text:
\section{Introduction}

    Adversarial attacks aim to fool the deep neural networks to provide incorrect outputs \cite{biggio2013evasion,carlini2019evaluating}. The distance between the outputs of clean images and adversarial images is maximized to construct adversarial images. Following this path, a series of white-box attacks, including Fast Gradient Sign Method (FGSM) \cite{goodfellow2014explaining}, Basic Iterative Method (BIM) \cite{kurakin2016adversarial}, and Projected Gradient Descent (PGD) \cite{madry2017towards}, craft adversarial images according to the gradients of the loss function with respect to the input. The gradient measures how small changes at each input pixel affect the model outputs \cite{chan2020thinks}. With access to the gradient, adversaries are able to craft adversarial examples along the direction of loss
   ascending. Thus, gradient-based attacks achieve high success rates in attacking deep learning models \cite{dong2020benchmarking,tramer2017ensemble,kannan2018adversarial,zhang2019theoretically,naseer2020self}.

    Many proposed defense methods are able to defend against gradient-based attacks. The robust training based methods \cite{madry2017towards, goodfellow2014explaining, tramer2017ensemble,zhang2019theoretically,hein2017formal,ross2017improving,yan2018deep,lecuyer2019certified} aim to train a robust target model either by including adversarial examples in training data or by regularization. The input transformation based methods \cite{guo2017countering,jia2019comdefend,xie2017mitigating,liao2018defense,naseer2020self,yuan2020ensemble,samangouei2018defense,song2017pixeldefend} modify the image examples to invalidate the adversarial effect before feeding them to the target model. The randomization based methods \cite{xie2017mitigating,pang2019mixup,cohen2019certified,dhillon2018stochastic,liu2018towards,liu2018adv} reduce the adversarial effects by randomizing the inputs or model parameters.  However, these methods can hardly recover the performance of the target model to the original level. Comparing to the attack methods which explore the gradient to craft adversarial images, defense methods unequally ignore the critical information about how each pixel changes of inputs affect the model outputs. The information provided by adversarial images is insufficient for defense methods to mitigate the adversarial effects since images contain the zero-order information of the target model while the gradient contains the first-order information of the target model \cite{madry2017towards}. Therefore, we leverage the adversarial images and their gradients to defend against gradient-based attacks. The gradients of adversarial images measure how small changes at each pixel of adversarial images affect the model outputs. This enables our method to incorporate extra information beyond the adversarial images and further provide a more robust defense.

    In this paper, we propose a Two-stream Restoration Network (\textbf{TRN}) to explore and fuse the information in adversarial images and its gradients, and further restore the adversarial images to a benign state (see Figure \ref{fig:Idea}). To utilize the gradient on the defense side, we addressed two challenges. The first one is to obtain the gradient. The gradients are available in attack methods because white-box attacks know both the network parameters and the label of input images. Adversaries can easily compute the gradient through backward propagation \cite{papernot2016technical,rauber2017foolbox,art2018,dong2020benchmarking}. However, the gradient is hard to obtain on the defense side since defenders do not have access to the label of original images. To solve this problem, we design a Gradient Map Estimation Mechanism (\textbf{GMEM}) to derive a set of estimated gradient maps. Another challenge is how to integrate information in the image stream and gradient stream since the gradient and the adversarial images are from different spaces. To bridge the features of two streams, we design a Fusion Block (\textbf{FB}) in TRN to fuse these two streams. The Fusion Block can be simply concatenated to construct a deeper network. In this case, we are able to train a TRN with different network depths to handle target models with different complexities.

    Therefore, our contributions can be summarized as:
    \begin{itemize}
    \item We show that the gradients of adversarial images can be exploited to defend against gradient-based attacks. Considering both adversarial images and the corresponding gradients, we propose a Two-stream Restoration Network for defense. To our best knowledge, this is the first attempt to leverage the gradients of adversarial images to defend against adversarial attacks.
    \item To solve two major challenges in using gradients on the defense side, we propose a novel Gradient Map Estimation Mechanism to estimate the gradients of adversarial images. Also, a Fusion Block is designed to fuse the image stream and gradient stream.
    \item Our new method outperforms the state-of-the-art defense methods on CIFAR10, SVHN, and Fashion MNIST. Experiments also demonstrate that our defense is generalizable, scalable, and hard to bypass.

\end{itemize}

\section{Related Work}

\textbf{Attack}: Gradient-based attacks \cite{goodfellow2014explaining,kurakin2016adversarial,madry2017towards} exploit the benefits of back-propagation to generate gradients and further craft the adversarial images. Specifically, the FGSM \cite{goodfellow2014explaining} is a single-step attack that transforms the gradient of the loss function with a sign function to generate the perturbation.
Goodfellow \textit{et al.} \cite{kurakin2016adversarial} provide the Basic Iterative Method to generate perturbations in an iterative fashion. Perturbations are generated based on the gradients of the loss function with respect to the adversarial images in the last iteration. Similarly, the PGD \cite{madry2017towards} was proposed to improve the BIM by randomly initializing the perturbation within the perturbation budget. It also applies random restarts after certain iterations. MIM \cite{dong2018boosting} uses momentum terms to prevent iterative optimization from staying in poor local maxima. FFGSM \cite{wong2020fast} improves FGSM in adversarial training to make the performance comparable to PGD while reducing the training time. Besides the attack that directly uses gradients to compute adversarial images, the C{\&}W attack \cite{carlini2016defensive} crafts adversarial examples by minimizing perturbations. Moreover,
Carlini \textit{et al.} \cite{athalye2018obfuscated} first train a network to approximate the input transformation, then compute the gradient based on the approximation network.

\textbf{Defense}: Many input transformation based methods \cite{guo2017countering,xie2017mitigating,liao2018defense,samangouei2018defense} are proposed to eliminate the adversarial patterns on images. Guo \textit{et al.} \cite{guo2017countering} transform images with bit-depth reduction, JPEG compression, total variance minimization, and image quilting to process images before feeding them to networks. Xie \textit{et al.} \cite{xie2017mitigating} use random resizing and random padding to modify inputs to mitigate the adversarial effect. HGD \cite{liao2018defense} proposes a high-level representation guided denoiser to process images to defend against attacks. Defense-GAN \cite{samangouei2018defense} trains a Generative Adversarial Network to transform the adversarial images into clean examples. Recent methods \cite{naseer2020self,yuan2020ensemble,madry2017towards} leverage adversarial training to design pre-processing networks, which can successfully defend against a variety of attacks.
Other methods \cite{lecuyer2019certified,cohen2019certified} leverage differential privacy or randomization to make the model more robust. However, all the above defenses solely rely on the adversarial images as inputs. The gradients of adversarial images, which contain first-order information of target models, are ignored in these methods. Thus, these methods fail to provide optimal defense performance to the target model.

\begin{figure}
    \centering
    \includegraphics[scale=0.33]{Framework.png}
    \caption{Two-stream Restoration Defense. To achieve the optimal robustness against gradient-based attacks, we adopt adversarial training to train a Two-stream Network as the pre-processing network. The training process iterates the following three steps: \textbf{(1)} Generating adversarial images using gradient-based attack algorithms. \textbf{(2)} Computing the gradient maps of adversarial images using GMEM. \textbf{(3)} Optimizing the TRN to restore the adversarial images with gradient maps and adversarial images. }
    \label{fig2:Framework}
\end{figure}
\section{Two-stream Restoration Network}

Our TRN serves as a pre-processing network to restore the adversarial images (see Figure \ref{fig2:Framework}). Different from other defense methods \cite{naseer2020self,yuan2020ensemble}, our TRN incorporates not only the adversarial images but also the gradients of adversarial images to defend against gradient-based attacks. Recall that the gradients measure how small pixel changes affect the model output. Considering the gradient maps as the second stream, our defense can outperform single-stream defense methods (See experimental results in Table \ref{tab:table1} and \ref{tab:table4}).

\textbf{Adversarial Training}: We adopt adversarial training \cite{madry2017towards} to optimize our TRN. Given the benign image $\textbf{x}$ and label $\textbf{y}$, we first generate the adversarial images $\textbf{x}'$: $\textbf{x}'=\textbf{x}+\delta$ by maximizing the adversarial loss $\mathcal{L}_{adv}$.

\begin{equation}\label{eq2}
\delta=\mathop{\arg\max}_{\delta}\mathcal{L}_{adv}(\mathcal{C}(\textbf{x}'),\textbf{y}), ||\delta||_{\infty} \leq \epsilon
\end{equation} where $\delta$ is the perturbation. $\epsilon$ is the perturbation budget in $\ell_\infty$ norm. $\mathcal{C}(\cdot)$ denotes the target model. Many gradient-based attack algorithms can be adopt to solve the maximization problem. For example, the Fast Gradient Sign Method \cite{goodfellow2014explaining} solves this optimization by $\delta=\epsilon sgn(\nabla_{\textbf{x}}{\mathcal{L}_{adv}})$, where $sgn(\cdot)$ denotes the sign function. Usually, the adversarial loss for classification task is the cross-entropy loss.

Once the adversarial images are crafted, we then compute the gradient maps $\textbf{G}$ of adversarial images using GMEM (see Section 3.1). The restored image $\textbf{x}_r$ will be generated using our TRN.

\begin{figure}
    \centering
    \includegraphics[scale=0.32]{gradientmaps.png}
    \caption{Visualization of Gradient Maps on CIFAR10 dataset. The red box is zoomed in for details. The spatial distributions of gradient maps are similar, while the colors of the gradient map in the adversarial class (Class 8) are different from those in other classes. The color difference results from the difference of adversarial losses since the adversarial image is classified into the adversarial class}
    \label{fig:gradient_maps}
\end{figure}

\begin{algorithm}
\small
\caption{Two-stream Restoration Defense}
\label{alg:A}
\renewcommand{\algorithmicrequire}{\textbf{Input:}}
\renewcommand{\algorithmicensure}{\textbf{Output:}}
\begin{algorithmic}[1]
\REQUIRE Training data $D$, Two-stream Restoration Network $TRN$, classifier $\mathcal{C}$, perturbation budget $\epsilon$ and loss criteria $\mathcal{L}$
\ENSURE Randomly initialize $TRN$
\REPEAT
\STATE Sample mini-batch of data $\textbf{x}$ from the training set.
\STATE Find adversarial images $\textbf{x}'$ by Eq. \ref{eq2}.
\STATE Generating gradient maps $\textbf{G}$ using Eq. \ref{eq6}.
\STATE Forward-pass $\textbf{G}$ and $\textbf{x}'$ through $TRN$ (Eq. \ref{eq3}) to generate $\textbf{x}_r$ and calculate $\mathcal{L}_{pix}$.
\STATE Forward-pass $\textbf{x}_r$ through $\mathcal{C}$ and calculate $\mathcal{L}_{smt}$.
\STATE Back-pass and update $\theta_{TRN}$ to minimize $\mathcal{L}_{pix}$ and $\mathcal{L}_{smt}$.
\UNTIL{$TRN$ converges.}
\end{algorithmic}
\end{algorithm}

\begin{equation}\label{eq3}
    \textbf{x}_{r}=TRN(\textbf{G},\textbf{x}',\theta_{TRN})
\end{equation} where $\theta_{TRN}$ denotes the network parameters of TRN. Different from other input transformation based defenses \cite{naseer2020self,yuan2020ensemble,mustafa2019image}, we leverage both image and gradient streams to process the adversarial image, which can provide a more robust defense. To obtain an optimal TRN that can restore the adversarial images in both pixel space and semantic space, we minimize the empirical risk $\rho$ given the dataset $D$:

\begin{equation}\label{eq4}
    \min_{\theta_{TRN}} \rho(\theta_{TRN})
\end{equation}
\begin{equation}\label{eq5}
    \rho(\theta_{TRN})=\mathbb{E}_{(\textbf{x},\textbf{y}){\thicksim}D}[\mathcal{L}_{pix}(\textbf{x},\textbf{x}_r)+\mathcal{L}_{smt}(\mathcal{C}(\textbf{x}_r),\textbf{y})]
\end{equation}

Note that we have two loss functions: pixel loss $\mathcal{L}_{pix}$ and semantic loss $\mathcal{L}_{smt}$. The pixel loss is proposed to restore the pixel domain of adversarial images, which is the $\ell_2$ distance between restored image $\textbf{x}_r$ and benign image $\textbf{x}$. The semantic loss aims to revert the performance of adversarial images on the classifier, which is the cross-entropy loss of classifier outputs $\mathcal{C}(\textbf{x}_r)$ and the label $\textbf{y}$. The detailed procedures are summarized in Algorithm \ref{alg:A}.

\begin{figure*}
    \centering
    \includegraphics[scale=0.45]{FusionBlock.png}
    \caption{Fusion Block. The Fusion Block contains image stream and gradient stream. With the connections between two streams, the Fusion Block learns to fuse the information from two streams to restore images.}
    \label{fig2:fusion block}\vspace{-0.1in}
\end{figure*}

\subsection{Gradient Map Estimation Mechanism}

To incorporate the gradients of adversarial images for further restoration, we need to first compute the gradients. However, on the defense side, the gradients of adversarial images are unavailable since defenders do not have access to the original label of adversarial images. Thus, to this end, we propose a Gradient Map Estimation Mechanism for estimating the gradients.

For gradient-based attacks, the gradient is commonly computed according to $\nabla_\textbf{x}\mathcal{L}_{adv}(\mathcal{C}(\textbf{x}),\textbf{y})$ \cite{madry2017towards,goodfellow2014explaining,kurakin2016adversarial}, which requires the ground truth label $\textbf{y}$. For gradients of adversarial images $\nabla_{\textbf{x}'}\mathcal{L}_{adv}(\mathcal{C}(\textbf{x}'),\textbf{y})$, the ground truth label $\textbf{y} $ is unknown, but $\textbf{y}$ should be one of the possible labels $\{y_i\}, i=1,2,3 ..., n$, since the sample should belong to one class in a dataset. Thus, for each possible label $\textbf{y}_i$, we compute the gradient of adversarial images. In this way, we generate the gradient maps as below:

\begin{equation}\label{eq6}
    \textbf{G}=\{\nabla_{\textbf{x}'}\mathcal{L}(\textbf{x}',\textbf{y}_i)\}, i=1,2,3,...n
\end{equation} where $y_i$ denotes the possible label in class $i$, $n$ denotes the total classes of the dataset. The gradient maps $\textbf{G}$ are considered as an estimator of the gradient computed by the real label. In our TRN, the gradient maps of all classes are then concatenated along the channel dimension.

In Figure \ref{fig:gradient_maps}, we visualize the gradient maps of one adversarial image. These gradients measure how each pixel of the adversarial image contributes to the model outputs. In this case, our TRN can focus on the critical pixels that strongly affect the model outputs to provide more precise restoration. By comparing the area in red boxes, we find that the gradient maps generated using possible labels share similar spatial patterns, while the gradient map in the adversarial class share different colors with gradient maps in other classes. The similarity of spatial patterns results from the same input, target model, and loss function, while the color difference is due to different loss values. Since the image is crafted to be in the adversarial class, the loss value for the adversarial class is low, while the loss values for other classes are high.

\begin{table*}
\caption{Performance (classification accuracy after defense) of our method against adversarial attacks on CIFAR10 ($\epsilon=8/255$) , SVHN ($\epsilon=12/255$) and Fashion MNIST ($\epsilon=8/255$). Our method significantly outperforms the state-of-the-art defense by a large margin.}
\centering
\begin{tabular}{c|l|c| c c c c c c c}
\toprule[1.5pt]
                &Defense Methods                                 & No attack     & PGD \cite{madry2017towards}       & BIM \cite{kurakin2016adversarial}          & FGSM \cite{goodfellow2014explaining}          & MIM \cite{dong2018boosting}       & FFGSM \cite{wong2020fast}\\ \midrule[1pt]
\multirow{10}*{\rotatebox{90}{CIFAR10 \cite{krizhevsky2009learning}}}         &No defense     & 95.34         &0.01       & 0.01         & 37.60         & 0.08      & 33.02       \\ \cline{2-8}
                &JPEG \cite{guo2017countering}                                   & 76.91         &67.16      & 64.6         & 59.27         & 52.75     & 63.11        \\
                &Label Smoothing \cite{warde201611}                        & 92.93         &36.69      & 37.9          &57.62          &45.60       & 57.27     \\
                &Feature Squeezing \cite{xu2017feature}                      & 27.13         & 23.55     & 22.82         & 23.41         &21.80       & 24.18         \\
                &R{\&}P \cite{xie2017mitigating}                                 & 91.49          &60.09      &48.16          & 56.80          & 27.79     &56.84    \\
                &SR \cite{mustafa2019image}                                     & 82.31       &68.84      &65.00          &57.24          &45.07      &61.54     \\
                &NRP \cite{naseer2020self}                                    &89.96           &78.77      &72.67          &72.86          & 61.18     &75.91         \\
                &Adv. Training \cite{madry2017towards}                          & 76.48          &44.16      &38.66          &42.49          & 40.46     &51.06     \\ \cline{2-8}
                &Ours                                           & \textbf{94.37}          &\textbf{86.96}      &\textbf{90.01}          & \textbf{87.10}         &\textbf{91.90}     & \textbf{87.51}    \\ \midrule[1pt]

\multirow{10}*{\rotatebox{90}{SVHN \cite{netzer2011reading}}}         &No defense        & 97.11         &0.16       & 0.15         & 37.25         & 1.40      & 39.65       \\ \cline{2-8}
                &JPEG \cite{guo2017countering}                                   & 96.81         &1.02      & 0.70         & 39.32        & 3.70     & 43.91        \\
                &Label Smoothing \cite{warde201611}                        & 96.66         &4.67      & 3.79          &50.30         &17.71       &57.62     \\
                &Feature Squeezing \cite{xu2017feature}                      & 60.88         & 36.29     & 34.36         & 43.13         &31.76      & 46.22         \\
                &R{\&}P \cite{xie2017mitigating}                                 & 94.42          &28.00     &24.09          & 47.64         & 18.78     &53.39    \\
                &SR \cite{mustafa2019image}                                     & 95.59      &55.12      &46.13          & 51.88         & 28.07     & 59.39    \\
                &NRP \cite{naseer2020self}                                    & 92.74          & 89.24     &   74.38         &  89.47        &  63.41    & 82.27        \\
                &Adv. Training \cite{madry2017towards}                       &  95.44        &     35.8       &   35.35   &   78.7       &     51.01     &  61.71    \\ \cline{2-8}
                &Ours                                           & \textbf{97.12}          &\textbf{95.34}      &\textbf{95.70}          & \textbf{98.13}         &\textbf{98.89}     & \textbf{98.41}    \\ \midrule[1pt]

\multirow{10}*{\rotatebox{90}{Fashion MNIST \cite{xiao2017fashion}}}  &No defense      & 95.08         &0.00       & 0.01          & 55.32         & 0.02      & 41.48       \\ \cline{2-8}
                &JPEG \cite{guo2017countering}                  & 90.87         &71.82      & 68.97         & 66.99         & 60.51     & 68.89        \\
                &Label Smoothing \cite{warde201611}             & 90.61         &11.58      &19.33          & 51.42         & 25.93     & 46.58     \\
                &Feature Squeezing \cite{xu2017feature}         & 60.95         & 59.58     & 59.21         & 59.73         & 58.88     & 60.12         \\
                &R{\&}P \cite{xie2017mitigating}                & 90.37         & 39.05     & 35.48         & 60.37         & 32.39     & 57.56        \\
                &SR \cite{mustafa2019image}                     &  92.92        & 66.66     & 59.89      & 67.97         & 45.37     & 66.37    \\
                &NRP \cite{naseer2020self}                      &  92.69        & 84.73     & 77.84         & 78.21         & 71.37     & 82.09        \\
                &Adv. Training \cite{madry2017towards}          &   90.56       &    86.15       &  85.22   &     85.65     &    85.35      &    87.01            \\ \cline{2-8}
                &Ours                                           & \textbf{95.08 }         &\textbf{93.11}    &\textbf{92.75}          & \textbf{95.12}       &\textbf{96.42}     & \textbf{95.12}   \\ \bottomrule[1.5pt]
\end{tabular}
\label{tab:table1}
\end{table*}

\subsection{Fusion Block (FB)}

Our TRN takes both image stream and gradient stream as inputs to restore adversarial images. Both two streams are related to each other but they are from different spaces, in which the value distributions are quite different. How to fuse these two streams to jointly guide the restoration is a challenge. To address this challenge, we design the Fusion Block in TRN to explore and fuse the information from two streams. The architecture is shown in Figure \ref{fig2:fusion block}.

\textbf{Residual Connection}: The nature of residual learning \cite{he2016deep} matches the goal of adversarial image restoration, which is to learn the benign perturbation adding to the adversarial images. Therefore, in the image stream, through the $6$ convolution layers, we learn the benign perturbation, then with the residual connection after convolution layer $i_6$, we add the benign perturbation to the adversarial image to obtain the restored image. The goal of the gradient stream is to help learn the benign perturbation since the natures of the gradient and perturbation are more close (perturbations are constructed from gradient in attack methods). Similarly, through the residual connection in the gradient stream, gradient stream learns to refine the gradient to provide more accurate guidance on generating perturbation.

\textbf{Dense Connectivity}: Motivated by Huang \textit{et al.} \cite{huang2017densely}, we use densely connected convolution in both streams, in which each layer takes all preceding feature-maps as input. In this way, the convolution layers can extract useful information from different level's features. Also, FBs can be concatenated to construct deeper networks to provide more accurate restoration without gradient vanishing.

\textbf{Fusion Connection}: Instead of fusing two streams into one stream \cite{Simonyan2014Two}, we use one stream to guide the other stream. First, by inserting features from image stream to gradient stream after the first two layers, we use the image features from shallow layers, which contains undistorted information, to guide the gradient refining. Second, as aforementioned, we use gradient stream to help learn the benign perturbation, so we concatenate the image features $i_5$ and the gradient features $g_4$ to learn the benign perturbation.

\textbf{Channel-wise Attention}: In the gradient stream, gradient maps and the features from two streams are all concatenated along the channel dimension. However, the contributions from gradient features and image features are different. Also, the gradient maps computed with different labels may have different contributions. Thus, we adopt the Channel-wise Attention (CA) \cite{hu2018squeeze} to re-weigh the feature in different channels. The first and second CA are used to re-weigh the features from different streams and the gradient map generated with different labels, respectively.

\section{Experiments}

In this section, we evaluate our TRN defense comprehensively. Our method is compared to $7$ state-of-the-art defense methods against $5$ gradient-based attacks on $3$ datasets. We evaluate our method in three aspects: (1) The defense performance against attacks. (2) The generalizability across attacks. (3) Robustness against bypass attacks.

\subsection{Experimental Setting}

The evaluation is based on three datasets including CIFAR-10 \cite{krizhevsky2009learning}, SVHN (Street View House Number) \cite{netzer2011reading} and Fashion MNIST \cite{xiao2017fashion}. The CIFAR-10 dataset has $60,000$ images in 10 classes. The SVHN dataset contains 10 classes of $99,289$ street view images in total.. The Fashion MNIST contains $60,000$ gray-scale images in 10 classes. Image sizes in CIFAR10 and SVHN datasets are $32\times32$. We also resize the images of Fashion MNIST to $32\times32$ from the original $28\times28$.

For a fair comparison, we test all the attack and defense methods on the same target model for each dataset. We use a ResNet-18 \cite{he2016deep} as the target model.  All the methods are implemented with PyTorch \cite{paszke2019pytorch}. In our method, we use Adam as the optimizer with an initial learning rate of $1\times 10^{-3}$ and reduce the learning rate by a factor of $10$ every $30$ epochs. In the training, besides the adversarial images and the gradient maps, we include the benign images and the corresponding gradient maps to train the TRN.

\begin{table*}
\caption{Generalizability across different attacks on CIFAR10, SVHN, and Fashion MNIST (Fashion). We compare our method (Right) with adversarial training \cite{madry2017towards}  (Left). Methods are trained with attacks in the second column and test on the attack in the first row. We show the classification accuracy after the defense. Variation is the difference between maximum accuracy and minimum accuracy. Our method is generalizable across different attacks.}
\centering
\begin{tabular}{c| c | c c c c c c}
\toprule[1.5pt]
\multicolumn{2}{c|}{}    &  PGD &  BIM &  FGSM &  MIM &  FFGSM & Variation\\ \midrule[1pt]
    \multirow{3}{*}[-2.2ex]{\rotatebox{90}{\footnotesize CIFAR10 \cite{krizhevsky2009learning}}}     & PGD &43.04 / 89.72            &37.33 / 87.33            &41.85 / 73.44          &39.36 / 75.26         &49.87 / 79.74 &      12.54 / 16.28\\
                                                                                & BIM &45.29 / 85.18           &40.04 / 89.67            &43.17 / 78.01          &41.41 / 84.62          &50.72 / 80.77     &   10.68 / 11.66 \\
                                                                                & FGSM&2.39 / 87.88          &2.24 / 88.61            &74.29 / 87.87          &2.60 / 88.25          &90.67 / 87.77         &  88.43 / 0.84\\
                                                                                & MIM &44.16 / 86.96           &38.66 / 90.01           &42.49 / 87.10           &40.46 / 91.90         &51.06 / 87.51      &     12.40 / 4.94\\
                                                                                & FFGSM&39.15 / 88.66           &33.12 / 88.63            &38.79 / 86.97         &35.32 / 86.65          &47.72 / 88.80     &      14.60 / 2.15 \\\midrule[1pt]

    \multirow{3}{*}[-2.2ex]{\rotatebox{90}{\footnotesize SVHN \cite{netzer2011reading}}}         & PGD &52.83 / 97.54            &10.58 / 95.52          &69.62 /  98.66          &4.61 / 98.66         &85.28 / 98.49           &              80.67 / 3.14\\
                                                                                & BIM &31.58 / 97.01           &30.84 / 97.26           &41.45 / 94.23          &37.16 / 98.25           &50.39 / 98.14         &       19.55 / 4.02 \\
                                                                                & FGSM&0.04 / 86.59           &0.18 / 71.88           &83.52 / 99.17          &0.21 / 70.05          &81.86 / 93.61             &         83.48 / 29.12 \\
                                                                                & MIM&35.80 / 95.34           &35.35 / 95.70            &78.70 / 98.13            &51.01 / 98.89        &61.71 / 98.41          &              43.35 / 3.55 \\
                                                                                & FFGSM&0.00 / 95.07          &0.00 / 94.07            &40.53 / 98.52          &0.00 / 94.45          &99.02 / 99.17            &               99.02 / 5.10\\\midrule[1pt]

        \multirow{3}{*}[-2.2ex]{\rotatebox{90}{\footnotesize Fashion \cite{xiao2017fashion}}}    & PGD &85.52 / 94.63            &84.30 / 92.01           &84.89 / 89.24           &84.55 / 91.47              &86.62 / 92.75    &         2.32 / 5.39\\
                                                                                & BIM &87.00 / 93.96            &86.14 / 94.68              &86.54 / 91.95           &86.34 / 94.99              &87.54 / 94.01 &      1.40 / 3.04\\
                                                                                & FGSM&64.82 / 90.68             &65.24 / 80.54            &90.50 / 96.35           &66.36 / 93.57              &76.82 / 95.47  &         25.68 / 15.81 \\
                                                                                & MIM&86.15 / 93.11             &85.22 / 92.75            &85.65 / 95.12             &85.35 / 96.42              &87.01 / 95.12 &           1.79 / 3.67 \\
                                                                                & FFGSM&57.28 / 80.06             &57.04 / 54.70          &88.48 / 95.98             &57.92 / 88.14              &87.81 / 96.95 &        31.44 / 42.25\\\bottomrule[1.5pt]

\end{tabular}
\label{tab:table2}
\end{table*}

\subsection{Experimental Results and Analysis}

\subsubsection{Defense Performance}

We compare the performance of our TRN with state-of-the-art defense methods including input transformation based methods \cite{guo2017countering,xu2017feature,xie2017mitigating,mustafa2019image,naseer2020self}, adversarial training \cite{madry2017towards}, and label smoothing \cite{warde201611}. We evaluate these defense methods against attack methods including FGSM \cite{goodfellow2014explaining}, BIM \cite{kurakin2016adversarial}, PGD \cite{madry2017towards}, MIM \cite{dong2018boosting} and FFGSM \cite{wong2020fast}. For adversarial training method and our method, we use the MIM attack method to generate adversarial images for training, and test on different attack methods. Follow the setting in \cite{yuan2020ensemble}, we set the iteration number as $10$ for iterative attack methods. For CIFAR10 and Fashion MNIST datasets, all attack methods are constrained in the perturbation budget $\epsilon = 8/255$ with each iteration step size as $1/255$. For SVHN dataset, the perturbation budget is set to $\epsilon = 12/255$ with a $3/255$ iteration step size following the setting in \cite{yuan2020ensemble}. In the experiment in Table \ref{tab:table1}, our TRN consists of 3 Fusion Block.

Table \ref{tab:table1} shows the classification accuracy of different defense methods under different attacks on three datasets. Our method outperforms other state-of-the-art defense methods under all five attacks on all three datasets. In particular, when under the MIM attack, our method outperforms other defenses by $30.72\%$, $35.48\%$, and $11.07\%$ on CIFAR10, SVHN, and Fashion MNIST, respectively.

Our method can keep the performance of the target model on benign images, as validated on SVHN and Fashion MNIST (it only slightly degrades from 95.34\% to 94.37\% on CIFAR10). It can even improve the accuracy on benign images by $0.01\%$ on the SVHN dataset since our TRN can focus on the critical pixels that lead to the erroneous outputs with the guidance of gradient maps. We also observe that defended by our method, the model can perform better on adversarial images than on benign examples on the SVHN dataset (``Label Leaking Effect \cite{kurakin2016adversarial}''). To our experience, the Label Leaking Effect occurs only when the defense model has enough capacity to capture both the label-leaking and benign information. Thus, the results show our model has a larger capability to explore the patterns in adversarial examples.

\subsubsection{Generalizability across Attacks}

We also evaluate the generalizability across different attacks when the TRN is trained with one attack method. As Table \ref{tab:table2} shows, we use the attack methods in the first column to train our TRN and test on the attack methods in the first row. By comparing our method with adversarial training \cite{madry2017towards}, we can see that the adversarial training may fail to generalize to other attacks, but our method can generalize from one attack to another without significant degradation in performance. The results also demonstrates that our TRN obtains the most robust performance when training with the MIM attack. The largest performance degradation when training with the MIM attack is $4.96\%$ on CIFAR10.

\subsubsection{Robustness against Bypass Attacks}

Pre-processing defense methods that obfuscate gradients are vulnerable to the Backward Pass Differentiable Approximation attack (BPDA) \cite{athalye2018obfuscated}. BPDA can bypass the pre-processing defense by using an approximation network to estimate the gradients, though the gradient is unavailable in some non-linear pre-processing algorithms. We show that our defense method is resistant to BPDA.

Incorporating two streams in our TRN, the gradient map is hard to be attacked since the attacker will need to compute the gradients of the gradient map. In this case, we choose the image stream to estimate the gradients of inputs and further attack our TRN using PGD. As shown in Table \ref{tab:table3}, comparing to the performance of our defense under the PGD attack, the PGD-based BPDA attack only slightly degrades the accuracy. On the SVHN dataset, the performance only degrades the accuracy of TRN by $0.02\%$. This is because our defense contains gradient-stream and image-stream, and two streams complement each other to make our defense robust.

\begin{table}

\caption{The comparison of TRN's performance under the PGD attack and the BPDA attack. The gap between the performance of the PGD attack and the BPDA attack is also shown.}
\centering
\begin{tabular}{l c c c }
\toprule[1.5pt]
Attacks   & CIFAR10 & SVHN & Fashion MNIST \\ \midrule[1pt]
PGD \cite{madry2017towards}  & 89.72  & 97.54 &  94.63\\
BPDA \cite{athalye2018obfuscated} & 87.86  &  97.52 & 93.75  \\ \midrule[1pt]
gap & 1.86 & 0.02  & 0.88 \\
\bottomrule[1.5pt]
\end{tabular}
\label{tab:table3}
\end{table}

\section{Ablation Study}

\subsection{Is two-stream better than one-stream?}

\begin{figure*}
    \centering
    \includegraphics[scale=0.5]{Abalation1.png}
    \caption{Examples of a pre-processed image with two streams and single stream. We show the images and the perturbation of restored or perturbed images. We can see that relying on a single stream, the restored images contain high-vibration perturbation, while with gradient and image streams, the network can restore the images more precisely (flatten and small perturbation). }
    \label{fig:Ablation}
\end{figure*}

\begin{figure}
    \centering
    \includegraphics[scale=0.45]{Visualization.png}
    \caption{Examples of restored images on CIFAR10, SVHN and Fashion MNIST.}
    \label{fig: Discussion}\vspace{-0.2in}
\end{figure}

\begin{table}

\caption{The comparison of different streams against PGD on CIFAR10. We show the results (classification accuracy after defense) against PGD attack from 10 iterations to 40 iterations. }
\centering
\begin{tabular}{l c c c c}
\toprule[1.5pt]
Methods         & 10 iter.  & 20 iter.      & 30 iter.      & 40 iter. \\ \midrule[1pt]
No defense      & 0.01      & 0.00          & 0.00          & 0.00      \\
Gradient Stream & 87.09     &  85.78        & 85.92              & 85.34            \\
Image Stream    & 87.41     &   87.60       & 87.34              & 86.80          \\ \midrule[1pt]
Two-stream      & \textbf{89.72}     &   \textbf{89.19 }      &  \textbf{88.86}             & \textbf{88.18}           \\ \bottomrule[1.5pt]
\end{tabular}
\label{tab:table4}\vspace{-0.1in}
\end{table}

In this section, we compare our two-stream restoration network with one-stream restoration network. We implement the gradient stream restoration network by removing the convolution layers in the image stream except for the last layer. This means we only use the gradient maps to estimate the benign perturbation and add the benign perturbation on adversarial images without feeding adversarial images to the network. In this case, we do not extract any features from the adversarial images. To implement the image stream restoration network, we simply remove the gradient stream.

In Table \ref{tab:table4}, we compare the complete TRN with restoration networks with image stream or gradient stream, respectively. From the results of gradient stream defense, we can see that by only relying on the gradient maps, we can still recover the adversarial images to defend against strong attacks like PGD. This means that the gradient maps contain enough information to defend against gradient-based attacks. Comparing the results of the two-stream method and one-stream methods, we can see that the two-stream manner reaches the best accuracy on PGD-attacked images. It indicates that the TRN can explore the complementary information in two streams. Since the gradient of the adversarial images measures how each pixel of adversarial images contribute to the erroneous outputs, the restoration network can focus on the critical pixels with the guidance of the gradient stream (see Figure \ref{fig:Ablation}). Thus, our TRN provides stronger defense to the target model.

\subsection{Scalability}

\begin{table}

\caption{The performance of our method (Classification accuracy after defense) with different number of Fusion Block against PGD attack on CIFAR10, SVHN and Fashion MNIST.}
\centering
\begin{tabular}{c c  c  c }
\toprule[1.5pt]
\small Block Number    &\small  CIFAR10 &SVHN & Fashion MNIST  \\ \midrule[1pt]
No defense & 0.01  & 0.16  &0.00   \\
1 &  89.90 &  97.18 & 94.01\\
2 &  90.05 &  97.68 & 94.04 \\
3 & \textbf{90.29}  & 97.30  & 94.30 \\
4 & 90.13  &  96.79 &94.22    \\
5 & 89.92  &  96.31 &\textbf{94.65}\\
6 & 90.04  &  97.58 &93.46\\
7 & 90.12  &  \textbf{97.84} &94.34\\
8 & 90.06  &  96.18 &92.93\\
9 & 90.11  &  92.15 &93.38\\ \bottomrule[1.5pt]
\end{tabular}
\label{tab:table5}\vspace{-0.1in}
\end{table}

With both image and gradient streams as inputs, our FB refines the gradient map and restores the adversarial images progressively. The residual connections also enable the outputs to maintain the raw information in inputs. Then, our FB can be simply cascaded to construct a deeper network. In Table \ref{tab:table5}, we evaluate our TRN with different Fusion Block numbers against the PGD attack. Table \ref{tab:table5} shows that TRN achieves the best accuracy on adversarial images with 3, 7 and 5 Fusion Blocks on CIFAR10, SVHN and Fashion MNIST, respectively. Due to the scalability of our method, we can change the number of FB to obtain the optimal defense on different datasets which need different model capabilities. Note that reducing the FB numbers to one only leads to a minor degradation from the performance with optimal block numbers. Thus, our method can approximate the best performance by training a small-scale TRN when the computational resources are limited.

\subsection{Discussion}

\begin{figure}
    \centering
    \includegraphics[scale=0.38]{illustration.png}
    \caption{A conceptual illustration of benign, adversarial, and restored examples. In (a) and (c), a set of points can be separated by the simple (Linear) or complicated decision boundary. In (b), the decision boundary does not separate the $\ell_\infty$-balls around the data points, but after the strict restoration, the decision boundary can successfully separate the restored data points. In (d), to defend the target model with a simple decision boundary, the loose restoration is sufficient, but the loose restoration can not defend target models with a complicated decision boundary.}
    \vspace{-0.2in}
    \label{fig:Illustration}
\end{figure}

We visualize the image restored by our method under the MIM attack (see Figure \ref{fig: Discussion}). The examples of CIFAR10 illustrate that our method can clean the adversarial pattern on images. However, on the SVHN and Fashion MNIST, we surprisingly observe that our TRN add some grid structures to defend the attack. This inspires us to think about the nature of restoration. As shown in Figure \ref{fig:Illustration}, since the data distribution is simpler in SVHN and Fashion MNIST than CIFAR10, it is sufficient for TRN to defend these datasets by adding some robust grid patterns (``loose restoration''). Noticeably, these robust grid patterns will revert the accuracy on adversarial examples without affecting the benign examples since these patterns are generated along the decision boundary (see (d) in Figure \ref{fig:Illustration}). However, for a dataset with complicated distributions like CIFAR10, TRN needs to learn a strict restoration to revert the performance of the target model. Thus, Figure \ref{fig: Discussion} shows that the restored examples on CIFAR10 will be closer to the benign images.

\section{Conclusion}

To our best knowledge, we propose the first defense method that incorporates the gradients into pre-processing for defending against gradient-based attacks. A novel Two-stream Restoration Network is proposed to jointly use the estimated gradient maps and adversarial images to restore adversarial images. A Fusion Block is also proposed to explore and fuse the information in the gradient space and image space. Our defense provides a new insight to input transfromation based defenses in which the estimated gradient maps may contain complementary information besides the adversarial images. It successfully defends against the state-of-the-art attacks on three representative datasets.

{\footnotesize
\bibliographystyle{ieee_fullname}

redpajama_set_name: RedPajamaArXiv


text:
Video announcements
AMV Award Winner Iron Chef Videos
IC: Bakuretsucon 2011 Iron Editor
Oct 21, 2011 LantisEscudo
Anime - Various Song - Beach Boys - I Get Around Completed October 2011 Premiered October 21, 2011 Animemusicvideos.org link This was my winning video from the Bakuretsucon 2011 Iron Editor. The sources were agreed on before the match, and the song was from a list of six that was given to us a week before the match.
Anime - Various Song - the HIATUS - Betelgeuse no Akari Completed October 2011 Premiered October 15, 2011 Animemusicvideos.org link This was the runner-up video in the AAC '11 Extreme Iron Editor contest. I can't remember all of the challenges that were thrown at us; the one that sticks out was having to roll a d20 and add that many lens flares to the video. We rolled a 15, and there are 17 in the video.
Anime - Various Song - Two Steps From Hell - Dragon Rider Completed October 2011 Premiered October 14, 2011 Animemusicvideos.org link At Another Anime Convention 2011, OtakuForLife ran a workshop on AMVs and asked for people to bring betas for critique. Having nothing in progress at the time, and fearing that no one would be bringing anything, I spent about an hour and 45 minutes throwing together a short video with the sources I had on my hard drive for an Iron Chef match the next week. There's a lot of problems with it, given the short period it was put together in, but I kind of like it. It got a decent reaction at the workshop, too. I like the idea, and may revisit it as a whole video someday, using the rest of the series that I didn't have available at the time.
Anime - Heidi, Girl of the Alps Song - Edison Lighthouse - Love Grows Completed September 2011 Premiered October 14, 2011 Animemusicvideos.org link This video was specifically made for the Theme category of Another Anime Con 2011, which was "70's Soul Train." Finding a source from the 1970's on DVD was difficult, since so few of them have enough nostalgia strength to get rereleased, and I was considering tracking down the Original Gundam DVDs before stumbling across Heidi. The song was a lot simpler, as I had a collection of 70's hits on CD from a previous project that didn't see completion, but several of the songs had potential. When I actually started planning it, I only scanned through the first two DVDs before deciding to make a romance video between Heidi and Peter. Not having read the book, I was completely unaware of Klara's later appearance or how large her role would become, so editing around her became more difficult as the video went along. Overall, this was a silly little romance video that came off decently, and ended up winning the category.
Project OrgEditor Round 4: Collaboration
Jul 14, 2011 LantisEscudo
Anime - Various Song - Kurt Schneider & Sam Tsui - Summer Pop Medley Completed July 2011 Premiered July 14, 2011 Animemusicvideos.org link This was a collaboration between me and Replay Studios for the fourth round of Project OrgEditor. After deciding that we wanted to use a mashup song, Replay found the one we eventually decided on, and we came up with the overarching theme of "Summer" for the whole video, using a different anime for each segment. Replay edited the odd segments, and I edited the evens. This video finished fourth in the round and saw both of us eliminated.
Award Winner LAMV Videos
Jul 8, 2011 LantisEscudo
Source - Mythbusters Song - Oingo Boingo - Weird Science Completed June 2011 Premiered July 8, 2011 This was a silly idea that I'm surprised hadn't been done before (at least that I found in my quick search at the time), and pretty much sums up what makes Mythbusters so much fun: Making things go BOOM. This was a finalist at Connecticon 2011 and won Best Live Action at AWA Pro 2011.
Jun 26, 2011 LantisEscudo
Anime - Various Song - Parry Gripp - Nom Nom Nom Nom Nom Nom Nom Completed June 2011 Premiered June 26, 2011 Animemusicvideos.org link This was my video for the third round of the 2011 edition of Project OrgEditor. The round's theme was "7-7-7" and we had to illustrate one of the Seven Deadly Sins or Seven Heavenly Virtues. The sins are a lot more fun and a lot more concrete than the virtues, so I decided to go with Gluttony. I remember hearing Parry Gripp's Nom song in one of the AMV Hells, and thought it'd be an easy, silly, fun video. I ended up raiding my entire collection of disks for potential clips, and even hitting up several friends for other shots. Overall, this was a lot of fun to make, and came in third in a round of 13 videos.
Anime - Various Song - Various Completed June 2011 Premiered June 16, 2011 Animemusicvideos.org link This was my video for the second round of Project OrgEditor's 2011 edition. The theme for the round was "After Story" and we were to show a conclusion, and then a continuation of a story. I decided to make something like the opening of a sequel to a game, which led to the game's "title" of "Operation Revolver: The Second Chamber." I completely ignored the storylines from the original series, and assigned each of the characters new roles for their earlier exploits, ten years ago in-story, and then moving on in a "Where Are They Now" type of montage. With the two younger girls, not having any scenes of them looking older, I decided to ramp up the "drama" factor by killing them off, one gloriously in battle, one after a depressive spiral. In retrospect, it was a bad decision to rely so much on text to tell the story, but by the time I realized that, it was too late to change the concept before the deadline. It ended up finishing 11th out of 15 videos, though one judge knocked it heavily because they hadn't seen the original series and thought understanding the original plots was necessary for understanding the video.
Original Work Project Editor Videos
Project OrgEditor Round 1: Org Ad
Jun 7, 2011 LantisEscudo
Anime - Various Song - Magical Girl Lyrical Nanoha OST - Drive Ignition Completed June 2011 Premiered June 7, 2011 Animemusicvideos.org link This was my video for the first round of the 2011 edition of Project Editor. The theme for the round was "Home Sweet Home" and we were to create an ad for the site we considered "home." As the Org is most certainly my AMV home, I decided to go with a fairly standard 30-second advertisement focusing on things the Org had to offer. I deliberately chose previous Project Editor-related pages on the Org as illustrations, including the coordinator - Code's user page, the page for Ileia's Finals-winning video RAH HEY!, and the stream of one of my favorite videos from the previous competition, Kitsuner's Deadlines. It was kind of dry and boring, and finished 11th in a round of 24 videos.
IC: Online Iron Chef 4.0 Round 2 – Experimentation
May 14, 2011 LantisEscudo
Anime - Magical Girl Lyrical Nanoha the Movie 1st Song - Richard Cheese - Fell In Love With a Girl Completed May 2011 Premiered May 14, 2011 Animemusicvideos.org link This was my video for the second round of Brad's Online Iron Chef Tournament 4, from my match against Ileia. This was a very different song from what I expected, but I think I managed a decent silly romance video out of it, focusing on the only male character in the movie with significant screen time. I unfortunately lost this match to Ileia's spectacular video.

redpajama_set_name: RedPajamaCommonCrawl


text:
Is Rachael Ray Pregnant? What is Ray's Net Worth?
By Jasmine On Jan 7, 2023
Rachael Ray is an American chef and TV personality. Is she pregnant? On her shows, in her magazine, and in her many cookbooks, Rachael Domenica Ray promoted easy, quick meals. Find out more about "Is Rachael Ray Pregnant?" by reading the article below.
Who Is Rachael Ray?
Is Rachael Ray Pregnant?
Rachael Ray's Talk Show
What is Rachael Ray's Net Worth?
How Much Does She Make From Endorsement Earnings?
Rachael Domenica Ray is a famous chef, TV host, businesswoman, and author from the United States. She is the host of the Food Network show 30 Minute Meals and the daily talk and lifestyle show Rachael Ray, which is seen all over the country.
She has done Rachael vs. Guy: Celebrity Cook-Off, Rachael Ray's Kids Cook-Off, $40 a Day, Rachael Ray's Week in a Day, and other shows. Ray has written a number of cookbooks based on the idea of "30 Minute Meals." In 2006, she also began publishing a magazine called Every Day with Rachael Ray. Ray has won three Daytime Emmy Awards for his TV shows.
Some of you will notice the baby bump and the extra weight, but they are hard to see in this photo. In 2005, Rachael and John got married. They are known to a lot of people, and everyone knows they don't have kids yet.
In an interview, Rachael Ray said she wasn't pregnant and didn't want to have kids. Some of us might think she's pregnant, but she's 54, so that might not be true.
Rachel has gained weight, but it's not because she's pregnant. Instead, it's because she's going through menopause. Even though she said she wasn't pregnant, we liked her, especially the simple food she made.
The American talk show Rachael Ray, which is also called Rachael and The Rachael Ray Show, is hosted by Rachael Ray. The first episode of the show aired in the US, Canada, and other places on September 18, 2006. It is taped in New York City at the Chelsea Television Studios.
A post shared by Rachael Ray (@rachaelray)
The purpose of this show is to show off how good Ray is at cooking. She talks to famous people on camera about how they got where they are in the media, sports, and entertainment worlds. Ray is like The Oprah Winfrey Show in that it has makeover segments and musical performances. Ray also asks different health and lifestyle experts for advice on how to stay healthy and safe.
When she taught her customers how to cook live at the gourmet food market where she worked, a local news station heard about her. They convinced her to teach instead, and 30 Minute Meals was born. She later wrote a book with the same name, and both the Food Network and the Today show asked her to be on their shows.
From 2001 to 2012, the Food Network showed 11 seasons of 30 Minute Meals. The show came back in 2019 for a new run. Ray had been on The Oprah Winfrey Show a lot, so in 2005 she got her own talk show. Rachael Ray was all it said.
Venus Williams: Net Worth What is Her Age?
Miranda Lambert Net Worth: How the Country Star Makes…?
Rachael Ray's business is so well diversified that she usually makes more than $25 million a year from a variety of sources. Ray makes a lot of money from her TV shows, books, and endorsements of products.
In 2007, she became a well-known person who endorsed Dunkin' Donuts. This was a joke because she had already said she couldn't make her own coffee. In the same year, all AT&T Wireless phones came with an app that let users access her recipes.
In 2008, Rachael Ray started selling Nutrish pet food. Ray made the recipes for the dog food for her own pit bull. All of the money from the sales of Nutrish goes to Rachael's Rescue, an organisation that helps animals. With PulteGroup, she released a new line of furniture and home decor in 2016. She made the furniture herself.
Is Michael Damian Married? He is Married to Janeen Damian, as You Can See.


The Controller of Examination RRBs, RRCs shall conduct the Examinations as per scheduled Railway Jobs 2017 Exam Calendar as briefed below. The Dates of Downloading of Railway Examination Admit Cards/ Hall Tickets of SSC upcoming Exams shall be as briefed in the Calendar. From AMS, Goods Guard, Engineers, Group C & D, Combined Competitive Examinations etc Examinations shall be conducted as given below.
Railway Recruitment Boards/ Cells is likely to intimate the RRB, RRC Group D- Group A Examination Admit Cards downloading links/ instructions/ dates for downloading the Exam Admit Cards etc.
Here through we are paving the common platform for Applicants apply for Railway Examinations to be held in 2018. Indian Railways Examinations Competitive and Recruitment Tests shall be organized as per the schedule portrayed hereunder & Admit Cards for the Exams shall be downloaded from the here shared links prior to the Examinations.
Hereby we anticipate the Indian Railways Competitive/Recruitment Examinations organizations to organize the Competitive and Recruitment Examinations as per schedule given above & shall release the Examination Admit Cards/ Hall Tickets tentatively prior to 10-15 Days before the Commencement of Examinations.
This section of Previous Question Papers and Examination Syllabus (Tier-1/ Tier-2) along with the Examination scheme shall be obtained from the website of the Organizations and Notifications Advertisements.


Rin Okumura bears the curse of being Satan's illegitimate son. He decides to hide his origins and enrolls at the Exorcism Cram School, a training institute for exorcists located on the True Cross Academy grounds. During an attack by Amaimon, the King of Earth, his identity as the Son of Satan is revealed. His friends are terrified and start to distance themselves. Then someone steals the Left Eye of the Impure King that had been sealed away in the deepest part of the academy. Rin and the others find themselves in an unexpected crisis.
Blue Exorcist (Japanese: 青の祓魔師エクソシスト Hepburn: Ao no Ekusoshisuto) is a Japanese manga series written and illustrated by Kazue Katō. The story events which take place in the second season correspond to volumes 5 through 9 of the manga.
Blue Exorcist: Kyoto Saga pick right up after episode 15 from the first season. Episodes 16-25 from season one was completely ret-con and ignored. This season deal with the Kyoto Impure King arc.
The animation is beautiful, just like the first season. Perhaps more green as it takes place in a lot of forest backdrop.
The opening and closing themes are "Itteki no Eikyō" (一滴の影響; Drop's Influence) by UVERworld and "Kono Te de" (コノ手デ; With This Hand) by Rin Akatsuki, respectively. Both are good songs, but I still like the first season-opening song "Core Pride" by Uverworld the best.
Blue Exorcist continue to do what it does best, which is character development. We see Rin struggle to control his power while trying to mend the rift with his friends, due to being the son of Satan. We also see the other teammate struggle with their inner feeling of how they should treat the son of Satan.
Despite all these characters' growth moments, the anime pacing is fast and I wasn't bored. These characters could easily be annoying under certain circumstance, but I find I like all of them.
The only flaw I can think of for this season is one of the main villains constantly flaunt and taunt while he fights, instead of just finishing off his opponent. This seems out of place as most characters in Blue Exorcist are well developed. This either is intentional as the villain goal is something we don't understand yet, or he's just a poorly written villain.
I highly recommend Blue Exorcist as the characters, story, and animation are all top notch. Let just hope we don't have to wait another 6 years before season three. This anime contains graphic violence and mild suggestive themes/dialogs. Intended for young adults.


Why Modi Matters to India?
The week of the Uri attack was also a testing time for the Prime Minister's leadership. Modi, adept at judging the public mood, was aware that people expected him to 'walk the talk' in acting tough against India's implacable enemy.
Public opinion in the country was inflamed. People were calling for an all-out war against Pakistan. Even saner voices were advocating at least some demonstrable retribution. Modi was aware of the public sentiment and the anger that was building up in popular perception. He vowed immediate retribution. 'I assure the nation that those behind this despicable attack will not go unpunished,' he tweeted on the day of the Uri attack. Not many people took the statement at face value. After all, politicians and prime ministers in the past had pledged stern action against terrorists and their handlers many times, but had ultimately refrained from giving that final go ahead required to retaliate, urging restraint instead.
Almost everyone thought it to be the standard condemnation – kadi ninda (severe condemnation) – as some commentators have often categorised politicians' routine statements after each terrorist attack. However, the PM was clear in his mind that he cannot afford to be taken for granted like his predecessor, Dr Manmohan Singh, who often dithered and failed to launch any punitive action against Pakistan even after the outrageous attack on Mumbai in November 2008 that killed more than 170 people and made a mockery of India's security apparatus.
Modi had himself strongly criticised Dr Singh's inaction then. Now, the ball was in Prime Minister Modi's court.
The Opposition was up in arms. After a day or two of showing solidarity with the government and sympathy for those martyred, it was now mocking the government's inability to punish Pakistan. The Congress targeted Modi, holding him 'singularly responsible for the complete disarray' in the government's policy vis-a-vis Pakistan. Congress' chief spokesman Randeep Singh Surjewala had alleged that the Uri attack, which was planned and executed by Pakistan, was the result of the 'failure' of Modi government's political leadership. He had also alleged that the tragedy was the result of a massive intelligence and operational failure inside the government.
Amidst all the criticism, the Prime Minister continued to be unruffled. Recall his aides: 'The PM went through with his daily routine and pre-scheduled appointments and programmes without any change, but made sure he had all possible options presented to him before giving the final go ahead (for a punitive strike against Pakistan).
All options, economic, political, and diplomatic were considered. They ranged from downgrading diplomatic ties, revisiting the provisions of the Indus Water Treaty, mobilising international opinion by furnishing proof of Pakistan's complicity in terrorist attacks, and of course punish Pakistan militarily.
But he was not about to be rushed into any hasty decision.
The Prime Minister however made up his mind by 23 September, five days after the Uri attack. Later that evening, he and Doval, escorted by a Major General from the MO Directorate, walked the length of the South Block Corridor from the PMO to the Army HQ Ops room around 2100 hours, much long after the corridors had been emptied and offices had closed. Already present in the room were Defence Minister Parrikar, Army Chief Gen Dalbir Singh, DGMO, Lt Gen Ranbir Singh, and a couple of MO Directorate senior functionaries.
The PM sat through the briefing silently, listening with rapt attention. He was presented various options, shown targets that were planned to be hit inside PoK, and briefed on the possible retaliation/ reaction by Pakistan.
Once the initial briefing was over, Modi had a couple of questions on other possible options like a precise air strike on terrorist camps, remembers a participant. Eventually, the Prime Minister agreed that a Special Forces raid across the frontier was the best possible course of action at that point, the participant added. The PM's situational awareness and sharp memory was also were also in evidence that night. When the subject of international reaction came up, he reminded everyone that External Affairs Minister Sushma Swaraj was to deliver her speech at the UN General Assembly in the next one week or so. The PM therefore ordered that the operation be timed after her speech. However, beyond giving general directions about the need to be careful, Modi did not get into specifics.
'He left the planning and execution to us professionals,' Doval told me.
So the professionals got down to business. Parrikar, Gen Dalbir and Doval analysed the situation and sent out instructions to the Northern Command for an 'in-principle' go ahead for a trans-LoC strike. 'We told Udhampur (Northern Command HQ) to look for major terrorist camps. The idea was to destroy some of that infrastructure across the LoC in PoK,' Gen Dalbir remembers.
India's external intelligence agency, the RA&W, and the Intelligence Bureau asked its operatives to assess the possible impact of a retaliatory strike inside PoK. 'We needed to be ready for any possible backlash,' a top intelligence operative recalls.
Gen Dalbir, cautious but pragmatic, also sought permission from Parrikar to redeploy some key formations just in case Pakistan decided to up the ante after the retaliation. 'As Army Chief, the onus was on me to make sure we did not fall short of firepower,' Gen Dalbir recalls thinking throughout the week following the Uri attack, planning far beyond the retaliatory strikes. 'I had already moved some additional forces to the Kashmir Valley to reinforce the existing security grid following large scale unrest, after we had eliminated Burhan Wani (the Kashmiri local commander),' Gen Dalbir revealed. 'Now I had to make sure that other areas too were ready for any possible backlash,' he told me.
So formations responsible for borders in Punjab and Rajasthan were put on alert. A review of shortages at the local level was carried out and orders passed to replenish inventory. Meanwhile as debates – mostly acrimonious and bitter – continued to rage on television channels and in public discourse, the Indian security establishment was quietly preparing for a counterstrike on a scale that was always possible in theory, but had never been implemented in practice for fear of escalation.
As one week passed after the Uri attack, the debates tapered off; people seemed resigned to live with the bitter fact that the situation in J&K and on the LoC would continue to be volatile with the Indian army unable to take any deterrent steps.
Little did anyone know that India was about to unleash unprecedented and audacious cross-border strikes.


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Five Tips for Machining Carbon Fiber
06/29/16 Aerospace Testing Services
By Engaged Expert Ryan Castells
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The use of carbon fiber reinforced polymers (CFRP) is growing in many industries and as a result there is a higher demand for mechanical testing of composites. To achieve accurate and repeatable results, precisely machined test specimens are required. Due to the nature of the material, carbon fiber machining presents machine shops with new challenges, as compared to machining traditional materials like metals.
Waterjet cutting is a fast and efficient way to cut carbon fiber. One advantage of waterjet cutting is that no special tools are required. It is best to drill a starting hole; otherwise the waterjet can cause delamination. When tight tolerances are required, it may be best to waterjet the part oversized and machine to final dimensions.
Machining of carbon fiber is generally done with a router; however, standard metal machining methods can be used. Machining carbon fiber requires higher spindle speeds than metals, but lower feed rates. The feed rates need to be adjusted to minimize the heat in the part while machining. Carbon fiber has a low thermal conductivity and the majority of the heat remains in the part since there are no chips to help dissipate the heat when machining. This heat from cutting can cause damage to the resin. Since coolant might not be permitted when machining carbon fiber, the tool path and tool must be used to control the heat in the part while machining. Additionally, fracturing of the fibers creates considerable abrasion on the cutting tool, so special tooling is required when machining carbon fiber (see below).
Carbon fiber is abrasive and will wear down tools quickly. When a tool loses its sharpness, it can catch fibers and pull them from the part instead of cutting them. To avoid this, tool life must be monitored during machining as cut quality will decrease quickly. The best way to do this is to monitor the machining time on the tool and change it before it becomes dull.
There are several types and shapes of cutters for different applications, but they are generally made from two materials: carbide and polycrystalline diamond (PCD). Carbide provides good wear resistance, but is more applicable to smaller jobs. PCD cutters offer better wear resistance and are the cutter of choice for several composites machine shops. However, the benefit of PCD cutters does come at a significantly higher cost compared to carbide.
Diamond coated grinding wheels are also a staple of the well-rounded composite machine shop. They allow precise dimensional control, reasonable tool life-expense ratio, and effective heat management.
Drilling is a difficult machining procedure on carbon fiber. Carbon fiber is constructed of layers of material that could easily splinter or delaminate during machining. Drilling speed must be adjusted based on the size and depth of hole. Special drill bits designed specifically for carbon fiber help eliminate delamination during drilling.
The dust from carbon fiber is conductive, and can migrate into electronics and short them out. Hence you must vacuum the dust frequently when machining to avoid causing damage to the machine. Carbon fiber dust can also irritate the skin (similar to working with fiberglass). It is good practice to wear gloves and a dust mask when machining CFRP.
Although it sounds complex, machining carbon fiber is not as difficult as it may seem from the first impression. Once you understand the basics, carbon fiber can be machined to the tight tolerances required for material testing without problems.
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David Cockman received 40 acres located on both sides of Locust Branch adjoining McKay, John Cockman, Griffith and James H. Muse. John Cockman and Noah Cockman were chain carriers.
Ordered that Westley Wallace be appointed overseer of the road from George Moore's to the branch near McQueen's and work the following hands: Joseph Upton, Cornelius Dowdy, Zeno Cockman, Henry Melton, George Cockman, Noah Richardson & hands, Noah Cockman, David Cockman, Armstead Stutts, Isaac Williams, George W. Horner, John Jones and Quimby Wallace.
Ordered that William Stutts be appointed overseer of the road from George Moore's to the branch near McQueen's and work the following hands: Westley Wallace, Cornelius Dowdy, Zeno Cockman, Henry Melton, George Cockman, Sampson Cockman, Noah Cockman, David Cockman, Noah Richardson & hands, Armstead Stutts, Isaac Williams, G.W. Horner, John Jones, Quimby Wallace, Joseph Upton, Lochart Wallace, Geo. Williams and Jefferson Williams.
Ordered that George Cockman be appointed overseer of the road from George Moore's to the branch near McQueen's and work the following hands: William Stutts, Wesley Wallace, Cornelius Dowdy, Zeno Cockman, Henry Melton, Sampson Cockman, Noah Cockman, David Cockman, Noah Richardson hands, Armsted Stutts, Isaac Williams, G.W. Horner, John Jones, Quimby Wallace, Joseph Upton, Lochart Wallace, George Williams and Jefferey Williams.
Estate of John Cockman, Dec'd. by Administrator George Cockman. Items were purchased by the following: Noah Richardson, Noah Cockman, D. Davis, Bryant Tyson, D.F. Muse, J.A. Stutts, Enoch Stewart, J.J. Richardson, Saml. Wallace, C.T. Horner, Mary Cockman, J.S. Ritter, L.M. Seawell, Emsley Wallace, Miles Muse, Thomas Horner, Sampson Cockman, S.W. Seawell, James Horner, M.A. Cockman, B.C. Brady, Z.B. Moore, D. Stutts, W. Wallace, J.H. Williams, Isham Wallace, Zeno Richardson, Armstrong Maness, N.R. Currie, John Muse, Henry Brown, George Horner, A. Brown, W. Williams and Wm. King.
Noah Cockman deeded M.J. Brown 15 acres adjoining John Garner. C.R. Cockman and M.D. Cockman were witnesses.
Noah Cockman mortgaged Ann R. Burns 150 acres located on Buffalo Creek adjoining Isham Wallace, J. Stutts, James Garner, Mrs. Jones and William Stutts. J.R. Marley was a witness.


This 4 star venue offers a unique location at the Lingfield Park Racecourse in Surrey and is within easy reach of the M25 at Junction 6.
Why not combine your stay with a round of golf on the 18 hole course, a trip to the races followed by a meal in the Cyprium Bar & Grill. Leisure facilities include an indoor swimming pool and a luxury spa.
The course welcomed the Europro Tour in 2011.


Perhaps you remember a nation that proclaimed gross domestic happiness was more important than gross domestic product? Well, the very same country, Bhutan, nestled in the Himalayan mountains, has decided to become the first 100% organic nation. Anthony Gucciardi previously reported on this exciting news, but it's so exciting that we need to report on it again with some new information.
With a population of 700,000 people, Bhutan is known as a country of farmers. If they didn't think they could feed a large population on organic crops, they wouldn't declare organic farming to be the best form of agriculture for their country with a comprehensive plan to make the change.
Many farmers like to use chemical fertilizers, even in Bhutan, because they believe it enriches their soil and helps to keep diseases and pest in check, but admirably, the Prime minister, as well as an Australian advisor to Bhutan named Andre Leu, Bhutan says that all-out organic farming is very doable.
Leu has said, "I don't think it's going to be that difficult given that the majority of the agricultural land is already organic by default." This is because the expensive fertilizers used by U.S. farmers are not readily available to most farmers in Bhutan.
The World Food Program states that Bhutan grows primarily rice, corn, and potatoes as well as oranges, but is it possible as organic food demands increase, that farmers in Bhutan can start to enjoy demand for more varied crops? Currently most organic farms in Bhutan are not certified (only 1% of all organic food is currently 'certified' organic) but if the whole country becomes organic, might it not enjoy larger exports of pure, non GMO food?
Right now Bhutan exports red beans to U.S. companies like Whole Foods, but as the new all-organic program spreads, and farmers in Bhutan are trained in new methods that will help them grow organic and become closer to self-sufficiency, we might see 'made in Bhutan' more often as a welcome change to the frankenfoods being offered by companies in our own country.
With current and past research finding that GMOs lead to tumor in rats, severe stomach inflammation in pigs, and increased disease rates, it's more important than ever to spread to the word on the importance of going organic and shifting to sustainable, safe farm practices. It is you who will ignite change.
I'm excited for Bhutan. I'm also apprehensive that farmers there will face bureaucratic fees and hardships associated with becoming "Certified Organic". The better route might be to ban the sale, manufacture, and import of petroleum-based fertilizers and pesticides as well as GMO's. Without these additives, the country goes organic by default rather than via permitting.
Yes the fertilizers are not good it goes right into the land and the food so it can't be organic like what they claim about most food here the fertilizers are still very bad for the environment, and the bees why can't they work with the crops like back in the old days w hen there were no fertilizers like this .I've seen some inside hydro gardens ,that get set out in the sun and are netted and they are beautiful using no bad fertilizers .The butter lettuce is delicious & looks so healthy it's grown year round inside the clear domed building,where nothing seems to bother it .People are using jars ,bottles . Plenty of old windows wind up in landfills and plastic bottles and buckets to make large green buildings ,even cans painted black can heat the water from the sun and recycled back in in the winter months seems to work great for them .


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Jester's reCap
Jester's ReCap Episode 3: Arena is here, and…I..like..it?
Blake Treleaven
Hello again, Magic Community
Despite the better judgement of all involved parties, I continue to produce this article on a biweekly basis. Jester's ReCap is a short, off-the-cuff article which can keep you up to to date on the latest Magic: The Gathering happenings. In theory, it will also make you laugh in the way only the impotent rage of a tiny little man with a sizable vocabulary can. I don't research stuff that happened, I just go from memory so you'll forgive me if this fortnight's article is a little one-dimensional, but there was one thing that stood out significantly for me over these past two weeks: the first reveal of Magic Digital's newest little bundle of joy, Magic Arena. The short version is, I'm a fan. But then, I'm also a negative, cynical, whiny sonovabitch who should really get medicated, laid or both. So naturally, I can't just be happy. We'll get into the details in a moment, but first:
1) MTGO: Keep On Suckin'
MTGO is an unrelenting suck machine. I've been thinking about how many Magic Digital products have come into existence, made some amount of money, then died unceremonious deaths at the hands of a their cruel WOTC taskmasters. The answer is, a whole fuzzy bunch. WOTC digital products have the lifespan of a piece of pre-chewed gum. And the only one anyone seems to remember liking is the Microprose one that was built for DOS. I guess Magic Puzzle Quest still exists? That hardly counts though, as Match-3 games are the Tardigrade of mobile gaming; Unkillable parasites that will outlive every meaningful human connection on earth. But through it all, MTGO's been around. Still charging an unbelievable ten dollar account fee. Still charging real money for every card, every pack. Still working about half the goddamn time. Sucking but surviving, like some kind of money-consuming Remora fish. It. Just. Keeps. Sucking. It can't be bargained with. It can't be reasoned with. It doesn't feel pity, or remorse or fear. And it absolutely will not stop until you are so salty Romans use you to end wars.
2. Magic: Arena, Now with more Hearthstone?
WOTC, buddy. Pal. Let's rap, m'kay homeboy? Is it ok if I sit on this chair backwards in a move that shows I'm both approachable and "hip to your style"? Coolio, brah. Listen, I get it. You see a lot of the people who used to be paying all their attention to you, lured away by the slick interface and you know…Free-ness, of the competition. Suddenly this billion dollar mega-behemoth called Blizzard is making a game on your turf. And they're sorta good at it! At least, they're great at all the things you suck at. Their app is effortless to use. They give shit away like a drug-dealer in an after school special. They have visually interesting animations, and voice acting and an artistic style that isn't just beautiful but also consistent and coherent. You got a little scared, and there's no shame in that. Well…there's a little shame in it, because how you handled yourself was so impossibly shameful. What with the flailing and screaming and wetting yourself. You can be a little ashamed of that part. But mostly, no company was going to handle their direct competition suddenly being Blizzard"We have as much money and talent as God but a better design philosophy" Entertainment. So you got nervous and you start tossing stuff at the wall, and seeing what sticks. And now, you've decided Arena is the silver bullet. All of Magic's complexity and glorious interaction, but with lights, and sounds, and voice acting and animation. It's pretty, which is a nice change. It's not compromising Magic so that it can be a Bejeweled clone which is relieving. It's free to play, which is fantastic! I honestly think Magic: Arena, outside its incredibly stupid name and apparently limited card pool, actually looks awesome. You're going to have a closed beta where you take player feedback and put it somewhere other than the usual woodchipper, and you're going to give priority to get into that Beta to Duels players, MTGO players and people who do the Ixalan prerelease at their local LGS. You are, in the face of all historical precedent, doing very well.
But WOTC, as excited as I am for you…for us, you are scaring the shit out of me. See, you've shown me something that looks pretty, and you're promising it won't compromise Magic's biggest draw, it's never-ending mind-bending depth. But you haven't shown us a single deckbuilding interface. Or trading interface. Or discussed the in-game economy. Or talked about organized play. You keep saying it's too early, and that's totally true, but you haven't sealed this deal. I don't want you spiking the football just yet ok? You've shown a lot of promise. I'm cautiously optimistic. But don't get cocky. This is the part where traditionally you trip on the two yard line, cartwheel into a pyramid of cheerleaders and end up choking to death on your own jockstrap. Stay the course guys. I want this for you.
3. Iconic Masters, for a given value of "Iconic"
Just so this article isn't completely positive and all about me actually being excited for Arena, let's take a stroll through this particular clusterfudge, shall we? Iconic Masters shouldn't exist. The Masters sets to this point have been exciting, fun draft environments and an excellent way to get a few reprints of some expensive cards out there. How effective they've been at anything other than selling more Magic cards is debatable, but the theory was sound. The problem is, you can't make a set with all Iconic cards. That's not what the word Iconic means. An Icon is the Ur example of a thing. The natural, platonic ideal which all other similar things are necessarily seen as a derivative of. Serra Angel? Sure, that's most people's default Angel. Bloodghast? Yeah, that's guy is probably an Iconic aggro-vampire.
But your set also contains Dragon Bell Monk. I was going to list a bunch of other cards in the set that I think fall short of the "Iconic" moniker, but screw it, let's spend a few hundred words talking about how awful Dragon Bell Monk is.
Dragon Bell Monk is a 3-mana 2/2 Human Monk. That's not just boring, it's tragic. It's the kind of card that proves you can print disappointment onto cardboard. That's what sad clowns think about while they masturbate. It has Vigilance, which is fantastic on large things. It has Prowess, which is great on cheap things. You will note that Dragon Bell Monk is both expensive and tiny; It's like the world's worst male prostitute. The flavour text on Dragon Bell Monk actually says "Steve, write something about Dragons and maybe a bell here – Adam". You might claim I made that up, but no one has ever stayed interested long enough to finish reading Dragon Bell Monk so I guess we'll never know. If you're drafting Khans block and Dragon Bell Monk doesn't wheel, a level 2 judge will know to stop everything to make sure no one is having a stroke. If you play a Defensive Stance on a Dragon Bell Monk you are actually considered legally barred from having fun, forever. It's true. You have to get work as an insurance actuary or as one of the voices on a Big Bang Theory laughtrack. Dragon Bell Monk cries himself to sleep every night, remembering the time Monastary Swiftspear said "Hi" to a person standing behind him at school and he said hi back, and then she laughed at him and he got so embarrassed he shame-vomited on her. Dragon Bell Monk is worse than Scathe Zombies because at least the zombies aren't trying so hard. Dragon Bell Monk wishes it had Bushido.
That's it for me this week guys, I hope you enjoyed a slightly more positive outlook on things.
arenaauthorBlake Treleaveniconic mastersjester's recapmagic onlineMTGO
Modern Musings: Ixalan Spoilers Round 2
The Metaworker – Casual Deckbuilding Part 2
Rant of Valakut: A Small Overview of Segovia
Ben Iverach-Brereton
Rant of Valakut: There's More to Speak than Common
Channel Nikachu: Strictly Better Cards Over MTG History
Rant of Valakut: Historic Vesperlark


The United Nations system includes 15 specialized agencies, 23 funds, programmes, research institutes and other entities plus numerous functional and regional commissions and subsidiary bodies. The ILO was the first specialized agency to join the United Nations in 1946.
The ILO participates in United Nations coordinating mechanisms and works with the rest of the UN family, at all levels, to support countries in their promotion and achievement of sustainable development. In this context the ILO takes a particular responsibility for assisting member states to achieve SDG 8 – to promote inclusive and sustainable economic growth, employment and decent work for all – while also contributing to all the SDGs and targets, as appropriate, within the ILO's mandate.
The 2030 Agenda for Sustainable Development is built on the same principles as the Decent Work Agenda in that it is universal, inclusive, transformative and firmly based on the normative framework of the UN system. It is based on a vision that we should protect our planet for our children, who will learn and grow up to live healthy lives, in peaceful and safe societies with strong institutions and inclusive and sustainable economies, and where poverty and inequality are things of the past.
We must invest much more to eradicate poverty, reduce inequalities and end exclusion. We must help countries strengthen institutions so they are able to efficiently deliver services to people. Our support to countries must contribute to creating decent jobs and entrepreneurship opportunities, particularly for young people, and increase women's participation in the workforce, empowering people to have stewardship over their futures.
The ILO supports and takes part in United Nations system-wide coherence initiatives to position decent work and employment within the UN. In this work, the ILO contributes to UN coordinating mechanisms at all levels.
Partnerships – in many different forms – help the ILO in providing more women and men with decent work opportunities all over the world, and a key part of this is working towards developing synergies with key players in the multilateral system.
The ambitious sustainable development agenda calls for the UN system to work together even more efficiently. It is important to ensure that the UN is "Fit for Purpose".
The General Assembly (GA) is the main deliberative, policy making and representative body of the United Nations. The ILO mainly works with its 2nd and 3rd committees, which address economic and financial issues, and social, humanitarian and cultural areas, respectively.
The United Nations Economic and Social Council (ECOSOC) examines international economic and social issues and formulates policy recommendations aimed at Member States and the UN system.
The Commission on the Status of Women (CSW) is the functional commission of ECOSOC exclusively dedicated to the promotion of gender equality and the empowerment of women, which is integral to the attainment of decent work for all.
The Commission for Social Development (CSocD) is the UN body advising ECOSOC on key social development themes, including ageing, cooperatives, disability, employment, family, indigenous peoples, poverty, social integration and protection and youth – all key elements of decent work.
The High-level Political Forum on Sustainable Development (HLPF), replaces the former Commission on Sustainable Development meets annually under the auspices of ECOSOC or every four year as part of the UN General Assembly. The HLPF is the central UN platform for the follow-up and review of the 2030 Agenda for Sustainable Development adopted at the United Nations Sustainable Development Summit on 25 September 2015.
The Permanent Forum on Indigenous Issues (UNPFII) is an advisory body to ECOSOC, which discusses indigenous issues related to economic and social development, culture, the environment, education, health and human rights.


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Ne Obliviscaris > The Aurora Veil > Reviews > Noktorn
The Aurora Veil
View all reviews for Ne Obliviscaris - The Aurora Veil
Good but not THAT good, damn - 79%
Noktorn, October 14th, 2007
Ne Obliviscaris is a band that's been receiving a surprisingly large amount of attention in the metal underground as of late. The band's debut demo, 'The Aurora Veil', seems to have taken the Australian, and, to some degree, international metal scenes by storm. After all, today is a good day to be a progressive black or death metal band: such artists are receiving much more attention these days, and above and beyond mere displays of technicality; audiences seem to be seeking more abstract music as a whole. This band in particular's brand of melodic, progressive black metal has seemed to entrance numerous metal fans with their unique sound.
Okay, so that's the way most people seem to be opening their reviews of 'The Aurora Veil': with a sort of slavering devotion to the status quo minus any sort of critical eye. Progressive black metal, like most 'progressive' metal, generally ends up being not very progressive at all. Hell, most of it's pretty REGRESSIVE because they're all depending on the same 'progressive' elements: weird time signatures, extra, unusual instruments, lots of instrumental flourishes, etc. None of these are progressive in the least. Progressive is doing something that's actually abstract and unique, but isn't that way simply to be such. Additionally, everyone is attempting to jump onto the progressive bandwagon when they really shouldn't be. More bands need to understand that they should be making solid, generic heavy metal that reinforces the genre instead of going off on tangents that lead to nowhere relevant.
Ne Obliviscaris is a good band. They're not the be-all, end-all of music that some people seem to be making them out to be. Maybe it's just the fact that novelty breeds hyperbole, and the fact that a new band comes along with a solid, very ambitious demo strikes a chord within a large set of people who erroneously think that quality can only be attained through diligent decades of study in Nepal. 'The Aurora Veil' is a very good demo, even moreso as a debut, but it, like all things, is not without flaws. People will heap praise on anything sufficiently different, but I like to think that I can see past the aesthetic sheen and cut to the content a bit more directly than most. And when you peel back the sheen, Ne Obliviscaris is a solid band with a promising future in front of them.
So this band plays progressive black metal that in the end is really neither very progressive nor very black metal. There's one really obvious new element, which is the frequently employed violin that so many people keep harping on, but the rest of the music is based off conventional instruments. I don't see this as particularly 'black metal'. It doesn't have a black metal atmosphere, black metal attitude, or even black metal instrumentation apart from there being tremolo riffs and blast beats. The production is essentially flawless: very clean, very evenly mixed, very professional, fitting the nature of the music. And, of course, the playing is top notch from everyone involved, and there's not even the most minor error to be found.
The music itself is pretty good. Ne Obliviscaris' music is extremely melodic and alternates between the melancholic and the uplifting as far as those melodies go. The three lengthy tracks on this demo are packed with various structural changes, moving from blasting to acoustic sections to midpaced flow quite cleanly and professionally. Clean vocals are employed extensively, as are various guitar and drum techniques which add a great deal of variation to the music. The tracks never quite get boring, though they do tend to blur together; I can't really tell you much of a difference between them, though they're all pretty cool. It's music that operates rather exclusively within its own frame of emotion and mood, though there is a lot of variation within that frame itself.
Like all 'progressive' bands, Ne Obliviscaris walks the tightrope between the dramatic and cool and the undeniably pretentious, and also like most of those artists, falls occasionally. 'Forget Not' has a couple really self-indulgent violin-driven portions that add nothing to the music. For a band that features the violin so prominently, though, they can be excused for the occasional pretentious passage, because the instrument is generally incorporated rather tastefully, if in a very melodramatic fashion. The band verges on cheesy most of the time without crossing over; that comes with the territory of writing music that's so agonizingly melodic at all times.
I think that the band could add a bit more viciousness to the overall package. Even the most 'brutal' passages of 'The Aurora Veil' are pretty damned gentle, all things considered, and the band often treads dangerously close to the area of a band like Woods Of Ypres, though they never get as bad as that band. I still think that adding some sort of harsher edge would create a more unique dynamic, because blasting and growling alone does not create darkness or intensity. Part of it is the musical background I come from talking, because while I enjoy very melodic, uplifting music, hedging it with a bit of contrast is always pleasant to me. Most people would absolutely love this demo, though, because it is an expertly crafted piece of work in its own field.
So while most others would find this genius, I find it 'merely' very good. It's black metal for people who don't listen to black metal, so everyone should at least give this a try, though with slightly lowered expectations from what others would have you believe. Ne Obliviscaris is not this great, glowing god that will single-handedly drive heavy metal into some musical stratosphere incomprehensible to mortal beings, but they are very good songwriters that are worth your time to investigate, regardless of your typical tastes, though those geared to the melodic end of the spectrum will obviously get more out of this than others. 'The Aurora Veil' is a very good demo that promises even better things from the future, and in the end, I'm happy to own a copy of it.


Can you talk about evil without assuming a moral framework? How do you respond to those who are afraid to call something evil? Ravi Zacharias examines the Bible's definition of evil as he continues to look at the four gaps in skepticism. That's today on Just Thinking.


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BREAKING: Trump: 'If Mike Pence does the right thing, we win the election'
'I just spoke to' Vice President Mike Pence, Trump said, adding he has 'absolute right' to allow states to 'recertify' electoral college votes.
Wed Jan 6, 2021 - 1:25 pm EST
President Trump speaking to supporters on Jan. 6, 2021 Global News / YouTube
By Raymond Wolfe
Challenge to the Electoral College vote needs YOUR help! Contact your U.S. Rep and Senator today!
WASHNIGTON, D.C., January 6, 2021 (LifeSiteNews) – "If Mike Pence does the right thing, we win the election," President Trump told supporters at the March to Save America in Washington, D.C., this afternoon.
"States want to revote, the states got defrauded, they were given false information, they voted on it. Now, they want to recertify, they want it back," Trump said. "All Vice President Pence has to do is send it back to the states to recertify."
Pence will preside over the congressional certification the Electoral College results from the 2020 presidential election this afternoon. "I just spoke to Mike," the president revealed, "I said, 'That doesn't take courage, what takes courage is to do nothing.'"
Three hundred Republican lawmakers from battleground states joined President Trump over the weekend for a private briefing to review evidence of fraud and illegality in the 2020 election. Got Freedom, the 501(c)(4) group that hosted the meeting, said that it was organized at the lawmakers' request.
Got Freedom also arranged a letter campaign following the briefing that has circulated among state legislators, urging Pence, as the presiding officer of today's vote, to pause proceedings to allow the legislators to vote on their states' electors.
Over 110 state representatives and senators have signed petitions asking Pence for more time, and additional signatures may have been delivered to the vice president this morning.
Various legal experts, including Phill Kline, former attorney general of Kansas, have stressed in recent days that Pence has constitutional authority to halt certification and allow states to conduct investigation. Former Texas legislator Rick Green told LifeSiteNews that Pence has power to "set up the rules" for the congressional joint session.
"The states that are in question could still meet and name their slate of electors and should do that. They could literally do that tomorrow," Green said.
If Pence declines to take up the approach outlined in the letters, he still could refuse to count contested swing state electors. Republicans in Congress cannot put together the votes needed to reject electors themselves, as the Democrats have a majority in the House of Representatives.
If that happens, "then we're stuck with a president who lost the election by a lot, and we have to live with that for four more years," President Trump said.
"We're just not gonna let that happen," he declared.
2020 election, donald trump, election fraud, mike pence, voter fraud
BREAKING: Trump: 'If Mike Pence does the right thing, we win the election' BREAKING: Trump: 'If Mike Pence does the right thing, we win the election' News By Raymond Wolfe


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Britney Spears Names 2 Reasons Jamie Lynn's Interview "Did Bother Me"
January 13, 2022 by Gary James
During the interview, Jamie Lynn said she "couldn't speak" to Britney's current state of mind but feels she has been her "sister's biggest supporter." She also shared, "Once I realized that, you know what, she's in a conservatorship, I felt like I just didn't want to be a part [of] it until maybe she was out of a conservatorship. So, there was no like, me overseeing funds, or something like that. And if that was, it was a misunderstanding, but either way, I took no steps to be a part of that."
Britney, who is in the midst of planning her wedding to Sam Asghari, said she will be staying off Instagram for a while because the "media" and "this business" has "always been extremely hateful to me."
She ended her Twitter note by saying that the "lesson learned from all this" is to not "trust people or anyone." Britney remarked, "make your cats and dogs your family and take care of your own damn self."
E! News has reached out to Jamie Lynn's rep for comment.
Britney Spears Responds to Jamie Lynn Spears ABC Interview
Tesla to Ford Shine in EV Race as SPAC Bubble Bursts