zhang2010discriminative} and \cite{jiang2011learning}, where the classification
   error was taken into the objective function.
   To alleviate the effect of image noise contamination, low-rank minimisation of dictionary
   was utilised with the help of class discrimination \cite{ma2012sparse} or structural
   incoherence of basis \cite{chen2012low}.

   Almost all of these methods use holistic images. Local feature descriptors such as
   histograms of Local Binary Patterns (LBP) \cite{FR_LBP06}, Gabor wavelets \cite{GaborWavelet93}
   have been proven to improve the robustness of face recognition systems.
   A {\em heuristic} method is the modular approach, which first partitions the entire facial
   image into several blocks and then classification is made independently
   on each of these local blocks. The intermediate results on local patches are finally
   aggregated, e.g., by distance-based evidence fusion (DEF) \cite{LRC10} or majority voting
   \cite{Wright09}. Zhu et al.~\cite{zhu2012multi} proposed a multi-scale patch
   based method, which fused the results of patches of all scales with the scale weights
   learned by regularized boosting.

   {\em All the aforementioned representation based methods make classification decisions based on the
   representation errors/residuals}. In contrast, the encoded coefficients
   themselves have not been fully exploited for face recognition.
   This is very different from the simplest, standard pipeline of generic image classification,
   such as the Bag-of-Features (BoF) model.
   \cite{coates2011importance}.
   %
   The BoF model
   \cite{LLC2010,coates2011importance} is generally composed of dictionary learning
   on local features (either raw pixels or dense SIFT features),
   feature encoding and spatial pyramid pooling. The locally encoded and
   spatially aggregated feature representation, coupled with an efficient linear classifier (e.g.,
   linear support vector machines (SVM)), has been the standard approach to generic image classification,
   including fine-grained image classification, and object detection.

   A striking and surprising result of our work here is that
   {\em the simple,  standard generic image classification approach
   using unsupervised feature learning outperforms the best face recognition methods.}
   This finding suggests that face recognition may not need to be tackled as a separate problem---generic object
   recognition approaches work extremely well on face recognition.
   A bold suggestion is that for the problem of face recognition, it may be better to
   shift the research focus from the recognition component to the pre-processing component, e.g.,
   facial feature detection and alignment, which often benefits the subsequent recogniser.

\begin{figure*}[t!]
\centering
\includegraphics[width = 0.65\textwidth]{pipeline}
\caption{An illustration of the standard image classification pipeline.}
\label{Fig:chart}
\end{figure*}

   Recently, Coates et al.\ showed that for  image classification, a very simple module combination
   (dictionary of patches learned using K-means or even random selection with a simplified non-linear
   encoder) can achieve state-of-the-art image classification performances on benchmark datasets
   such as CIFAR and CalTech101
   \cite{coates2011importance,coates2011analysis}.
   Our baseline face recognition system follows this simple pipeline of
   \cite{yang2009linear,coates2011importance},
   which is illustrated in Fig.\ \ref{Fig:chart}.
  Namely, we solve the face recognition problem using the pipeline of
  dictionary learning, feature encoding and spatial pooling followed by linear classification.
  We also thoroughly evaluate the impact of each stage in the pipeline on the final face
  recognition performance. Some interesting results are found, which provide insightful
  suggestions to the face recognition community.

  In summary, our main contributions are as follows.
\begin{itemize}

\setlength{\itemsep}{-1.2mm}

\item[1.]

   We show that, given the raw-pixel face images, simple feature learning coupled with a highly
efficient linear classifier can achieve state-of-the-art face recognition performances on
widely-used face benchmark datasets. We thus suggest that face recognition can be tackled
as an instance of generic image classification, advances of which can help boost
the face recognition research.

\item[2.]
The generic image classification pipeline (including dictionary learning, encoding and
pooling) is thoroughly evaluated in the scenario of face recognition.
We show that dictionary learning is less important than that of
feature encoding. This is consistent with the observation in \cite{coates2011importance}.
   In particular, we show that a dictionary using random patches, without learning involved,
   can work very well.

   Another interesting finding is that, using this recognition pipeline, a dictionary learned
   using {\em non-face patches} can also achieve excellent results.
   That is, a face image patch can be represented by using non-face image patches.
   In contrast, all the previous representation based face recognition methods (SRC \cite{Wright09},
   CRC \cite{CRCzhanglei2011}, LRC \cite{LRC10} etc.) are restricted to use facial
   training images to form the dictionary.

\item[3.]
Composed of inner product and simple non-linear activation function, the soft
threshold encoding performs close to (however much faster than) sparse coding. Actually in
some situations (e.g., with large class number and less training data) soft threshold
achieves even better results than sparse coding.
This deviates the results found on the CIFAR and CalTech101
image classification \cite{coates2011importance}.

\item[4.]
Last, we show that a simple fusion of the learned features using raw-pixel intensity and
LBP  further improves the classification performance over features learned using raw-pixel or LBP alone.

\end{itemize}

   Next, before we present our main results, we briefly introduce the image classification pipeline using
   unsupervised feature learning.

\section{Image classification and feature learning}

Image classification, which classifies an image into one or a few categories, has been one
of the most fundamental problems in computer vision.
Possibly due to the special characteristics of face images and historical reasons,
face recognition has so far been considered as a different task from generic image classification.
However, in essence, face recognition is a sub-category or fine-grained object classification problem.
Indeed, for the first time, we show that generic object recognition methods work extremely well on face
recognition.

The generic image classification pipeline (shown in Fig.~\ref{Fig:chart}) has achieved
state-of-the-art performances \cite{LLC2010,coates2011importance}. Low-level features on
local patches are usually densely extracted and pre-processed (normalisation and whitening)
in the first step. It has
been shown that the pre-processing steps can have considerable influence on the final classification performance
 \cite{coates2011analysis}.
   With the extracted local patches, an over-complete dictionary is formed by using unsupervised
learning, e.g, K-means, K-SVD, or sparse coding \cite{yang2009linear}.
The image patches are then encoded with the learned dictionary, e.g., using the hard vector
quantisation, variants of sparse encoding \cite{yang2009linear,LLC2010} or soft threshold
\cite{coates2011importance}. To encode the spatial information as well as reduce the dimension of the generated features,
   the encoded features are pooled over the
pre-defined spatial cells either using average pooling or max-pooling, results of which are
concatenated to form the final representation of an image. Last, a
linear classifier  is trained with the learned image features.

\subsection{Unsupervised dictionary learning}

In this work, we use the following methods to construct the dictionary ${\mathbf{D}} \in
\mathbf{R}^{d \times m}$. Here  $m$ is the number of atoms, and $ d $ is the input dimension.
   We have chosen these methods  mainly due to their computational scalability and efficiency.
Suppose that we have extracted $N $ small local patches
$\{{\mathbf{x}}_i\}$ from the training data.
\begin{itemize}
\item[1.] \textbf{Random selection:}
The dictionary ${\mathbf{D}}$ is directly formed by the $m$ patches randomly selected from $\{{\mathbf{x}}_i\}$.
No learning is involved.
\item[2.] \textbf{K-means clustering:}
   We apply K-means clustering on  local patches $\{{\mathbf{x}}_i\}$ and
   fill the columns of ${\mathbf{D}}$ with the $m$ cluster centres.
\item[3.]  \textbf{Sparse coding:}
Sparse coding forms  an over-complete dictionary by minimising the reconstruction error
with the $L_1$ sparsity constraint. The objective function can be written as
\cite{coates2011importance}:
\begin{align}
\min_{D, {\mathbf{f}}_i} & \sum_i \|{\mathbf{D}}{\mathbf{f}}_i - {\mathbf{x}}_i\|^2_2 + \lambda \|{\mathbf{f}}_i\|_1 \notag\\
{\,\,\mathrm{s.t.\,\,}} & \|{\mathbf{D}}_j\|_2 = 1, \quad \forall j.
\label{EQ:SC}
\end{align}
Here ${\mathbf{D}}_j$ is the $j$th dictionary atom ($j$th column of the matrix $ {\mathbf{D}} $).

\item[4.] \textbf{K-SVD:}
   As a generalization of K-means,
   K-SVD  shares a similar objective function as sparse coding \cite{KSVD2006}.
   It has been  used for generating dictionary for residual based face recognition
   \cite{zhang2010discriminative,jiang2011learning}.
\end{itemize}

\subsection{Feature encoding}

     Once the dictionary is obtained,
     local patches are then fed into the feature encoder to generate
     a set of codes $\{{\mathbf{f}}_i\}$.
     We use the following encoding methods.
\begin{itemize}
\item[1.] \textbf{Sparse coding: } With ${\mathbf{D}}$ fixed in \eqref{EQ:SC},
   ${\mathbf{f}}_i$ can be obtained by solving the LASSO problem.
\item[2.] \textbf{Locality-constrained linear coding:}
Compared with sparse encoding, LLC \cite{LLC2010} is more efficient,
         which solves for the code of a sample by a constrained least square problem with
         its $K$ nearest neighbours.
\item[3.] \textbf{Ridge regression:}
If one replaces the $ L_1 $-norm regularisation with the $ L_2 $-norm regularisation,
   one has the ridge regression problem, which admits a closed-form solution.
   We can generate the code directly by the ridge regression problem.

   In \cite{CRCzhanglei2011}, the authors reformulate the $ L_1 $
   optimisation of SRC into a $ L_2 $ ridge regression problem for face recognition.

\item[4.] \textbf{Soft threshold:} With a fixed threshold $\alpha$,
   this simple feed-forward non-linear encoder writes \cite{coates2011importance}:
\begin{align}
{\mathbf{f}}_j({\mathbf{x}}) =& \max\big\{0, {\mathbf{D}}_j^{\!\top}{\mathbf{x}} - \alpha \big\},\\
{\mathbf{f}}_{j+m}({\mathbf{x}}) = &\max\big\{0, -{\mathbf{D}}_j^{\!\top}{\mathbf{x}} - \alpha \big\}.
\label{EQ:ST}
\end{align}
Here ${\mathbf{f}}_j$ is the $j$th entry of the encoded feature vector ${\mathbf{f}}$.
\item[5.] \textbf{K-means triangle:} As a `softer' extension of the hard-assignment coding, with the $m$ centroids $\{{\mathbf{c}}_j\}$ learned by K-means, K-means triangle encode ${\mathbf{x}}$ as:
\begin{equation}
{\mathbf{f}}_j({\mathbf{x}}) = \max\big\{0, \mu({\mathbf{z}}) - {\mathbf{z}}_j\big\},
\label{EQ:KT}
\end{equation}
where ${\mathbf{z}}_j = \|{\mathbf{x}} - {\mathbf{c}}_j\|$ and $\mu({\mathbf{z}})$ is the mean of ${\mathbf{z}}$.

We can also use this encoding method by replacing $\{{\mathbf{c}}_j\}$ with the
bases generated by other dictionary learning methods other than  K-means.
See \cite{coates2011analysis}
   for details.
\end{itemize}

\subsection{Linear classifiers}
\label{SEC:classifier}

A simple ridge regression based classifier is used in  \cite{gong2011comparing}.
We use the same linear classifier for its computational efficiency.
One can of course use linear SVM such as LIBLINEAR  \cite{liblinear08}.
However, for large-scale high-dimensional data, LIBLINEAR is still slow.
The ridge regression classifier has a closed-form solution, which is very fast.
Despite its simplicity, the classification performance of this ridge regression approach
is on par with linear SVM \cite{gong2011comparing}.

Let ${\mathbf{X}} \in \mathbf{R}^{n \times d}$ be the training data of $n$ samples of dimension
$d$ and ${\mathbf{Y}} \in \{0, 1\}^{n \times c}$ the label matrix: ${\mathbf{Y}}_{ij}=1$ if data point $i$
belongs to class $j$ of total $c$ classes and 0 otherwise.
Unlike SVM, this classifier generates the classifier matrix with a closed-form solution
${\mathbf{W}} = ({\mathbf{X}}^{{\!\top}}{\mathbf{X}} + \delta {\mathbf{I}})^{-1}{\mathbf{X}}^{{\!\top}}{\mathbf{Y}}$, where ${\mathbf{I}}$ is the identity matrix.
Then an image ${\mathbf{z}}$ is classified by the maximum of ${\mathbf{W}}^{{\!\top}}{\mathbf{z}}$.
Another benefit of this classifier is that, compared to the one-versus-all SVM, it only needs
to compute ${\mathbf{W}}$ once. When the number of classes is large, training of multiple one-versus-all classifiers also
creates the problem of imbalanced data (the number of positive data is much smaller than that of negative data).
This ridge regression classifier does not have this problem.
In practice the dimension $d$ could be over hundreds of thousands when with several
pooling pyramid levels.

We thus compute the matrix ${\mathbf{W}}$ by its equivalent ${\mathbf{W}} = {\mathbf{X}}^{{\!\top}}({\mathbf{X}}\BX^{{\!\top}} +
\delta {\mathbf{I}})^{-1}{\mathbf{Y}}$, where it only needs to invert much smaller $n \times n$ matrix\footnote{The
   parameter $\delta$ is empirically set to $0.005$ in all of our experiments. Careful tune of this
parameter might slightly improve the performance.}.

\section{Evaluation of the pipeline for face recognition}

In this section, we  test the feasibility and performance of classifying face images exactly same as
 in classifying natural images, and try to discover what the most important component is.
Thorough cross evaluations of the components in the image classification pipeline are
conducted on face recognition.

Following  \cite{coates2011importance},
 local patches are extracted on data with patch size of $6 \times 6$ pixels and a stride of $1$
pixel, followed by contrast normalization and ZCA whitening \cite{ICA2000}. It has been
proven that dense feature extraction and the pre-processing step are critical for achieving
better performance.

Unless otherwise specified, we generate the dictionary from 50,000 randomly extracted patches on the training data.
In all the experiments except those in section \ref{SEC:dict}, the threshold $\alpha$ in soft threshold and the regularization
parameter $\lambda$ in sparse coding are set to 0.25 and 1, respectively.
As a default setup in the evaluation, the final features are formed by max pooling over 3 pyramid levels.
 %

We mainly conduct evaluations on the datasets of AR and Extended Yale B, and LFW. The datasets
description can be found in section \ref{SEC:Exp}. In this section we use 13 and 32
training samples each class for AR and Extended Yale B, respectively. The rest are used
for testing. For LFW-a, 5 samples are randomly
selected for training and another 2 samples for testing in each class.

\subsection{Dictionary learning and feature encoding}
\label{SEC:dict}

First, we evaluate the impact of the dictionary size on face recognition performance.
Fig.~\ref{Fig:DictionarySize} shows the results of our method with the dictionary size
varying from 100 to 2,000. It is not surprising to see that {\em the accuracy is consistently
improved as the dictionary size increases.} Coates et al.\ \cite{coates2011importance} observed
that a large dictionary often leads to improved classification accuracy, especially
when one has a limited number of labelled training data for training the final classifier.
     This is the case for face recognition.
In the following experiments we set the dictionary size to 1,600.

\begin{figure}[]
\centering
\includegraphics[width=0.45\textwidth]{DictionarySize}
\caption{Impact of the size of the dictionary (obtained by random selection, K-means
   and K-SVD) on the recognition accuracy (\%). The AR dataset is used.
   Here the encoding strategy is soft
   thresholding for efficiency.}
\label{Fig:DictionarySize}
\end{figure}

Next, we then examine the importance of dictionary learning and feature encoding methods for
face recognition. Four methods of dictionary construction: randomly selected patches (uniform sampling),
K-means, K-SVD \cite{KSVD2006}, sparse coding \cite{yang2009linear}; and five encoding
algorithms: sparse coding, LLC \cite{LLC2010}, $ L_2 $ ridge regression, soft threshold, K-means
triangle \cite{coates2011analysis} are  evaluated.
We set the maximum number of iterations for K-SVD to 30,
and  the number of nearest neighbours $K = 5$ for LLC.
We conduct K-SVD with  sparsity level $T = \{3, 5, 10, 15, 30\}$, sparse coding with $\lambda = \{0.25, 0.5, 1.0, 1.5\}$, LLC with the regularization parameter $\delta = \{0.001, 0.01, 0.1, 1\}$, soft threshold with $\alpha = \{0.1, 0.25, 0.5, 0.75, 0.1\}$.
By maximizing over these parameters, best cross validation results with each combination of the dictionary learning and encoding methods are reported.
Table~\ref{Tab:Dict_Encoder_AR} and Table~\ref{Tab:Dict_Encoder_LFW} list the recognition
results on the AR and LFW datasets, respectively.

\begin{table}[]
\centering
\begin{tabular}{r cccccc}
\hline
Encoder & ST & SC & LLC & RR &  KT & VQ\\
 \hline
Random & 97.8   & 98.8  &  98.2  &  95.7 &   97.5 & 94.5\\
 K-means&    97.8 &  98.5 &  97.2  &  95.6   & 97.1 &92.9\\
K-SVD&     98.2 &   98.6  &  98.4  &  96.1   & 97.8 &95.1\\
 SC &    98.1 &  98.4   & 98.9  &  97.7 &   98.5 &94.3\\
 \hline
\end{tabular}
\caption{ Accuracies (\%) with various dictionary learning methods (shown in the first
column) and encoding methods (shown in the first row) on AR. `SC', `LLC', `RR', `ST',
`KT' and `VQ' represent `Sparse Coding',
   `Locality-constrained Linear Coding',
   `Ridge Regression', `Soft Threshold', `K-means Triangle' and hard `Vector Quantization', respectively.
      {\em The results at each column are similar,
which shows that dictionary learning methods
   do not  have a significant impact on the final performance.
}
}
\label{Tab:Dict_Encoder_AR}
\end{table}

\begin{table*}[]
\centering
\begin{tabular}{r cccccc}
\hline
Encoder & ST & SC & LLC & RR &  KT & VQ\\
 \hline
Random & $75.4 \pm 1.6$   & $76.4 \pm 1.8$  &  $76.5 \pm 0.5$  &  $68.2 \pm 3.6$ &   $78.0 \pm 1.2$ & $62.5 \pm 2.1$\\
 K-means&    $75.4 \pm 1.6$ &  $77.2 \pm 2.3$  & $72.9 \pm 2.2$  &  $64.6 \pm 2.1$   & $77.9 \pm 0.9$ &$60.6 \pm 2.1$\\
K-SVD&     $75.4 \pm 1.6$ &   $77.6 \pm 2.8$  &  $75.1 \pm 0.8$  &  $69.1 \pm 1.4$   & $79.0 \pm 1.2$ &$61.6 \pm 1.1$\\
 SC &    $74.7 \pm 1.9$  &  $74.3 \pm 3.7$   & $75.7 \pm 3.1$  &  $72.6 \pm 1.1$ &   $80.0 \pm 2.3$ &$60.3 \pm 0.4$\\
 \hline
\end{tabular}
\caption{ Accuracies (\%) with various dictionary learning methods (shown in the first
column) and encoding methods (shown in the first row) on LFW. Results are based on 5 independent runs.
}
\label{Tab:Dict_Encoder_LFW}
\end{table*}

   As we can see, with an encoder fixed (such as soft threshold or sparse coding),
   different dictionary learning methods lead to similar classification results.
   In other words, dictionary learning is less critical in terms of the final performance.
   As observed in \cite{coates2011importance}, for face recognition we see similar observations:
   simple algorithms like K-means can work as well as sparse coding and K-SVD in most
   cases.
   In general, Vector Quantization (VQ) as in the traditional bag-of-visual-words model,
   performs  worse than other encoding methods because  VQ
   may have lost much useful information due to the aggressive hard-assignment
   coding.

   In \cite{coates2011importance}, the authors show that
   a dictionary of randomly selected patches usually performs slightly
   worse on CIFAR and CalTech101.
   While for face recognition, we see that even with a dictionary of
   randomly selected patches, the performance is as good as other dictionary learning methods such as
   sparse coding when the size of the dictionary is large (1,600 is sufficient in our experiments).

   This is in contrast to the representation error based methods, where it is important
   to design an effective dictionary (in order to reconstruct the test image well), e.g., by sample
   variations \cite{ESRC2012,dengdefense2013}, discriminative  learning \cite{yang2011fisher}
   or low-rank minimization \cite{ma2012sparse,chen2012low}.

   Among the encoding approaches, sparse coding  achieves the best results on
   the AR dataset, which marginally outperforms other encoding methods with most dictionary training methods.
   The simple and computationally efficient encoder `soft threshold' and `K-means Triangle' also stably offer
   promising results, which is much better than ridge regression and VQ on this dataset. On the
   relatively challenging dataset LFW, the simple algorithm K-means triangle works even better than all other methods with any dictionary learning methods. Note that K-means triangle does not have a parameter to tune.

   Consistent with the finding for generic image classification \cite{coates2011importance}, {\em
   we can see from this experiment that the choice of dictionary learning methods  is less
   important than that of feature encoder in the face image classification pipeline.}
   Random patches combined with a simple encoder, which introduces non-linearity, can work very
   well. In general, the conclusion is that
   most non-linear encoding methods yield promising results for face recognition.
   Here we show some learned bases generated by different dictionary learning methods
   in Fig.\ \ref{Fig:dictionary}.

\begin{figure*}[]
\centering
\begin{subfigure}{0.23\textwidth}
\centering
\includegraphics[width=0.95\textwidth]{dictionary_patches_with_whiten}
\caption{Randomly selected patches}
\end{subfigure}
\begin{subfigure}{0.23\textwidth}
\centering
\includegraphics[width=0.95\textwidth]{dictionary_kmeans_with_whiten}
\caption{K-means}
\end{subfigure}
\begin{subfigure}{0.23\textwidth}
\centering
\includegraphics[width=0.95\textwidth]{dictionary_ksvd_with_whiten}
\caption{K-SVD}
\end{subfigure}
\begin{subfigure}{0.23\textwidth}
\centering
\includegraphics[width=0.95\textwidth]{dictionary_sc_with_whiten}
\caption{Sparse coding}
\end{subfigure}
\caption{Visualisation of some generated dictionary bases by a few different dictionary construction
   methods. The bases generated by different methods appear similarly.}
\label{Fig:dictionary}
\end{figure*}

 %
 %
 %
 %
   %

 %
 %
 %

\begin{figure}[t]
\centering
\includegraphics[width=0.45\textwidth]{AR_bar}
\includegraphics[width=0.45\textwidth]{LFW_bar}
\caption{Accuracies (\%) with different number of spatial pyramid levels on the
   AR and LFW-a datasets. `ST', `SC', `AP' and `MP' represent `Soft Threshold', `Sparse Coding', `Average Pooling' and `Max Pooling', respectively.}
\label{Fig:Pooling}
\end{figure}

\subsection{Spatial pyramid pooling}

In this subsection, we evaluate the two popular pooling method: sum/average pooling and
max pooling with varying pyramid levels.
With pyramid pooling, the encoded features for all local patches are pooled over each grid of all
spatial pyramid levels \cite{SPM06}. We set the first a few levels to have \{$1 \times 1$,
$2 \times 2$, $4 \times 4$, $6 \times 6$, $8 \times 8$\} equal-sized rectangular pooling
grids.
The pooled features at each level are concatenated to form the final feature vectors.

Fig.~\ref{Fig:Pooling} shows the results on AR and LFW-a \cite{LFW_a}.
{\em The first observation is that spatial pyramid pooling is important for the final classification
   performance}.

We can clearly see that, max pooling is almost always better than average pooling except for
soft threshold with fewer pyramid levels.      This is consistent with recent results for image
classification \cite{boureau2010learning}.
From Fig.~\ref{Fig:Pooling}, we also see that soft threshold encoding achieves increasing
accuracies as pyramid depth increases, especially on the challenging LFW-a dataset \cite{LFW_a}.
As for sparse coding, the performance does not improve significantly when the pyramid
depth is already more than three.
With max pooling   and   three or more pyramid levels, soft
threshold obtains close results with sparse  coding on AR and even better results on LFW-a.
Note that soft threshold encoding is simple and computationally much more efficient than sparse coding.

\begin{table}[]
\centering
\begin{tabular}{r ccc}
\hline
\multirow{2}{*}{Test ~ ~ } & \multicolumn{3}{c}{Dictionary}\\%& \multicolumn{3}{c}{sparse coding}\\
 \cline{2-4}
 & AR & ExYale B  & CIFAR-10\\
 \hline
 AR (ST) & 97.8 & 97.6 & 97.5\\%\\
AR (SC) & 98.2 & 98.2 & 97.8\\
  ExYale B (ST) &99.8  & 99.8 & 99.8\\%
  ExYale B (SC) & 98.0 & 98.7 & 98.3\\%
 \hline
\end{tabular}
\caption{Recognition accuracies (\%) on AR and Extended Yale B (ExYale B) of our method using different datasets (listed on the second row) for dictionary generation. }
\label{Tab:datasets}
\end{table}

\subsection{Representing faces using a dictionary learned from non-face image patches}

In this subsection, we test whether the training data for dictionary learning is
critical. We generate the dictionary using both the training data from the testbed dataset
and on additional datasets. For example, we test on the AR dataset with dictionary trained
on AR or Extended Yale B.
From Table~\ref{Tab:datasets}, we can see that on par
results are obtained by training on the same  dataset from the test data or a different one.

In addition, we further evaluate the dictionary generated
on datasets of {\em non-face} images. Here we use a subset of
CIFAR-10\footnote{\url{http://www.cs.toronto.edu/~kriz/cifar.html}}, which includes 10,000
natural images.
It is surprising that learning a dictionary using the dataset of non-face images also works very
well.
\emph{That is, a face image can be recognised using a representation learned from
non-face images.} This is impossible for the representation error based methods like SRC.
This property is desirable because one can obtain a dictionary that is as large as needed
even when lacking of training face data.

   The reason why learning face representations using non-face data works  so well
   might be as follows.   We have used the single-layer unsupervised feature learning
   to generate the dictionary. It is well known that single-layer dictionary learning
   such as sparse coding mainly learns image filters that resemble Gabor-like filters.
   Small patches extracted from either face images or non-face images would both learn
   Gabor-like filters.
   However, for multiple-layer hierarchical feature learning, one expects that using
   object-specific training data might learn more meaningful high-level features.

   %

\section{Experimental results}
\label{SEC:Exp}

In this section, we compare our methods with several state-of-the-art algorithms on
four benchmark face recognition databases. These compared algorithms include SRC, CRC,
robust sparse coding (RSC \cite{RSC11}), the Fisher discrimination criterion based dictionary
learning method FDDL \cite{yang2011fisher}, patched based MSPCRC \cite{zhu2012multi}
and PNN \cite{kumar2011maximizing}. For these methods we use the codes and recommended
settings provided by the authors.

From the pilot experiments in the last section, we know that the important factors for the final
face recognition performance are: the image patch size and stride step, encoding methods, and the pooling strategy.
We now report results using various encoding methods; and the patch size and stride step are set to be small
      ($6\times 6$ pixels and the stride step is 1 pixel in all of the experiments).
All the results of our method in this section are based
on max-pooling with 5 pyramid pooling levels.
We set the dictionary size to 1,600 in all experiments, which are randomly sampled from 50,000 small patches.
Therefore,
   no learning is involved in dictionary learning for our approach.

\subsection{Extended Yale B}
The Extended Yale B dataset \cite{GeBeKr01} consists of 2414 frontal face images from 38
subjects under various lighting conditions. The images are cropped and normalized to $192
\times 168$ pixels [31].
To increase the difficulty, in each class we randomly choose only 10 sample for
training and another 5 samples for testing. All the images are finally resized to $32
\times 32$. We run experiments on 5 independent data splits and report the average results in
Table~\ref{Tab:Yale}.

It is clear that our method with soft threshold encoding achieves the highest average accuracy of
97.3\%, which is  better than MSPCRC. In contrast, the best result among all other
methods is only 92.6\% (obtained by FDDL). Our method with sparse coding does
not perform as
well as soft threshold on this dataset. We have not carefully tuned the regularization parameter in sparse coding.
Careful tuning of this parameter might improve the performance of sparse coding.

We also list  in Table~\ref{Tab:Yale2} the recent published results of several sate-of-the-art methods on this dataset. It is clear that our method outperforms all other methods with 99.8\% accuracy.
{\em To  our knowledge, ours is the best published result with the same settings on the Extended Yale B dataset.
}

\begin{table*}[]
\centering
\begin{tabular}{r  ccccccc}
\hline
Method & CRC & SRC & RSC &FDDL & MSPCRC & Ours (ST)&Ours (SC)
\\   \hline
Accuracy&$91.9 \pm 0.8$&$89.0 \pm 1.2$&$89.7 \pm0.5$&$92.6\pm 1.3$&$97.1 \pm 1.5$&${\bf 97.3 \pm 1.3}$&$91.0 \pm 2.1$\\
 \hline
\end{tabular}
\caption{Recognition accuracies (\%) and their standard deviations on the Extended Yale B dataset with dimension $32 \times 32$. Results are based on 5 independent runs.
 }
\label{Tab:Yale}
\end{table*}

\begin{table}[h]
\begin{tabular}{r | ccc}
\hline
Method & Training size & Dimension & Accuracy\\
\hline\hline
DLRD \cite{ma2012sparse} & 32 & $24 \times 21$ & 98.2\\
FDDL \cite{yang2011fisher} & 20 & $54 \times 48$ & 91.9\\
LC-KSVD \cite{jiang2011learning} &32 & $24 \times 21$ & 96.7\\
LSC \cite{theodorakopoulos2011face} & 32 & $104 \times 91$ & 99.4\\
Low-Rank \cite{chen2012low} & 32 & 100 (PCA) & 97.0\\
RSC \cite{RSC11} & 32 & 300 (PCA) & 99.4\\
Ours & 32 & $32 \times 32$ &\textbf{99.8}\\
\hline
\end{tabular}
\caption{Published results (\%) in the literature on the Extended Yale B dataset. Number of training images
per class are shown in the second column and the rest are for test.
   %
Soft threshold encoding is used for our method.
 }
\label{Tab:Yale2}
\end{table}

\begin{table*}[]
\centering
\begin{tabular}{r ccccccc}
\hline
Method & CRC & SRC & RSC &FDDL & MSPCRC & Ours (ST)&Ours (SC)\\
          \hline
$32 \times 32$&$59.4 \pm 1.1$&$75.5 \pm 1.5$&$73.0 \pm 2.2$&$79.4\pm 1.4$&$41.0 \pm 1.6$&${\bf 94.3 \pm 0.5}$&${\bf 95.9 \pm 1.0}$\\
$64 \times 64$&$54.0 \pm 0.8$ & $73.6 \pm 1.5$&$73.0 \pm 2.2$ & $79.5 \pm 1.8$ &$41.9 \pm 2.2 $ & ${\bf 97.0 \pm 0.7}$ & ${\bf98.8 \pm 0.5}$\\
 \hline
\end{tabular}
\caption{Recognition accuracies (\%) and their standard deviations on FERET with dimension $32 \times 32$ and $64 \times 64$. Results are based on 5 independent runs.}
\label{Tab:FERET}
\end{table*}
\subsection{FERET}
The FERET dataset \cite{FERET} is another widely-used standard face recognition benchmark set
provided by DARPA.
We use a subset of FERET which includes 200 subjects.
Each individual contains
7 samples, exhibiting
 facial expressions, illumination changes and up to 25 degrees of pose variations. It is
composed of the images whose names are marked with `ba', `bj', `bk', `be', `bf', `bd' and
`bg'. The images are cropped and resized to $80 \times 80$ \cite{yang2003combined}. We
randomly choose 5 samples in each class for training and the rest 2 samples for testing.
The mean results of 5 independent runs with down-sampled $32 \times 32$ and $64 \times 64$
images are reported in Table~\ref{Tab:FERET}.

Different from Extended Yale B, FERET has a large number of classes and exhibits pose variations.
On this dataset, we can see that our methods  perform significantly better than previously reported results.
   Almost all other methods do not achieve satisfactory results.

{\em Our method with sparse coding obtains an average accuracy of 98.8\% with image dimension $64
\times 64$ pixels, which outperforms the previous best result of FDDL \cite{yang2011fisher}
by a large margin of about 20\%.}
Note that our method is based on randomly extracted
patches, while the dictionary of FDDL is trained by a very expensive process. MSPCRC does
not perform well on this dataset.

\subsection{AR}

\begin{table*}[]
\centering
{
\begin{tabular}{r cccccc}
\hline
Method & CRC & SRC & RSC &FDDL  & Ours (ST) &Ours (SC) \\
\hline
AR (Occlusion)& 84.5&86.5&88.5&83.5& \textbf{97.8}&\textbf{98.2} \\
AR (Clean) &88.4&92.9&92.3&85.9&\textbf{98.4}&\textbf{98.9}
\\
 \hline
\end{tabular}
}
\caption{Recognition accuracies (\%) on the AR dataset with dimension of $32 \times 32$ pixels. ST and SC are our methods with soft threshold and sparse coding, respectively.}
\label{Tab:AR}
\end{table*}

\begin{table}
\centering
\begin{tabular}{r | ccc}
\hline
Method & Situation & Dimension & Accuracy\\
\hline\hline
LSC \cite{theodorakopoulos2011face} &Clean &$116 \times 84$&90.0\\
 FDDL \cite{yang2011fisher} & Clean&$60 \times 43$&92.0\\
DLRD \cite{ma2012sparse}& Clean & $27 \times 20$ & 95.0\\
RSC \cite{RSC11} & Clean & 300 (PCA) & 96.0\\
Ours & Clean & $64 \times 64$ & \textbf{99.1}\\
L2 \cite{Javen2011} & Occlusion & $168 \times 120$&95.9\\
ESRC \cite{ESRC2012} & Occlusion &$168 \times 120$&97.4\\
SSRC \cite{dengdefense2013} & Occlusion&$168 \times 120$ & 98.6\\
Ours  & Occlusion & $64 \times 64$ & \textbf{99.2}\\
\hline
\end{tabular}
\caption{Published results (\%) on AR dataset in two situations. Sparse coding is used for our method.}
\label{Tab:AR2}
\end{table}
The AR dataset \cite{AMM98} consists of over 4000 facial images from 126 subjects (70 men
and 56 women). For each subject 26 facial images were taken in two separate sessions.
The images exhibit a number of variations including various facial expressions (neutral,
smile, anger, and scream), illuminations (left light on, right light on and all side
lights on), and occlusion by sunglasses and scarves. Of the 126 subjects available 100
have been selected in our experiments (50 males and 50 females). All the images are
finally resized to $32 \times 32$ pixels.

We first test on the AR dataset in two situations: with occlusions and without occlusions. For
the first situation, the 13 samples in the first session are used for training and the
other 13 images in the second session for test. For the second `clean' situation, the
7 samples in each session with only illumination and expression changes are used. From
Table~\ref{Tab:AR}, we can see that {\em both of our two methods with  soft threshold encoding
and sparse coding encoding obtain the best results on these two situations}. The previous best results of other
methods in the occlusion and clean situation are obtained by RSC and SRC, which are worse
than our method with sparse coding by 9.8\% and 10\%, respectively.

We also list in Table~\ref{Tab:AR2} the recent published results of several
sate-of-the-art methods on AR. It is clear that our method achieves the highest accuracies
(99.1\% and 99.2\% for the clean and occlusion situation, respectively). To the best of
our knowledge, these are the best results obtained on AR in these two situations.

We further test these methods with less number of training samples per class. Among the samples
with only illumination and expression changes in each class, we randomly select 2 to 5
images of the first session for training and 3 of the second session for test. We run
the experiments 5 times and report the average results in Fig.~\ref{Fig:AR_trn_sz}. It
is clear that, our methods consistently outperform all other methods by large margins in
all situations. An accuracy of 88\% is obtained by soft threshold with only 2 training
samples per class. With less training samples, soft threshold performs better than sparse
coding on this dataset. Holistic image based method CRC achieves the worst results, while
multi-scale patch based CRC performs the second best.
This demonstrates that  relatively smaller-size local patches might encode more discriminative information,
which helps classification, than bigger-size patches or the whole image.

\begin{figure}[]
\centering
\includegraphics[width=0.48\textwidth]{AR_trn_sz2}
\caption{Aveage recognition accuracies (\%) of 5 independent runs on AR dataset with number of training samples per class from 2 to 5. Standard deviations are shown with error bars. Results for PNN and MSPCRC are cited from \cite{zhu2012multi} with the same setting, where the deviation results are not shown here due to their large values.}
\label{Fig:AR_trn_sz}
\end{figure}

\subsection{LFW-a}
The original LFW database \cite{LFWTech} contains images of 5,749 subjects in unconstrained environment.
With the same settings as in \cite{zhu2012multi},
     here we use LFW-a, an aligned version of LFW using commercial face alignment software \cite{LFW_a}.
     A subset of LFW-a including 158 subjects with each subject more than 10 samples are used. The images are cropped to $121 \times 121$ pixels.
Following the settings in \cite{zhu2012multi},
          2 to 5 samples are randomly selected for training and another 2 samples for
testing. All the images are finally resized to $32 \times 32$.

Fig.~\ref{Fig:LFW_trn_sz} lists  the average results of 5 independent runs. Similar results
are obtained as in Fig.~\ref{Fig:AR_trn_sz}. Our methods perform considerably better than all other
methods with even larger gaps on this challenging dataset. Same as on the AR dataset, soft threshold encoding
achieves slightly better results than sparse coding here.
 On this dataset, residual based methods (even MSPCRC and PNN that use local patches) obtain poor results.
{\em With 5 training samples each class, our approach with
 soft threshold encoding outperforms the state-of-the-art method MSPCRC by around 29\%.
}

\begin{figure}[]
\centering
\includegraphics[width=0.48\textwidth]{LFW_trn_sz2}
\caption{Average recognition accuracies (\%) of 5 independent runs on the LFW-a dataset.
Standard deviations are shown with error bars. Results for PNN and MSPCRC are cited from
[3] with the same setting.}
\label{Fig:LFW_trn_sz}
\end{figure}

\subsection{Feature combination}
All the previous evaluations  are based on raw intensity.
To achieve better performance, multiple complementary features can be extracted for input of the feature learning pipeline.
In this experiment, we take local binary patterns (LBP) for example to test the performance by feature fusion.
We first represent each image as an LBP code image, and then apply the feature learning pipeline  on images of raw pixels and LBP codes  independently. The learned feature vectors through the two channels for each image are then concatenated  into one large vector.

Table~\ref{Tab:Fusion} shows the compared results between raw intensity and its fusion
with LBP. Soft threshold is used for feature encoding. On both AR and LFW-a, the fused
features obtain improved accuracies with all training sizes. For example with 2 training
samples each class on LFW-a, the accuracy is averagely increased by 1.7\% with the
combined feature.
It is an interesting research topic to explore how to improve the classification by combining
more low-level image statistics.
We leave this as a future research topic.

\begin{table*}[]
\centering
\begin{tabular}{r | cccccc}
\hline
 Training size & 2 & 3 & 4 & 5\\
 \hline\hline
AR (Raw) & $88.6 \pm 0.9$ & $94.1 \pm 0.8$& $96.7 \pm 0.9$& $97.6 \pm 0.9$   & \\
AR (Raw + LBP) & $ 89.2 \pm 2.4$  &$ 95.1 \pm 1.1$ & $ 97.9 \pm 1.0$ & $ 98.3 \pm 0.6$
   & \\
 %
  \hline
 LFW-a (Raw) & $52.9\pm3.5$  &$66.5\pm 2.9$ &$73.8 \pm 1.8$  &$77.9 \pm 1.5$ &\\
 LFW-a (Raw + LBP)&$ 54.6 \pm 2.7$& $ 66.8 \pm 1.9$&$ 74.6 \pm 2.5$& $ 78.9 \pm 1.8$\\
 %
\hline
\end{tabular}
\caption{Average results (\%) of 5 runs on AR and LFW-a with fusion of learned features. The
first column shows the training size per class. Soft threshold is used for encoding.
}
\label{Tab:Fusion}
\end{table*}

\subsection{Modular methods vs.\ our pipeline}

In this experiment, we want to compare our approach against the modular approach which is also
based on local patches.

The modular approach has been widely used for robust face recognition.
For example, the voting strategy is used to aggregate the intermediate results on local patches in
\cite{Wright09,CRCzhanglei2011}, which achieved much better results than the original methods on occlusion problems.
For efficiency, here we take CRC  \cite{CRCzhanglei2011} for example to compare the modular approach with our image classification
model on the AR dataset. Local patches are densely extracted from the images of $32 \times 32$  pixels
with a stride of 1 pixel.
The comparative results with different patch sizes are shown in Fig.~\ref{Fig:Modular}.
In addition  to voting, we also conduct the modular approach by combining the residuals of all local patches
for classification (`Sum' in the figure).
The observations are:
\begin{enumerate}
\item
Our generic image classification model achieves significantly better results than the modular approaches
with all the setting of different patch sizes.
\item
Our approach tends to achieve better results with smaller patches
while the modular approaches favour mid-size patches.
This is mainly due to the spatial pyramid pooling step in our pipeline.
It remains unclear how to introduce spatial pooling into the residual based approaches such as SRC and CRC.
\item
Also note that the simple sum operation consistently obtains higher accuracies
than the majority voting method in the modular approaches.
\end{enumerate}
   This experiment shows that besides the advantage of local patches,
   other components in our pipeline such as feature encoding and spatial pooling must have contributed to
   the superior performance of the image classification pipeline for face recognition.

\begin{figure}
\includegraphics[width=0.48\textwidth]{ModularVSPooling}
\caption{Comparison between modular methods (with `Votin'g and `Sum' strategy)
   and our model (with soft threshold encoding) on the AR dataset.
      The patch size varies from $4 \times 4$ to $20 \times 20$ pixels.}
\label{Fig:Modular}
\end{figure}

\section{Conclusion}

Can face images be classified using  the same strategy as in generic image classification?
In this work, we have answered this
question by applying the generic image classification pipeline to face recognition,
which {\em outperforms all the reported state-of-the-art face recognition methods by large margins
on four standard benchmark datasets.}

A large body of recent published face recognition methods, especially SRC-scheme
based ones, have focused on designing complex dictionaries (e.g., using discriminant
learning or K-SVD etc). By a thorough cross evaluation of the components in the image classification
pipeline on face recognition problems, we show that the choice of dictionary learning algorithms
is less critical than that of encoders.  In particular, we show that randomly  selected
patches can work very well. Interestingly, we find that the dictionary is even not
necessarily generated using patches extracted from face images. That is, a face image can be recognised using a
representation generated from non-face image patches.

Another finding of this work is that the simple and efficient soft threshold algorithm
surprisingly obtains on par results with the expensive $L_1$-constrained sparse coding.
Note that, the best results on some datasets that we have tested are obtained by the
components of a dictionary of randomly selected patches, soft threshold encoding, and pooling,
followed by a simple linear classifier. In this entire procedure,
{\em the only component involving `learning' is the simple linear classifier training}.

   In the future, we plan to explore the performance of multiple-layer hierarchical feature learning for face recognition.
   Our initial experiments on learning two-layer features using the approach of \cite{KirosS12} and sparse filtering of
   \cite{SP11}
   did not improve the performance over the single-layer features of this work on the AR and LFW-a datasets.
   However, more thorough experiments are needed before we can draw a conclusion.
   The facial image descriptor extracted with the pipeline presented here has potentials for other
   faces-related applications, such as face detection, retrieval,  alignment, and tagging.
   Our preliminary experiments show very encouraging results on large-scale face image retrieval.

{
\small
\bibliographystyle{ieee}

redpajama_set_name: RedPajamaArXiv


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Iker Salaberria fulfilled the forecasts and will play the final of the Four and a Half promotion after beating Expósito yesterday at the Amorebieta fronton. The Navarrese made use of his tables to overcome a newcomer to professionalism who still has many things to learn.
The one from Goizueta made his serve profitable to perfection and, although he had to suffer more than necessary because he was not very fine in the auction, he did not make any concessions either. He managed much better the second part of the contest against a rival that went from more to less. The Gipuzkoan had nothing to lose and had a good first half. Despite the fact that the clash was uphill for him in the first moments (6-1), he managed to react and put pressure on the one who wore red.
He took advantage of his biggest blow to prevent him from approaching the front and found the gaps he was looking for. In this way he managed to tie the match at eight and managed to hold on until shortly after. It was after the tie to eleven when Salaberria certified his pass the summit clash in which he will face Egiguren V.
Hit of seven points
He served much better than the one in blue and was more confident in finishing. The Gipuzkoan's mistakes paved the way for him. Expósito was also not successful in making decisions and he sold himself on a couple of occasions when he had the goal controlled.
A run of seven goals allowed the Navarrese to face the end of the contest much calmer. The one from Anoeta had a timid attempt at reaction that allowed him to get to three (19-16), but he lacked continuity and belief. After 255 balls in 52 minutes the contest was closed.
Topics: final, four, medium, New Year, promotion, will play
Basketball – NBA: Doncic, still stratospheric, scores 53 points and saves Dallas
VIRTUS – RED STAR, REPORT CARDS AND INTERVIEWS – BOLOGNABASKETBALL
Ligue 2: "Frustration but no renunciation" … Paris FC sinks after a 3rd defeat in a row
Find out where to watch Sport x Afogados and check out the likely lineups

redpajama_set_name: RedPajamaCommonCrawl


A historical region and former province of northwest France on the English Channel. Part of ancient Gaul, the region was successively conquered by the Romans, Franks, and Norse; during the Middle Ages it was sporadically controlled by the Anglo-Norman kings of England, but passed permanently to France in 1450. Its beaches were the focal point of Allied landings on D-day (June 6, 1944) in World War II.
a historic region in NW France along the English Channel: Allied invasion in World War II began here June 6, 1944.
Conan, Duke of Brittany, and William, Duke of Normandy, were related to each other, and in a manner the Bretons owned the Duke of Normandy as overlord.
Don't you really know, Durbeyfield, that you are the lineal representative of the ancient and knightly family of the d'Urbervilles, who derive their descent from Sir Pagan d'Urberville, that renowned knight who came from Normandy with William the Conqueror, as appears by Battle Abbey Roll?
Bertuccio," said he, "I intend going this evening to Normandy, instead of to-morrow or the next day.
Normandy and Picardy had taken positions in the rocks dominated by the heights of the mountain, upon the declivity of which were raised the bastions of Gigelli.
They were a large kind of "chaluts," like those on the Normandy coasts, great pockets that the waves and a chain fixed in the smaller meshes kept open.
As he left no children, his estate was inherited by his nephew, from whom is descended the present Marquis of Normandy.
The class are going for a sketching tour to Normandy," she said, "and Mr.
When they are, it is easier to hold them, especially when they have not been accustomed to self- government; and to hold them securely it is enough to have destroyed the family of the prince who was ruling them; because the two peoples, preserving in other things the old conditions, and not being unlike in customs, will live quietly together, as one has seen in Brittany, Burgundy, Gascony, and Normandy, which have been bound to France for so long a time: and, although there may be some difference in language, nevertheless the customs are alike, and the people will easily be able to get on amongst themselves.
Come, Athos and Aramis, go to the right; Porthos, come with me to the left; these gentlemen should file off into Normandy, whilst we, by the nearest road, reach Paris.
Those bands of them with whom we are here concerned, and who became known distinctively as Normans, fastened themselves as settlers, early in the eleventh century, on the northern shore of France, and in return for their acceptance of Christianity and acknowledgment of the nominal feudal sovereignty of the French king were recognized as rightful possessors of the large province which thus came to bear the name of Normandy.
A circumstance which greatly tended to enhance the tyranny of the nobility, and the sufferings of the inferior classes, arose from the consequences of the Conquest by Duke William of Normandy.


text:
Q: How do I merge MP3 files without re-encoding? I would like to merge multiple MP3 files on a Windows PC without decoding and re-encoding them. I don't mind if the final MP3 files loses the ID3 tags, but if it could somehow preserve the ID3 tags, that would be a bonus. I would also like to avoid issues with time.
How can I accomplish this?

A: MP3DirectCut does what you want.
ffmpeg will probably do it too if you'd prefer a command-line method.

A: A Highly rate software Free-MP3-Joiner link here
Hope this helps.

redpajama_set_name: RedPajamaStackExchange


Short and sour: if you're in any affiliate programs requiring referral tracking and you use Wordpress, you need to check if last WP update is killing your sales... AND putting your web at risk!
WP updated TinyMCE text editor to include certain tags on any links that open in a new tab or window which will block the referral tracking. This will lead to you losing sales and possibly being banned from programs like Amazon.
After update, WP adds the noopener and noreferrer tags to links that open in a new tab.
A brief summary can be found here.
What they do is disallow spammers from hijacking the off-tab link.
Are now open for a kind of man-in-the-middle attack.
Google calls it reverse tabnabbing and says that it's part and parcel of using browsers and to not submit it to them as a bug for a reward, etc.
I suggest doing a Wordpress search inside the dashboard for the phrase "noopener noreferrer" to find any that remain and remove them.


text:
package org.archive.util;

import junit.framework.Test;
import junit.framework.TestCase;
import junit.framework.TestSuite;

/**
 * JUnit test suite for UriUtils.
 *
 * Several of the tests for the 'legacy' (H1 through at least 1.14.4)
 * heuristics are disabled by renaming, because those heuristics have known
 * failures; however, until more experience with the new heuristics is
 * collected, H1 still uses them for consistency.
 *
 * @contributor gojomo
 * @version $Id: ArchiveUtilsTest.java 5052 2007-04-10 02:26:52Z gojomo $
 */
public class UriUtilsTest extends TestCase {

    public UriUtilsTest(final String testName) {
        super(testName);
    }

    /**
     * run all the tests for ArchiveUtilsTest
     *
     * @param argv
     *            the command line arguments
     */
    public static void main(String argv[]) {
        junit.textui.TestRunner.run(suite());
    }

    public static Test suite() {
        return new TestSuite(UriUtilsTest.class);
    }

    /** image URIs that should be considered likely URIs **/
    static String[] urisRelativeImages = {
        "photo.jpg",
        "./photo.jpg",
        "../photo.jpg",
        "images/photo.jpg",
        "../../images/photo.jpg" };

    /** check that plausible relative image URIs return true with legacy tests */
    public void xestLegacySimpleImageRelatives() {
        legacyTryAll(urisRelativeImages, true);
    }

    /** check that plausible relative image URIs return true with new tests */
    public void testNewSimpleImageRelatives() {
        tryAll(urisRelativeImages,true);
    }

    /** absolute URIs that should be considered likely URIs **/
    static String[] urisAbsolute = {
        "http://example.com",
        "http://example.com/", "http://www.example.com",
        "http://www.example.com/", "http://www.example.com/about",
        "http://www.example.com/about/",
        "http://www.example.com/about/index.html", "https://example.com",
        "https://example.com/", "https://www.example.com",
        "https://www.example.com/", "https://www.example.com/about",
        "https://www.example.com/about/",
        "https://www.example.com/about/index.html",
        "ftp://example.com/public/report.pdf",
        "http://a.example.com/combiner/c?js=analytics/sOmni.js,analytics/analytics.js,analytics/zf.js,analytics/externalnielsen.js",
        "http://l.example.com/jn/util/anysize/74*74c-86400,http%3A%2F%2Fl.example.com%2Fa%2Fi%2Fus%2Fshine%2Fmoreon%2F74.upallnight.jpg",
        // TODO: other schemes? mailto?
    };

    /** check that absolute URIs return true with legacy tests */
    public void testLegacyAbsolutes() {
        legacyTryAll(urisAbsolute,true);
    }

    /** check that absolute URIs return true with new tests */
    public void testAbsolutes() {
        tryAll(urisAbsolute,true);
    }

    protected static String[] urisRelative = new String[] {
        "default.asp?type=1",
        "\\/add\\/page?.crumb=O2.eArRHJUUWRkVHN6L0Y.&frompg=p1",
        "/wiki/Ficheiro:Wikiversity-logo.svg",
        "cssp!gelui-1/overlay",
        "/wiki/%E0%B4%B8%E0%B4%B9%E0%B4%BE%E0%B4%AF%E0%B4%82:To_Read_in_Malayalam",
        "/wiki/Wikiversity:Why_create_an_account%3F",
        ";s.channel=d.channel?d.channel:"
    };
    public void testRelatives() {
        tryAll(urisRelative, true);
    }

    /** path-absolute images URIs that should be considered likely URIs **/
    static String[] urisPathAbsoluteImages = {
        "/photo.jpg",
        "/images/photo.jpg",
    };

    /** check that path-absolute image URIs return true with legacy tests*/
    public void testLegacySimpleImagePathAbsolutes() {
        legacyTryAll(urisPathAbsoluteImages, true);
    }

    /** check that path-absolute image URIs return true with new tests*/
    public void testSimpleImagePathAbsolutes() {
        tryAll(urisPathAbsoluteImages, true);
    }

    /** URI-like strings risking false positives that should NOT be likely URIs **/
    static String[] notUrisNaiveFalsePositives = {
        "0.99",
        "3.14157",
        "text/javascript"
    };

    /** check that typical false-positives of the naive test are not deemed URIs */
    public void xestLegacyNaiveFalsePositives() {
        legacyTryAll(notUrisNaiveFalsePositives, false);
    }

    /** check that typical false-positives of the naive test are not deemed URIs */
    public void testNaiveFalsePositives() {
        tryAll(notUrisNaiveFalsePositives, false);
    }

    /** strings that should not be considered likely URIs **/
    static String[] notUrisNaive = {
        "foo bar",
        "<script>foo=bar</script>",
        "item\t$0.99\tred",
    };

    /** check that strings that fail naive test are not deemed URIs legacy tests*/
    public void testLegacyNaiveNotUris() {
        legacyTryAll(notUrisNaive, false);
    }

    /** check that strings that fail naive test are not deemed URIs new tests*/
    public void testNaiveNotUris() {
        tryAll(notUrisNaive, false);
    }

    protected static final String[] unusualCharacterFalsePositives = new String[] {
        "),f=document.getElementsByTagName(",
        "window.location.href='/'",
        "location='http://example.com/blah/'",
        "http://example.com/intent/user?screen_name='+p.user+'",
        ").append(",
        "[\\x3cb\\x3e−\\x3c/b\\x3e]",
        "http://demo.example.net/panama.php?cgroup=ron728x90&pid=\"+pid+\"&uid=\"+uid+\"&rid=\"+rid+\"&kw=10&cx=10&bh=10",
    };
    public void testUnusualCharacterFalsePositives() {
        tryAll(unusualCharacterFalsePositives, false);
    }

    protected static final String[] mimetypesFalsePositives = new String[] {
        "text/javascript",
        "text/css",
        "application/x-shockwave-flash",
        "text/javaScript",
        "text/html",
        "application/x-www-form-urlencoded",
        "text/xml",
        "text/plain",
        "application/x-mplayer2",
        "application/json",
        "image/jpeg",
        "image/x-icon",
        "audio/mpeg",
        "image/gif",
        "audio/ogg",
        "video/quicktime",
        "audio/x-pn-realaudio-plugin",
    };
    public void testMimetypesFalsePositives() {
        tryAll(mimetypesFalsePositives, false);
    }

    protected static final String[] startsOrEndsWithPlusFalsePositives = new String[] {
        "+resp.result+",
        ";overlay.style.width=viewport_dimensions.width+",
        "+_ti;bb.src=",
    };
    public void testStartsOrEndsWithPlusFalsePositives() {
        tryAll(startsOrEndsWithPlusFalsePositives, false);
    }

    /**
     * Test that all supplied candidates give the expected result, for each of
     * the 'legacy' (H1) likely-URI-tests
     *
     * @param candidates String[] to test
     * @param expected desired answer
     */
    protected void legacyTryAll(String[] candidates, boolean expected) {
        for (String candidate : candidates) {
            assertEquals("javascript context: " + candidate,
                    expected,
                    UriUtils.isLikelyUriJavascriptContextLegacy(candidate));
            assertEquals("html context: " + candidate,
                    expected,
                    UriUtils.isLikelyUriHtmlContextLegacy(candidate));
        }
    }

    /**
     * Test that all supplied candidates give the expected results, for
     * the 'new' heuristics now in this class.
     * @param candidates String[] to test
     * @param expected desired answer
     */
    protected void tryAll(String[] candidates, boolean expected) {
        for (String candidate : candidates) {
            assertEquals("new: " + candidate,
                    expected,
                    UriUtils.isLikelyUri(candidate));
            assertEquals("html context: " + candidate,
                    expected,
                    UriUtils.isLikelyUri(candidate));
        }
    }
}

redpajama_set_name: RedPajamaGithub


text:
Department of Heavy Industry and Ministry of Skill Development & Entrepreneurship Sign an MoU for Capital Goods and Automotive Sector for Large Scale Capacity Building
Department of Heavy Industry and Ministry of Skill Development & Entrepreneurship have signed a MoU for capital goods and automotive sector for large scale capacity building by providing infrastructure and facilities, improving training content, curriculum and methodology and use of latest technologies, during DHI-FICCI-HMFI WIN (World of Industry) India conference.
The MoU was signed by Dr Rajan Katoch, Secretary, Department of Heavy Industry and Mr Rohit Nandan, Secretary, Ministry of Skill Development & Entrepreneurship in the presence of Shri Anant Geete, Union Minister for Heavy Industries and Shri Rajiv Pratap Rudy, Minister of State for Skill Development and Entrepreneurship at an event last evening.
The key activities to be undertaken under the MoU, inter alia, are:
• Create jointly a National Ecosystem for Development of Skills for the Manufacturing Sector, in particular the Capital Goods sector and automotive sector and other sectors/subjects covered by DHI
• Develope a Multi-location 'National Institute of Manufacturing Technology (NIMT)' for training, education and research in manufacturing technologies by leveraging the infrastructure and facilities of PSUs under DHI
• Creating Centres of Excellence in different categories by utilising/up-grading of exiting training facilities of the PSUs under DHI in collaboration with NSDC/Sector Skill Council and/or DGT
• Bring in the internationally known best practices in the field of skill development and entrepreneurship for manufacturing sector and for that purpose get the services of internationally renowned experts and to leverage global partnerships and technical
There would be top-level institutional arrangement for this initiative so that it is properly implemented and monitored on regular basis.
Speaking at inaugural session ceremony of FICCI-DHI-HMFI WIN India Shri Anant Geete said "We know that our priority today is to create jobs for our 65% population which is young. Partnership with Ministry of Skill Development is important to make people job-ready for our capital goods sector. The sector provides direct employment to ~1.4 million people and indirect employment to ~7 million people and impacts users of capital goods estimated to be 50 times of the direct employment. We are aiming to create direct and indirect employment for 30 million workers in the capital goods sector by 2025. Hence, we have signed the MoU with Ministry of Skill Development and Entrepreneurship to ensure that these targets are met".
Draft National Policy on Capital Goods has been prepared after extensive industry consultations which we would be taking to the Cabinet shortly, Mr Geete said.
Speaking on the occasion, Shri Rajiv Pratap Rudy pointed out that "availability of talent and productivity of the workforce are amongst the top drivers for manufacturing competitiveness of a nation. The Central Government is committed to ensure that there is adequate supply of quality talent for the manufacturing sector. This would give India a competitive edge in manufacturing. This Agreement between the Skills Ministry and Ministry of Heavy Industries will go a long way in ensuring supply of such talent pool."
Shri Raman Singh, Chief Minister of Chhattisgarh also addressed the delegates in the inaugural session. Chhattisgarh is the partner State for the WIN India 2015.
Lower Saxony's State Secretary, Daniela Behrens, is leading the business delegation from Lower Saxony, Germany to visit WIN INDIA with the specific goal of meeting Indian companies and intensifying Lower Saxony's economic relations with India. This is a direct result of India's Partner Country appearance in Hannover, which is the capital of Lower Saxony. Learning that there are numerous investment and cooperation opportunities in India, the delegation is visiting WIN India 2015.
Hannover Milano Fairs India Pvt Ltd, the Indian subsidiary company of the Deutsche Messe, organizers of Hannover Messe (Germany) along with Department of Heavy Industries and FICCI today announced the 9th edition of WIN India 2015, India's leading Industrial and engineering trade fair, schedule to be held from December 9-11, 2015 at the Pragati Maidan, New Delhi.
Department of Heavy Industries (DHI), Ministry of Heavy Industries and Public Enterprise, Government of India along with the support of FICCI, will work jointly with the organizers towards promoting India's high tech engineering and capital goods manufacturing industry as a part of Government of India's 'Make In India' campaign. Department of Heavy Industries will also be organising DHI 'Make in India' pavilion at WIN India 2015, which will showcase the prowess of India's manufacturing in technology.
at December 09, 2015 No comments: Links to this post
#OCCUPY UGC
March To Parliament
9th December 2015, 1:00pm
@University Grants Commission, Near ITO metro station Exit Gate no -3
EDUCATION IS OUR RIGHT, NOT A PRIVILEGE!
Our Demands
1. Restore, Increase and Expand Non-Net Fellowship- no merit/criteria based exclusion;
2. India withdraw from WTO negotiation;
3. Halt the process of fund cut in Education- spend 10% of the budget in education;
Since 21st of October 2015, students across the country in every college have been protesting against the scrapping of the Non-NET Fellowship given to the M.Phil/Ph.D students by the University Grants Commission (UGC) ( a sum of Rs.5000 and Rs.8000 per month respectively). It is a tragedy that the decision to scrap the non-NET fellowship was taken on 7th of October by a committee that was in fact set up to enhance the fellowship but it decided to scrap the fellowship entirely. Undergraduates, post-graduates, teachers, intellectuals and people from all walks of life have joined the protest in solidarity against many measures which have been taken by the government in past few years such as semesterization, fee hikes, fund cuts etc in order to privatize education and parcel out its vital responsibilities at every level to serve the interests of the corporations. At the outset, this is visible in the decrease in allocation for education in Union Budget by 17% (currently 68,980 crores) while there is a concurrent increase in the allocation for defence by 7.7% to 2.47 lakh crores for the year 2015-16. The allocation for higher education remains abysmally low within the education sector. In the name of 'national interest', it is clear that the central government would rather display military might to the world than nurture its young intellectual minds.
Here in Delhi, students have waged a united struggle outside the UGC office and have been occupying it 24X7. Protesting students were lathi-charged and detained twice, brutally beaten by the Delhi police, abused and intimidated and threatened with dire consequences. Women students have been repeatedly beaten up by male police officers. Meanwhile, the UGC, a body answerable to the students, remained conspicuously silent. Despite these circumstances, the students have sustained their spirit and have constantly occupied the space outside the UGC till now. The students have shown unprecedented unity in the face of police brutality and uncompromisingly fought for their democratic right.
It is important to recognize the objective behind these efforts by the government to privatize education. The current government is furthering a policy that began with the opening up of the economy in the early 90s. On the 1st of January 1995, India signed an agreement with the World Trade Organisation (WTO) called the General Agreement on Trade in Services (GATS) which effectively subsumed 'education' as a service provided for industry. The agreement asserted that benefits of providing education will invariably 'spillover' as a consequence of furthering industry and trade. In this country, this understanding was enhanced by the Birla-Ambani Report on Higher Education in 2000. This report unilaterally claimed that in order to make education conducive to the demands of the market economy, universities need to be privatized. Further, organized students movements were seen as a threat to the effort to make education 'profitable' and therefore needed to be curbed. Unsurprisingly, this report was followed by another government report now infamous as the Lyngdoh Committee Recommendations (LCR) which was imposed on all universities in the country with the logic of 'national integration' as the parameter for student assertion wherein any student protest could be simply deemed 'anti-national' if it didn't serve the interests of those in power. At the same time, the university administration officials, funded generously by corporate sponsors, started speaking of the need for 'world-class universities'. Since 2008, we have seen this vision being imposed on the country's biggest university, Delhi University, through the semester system, then Four-Year Undergraduate Programme (FYUP) and now the rehashed Credit Based Choice System (CBCS).
Nowhere in this entire process were students or teachers, the real stakeholders of education, consulted. The Central University Bill echoes this policy a centralized, homogenous curriculum across the country. This aims to scuttle critical thinking, voices of dissent and take away the autonomy of university spaces.
Bending under the World Bank's insistence to reduce fiscal deficit, Indian government has already been cutting funding and subsidy to many important sectors like health and education. December 2015 marks the WTO-GATS Conference wherein the Government of India is all set to allow 160 member countries of WTO to establish universities in India as commercial ventures. To create a "level playing field" for these profit making entities, the government will need to dismantle all subsidies and support to public universities, so that these private and foreign entities can "compete" with public universities in the market. Indian universities/educational institutions and policy in all sense will come under the WTO rules to the disastrous extent that Indian parliament or constitution would have no control over these policies. This will completely dismantle our right to education. It is in preparation for this commitment to the WTO that the government took this move to scrap the non-NET scholarship. Thus we need to understand this as a deliberate attempt to sabotage public universities in preparation for the planned privatization and marketization of higher education of India.
It comes as no surprise then that these policies seem to adversely affect the economically most vulnerable sections of the society. In a country where structural oppression based on class, caste and gender has kept large sections of the people historically deprived and away from access to education, and education is promised as an opportunity to break free from these structural inequalities, then/ and it is criminal to deprive these sections of our society of education. The rising cost of education, the professionalized courses, inadequate housing for students and the prohibitive criteria for admission render higher education inaccessible to the poorest and pushing the middle class towards private universities. The courses provided in such institutions remain both opaque and irrelevant for those coming from across social, economic and regional differences. By introducing merit/income criteria based fellowship, (which is a prerogative of brahminical response to any inclusive provision of keeping Dalits, women, minorities, differently abled in to the education system, employment opportunities) students from struggling backgrounds will get even further excluded from the higher education.
On the 5th of November, the students led a protest march till the Ministry of Human Resource Development (MHRD) where we were asked to give our feedbacks to the review committee. On 18th of November, students, in large numbers marched to MHRD for submitting mass depositions regarding the same but no official from the review committee listened to us, our feedback was not submitted, and instead police action was unleashed on to us brutally. The hundreds of students have united to fight not just the scrapping of non – net fellowship but the entire machinery that aims to churn out unthinking cogs for the service of corporate profits and continue to push their demands of:
We appeal everyone to join the All India March to Parliament on 9th of December to raise questions on the 'selling of education' at WTO; on the government's policy on public education; and its commitment to the people of the country.
Student Unity Long Live! We Shall Fight, We Shall Win!
Contact : 7838503203, 9643546968, 9013275506, 9582671784
Facebook page: Occupy UGC (https://www.facebook.com/Occupy-UGC-1481899532116484/?pnref=story)
Shabnam Shaikh


The Conflict between the Buddhist and the Naiyayika Philosophers: A Brief Survey - Download this document for free, or read online. Document in PDF available to download.
Conflict and Networks - Dziubinski, M.Goyal, S.Vigier, A.


Strata Health is a SaaS solution provider that helps health care regions in Canada, the United States and the UK manage patient flow through electronic referral.
B2B Marketing Project: As part of a project team brought together to rebrand the website, I rewrote website copy in a refreshed brand voice.
Home Page Content Tag lines and descriptions.
An ultimate guide on how to position your blog in your niche by using key messages.
Ed Gandia, author of , interviewed me for his podcast B2B Launcher, a show for high income writers. We discussed the B2B writing business and how to market and land ideal clients from global trade shows without ever leaving the house.
Landing Clients Make a Living Writing is an online community of professional freelance writers. As a member of the community, I pitched a guest post that helps freelance writers break the feast and famine cycle of work by going after global clients.
Interested in this book? Here's an affiliate link.
My guest blog post was included in this book by Carol Tice.
Author Page: Profiles on thriving Vancouver women entrepreneurs. From executives, award winners and up and comers to artists bringing fire and ice into the lobby of downtown Vancouver's Four Seasons Hotel.
CrowdExpert is an online crowdsourcing platform.


When talking about the future, we can use will..., going to...or the Present Continuous.
Use will to talk facts or things that we believe are true.
"I'm sure you will love learning English in Malta. It's a great place."
Going to is used with predictions.
When you are making a decision use will; use going to after the decision has been made. We sometimes also use the present continuous for planned events in the near future.
When we want to talk about future facts or things we believe to be true about the future, we use will.
1. "Where are you going on holiday?" I don't know yet, maybe ___ to India.
2. Are you watching the game? It's 5 - 2, they ___ win the match!
3. I think you ___ like this movie.
4. We ___ get married next year.
5. I can't join you at the party, I ___ away for two weeks.
6. You ___ Mr Thompson tomorrow at 10.00.
7. Have you heard? Jean is pregnant – ___ have a baby.
8. This exercise looks really hard – I ___ help you.
9. Don't forget that we ___ out with Sue and Peter tonight!
10. Look at that beautiful sky! It most certainly ___ rain today.


Organize your personal work space by adding a folding file organizer or file pack. If you are looking for the perfect office gift, these unique filing items will for sure be a hit!
This 19 pocket expandable file organizer helps you easily manage and store your legal size documents. Die-cut tab dividers and secure-lock closure will keep your papers in place.


text:
using NaC.Engine.Models;
using NandC.Engine.Models;
using System;
using System.Collections.Generic;
using System.Linq;

namespace NaC.Engine
{
    public class Program
    {
        public static void Main(string[] args)
        {
            var game = new GameBootstrapper();

            game.Game.Board.Updated += (s, e) =>
            {
                RenderGrid(game.GameMatrix);

                Console.WriteLine();
                Console.WriteLine();
                Console.WriteLine("Last move: {0} ({1},{2})", e.Player.Name, e.ContextData.X, e.ContextData.Y);
            };

            game.Game.Board.Completed += (s, e) =>
            {
                Console.WriteLine("Game over");
                if (game.Game.Board.HasWinner)
                {
                    Console.WriteLine("Winner: {0}", game.Game.Board.Winner);
                }
            };

            Console.Clear();
            Console.WriteLine("Running");

            Console.Write("Train robot? ");

            if( Console.ReadKey().Key == ConsoleKey.Y)
            {
                Console.Clear();
                Console.WriteLine("Training...");
                game.TrainRobot();
            }

            game.Start();

            var p = new RefParser();

            while (true)
            {
                var k = Console.ReadKey();

                if (k.Key == ConsoleKey.Escape) break;

                if (p.SetNext(k.KeyChar).GetValueOrDefault())
                {
                    if (!game.SubmitGo(p.Point))
                    {
                        Console.WriteLine();
                        Console.WriteLine(game.Messages.Last());
                    }

                    p.Reset();
                }
            }

            Console.WriteLine("Exiting");
        }

        private static void RenderGrid(IEnumerable<GridReferenceViewModel> view)
        {
            int lastY = -1;

            Console.Clear();

            foreach (var refx in view.OrderBy(y => y.GridReference.Y).ThenBy(x => x.GridReference.X))
            {
                if(lastY < refx.GridReference.Y)
                {
                    Console.WriteLine();
                }

                Console.Write(" ");
                Console.Write(refx.Player == null ? "-" : refx.Player.Symbol);
                Console.Write(" |");

                lastY = refx.GridReference.Y;
            }
        }

        private class RefParser
        {
            private int x = -1;
            private int y = -1;

            public Point Point { get; private set; }

            public void Reset()
            {
                x = -1;
                y = -1;
                Point = new Point();
            }

            public bool? SetNext(char key)
            {
                byte v;

                if (byte.TryParse(key.ToString(), out v) && v > 0 && v <= 3)
                {
                    if (x > -1)
                    {
                        y = v;
                        Point = new Point(x, y);
                        return true;
                    }
                    else
                    {
                        x = v;
                        return false;
                    }
                }

                return null;
            }
        }
    }
}


text:
Ex-Pat
Mortgage Tables
Whole of Life
Getting a mortgage can be a challenging and complicated process. That's why you need regular access to expert guidance and support from people you know you can trust. Our aim is to provide you with professional knowledge of the market and excellence in customer service whilst being on hand to help every step of the way.
We're committed to helping you fully understand the broad range of options available to you so that you can work with us to make an informed decision about the product that is most suited to your individual requirements.
Harford Financial Limited
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Spring Villa Road
HA8 7EB
eli@harfordfinancial.co.uk
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Harford Financial Limited is an appointed representative of Quilter Mortgage Planning Ltd, which is authorised and regulated by the Financial Conduct Authority. Registered in England and Wales No: 08547970. Registered Office: Harford Financial Limited, 4 Spring Villa Park, Spring Villa Road, Edgware, Middlesex, HA8 7EB. Calls may be recorded for training and monitoring. The information and content within this website is subject to the UK regulatory regime, and is therefore targeted at consumers based in the UK.
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