Across the country, states are advocating for crisp, thirst-quenching laws.
State lawmakers are looking to spur business wherever they can, but one industry hopes to change local palates as well as local economies: breweries.
While they haven't had much luck in Washington, beer advocates are having more success at the state level. Last year, Alabama became the last state to legalize home brewing. A bill wending its way through the state Legislature this week would allow larger Alabama brewers to open restaurants at their breweries. The only hitch: No brewery in Alabama is big enough to qualify for the law.
Alabama is working to change that. The state is now trying to lure high-profile breweries from out of state, like Stone Brewing in California. In 2012, Alabama ranked 49th in breweries per capita. But with recent laws like the Alabama Brewery Modernization Act, which allows craft brewers to sell their wares where they're brewed, Alabama is hoping to raise its profile as a destination for brewers and beer aficionados alike.
Still, compared with other booze, the beer industry is struggling to keep its brand recognition. Unlike years ago, more people today say they prefer drinking wine or liquor instead of beer. Despite the relative decline of beer's popularity, there has been an explosion of craft breweries around the country — in 2012, the craft-brewing industry saw 15 percent growth. Larger mainstream products like Blue Moon and Pabst Blue Ribbon are enjoying rapid growth. There are almost 2,000 craft breweries in the U.S. There's even a congressional Small Brewers Caucus.
And the beer industry has economic ripple effects. According to the Beer Institute, the industry employs roughly 2 million Americans, directly and indirectly, for a combined $79 billion in wages and benefits.
Beer advocates have been fighting for years to decrease the federal excise tax on beer. In 1991, the federal beer tax was doubled from $9.00 to $18.00 per barrel as part of an effort to balance the federal budget. Taxes were also raised on select luxury goods, but were eventually repealed or phased out. Now, beer advocates want to roll back the federal beer tax to pre-1991 levels, arguing that it would correct for years of overtaxing and bring back lost jobs.
Beer-industry lobbyists argue that the beer tax is unfair, since it unduly burdens the lower- and middle-income workers who prefer a cheap, cold brewski over a glass of single-malt whisky. "The tax on beer is thus one of the most discriminatory of all taxes in the federal and states' tax codes," according to the Beer Institute.
"¢ Minnesota beer activists are agitating to reinstate Sunday sales.
"¢ New Hampshire lawmakers may reverse a state ban on advertising alcohol on billboards, and could even vote to allow restaurants to sell beer to go.
"¢ Connecticut may do away with excise taxes on beer, wine, and liquor, as the state has been losing business across the border to Rhode Island, which has lower alcohol taxes.
"¢ An Indiana lawsuit is challenging the state's ban on selling refrigerated beer in gas stations, groceries, and pharmacies, though the law is unlikely to be overturned.
The Congressional Budget Office has argued in favor of raising the excise tax, noting that the costs of alcoholism and alcohol-related incidents far exceed the revenue brought in by taxes on beer, wine, and spirits. "When adjusted for inflation, current excise-tax rates on alcohol are far lower than historic levels," CBO argues. "In the 1950s, excise taxes accounted for nearly half of the pretax price of alcohol; they now account for between 10 and 20 percent of the pretax price."
While beer advocates may be fighting a quixotic battle, lawmakers should recognize the economic good the craft brewing industry can bring to their states — or just shut up and drink.


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text:
Rajeev Dhavan
A Senior Advocate of the Indian Supreme Court, Dhavan was educated in Allahabad Cambridge and London Universities. He taught at Brunel with visiting assignment to the Universities of Texas, Madison, Delhi, the Indian Law Institute (where he is an Honorary Professor) and Melbourne. He was a Commissioner of the International Commission of Jurists of which he is now Hon. Member and called to the Allahabad Bar and Middle Temple.
Abdur Razzaq
Called to the Bar by the Honorable Society of Lincoln's Inn in 1980 and has practised in the Superior Courts of England from his London chambers at 9 Stone Buildings, Lincoln's Inn until 1985. He later returned to Bangladesh as a senior advocate of the Supreme Court.
In the three decades since the commencement of his practice in Bangladesh, Razzaq built up a substantial constitutional/administrative law practice, having been involved in numerous high profile public law cases. Between 2011 and 2013, he appeared as Chief Defence Counsel before Bangladesh's first ever War Crimes Tribunal to try commission of crimes against humanity and genocide during the War of Liberation in 1971. He represented six defendants before the War Crimes Tribunal and the Supreme Court and was instrumental in shaping the jurisprudence of international criminal law in Bangladesh. His robust defence before the War Crimes Tribunal was praised in an independent report on the Trial of International Crimes in Bangladesh authored by Geoffrey Robertson QC – 'defendants have been represented by capable and courageous counsel, especially … where defence teams have been led by Abdur Razzaq, a Lincoln's Inn barrister'.
Razzaq currently practices as a barrister in the English Bar from Legis Chamber, London.
Dr Thomas MacManus
A Research Fellow based at the International State Crime Initiative at Queen Mary University of London School of Law, MacManus has a BA (Hons) in Law and Accounting (University of Limerick), an LLM (with distinction) in International Law (University of Westminster) and a PhD in Law and Criminology (King's College London). He is admitted as an Attorney-at-Law (New York) and Solicitor (Ireland). He is the Editor-in-Chief of State Crime journal, and Joint Editor of Amicus Journal: Assisting Lawyers for Justice on Death Row. MacManus is also a Director of the Colombia Caravana.
AF Hassan Ariff
A Senior Advocate of the Supreme Court of Bangladesh specialising in constitutional, corporate and commercial law, he was enrolled in the Dhaka High Court since 1970. Arrif served in the Attorney General's Office as Assistant Attorney General between 1982-1985, Deputy Attorney General between 1985-1996, and in October 2001 as Attorney General for Bangladesh. He was an Adviser for the Ministry of Law Justice & Parliamentary Affairs, Ministry of Land and Ministry of Religious Affairs of the Caretaker Government, Bangladesh (2008-2009). Ariff is a member of the Supreme Court Bar Association, a Panel Arbitrator of the Bangladesh International Arbitration Centre, and President of ODIKHAR – a leading human rights organisation in Bangladesh.
Justice Mohammad Abdur Rauf
Born on 1 February 1934 in the district of Mymensingh, Abdur Rauf was a Practising Advocate of Bangladesh Supreme Court and was elevated to the bench of the High Court Division as Additional Judge on 29 January 1982 and confirmed on 26 January 1984. He was elevated to the Appellate Division of the Supreme Court on 8 June 1995. While in the post of Justice in the High Court Division, he was appointed as the Chief election commissioner 25 December 1990 by the then President of Bangladesh Justice Sahabuddin Ahmed. He later returned to the post of justice, elevated to the Appellate Division and retired on 1 February 1999.
A prominent Indian lawyer, politician and a social activist, he is the son of Shanti Bhushan, the former Law Minister of India. Bhushan is noted for his use of public interest litigation (PIL) to support corruption and human rights cases; also involved with Institute of Public Policy and Politics.
Sajjad Amin
A practising Solicitor-Advocate with Higher Rights having qualified in 2009. He volunteers in the community and helps with several projects including helping with youth work as a mentor and as a speaker on interfaith work. He is a governor at a local school, a member of the safeguarding board and sits as an advisor to the police at the Independent Advisory Group.
Sajid Miah
Former youth and community organiser, he gained an LLB at SOAS, University of London and qualified as a solicitor for a prominent city firm. Previous experience includes working as a senior manager for a local authority. Miah is involved in charity work and community development, and has a deep interest in human rights.

redpajama_set_name: RedPajamaCommonCrawl


text:
\section{INTRODUCTION}
In face recognition, the major issue to be addressed is the extraction of features which are discriminating in nature \cite{1}, \cite{2}. The accuracy of classification depends upon which texture feature of the face are extracted e.g., geometrical, statistical, local or global features in addition to representation of these features and the design of corresponding classifier. Normally, the feature extraction algorithm should produce little variance of features within the class and large variance between the classes. There are typically two common approaches to extract facial features: geometric-feature-based and appearance-based methods. The geometric-feature-based [\cite{3}, \cite{4}] method encodes the shape and locations of different facial components, which are combined into a feature vector that represents the face. An illustration of this method is the graph-based method \cite{5}, that uses several facial components to create a representation of the face and process it. The Local-Global Graph algorithm \cite{5} approach makes use Voronoi tessellation and Delaunay graphs to segment local features and builds a graph. These features are combined  into a local graph, and then the skeleton (global graph) is created by interrelating the local graphs to represent the topology of the face. The major requirements of geometric-feature-based methods is accurate and reliable facial feature detection and tracking, which is difficult to accommodate in many situations. In the case of appearance based methods, there are many methods for the holistic classes such as, Eigenfaces \cite{6} and Fisherfaces \cite{7}, which are built on Principal Component Analysis (PCA) \cite{6}, to the more recent 2D-PCA \cite{8}, and Linear Discriminant Analysis \cite{9} are also examples of holistic methods.  The \cite{10} and \cite{11} makes use of image filters, either on the whole face to create holistic features, or some specific face-region to create local features, to extract the appearance changes in the face image. The recognition rate of the appearance-based methods is good in constrained environment but their recognition rate deteriorates in environmental variation \cite{12}. Rui Huang et. al., have proposed a hybrid face recognition method that combines appearance and local feature analysis-based scheme  using a Markov Random Field (MRF). The face images are divided into small patches, and the MRF model is used to represent the relationship between the image patches and the patch ID's. The MRF model is first learned from the training image patches, given a test image. The most probable patch ID's are then inferred using the belief propagation (BP) hybrid model. Finally, the ID of the image is determined by a voting scheme from the estimated patch ID's. \\
Although these methods have been studied widely, local descriptors have gained attention because of their robustness to illumination and pose variations. Lu et.al., \cite{13} have presented a method for face recognition based on parallel neural networks and fuzzy clustering. The face patterns are divided into several small-scale neural networks based on fuzzy clustering and they are combined to obtain the recognition results. In \cite{14}, the authors have presented a method for face recognition combining modular neural networks and two interval type-2 fuzzy inference systems (FIS-2) for face recognition. The first (FIS-2) is used for edge detection in the training data, and the second one to find the ideal parameters for the Sugeno integral as a decision operator.\\
The theory of Information sets and Information processing developed to expand the scope of fuzzy sets and fuzzy modeling \cite{15} in bio-metrics. The two apparent drawbacks of fuzzy sets such as treating the information source (attributes) and their membership values separately in all the problems dealing with the fuzzy logic theory; and not accounting for probability (the occurrence) of information sources are the motivation for the present work. The membership function that gives the degree of association of a particular information source value in fuzzy sets is always considered ignoring the information source value. In reality, the information is a combination of information source and an agent's opinion about it (membership function).  An attempt is made to overcome these drawbacks in the present work  by introducing the concept of information set that represents the amount of information or uncertainty in an information source.\\
Motivation: The problem with many of the pattern recognition  system is the processing the binarized images that results in loss of information source. Especially, the LBP \cite{16} has a problem with process of encoding. The LBP encodes the local neighborhood intensity by using the centre pixel as a threshold for a sparse sample of the neighboring pixels. The few number of pixels used in this method introduce several problems: Firstly, it limits the accuracy of the method. Secondly, the method discards most of the information in the neighborhood. Moreover, most of the methods we saw in the literature were partition the  image into small segments and restricting the bins for histogram construction followed by  histogram concatenation which again leads to loss of information as the neighboring feature values are assigned to same bins. \\
The remaining part of the paper is organized as follows. In section 2, we present some basics of information set and information processing. The section 3 is devoted to describe the family of texture descriptors based on information set. In section 4, we present the proposed methodology of feature extraction based on the theoretical aspects presented in section 3. The classification based on SVM and KNN classifier is presented in section 5. Experimental results are shown in section 6 followed by comparative analysis in section 7 and section 8 summarizes the conclusions that are drawn from our work.
\section{INFORMATION SETS AND INFORMATION PROCESSING}
Consider a fuzzy set constituted by a number of gray levels I=$\left\{I_{ij}\right\}$ in a window. When these are fitted with a membership function, their membership function values denoted by $\left\{\mu_{ij}\right\}$ represent the degree of association of gray levels to the set. Thus, the pair formed by the gray level and the membership function is considered as part of a fuzzy set. However, when the fuzzy set is considered in the context of the information theoretic entropy called Hanman-Anirban entropy  \cite{17}, the pair becomes a product leading to the concept of the Information set. The proposed information theoretic methods originate from Hanman-Anirban entropy function when the probabilities are replaced by the information sources and the assumption that the sum of information sources is equal to unity is relaxed. With this, the resolution of the function improves leading to increased distinguish ability such as when the details of an image become more discernible as its size increases. \\
The multimedia components (i.e. an image, speech, text or video) after granualization are considered as the information sources. The granularization amounts to partitioning in the case of an image. The property values, attributes or cues comprising the information sources contained in windows or frames form the fuzzy sets. The distribution of these information sources in the fuzzy sets require an appropriate membership function. Let us consider the commonly used membership functions such as the exponential and Gaussian type functions. \\
The Exponential and Gaussian type membership functions are given by:
\begin{equation}
\mu_{ij}^{e}=e^{-\left \{ \frac{\left | I_{ij}-I(ref) \right \vert}{f_{h}^{2}} \right \}}
\end{equation}
\begin{equation}
\mu_{ij}^{g}=e^{{-\left \lbrack \frac{ I_{ij}-I(ref)}{\sqrt{2}f_{h}} \right \rbrack}^2}
\end{equation}

The fuzzifier $\left\{f_{h}^{2}\right\}$ in (1) is devised by Hanmandlu et al. in \cite{17} and it gives the spread of attribute values with respect to the chosen reference (symbolized as \textit{ref}). It is defined as

\begin{equation}
f_{h}^{2}= \frac{\sum_{i=1}^{w} \sum_{j=1}^{w} (I(ref)-I_{ij})^{4}}{\sum_{i=1}^{w} \sum_{j=1}^{w} (I(ref)-I_{ij})^{2}}
\end{equation}

One can take $I(ref)$ as $I_{avg}$ or $I_{max}$ or $I_{min}$ from the values in a window, where $I_{avg}$,$I_{max}$ and $I_{min}$ are the average, maximum and minimum  of  the window respectively. It may be noted that the above fuzzifier gives more spread than is possible with variance as used in the Gaussian function.

\section{NON BINARY GRADIENT CONTOURS}
Here, we define the non binary gradient contour of a 3x3 gray scale image patch.

Procedure: (1) The gradient between pairs of pixels is computed along a closed path around the central pixel S and
(2) The gradients are not binarized because we lose the information during process of binarization. We propose to define the closed path in three different ways, namely: single-loop, double-loop and triple-loop, as shown in Fig. ~\ref{Fig_1}. Let S be a matrix representing the pixel intensities of a generic square neighborhood with a support of 3x3 region. Let $I_{c}$ be the gray-level of the central pixel and $I_{j}$ the gray-levels of the peripheral pixels (j $\in [1, 2, 3, \cdots ,7])$ which are arranged as follows.\\

\begin{figure}
\centering
\includegraphics[width=60mm,scale=0.5]{Fig_1}
\caption{(a) Spatial arrangement of a 3x3 gray scale pattern and schematic representation of the texture models considered in this paper,(b)single-loop, (c) double-loop, and(d)triple-loop versions of the non binary gradient contour concept.}
\label{Fig_1}
\end{figure}

The corresponding versions of the non binary gradient contours can accordingly be expressed as
\begin{eqnarray}
G_1 = [\vert \xi \left( I_{7}-I_{0}\right) \vert + \vert \xi\left(I_{6}-I_{7}\right) \vert + \nonumber  \\
\vert \xi \left(I_{5}-I_{6} \right) \vert + \vert \xi \left(I_{4}-I_{5}\right) \vert + \nonumber \\
 \vert \xi \left( I_{3}-I_{4}\right) \vert +  \vert \xi \left( I_{2}-I_{3}\right) +  \nonumber  \\
 \vert \xi \left( I_{1}-I_{2}\right) \vert + \vert \xi \left( I_{0}-I_{1}\right)]\\ \nonumber
\end{eqnarray}

\begin{eqnarray}
G_{20} &= [\vert \xi \left( I_{6}-I_{0}\right) + \vert  \xi \left( I_{4}-I_{6}\right) \vert + \nonumber \\
&\qquad \vert \xi \left( I_{2}-I_{4}\right) \vert+ \vert \xi \left( I_{0}-I_{2}\right)\vert]
\end{eqnarray}

\begin{eqnarray}
G_{21} &= [\vert \xi \left( I_{7}-I_{1}\right) \vert + \vert \xi \left( ... I_{5}-I_{7}\right) \vert + \nonumber \\ &\qquad \vert \xi \left( I_{3} ...
-I_{5}\right) \vert+ \vert \xi \left( I_{1}-I_{3}\right) \vert ] ...
\end{eqnarray}

\begin{equation}
G_2= \left[ G_{20}+G_{21} \right]
\end{equation}

\begin{align}
G_3 &= [ \vert \xi \left( I_{5}-I_{0}\right) \vert + \vert \xi \left( I_{2}-I_{5}\right) \vert + \nonumber \\ &\qquad \vert \xi \left( I_{7}-I_{2}\right) \vert+ \vert \xi \left( I_{4}-I_{7}\right) \vert + \nonumber \\ &\qquad \vert \xi \left( I_{1}-I_{4}\right) \vert + \vert \xi \left( I_{6}-I_{1}\right) \vert + \nonumber \\ &\qquad \vert \xi \left( I_{3}-I_{6}\right) \vert + \vert \xi \left( I_{0}-I_{3}\right) \vert ]
\end{align}

It is useful to notice that the 8-tuples and 4-tuples above are actually functions of S, but the explicit dependency on S has been dropped from Eqs. $(4)-(8)$ to alleviate notation.

To enhance the image classification information and improve the recognition accuracy, here we propose to use entropy information based on the Shannon entropy \cite{18}. The entropy is usually used to describe the information contained in the system. Shannon defined the concept of information and described the essential components of a communication system includes a sender, a receiver, a communication channel, and an encoding of the information set \cite{18}. The Shannon entropy can also be computed for an image, where the probabilities of the gray level distributions are considered in the Shannon Entropy formula. An image consisting of a single intensity will have a low entropy value; it contains very little information.\\
A high entropy value will be yielded by an image, which has much different intensity. In this manner, the Shannon entropy is also a measure of dispersion of a probability distribution. A distribution with a single sharp peak corresponds to a low entropy value, whereas a dispersed distribution yields a high entropy value. \\
The set of features used to describe face image can be regarded as alphabets of symbols, in analogy with a discrete noiseless channel \cite{19}. It has been observed that the dimension of the feature space represents a theoretical limit to the amount of information that can be conveyed through a face model. It is well known that the highest efficiency attainable by an alphabet occurs when its symbols are equally likely.\\
The proposed feature extraction algorithm is based on extracting features from the windows which are non overlapping and membership value is computed based on central pixel and Fuzzifier precisely from high informative local zones of the face image instead of utilizing the entire image in the form of window. The information content of different regions of a face image varies widely. It can be shown that, if an image of a face were divided into windows, all the windows may contain the same amount of information. It is expected that a close neighborhood of eyes, nose and lips contains more information than that possessed by the other regions of a  face image.\\

\section{PROPOSED METHOD}
\begin{itemize}
  \item Normalization of an image: We propose to use unit normalization to eliminate the problem of illumination and intensity variations.
  \item Image Partitioning: We propose to partition the image into a non-overlapping blocks of size 3x3. The image is resized to obtain desired number of blocks which is optional.
  \item Membership computation: We compute the fuzzy membership of the central pixel (pixel under processing) and encodes the information within a window. In fact, we tend to lose the information about central pixel as in the case of LBP, LDP \cite{26} or any gradient pattern based approaches.
  \item Compute $f_{h}$ using equation 3.
  \item Membership value is given by
  \begin{equation}
  \mu _{w}=\frac{i_{c}}{f_{h}}
  \end{equation}
  \item We compute $G_1$ which is in the non-binary form. We take absolute value of difference and add them up. As shown in equation 4, the non absolute values are most of the time are zero or nearly zero. This is due to that fact that the face has got smooth texture and values are very close to each other (the variance is very small)
  \item Computation of Shannon entropy: We propose to compute the Shannon entropy of each window as a feature value of a block  i.e.,  \begin{equation}
  F_{w}=-\mu _{w}G_{1}logG_{1}
  \end{equation}
  \item The features for each block of an image are concatenated to construct a feature vector.  Compute $F_{w}$ for complete image say if we have image of size 63x63, we get 441 features.
\end{itemize}
The similar procedure is adopted for $G_2$ and $G_3$.\\
The above process is repeated for all the images considered for training to create knowledge base.

\section{Classification}

We propose here two ways of classification, one by having SVM \cite{20} as a trainer and other by using KNN classifier. In addition, we propose to use a new similarity measure instead of conventional Euclidean distance measure in the case of KNN classifier. The details are presented below.
\subsection{SVM based classification:} We divide the entire database into training set and testing set and apply Support Vector Machine. The SVM is a well-established machine learning approach which has been successfully adopted in various data classification problems. The concept of SVM is based on the modern statistical learning theory. For data classification, SVM firstly implicitly maps the data into a higher dimensional feature space and then constructs a hyper plane in such a way that the separating margin between the samples of different classes is optimal. This separating hyper plane then function as the decision surface.
\subsection{KNN based classification:} The k-Nearest Neighbour classifier is amongst the simplest of all the machine learning methods. The principle of nearest neighbour classification was first proposed by Skellam \cite{21}. It is a non-parametric method for classifying objects. The classification is done based on how much close the test feature vector is to the training feature vectors in the feature space. An object is classified based on the majority votes of its neighbors. If k = 1, then the object is simply assigned to the class of its nearest neighbor We have not considered Euclidean distance due to the following reasons.\\
\begin{itemize}
  \item The Euclidean distance measure suffers whenever there is a high noise-to-signal ratio and negative spikes values and hence any correlation is difficult to establish.
  \item The Euclidean distance measures the correlation between quantitative, continuous variables. It is not suitable for ordinal data, where preferences are listed according to rank instead of according to actual values.
\end{itemize}
Hence, We propose the new distance measure
\begin{equation}
d_{m}=\sum_{i=1}^{k}ln(1+\left | tr{_{i}} - te{_{i}} \right |)
\end{equation}
where $d{_{m}}$ is the distance measure, k is the dimension of the feature vector, $tr{_{i}}$  represents the $i{^{th}}$ element of training pattern. Similarly $te{_{i}}$ represents the $i{^{th}}$ element of a test pattern.

\section {EXPERIMENTAL RESULTS}
This section presents the experimental results on standard face data-sets such as ORL, Sheffield and YALE, which are used as benchmark data-sets to evaluate the suitability of proposed method for face recognition problems.
It shall be noticed that, in the real life scenario the received information is invariably pruned either by correcting or by normalizing. This is due to the fact that the information received by our senses are perceived differently as differing information values as the perception is different and also  depending on how much importance we attach to the source. Similar to fuzzy variables, the information values are also natural variables and hence normalization is done on each image for further processing.In all the three datasets, we normalize all of the gray (pixel) values by dividing them by the maximum gray value in that image. So the pixel values lies in the range 0 to 1 (also known as $I_{ij}$, the normalized information source values). But for simplicity and of imparting the discriminating power which is accrued by not scaling, we choose the basic information as H, which is the product of the information source values and its membership function values. This product mislead the readers that the information sets are no way different from the fuzzy sets.\\

\subsection{Experimentation on ORL dataset:}
The ORL dataset \cite{22} consist of images from 40 different individuals with 10 images of each person. All the face images were resized into images of 63x63 pixels to get 441 features. The datasets were partitioned into train and test set. The SVM classifier with a polynomial kernel of degree 1 and 2 is considered for training the classifier. The subset of some of the subjects of ORL dataset is shown in Fig. \ref{Fig_2}\\

\begin{figure}
\centering
\includegraphics[width=60mm,scale=0.5]{Fig_2}
\caption{Gray scale face images of some of the subject in ORL database.}
\label{Fig_2}
\end{figure}

The recognition accuracy of the proposed approach using SVM classifier and KNN classifier is reported in Tables \ref{Tab_1}, \ref{Tab_2}, and \ref{Tab_3} by respectively considering $G_1, G_2, G_3$.

\begin{table}[h]
\caption{Face Recognition accuracy on ORL dataset using NBLGC $G_2$.}
\label{Tab_2}
\begin{tabular}{|c|c|c|c|}
\hline
\multirow{2}{*}{\textbf{\begin{tabular}[c]{@{}c@{}}ORL  database\\ on NBLGC G2\end{tabular}}} & \multirow{2}{*}{\textbf{\begin{tabular}[c]{@{}c@{}}SVM\\ poly1\end{tabular}}} & \multirow{2}{*}{\textbf{\begin{tabular}[c]{@{}c@{}}SVM\\ poly2\end{tabular}}} & \multirow{2}{*}{\textbf{KNN}} \\
                            &                                                                                &                                      &                               \\ \hline
\multirow{2}{*}{\begin{tabular}[c]{@{}c@{}}Training images7.\\ Testing images3.\end{tabular}}          & \multirow{2}{*}{\begin{tabular}[c]{@{}c@{}}95\%\end{tabular}}                                                            & \multirow{2}{*}{81.16\%}             & \multirow{2}{*}{\begin{tabular}[c]{@{}c@{}}93.33\%\end{tabular}}        \\
                            &                                                                                &                                      &                               \\ \hline
\multirow{2}{*}{\begin{tabular}[c]{@{}c@{}}Training images6.\\ Testing images4.\end{tabular}}          & \multirow{2}{*}{\begin{tabular}[c]{@{}c@{}}91.25 \%\end{tabular}}                                                            & \multirow{2}{*}{\begin{tabular}[c]{@{}c@{}}88.12 \%\end{tabular}}             & \multirow{2}{*}{\begin{tabular}[c]{@{}c@{}}91.25 \%\end{tabular}}        \\
                            &                                                                                &                                      &                               \\ \hline
\end{tabular}
\end{table}

\begin{table}[h]
\caption{Face Recognition accuracy on ORL dataset using NBLGC $G_2$.}
\label{Tab_2}
\begin{tabular}{|c|c|c|c|}
\hline
\multirow{2}{*}{\textbf{\begin{tabular}[c]{@{}c@{}}ORL  database\\ on NBLGC G2\end{tabular}}} & \multirow{2}{*}{\textbf{\begin{tabular}[c]{@{}c@{}}SVM\\ poly1\end{tabular}}} & \multirow{2}{*}{\textbf{\begin{tabular}[c]{@{}c@{}}SVM\\ poly2\end{tabular}}} & \multirow{2}{*}{\textbf{KNN}} \\
                            &                                                                                &                                      &                               \\ \hline
\multirow{2}{*}{\begin{tabular}[c]{@{}c@{}}Training images7.\\ Testing images3.\end{tabular}}          & \multirow{2}{*}{\begin{tabular}[c]{@{}c@{}}93.33\%\end{tabular}}                                                            & \multirow{2}{*}{90\%}             & \multirow{2}{*}{\begin{tabular}[c]{@{}c@{}}93.33\%\end{tabular}}        \\
                            &                                                                                &                                      &                               \\ \hline
\multirow{2}{*}{\begin{tabular}[c]{@{}c@{}}Training images6.\\ Testing images4.\end{tabular}}          & \multirow{2}{*}{\begin{tabular}[c]{@{}c@{}}92.5\%\end{tabular}}                                                            & \multirow{2}{*}{\begin{tabular}[c]{@{}c@{}}88.12\%\end{tabular}}             & \multirow{2}{*}{\begin{tabular}[c]{@{}c@{}}91.87\%\end{tabular}}        \\
                            &                                                                                &                                      &                               \\ \hline
\end{tabular}
\end{table}

\begin{table}[h]
\caption{Face Recognition on ORL database using NBLGC $G_3$.}
\label{Tab_3}
\begin{tabular}{|c|c|c|c|}
\hline
\multirow{2}{*}{\textbf{\begin{tabular}[c]{@{}c@{}}ORL  database\\ on NBLGC G3\end{tabular}}} & \multirow{2}{*}{\textbf{\begin{tabular}[c]{@{}c@{}}SVM\\ poly1\end{tabular}}} & \multirow{2}{*}{\textbf{\begin{tabular}[c]{@{}c@{}}SVM\\ poly2\end{tabular}}} & \multirow{2}{*}{\textbf{KNN}} \\
                            &                                                                                &                                      &                               \\ \hline
\multirow{2}{*}{\begin{tabular}[c]{@{}c@{}}Training images7.\\ Testing images3.\end{tabular}}          & \multirow{2}{*}{\begin{tabular}[c]{@{}c@{}}93.33\%\end{tabular}}                                                            & \multirow{2}{*}{\begin{tabular}[c]{@{}c@{}}88.33\%\end{tabular}}             & \multirow{2}{*}{\begin{tabular}[c]{@{}c@{}}93.33\%\end{tabular}}        \\
                            &                                                                                &                                      &                               \\ \hline
\multirow{2}{*}{\begin{tabular}[c]{@{}c@{}}Training images6.\\ Testing images4.\end{tabular}}          & \multirow{2}{*}{\begin{tabular}[c]{@{}c@{}}91.25\%\end{tabular}}                                                            & \multirow{2}{*}{\begin{tabular}[c]{@{}c@{}}86.87\%\end{tabular}}             & \multirow{2}{*}{\begin{tabular}[c]{@{}c@{}}91.87\%\end{tabular}}        \\
                            &                                                                                &                                      &                               \\ \hline
\end{tabular}
\end{table}

It is a well known fact that the k-fold cross validation is a common technique for estimating the performance of a classifier. The advantage of k-fold cross validation is that all the examples in the dataset are eventually used for both training and testing. Hence, we have conducted experimentation considering 10-fold cross validation process. The k-fold results considering SVM classifier varies from 90\% at lower end and 100\% at higher end for k varies from 1 to 10. The results are shown in the Fig. \ref{Fig_3}.

\begin{figure}
\centering
\includegraphics[width=73mm,scale=0.6]{Fig_3}
\caption{K-fold accuracy on ORL dataset.}
\label{Fig_3}
\end{figure}

In addition, we have seen in the literature that most of the authors tested their approach and presented their results using ROC curves. We have presented the results of our approach on ORL dataset in Fig. \ref{Fig_4} considering the False Acceptance Rate (FAR) and Genuine Acceptance Rate (GAR). The FAR is the ratio of the number of successful fraud attempts to the total number of fraud attempts against a person and the GAR equals 100-FAR. Finally using these error rates, the receiver operating characteristics (ROC) which depicts the performance of an authentication system is plotted. The ROC plot is drawn FAR vs. GAR, with varying threshold values. From this plot, the threshold that yields the highest GAR corresponding to the lowest FAR need to be selected. It is evident from the graph that the $G_1$ outperform $G_2$ and $G_3$ for ORL databset.

\begin{figure}
\centering
\includegraphics[width=73mm,scale=0.6]{Fig_4}
\caption{The GAR Vs. FAR on ORL database with $G_1$, $G_2$ and $G_3$.}
\label{Fig_4}
\end{figure}

\subsection{Experimentation on Sheffield dataset:}
The Sheffield (previously UMIST) Face Database consists of 564 images of 20 individuals (mixed race/gender/appearance). The Sheffield data base consist of 20 classes with minimum number of images per class as 23 and maximum of 58 images. Each individual is shown in a range of poses from profile to frontal views - each in a separate directory labeled $1_a,1_b,...1_t$ and images are numbered consecutively as they were taken. All the images  are in PGM format of approximately 220 x 220 pixels with 256-bit grey-scale. In this dataset, there is large orientation of face exist. The subset of some of the subjects of Sheffield dataset is shown in Fig. \ref{Fig_5}. Similar to previous experimentation, here too, the datasets were partitioned into train and test set. The SVM classifier with a polynomial kernel of degree 1 and 2 is considered for training the classifier.

\begin{figure}
\centering
\includegraphics[width=60mm,scale=0.5]{Fig_5}
\caption{Gray scale face images of single subject in Sheffield database.}
\label{Fig_5}
\end{figure}

The recognition accuracy of the proposed approach on Sheffield database using SVM classifier and KNN classifier is reported in Tables \ref{Tab_4}, \ref{Tab_5} and \ref{Tab_6} by respectively considering $G_1, G_2, G_3$.

\begin{table}[h]
\caption{Face Recognition accuracy on Sheffield database using NBLGC $G_1$.}
\label{Tab_4}
\begin{tabular}{|c|c|c|c|}
\hline
\multirow{3}{*}{\textbf{\begin{tabular}[c]{@{}c@{}}Sheffield\\database\\
on NBLGC G1\end{tabular}}} & \multirow{3}{*}{\textbf{\begin{tabular}[c]{@{}c@{}}SVM\\ poly1\end{tabular}}} & \multirow{3}{*}{\textbf{\begin{tabular}[c]{@{}c@{}}SVM\\ poly2\end{tabular}}} & \multirow{3}{*}{\textbf{KNN}} \\
                            &                                                                                &                                      &                               \\
                            &                                                                                &                                      &                               \\ \hline
\multirow{2}{*}{\begin{tabular}[c]{@{}c@{}}Training images7.\\ Testing images21.\end{tabular}}          & \multirow{2}{*}{\begin{tabular}[c]{@{}c@{}}91.42\%\end{tabular}}     & \multirow{2}{*}{90\%}             & \multirow{2}{*}{90\%}        \\
                            &                                                                                &                                      &                               \\ \hline
\multirow{2}{*}{\begin{tabular}[c]{@{}c@{}}Training images14.\\ Testing images14.\end{tabular}}          & \multirow{2}{*}{\begin{tabular}[c]{@{}c@{}}96.42\%\end{tabular}}     & \multirow{2}{*}{\begin{tabular}[c]{@{}c@{}}94.28\%\end{tabular}}             & \multirow{2}{*}{90\%}        \\
                            &                                                                                &                                      &                               \\ \hline
\end{tabular}
\end{table}

\begin{table}[h]
\caption{Face Recognition accuracy on Sheffield database using NBLGC $G_2$.}
\label{Tab_5}
\begin{tabular}{|c|c|c|c|}
\hline
\multirow{3}{*}{\textbf{\begin{tabular}[c]{@{}c@{}}Sheffield\\database\\
on NBLGC G2\end{tabular}}} & \multirow{3}{*}{\textbf{\begin{tabular}[c]{@{}c@{}}SVM\\ poly1\end{tabular}}} & \multirow{3}{*}{\textbf{\begin{tabular}[c]{@{}c@{}}SVM\\ poly2\end{tabular}}} & \multirow{3}{*}{\textbf{KNN}} \\
                            &                                                                                &                                      &                               \\
                            &                                                                                &                                      &                               \\ \hline
\multirow{2}{*}{\begin{tabular}[c]{@{}c@{}}Training images7.\\ Testing images21.\end{tabular}}          & \multirow{2}{*}{\begin{tabular}[c]{@{}c@{}}90.95\%\end{tabular}}     & \multirow{2}{*}{\begin{tabular}[c]{@{}c@{}}87.61\%\end{tabular}}             & \multirow{2}{*}{\begin{tabular}[c]{@{}c@{}}90.47\%\end{tabular}}        \\
                            &                                                                                &                                      &                               \\ \hline
\multirow{2}{*}{\begin{tabular}[c]{@{}c@{}}Training images14.\\ Testing images14.\end{tabular}}          & \multirow{2}{*}{\begin{tabular}[c]{@{}c@{}}97.14\%\end{tabular}}     & \multirow{2}{*}{\begin{tabular}[c]{@{}c@{}}94.28\%\end{tabular}}             & \multirow{2}{*}{\begin{tabular}[c]{@{}c@{}}90.71\%\end{tabular}}        \\
                            &                                                                                &                                      &                               \\ \hline
\end{tabular}
\end{table}

\begin{table}[h]
\caption{Face Recognition accuracy on Sheffield database using NBLGC $G_3$.}
\label{Tab_6}
\begin{tabular}{|c|c|c|c|}
\hline
\multirow{3}{*}{\textbf{\begin{tabular}[c]{@{}c@{}}Sheffield\\database\\
on NBLGC G3\end{tabular}}} & \multirow{3}{*}{\textbf{\begin{tabular}[c]{@{}c@{}}SVM\\ poly1\end{tabular}}} & \multirow{3}{*}{\textbf{\begin{tabular}[c]{@{}c@{}}SVM\\ poly2\end{tabular}}} & \multirow{3}{*}{\textbf{KNN}} \\
                            &                                                                                &                                      &                               \\
                            &                                                                                &                                      &                               \\ \hline
\multirow{2}{*}{\begin{tabular}[c]{@{}c@{}}Training images7.\\ Testing images21.\end{tabular}}          & \multirow{2}{*}{\begin{tabular}[c]{@{}c@{}}90.47\%\end{tabular}}     & \multirow{2}{*}{\begin{tabular}[c]{@{}c@{}}86.66\%\end{tabular}}             & \multirow{2}{*}{\begin{tabular}[c]{@{}c@{}}89.52\%\end{tabular}}        \\
                            &                                                                                &                                      &                               \\ \hline
\multirow{2}{*}{\begin{tabular}[c]{@{}c@{}}Training images14.\\ Testing images14.\end{tabular}}          & \multirow{2}{*}{\begin{tabular}[c]{@{}c@{}}96.42\%\end{tabular}}     & \multirow{2}{*}{\begin{tabular}[c]{@{}c@{}}94.28\%\end{tabular}}             & \multirow{2}{*}{\begin{tabular}[c]{@{}c@{}}91.42\%\end{tabular}}        \\
                            &                                                                                &                                      &                               \\ \hline
\end{tabular}
\end{table}

The experiments conducted on Sheffield database considering 10-fold cross validation process is given here. The k-fold results, considering SVM classifier, varies from 98\% at lower end and 100\% at higher end for k varies from 1 to 10. The results are shown in the Fig. \ref{Fig_6}.

\begin{figure}
\centering
\includegraphics[width=73mm,scale=0.6]{Fig_6}
\caption{K-fold accuracy on Sheffield database.}
\label{Fig_6}
\end{figure}

The ROC plot is drawn FAR vs. GAR, with varying threshold values for Sheffield database also and shown in Fig. \ref{Fig_7}. From this plot, the threshold that yields the highest GAR corresponding to the lowest FAR need to be selected. It is evident from the graph that the $G_1$ outperform $G_2$ and $G_3$ for this databset.

\begin{figure}
\centering
\includegraphics[width=73mm,scale=0.6]{Fig_7}
\caption{The GAR Vs FAR on Sheffield database with $G_1$, $G_2$ and $G_3$.}
\label{Fig_7}
\end{figure}

\subsection{Experimentation on YALE dataset:}
The YALE dataset \cite{24} consists of 165 images of 15 individual. There are 11 images of each individual, one per different facial expression or configuration: center-light, with glasses, happy, left-light, without glasses, normal, right-light, sad, sleepy, surprised, and wink. The YALE database images have a fixed pose but different light illumination condition and expression. \\

The subset of some of the subjects of YALE dataset is shown in Fig. \ref{Fig_8}. Similar to previous experimentation, here too, the datasets were partitioned into train and test set. The SVM classifier with a polynomial kernel of degree 1 and 2 is considered for training the classifier.

\begin{figure}
\centering
\includegraphics[width=60mm,scale=0.5]{Fig_8}
\caption{Gray scale face images of some of the subject in YALE database}
\label{Fig_8}
\end{figure}

The recognition accuracy of the proposed approach on YALE database using SVM classifier and KNN classifier is reported in Tables \ref{Tab_7}, \ref{Tab_8} and \ref{Tab_9} by respectively considering $G_1, G_2, G_3$.

\begin{table}[h]
\caption{Face Recognition accuracy on  YALE database using NBLGC $G_1$.}
\label{Tab_7}
\begin{tabular}{|c|c|c|c|}
\hline
\multirow{2}{*}{\textbf{\begin{tabular}[c]{@{}c@{}}YALE database \\ on NBLGC G1
\end{tabular}}} & \multirow{2}{*}{\textbf{\begin{tabular}[c]{@{}c@{}}SVM\\ poly1\end{tabular}}} & \multirow{2}{*}{\textbf{\begin{tabular}[c]{@{}c@{}}SVM\\ poly2\end{tabular}}} & \multirow{2}{*}{\textbf{KNN}} \\
                            &                                                                                &                                      &                               \\ \hline
\multirow{2}{*}{\begin{tabular}[c]{@{}c@{}}Training images6.\\ Testing images5.\end{tabular}}          & \multirow{2}{*}{\begin{tabular}[c]{@{}c@{}}69.33\%\end{tabular}}                                                            & \multirow{2}{*}{68\%}             & \multirow{2}{*}{76\%}        \\
                            &                                                                                &                                      &                               \\ \hline
\multirow{2}{*}{\begin{tabular}[c]{@{}c@{}}Training images5.\\ Testing images6.\end{tabular}}          & \multirow{2}{*}{\begin{tabular}[c]{@{}c@{}}66.66\%\end{tabular}}                                                            & \multirow{2}{*}{\begin{tabular}[c]{@{}c@{}}61.11\%\end{tabular}}             & \multirow{2}{*}{70\%}        \\
                            &                                                                                &                                      &                               \\ \hline
\end{tabular}
\end{table}

\begin{table}[h]
\caption{Face Recognition accuracy on YALE database using NBLGC $G_2$.}
\label{Tab_8}
\begin{tabular}{|c|c|c|c|}
\hline
\multirow{2}{*}{\textbf{\begin{tabular}[c]{@{}c@{}}YALE database \\ on NBLGC G2
\end{tabular}}} & \multirow{2}{*}{\textbf{\begin{tabular}[c]{@{}c@{}}SVM\\ poly1\end{tabular}}} & \multirow{2}{*}{\textbf{\begin{tabular}[c]{@{}c@{}}SVM\\ poly2\end{tabular}}} & \multirow{2}{*}{\textbf{KNN}} \\
                            &                                                                                &                                      &                               \\ \hline
\multirow{2}{*}{\begin{tabular}[c]{@{}c@{}}Training images6.\\ Testing images5.\end{tabular}}          & \multirow{2}{*}{\begin{tabular}[c]{@{}c@{}}74.66\%\end{tabular}}                                                            & \multirow{2}{*}{\begin{tabular}[c]{@{}c@{}}70.66\%\end{tabular}}             & \multirow{2}{*}{62.22\%}        \\
                            &                                                                                &                                      &                               \\ \hline
\multirow{2}{*}{\begin{tabular}[c]{@{}c@{}}Training images5.\\ Testing images6.\end{tabular}}          & \multirow{2}{*}{\begin{tabular}[c]{@{}c@{}}63.33\%\end{tabular}}                                                            & \multirow{2}{*}{\begin{tabular}[c]{@{}c@{}}62.22\%\end{tabular}}             & \multirow{2}{*}{66.66\%}        \\
                            &                                                                                &                                      &                               \\ \hline
\end{tabular}
\end{table}

\begin{table}[h]
\caption{Face Recognition accuracy on YALE database using NBLGC $G_3$.}
\label{Tab_9}
\begin{tabular}{|c|c|c|c|}
\hline
\multirow{2}{*}{\textbf{\begin{tabular}[c]{@{}c@{}}YALE database \\ on NBLGC G3
\end{tabular}}} & \multirow{2}{*}{\textbf{\begin{tabular}[c]{@{}c@{}}SVM\\ poly1\end{tabular}}} & \multirow{2}{*}{\textbf{\begin{tabular}[c]{@{}c@{}}SVM\\ poly2\end{tabular}}} & \multirow{2}{*}{\textbf{KNN}} \\
                            &                                                                                &                                      &                               \\ \hline
\multirow{2}{*}{\begin{tabular}[c]{@{}c@{}}Training images6.\\ Testing images5.\end{tabular}}          & \multirow{2}{*}{72\%}                                                            & \multirow{2}{*}{72\%}             & \multirow{2}{*}{60\%}        \\
                            &                                                                                &                                      &                               \\ \hline
\multirow{2}{*}{\begin{tabular}[c]{@{}c@{}}Training images5.\\ Testing images6.\end{tabular}}          & \multirow{2}{*}{64\%}                                                            & \multirow{2}{*}{61\%}             & \multirow{2}{*}{55.55\%}        \\
                            &                                                                                &                                      &                               \\ \hline
\end{tabular}
\end{table}

The experimentation conducted on YALE database considering 10-fold cross validation process is given here. The k-fold results considering SVM classifier varies from 32\% at lower end and 95\% at higher end for k varies from 1 to 10. The results are shown in the Fig. \ref{Fig_9}.

\begin{figure}
\centering
\includegraphics[width=73mm,scale=0.6]{Fig_9}
\caption{K-fold accuracy on YALE database.}
\label{Fig_9}
\end{figure}

The ROC plot is drawn FAR vs. GAR, with varying threshold values for YALE database also and is shown in Fig. \ref{Fig_9}. From this plot, the threshold that yields the highest GAR corresponding to the lowest FAR need to be selected. It is evident from the graph that the $G_1$ outperform $G_2$ and $G_3$ for this databset.

\begin{figure}
\centering
\includegraphics[width=73mm,scale=0.6]{Fig_10}
\caption{The GAR Vs FAR on YALE database with $G_1$, $G_2$ and $G_3$.}
\label{Fig_10}
\end{figure}

\section{COMPARATIVE ANALYSIS}
In order to exhibit the suitability of the proposed approach to face recognition problems, we have made a comparative study with some of the well known and related face recognition approaches. We have considered Local Polynomial Approximation method, Eigenface approach, Local Gabor Binary Pattern Histogram Sequence( LGBP) method, Local Binary Pattern(LBP) method,Fisher and Local Polynomial Approximation-Local Binary Pattern method for comparative analysis. The recognition results on different datasets considering eight number of training images and the remaining number of testing images are shown in the following Table \ref{Tab_10}.

\begin{table}[h]
\caption{Comparative results with existing approaches}
\label{Tab_10}
\begin{tabular}{|l|l|l|ll}
\cline{1-3}
\multicolumn{1}{|l|}{Method} & ORL database & YALE database &  &  \\ \cline{1-3}
Eigenface        & 96.25        & 74            &  &  \\
LGBP             & 96.75        & 86            &  &  \\
LBP				 & 97.5			& 87			&  &  \\
LPA-LBP			 & 99.25		& 92			&  &  \\
MFA				 &92.5			&84				&  &  \\
LPP				 &90			&81				&  &  \\
NPDA			 &90			&82 			&  &  \\
\textbf{Proposed} &             &               &  &  \\
\textbf{method}  &\textbf{97.55}         &\textbf{86.5}           &  &  \\
\cline{1-3}
\end{tabular}
\end{table}

\section{CONCLUSIONS}
In this work, we have developed Non Binary Local Gradient Contours(NBLGC) as a descriptor for face recognition. A new approach is presented to account for the information from both the central pixel and the neighbourhood pixels of an image while matching test sample with the training samples in the face recognition process thus eliminating the drawback of traditional LBP and LDP approaches which accounts for the neighbouring pixels alone in the representation of texture in a window. We have shown that the newly developed NBLGC descriptor is suitable for face recognition under varying conditions. Since we are losing the information about central pixels and hence we compute the membership based on central pixels. And we are using Shannon entropy for computing the features of the window. Also we have developed a new distance measure which gives most of times better results when compared to conventional Euclidean distance measure. The proposed NBLGC poses good discrimination capability and shown to be worked well with subject having 10 images with 40 classes (ORL) database. As a future work, we are trying to build a classifier and looking for a new membership function.\\
The information value obtained from the window is based on communication theory where the information is defined as the log of the decimal value of a signal. In our work, the information from window is coupled with the concept of information sets in a multiplicative manner. The advantage of this approach is that the information sets allow the information to be modified in different ways. A comparison of performance of the proposed approach is made with that of PCA using two classifiers KNN and SVM. Better results are reported with SVM.\\
The proposed approach is found to be effective on images having variation in expression, illumination and pose. The experiments are reported on ORL, Sheffield and Yale face database. The new features are the result of better representation of the uncertainty in the local area like a window, which preserves the information of complete window. The experiments reveal that, better the representation of an uncertainty, better will be the recognition rates.\\

redpajama_set_name: RedPajamaArXiv


text:
HomeTeslaModel 3NewsGeneral
Tesla Model 3 Ownership Experience Cut Short By System Shutdown
Tesla Model 3 Ownership Experience Cut Short By System Shutdown product 2018-08-22 14:25:19 https://insideevs.com/news/339096/tesla-model-3-ownership-experience-cut-short-by-system-shutdown/ Tesla Model 3 General
Aug 22, 2018 at 10:25am
This Tesla Model 3 shut off after just three weeks
A Tesla Model 3 owner and a self-proclaimed "true loyal fan to Tesla and Elon" was thrilled to receive his car, ordered over two years earlier and really enjoyed the ownership experience for... three weeks.
In this particular example, after 22 days, on August 15, the Model 3 stopped work properly as the system experienced a shutdown.
"...I got in the car in the morning and noticed the turn signal no longer make the ticking noise.
My audio system was also off, and there was no internet connectivity.
Thinking that the system needs a reboot, I went to the Controls section on the screen and hit "Power Off"."
Tesla Model 3 problems?
UPDATE: Are Tesla Model 3 Rear Bumpers Falling Off In The Rain?
This All-White Tesla Model 3 Came With One Brown Door Panel
It was the last time the owner saw the screen on and after he returned to the car it was off and wouldn't reboot. Interestingly, there was still a possibility to select gears blindly (no screen to see which gear was selected) and to then drive the car.
Tesla Roadside Assistance team was helpful though. The team sent a trailer to take the Model 3 to a service center, as well as arranged Lyft transportation for the owner to pick up a loaner Model S.
What exactly happened to the Model 3 computer is the question that has since been "escalated to the Corporate Engineering team".
Hopefully, this was a one-off problem, though we've heard similar such reports. However, it doesn't seem like a widespread issue. Nonetheless, it's worth noting this inoperable experience with the car, which is rare across the broader automotive world.
Source: Teslainfo


Preheat oven to 350°F/177°C. Grease and line four 6-inch round pans. Set aside.
In a bowl, combine cake mix, eggs, mashed bananas, oil, water, and chocolate chips.
Divide the cake batter even among the prepared pans.
Bake for 20-25 minutes or until cake is set and lightly golden brown.
Prepare peanut butter frosting in a mixer bowl with the whisk attachment. Pour in the cream and whip until it forms soft peaks. Incorporate the peanut butter and powdered sugar. Fold in the gelatin. Set aside.
Prepare filling by placing the peanut butter and cream in a microwave safe bowl. Microwave in 15 second intervals until mixture is smooth. Repeat this step for the chocolate ganache filling.
Level the tops of the cake once they have cooled. Fill the cake layers with peanut butter filling, and then spread with chocolate ganache. Place the cake layers on top of each other. Place a dowel on the center to secure the cake.
Frost the cake with the prepared peanut butter frosting. Use a spoon to add texture the top part of the cake and place chocolate shavings on the bottom part. Serve.


Young Sydney midfielder Terry Antonis is hoping to keep his 18th birthday celebrations to a minimum but says he might not have a choice.
Antonis will be in New Zealand when he turns 18 on Saturday preparing for Sydney-s game against Wellington the following day.
He remained coy when asked about any birthday plans.
Those three points against the Phoenix are the only birthday present the teenager wants from his team mates on Sunday, but he admits they might persuade him to let his hair down with a win.


text:
What Goes Down Must Go Back Up
Inflation is rising but for how much longer? Peter Hemple reports
by Peter Hemple - 1 Jun 2021
If you find the April 2020 edition of PIN, there is an article looking at what impact the pandemic will have on commercial property in the UK. I wrote the article just a week or two into the first UK lockdown while global stock markets and economies were crashing, with no bottom in sight.
Here is an excerpt from that article: 'Any faster than expected turnaround in events regarding Covid-19…will very likely unleash a strong bout of inflation. Car plants are closing, clothing factories also, airlines are going bust. All the while, millions of employees will be earning close to full salaries while working at home in isolation, or being paid almost a full salary not to work at all.'
Regarding when the lockdown in the UK eventually ended I wrote: 'We will be treating ourselves to better cars and clothes and nicer homes with better furniture and, after such close confinement with our families for such a long period, demand to build extensions/extra rooms will also soar…in short, if you wake up to global headlines this summer of "over-80s to be vaccinated within weeks", it is probably worth a call to your mortgage lender to see what the best fixed-rate deals are.'
However, while Moneyfacts did indeed report in November 2020, just weeks before the vaccination rollout began, that average rates on two and five year fixed mortgage deals had risen for four consecutive months, resulting in the average two-year rate for borrowers with a 15% deposit exceeding 3%, in January this year lenders starting focusing on borrowers with large deposits, just as they did after the financial crisis.
This trend has continued and at the end of May Moneyfacts reported that interest rates for buy-to-let mortgages are beginning to decrease. Its figures revealed that since March 2021 the largest reductions have been seen at 65% LTV. While it said this was great news for landlords with the required level of deposit, or equity if remortgaging, for those landlords with lower levels of deposit or equity the news is not good as the average two-year fixed rate at 80% LTV has been rising and is now at 4.20%.
Overall though, interest rates are still relatively low despite a successful vaccine rollout and a strong economic rebound. There are two main reasons for this. Firstly, after cutting the base rate to a new record low of 0.1% in March 2020, the Bank of England (BoE) has so far decided against raising rates. Secondly, the Mortgage Guarantee Scheme was launched in April 2021, increasing lender confidence, not only to lend to first-time buyers but also to first-time landlords, which has resulted in more competitive mortgage deals being offered.


text:
Home / Business News / Horizon Discovery and Rutgers University Form Exclusive Partnership to Develop Next Generation Novel Gene Editing Technology
Horizon Discovery and Rutgers University Form Exclusive Partnership to Develop Next Generation Novel Gene Editing Technology
Nicole Watkins Business News, Technology & Devices Comments Off on Horizon Discovery and Rutgers University Form Exclusive Partnership to Develop Next Generation Novel Gene Editing Technology
CAMBRIDGE, England–(BUSINESS WIRE)–Horizon Discovery Group plc (LSE: HZD) ("Horizon" or "the Group"), a global leader in gene editing and gene modulation technologies, today announces that it has entered into an exclusive strategic partnership with Rutgers, The State University of New Jersey (US), to develop and commercialise a novel gene editing technology, known as base editing. The technology potentially has applications in the development of new cell therapies and will augment Horizon's research tools and services.
Horizon will collaborate with Rutgers University to further develop the novel base editing platform from the laboratory of Dr. Shengkan Jin, associate professor of pharmacology at Rutgers Robert Wood Johnson Medical School. As part of the agreement, Horizon has made a non-material payment to Rutgers for an option to exclusively license the base editing technology for use in all therapeutic applications. As part of the collaboration, Horizon will also fund further research in base editing at Rutgers University while undertaking evaluation and proof of concept studies at Horizon.
Base editing is a novel technology platform for engineering DNA or genes in cells that has the potential to correct errors or mutations in the DNA by modifying genes using an enzyme. Compared with currently available gene editing methodologies such as CRISPR/Cas9, which creates "cuts" in the gene that can lead to adverse or negative effects, this new technology allows for more accurate gene editing while reducing unintended genomic changes. The technology will have a significant impact in enabling cell therapies to be progressed through clinical development and towards commercialisation.
Terry Pizzie, Horizon's Chief Executive Officer, said: "Base editing is potentially transformative for all gene editing technologies with the potential to help target many diseases that to date have no treatment. As a world leader in the field of gene editing and gene modulation, both in research and applied markets, we are very excited to partner with Dr Jin and Rutgers University. By extending our scientific and IP capabilities, Horizon will now be able to more fully support our pharma, biotech and academic partners to deliver better cell therapy solutions to patients. As part of our five-year investment strategy, Horizon committed to investing in high value technologies that maintain our market leadership; base editing technology is a perfect example of that. We look forward to updating on the progress of the partnership and potential future next steps."
Dr. Shengkan 'Victor' Jin of Rutgers University stated: "The cytidine deaminase version of the technology alone could potentially be used for developing ex vivo therapeutics such as gene modified cells for sickle cell anemia and beta thalassemia, HIV resistant cells for AIDS, and over-the-shelf CAR-T cells for leukemia, as well as in vivo therapeutics for inherited genetic diseases. The potential is enormous. In addition to the 'simple' diseases caused by a single genetic alteration event, the therapeutic strategy, in principle, could also be useful for treating diseases where permanently targeting a disease-related gene is beneficial."
Dr. David Kimball, Interim Senior Vice President for Research and Economic Development at Rutgers University, added: "Gene editing technology has truly revolutionised how scientists think about their search for better options and outcomes in the treatment of disease. We look forward to advancing the shared goals of further developing this novel base editing platform and improving human health through this collaboration with Horizon."
Previous Alexion Announces Positive Top-Line Results from Phase 3 Study Evaluating Ultomiris in Complement Inhibitor-Naïve Patients with atypical Hemolytic Uremic Syndrome
Next FDA Approves Imbruvica Plus Obinutuzumab as First Non-Chemotherapy Combination Regimen for Treatment-Naïve Patients with Chronic Lymphocytic Leukemia
INGELHEIM, Germany & SEONGNAM, Korea–(BUSINESS WIRE)–Boehringer Ingelheim and Bridge Biotherapeutics Inc. today announced that they …


Domain names are the names of web sites on the Internet. They are names like dwightwatt.com, cnn.com, gvtc.edu, and whitehouse.gov When we want to see a website we use the domain name to go to the web site.
The Internet has not always used domain names. Domain names were created and started to be used in the early 1990s. Prior to the early 1990s the only way to pull up material that an organization had on the Internet was to use their IP address (which the Internet actually converts domain names to and still uses). Then you had to type in a number like 192.168.13.1 to go to where I had my information stored. Obviously that did not work well for most people and the only ones who used the Internet were government, military, colleges and libraries and research organizations. You had to remember the IP address for any you wanted to contact.
With domain names we can use a word that the organization has bought that represents them. Domain names are sold by domain name companies and administered by international organizations set up by the US government and still highly influenced by them.
In front of the domain name, the name of the server the web page is on is named. Most often the server is called www, but if you watch when you go to web sites quite often that first part will change at the site as they have their pages on different servers.
The first part of the domain name tells us the specific organization such as dwightwatt or cnn. Domain names are not case sensitive so they can be upper or lower case. However, if there is folder or file information at the end after a slash (/) that is case sensitive. The last part of the domain name is supposed to tell us the type of organization but you can buy most extensions. A few are tightly controlled such as .mil (military), .gov (government) and .edu (accredited educational institutions, although initially accreditation was not required so there are a few grandfathered in.


text:
GEC Home
Remarks and Releases
Technology Engagement Division
HomeGlobal Engagement Center...About Us – Global Engagement Center
About Us – Global Engagement Center
Mission: To direct, lead, synchronize, integrate, and coordinate U.S. Federal Government efforts to recognize, understand, expose, and counter foreign state and non-state propaganda and disinformation efforts aimed at undermining or influencing the policies, security, or stability of the United States, its allies, and partner nations.
Vision: To be a data-driven body leading U.S. interagency efforts in proactively addressing foreign adversaries' attempts to undermine U.S. interests using disinformation and propaganda.
The GEC carries out its mission along five lines of effort:
Analytics and Research: GEC's analysts and data scientists collect and analyze data from foreign state and foreign non-state actors to produce analysis on their foreign malign information influence narratives, tactics, and techniques. GEC shares these analyses with stakeholders within the Department, and among S. embassies, the interagency, and our international partners.
International Partnerships: GEC has built and participates in multiple international coalitions and partnerships with other national governments for the purpose of coordinating counter-disinformation analyses and actions, and collectively buttressing the integrity of the global information environment.
Programs and Campaigns: GEC's Russia, People's Republic of China, Iran, and Counterterrorism teams each designed to build societal and institutional resilience to foreign propaganda and disinformation efforts abroad. GEC tailors its initiatives to the specific challenges in unique overseas information environments and coordinates both internally within the Department, and with interagency and international partners.
Exposure: GEC plays a coordination role in the interagency's public exposure of foreign information influence operations, including the use of proxy sites and social media networks overseas.
Technology Assessment and Engagement: GEC hosts private sector technology demonstrations, assesses counter-disinformation technologies against specific challenges, and identifies technological solutions through technology challenge programs.
Establishment of the Global Engagement Center: GEC's founding traces back to 2011 and Executive Order 13584, which established within the Department of State the Center for Strategic Counterterrorism Communications (CSCC) for the purpose of "supporting agencies in Government-wide public communications activities targeted against violent extremism and terrorist organizations."[1] Executive Order 13721 in 2016 transformed the CSCC into the Global Engagement Center but left its counterterrorism mission largely unchanged.
GEC's mission expanded upon enactment of the National Defense Authorization Act for Fiscal Year 2017 to include the authority to address other foreign state and non-state propaganda and disinformation activities. The John S. McCain National Defense Authorization Act for Fiscal Year 2019 further refined this mission, and endowed it with a mandate, as reflected in GEC's mission statement.[2]
[1] John S. McCain National Defense Authorization Act for Fiscal Year 2019, Section 1284, Modifications to Global Engagement Center, P.L. 115-232, https://www.congress.gov/bill/115th-congress/house-bill/5515/text
[2] The White House Office of the Press Secretary, Executive Order 13584-Developing an Integrated Strategic Counterterrorism Communications Initiative, September 9, 2011, https://obamawhitehouse.archives.gov/the-press-office/2011/09/09/executive-order-13584-developing-integrated-strategic-counterterrorism-c
Data Disinformation Global Engagement Center
James P. Rubin
Appointment of James P. Rubin as Special Envoy and Coordinator of the Global Engagement Center
Targeting Russian Corruption in the Guatemalan Mining Sector


Family Health Guide: No meat, no milk, no animal anything. Oprah and 378 Harpo staffers go vegan for one week. Could you do it?
No meat, no milk, no animal anything. Oprah and 378 Harpo staffers go vegan for one week. Could you do it?


Our client had a interesting challenge of an older workforce but a degree of complacency coming in around areas with forklifts and trucks.
We worked with the client to observe current behaviors in order to understand the dynamics, also ensuring employee buy in. We then came up with a plan to separate workers from the risks, by implementing safety measure using our Walksafe Handrail, Forklift Barrier, and Bollards to protect assets.
Result is a safer, more productive facility and peace of mind for the site manager and his team that their hazards have been minimised.


text:
My Old Man Said
Home News Advice for Villa Supporters Heading to Wembley by Train
Advice for Villa Supporters Heading to Wembley by Train
Last week, MOMS was informed by Virgin Trains about the rail works Network Rail is undertaking on the Bank Holiday Monday weekend of the Championship play-off final. They'll be a reduced service on the day of the final. To counter this though, Virgin Trains have got the clearance to put on four extra train services between Birmingham New Street and London Euston (two on the outward journey and two after the game) on the 27th May to help supporters on the day.
Now that Villa have booked their place at Wembley, here's the details:
The extra morning trains will leave from Birmingham News Street at 09:24 and 10:30, while the returning services will depart from Euston at 19.05 and 20.06.
Advance booking is recommended, as it is expected to be busy due to it being a Bank Holiday.
Don't forget Villa season card holders and club/claret members are able to get 20% off some services if they book in advance. More details here – Aston Villa Virgin Train Discount
Below is Virgin's statement and full details…
Virgin Trains Announcement
Virgin Trains is encouraging fans wanting to travel to Wembley by train to book in advance as their services are expected to be extremely busy on Bank Holiday Monday. With Network Rail undertaking engineering works over the weekend, a reduced service will operate throughout the day.
After the game the service will be bolstered by two additional trains to Birmingham New Street leaving Euston at 19:05 and 20:06.
Boarding controls will be in place at London Euston and fans are asked to arrive at the station in good time ahead of their booked return service. There are a number of services after the match, with the last train departing London Euston at 23:30.
Fans can also choose to travel with West Midlands Trains (WMT) and Chiltern Railways (to and from Birmingham Moor Street. WMT only tickets are not valid on Virgin Trains services.
Check out the latest MOMS Podcast episode
Discounted Train Travel Continues for Aston Villa Supporters
Useful Aston Villa Fan Travel Benefit is Back & Get a Discounted Away Shirt
Virgin Trains Announce a 'Better' Version of the Football Specials
Aston Villa's Good Form Sees Villa Park Stand Open & Benefit Finally Announced
Aston Villa Arrange Useful New Benefit for Villa Season Ticket Holders
Aston Villa News 24/7
Villa Shirts Sale
My Old Man Said (MOMS) is an award-winning football blog, podcast and fan community. MOMS is sits on the Aston Villa Fan Consultation Group and is an active member of the Football Supporters Association (FSA). UTV
MOMS Members


text:
Hot Spot: The 50 Cent vs Teairra Mari…
The 50 Cent and Teairra Mari saga continues, JD and Dallas Austin are planning a different type of Instagram battle…
Struggle Swerve: Teairra Mari Could Face Felony Charge…
Teairra Mari might need some help getting off the bottle, especially if she's going to be hitting the streets operating…
Teairra Mari Popped For DWI In A 3-Wheeled…
Teairra Mari is living the life of full-blown struggle.
50 Cent Claims Teairra Mari Lied About Grandfather's…
The ongoing beef between 50 Cent and Teairra Mari has taken vicious turns with the two swiping at one another…
50 Cent Gets Teairra Mari Served At An…
50 is still trying to get his coin from Teairra Mari.
Teairra Mari's Revenge Porn Lawsuit Thrown Out After…
Teairra Mari suffered another legal setback on Thursday as a judge threw out her revenge porn case against her ex-boyfriend.
Teairra Mari Loses Revenge Porn Lawsuit Against 50…
Teairra Mari's lawsuit against 50 Cent for allegedly spreading her sex tape on social media has been dismissed.
Teairra Mari Names Ex-Boyfriend & 50 Cent In…
Teairra Mari found herself in the headlines for a terrible reason after her Instagram page was hacked and images of…
Teairra Mari Bashed Ex-Boyfriend's Benz G-Wagon Windows After…
Teairra Mari got exposed in embarrassing fashion after he ex-boyfriend allegedly posted a lewd photo and video of her performing…
Gary With Da Tea & "Bossip On WEtv"…
Teairra Mari was apparently leaking from her booty during filming for Love & Hip-Hop: Hollywood.


text:
Q: In which cases do we need protected inheritance? It is clear about public and private inheritance, but what about protected? Any example when we really need to use it and it gives us benefit?

A:
Protected inheritance is something whose meaning eludes me to this day.

This is Scott Meyers opinion (Effective C++, 3rd Edition) on protected inheritance :).
However, this page is interesting: Effective C++: discouraging protected inheritance?.

A: The base-from-member idiom needs protected inheritance at times.
The problem that idiom addresses is the following: you sometimes need to initialize the base class with a member of the derived class, like in
struct foo
{
    virtual ~foo() {}

protected:
    foo(std::ostream& os)
    {
        os << "Hello !\n";
    }
};

struct bar : foo
{
    bar(const char* filename)
      : foo(file), file(filename) // Bad, file is used uninitialized
    {}

private:
    std::fstream file;
};

But file is constructed after foo, and thus the ostream passed to foo::foo is invalid.
You solve this with an auxiliary class and private inheritance:
struct bar_base
{
    std::fstream file;

protected:
    bar_base(const char* filename)
      : file(filename)
    {}

    ~bar_base() {}
};

struct bar : private bar_base, public foo
{
    bar(const char* filename)
      : bar_base(filename), foo(file)
    {}
};

Now bar_base is constructed before foo, and the ostream passed to foo::foo is valid.
If you want file to become a protected member of bar, you must use protected inheritance:
struct bar : protected bar_base, public foo { ... }

A: protected means that the member variables will be accessible from subclasses but not from outside.
A very simple example may be a class that uses a variable (say x) to do some internal computation. If a subclass will need to do the same computation will probably need to access x. Making it private will avoid the subclass to access it, making it public will make it accessible to everyone. Protected is like a tradeoff.

A: Protected is particularly useful to abstract superclasses, written with knowledge of the children in mind.  Protected members and methods are available to the children, and can save code replication, without exposing them to the world outside the family of classes being implemented at the time.

redpajama_set_name: RedPajamaStackExchange


text:
Arsenal News Arsenal opinion
Ozil is made out to be the villain yet again – what about Kroenke?
By Admin Pat - 21 April 2020, 9:37
In Ozil I trust by Dan Smith
Even without a ball being kicked yet again the media picks on Ozil. The majority of the squad are doing a decent thing voluntarily, yet we focus on the German's decision not to agree to losing 12 percent of his income. Two others agree with him but of course it is the midfielder who makes the headlines
So, for weeks, Premiership club's players were criticised for not taking a wage cut. Even captains setting up their own initiative to help the NHS was not good enough.
As Wayne Rooney pointed out, that's why owners and the League itself has been happy to leak this out to the public, essentially backing footballers into a corner. Chairmen cannot legally slash salaries , that needs to be the individual's choice. They know that the average man and women in the street will find their wages even more crazy when the world's in lock down, pressuring these men if they dare say no.
It hurts the sports image if someone on 100 grand a week cannot take a pay cut while many of their fans are losing jobs or do not know when or if they return to work, etc. Yet is Ozil wrong in his stance?
Put emotion into it and of course the world is crazy that a man gets 300,000 grand a week for kicking a ball, while ordinary people are starving and/or homeless. Yet that is a different debate, one that is being going on for decades. 2 per cent of the richest individuals in this universe have enough to stop poverty so let us not act like things should change due to the Pandemic.
In other sports, genres, etc, I read 'how dare there be rich people', like they have done something evil, likes it is their fault that others are in their individual situations. The sense of entitlement scares me, it is like you are not happy so will not be unless someone else loses everything. The economy takes a hit, so how dare others still be wealthy?
Some, by the way, work very hard for their millions, others have bad luck. That is the way the ball bounces.
But yes, the likes of Ozil earn absurd money …. we know that… but that is not his fault. He gets that because – and this is the key thing – there is a hell of a lot of money in this game. That is what the likes of Stan Kroenke wants you to forget.
That is what Rooney means. They want us to debate what he has doing with his money, meaning we are not talking about what those who own the club are doing.
See, Ozil has not said no. Neither have the other two. They want evidence to see how much revenue is lost and what their employers are equally risking or helping?
That is not just Ozil. Bellerin, on behalf of the Arsenal squad, rejected the initial request from the club to give up a percentage of their money until they saw the shareholders, i.e. the Kroenke family, do the same. It is like those earning thousands are being told to pay the tea ladies wages because billionaires have found an excuse not to.
Peers at other clubs feel the same, hence why they are happy to set up their own initiative for the NHS – so they control where their money goes.
I cannot say Ozil is wrong because it is been my stance from the start. We all have opinions but how many truly know how contracts, insurance, etc, works?
What if the season does finish so there is no TV money to pay back?
What if FIFA turn around and offer compensation?
If that happens then the 12.5 per cent given up is simply going back into the club.
If I were a player my priority would be that all non-playing staff are paid in full and any wage cuts benefit the NHS, not a football club.
Remember Stan Kroenke is a billionaire. FIFA got 6 billion just for the last World Cup and they will get more for the next. So, an Ozil has every right to ask what other stakeholders are doing to help.
It is wrong to target him for not believing or wanting proof of this hit we might take. If it is to fund a new striker then that is not his responsibility.
In 2014 Ozil used all prize money from winning the World Cup to fund 23 surgeries in Brazil for 23 poor children. For wedding gifts, he and his wife raised enough to cover the cost of another 1,000 surgeries for children. He helped fund 100 000 takeaway meals to Turkey and Syria refugee camps through the Red Cross. He is funding a football school in Turkey and is the face of charity Rays of Sunshine.
Lots of that will not get mentioned in the press.
Now, just because we are in an era where we think we can tell someone he has to give 12.5 percent of his wage to Mr Kroenke, I would rather him to continue doing the good work independently, and wait to see the facts.
Admin Pat
stevo says:
Ozil could take a 300k p/w cut but wont. He is building a huge business empire and can't "afford" to take a cut.
'Ozil made a villain once again', what a statement for for a highest paid player who scores 12 goals in 60 games. Ozil has got no drive, determination. and willingness to help Arsenal. Its all about him
You see that clown up there who was licking Emery's balls months ago? Him alongside a few others on here, would as usual blame Ozil for anything going wrong with the club.
Let's ignore the fact that the club came out yesterday announced they've reached an agreement with the players to take a cut.
Let's ignore it and go with the crap these shameless blogs put up.
Watch a few other self entitled fans come on here come pick on Ozil for something nobody can say is true.
Bunch of jokers
True. They are all jokers. No one talks of the good works he has been doing and is still doing as pointed out by the author. This herd mentality is killing the world big time. No one wants to take the time to reason and figure out truths any more. Just jump on the bandwagon and kill the witch! Bunch of losers all. If you all have the talent go play football or other sports. They pay the highest tax and still you all want to judge them guilty?!
ThirdManJW says:
The reason Ozil gets the most air time, is because of two reasons: He's a big name, and he is most undeserving of their salary than any other Arsenal player, or any player in the whole of England, bar Alexis.
As I have said previously, this story needs to substantiated before we all lose our heads, but I really want to know who the other two are if this is true.
Chief says:
I think the other two are bellarin, Xhaka
Admin Pat says:
And why would you say that? Another made-up rumour?
A J says:
Not even going to attempt to justify what Mesut reputably earns.
Supply and Demand.
He demanded ………we supplied !!
More fool us !
One can only hope he puts at least a portion of his earnings to good use – and I mean GOOD use.
No,he didn't demand – the club offered the salary to keep him at the club – he accepted.
If you want to blame anyone, then blame our stupid club (in this instance) for offering such a ludicrous and obscene salary – would you have turned it down?
Come on Ken.
"No, he didn't demand" ?
So we dreamt up 350K a week !? (if true).
A "grand gesture" which has proved to be a millstone 'round our necks ever since.
Where are the tangible rewards for such a "gamble".
Ozil and his people were coming from a position of strength, on the back of the Sanchez departure and "dipped their bread".
They knew exactly what they were doing , and went in for "max out".
To quote my post ;
More fool us !" *
We agree * – I did blame our "stupid" club !
Would I have turned it down – no.
Would I have bust a gut to deliver value for money, and delivered on my "superstar" reputation.
Would I have made bloody sure I COULD be selected for certain games, when the view was I was a "risk" the manager couldn't take,
Would I have strolled through half the matches played waving my arms around at the failings of others around me .
In short – no.
Not demanded……..come on Ken, we may be *rap negotiators – but we're not a registered charity !
Of course his demands were on the table – and we bottled it.
I'm with you there AJ
So AJ, when one wants to employ or re – employ someone, they put forward a package to entice that person to stay – at least that is what I've always done in a situation that you describe.
If said person asks for more, as the employer, I had a decision to make – stick to my original offer, improve it or thank him/her for their contribution and withdraw the original offer… as it seems the club did with Ramsey.
So, why do you think that the club and player didn't have the first example in place, based on your Sanchez departure?
As for his play, once again it seems the fact that he has started every single Premier league game under Mikel Arteta, shows he has confidence in the player, even if some of the fans don't.
So let's cut to the chase Ken.
This is a FOOTBALL opinion site.
A straightforward question for you.
Do you think Mesut Ozil has been value for money (again, in footballing terms) since 2013 ?
As simple as that.
Cutting through all the fog, I'm quite clear – a resounding NO !
Jon, thanks for a full and interesting reply.
First of all Jon, I always judge a person on their personal views and actions in life.
As I have said many times before, one can be a leader without shouting to the world what he does.
Ozil has been contributing to worthy causes for years, without trumpeting his actions to the world and I also give you Bob Wilson and his amazing charity – people follow those who are dignified and resppectful way, at least I do.
Most else of what you write I can agree with, bar your blaming of the fans.
As you often say, we are the club and we support the club, wanting them to be winners.
Our club has never been, in our lifetime anyway, a club that has tried to buy success…it often seemed the opposite, buying players in order that w edidn't succeed!!!
I have never been jealous of manure, chelsea or city with their individual buying powers, to me their successes ring hollow when they have steam rolled over the transfer market.
But what did you expect their fans to do? Like us, they want and crave success, that's what competition is all about.
I view Ozil as a convenient scapegoat, with his salary being the main way to attack him.
I don't just blame the club BUT I react when he is held solely responsible for the contract offered him – for goodness sake, the club were not having a shotgun held to their heads when they offered it were they? That is why I defen him when he is blamed for all the ills our club have.
I have never said he is worth a reported £350,000 a week, or that he is still world class, or that he is the answer to our attacking midfield BUT he is the best we have by far and MA has improved him so much, I believe he can bring even more out of him.
Finally Jon, you say that you act from a morale and humane view and I'm sure you do…so do I and one of the things I detest more than anything is an individual being picked on as an excuse when things go wrong.
Name calling, singling out and persecuting another individual is, to me, the lowest thing one can do to another and I defend that individual with all my worth – hope this is not too profound either and as I have said dozens of times on here, IF Ozil was to leave tomorrow, I would still be in my seat at the next home game, cheering my club on…and wishing Ozil well, as I put him and Bob Wilson in the same catagory – wonderful human beings that I look up to.
Using Ozil's generosity to support him in this article isn't really the point. I am sure that other players do likewise.
There is a lot of money in football in the good times, but at the moment clubs must be haemorhagging money due to loss of match day revenue. I am sure that the club and players taking this cut must be really important to the future way forward for the club, otherwise how can it be justified?
With regard to Kroenke I am not a fan at all and you rightly point out that he and his family should be doing something to help although I don't know if they are. Expecting any of the players to take a cut to fund a new player would of course be wrong too.
Because Ozil is so high profile for the reasons mentioned soooo many times before, it should not be used as a stick to beat him with here. His agent and the club negotiated the deal and as for Ozil, he must have known that to earn that much money brings pitfalls if the fan base become disillusioned in any way.
I agree with others before me, that this was mucky tabloid journalism and the club statement should be taken at face value
I think with Ozil, it's a case of 'damned if you do, damned if you don't'… he can't win no matter what he does!
Yes his wages are obscene, but so are plenty of others!! I don't agree with how much they're paid.. but c'est la vie 🤷♀
If he was on 150-180k a week, nobody would bat an eyelid!
Hope you're keeping O.K.
I think I would "bat an eyelid".
Purely talking football here.
Overall, in my opinion, Mesut has been a massive disappointment whether earning 20K, 50k, 100k or indeed 350k per week.
My view is not based on his earnings, but his input since 2013.
Undoubtedly a good guy (benefit of the doubt there), but forget the money and let's talk football.
Simple statement , simply put, but just my view.
What do you and others reckon on his FOOTBALLING input since joining ?
This should draw the "Cult of Ozil" out !
A J… I'm a fan, so you're asking the wrong person 😂
In all seriousness, I'm not going to lie, I'd have loved to see him score and assist a few more this season! But overall, I don't see his Arsenal career as a 'disappointment'…. but hey that's just my opinion, and yes I'm braced for the onslaught 🙈🙉 hahaha!!
It's just the money that has and always will be an issue….
Kenny Rolfe says:
AJ' if you're just talking football then why not pick on Luis, Xhaka, Mkhy, Mustafi, Bellerin. There's five players that contributed less for our club than Ozil. Played in the right position, with the right players around him, Ozil could still be useful addition unlike the five I previously mentioned.
Lenohappy says:
Sue, I don't think your last statement is right about if he's on 180k nobody will talk about him, most of Barcelona players are on less than 180k and they have all agreed that they will take a 70% wage cut. But the truth is on this particular issue I will not blame ozil even if he decides not to take a pay cut, the club gave him that amount and its his decision baby, it's been reported on goal that his agent has refused to pick call when been contacted by some journalist, so nobody should blame him, it's his decision.
His agent had come out before now to advice players not tootle any pay cut, hence the reason he is not picking his calls(@Lenohappy).
It has been mentioned that Ozil is willing to accept a deferral worth more than the 12.5% that has been agreed by others…
Well, he wouldn't be the scapegoat, Lenohappy!!
Good on the Barca players… as well as Roma's…
Eddie my brother, we are in a world where anyone can make his or her opinions known without the fear of being arrested or intimated, I was also an emery supporter even till the beginning of this season, and am not ozil biggest fan but have decided to stay away from this very topic because I know whatever I say will be seen as me not liking ozil, so please stop calling anyone a clown and an emery ass licker simply because he supported our coach, its his choice to support anyone he choose to, you are way more nature than calling people names just because they don't agree with you.
We want to clarify that our desire has always been for a reduction to be applied to our salaries because we understand that this is an exceptional situation and we are the first that have ALWAYS helped the club with what they have asked of us," the statement first published by Messi read.
"Many times we have even done things on our own accord, at moments when we felt it necessary or important to do so.
"For that reason, it doesn't cease to surprise us that from inside the club there would be people that want to put us under a magnifying glass or try to pressure us into something that we were always clear we wanted to do. In fact, if the agreement has dragged on, it's because we were looking for a formula to help the club and its workers in these difficult times.
"For our part, the moment has arrived to announce that, aside from the 70% wage cut during the State of Emergency, we are going to also make contributions so that all the club's employees can earn 100% of their salaries for as long as this situation lasts." Lionel Messi.
Grandad says:
If any good comes from this Pandemic,it will be the realization of the unjustifiable gap between rich and poor not only in this Country but throughout the World.Hopefully those in authority will recognise the efforts of our "front line troops" and appreciate the fact that they are indispensable in our time of need unlike actors, rock stars,professional sportsman, TV pundits, etc who are all grossly overpaid and who at our time of need pale into insignificance.Those who run our Premier League Clubs have enjoyed a period of sustained wealth due to the stupidity of the likes of TV Companies and the undying support of fans.Those same fans will after Coronavirus resist any attempts to raise prices at the gate or for their TV sport and as a consequence, the owners of Clubs and their overpaid Management and players will be obliged to operate within the wage guidelines imposed on public servants including our wonderful people in the NHS. Ozil has always polarised opinion with our fans,yet he just happens to be the classic example of the incompetence displayed by our Management at Arsenal for the past decade.
Glorious, Grandad!
That came from the heart and I wholeheartedly agree
Well said grandad, its not ozil fault that the stupid board gave him that huge amount.
A very glorious statement …
The drama never ends with Ozil even when there is no football. One more season, one more season is what I keep telling myself…
Gunner SA says:
So again mass hysteria for the purpose of clicks. Ozil was happy to defer his salary ……think about the amount of money which would be available immediately for the needs of non playing staff and the club's needs. This is not a self centred young man. Look at his funding of the needy the poor and those with health challenges. People are ready to jump on a wagon as long as it faces their intended direction. One last thing Club's today pay for a player s commercial appeal in greater measure than playing capability. If someone is gonna make you money …you pay them .
How can you critisze Kronke for paying 87.5% and support someone who refuses to take a pay cut of 12.5% for staying home. These bunch of mediocre players who are struggling to qualify for Europa league should be paid 50%. They are a disgrace to Arsenal brand.
None of them are worth defending or truly earn their wages besides Auba & some of the kids. I guess I'm not surprised they latch onto their wages, because only at Arsenal will they ever get paid this much. All their next offers will be lower than what they earn now.