text:
\section{Introduction}
\begin{figure*}
	\begin{center}
		\includegraphics[width=\linewidth]{framework.pdf}
		\vskip -0.1in
		\caption{Overview of ACNet. For example, we replace every $3\times3$ layer with an ACB comprising three layers with $3\times3$, $1\times3$ and $3\times1$ kernels, respectively, and their outputs are summed up. When the training is completed, we convert the model back into the same structure as the original by adding the asymmetric kernels in each ACB onto the skeleton, which is the crisscross part of the square kernel, as marked on the figure. In practice, this conversion is implemented by building a new model with the original structure and using the converted learned parameters of the ACNet to initialize it.}
		\label{fig-framework}
	\end{center}
\vskip -0.2in
\end{figure*}

Convolutional Neural Network (CNN) has achieved great success in visual understanding, which makes them useful for various applications in wearable devices, security systems, mobile phones, automobiles, \etc. As the front-end devices are usually limited in computational resources and demand real-time inference, these applications require CNN that delivers high accuracy with the constraints of a certain level of computational budgets. Thus it may not be practical to enhance the model by simply employing more trainable parameters and complicated connections. Therefore, we consider it meaningful to improve the performance of CNN with no extra inference-time computations, memory footprint, or energy consumption.

On the other hand, along with the advancements in the CNN architecture designing literature, the performance of the off-the-shelf models has been significantly improved. However, when the existing models cannot meet our specific needs, we may not be allowed to customize a new architecture at the costs of heavy human works or numerous GPU hours \cite{zoph2018learning}. Recently, the research community is soliciting innovative architecture-neutral CNN structures, \eg, SE blocks \cite{hu2018squeeze} and quasi-hexagonal kernels \cite{sun2016design}, which can be directly combined with various up-to-date architectures to improve the performance on our real-world applications.

Some recent investigations on CNN architectures focus on \textbf{1)} how the layers are connected with each other, \eg, simply stacked together \cite{krizhevsky2012imagenet,simonyan2014very}, through identity mapping \cite{he2016deep,szegedy2017inception,zagoruyko2016wide} or densely connected \cite{huang2017densely} and \textbf{2)} how the outputs of different layers are combined to increase the quality of learned representations \cite{ioffe2015batch,szegedy2017inception,szegedy2015going,szegedy2016rethinking}. Considering this, in quest of a generic architecture-neutral CNN structure which can be combined with numerous architectures, we seek to strengthen standard convolutional layers by digging into an orthogonal aspect: \textit{the relationship between the weights and their spatial locations in the kernels}.

In this paper, we propose Asymmetric Convolution Block (ACB), an innovative structure as a building block to replace the standard convolutional layers with square kernels, \eg, $3\times3$ layers, which are widely used in modern CNN. Concretely, for the replacement of a $d\times d$ layer, we construct an ACB comprising three parallel layers with $d\times d$, $1\times d$ and $d \times1$ kernels, respectively, of which the outputs are summed up to enrich the feature space (Fig. \ref{fig-framework}). As the introduced $1\times d$ and $d \times 1$ layers have non-square kernels, we refer to them as the asymmetric convolutional layers, following \cite{szegedy2016rethinking}. Given an off-the-shelf architecture, we construct an Asymmetric Convolutional Network (ACNet) by replacing every square-kernel layer with an ACB and train it until convergence. After that, we equivalently convert the ACNet into the same original architecture by adding the asymmetric kernels in each ACB onto the corresponding positions of the square kernels. Due to the additivity of convolutions with compatible kernel sizes (Fig. \ref{fig-theory}), which is obvious but has long been ignored, the resulting model can produce the same outputs as the training-time ACNet. As will be shown in our experiments (Sect. \ref{sec-improvement-cifar}, \ref{sec-improvement-imagenet}), doing so can improve the performance of several benchmark models on CIFAR \cite{krizhevsky2009learning} and ImageNet \cite{deng2009imagenet} by a clear margin. Better still, ACNet \textbf{1)} introduces NO hyper-parameters, such that it can be combined with different architectures without careful tuning; \textbf{2)} is simple to implement on the mainstream CNN frameworks like PyTorch \cite{paszke2017automatic} and Tensorflow \cite{abadi2016tensorflow}; \textbf{3)} requires NO extra inference-time computational burdens compared to the original architecture.

Through our further experiments, we have partly explained the effectiveness of ACNet. It is observed that a square convolution kernel distributes its learned knowledge unequally, as the weights on the central crisscross positions (which are referred to as the \textit{``skeleton''} of the kernel) are usually larger in magnitude, and removing them causes higher accuracy drop, compared to those in the corners. In each ACB, we add the horizontal and vertical kernels onto the skeletons, thus explicitly making the skeletons more powerful, following the nature of square kernels. Interestingly, the weights on the corresponding positions of the square, horizontal and vertical kernels are randomly initialized and have a possibility to grow opposite in sign, thus summing them up may result in a stronger or weaker skeleton. However, we have empirically observed a consistent phenomenon that the model always learn to enhance the skeletons at every layer. This observation may shed light on future researches on the relationship among the weights at different spatial locations. The codes are available at \url{https://github.com/ShawnDing1994/ACNet}.

Our contributions are summarized as follows.
\begin{itemize}[noitemsep,nolistsep,,topsep=0pt,parsep=0pt,partopsep=0pt]
	\item We propose to use asymmetric convolutions to explicitly enhance the representational power of a standard square-kernel layer in a way that the asymmetric convolutions can be fused into the square kernels with NO extra inference-time computations needed, rather than approximate a square-kernel layer like many prior works \cite{denton2014exploiting,jaderberg2014speeding,jin2014flattened,lo2018efficient,paszke2016enet,szegedy2016rethinking}.
	\item We propose ACB as a novel architecture-neutral CNN building block. We can construct an ACNet by simply replacing every square-kernel convolutional layer in a mature architecture with an ACB without introducing any hyper-parameters, such that its effectiveness can be combined with the numerous advancements in the CNN architecture designing literature.
	\item We have improved the accuracy of several common benchmark models on CIFAR-10, CIFAR-100, and ImageNet by a clear margin.
	\item We have justified the significance of skeletons in standard square convolution kernels and demonstrated the effectiveness of ACNet in enhancing such skeletons.
	\item We have shown that ACNet can enhance the model's robustness to rotational distortions, which may inspire further studies on the rotational invariance problem.
\end{itemize}

\section{Related work}

\subsection{Asymmetric convolutions}
Asymmetric convolutions are typically used to approximate an existing square-kernel convolutional layer for compression and acceleration. Some prior works \cite{denton2014exploiting,jaderberg2014speeding} have shown that a standard $d\times d$ convolutional layer can be factorized as a sequence of two layers with $d\times 1$ and $1\times d$ kernels to reduce the parameters and required computations. The theory behind is quite simple: if a 2D kernel has a rank of one, the operation can be equivalently transformed into a series of 1D convolutions. However, as the learned kernels in deep networks have distributed eigenvalues, their intrinsic rank is higher than one in practice, thus applying the transformation directly to the kernels results in significant information loss \cite{jin2014flattened}. Denton \etal \cite{denton2014exploiting} tackled this problem by finding a low-rank approximation in an SVD-based manner then finetuning the upper layers to restore the performance. Jaderberg \etal \cite{jaderberg2014speeding} succeeded in learning the horizontal and vertical kernels by minimizing the $\ell$-2 reconstruction error. Jin \etal \cite{jin2014flattened} applied structural constraints to make the 2D kernels separable and obtained comparable performance as conventional CNN with $2\times$ speed-up.

On the other hand, asymmetric convolutions are also widely employed as an architectural design element to save the parameters and computations. For example, in Inception-v3 \cite{szegedy2016rethinking}, the $7\times7$ convolutions are replaced by a sequence of $1\times7$ and $7\times1$ convolutions. However, the authors found out that such replacement is not equivalent as it did not work well on the low-level layers. ENet \cite{paszke2016enet} also adopted this approach for the design of an efficient semantic segmentation network, where the $5\times5$ convolutions are decomposed, allowing to increase the receptive field with reasonable computational budgets. EDANet \cite{lo2018efficient} used a similar method to decompose the $3\times3$ convolutions, resulting in a 33\% saving in the number of parameters and required computations with minor performance degradation.

In contrast, we use 1D asymmetric convolutions not to factorize any layers as part of the architectural designs but enrich the feature space during training and then fuse their learned knowledge into the square-kernel layers.

\subsection{Architecture-neutral CNN structures}

We intend not to modify the CNN architecture but use some architecture-neutral structures to enhance the off-the-shelf models. Thus the effectiveness of our method is supplementary to the advancements achieved by the innovative architectures. Specifically, a CNN structure can be called architecture-neutral if it \textbf{1)} makes no assumptions on the specific architecture, thus can be applied on various models, and \textbf{2)} brings universal benefits. For example, SE blocks \cite{hu2018squeeze} can be appended after a convolutional layer to rescale the feature map channels with learned weights, resulting in a clear accuracy improvement at reasonable costs of extra parameters and computational burdens. As another example, auxiliary classifier \cite{szegedy2015going} can be inserted into the model to assist in supervising the learning process, which can indeed improve the performance by an observable margin but requires extra human works to tune the hyper-parameters.

By contrast, ACNet introduces \textit{NO hyper-parameters} during training and requires \textit{NO extra parameters or computations} during inference. Therefore, in real-world applications, the developer can use ACNet to enhance a variety of models without exhausting parameter tunings, and the end-users can enjoy the performance improvement without slowing down the inference. Better still, since we introduce no custom structures into the deployed model, it can be future compressed via techniques including connection pruning \cite{guo2016dynamic,han2015learning}, channel pruning \cite{ding2019centripetal,ding2019approximated,liu2017learning,luo2017thinet}, quantization \cite{courbariaux2016binarized,gupta2015deep,rastegari2016xnor}, feature map compacting \cite{wang2017beyond}, \etc.

\section{Asymmetric Convolutional Network}

\subsection{Formulation}

For a convolutional layer with a kernel size of $H\times W$ and $D$ filters which takes a $C$-channel feature map as input, we use $\bm{F}\in\mathbb{R}^{H\times W\times C}$ to denote the 3D convolution kernel of a filter, $\bm{M}\in\mathbb{R}^{U\times V\times C}$ for the input, which is a feature map with a spatial resolution of $U\times V$ and $C$ channels, and $\bm{O}\in\mathbb{R}^{R\times T\times D}$ for the output with $D$ channels, respectively. For the $j$-th filter at such a layer, the corresponding output feature map channel is
\begin{equation}\label{eq-def-conv}
\bm{O}_{:,:,j}=\sum_{k=1}^{C}\bm{M}_{:,:,k}\ast\bm{F}^{(j)}_{:,:,k} \,,
\end{equation}
where $\ast$ is the 2D convolution operator, $\bm{M}_{:,:,k}$ is the $k$-th channel of $\bm{M}$ in the form of a $U\times V$ matrix, and $\bm{F}^{(j)}_{:,:,k}$ is the $k$-th input channel of $\bm{F}^{(j)}$, \ie, a 2D kernel of $H\times W$.

In modern CNN architectures, batch normalizations \cite{ioffe2015batch} are widely adopted to reduce overfitting and accelerate the training process. As a common practice, a batch normalization layer is usually followed by a linear scaling transformation to enhance the representational power. Compared to Eq. \ref{eq-def-conv}, the output channel then becomes
\begin{equation}\label{eq-conv-with-bn}
\bm{O}_{:,:,j}=(\sum_{k=1}^{C}\bm{M}_{:,:,k}\ast\bm{F}^{(j)}_{:,:,k} - \mu_j)\frac{\gamma_j}{\sigma_j} + \beta_j \,,
\end{equation}
where $\mu_j$ and $\sigma_j$ are the values of channel-wise mean and standard deviation of batch normalization, $\gamma_j$ and $\beta_j$ are the learned scaling factor and bias term, respectively.

\subsection{Exploiting the additivity of convolution}

\begin{figure}
	\begin{center}
		\includegraphics[width=0.7\columnwidth]{theory.pdf}
		\caption{We use sliding windows to provide some intuitions of the additivity of 2D convolutions with different kernel sizes. Here we have three convolutional layers with a kernel size of $3\times3$, $1\times3$ and $3\times1$, respectively, which take the same input. We only depict the sliding window at the top-left and bottom-right corners, for example. It can be observed that the key for the additivity to hold is that the three layers can \textit{share the same sliding window}. Therefore, if we add the kernels of conv2 and conv3 to conv1 on the corresponding positions, using the resulting kernel to operate on the original input will produce the same result, which can be easily verified only using the distributive property of multiplication (Eq. \ref{eq-mult-property}). Best viewed in color.}
		\label{fig-theory}
	\end{center}
\vskip -0.1in
\end{figure}

We seek to employ asymmetric convolutions in a way that they can be equivalently fused into the standard square-kernel layers, such that no extra inference-time computational burdens are introduced. We notice a useful property of convolution: if several 2D kernels with \textit{compatible} sizes operate on the same input with the same stride to produce outputs of the same resolution, and their outputs are summed up, we can add up these kernels \textit{on the corresponding positions} to obtain an equivalent kernel which will produce the same output. That is, the \textit{additivity} may hold for 2D convolutions, even with different kernel sizes,
\begin{equation}\label{eq-additivity}
\bm{I} \ast \bm{K}^{(1)} + \bm{I} \ast \bm{K}^{(2)} = \bm{I} \ast (\bm{K}^{(1)} \oplus \bm{K}^{(2)}) \,,
\end{equation}
where $\bm{I}$ is a matrix, $\bm{K}^{(1)}$ and $\bm{K}^{(2)}$ are two 2D kernels with compatible sizes, and $\oplus$ is the element-wise addition of the kernel parameters on the corresponding positions. Note is that $\bm{I}$ may need to be appropriately clipped or padded.

Here \textit{compatible} means that we can ``patch'' the smaller kernel onto the bigger. Formally, this kind of transformation on layer $p$ and $q$ is feasible if
\begin{equation}
\bm{M}^{(p)}=\bm{M}^{(q)} \,, H_p \leq H_q \,, W_p \leq W_q \,, D_p=D_q \,.
\end{equation}
\Eg, $3\times1$ and $1\times3$ kernels are compatible with $3\times3$.

This can be easily verified by investigating the calculation of convolution in the form of sliding windows (Fig. \ref{fig-theory}). For a certain filter with kernel $\bm{F}^{(j)}$, a certain point $y$ on the output channel $\bm{O}_{:,:,j}$ is given by
\begin{equation}\label{eq-mult-property}
y = \sum_{c=1}^{C}\sum_{h=1}^{H}\sum_{w=1}^{W} F^{(j)}_{h,w,c} X_{h,w,c} \,,
\end{equation}
where $\bm{X}$ is the corresponding sliding window on input $\bm{M}$. Obviously, when we sum up two output channels produced by two filters, the additivity (Eq. \ref{eq-additivity}) holds if for each point $y$ on one channel, its corresponding point on the other channel \textit{shares the same sliding window} $\bm{X}$.

\subsection{ACB for free inference-time improvements} \label{sec-fusion}

\begin{figure}
	\begin{center}
		\includegraphics[width=0.9\columnwidth]{fusion.pdf}
		\caption{BN and branch fusion. Let $\bm{I}$ be an arbitrary channel of the input feature map $\bm{M}$, for each branch, we first equivalently fuse the parameters of batch normalization into the convolution kernel and a bias term, then add up the fused kernels and bias terms to obtain a single layer.}
		\label{fig-fusion}
	\end{center}
\vskip -0.1in
\end{figure}

In this paper, we focus on $3\times3$ convolutions, which are heavily used in modern CNN architectures. Given an architecture, we construct an ACNet by simply replacing every $3\times3$ layer (together with the following batch normalization layer, if any) with an ACB which comprises three parallel layers with kernel size $3\times3$, $1\times3$ and $3\times1$, respectively. Similar to the common practice in standard CNN, each of the three layers is followed by batch normalization, which is referred to as a branch, and the outputs of three branches are summed up as the output of ACB. Note that we can train the ACNet using the same configurations as the original model without any extra hyper-parameters to be tuned.

As will be shown in Sect. \ref{sec-improvement-cifar} and Sect. \ref{sec-improvement-imagenet}, the ACNet can be trained to reach a higher level of accuracy. When the training is completed, we seek to convert every ACB to a standard convolutional layer which produces identical outputs. By doing so, we can obtain a more powerful network which requires no extra computations, compared to a normally trained counterpart. This conversion is achieved through two steps, namely, BN fusion and branch fusion.

\paragraph{BN fusion.} The \textit{homogeneity} of convolution allows the following batch normalization and linear scaling transformation to be equivalently fused into the convolutional layer with an added bias. It can be observed from Eq. \ref{eq-conv-with-bn} that for each branch, if we construct a new kernel as $\frac{\gamma_j}{\sigma_j}\bm{F}^{(j)}$ along with an added bias term $-\frac{\mu_j \gamma_j}{\sigma_j} + \beta_j$, we will produce the same output, which can be easily verified.

\paragraph{Branch fusion.} We merge the three BN-fused branches into a standard convolutional layer by adding the asymmetric kernels onto the corresponding positions of the square kernel. In practice, this transformation is implemented by building a network of the original structure and using the fused weights for initialization, thus we can produce the same outputs as the ACNet with the same computational budgets as the original architecture. Formally, for every filter $j$, let $\bm{F}^{\prime(j)}$ be the fused 3D kernel, $b_j$ be the obtained bias term, $\bar{\bm{F}}^{(j)}$ and $\hat{\bm{F}}^{(j)}$ be the kernels of the corresponding filter at the $1\times3$ and $3\times1$ layer, respectively, we have
\begin{equation}\label{eq-fused-kernel}
\bm{F}^{\prime(j)} = \frac{\gamma_j}{\sigma_j}\bm{F}^{(j)} \oplus \frac{\bar{\gamma}_j}{\bar{\sigma}_j}\bar{\bm{F}}^{(j)} \oplus \frac{\hat{\gamma}_j}{\hat{\sigma}_j}\hat{\bm{F}}^{(j)} \,,
\end{equation}
\begin{equation}\label{eq-fused-bias}
b_j = - \frac{\mu_j\gamma_j}{\sigma_j} - \frac{\bar{\mu}_j\bar{\gamma}_j}{\bar{\sigma}_j} - \frac{\hat{\mu}_j\hat{\gamma}_j}{\hat{\sigma}_j} + \beta_j + \bar{\beta}_j + \hat{\beta}_j \,.
\end{equation}

Then we can easily verify that for an arbitrary filter $j$,
\begin{equation}
\bm{O}_{:,:,j} + \bar{\bm{O}}_{:,:,j} + \hat{\bm{O}}_{:,:,j}=\sum_{k=1}^{C}\bm{M}_{:,:,k}\ast\bm{F}^{\prime(j)}_{:,:,k} + b_j \,,
\end{equation}
where $\bm{O}_{:,:,j}$, $\bar{\bm{O}}_{:,:,j}$ and $\hat{\bm{O}}_{:,:,j}$ are the outputs of the original $3\times3$, $1\times3$ and $3\times1$ branch, respectively. Fig. \ref{fig-fusion} shows an example on a single input channel for more intuitions.

Of note is that though an ACB can be equivalently transformed into a standard layer, the equivalence only holds at inference-time because the training dynamics are different, thus giving rise to different performance. The non-equivalence of the training process is due to the random initialization of kernel weights, and the gradients derived by different computation flows they participate in.

\section{Experiments}
We have conducted abundant experiments to verify the effectiveness of ACNet in improving the performance of CNN across a range of datasets and architectures. Concretely, we pick an off-the-shelf architecture as the baseline, build an ACNet counterpart, train it from scratch, convert it into the same structure as the baseline, and test it to collect the accuracy. For the comparability, all the models are trained until the complete convergence, and every pair of baseline and ACNet uses identical configurations, \eg, learning rate schedules and batch sizes.

\subsection{Performance improvements on CIFAR}\label{sec-improvement-cifar}

In order to preliminarily evaluate our method on various CNN architectures, we experiment with several representative benchmark models including Cifar-quick \cite{snoek2012practical}, VGG-16 \cite{simonyan2014very}, ResNet-56 \cite{he2016deep}, WRN-16-8 \cite{zagoruyko2016wide} and DenseNet-40 \cite{huang2017densely} on CIFAR-10 and CIFAR-100 \cite{krizhevsky2009learning}.

For Cifar-quick, VGG-16, ResNet-56, and DenseNet-40, we train the models using a staircase learning rate of 0.1, 0.01, 0.001 and 0.0001 following the common practice. For WRN-16-8, we follow the training configurations reported in the original paper \cite{zagoruyko2016wide}. We use the data augmentation techniques adopted by \cite{he2016deep}, \ie, padding to $40\times40$, random cropping and left-right flipping.

As can be observed from Table. \ref{table-exp-cifar10} and Table. \ref{table-exp-ch},
the performance of all the models is consistently lifted by a clear margin, suggesting that the benefits of ACBs can be combined with various architectures.

\begin{table}
	\caption{Top-1 accuracy of ACNets and the normally trained baselines on CIFAR-10.}
	\label{table-exp-cifar10}
	\vspace{-0.15in}
	\begin{center}
		\begin{small}
			\begin{tabular}{lccccccc}
				\toprule
				Model		&	Base Top-1					&ACNet Top-1		&Top-1 $\uparrow$\\
				\midrule
				Cifar-quick	&	83.13						&	84.24		&	1.11	\\
				VGG			&	94.12						&	94.47		&	0.35	\\
				ResNet-56	&	94.31						&	95.09		&	0.78	\\
				WRN-16-8	&	95.56						&	96.15		&	0.59	\\
				DenseNet-40	&	94.29						&	94.84		&	0.55	\\
				\bottomrule
			\end{tabular}
		\end{small}
	\end{center}
\vskip -0.1in
\end{table}
\begin{table}
	\caption{Top-1 accuracy of ACNets and the normally trained baselines on CIFAR-100.}
	\label{table-exp-ch}
	\vspace{-0.15in}
	\begin{center}
		\begin{small}
			\begin{tabular}{lccccccc}
				\toprule
				Model		&	Base Top-1					&ACNet Top-1		&Top-1 $\uparrow$\\
				\midrule
				Cifar-quick	&	53.22						&	54.30		&	1.08\\
				VGG			&	74.56						&	75.20		&	0.64\\
				ResNet-56	&	73.58						&	74.04		&	0.46\\
				WRN-16-8	&	78.65						&	79.44		&	0.79\\
				DenseNet-40	&	73.14						&	73.41		&	0.27\\
				\bottomrule
			\end{tabular}
		\end{small}
	\end{center}
	\vskip -0.1in
\end{table}

\subsection{Performance improvements on ImageNet}\label{sec-improvement-imagenet}
\begin{table*}
	\caption{Accuracy of the ACNet counterparts of AlexNet, ResNets, DenseNet-121 and the baselines on ImageNet.}
	\label{table-exp-imagenet}
	\vspace{-0.15in}
	\begin{center}
		\begin{small}
			\begin{tabular}{lccccccc}
				\toprule
				Model		&	Base Top-1					&ACNet Top-1		&Top-1 $\uparrow$	&	Base Top-5					&ACNet Top-5		&Top-5 $\uparrow$	\\
				\midrule
				AlexNet		&	55.92						&	\textbf{57.44}		&	1.52	&	79.53	&	\textbf{80.73}		&	1.20		\\
				ResNet-18	&	70.36						&	\textbf{71.14}		&	0.78	&	89.61	&	\textbf{89.96}		&	0.35	\\
				DenseNet-121&	75.15						&	\textbf{75.82}		&	0.67	&	92.45	&	\textbf{92.77}		&	0.32	\\
				\bottomrule
			\end{tabular}
		\end{small}
	\end{center}
\vskip -0.1in
\end{table*}

We then move on to the effectiveness validation of our method on the real-world applications through a series of experiments on ImageNet \cite{deng2009imagenet} which comprises 1.28M images for training and 50K for validation from 1000 classes. We use AlexNet \cite{krizhevsky2012imagenet}, ResNet-18 \cite{he2016deep} and DenseNet-121 \cite{huang2017densely} as the representatives for the plain-style, residual and densely connected architectures, respectively. Every model is trained with a batch size of 256 for 150 epochs, which is longer than the usually adopted benchmarks (\eg, 90 epochs \cite{he2016deep}), such that the accuracy improvement cannot be simply attributed to the incomplete convergence of the base models. For the data augmentation, we employ the standard pipeline including bounding box distortion, left-right flipping and color shift, as a common practice. Especially, the plain version of AlexNet we use comes from the Tensorflow GitHub \cite{Tensorflow-AlexNet}, which is composed of five stacked convolutional layers and three fully-connected layers with no local response normalizations (LRN) or cross-GPU connections. For the faster convergence, we apply batch normalization \cite{ioffe2015batch} on its every convolutional layer. Of note is that since the first two layers of AlexNet use $11\times11$ and $5\times5$ kernels, respectively, it is possible to extend ACBs to have larger asymmetric kernels. However, we still only use $1\times3$ and $3\times1$ convolutions for these two layers, because such large-scale convolutions are becoming less favored in modern CNN, making large ACBs less useful.

As shown in Table. \ref{table-exp-imagenet}, the single-crop Top-1 accuracy of AlexNet, ResNet-18 and DenseNet-121 is lifted by 1.52\%, 0.78\% and 1.18\%, respectively. In practice, aiming at the same target of accuracy, we can use ACNet to enhance a more efficient model to achieve the target with less inference time, energy consumption, and storage space. On the other hand, with the same constraints on computational budgets or model size, we can use ACNet to improve the accuracy by a clear margin such that the gained performance can be viewed as free benefits, from the viewpoint of end-users.

\subsection{Ablation studies}
\begin{table*}
	\caption{Top-1 accuracy of the ACNets with different design configurations and rotational distortions on ImageNet.}
	\vspace{-0.1in}
	\label{table-exp-ablation}
	\begin{center}
		\begin{small}
			\begin{tabular}{lccccccc}
				\toprule
				Model						&	\makecell{Horizontal\\ kernel}	&	\makecell{Vertical\\ kernel}	&	\makecell{BN \\in branch}	&	Original input	& Rotate \ang{90}		&	Rotate \ang{180}		&	Up-down flip 	\\
				\midrule
				AlexNet						&				&				&				&	55.92			&	28.18				&	31.41				&	31.62			\\
				AlexNet						&				&	\checkmark	&	\checkmark	&	57.10			&	29.65				&	32.86				&	33.02			\\
				AlexNet						&	\checkmark	&				&	\checkmark	&	57.25			&	29.97				&	33.74				&	33.74			\\
				AlexNet						&	\checkmark	&	\checkmark	&	\checkmark	&	\textbf{57.44}	&	\textbf{30.49}		&	\textbf{33.98}		&	\textbf{33.82}	\\
				AlexNet						&	\checkmark	&	\checkmark	&				&	56.18			&	28.81				&	32.12				&	32.33			\\
				\midrule
				ResNet-18					&				&				&				&	70.36			&	41.00				&	41.95				&	41.86			\\
				ResNet-18					&				&	\checkmark	&	\checkmark	&	70.78			&	41.61				&	42.47				&	42.66			\\
				ResNet-18					&	\checkmark	&				&	\checkmark	&	70.70			&	42.06				&	\textbf{43.22}		&	43.05			\\
				ResNet-18					&	\checkmark	&	\checkmark	&	\checkmark	&	\textbf{71.14}	&	\textbf{42.20}		&	42.89				&	\textbf{43.10}	\\
				ResNet-18					&	\checkmark	&	\checkmark	&				&	70.82			&	41.70				&	42.92				&	42.90			\\
				\bottomrule
			\end{tabular}
		\end{small}
	\end{center}
\vskip -0.1in
\end{table*}
\begin{figure}
	\begin{center}
		\includegraphics[width=1\columnwidth]{flipping.pdf}
		\caption{Compared to square kernels, horizontal kernels are more robust to up-down flipping. As shown above, the $1\times3$ kernel will produce the same results on the symmetric positions of the flipped inputs, but the $3\times3$ kernel will not.}
		\label{fig-flipping}
	\end{center}
\vskip -0.2in
\end{figure}

Though we have empirically justified the effectiveness of ACNet, we still desire to find some explanations. In this subsection, we seek to investigate ACNet through a series of ablation studies. Specifically, we focus on the following three design decisions: the usage of \textbf{1)} horizontal kernels, \textbf{2)} vertical kernels, and \textbf{3)} batch normalization in every branch. For the comparability, we train several AlexNet and ResNet-18 models on ImageNet with different ablations using the same training configurations. Of note is that if the batch normalizations in the branches are removed, we batch-normalize the output of the whole ACB instead, \ie, the position of batch normalization layer is changed from pre-summation to post-summation.

As can be observed from Table. \ref{table-exp-ablation}, removing any of the three designs degrades the model. However, though the horizontal and vertical convolutions can both improve the performance, there may exist some difference because the horizontal and vertical directions are treated unequally in practice, \eg, we usually perform random left-right but no up-down image flipping to augment the training data. Therefore, if an upside-down image is fed into the model, the original $3\times3$ layers should produce meaningless results, which is natural, but a horizontal kernel will produce the same outputs as on the original image at the axially symmetric locations (Fig. \ref{fig-flipping}). \Ie, a part of the ACB can still extract the correct features. Considering this, we assume that ACBs may enhance the model's robustness to rotational distortions, enabling the model to generalize better on the unseen data.

We then test the formerly trained models with rotationally distorted images from the whole validation set including counterclockwise \ang{90} rotation, \ang{180} rotation, and up-down flipping. Naturally, the accuracy of every model is significantly reduced, but the models with horizontal kernels deliver observably higher accuracy on the \ang{180} rotated and up-down flipped images. \Eg, the ResNet-18 equipped with only horizontal kernels delivers an accuracy slightly lower than that of the counterpart with only vertical kernels on the original inputs, but 0.75\% higher on the \ang{180} rotated inputs. And when compared with the base model, its accuracy is 0.34\% / 1.27\% higher on the original / \ang{180} flipped images, respectively. Predictably, the models exert similar performance on the \ang{180} rotated and up-down flipped inputs, as \ang{180} rotation plus left-right flipping is equivalent to up-down flipping, and the model is robust to left-right flipping due to the data augmentation methods.

In summary, we have shown that ACBs, especially the horizontal kernels inside, can enhance the model's robustness to rotational distortions by an observable margin. Though this may not be the primary reason for the effectiveness of ACNet, we consider it promising to inspire further researches on the rotational invariance problem.

\subsection{ACB enhances the skeletons of square kernels}

Intuitively, as adding the horizontal and vertical kernels onto the square kernel can be viewed as a means to explicitly enhance the skeleton part, we seek to explain the effectiveness of ACNet by investigating the difference between the skeleton and the weights at the corners.

Inspired by the CNN pruning methods \cite{guo2016dynamic,han2015deep,han2015learning}, we start from removing some weights at different spatial locations and observing the performance drop using ResNet-56 on CIFAR-10. Concretely, we randomly set some individual weights in the kernels to zero and test the model. As shown in Fig. \ref{fig-sparsity-normal}, for the curve labeled as \textit{corner}, we randomly select the weights from the four corners of every $3\times3$ kernel and set them to zero in order to attain a given global sparsity ratio of \textit{every} convolutional layer. Note that as $4/9=44.4\%$, a sparsity ratio of 44\% means removing most of the weights at the four corners. For \textit{skeleton}, we randomly select the weights only from the skeleton of every kernel. For \textit{global}, every individual weight in the kernel has an equal chance to be chosen. The experiments are repeated five times with different random seeds, and the \textit{mean}$\pm$\textit{std} curves are depicted.
\begin{figure}[t]
	\begin{subfigure}{0.90\columnwidth}
		\includegraphics[width=1\linewidth]{rc56-sparsity-normal-random.pdf}
		\vskip -0.1in
		\caption{Normally trained ResNet-56.}
		\label{fig-sparsity-normal}
		\vspace{0.1in}
	\end{subfigure}
	\begin{subfigure}{0.90\columnwidth}
		\includegraphics[width=1\linewidth]{rc56-sparsity-acg-random.pdf}
		\vskip -0.1in
		\caption{ACNet counterpart of ResNet-56.}
		\label{fig-sparsity-ACB}
		\vspace{0.1in}
	\end{subfigure}
	\begin{subfigure}{0.90\columnwidth}
		\includegraphics[width=1\linewidth]{rc56-sparsity-border-random.pdf}
		\vskip -0.1in
		\caption{ACNet with the asymmetric kernels added to the border.}
		\label{fig-sparsity-border}
	\end{subfigure}
	\caption{Validation accuracy of different ResNet-56 models on CIFAR-10 with increasing sparsity ratios attained by pruning weights at different locations of the $3\times3$ kernels.}
\vskip -0.1in
\end{figure}

As can be observed, all the curves show a tendency of decreasing as the sparsity ratio increases, but not monotonically, due to the random effects. It is obvious that removing the weights from the corners causes less damage to the model, but pruning the skeletons does more harm. This phenomenon suggests that the skeleton weights are more important to the model's representational capacity.

We continue to verify if this observation holds for ACNet. We convert the ACNet counterpart via BN and branch fusion, then conduct the same experiments on it. As shown in Fig. \ref{fig-sparsity-ACB}, we observe an even more significant gap, \eg, pruning almost all the corner weights only degrades the model's accuracy to above 60\%. On the other hand, pruning the skeletons causes more damage, as the model is destroyed when the global sparsity ratio attained by pruning the skeletons merely reaches 13\%, \ie, $13\% \times 9 / 5 = 23.4\%$ weights of the skeletons are removed.

Then we explore the cause of the above observations by investigating the numeric values of the kernels. We use the magnitude (\ie, absolute value) as the metric for the importance of parameters, which is adopted by many prior CNN pruning works \cite{ding2018auto,guo2016dynamic,han2015learning,li2016pruning}. Specifically, we add up all the fused 2D kernels in a convolutional layer, perform a layer-wise normalization by the max value, and finally obtain an average of the normalized kernels of all the layers. Formally, let $\bm{F}^{(i,j)}$ be the 3D kernel of the $j$-th filter at the $i$-th $3\times3$ layer, $L$ be the number of all such layers, $max$ and $abs$ be the max and element-wise absolute value, respectively, the average kernel magnitude matrix is computed as
\begin{equation}\label{eq-average-kernel}
\bm{A} = \frac{1}{L} \sum_{i=1}^{L} \frac{\bm{S}^{(i)}}{max(\bm{S}^{(i)})} \,,
\end{equation}
where the sum of absolute kernels of layer $i$ is
\begin{equation}
\bm{S}^{(i)} = \sum_{j=1}^{D_i}\sum_{k=1}^{C_i} abs(\bm{F}^{(i,j)}_{:,:,k}) \,.
\end{equation}

We present the $\bm{A}$ values of the normally trained ResNet-56 and the fused ACNet counterpart in Fig. \ref{fig-accumulate-kernel-normal} and Fig. \ref{fig-accumulate-kernel-ACB-crisscross}, where the numeric value and color at a certain grid indicate the average relative importance of the parameter on the corresponding position across all the $3\times3$ layers, \ie, a larger value and darker background color indicates a higher average importance of the parameter.

As can be observed from Fig. \ref{fig-accumulate-kernel-normal}, the normally trained ResNet-56 distributes the magnitude of the parameters in an imbalance manner, \ie, the central point has the largest magnitude, and the points at the four corners have the smallest. Fig. \ref{fig-accumulate-kernel-ACB-crisscross} shows that ACNet aggravates such imbalance, as the $\bm{A}$ values of the four corners are decreased to below 0.400, and the skeleton points have the $\bm{A}$ values above 0.666. In particular, the central point has an $\bm{A}$ value of 1.000, which means that this location has a dominant importance consistently in \textit{every} single $3\times3$ layer. It is noteworthy that the weights on the corresponding positions of the square, horizontal and vertical kernels have a possibility to grow opposite in sign, thus summing them up may result in a larger or smaller magnitude. But we have observed a consistent phenomenon that the model always learn to enhance the skeletons at \textit{every layer}.

\begin{figure}[t]
	\begin{subfigure}{0.325\columnwidth}
		\includegraphics[page=1,width=0.9\linewidth]{rc56-accumulate-kernel.pdf}
		\caption{Normal.}
		\label{fig-accumulate-kernel-normal}
	\end{subfigure}
	\begin{subfigure}{0.325\columnwidth}
		\includegraphics[page=2,width=0.9\linewidth]{rc56-accumulate-kernel.pdf}
		\caption{ACNet, skeleton.}
		\label{fig-accumulate-kernel-ACB-crisscross}
	\end{subfigure}
	\begin{subfigure}{0.325\columnwidth}
		\includegraphics[page=3,width=0.9\linewidth]{rc56-accumulate-kernel.pdf}
		\caption{ACNet, border.}
		\label{fig-accumulate-kernel-ACB-border}
	\end{subfigure}
	\caption{The average kernel magnitude matrix $\bm{A}$ of ResNet-56 models trained in different ways on CIFAR-10.}
	\label{fig-accumulat-kernel}
\vskip -0.1in
\end{figure}

We continue to study how the model will behave if we add the asymmetric kernels onto the other positions rather than the central skeletons. Specifically, we train an ACNet counterpart of ResNet-56 using the same training configurations as before, but shift the horizontal convolutions one pixel towards the bottom on the inputs and shift the vertical convolutions towards the right. Accordingly, during branch fusion, we add the BN-fused asymmetric kernels to the bottom-right borders of the square kernels (Fig. \ref{fig-accumulate-kernel-ACB-border}) in order for an equivalent resulting network. It is observed that such ACBs can also enhance the borders, but not as intensively as the regular ACBs do to the skeletons. The model delivers an accuracy of 94.67\%, which is 0.42\% lower than the regular ACNet (Table. \ref{table-exp-cifar10}). Moreover, similar pruning experiments are conducted on the fused model (Fig. \ref{fig-sparsity-border}). As observed, pruning the corners still delivers the best accuracy, and pruning the enhanced bottom-right borders gives no better results than the top-left $2\times2$ squares, \ie, though the magnitudes of the borders have increased, the other parts remain essential to the whole kernels.

In summary: \textbf{1)} the skeletons are inherently more important than the corners in standard square kernels; \textbf{2)} ACB can significantly enhance the skeletons, resulting in improved performance; \textbf{3)} adding the horizontal and vertical kernels to the borders degrades the model's performance compared to regular ACBs; \textbf{4)} doing so can also increase the magnitude of the borders but cannot diminish the importance of the other parts. Therefore, we partly attribute the effectiveness of ACNet to its capability of further strengthening the skeletons. Intuitively, ACNet follows the nature of the square convolution kernels.

\section{Conclusion}

In order to improve the performance of various CNN architectures, we proposed Asymmetric Convolution Block (ACB), which sums up the outputs of three convolutional branches with square, horizontal and vertical kernels, respectively. We construct an Asymmetric Convolutional Network (ACNet) by replacing the square-kernel layers in a mature architecture with ACBs and convert it into the original architecture after training. We have evaluated ACNet by improving various plain-style, residual and densely connected models on CIFAR and ImageNet. We have shown that ACNet can enhance the model's robustness to rotational distortions by an observable margin, and explicitly strengthening the skeletons following the nature of square kernels. Of note is that ACNet introduces NO hyper-parameters to be tuned, requires NO extra inference-time computations, and is simple to implement using mainstream frameworks.

\section*{Acknowledgement}
This work was supported by the National Key R\&D Program of China (No. 2018YFC0807500), National Natural Science Foundation of China (No. 61571269), National Postdoctoral Program for Innovative Talents (No. BX20180172), and the China Postdoctoral Science Foundation (No. 2018M640131). Corresponding author: Guiguang Ding, Yuchen Guo.

{\small
\bibliographystyle{ieee_fullname}

redpajama_set_name: RedPajamaArXiv


Odin, I thought I would drop you this letter to thank you for all your help and quick responses in helping us to decide to purchase the istill 250.
As you know we are a little different than some of your buyers. We are already an established craft distillery that's been in business for over 5 years and with a flagship award winning vodka.
We have a 600 liter Arnold Holstein Copper Still which we are extremely happy with. The reason we purchased the iStill 250 was to make gin. Our big still has a fully implemented CIP (clean in place) system however after a gin run the copper requires a complete chemical cleaning rather than a normal wash down which requires many hours in our case to remove the residual traces of a gin distillation before we can again distill neutral alcohol or whiskey. By purchasing the iStill we can now make Gin in a cost effective way.
A second benefit to us is that we run a successful distilling school 3 times a year. We often have to turn people away because we are full. The iStill is an exceptional piece of equipment for those interested in getting into commercial craft distilling. The cost, ease of operation and its ability to educate a new distiller is just amazing. I expect once would be craft distillers find that we have incorporated the iStill into our program, there will be even more interest in our School.


text:
Charles Parsons:Mathematics in Philosophy: Selected Essays,:Mathematics in Philosophy: Selected Essays [Book Review]
Philosophy of Science 74 (4):549-552 (2007)
Philosophy of Mathematics, General Works in Philosophy of Mathematics
journals.uchicago.edu (no proxy)
journals.uchicago.edu [2] (no proxy)
Mathematics in Philosophy: Selected Essays.Charles Parsons - 1983 - Cornell University Press.
Philosophy of Mathematics in the Twentieth Century: Selected Essays By Charles Parsons.Bob Hale - 2015 - Analysis 75 (3):532-535.
Mathematics in Philosophy, Selected Essays.Stewart Shapiro - 1983 - Journal of Symbolic Logic 53 (1):320.
Mathematics in Philosophy: Selected Essays.W. V. Quine - 1984 - Journal of Philosophy 81 (12):783-794.
Mathematics in Philosophy: Selected Essays.Charles Parsons - 1985 - British Journal for the Philosophy of Science 36 (4):437-457.
Charles Parsons, Philosophy of Mathematics in the Twentieth Century: Selected Essays. [REVIEW]Shay Logan - 2014 - Polish Journal of Philosophy 8 (2):84-87.
Between Logic and Intuition: Essays in Honor of Charles Parsons.Gila Sher & Richard Tieszen (eds.) - 2000 - Cambridge University Press.
C. PARSONS "Mathematics in Philosophy. Selected Essays". [REVIEW]A. Urquhart - 1985 - History and Philosophy of Logic 6 (2):239.
El papel de la intuición en la filosofía de las matemáticas. La propuesta de Charles Parsons The role of intuition in the philosophy of mathematics. Charles Parsons' proposal.María Azcárate - 2007 - Laguna 20.
Mathematics, Models, and Modality: Selected Philosophical Essays.John P. Burgess - 2008 - Cambridge University Press.
New Model Naturalism.Øystein Linnebo - 2009 - Metascience 18 (3):433-436.
The Philosophy of Mathematics Today: Papers From a Conference Held in Munich From June 28 to July 4,1993.Matthias Schirn (ed.) - 1998 - Oxford, England: Clarendon Press.
Charles S. Peirce. Philosophy of Mathematics: Selected Writings: Books of Essays.[author unknown] - 2011 - Philosophia Mathematica 19 (2):226-226.
Mathematics in Philosophy: Selected Essays.C. Parsons - 1983 - Cornell Univ Pr.
Philosophy of Mathematics in the Twentieth Century: Selected Essays.John P. Burgess - 2015 - History and Philosophy of Logic 36 (1):93-95.

redpajama_set_name: RedPajamaCommonCrawl


This Lent, we are going the 'extra mile' to walk to Jerusalem with acts of kindness. As a parish we have mapped out a Christian Pilgrimage route for us to follow, stopping at significant destinations. Together with the parish of Guardian Angels and Our Lady of Good Counsel, Guardian Angels and Our Lady of Lourdes primary schools have created some information videos about the different places on our journey.
On the 8th February, Parishioners and children joined together to research different places of pilgrimage on our route to Jerusalem.


text:
5% TO FIGHT FOOD INSECURITY
More than 10% of Americans are food insecure due to interconnected issues related to poverty under-employment and inconsistent access to healthy food. While the Bronx is home to the largest produce market in the country, it also has the highest rate of food insecurity. Bronx-based culinary collective Ghetto Gastro and CRUX are on a mission to change that. 5% of all proceeds are donated to a collective of nonprofits democratizing access to food. Read more about our partners.
Project EATS
Project EATS' mission is for everyone to have the opportunity - regardless of income - to live healthily and to thrive. To achieve our mission, we join with residents and other stakeholders in neighborhoods to work as both a catalyst and support in creating community-based enterprises that grow and distribute food and programs that inspire, engage, cultivate understanding and strengthen ties between neighbors. The end goal is to increase the well-being of individuals and to improve the social, economic, and cultural viability of their communities.
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Sky High Farm is a nonprofit organization working in New York State to increase access to fresh, nutritious, locally produced food while investing in collaborative long-term solutions for food security. Committed to sustainable farming practices and direct donation of 100% of food produced on the farm, Sky High works to close the gap between regenerative farming and food access initiatives. Through its agricultural training programs and partnerships with local farms and frontline organizations, Sky High serves as a laboratory for innovative community-driven food production, distribution, and equity-building. Sky High reaches beyond conventional philanthropy, collaborating with a diverse and inspiring network of fashion brands, artists, and designers to generate resources that sustain our work locally and cultivate awareness around food justice and sustainability worldwide.
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SUMMAEVERYTHANG
SUMMAEVERYTHANG is a community center based in South Central Los Angeles dedicated to the empowerment and transcendence of black and brown folks socio-politically, economically, intellectually and artistically.
Visit SUMMAEVERYTHANG
Culture Aid Nola
Culture Aid NOLA organizes twice-weekly distributions of free, fresh food open to all community members on both a walk-up and drive-up basis. We are committed to no-barrier, no-stigma aid and will never request paperwork or ID.
VISIT Culture Aid Nola
Isuroon
Isuroon is a community-driven nonprofit organization that
was founded in 2010 to address the unmet health needs of Somali women. Isuroon works to promote the health, wellness and empowerment of Somali women in Minnesota and beyond. It provides culturally appropriate healthcare education, halal food support, resettlement support, and advocacy for Somali women and health care providers. Taking its name from a Somali word that means "a woman who cares for herself," Isuroon believes that empowering women is the key to building a thriving community.
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this is mainly due to an SSD failing on the Brisbane server, and as this is the OS drive there may be issues replacing it.
Also due to the Brisbane server being sub-standard for games (causing lag and slow loading games...) i have decided to minimise the use of this server to disposable games until the contract is finished end of next year, which will then be replaced with hardware I own.
As the new VM has a different IP address, you will need to find it again in the in-game browser.
just search for [au/nz]playark.com.au and you will see it.
Servers name will have PVE2 in them so u can tell them apart.


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Home » D'vrei Torah » Chukat: "Do It!" Why" Just Because I Said So?
Chukat: "Do It!" Why" Just Because I Said So?
June 29, 2017Rabbi Richard AddressD'vrei Torah0
Written by: Rabbi Richard Address on June 29, 2017.
One of the more interesting and obscure rituals appears in this week's portion. It concerns the red heifer, one without blemish which is to be sacrificed. An elaborate ritual ensues at the beginning of Numbers 19. We do not know where this ritual originated (some say within Cannanite culture). The red cow without blemish ritual also speaks to the concerns about being clean and unclean, especially, as commentaries have pointed out, regarding someone who would come in contact with death. especially a dead person.
"Chukat" stems from a word that is used to refer to a "law that defies rational explanation." In the Plaut commentary there is a Midrash quoted (page 1149) that speaks of four laws that "cannot be explained by human reason but, being Divine, demand implicit obedience". The ritual of the red cow is one. This of course, relates to a larger discussion of obedience to God's word, even if you cannot see a rational explanation. One obeys just because it is God's word. Indeed, Torah is a source of that God. The Wilderness experience is replete with instances where there is no toleration of questioning.
The idea of obedience to a law because it comes from God, the theme of this portion and others, has obviously been reviewed and redefined within our contemporary world. Indeed, the questioning of the Divine Revelation at Sinai and the restructuring of that belief, gave rise to modern forms of non revelatory Judaism.
As we get a little older and try to see the spiritual aspects of our own life, we often again come to questions of who or what is behind the laws of Torah.Faith, for many of us, becomes a subject of intense discussion and questioning. Once we begin this process of openly questioning what we believe, what we have been taught and how these "truths" can relate to our life, we often retreat from religion. So many of us carry with us a theology that was last debated when we were 13 or 16. It is THAT religion that we leave and, for so many of us, seek a more adult religious embrace.
I think one of the great gifts of passages such as Chukat is that is again allows us to confront what we believe in and know that Judaism allows us to questions these beliefs, and even question how we choose to define that concept we call God. Perhaps this strange ritual is really a symbol of the struggle we are permitted to engage in to come to a better, more mature understanding of who we are in relationship to God, how we choose to model that relationship and how we can seek a sense of purification of our soul in light of the reality of our own mortality.
Rabbi Richard F Address
About Rabbi Richard Address
Rabbi Richard F. Address, D.Min, is the Founder and Director of www.jewishsacredaging.com. Rabbi Address served for over three decades on staff of the Union for Reform Judaism; first as a Regional Director and then, beginning in 1997, as Founder and Director of the URJ's Department of Jewish Family Concerns and served as a specialist and consultant for the North American Reform Movement in the areas of family related programming. Rabbi Address was ordained from Hebrew Union College-Jewish Institute of Religion in 1972 and began his rabbinic career in Los Angeles congregations. He also served as a part time rabbi for Beth Hillel in Carmel, NJ while regional director and, after his URJ tenure, served as senior rabbi of Congregation M'kor Shalom in Cherry Hill, NJ from 2011-2014.
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Jewish Sacred Aging is a forum for the Jewish Community with resources and texts that feature discussions on the implications of the revolution in longevity for Baby Boomers and their families. We encourage you to participate in the conversation!
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Sign on Property, call office first. Best to show with ATV. Park on the 6 Acre tract. Not the neighbors drive access. GPS Coordinates 31.4363, -97.78515 From Coryell County Courthouse: Head west on E Main St. toward N 6th St. for 1.8 miles. Turn right onto C R D 171, continue for 0.4 miles. Destination will be on the left.


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About CoPIRG
Our team of researchers uncover the facts; our staff bring our findings to the public, through the media as well as one-on-one interactions; and our advocates are bringing the voice of the public to the halls of power on behalf of consumers.
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CoPIRG, Colorado Public Interest Research Group, is a consumer group that stands up to powerful interests whenever they threaten our health and safety, our financial security or our right to fully participate in our democratic society.
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Using the time-tested tools of investigative research, media exposés, grassroots organizing, advocacy and litigation, CoPIRG stands up to powerful interests and delivers concrete results.
The overuse of antibiotics on factory farms is contributing to the rise of antibiotic-resistant bacteria, threatening millions of Americans every year. We're calling on major restaurants to take strong action to address this growing public health threat. >LEARN MORE
We're working to stanch the flow of big money in our democracy by increasing the influence of small donors in elections. We're also part of a national coalition that's helped 16 states and more than 650 cities call for an amendment to overturn Citizens United. >LEARN MORE
Consumer Program Director Ed Mierzwinski has fought for consumers for more than 20 years. He spearheaded our fight to create the Consumer Financial Protection Bureau, which has provided more than $11 billion in relief for consumers harmed by illegal practices. >LEARN MORE
When consumers are cheated or the voices of ordinary citizens are drowned out by special interest lobbyists, CoPIRG, the Colorado Public Interest Research Group, speaks up and takes action. We uncover threats to public health and wellbeing and fight to end them, using the time-tested tools of investigative research, media exposés, grassroots organizing, advocacy and litigation. CoPIRG's mission is to deliver persistent, result-oriented public interest activism that protects consumers, encourages a fair, sustainable economy, and fosters responsive, democratic government.
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To break through the ideological divide that has dominated Washington this past year and offer a
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Bank of America leads charge in fee changes
Bank of America customers will now pay $5 a month for purchases they make with their debit cards as the bank moves to recoup an estimated $6.6 billion in lost revenue.
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Opinion: Glass Half Full...of Corn Syrup
Opinion piece by Director Danny Katz on ending agricultural subsidies that fuel obesity.
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House Committee Takes Actions To Clean Up Credit Bureau Mistakes
In committee votes this week and last week, the House Financial Services Committee sent a package of credit reporting reforms on to the House floor. It's the first major Congressional action to rein in the so-called Big 3 credit bureaus - Equifax, Experian and Trans Union - and other smaller, specialized bureaus and credit scoring companies, since 2003. The Big 3 national credit bureaus have been the most complained about financial firms to the CFPB for four years running, predating the massive Equifax data breach.
Why does agribusiness keep overusing antibiotics? Consider 'Pig Zero.'
"Don't wait for Pig Zero," declared the poster, featuring a pig peeking through a giant blue zero, that appeared at last year's swine industry trade show.
Claire's recalls more sparkly children's makeup after FDA finds asbestos
Like the teen star herself, Claire's JoJo Siwa Makeup Set is colorful, sparkly and shimmery—but it's now been found to contain asbestos.
Flying this summer? You'll want to know your rights.
Everybody knows somebody who has a "bad airline story" involving a long flight delay, sitting on the tarmac, being "bumped," losing baggage or some nightmare combination thereof.
It's time for Wendy's to get antibiotics off the menu
The fast food chain Wendy's has a role to play in preventing a future in which antibiotics no longer work to protect our health.
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We want the right to repair our stuff
Companies make it hard to repair our phones and other electronics so more of us trash our old stuff and buy new stuff. The Federal Trade Commission can make it easier.
Let's move beyond plastic
Nothing we use for a few minutes should threaten our health and pollute our future for hundreds of years. One of the best ways to reduce the amount of trash headed to landfills is to ban items such as plastic foam cups and takeout containers.
Antibiotics | U.S. PIRG
Hold the Antibiotics Wendy's
A recent estimate suggests that as many as 162,000 people die from antibiotic-resistant infections every year. We delivered a message to Wendy's CEO and shareholders: Get antibiotics out of your beef supply chain.
Transportation | U.S. PIRG
Volkswagen settlement scorecard
Volkswagen was caught cheating emissions laws and settled with federal authorities. The settlement included nearly $3 billion for the Environmental Mitigation Trust. How well does our state rank on plans for investing VW mitigation trust funds in clean transportation projects?
CoPIRG Updates
2017 PROGRAM REPORT


text:
Code Geass: Hangyaku no Lelouch
Title: Code Geass: Hangyaku no Lelouch
English: Code Geass: Lelouch of the Rebellion
Japanese: コードギアス 反逆のルルーシュ
Aired: Oct 6, 2006 to Jul 29, 2007
Genres: Action, Mecha, Military, School, Sci-Fi, Super Power
Rating: R - 17+ (violence & profanity)
Summary: In the year 2010, the Holy Empire of Britannia is establishing itself as a dominant military nation, starting with the conquest of Japan. Renamed to Area 11 after its swift defeat, Japan has seen significant resistance against these tyrants in an attempt to regain independence. Lelouch Lamperouge, exiled prince of Britannia, unfortunately finds himself caught in a crossfire between the two nations' armed forces. He is able to escape, however, thanks to the timely appearance of a mysterious girl named C.C., who bestows upon him Geass, the "Power of Kings." Realizing the vast potential of his newfound "power of absolute obedience," Lelouch embarks upon a perilous journey as the masked vigilante known as Zero, leading a merciless onslaught against Britannia in order to get revenge once and for all. [Written by MAL Rewrite]
Code Geass: Hangyaku no Lelouch Episode 25 English Sub
Code Geass: Hangyaku no Lelouch Episode 9 English Sub
Code Geass: Hangyaku no Lelouch Episode 25 English Dub
Code Geass: Hangyaku no Lelouch Episode 9 English Dub


Riz Global Foods is offering the most High Quality Halal products which are not just Halal but are also Tayyib. These are the products consumers are looking for today.
Riz Global Foods had the opportunity to have successfully launched a range of Frozen Halal Desserts from Chudleigh's at the AAHAR show in India.
A study by the Conference Board of Canada found that SMEs owned by recent immigrants to Canada, are more likely to export, and they are also among the fastest-growing SMEs.
Riz Global specializes in Canadian Halal produced Meats, Prepared Foods, and their New Innovative Gourmet offerings.


At Dicom Express, a Dicom Group company, safety and protection of our clients' privacy are essential. This document informs our clients about the Dicom Group's practices regarding the collection, use and disclosure of personal information we receive through the use of our shipping services, our products and related services, the consultation or use of our websites or any interactions between our clients and us.
Meaning of "Client" and "Personal Information"
For the purposes of this Privacy Statement, the term "client" means any person using the products and services of the Dicom Group, whether as a shipper, receiver, person responsible for shipping or otherwise. Anyone using the Dicom Group websites and anyone who registered to receive emails to learn about promotions, special offers and advice of the Dicom Group through the Dicom Freight or Dicom Express Inc. is also considered a client.
"Personal information" means information about an identifiable person, excluding name, title, business address and telephone number of an employee, that is to say, information belonging specifically to a company.
We collect personal information on shipments entrusted to the Dicom Group. This allows us to provide our clients with the services they have requested, to facilitate shipments and to bill clients accurately for all services and products provided by the company.
Financial information and billing information (information about the credit card or history of credit history).
Provide our clients with reliable and punctual products and services, particularly with pickups, deliveries, provision of logistics services and communications with clients, related to their shipments.
Assist law enforcement agencies when necessary, to recover money owed to the Dicom Group, protect the commercial interests of the Dicom Group (including application of the General Conditions of Service), and comply with the requirements of the law.
Does Dicom disclose personal information concerning you?
It is possible that we release personal information to third parties who assist us in providing products and services to our clients and/or to achieve any other purpose mentioned above, or to manage our mailing lists. In particular, such third parties may be transport companies, local pickup and delivery services, which are mandated for parcel pickup, transport and delivery, and IT service brokers and suppliers. When we disclose personal information to others, we offer a promise of confidentiality comparable to ours, during the period when the personal information is in the custody of third parties. It is also possible that we will share personal information with affiliates of the Dicom Group, thereby facilitating the achievement of one or more of the objectives stated above. Due to the geographical location of some of these third parties, personal information may sometimes be transferred, processed and stored outside of Canada or on some independent virtual servers of the Dicom Group.
We reserve the right to cooperate fully with local, state and federal agents in the context of any presumed illegal activity, or any investigation requiring personal information collected by DICOM (including private electronic communications exchanged on any corporate Dicom site). We also reserve the right to disclose or communicate personal information to a third party in the context of a sale, a merger or a transfer of the assets of Dicom Freight or Dicom Express Inc., to the extent that the third party consents to comply with the principles set out in this Privacy Statement and that such information does not constitute all or substantially all of the assets transferred.
Except as specified above, we may not sell, share, transfer, license, trade or rent personal information about our clients to third parties without the consent of said clients.
How do we obtain our clients' consent?
The type of explicit or tacit consent obtained from a person depends largely on the degree of sensitivity of the information involved and the reasonableness of the expectations from that person in this case. We do not require a person to consent to the collection, use or disclosure of his personal information beyond what is necessary in order to achieve the business objectives set out above or any other goals, specified at the time of the request for consent.
In the event a client provides us with personal information about another person (as the recipient of the shipment), the client providing the personal information has the responsibility to obtain the consent of that person for the use of his personal information for collection, use and disclosure.
At any time the client may withdraw consent allowing us to use, retain or disclose personal information subsequently, subject to legal or contractual restrictions and reasonable notice. You may withdraw your consent by contacting Dicom Express at 1-888-463-4266 or Dicom Freight at 1-888-884 6548 or at privacy@dicom.com. We inform all persons wishing to withdraw their consent of the consequences of withdrawal.
A cookie is a small file created to contain information about the user, generated by a website when you visit it. A cookie can detect and store information on the use made by a user of one or more websites at the time of such use.
There are two main types of cookies, "session cookies" and "persistent cookies".
Session cookies only store information for the period spent on a website – the data is not stored on the user's hard drive. When the user closes the browser, the issuer of the cookie no longer has the information collected by the cookie. In DICOM, we use session cookies only as additional security measures.
When clients access DICOM websites and confirm their identity (through their user ID and password), a cookie remembers the identification number of their browser (secure cookie). During the active session, the cookie is used as a digital signature to identify the session and our client on our Web server. The Web server then checks the identification number of the client browser to verify the client's identity, at any time during the session.
What types of cookies does Gogroup use?
We use only the bare essentials. Without these cookies, we cannot provide you with personalized, secure and enjoyable services.
-utma Google Analytics These cookies are used to collect information on usage patterns of visitors to our site. We use the information to analyze and improve our site. This information is collected in an anonymous format, including the number of visits to our site, where the visitors come from and what pages they visited. You can click on the following link for more details on Google data retrieval techniques.
We also collect your IP address for each connection in order to increase security. This traceability helps detect fraudulent navigation or identity theft. Finally, Bill C-28 (the anti-spam law) requires us to collect information to provide traceability on the consent of visitors to subscribe to our newsletter.
Currently, most browsers do not distinguish a session cookie from a persistent cookie. To use our websites, our clients must configure their browser to accept cookies. We recommend that clients who are concerned about the idea that their browser accepts cookies from other sites configure their browser so that there is a notification upon receipt of a cookie. This will allow clients to accept or decline cookies transmitted by the foreign Web server visited. We suggest that you contact the provider of your browser for information on the steps to perform this configuration.
We make every possible effort to protect personal information collected from any loss or unauthorized access. Access to our clients' personal information is limited to employees and agents of the Dicom Group who have been carefully selected. We use recognized information security techniques as firewalls, access control procedures and encryption, to protect personal information against loss of data or unauthorized access. We cannot guarantee the confidentiality of the information contained on any package or shipping label.
The Dicom Group retains its clients' personal information as long as necessary, to meet the needs for which it was collected, in addition to complying with the applicable laws.
Our websites may sometimes contain links to other sites. Please note that the operators of those websites may also collect personal information from our clients (especially information stored by cookies) when they click on these links. We are not responsible for the collection, use or disclosure of our clients' personal information by these third parties. It is important that our clients become aware of the privacy policies of these third parties before providing them with any personal information.
You can contact us by phone at 1-888-463-4266 or Dicom Freight at 1-888-884-6548 or by email at privacy@dicom.com to request access to your personal information in order to make corrections or updates.
Otherwise, you can send an email to privacy@dicom.com. Please note that all comments, suggestions or documents provided are considered non-confidential and non-exclusive (becoming the property of Dicom Express Inc. and Dicom Freight upon receipt) and that we may use and distribute these comments, suggestions or documents to any third party and for any purpose, without limitation, for the development and marketing of products or services, including your future comments, suggestions or documents.


Located in Costa Nova, this sweet little chocolate box property is south facing and built on 2 levels offering 3 bedrooms (Master bedroom with large naya and balcony). The property is part centrally heated, bedroom 2 has built in wardrobes and access to the terrace which overlooks the 8mx4m pool, bedroom 3 with built in wardrobes. The lounge area has access directly to the pool and also has a dining area. The kitchen is of good size and is fully fitted, also offering a very large larder for storage. Fully new double glazing, Valencian Style beamed ceilings, also having off street parking, walking distance to bars and restaurants.


JavaOne sessions are now online for you to view. We captured the audio and synched it with the presentations so you can experience the sessions even if you didn't attend JavaOne. The first 60 sessions are now available. We'll be rolling out new sessions every couple of weeks, so check back often for the latest content. It's all free, brought to you by the Oracle. Check them out!


Here is what is happening for the week of February 6th through February 10th. Sports are wrapping up this week, with the wrestling and track teams moving towards their regional meets. The basketball teams play their final games of the season this week and prepare for the Conference 12 South 5A tournament next week. Here is the rest of what is going on this week.
Young Authors will have a meeting after school from 2:30-3:30 pm on Thursday in A9. Please bring an original work to share. It can be finished or unfinished. Snacks will be provided- just bring your own drink.
SGA members are reminded to attend the meeting Wednesday, February 8 after school in the LMC. Members who are going to the VSCA State Convention need to plan to stay after for a brief information session.
All Spanish Club members need to stop by room A29 to pick up a bag of candy to sell for the fundraiser. Spanish Club will be selling candy starting on Tuesday, February 14th through Friday, February 28th.
The Hopewell Kiwanis Club is holding their annual Leprechaun Dash 5K race on Saturday, March 18. Registration is $5 for students and $15 for adults. All of the proceeds go back to the school of your choice, so make sure when you register that you select PGHS! You can register online at www.leprechaundash.weebly.com or grab a registration form from outside of D-12.
There will be an outdoor track interest meeting in the commons on Wednesday, February 8th at 6 PM. All students and parents are invited to this meeting.
Interact club will be meeting on Feb. 8th, 2017 at 2:40 pm in room A34.
Any girl interested in participating in a short spring volleyball league, please see Ms. Gilbert in the girls PE office. League play will take place on Tuesdays and Thursdays from March 2nd to May 4th. See Coach Gilbert.
The library has added titles to the free book cart. Stop by and take the titles related to social issues, sign language, and folklore.


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El municipio de Half Moon Lake (en inglés: Half Moon Lake Township) es un municipio ubicado en el condado de Misisipi en el estado estadounidense de Arkansas. En el año 2010 tenía una población de 601 habitantes y una densidad poblacional de 6,29 personas por km².

Geografía
El municipio de Half Moon Lake se encuentra ubicado en las coordenadas . Según la Oficina del Censo de los Estados Unidos, el municipio tiene una superficie total de 95.61 km², de la cual 89,13 km² corresponden a tierra firme y (6,78 %) 6,48 km² es agua.

Demografía
Según el censo de 2010, había 601 personas residiendo en el municipio de Half Moon Lake. La densidad de población era de 6,29 hab./km². De los 601 habitantes, el municipio de Half Moon Lake estaba compuesto por el 95,01 % blancos, el 1,16 % eran afroamericanos, el 0,67 % eran amerindios, el 0,17 % eran isleños del Pacífico, el 0,33 % eran de otras razas y el 2,66 % eran de una mezcla de razas. Del total de la población el 2 % eran hispanos o latinos de cualquier raza.

Referencias

Enlaces externos

Municipios de Arkansas
Localidades del condado de Misisipi (Arkansas)

redpajama_set_name: RedPajamaWikipedia


text:
> text utilities in go

## Install

## Usage

redpajama_set_name: RedPajamaGithub


text:
Of Governments and Cybercriminals
June 5, 2020 Deliverability, Email Marketing, Technology
Imagine being one of the richest people on the planet, and having your nude photographs stolen from your smartphone. Imagine the frustration when you're powerful enough to affect international trade, but your marriage is held hostage by the actions of hacker. At a certain point in the life of Jeff Bezos, owner of the Washington Post and founder of Amazon, it would have been reasonable to think that things couldn't get any worse.
Then they got worse.
Last week, vice.com blog published the full findings of a forensic investigation performed on on Bezos' iPhone (1), and although no malware was found on the device, there are strong indications that spyware was transmitted from the WhatsApp account belonging to Mohammad Bin Salman, Crown Prince of Saudi Arabia. According to a Guardian report, it wasn't only his iPhone that was compromised this way by Saudi officials, friends and colleagues of journalist Jamal Khashoggi had also experienced similar attacks (3). What makes this particularly worrying for Bezos is that he appears to have caught the attention of Mohammad Bin Salman, who is also being accused of orchestrating the murder of Jamal Khashoggi in Istanbul in 2018 (2).
So how is it possible for someone's life to be so readily compromised if there was no malware behind it?
Anyone who has ever used E-Mail knows that you never click on links or open attachments from unknown sources. It's even become standard that most E-Mail clients do not allow attachments or images to be automatically opened, and even the pre-loading of images is disabled by default. When it comes to E-Mail, people understand the dos and don'ts, but being security conscious in this way doesn't seem to apply to Messenger apps. Maybe it's a combination of apps being closed systems so there aren't that many settings to worry about, or maybe it's because app creators know that user convenience is king. Even if the automatic preview of incoming files can be disabled, as is the case in Telegram messenger, it's mostly enabled by default because people just want things to work. The problem is that they don't ever wonder what the tradeoff is for this level of convenience.
So when Jeff Bezos received the message, it probably didn't cross his mind to question the message or to double check if it was safe to watch the video. But with smartphone apps it's not even as convenient as looking at the app, or the individual, because it's the curse of convenience which basically complicates things further by adding more grey areas. In theory, the preview of that video would have been sufficient for the attack to succeed – but if the user doesn't preview the video then they're safe right?
In this specific case, Facebook claims the vulnerability abused was not a WhatsApp-bug, but rather an IOS-bug (8). Because it is possible to have previews of incoming messages in parts of the operating system, such as the lock screen, the phone's operating system would need to access the content of messages as well. So this means that by trying to be helpful and give the user a preview of a message they would never have clicked on themselves, the phone OS can end up installing the malware.
According to the forensic researchers that have researched Jeff Bezos iPhone, the software used to epxloit the vulnerabilities was purchased from a company that provides spyware for governments worldwide. One example of such company is the Israeli NSO group (4), who has been sued by Facebook / WhatsApp for the abuse of their messenger apps. An older vice.com blog describes the contents of the lawsuit and how NSO group operates (5).
There were reports relating to some early Android versions, where the operating system prevented users from changing certain settings which made it practically impossible for users to protect themselves from execution of malicious incoming files. WhatsApps's own Signal protocol has also reportedly had vulnerabilities found and abused by third parties like the NSO group or FinFisher.
So can you stay safe while using a mobile?
These days desktop computers have become relegated for business use, while personal communication has largely shifted to mobile – because you can take it with you wherever you go. This means that the data generated on mobile devices becomes much more valuable because of how much more of the person's life it touches – which then makes mobile devices a much more interesting target for malicious actors.
While large tech companies generally refuse to allow governments backdoor access to their applications to prevent citizens being spied on, some of the very same governments engage with organizations that take advantage of software vulnerabilities. Interestingly, these organizations employ the same methods as cyber criminals when they attack their victims. Wikipedia mentions Germany, Turkey, Israel, Thailand, Qatar, Kenya, Uzbekistan, Mozambique, Morocco, Yemen, Hungary, Saudi Arabia, Nigeria, and Bahrain as countries where software from NSO group or Finfisher (6) has been used.
While the data is currently the most appealing asset in your mobile device, it's important to note that a compromised phone would also give access to all your other channels including E-Mail, chat, messenger, SMS, etc. Trends in E-Mail consumption already show that more than half of all E-mails is being read on mobile devices. This is where E-Mail may become the target of attacks more often in the future, rather than just the tool for transmission of spyware. Imagine what someone with control over Jeff Bezo's phone could achieve pretending to be him…
But the thing is, with E-Mail the challenge is to detect if the person you believe to be speaking to actually is the person you are speaking to. Even with mechanisms like SPF, DKIM and DMARC that help make sure that an E-Mail message look entirely authentic, you can't be sure because it's much easier to attack and abuse any open protocol than it is to attack and abuse proprietary software, which is usually owned and hosted by one single multinational.
In contrast to E-Mail, most messengers largely verify users by their SIM-cards' numbers, not leaving much room for doubt about their identity – even though this confirmation of identity can ironically become a threat to the users' anonymity. But there is some hope as there is one app which attempts to balance user anonymity and sender authentication is Telegram messenger (7). Since mobile apps are all designed around convenience, it also introduces further risks because a compromised phone will still be sending as the legitimate owner – which then neutralizes any benefits that anti-spam measures may bring, e.g. spam from Jeff Bezos' phone will in fact be from Jeff Bezos…
Shouldn't Providers keep us safe?
Pretty much any provider for software or technology will have loads to say about their dedication to security and privacy, etc. – because on average they all generally do try to keep their customer safe. Unsecured data doesn't result in a very good long-term userbase for anyone. However, most vulnerabilities used to attack any software are so-called zero-day-bugs – which ironically are introduced as software is updated, or other bugs are fixed.
To help encourage user-safety a lot of providers have rewards programs where individuals, or hackers, who find security flaws can report these for a reward. However, the reward they receive from the software company is not always as high as the amount they can earn by selling this information on in the darker corners of the web.
Wikipedia reads "While selling and buying these vulnerabilities is not technically illegal in most parts of the world, there is a lot of controversy over the method of disclosure. A 2006 German decision to include Article 6 of the Convention on Cybercrime and the EU Framework Decision on Attacks against Information Systems may make selling or even manufacturing vulnerabilities illegal." (9).
So as usual with regards to security it's an arms race – with some very shady actors involved.
But what is possibly confusing is when Governments spend tax money to buy licenses for the kind of tools that would allow them to steal data, e.g. like when the German government spent 147.000 Euro on tax money to buy a license for Finfisher's FinSpy in 2013 (10). On the face of it, it looks like quite the contradiction – on the one hand how trustworthy does that make the government, but on the other is it because they want to use the same kind of tools to test their own security?
It's a dark day when you start wondering if the leader of a country has abused day-to-day communication tools to the extent where murders are orchestrated, or to blackmail one of the richest men on earth. It reads more like a crime thriller you'd take on holiday. But when the same tools that cyber criminals use are available to well-funded government actors, then where does it all end? Wikipedia quotes: "In June 2018, an Israeli court indicted a former employee of NSO Group for allegedly stealing a copy of Pegasus and attempting to sell it online for $50 million worth of cryptocurrency." (4).
The moral of this story?
I'm not sure if there is any positive aspect to E-Mail in this story. We are still struggling to authenticate the legitimacy of senders and many E-Mail experts still live in a bubble where they aren't looking beyond their channel of choice. The past 10 years in E-Mail security was mainly about authenticating E-Mail with SPF and DKIM, and the reason we need to patch SPF and DKIM with DMARC was mainly the ignorance of our own policies and mechanisms.
But now we live in times where each messenger can do end-to-end encryption straight out of the box, but with E-Mail, the same level of security is still a huge challenge. Because E-Mail isn't an app but a channel at the mercy of a number of different mechanisms, and protocols it's much harder to ensure a unified level of security. But to make E-Mail safe it would require a monumental collaborative effort across a host of different stakeholders, which is a hard sell given that the benefits for that isn't as high as say developing the next big communications app.
But at the same time, with vulnerabilities having been reported in the Signal protocol that WhatsApp uses, it opens up the possibility of it being taken advantage of by software from the NSO group and FinFisher. So when looking at communications from a single device, then it's important to remember that the chain of communication is only as secure as its weakest link, or app.. or OS!
Maybe it's time to look beyond our bubble and realize that despite all the security measures we're putting together for E-Mail, it's now the stronger part of a pretty weak chain. If any spammer ever gets hold of Pegasus-like technology, they can start sending spam straight out of our pockets and sender authentication will not solve that problem – because in more ways than one your phone is now the digital version of you.
Convenience will be the death of our security.
1. https://www.vice.com/en_us/article/v74v34/saudi-arabia-hacked-jeff-bezos-phone-technical-report
2. https://en.wikipedia.org/wiki/Jamal_Khashoggi
3. https://www.theguardian.com/world/2020/jan/21/revealed-the-saudi-heir-and-the-alleged-plot-to-undermine-jeff-bezos
4. https://en.wikipedia.org/wiki/NSO_Group
5. https://www.vice.com/en_us/article/gyznnq/how-nso-group-helps-countries-hack-targets
6. https://en.wikipedia.org/wiki/FinFisher
7. https://telegram.org/blog/scheduled-reminders-themes#new-privacy-settings
8. https://mashable.com/article/facebook-whatsapp-jeff-bezos/
9. https://en.wikipedia.org/wiki/Zero-day_(computing)#Ethics
10. https://de.wikipedia.org/wiki/FinFisher#Kauf_durch_deutsche_Beh%C3%B6rden
cybercriminals deliverability email Germany hack Mobile security
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