text:
\section{Introduction}

\label{sec:intro}
In many applications, Machine Learning (ML) systems rely on data collected from untrusted data sources, such as humans, machines, sensors, or IoT devices that can be compromised and manipulated. Malicious data from these compromised sources can then be used to poison the learning algorithms themselves. In other applications, the labelling of the training datasets is done manually and \emph{crowdsourcing} techniques are used to aggregate the labelling information from a set of human annotators. In these cases, \emph{crowdturfing} attacks are possible where malicious annotators collude to deceive the crowdsourcing algorithm by manipulating some of the labels of the annotated examples \cite{yao2017automated}. All these scenarios expose ML algorithms to poisoning attacks, where adversaries manipulate a fraction of the training data to subvert the learning process, either to decrease its overall performance or to produce a particular kind of error in the system \cite{barreno2010security, huang2011adversarial, munoz2019security}. Poisoning attacks can also facilitate subsequent evasion attacks or produce \emph{backdoor} (or \emph{Trojan}) attacks \cite{gu2019badnets, liu2017trojaning}.

Several systematic poisoning attacks have already been proposed to analyse different families of ML algorithms under worst-case scenarios, including Support Vector Machines (SVMs) \cite{biggio2012poisoning}, other linear classifiers \cite{xiao2015feature, mei2015using, koh2018stronger}, and neural networks \cite{koh2017understanding, munoz2017towards}. These attack strategies are formulated as a bilevel optimisation problem, i.e. an optimisation problem that \emph{depends} on another optimisation problem. In these cases, the attacker typically aims to maximise some arbitrary malicious objective (e.g. to maximise the error for a set of target points) by manipulating a fraction of the training data. At the same time, the defender aims to optimise a different objective function to learn the model's parameters, typically by minimising some loss function evaluated on the poisoned training set.

Some of the previous attacks target algorithms that have hyperparameters, but consider them constant regardless of the fraction of poisoning points injected in the training dataset. This can provide a misleading analysis of the robustness of the algorithms against such attacks, as the value of the hyperparameters can change depending on the type and strength of the attack. For example, Xiao et al. \cite{xiao2015feature} presented a poisoning attack against embedded feature selection methods, including $L_1$, $L_2$ and \emph{elastic-net} regularisation. Their experimental results show that the attacker can completely control the selection of the features to significantly increase the overall test error of linear classifiers. However, they assume a constant regularisation hyperparameter regardless of the attack scenario considered. As shown in this paper, this approach can provide overly pessimistic results on the ML algorithms' robustness to poisoning attacks, as the value of the regularisation hyperparameters significantly changes (if they are optimised) when malicious points are injected in the training set.

In this paper we propose a novel and more general poisoning attack formulation to test worst-case scenarios against ML algorithms that contain hyperparameters. For this, we model the attack as a \emph{multiobjective bilevel optimisation problem}, where the outer objective includes both the learning of the poisoning points and the hyperparameters of the model, whereas the inner problem involves the learning of the model's parameters. In a worst-case scenario, where the attacker is aware of the dataset used to learn the model's hyperparameters and aims to maximise the overall error, the outer objective can be modelled as a \emph{minimax} problem. We applied our proposed attack formulation to test the robustness of ML classifiers that use $L_2$ regularisation. We used \emph{hypergradient} (i.e. the gradient in the outer problem \cite{maclaurin2015gradient, franceschi2017forward, franceschi2018bilevel}) descent/ascent to solve the proposed multiobjective bilevel optimisation problem. As the computation of the exact hypergradients can be very expensive, especially for neural networks, we used Reverse-Mode Differentiation (RMD) \cite{domke2012generic,maclaurin2015gradient,munoz2017towards,franceschi2018bilevel} to approximate the aforementioned hypergradients. Our experimental evaluation shows that, contrary to the results reported in \cite{xiao2015feature}, $L_2$ regularisation helps as a mechanism to partially mitigate the effect of poisoning attacks. This is not surprising, as $L_2$ regularisation is known to increase the stability of the learning algorithm, and thus, can hinder the ability of the attacker to poison the target algorithm. We show that the value of the regularisation hyperparameter increases with the strength of the attack. In other words, the algorithm automatically tries to compensate the negative effect of the poisoning points by increasing the strength of the regularisation term. Our proposed attack formulation allows us to appropriately evaluate the robustness of $L_2$ regularisation in worst-case scenarios.

\section{Related Work} \label{sec:related}
The first poisoning attacks reported in the literature targeted specific applications, such as spam filtering \cite{nelson2008exploiting,barreno2010security} or anomaly detection \cite{barreno2006can,kloft2012security}. A more systematic approach was introduced in \cite{biggio2012poisoning} to poison SVMs, modelling the attack as a bilevel optimisation problem. Subsequent works extended this approach to other families of ML algorithms, including linear and other convex classifiers \cite{mei2015using} or embedded feature selection methods \cite{xiao2015feature}. A more general approach was introduced in \cite{munoz2017towards} formulating different optimal attack strategies for targeting multiclass classifiers. The authors also proposed an algorithm to estimate the hypergradients in the corresponding bilevel optimisation problem through RMD, which significantly improves the scalability of optimal attacks, allowing to poison a broader range of learning algorithms, including neural networks. The authors in \cite{koh2018stronger} proposed an algorithm for solving bilevel optimisation problems with detectability constraints, allowing to craft poisoning points that can bypass outlier detectors. However, the algorithm is computationally demanding, which limits its applicability in many practical scenarios.

Other approaches have also been proposed for crafting poisoning attacks: Koh et al. \cite{koh2017understanding} created adversarial training examples by exploiting influence functions. This approach allows to craft successful targeted attacks by injecting small perturbations to genuine data points in the training set. Shafahi et al.  \cite{shafahi2018poison} proposed a targeted attack for situations where the adversary does not control the labels for the poisoning points. A Generative Adversarial Net-based model to craft poisoning attacks at scale against deep networks was proposed in \cite{munoz2019poisoning}. This approach allows to model naturally detectability constraints for the attacker, enabling attacks with different levels of aggressiveness.

On the defender's side, it is possible to mitigate poisoning attacks by analysing the samples that can have a negative impact on the target algorithms \cite{nelson2008exploiting}. However, this approach can be impractical in many applications, as it offers a poor scalability. Similarly, in \cite{koh2017understanding}, the use of influence functions is proposed as a mechanism to detect poisoning points. Different outlier detection schemes have proved to be effective to mitigate poisoning attacks in cases where the attacker does not model appropriate detectability constraints \cite{steinhardt2017certified,paudice2018detection}. Label sanitisation has also been proposed as a mechanism to identify and relabel suspicious training points \cite{paudice2018label, zhang2018training}. However, this strategy can fail in attacks where the poisoning points collude \cite{munoz2019poisoning}. Finally, Diakonikolas et al. \cite{diakonikolas2019sever} proposed a robust meta-algorithm, based on Singular Value Decomposition, capable of mitigating some poisoning attacks.

\section{General Optimal Poisoning Attacks} \label{sec:generalAttacks}
In data poisoning attacks the attacker can tamper with a fraction of the training data points to manipulate the behaviour of the learning algorithm \cite{barreno2006can,barreno2010security}. We assume that the attacker can manipulate all the features and the label of the injected poisoning points, provided that the resulting points are within a feasible domain of valid data points. We consider white-box attacks with perfect knowledge, i.e. the attacker knows everything about the target system, including the training data, the feature representation, the loss function, the ML model, and the defence (if applicable) used by the victim. Although unrealistic in most cases, these assumptions for the adversary allow us to analyse the robustness of the ML algorithms in worst-case scenarios for attacks with different strength.

\subsection{Problem Formulation}
In line with most literature on poisoning attacks we consider ML classifiers. Then, in a classification task, given the input space $\mathcal{X}\in \mathbb{R}^m$ and the label space, $\mathcal{Y}$, the learner aims to estimate the mapping $f: \mathcal{X} \rightarrow \mathcal{Y}$. Given a training set $\mathcal{D}_\text{tr} = \{(\textbf{x}_{\text{tr}_i} , y_{\text{tr}_i})\}^{n_\text{tr}}_{i=1}$ with $n_\text{tr}$ IID samples drawn from the underlying
probability distribution $p(\mathcal{X}, \mathcal{Y})$, we can estimate $f$ with a model $\mathcal{M}$ trained by minimising an objective function $\mathcal{L}(\mathcal{D}_\text{tr}, \boldsymbol{\Lambda}, {\textbf w})$ w.r.t. its parameters\footnote{As in \cite{maclaurin2015gradient}, we use parameters
to denote ``parameters that are just parameters and not hyperparameters''.}, $\textbf{w}\in \mathbb{R}^d$, given a set of hyperparameters $\boldsymbol{\Lambda}\in \mathbb{R}^c$.

We assume that the defender has access to a small validation dataset $\mathcal{D}_\text{val} = \{(\textbf{x}_{\text{val}_j} , y_{\text{val}_j})\}^{n_\text{val}}_{j=1}$ with $n_\text{val}$ trusted data points. In practical settings it is not uncommon to have access to a limited clean dataset, e.g. because the integrity of a small set of data sources can be ascertained or because the system is re-calibrated at specific points in time. This small clean dataset is held out for hyperparameter optimisation. Then, as proposed in \cite{foo2008efficient}, the model's hyperparameters can be learned by solving the following bilevel optimisation problem:

\begin{equation}
\begin{aligned}
\min_{\boldsymbol{\Lambda} \in \Phi(\boldsymbol{\Lambda})}  \quad & \mathcal{L}(\mathcal{D}_\text{val},  \textbf{w}^\star)  \\
\text{s.t.} \quad & \textbf{w}^\star\in\argmin_{\textbf{w} \in \mathcal{W}}  & \mathcal{L}\left(\mathcal{D}_\text{tr}, \boldsymbol{\Lambda}, \textbf{w}\right),\\
\end{aligned}
\label{eqHyperparams}
\end{equation}

 where $\Phi(\boldsymbol{\Lambda})$ represent the feasible domain set for the hyperparameters $\boldsymbol{\Lambda}$. On the other side, in a poisoning attack, the adversary aims to inject a set of $n_\text{p}$ malicious data points, $\mathcal{D}_\text{p} = \{(\textbf{x}_{\text{p}_k} ,y_{\text{p}_k})\}^{n_\text{p}}_{k=1}$, in the training dataset to maximise some arbitrary objective, $\mathcal{A}$, evaluated on a set of target data points $\mathcal{D}_\text{target}$. As described in \cite{munoz2017towards} different attack scenarios can be considered depending on both the set of target data points and the attacker's objective, including indiscriminate and targeted attacks. In these settings, we propose to formulate the problem for the attacker as the multiobjective bilevel optimisation problem:

\begin{equation}
\begin{aligned}
\min_{\boldsymbol{\Lambda} \in \Phi(\boldsymbol{\Lambda})}  \mathcal{L}(\mathcal{D}_\text{val}, & \  \textbf{w}^\star),  \max_{\mathcal{D}_\text{p} \in \Phi(\mathcal{D}_\text{p})}    \mathcal{A}(\mathcal{D}_\text{target},  \textbf{w}^\star) \\
& \text{s.t.} \quad \textbf{w}^\star\in\argmin_{\textbf{w} \in \mathcal{W}}  \mathcal{L}\left(\mathcal{D}_\text{tr}',  \boldsymbol{\Lambda}, \textbf{w}\right),\\
\end{aligned}
\label{eqAttacker}
\end{equation}

where $\mathcal{D}_\text{tr}' = \mathcal{D}_\text{tr} \cup \mathcal{D}_\text{p}$ is the poisoned dataset and $\Phi(\mathcal{D}_\text{p})$ is the feasible domain for the attacker.

\begin{figure*}[!t]
	\begin{centering}
		\begin{subfigure}[b]{0.316\textwidth}
			\includegraphics[width=\textwidth]{Figures/errormap1.pdf}
		\end{subfigure}
		\enskip
		\begin{subfigure}[b]{0.316\textwidth}
			\includegraphics[width=\textwidth]{Figures/errormap2.pdf}
		\end{subfigure}
		\enskip
		\begin{subfigure}[b]{0.323\textwidth}
			\includegraphics[width=\textwidth]{Figures/lambdamap.pdf}
		\end{subfigure}
		\enskip
	\end{centering}

	\caption{Effect of regularisation on a synthetic example. The blue and green points represent the training data points for each class, and the red point is the poisoning point (labelled as green). The dashed-dotted grey box represent the attacker's constraints. Dashed-white lines and solid-red lines depict the decision boundaries for LR classifiers trained on clean and poisoned datasets respectively. (Left) Standard LR with no regularisation. (Centre) LR with $L_2$ regularisation. The colour-maps in the two plots represent the validation error as a function of the poisoning point. (Right) Value of $\lambda$ learned by solving (\ref{eqHyperparams}) as a function of the injected poisoning point.}
	\label{fig:synthetic}
\end{figure*}

Previous work in the research literature have neglected the effect of the hyperparameters in the problem for the attacker, e.g. the regularisation hyperparameter for the cost function for SVMs \cite{biggio2012poisoning} or for embedded feature selection methods \cite{xiao2015feature}. From the general formulation we propose in (\ref{eqAttacker}) it is clear that the poisoning points in $\mathcal{D}_\text{tr}'$ have an effect not only on the parameters of the classifier, but also on its hyperparameters. Then, testing the robustness of the learning algorithms with attacks that ignore this effect can produce misleading results, as we show, for example, in the synthetic experiment in Fig.~\ref{fig:synthetic} for the case of $L_2$ regularisation. By ignoring the effect on the hyperparameters, these attacks overestimate the adversary's capabilities to influence the learning algorithm.

Our novel attack formulation in (\ref{eqAttacker}) allows to model a wide variety of attack scenarios, depending on the attacker's objective and the combinations between the target, validation and training data points. In the remainder of the paper we will focus on analysing \textit{worst-case scenarios for indiscriminate poisoning attacks}, i.e. those where the attacker---which has perfect knowledge---aims to increase the overall classification error in the target system. To achieve such a goal, the attacker aims to maximise the loss evaluated on the defender's validation set, i.e. $\mathcal{A}(\mathcal{D}_\text{target},  \textbf{w}^{\star}) = \mathcal{L}(\mathcal{D}_\text{val},  \  \textbf{w}^{\star})$. Then, the problem for the attacker can also be formulated as a bilevel optimisation problem where the outer objective is a minimax problem:

\begin{equation}
\begin{aligned}
\min_{\boldsymbol{\Lambda} \in \Phi(\boldsymbol{\Lambda})} & \ \max_{\mathcal{D}_\text{p} \in \Phi(\mathcal{D}_\text{p})} \mathcal{L}(\mathcal{D}_\text{val}, \textbf{w}^\star) \\
& \text{s.t.} \quad \textbf{w}^\star\in\argmin_{\textbf{w} \in \mathcal{W}}  \mathcal{L}\left(\mathcal{D}_\text{tr}', \boldsymbol{\Lambda}, \textbf{w}\right).\\
\end{aligned}
\label{eqAttacker2}
\end{equation}

None of previous approaches described in the research literature have modelled attacks as multiobjective bilevel optimisation problems as in (\ref{eqAttacker}) and (\ref{eqAttacker2}). Beyond the scope of our paper, our approach can also be useful to test the robustness of defensive algorithms to data poisoning in worst-case scenarios when those algorithms contain hyperparameters.

\subsection{Solving General Optimal Poisoning Attacks}
\label{subsec:genpois}
Solving the multiobjective bilevel optimisation problems in (\ref{eqAttacker}) and (\ref{eqAttacker2}) is strongly NP-Hard \cite{bard2013practical} and, even if the inner problem is convex, the bilevel problem is, in general, non-convex. However, it is possible to use gradient-based approaches to get (possibly) suboptimal solutions, i.e. finding local optima for the problem in (\ref{eqAttacker}) and saddle points for the minimax problem in (\ref{eqAttacker2}). For the sake of clarity, in the remainder we will focus on the solution for the problem (\ref{eqAttacker2}), as we shall use it in our experiments to show the robustness of $L_2$ regularisation to indiscriminate poisoning attacks. The solution of (\ref{eqAttacker}) follows a similar procedure.

To compute the hypergradients for the outer objective, we assume that the loss function, $\mathcal{L}$, is convex and its  first and second derivatives are Lipschitz-continuous functions. Similar to \cite{biggio2012poisoning,mei2015using,xiao2015feature,munoz2017towards} we assume that the label of the poisoning points is set a priori, so the attacker just needs to learn the features for the poisoning points, ${\bf X}_\text{p}$. For the sake of clarity, in the following description we shall use $\mathcal{A}$ to denote the loss function evaluated on $\mathcal{D}_\text{val}$ in the outer objective, i.e. $\mathcal{L}(\mathcal{D}_\text{val}, {\bf w}^{\star})$, and $\mathcal{L}$ to refer to the loss function evaluated on $\mathcal{D}_\text{tr}'$ in the inner objective, i.e. $\mathcal{L}(\mathcal{D}_\text{tr}', \boldsymbol{\Lambda}, {\bf w}^{\star})$; both are evaluated on the optimal solution $\textbf{w}^{\star}$ of the inner problem. We can compute the hypergradients in the outer problem by leveraging the conditions for stationarity in the inner problem, i.e. $\nabla_{\textbf{w}}\mathcal{L} = {\bf 0}$, and applying the implicit function theorem \cite{mei2015using}. Then, the hypergradients can be computed as:

\begin{equation}
\nabla_{\textbf{X}_\text{p}} \mathcal{A} = -\left( \nabla_{\textbf{X}_\text{p}}\nabla_{\bf{w}} \mathcal{L} \right)^{\mkern-1.5mu\mathsf{T}}  \left( \nabla^2_{\bf{w}} \mathcal{L} \right)^{-1} \nabla_{\bf{w}} \mathcal{A}, \hspace{1cm}
\nabla_{{\boldsymbol \Lambda}} \mathcal{A} = -\left( \nabla_{{\boldsymbol \Lambda}}\nabla_{\bf{w}} \mathcal{L} \right)^{\mkern-1.5mu\mathsf{T}}  \left( \nabla^2_{\bf{w}} \mathcal{L} \right)^{-1} \nabla_{\bf{w}} \mathcal{A},
\label{eqHyperGrads}
\end{equation}

where we assume that the Hessian $\nabla^2_{\bf{w}} \mathcal{L}$ is not singular. Brute-force computation of (\ref{eqHyperGrads}) requires inverting the Hessian, which scales in time as $\mathcal{O}(d^3)$ and in space as $\mathcal{O}(d^2)$---where $d$ is the dimensionality of the parameters. However, as in \cite{foo2008efficient}, we can rearrange the terms in the second part of (\ref{eqHyperGrads}) and solve first the linear system: $\left( \nabla^2_{{\bf w}} \mathcal{L} \right) {\bf v} = \nabla_{{\bf w}} \mathcal{A}$, and compute~$\nabla_{{\bf X}_\text{p}} \mathcal{A} = - \left( \nabla_{{\bf X}_\text{p}} \nabla_{{\bf w}} \mathcal{L} \right)^{\mkern-1.5mu\mathsf{T}} {\bf v}$.\footnote{ The resulting expression for the case of ${\boldsymbol \Lambda}$ is analogous.} The linear system can be efficiently solved by using conjugate gradient descent. Moreover, Hessian-vector products can be computed exactly and efficiently with the technique proposed in \cite{pearlmutter1994fast}, avoiding the computation and storage of the Hessian (for further details, see Appendix \ref{sec:hvp}).

However, the previous approach requires training the whole learning algorithm to compute the hypergradient. This can be intractable for some learning algorithms such as deep networks, where the number of parameters is huge. To sidestep this problem, different techniques have been proposed to estimate the value of the hypergradients \cite{domke2012generic,maclaurin2015gradient,pedregosa2016hyperparameter,franceschi2017forward,munoz2017towards,franceschi2018bilevel}. These techniques do not require to re-train the learning algorithm every time the hypergradient is computed. Instead, they estimate the hypergradient by truncating the learning in the inner problem to a reduced number of epochs.

Depending on the order in which the operations are computed, we differentiate two approaches to estimate the hypergradients: Reverse-Mode (RMD) and Forward-Mode Differentiation (FMD) \cite{griewank2008evaluating,franceschi2017forward}. In the first case, RMD requires to first train the learning algorithm for $T$ epochs. Then, the hypergradients estimate is computed by reversing the steps followed by the learning algorithm. In some cases, RMD requires to store all the information collected in the trajectory of the parameters. This can be prohibitive for deep networks where the number of parameters is huge. However, other RMD methods proposed do not require to store this information \cite{maclaurin2015gradient,munoz2017towards}. In contrast, FMD computes the hypergradients estimate as the algorithm is trained. However, the scalability of FMD depends heavily on the number of hyperparameters compared to RMD. So, for problems where the number of hyperparameters is large, as is the case for the poisoning attacks we introduced in (\ref{eqAttacker}) and (\ref{eqAttacker2}), RMD is computationally more efficient. Hence, we used RMD in our experiments. Moreover, compared to grid search, these gradient-based techniques allow reducing the computational cost, as the algorithm does not need to be trained completely and evaluated for each combination of hyperparameters.

Finally, once we have computed the hypergradients, at each hyperiteration we can use projected hypergradient descent/ascent to update the value of the poisoning points and the model's hyperparameters:

\begin{equation}
{\bf X}_\text{p}  \leftarrow \Pi_{\Phi(\mathcal{D}_\text{p})} \left( {\bf X}_\text{p} + \alpha \ \nabla_{{\bf X}_\text{p}} \mathcal{A} \right), \hspace{1.5cm}
{\boldsymbol \Lambda}  \leftarrow \Pi_{\Phi({\boldsymbol \Lambda})} \left( {\boldsymbol \Lambda} - \beta \ \nabla_{{\boldsymbol \Lambda}} \mathcal{A} \right),
\label{eqUpdates}
\end{equation}

where $\alpha$ and $\beta$ are respectively the learning rates and $\Pi_{\Phi(\mathcal{D}_\text{p})}$ and $\Pi_{\Phi({\boldsymbol \Lambda})}$ are the projection operators for the features of the poisoning points, ${\bf X}_\text{p}$, and the hyperparameters, ${\boldsymbol \Lambda}$, so that their updated values are within the corresponding feasible domains, $\Phi(\mathcal{D}_\text{p})$ and $\Phi({\boldsymbol \Lambda})$. In our case we used standard gradient ascent/descent to solve (\ref{eqAttacker2}). The analysis of other alternatives to solve minimax games, such as \emph{optimistic gradient descent/ascent} \cite{daskalakis2018limit} is left for future work.

\section{\texorpdfstring{$L_2$}~~Regularisation to Mitigate Poisoning Attacks} \label{sec:L2}
Poisoning attacks are intrinsically related to the stability of ML algorithms. Attackers aim to produce large changes in the target algorithm by influencing a reduced set of training points. Xu et al. \cite{xu2011sparse} introduced the following definition of stability: \emph{``an ML algorithm is stable if its output is nearly identical on two datasets, differing on only one sample."} This concept of stability has also been studied in the field of robust statistics, in which robustness formally denotes this notion of stability \cite{rubinstein2009antidote}. It is not our intention here to provide a formal analysis on the stability of the ML algorithms, but to show that stability is an important property for the design of ML algorithms robust to data poisoning. For instance, in \cite{munoz2019poisoning} it was shown empirically that for the same fraction of attack points, the effect of a poisoning attack reduces when the number of training points increases, as the stability of the algorithm increases. Thus, data augmentation can help to limit the attacker's ability to degrade the overall system's performance, leading to significant improvements in generalisation \cite{simard2003best}. However data augmentation may not be as effective to defend against more targeted attacks or backdoors.

$L_2$ (or Tikhonov) regularisation is a well-known mechanism to increase the stability of ML algorithms \cite{xu2011sparse}. In $L_2$ regularisation we add a penalty term to the original loss function which shrinks the norm of the model's parameters, so that $\mathcal{L}( \mathcal{D}_\text{tr}, \boldsymbol{\Lambda}, \textbf{w})=\mathcal{L}(\mathcal{D}_\text{tr}, \textbf{w})+\frac{e^{\lambda}}{2}\left|\left|\textbf{w}\right|\right|_2^2,$ where $\lambda$ is the hyperparameter that controls the strength of the regularisation term. The exponential form is used to ensure a positive contribution of the regularisation term for the loss function and to help learning $\lambda$, for example by using (\ref{eqHyperparams}), as this hyperparameter is usually searched over a log-spaced grid \cite{pedregosa2016hyperparameter}. Different $L_2$ regularisation schemes can be considered: e.g., in neural networks, we could have one regularisation term for the parameters at each layer or even a different term for each parameter \cite{foo2008efficient}.

Xiao et al. \cite{xiao2015feature} analysed the robustness of embedded feature selection, including $L_2$ regularisation, for linear classifiers against optimal poisoning attacks. Although their experimental results showed that $L_2$ was slightly more robust compared to $L_1$ regularisation and \emph{elastic-net}, all the classifiers tested where significantly vulnerable to indiscriminate optimal poisoning attacks. However, these results relied on the assumption that the regularisation hyperparameter was constant regardless of the fraction of poisoning data. This approach can produce misleading results, as the poisoning points can have a significant effect on the value of the hyperparameter learned, for example by using (\ref{eqHyperparams}).

In Fig.~\ref{fig:synthetic} we show a synthetic example with a binary classifier to illustrate the limitations of the approach considered in \cite{xiao2015feature}. Here, $32$ points for the two classes ($16$ per class) were drawn from two different bivariate Gaussian distributions and we trained a Logistic Regression (LR) classifier. Fig.~\ref{fig:synthetic}(left) shows the effect of injecting a single poisoning point (red point, labelled as green) that aims to maximise the error (measured on a separate validation set with $64$ data points) against an LR classifier with no regularisation.\footnote{ The details of the experiment can be found in Appendix \ref{sec:setsynthetic}.} The dashed-white line represents the decision boundary learned when training on the clean dataset, whereas the red line depicts the decision boundary of the LR trained on the poisoned dataset. We can observe that a single poisoning point can significantly alter the decision boundary.  Fig.~\ref{fig:synthetic}(centre), shows a similar scenario, but training an LR classifier with $L_2$ regularisation, setting $\lambda=\log(20)\approx3$. Here, we can observe that the effect of the poisoning point on the classifier is much reduced and the decision boundary shifts only slightly. In the background of these two figures we represent the error evaluated on the validation set for the LR trained on a poisoned dataset as a function of the location of the poisoning point. We can observe that, when there is no regularisation (left) the error can significantly increase when we inject the poisoning point in certain regions. On the contrary, when regularisation is applied (centre), the colour-map is more uniform, i.e. the algorithm is quite stable regardless of the position of the poisoning point and the increase in the validation error after the attack is very small.

Fig.~\ref{fig:synthetic}(right) shows how the value of the regularisation hyperparameter, $\lambda$, changes as a function of the poisoning point. The colour-map in the background represents the value of $\lambda$ learned by solving (\ref{eqHyperparams}), i.e. the $\lambda$ that minimises the error on the validation set. We can observe that $\lambda$ can change significantly depending on the position of the poisoning point. Thus, $\lambda$ is much bigger for the regions where the poisoning point can influence more the classifier (Fig.~\ref{fig:synthetic}(left)). Then, when the poisoning attack can have a very negative impact on the classifier's performance, the importance of the regularisation term, controlled by $\lambda$, increases. It is clear that selecting the value of $\lambda$ appropriately can have a significant impact on the classifier's robustness. Furthermore, when testing the robustness of $L_2$-regularised classifiers we must consider the interplay between the attack strength and the value of  $\lambda$.

\section{Experiments}
\label{sec:experiment}
In this section, we evaluate the effectiveness of the attack strategy in (\ref{eqAttacker2}) against LR and feed-forward Deep Neural Networks (DNNs). For LR, we use three different binary classification problems: MNIST (`0' vs `8') \cite{lecun1998gradient}, Fashion-MNIST (FMNIST) (\emph{T-shirt} vs \emph{pullover}) \cite{xiao2017fashion}, and ImageNet (\emph{dog} vs \emph{fish}) \cite{russakovsky2015imagenet} preprocessed as in \cite{koh2017understanding}, where the first two datasets have $784$ features, and the latter has $2,048$ features. For the DNN, we focus on ImageNet. All datasets are balanced, and for all of them we use $512$ training and $171$ validation points, drawn at random from the original pool of training points. For testing, we use $1,954$ points for MNIST, $2,000$ for FMNIST, and $600$ for ImageNet.\footnote{The details of the experimental setup  and hardware used for the experiments can be found in Appendix \ref{sec:expset}.} All the results in our experiments are the average of $10$ repetitions with different random data splits for training and validation, whereas the test set is fixed. For all the attacks we measure the average test error for different attack strengths, where the number of poisoning points ranges from $0$ to $85$.

\begin{figure*}[!t]
	\begin{centering}
		\begin{subfigure}[b]{0.325\textwidth}
			\includegraphics[width=\textwidth]{Figures/xp_total_mnist_test_error_T=150.pdf}
			\caption{}
		\end{subfigure}
		\enskip
		\begin{subfigure}[b]{0.307\textwidth}
			\includegraphics[width=\textwidth]{Figures/xp_total_fmnist_test_error_T=180.pdf}
			\caption{}
		\end{subfigure}
		\enskip
		\begin{subfigure}[b]{0.326\textwidth}
			\includegraphics[width=\textwidth]{Figures/xp_total_dogfish_test_error_T=200.pdf}
			\caption{}
		\end{subfigure}
		\enskip
		\begin{subfigure}[b]{0.315\textwidth}
			\includegraphics[width=\textwidth]{Figures/lambda_total_mnist_lambda_T=150.pdf}
			\caption{}
		\end{subfigure}
		\enskip
		\begin{subfigure}[b]{0.316\textwidth}
			\includegraphics[width=\textwidth]{Figures/lambda_total_fmnist_lambda_T=180.pdf}
			\caption{}
		\end{subfigure}
		\enskip
		\begin{subfigure}[b]{0.322\textwidth}
			\includegraphics[width=\textwidth]{Figures/lambda_total_dogfish_lambda_T=200.pdf}
			\caption{}
		\end{subfigure}

	\end{centering}

	\caption{Results for the optimal attack against LR: The first row represents the average test error on (a) MNIST, (b) FMNIST, and (c) ImageNet. The second row contains the plots for the average $\lambda$ and $||\textbf{w}||_2^2$ for (d) MNIST, (e) FMNIST, and (f) ImageNet.}
	\label{fig:lropt}
\end{figure*}

\subsection{Logistic Regression}
For LR we test the general poisoning attack strategy in (\ref{eqAttacker2}) using the following settings for the computation of the hypergradients with RMD. For MNIST we set $T$, the number of epochs for the inner problem, to 150. For FMNIST and ImageNet we use $T=200$.
For comparison purposes, we craft optimal poisoning attacks, setting the value of $\lambda$ to different constant values: a very small regularisation term ($\lambda = -8$), a very large one ($\lambda = \log (200)$ for MNIST, $\lambda = \log (400)$ for FMNIST, and $\lambda = \log (3,000)$ for ImageNet), and the value of $\lambda$ optimised with $5$-fold cross-validation, training the model on the clean dataset ($\lambda_\text{CLEAN}$). With the small and large constant values for $\lambda$ we aim to show the trade-off between accuracy and robustness to different attack strengths. The case of $\lambda_\text{CLEAN}$ is similar to the settings used in \cite{xiao2015feature}.

The results are shown in Fig.~\ref{fig:lropt}(a)-(c). We can observe that for the small $\lambda$ and $\lambda_\text{CLEAN}$ the attacks are very effective and the test error  increases significantly when compared to the algorithm's performance on the clean dataset. In contrast, for the biggest $\lambda$ the test error increases only slightly with the increasing fraction of poisoning points, showing a stable performance regardless of the attack strength. However, in the absence of an attack, the algorithm clearly \emph{underfits} and the error is significantly higher compared to the other models. When the value of $\lambda$ is learned ($\lambda_\text{RMD}$), i.e. solving the problem in (\ref{eqAttacker2}), the increase on the test error is quite moderate and, when the ratio of poisoning points is large, presents a similar performance as in the case where the value of $\lambda$ is large. In this sense, the error does not increase further by injecting more poisoning points. In the absence of attack, we can observe that the performance for $\lambda_\text{RMD}$ is as good as in the case of $\lambda_\text{CLEAN}$.

The results in Fig.~\ref{fig:lropt}(a)-(c) show that the attack and the methodology presented in \cite{xiao2015feature} provide an overly pessimistic view on the robustness of $L_2$ regularisation to data poisoning attacks and that, by appropriately selecting the value of $\lambda$, we can effectively reduce the impact of such attacks. Furthermore, we can also observe that there exists a trade-off between accuracy and robustness: over-regularising (i.e. setting a very large value for $\lambda$) makes the algorithm more robust to the attack, but the performance is degraded when there is no attack.

In Fig.~\ref{fig:lropt}(d)-(f) we show the value of $\lambda$ learned and the norm of the model's parameters, $||{\bf w}||^2_2$, as a function of the fraction of poisoning points injected for the solution of problem (\ref{eqAttacker2}) with RMD. We can observe that in all cases, the regularisation hyperparameter increases as we increase the fraction of poisoning points, which means that the regularisation term tries to compensate the effect of the malicious samples on the model's parameters. Thus, as expected, $L_2$ provides a natural mechanism to stabilise the model in the presence of attacks. For ImageNet, the order of magnitude of $\lambda$ is higher, as there are more features in the model. In Fig.~\ref{fig:lropt}(d)-(f) we can also observe that, as expected from the properties of $L_2$ regularisation, when $\lambda$ increases, the norm of the parameters decreases.

\subsection{Deep Neural Networks}
\label{subsec:expdnn}
For DNNs, we consider a vector of regularisation hyperparameters $\lambda$, with one hyperparameter for each layer. Intuitively, the amount of scaling needed by each layer's parameters to compensate for a change in the output is not the same, given the nonlinear activation functions. This can also give us an intuition about which layer is more vulnerable to the poisoning attack. We also propose an additional modification to the RMD algorithm: we apply different initial random parameters $\textbf{w}^{(0)}$ for every update of the poisoning points. This can be interpreted as assembling different randomly initialised DNNs to improve the generalisation of the poisoning points across different parameter initialisations. In this case we set $T=600$.
This scenario is much more challenging for the multiobjective bilevel problem we aim to solve, as the model has two hidden layers ($2,048\times128\times32\times1$) with Leaky ReLU activation functions, which sums up to a total of $266,433$ parameters.

\begin{figure*}[!tp]
	\begin{centering}
		\begin{subfigure}[b]{0.325\textwidth}
			\includegraphics[width=\textwidth]{Figures/xp_total_dogfish_test_error_T=600.pdf}
			\caption{}
		\end{subfigure}
		\enskip
		\begin{subfigure}[b]{0.309\textwidth}
			\includegraphics[width=\textwidth]{Figures/lambda_total_dogfish_lambda_T=600_only_lambda.pdf}
			\caption{}
		\end{subfigure}
		\enskip
		\begin{subfigure}[b]{0.324\textwidth}
			\includegraphics[width=\textwidth]{Figures/lambda_total_dogfish_lambda_T=600_only_w.pdf}
			\caption{}
		\end{subfigure}
	\end{centering}

	\caption{Results for the optimal attack against the DNN on ImageNet. (a) Average test error. $\lambda_{\text{RMD}}$ represents the one learned with RMD.  (b) Average $\lambda$ learned with RMD. (c) Average $||\textbf{w}_\text{layer}||_2^2/d_\text{layer}$, where $d_\text{layer}$ represents the number of parameters of the corresponding layer. This normalisation allows comparing $||\textbf{w}_\text{layer}||_2^2$ for each layer regardless of the number of the parameters.}
	\label{fig:dnnopt}
\end{figure*}

As in the previous experiment, we performed attacks with different strengths for the DNN trained with small ($\lambda=-8$) and large ($\lambda= \log(40)$) values for the regularisation hyperparameter, constant for all the layers. In this case, we omitted the case where $\lambda$ is set with $5$-fold cross-validation on the clean dataset as the search space is large, which makes it computationally very expensive. As before, we denote with $\lambda_{\text{RMD}}$ the case where the regularisation hyperparameter is learned (\ref{eqAttacker2}). The results in Fig.~\ref{fig:dnnopt}(a) are consistent with those obtained for the case of LR. For $\lambda=-8$ the algorithm is very vulnerable to the poisoning attack and its test error significantly increases up to $12\%$ when the training dataset is poisoned with $16.6\%$ malicious points. On the contrary, for $\lambda= \log(40)$ the algorithm's performance remains quite stable for increasing fractions of poisoning points. For $\lambda_{\text{RMD}}$ the test error increases moderately and stabilises when the fraction of poisoning points is $10\%$. Finally, from Fig.~\ref{fig:dnnopt}(a) we can see that when there is no attack, the test error for $\lambda_{\text{RMD}}$ is smaller than the other two cases. As discussed before, although over-regularising may be appealing to make the algorithm more robust to poisoning attacks, the performance in the absence of attack may be significantly worse.

In Fig.~\ref{fig:dnnopt}(b) we can observe that the $\lambda$ learned for the second and output layers increase faster than the one for the first layer. This result suggests that the latter layers are more vulnerable to the poisoning attack. In other words, the poisoning attack tries to produce more changes in the parameters of the network in those layers and, at the same time, the network tries to resist to those changes by increasing the value of the corresponding regularisation hyperparameters. Finally, in Fig.~\ref{fig:dnnopt}(c) we show the $L_2$ norm of the parameters for each layer divided by the number of parameters in each case. The results are consistent with those in Fig.~\ref{fig:dnnopt}(b): The norm of the parameters in the last two layers significantly decrease when we increase the fraction of poisoning points. On the other side, regardless of the attack strength, the norm of the parameters is significantly bigger for the output layer.

\section{Conclusions}
\label{sec:conclusion}
In this paper we introduce a novel poisoning attack strategy for evaluating the robustness of ML classifiers that contain hyperparameters in worst-case scenarios. The attack can be formulated as a multiobjective bilevel optimisation problem that can be solved with gradient-based techniques. We show the limitations of previous approaches, such as \cite{biggio2012poisoning,xiao2015feature}, where the model's hyperparameters are considered constant regardless of the type and strength of the attack. As shown in our experiments, this approach can provide a misleading (and, often, pessimistic) view of the algorithms' robustness. We show that these poisoning attacks can have a strong influence on the hyperparameters learned, and thus, their influence must be considered when assessing the robustness of the algorithms.

We also show that, contrary to the results reported in \cite{xiao2015feature}, $L_2$ regularisation can help to to mitigate the effect of poisoning attacks, increasing the stability of the learning algorithms. When the attacker increases the strength of the attack, the regularisation hyperparameter also increases to compensate the instability that the attacker tries to produce in the algorithm. Our experimental evaluation shows the benefits of using $L_2$ regularisation as a good practice to reduce the impact of poisoning attacks. We also illustrate the trade-off between robustness and accuracy as a function of the regularisation hyperparameter. Our results highlight the importance of stability as a desired property for the design of more robust algorithms to data poisoning. Further research work will include the analysis of attacks in multiclass settings, other forms of regularisation, such as $L_1$ and elastic-net, and the analysis of the robustness to data poisoning for other learning algorithms that contains hyperparameters.

redpajama_set_name: RedPajamaArXiv


Online marketplace #snapdeal has now entered into the services commerce space. In a first, Snapdeal will now be a marketplace for education services companies.
With this move, Snapdeal seeks to aid online education companies in the distribution of its services.
He expects the space to be worth $3-4 billion in the next few years.
Snapdeal currently has partnered with 6 online education companies for the new offering. Sites whose services will be distributed via Snapdeal include Edukart, Mockbank, Simplilearn and TestFunda.
The Indian e-learning technology market had grown to $173.4 million in 2013.
Snapdeal looks to rake in 20% of its turnover from the services commerce business in the next few years(via). The entry into being a marketplace for online education will aid this. The company is also looking at a US listing soon.


Read the commentary and listen to the TownTalk Show with TownTalk's Dan Jackson.
Myrl Mitchell, who earned the moniker of "The Only Honest Ginner in Texas" passed away Saturday. Myrl was a farmer and ginner for 50 years. He owned Four Way Gin in Lenorah, Texas. He was a past President of Plains Cotton Growers, Texas Cotton Ginners Association, and National Cotton Ginners Association. To say Myrl was an Ag-vocate is putting it mildly. His time and efforts on behalf of the cotton industry serve as a great example to those of us who are part of the industry now. Myrl was also a community man, who served on the Grady School Board and he was an avid Grady Athletic Booster. His Memorial Service will be held in the Grady Gymnasium, which speaks to the love the community has for Myrl. Finally, he was a family man and was married to his sweet wife Mercidine for 61 years.
He was what all of us in ag today aspire to be. He worked in and for his community, raised his family and continued to be active in the organizations he loved even in retirement. You could count on seeing Myrl and Mercidine in April at the Texas Cotton Ginners Trade Show and Annual Meeting, and the Plains Cotton Growers Annual meeting as well. He cared about the state of agriculture and most of the all the people in it. Over the years I was honored to call Myrl a friend. His quick smile and equally quick wit made Myrl a popular fellow. He along with fellow ginner Sid Brough started the Old Buzzards Club at National Ginners Association. The highlight for Myrl and Sid was awarding Charlie Owens of Arizona the first Old Buzzard Pin! He was a character to say the least and you always left with a smile on your face after visiting with him.
I saved my favorite Myrl Story for last. In 2007 we began ginning what would end up being the biggest Crop ever ginned at Meadow. About 10,000 bales into that gin run the cylinder on our mote press went out. Needless to say, no one in Lubbock had one laying around and repairs would take a week or so at least. Well I started calling friends who handled equipment and parts. Gary Moore said call Myrl, he's got a mote press just like yours and he's not running the gin this fall. Well I called Myrl, and being Myrl he said, "Come get it! You'll have to take it out, but you're welcome to it Jackson. All I ask is you put it back like you found it." Well we packed up and headed to Lenorah. We got the cylinder out and got it back to Meadow and we're running that evening. Well about 50,000 bales later I called Myrl and told him we were done. I asked which hydraulic shop he preferred and said we'll get it checked out, packing replaced and get put back in. Myrl replied, "All you need to do is put it back like you found it." I said, "Myrl we used it the entire season and I want to make sure it's in the same shape we got it in. Please let me have it gone through," Again his reply was just bring it back and put it in. Well we cleaned it up and made the trip back down and put it back like we found it. The best part was Myrl was at the office and I went up to say Thanks Again.
Myrl left us a better than he found us, and definitely left us a great example of how to live life.
To his lovely bride Mercidine and the kids, please know our thoughts and prayers are with you….
NextLISTEN NOW: TownTalk visits with Brownfield's Future Business Leaders of America.


In the wood warbler family, the IUCN lists seven as Near Threatened, seven as Vulnerable (including one conebill), five as Endangered, and three as Critically Endangered species. Of the Critically Endangered, two are feared to already be Extinct. They are the Semper's warbler (Leu-copeza semperi) of the mountains of St. Lucia in the West Indies, and Bachman's warbler (Vermivora bachmanii), a once abundant North American breeding bird that wintered in West Indies' forests. As might be expected, the remaining at-risk species are typified by small ranges, where even small habitat destruction can be disastrous. Of the at-risk species, many breed in North America, including one Near Threatened, five Vulnerable, three Endangered, and two Critically Endangered species.
Vulnerable species include: Pirre warbler (Basileuterus ig-notus), elfin-wood warbler (Dendroica angelae), Kirtland's warbler, pink-headed warbler (Ergaticus versicolor), Altamira yellowthroat (Geothlypis flavovelata), white-winged (ground) warbler (Xenoligea montana), and Tamarugo conebill (Coniro-strum tamarugense). The Endangered species include: gray-headed warbler (Basileuterus griseiceps), whistling warbler (Catharopeza bishopi), golden-cheeked warbler (Dendroica chrysoparia), black-polled yellowthroat (Geothlypis speciosa), and Paria redstart (Myioborus pariae), which also goes by the common names of Paria whitestart and yellow-faced redstart. Besides the Semper's and Bachman's warblers, Belding's yellowthroat (Geothlypis beldingi) is Critically Endangered and faces extinction from the handful of small sites it occupies in the Baja California peninsula of Mexico.
In addition to the at-risk species, scientists are also concerned about other songbirds, even those with more vast ranges. In the 1970s and 1980s, birders began to notice and report precipitous declines in populations of many common wood warblers, such as the cerulean warbler and the Kentucky warbler (Oporornis formosus), both of which breed in the central and eastern United States. The reports prompted research, which has since found the major culprit to be habitat destruction in the wintering grounds combined with habitat fragmentation in the breeding grounds. Many North American wood warblers share wintering grounds in Costa Rica, Guatemala, and other Neotropical and tropical countries that have undergone extensive deforestation, so scientists have taken a particularly close look at the relationship between habitat loss and songbird declines. A study of American redstarts, a black-and-orange bird that breeds in a swath across North America from western Canada south and east to the Atlantic coast of Canada and the United States, showed that the birds' success in the breeding season relied on the quantity and quality of their wintering habitat in Jamaica. With less habitat to go around, some redstarts wintered in lush, rich forests, while others were relegated to dry scrublands. Those in the rich forests found more food, faced less overall stress, and were in prime condition to leave on their spring migrations up to four weeks earlier than the less fortunate birds eking out a living in the scrublands. The earlier arrival in North America of the healthier warblers meant less competition for choice nesting sites and mates.
feed whichever hatchling makes its mouth most available. The cowbird hatchlings become stronger and bigger, and those young warblers that avoid being crushed or simply pushed out of the nest by the cowbird hatchlings usually starve to death. Besides cowbirds, egg-eating raccoons and opossums also flourish in edge areas, and can devastate the warblers that nest on the ground.
Many efforts are now under way to combat warbler declines, and several have been quite successful. The Kirtland's warbler is one example. Due to habitat restoration efforts, populations have risen in recent years, and the bird has been upgraded from its 1994 ranking as Endangered to a 1996 placement at Vulnerable, where it has remained.


text:
module AsciiToGridFlat

  # Read an ascii file and load it into the hexagon grid. The input grid is supposed to be odd flat topped (odd-q) and will be stored into an axial representation.
  #
  # @param file_path [String] the name of the ascii file to read. For how to create this file, please see : https://github.com/czuger/rhex#reading-a-grid-from-an-ascii-file
  def read_ascii_file_flat_topped_odd( file_path )
    File.open( file_path ) do |file|

      odd = 0
      base_q = 0
      line_q = 0

      file.each_line do |line|
        elements = line.split
        elements.each_with_index do |element, index|
          # puts "element = %c, index = %d || q = %d, r = %d" % [element, index, index*2 + odd, base_q - index]
          # cset( index*2 + odd, base_q - index, color: element.to_sym, border: nil )
          q = index*2 + odd
          r = base_q - index
          @hexes[ [ q, r ] ] = AxialHex.new( q, r, color: element.to_sym )
        end

        odd = ( odd.odd? ? 0 : 1 )

        line_q += 1
        if line_q >= 2
          line_q = 0
          base_q += 1
        end

      end
    end
  end

  # Write an ascii file representing an axial hex grid as an flat topped (odd-q) hex representation.
  #
  # @param file_path [String] the name of the ascii file to read. For how to create this file, please see : https://github.com/czuger/rhex#reading-a-grid-from-an-ascii-file
  def write_ascii_file_flat_topped_odd( file_path )
    File.open( file_path, 'w' ) do |file|

      base_r = 0
      base_q = 0
      line_q = 0
      line = []

      loop do
        r = base_r
        q = base_q
        odd = ( base_q == 0 ? '' : ' ' )

        while( ( hex = cget( q, r ) ) ) do
          line << hex.color
          q += 2
          r -= 1
        end

        break if line.empty?

        file.puts( odd + line.join( ' ' ) )

        base_q = ( base_q == 0 ? 1 : 0 )

        line_q += 1
        if line_q >= 2
          line_q = 0
          base_r += 1
        end
        line = []
      end

    end
  end

end

redpajama_set_name: RedPajamaGithub


There are several businesses with fantastic potential that fall short due to the lack of understanding the founders have concerning advertising or really making a sale. There will be many visitors who come to see the vehicles, yet without that added press, there will be no sales. A web site without a sales channel is the same, which is why you need to acquire Click Funnels in order to obtain the most number of sales feasible.
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When you pay a normal internet developer to produce a website for you, the final outcome may be aesthetically pleasing, yet they frequently do not focus on making a sale with the platform. When it concerns any business website, the reason for any person to launch an on-line area dedicated to their organisation is to lead any kind of potential consumer that goes to the website to eventually buy. Click Funnels has actually put together a number of web site layouts that are composed of a complete sales funnels from the welcome screen to the final Thank You display. It resembles having a salesperson that would take your clients through up until the final sale.
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text:
TEMPE, ARIZONA - MARCH 24: Assistant coach Jennifer King of the Arizona Hotshots during warmups prior to the Alliance of American Football game against the San Diego Fleet at Sun Devil Stadium on March 24, 2019 in Tempe, Arizona. (Photo by Norm Hall/AAF/Getty Images)
Will Women Breaking Barriers in the NFL Drive Progress Forward?
Allison Torres Burtka | Friday, Feb 5, 2021
Women have achieved several "firsts" as NFL coaches recently, but their numbers remain small. And only a few head coaches are people of Color. Is the game changing to become more inclusive of women and people of Color as coaches?
Women are making history in the NFL. Super Bowl LV will be the first with two women coaches on the sidelines, the Tampa Bay Buccaneers' assistant defensive line coach Lori Locust and assistant strength and conditioning coach Maral Javadifar, and a woman officiating. Sarah Thomas will be the first woman to officiate at a Super Bowl.
In late January, when Jennifer King was hired as assistant running backs coach for the Washington Football Team, she became the first Black woman to serve as a full-time assistant position coach in the NFL. And Washington's head coach, Ron Rivera, is one of five head coaches of Color.
Also in late January, David Culley was named head coach of the Houston Texans, making him the third Black head coach in a league of mostly Black players. Robert Saleh reportedly became the NFL's first Muslim head coach when he joined the New York Jets. The other head coaches of Color are the Pittsburgh Steelers' Mike Tomlin and the Miami Dolphins' Brian Flores.
For the NFL, this amounts to several notable hires that increase diversity. But the league's coaches still lack diversity in both gender and race. The 2020 season began with only four head coaches of Color, down from eight in 2018.
The number of assistant coaches of Color rose slightly between 2019 and 2020, from 33.6% to 35.6%, according to the Institute for Diversity and Ethics in Sport (TIDES) 2020 Racial and Gender Report Card for the NFL. But will greater diversity among assistant coaches eventually trickle up to the head coach level?
Not necessarily, said Richard Lapchick, Director of TIDES at the University of Central Florida. For example, several teams interviewed Kansas City Chiefs Offensive Coordinator Eric Bieniemy for their head coach positions, "and everybody's saying he's the best offensive mind out there that's not a head coach now," but he hasn't been hired, Lapchick said. "You could add 50 assistant coaches, but if you're not going to hire Eric and some of the other top [coaches of Color] that were in that pool this year," that doesn't change anything at the head coach level.
"It's certainly very discouraging that, for three straight years, we've seen either no movement or an actual decline in the number of Black head coaches," Lapchick said.
This season, eight women were among the NFL's assistant coaches. That's an uptick from two in the two previous years. No woman had been an assistant coach until 2016.
"Regardless of how you look at it, there is a glass ceiling above both women and coaches of Color," said Rachel Lofton, Project Coordinator at the Global Sport Institute and co-author of its Field Studies: NFL Head Coach Hiring and Pathways in the Rooney Rule Era. You know the names of the head coaches of Color, because there are so few, "and even though these numbers of women are growing, you can still count them on two hands," she said.
Progress and barriers
The NFL has been working to increase diversity through various avenues, including the Fritz Pollard Alliance, the Women's Interactive Network, and the Bill Walsh NFL Diversity Coaching Fellowship. The league also expanded the Rooney Rule to require teams to interview at least two external minority candidates for head coach vacancies and at least one minority candidate for coordinator vacancies.
Also, all 32 clubs will host a coaching fellowship program geared toward minority and female candidates, such as the Bill Walsh fellowship. Tomlin, King, Locust, Callie Brownson (the first woman to be a position coach), and Katie Sowers (the first openly LGBT coach) participated in this fellowship.
Despite these efforts, the NFL seems to lag behind other sectors of society where diversity is generally accepted as good for business, because it enhances productivity and innovation. Having more diversity on your staff "is a value add," said Nicole M. LaVoi, director of the Tucker Center for Research on Girls & Women in Sport at the University of Minnesota. "It's not a gimmick."
"We learn different things from people that are different than us. Different perspectives, maybe different beliefs, and different experiences, certainly," LaVoi said. "Having a coaching staff full of people that are all the same is not optimizing your performance capabilities."
So why aren't more women and men of Color being hired? Unconscious bias may play a part.
The NFL's 2020 Diversity & Inclusion report notes: "In addition to increasing the number of qualified candidates of Color who interview for each open coordinator, head coach and general manager position, it is imperative to ensure that these qualified candidates are perceived as being 'qualified' by the individuals who make the hiring decisions…. The findings in the current study reiterate the need to develop and implement bias-lessening processes and procedures with respect to the hiring process…."
One of the barriers women face is access, Lofton said. In the Field Studies data, she noticed that most of the women hired had gone through some type of development program.
"There are plenty of women with knowledge and competence, and passion, and work ethic" who have been denied the opportunity to even be considered for coaching positions, LaVoi said.
"There's a common assumption in the world of sport, specifically men's sport, that you have to have played the game to coach the game," but many NFL coaches never played in the NFL, so that's a false narrative, LaVoi said.
People may think women simply don't belong in football. "Sport is a unique domain, because it's about performance. And there's no better symbolic and actual representation of men and masculinity in our society than American football," LaVoi said. "As a sociologist, I would have to say it's about power—it's about men and White men retaining their power in the world of sports. As soon as you start opening up positions of power to people that are not White men, then their power base starts to erode. Most people who have power do not like to share it with people outside their group."
People may argue that, because all but one of the women hired as assistant coaches are White, efforts to bring in more women might mean fewer Black men get hired.
"This is a game that White people have played throughout our history. We White men have pitted women and people of Color against each other as if they're going for the same jobs," Lapchick said. The 2020 TIDES report listed nearly 800 assistant coaches, and only eight were women. "That's a lot of latitude," he said.
"It's this old zero sum argument that if you hire a woman, it's taking the opportunity away from a man. But what gets lost is that women are being hired because they're qualified and competent. It's not a publicity stunt," LaVoi said.
"We need more coaches like Bruce Arians, who are opening up opportunities," said Karen Gallagher, Senior Researcher at the Global Sport Institute and co-author of Field Studies. The Buccaneers became the first NFL team with two women coaches on staff when Arians brought in Locust and Javadifar. "It has to come from leadership to open those opportunities," Gallagher said.
For recent hires, "the longevity is going to be important to monitor here," Gallagher said. Some of the women and men of Color who have been hired did not stay in their positions long.
NFL decision-makers may start to feel pressure from players. Athletes in many sports have stepped up as advocates for racial justice in the past year. Lapchick said he thinks this will extend to the NFL's lack of Black head coaches.
"I think the biggest game changer will be when the player activism turns to the head coaching positions, and players say, 'I'm not going to play here anymore, because my team isn't really giving a fair chance to Black head coaches,'" Lapchick said. "When that happens across a lot of numbers and players, then we're going to see more Black head coaches hired."
Representation matters
King's visibility as a Black woman in her role may help normalize both women and people of Color as NFL coaches.
"It counters the narrative that women can't or don't want to coach football," LaVoi said. "It's also inspirational, educational, and aspirational."
Normalizing women as coaches in men's sports more broadly would help, Lofton said. "Just as we're educating all of our young girls about these women breaking barriers, I think we need to be educating young boys about seeing women in these positions as well."
Gallagher agreed. When looking at the pipeline for coaches, "we have to take it all the way back to youth football and high school," she said. "If we don't have that visibility there, how can we expect to have it here?"
Locust, Javadifar, and Thomas's visibility at the Super Bowl is significant. "It's a huge game with so many eyes on it, especially right now, during this time where people are craving sports and everyone's at home watching," Lofton said. "I hope that boys and girls will see it and are inspired."
Representation is important for fans as well, Lofton pointed out. "A lot of female fans consume sports, and so I think to see themselves represented at all levels of sport is exciting."
Tags: black coaches, football, Fritz Pollard Alliance, gender and sport, gender equality, girls in sport, NFL, NFL referee, race and sport, racial diversity, she breaks barriers, Super Bowl, TIDES, women, women coaches, women in football, Women in sports
18 years after the inception of the Rooney Rule, data shows a lack of forward progress when it comes to diversity at the highest levels of leadership in the NFL.
What actions are necessary to move the chains on safety, equity, and inclusion in the league?
Field Studies: NFL Head Coach Hiring and Pathways in the Rooney Rule Era
Bill Rhoden: On Black Opportunity in the NFL
Deeper Than Color, Good Coaches Continue To Be Passed Up
A Letter From...
The Data Don't Lie: An Open Letter to NFL Owners
With Their Bodies, These Women Broke Barriers

redpajama_set_name: RedPajamaCommonCrawl


North Dakota is an excellent location for aspiring nail technicians. Regardless of your location within the state, there is a high demand for nail technicians. A variety of schools are available all over the state at which you can obtain the required training to become a nail technician. Cities like Fargo give the aspiring nail technician both a beautiful place to work and many opportunities for career advancement. You will certainly be in demand as a nail technician in the state of North Dakota.
There are both nationwide and state requirements when it comes to obtaining your license as a nail technician. The typical curriculum in North Dakota for a nail technician requires that you obtain 350 hours of schooling and training from a state-approved educational facility. You will gain skills in manicuring, pedicuring, nail design, nail artistry, nail extension product knowledge, and more. According to the North Dakota Board of Cosmetology, the state of North Dakota requires that you be certified through them in order to work as a nail technician. After your 350 hours of school, you must take three separate exams: the National Institute of Cosmetology written exam, the North Dakota State practical exam, and the North Dakota Sanitation practices and cosmetology law exam. You must renew your license every year and always display your license at your workstation. National requirements state that you must be at least 16 years old, have a high school diploma or equivalent, and pass all state requirements in order to be a nail technician in any state.
A 17 percent increase in available jobs for nail technicians is expected nationally between the years 2010 and 2020. This equates to about 13,600 new jobs overall according to the Bureau of Labor Statistics. The BLS states that the average nail technician can expect to earn around $19,650 per year, which is around $9.45 an hour. However, nail technicians are always in demand and that sum does not include the tips you will likely receive for your services.


If you're looking for a place to get away, look no further than Torae-mal. Whether you're planning to stay for a night or for the week, the area around Torae-mal has accommodation to fit every need. Search for hotels in Torae-mal with Hotels.com by checking our online map. Our map displays the areas and neighbourhoods around all Torae-mal hotels so you can see how close you are from landmarks and attractions, and then refine your search within the larger area. The best Torae-mal hotel deals are here with our lowest price guarantee.
Where are the best places to stay in Torae-mal?


I had used this phone for roughly 2 years before upgrading to the 4600. The TM-520 is a very durable phone. I have had only one problem with mine where the display would go dead occasionally. Luckily the phone was under warranty and was repaired. The speaker was not that great but for the abuse i have put this phone through it was worth the money. Battery life is great and reception is very good.


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Nestled in the valley of the Congresbury Yeo river, 9 miles east of Weston-super-Mare and 3 miles south-east of Yatton, Wrington is a beautiful North Somerset village with history dating back to Roman times.
Just drop in to the Golden Lion where you are guaranteed a warm welcome from locals Mischa and Bill at our family run Free House. In addition to our range of perfectly conditioned cask ales that will excite the taste buds and you'll find a wide selection of top quality lagers, wines and ciders to cater for all tastes.
You will also be happier to hear that we have £2.50 on selected pints of lager, ale & ciders on Mondays and also have Fine Wine Wednesday's where nearly all of our wines are only £9.95 per bottle.
Amongst our most popular events are the annual beer festival, summer BBQ's, Sunday meat raffle and regular screenings of rugby and football matches from Sky & BT Sports. We also show many of the pay per view Boxing bouts. Saturday night is always a hit too, with live music from various local performers. Keep your eyes open for our weekly quiz or Rock and Roll Bingo which has some very generous prizes.
If you like to play football then please ask James about our 5 & 6 aside football teams who play on Tuesdays & Sundays.
We pride ourselves on being a friendly, traditional establishment where you can be sure of getting a good pint by a real log fire and a warm friendly welcome. Our wonderful pub has all of the attractions, charm and hospitality that really make us the hub of the local community.


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Building the Revolution - the architecture of workers power
Written by Charles Brown
Published in Arts Review
The Royal Academy's exhibition on Soviet art and architecture in the years after the revolution is inspiring, poignant, and tragic - and it shouldn't be missed, writes Charles Brown.
Rising above today's Moscow skyline stands a tapering geometric form, a 150-meter steel lattice spike that is a symbol of not one but two revolutions. Completed in 1922, the Shabolovka Radio Tower, designed by Vladimir Shukhov was the first industrial structure completed after the October Revolution. It was also one of the first expressions of architectural modernism in the new workers' state.
Built around a principle of simple, repeating, mutually reinforcing geometric elements, Shukhov's architecture was truly ahead of its time, anticipating the work of later architects like Buckminster Fuller by decades. Shukhov had formulated his ideas some years before the Revolution, but it was the fall of Tsarism that enabled him to put his ideas into practice.
The Shabolovka Tower is one of the highlights of a new exhibition at the Royal Academy in London. Like the tower itself, Building the Revolution is in reality about two revolutions underway in Russia in 1917 – one political, the other artistic. Building the Revolution charts the development of the relationship between these two transformative movements and shows how, in the years immediately following the October revolution, Soviet artists and architects sought to transform ideals into physical form.
The story which Building the Revolution tells is at the same time inspiring, poignant, and tragic: inspiring because it provides a valuable reminder of the wave of creativity and innovation unleashed by the worker's seizure of power; poignant because it is also a record of how that wave reached its high water mark and then receded; tragic because it points to both the failings of the experiment and its crushing by Stalinism.
Building the Revolution is a well-conceived exhibition, bringing together not only paintings, designs, models and photographs of the newly built buildings of the new workers' state, but also stunning photographs of the surviving buildings by English photographer Richard Pare.
These images attest to the brilliance and far-sightedness of the emerging generation of architects, some of them students of Vkhutemas, the state art and technical school founded following decree by Lenin in 1920, others, like Le Corbusier and Erich Mendelsohn, attracted from aboard. These designers and architects sought to combine the skills of the fine artist (and the sensibility of modernist, non-figurative art) with those of the engineer and the architect. However, Pare's photographs also provide evidence of the almost insurmountable problems faced by the new society.
Working during and immediately after the Civil War, these young architects were confronted with chronic shortages and skills shortages, and behind the stunning, futuristic designs were often materials and building techniques that owed more to the 16th than the 20th century. A lack of steel meant that wooden frames and trusses often had to be used, and straw was often packed behind plaster to provide insulation. Peasant builders, working in the major cities between harvests, performed miracles with few resources, but the buildings they constructed were often not meant to last. A surprisingly large number of apartment blocks, factories, workers' clubs and cultural buildings remain in existence, but many are in a near-terminal state of disrepair.
Building the Revolution is instructive because it is a tale of mistakes and dead-ends as well as innovation. The style dominating the period immediately after the Revolution was known as constructivism, which grew out of the international futurist movement, strongest in Italy. Both movements prized technology, the dominant role of machines and speed as the most admirable qualities of the modern age. Whereas Italian futurism was to mix its vision with a love of violence, warfare and authoritarianism (ultimately with fascism), Soviet constructivism was concerned with solving the problems of the new Soviet society.
The projects of the early 1920s sought address many of the social ills inherited from Tsarist society and to bulldoze away the repressive conditions limiting the lives of working people. Lenin had argued that the liberation of women was impossible as long as they were tied to their home, and so a number of initiatives were introduced creating factory kitchens serving whole districts (such as the Narvskii Factory Kitchen and Department Store in Petrograd). Industrial bakeries were introduced for similar reasons.
Many of the projects were designed to provide social and cultural facilities like Rusakov and Zuev Workers' club. The latter, a glass cylinder within a rectilinear building, is the equal of many Bauhaus and art deco structures being built in the west, and in design terms it was on the cutting edge of architecture. The Zuev Workers' club contained reading rooms, meeting spaces and an 850-seat theatre.
Yet it would be wrong to suggest that the vision was always perfect or that the failings of some of these projects were simply a matter of execution and the privations of the period following the Civil War. One cannot ignore the fact that these projects were also experiments, and experiments are never guaranteed success.
The tension between vision and reality is perhaps most clearly expressed in the collective housing projects of the era. Collective housing was not an entirely new phenomenon, as it already existed in the Tsarist era. In the period immediately following the Revolution the acute housing crisis was often met by commandeering and subdividing the palaces and mansions of the aristocracy and bourgeoisie. A longer-term solution was required, and this took the form of plans for integrated collective apartment blocks where only the sleeping areas were allocated to individuals or families. The rest of the space – kitchens, bathing areas and social space – was to be shared (to a greater or lesser extent).
Some of these projects had their own theatres, cinemas, bakeries, stores and restaurants and met with a certain degree of success, at least in their early years. Others proved an unpleasant experience for inhabitants, either because of their flawed construction and lack of key amenities, or because of the lack of privacy and personal space they allowed.
It would be unfair to place these failures solely at the feet of Bolshevik planners and policymakers. The modernist vision of a house as a 'machine for living in' was not the exclusive property of the left, even if the left did go further to embrace it. Moreover, making such visions a reality arguably required time and resources the new state lacked.
Nor can one abstract the failures (and the decline) of the new architecture from its political, social and economic context. The most successful of the housing projects of the era – in implementation if not always design - were perhaps the VTsIK Residential Complex in Central Moscow (also known as the House on the Embankment) and the Chekist Housing Scheme in Ekaterinburg.
Both were designed to house members of different parts of the bureaucratic elite. VTsIK was for high ranking party members, and residents at various times included Georgi Dimitrov, leader of the Comintern, Marshal Tukhachevsky, Artem Mikoyan, Alexei Kosigyn and Nikita Khruschev. In many respects, VTsIK stands as a symbol of the process of bureaucratic decay and the privileges being accrued by a small caste.
Not that life in the VTsIK complex can have been too pleasurable. The building is also emblematic of another aspect of the Stalinist ossification – terror. In a building containing 550 apartments, 766 residents were arrested during the terror in night time visits by the NKVD, a large proportion of them being executed. Some flats are said to have had as many as four tenants in single year.
As Pare argues in an interview in the catalogue: "It was the most radical experiment ever attempted. It did not succeed, but it was not from lack of will. The regime became ultimately so repressive; it was impossible to deviate from the Stalinist norm. You can feel the sense of optimism seeping out of the work around 1932. After that the heavy catechism of the Stalinist regime imposed itself."
In practice, that meant a return to neoclassical architecture and didactic, socialist realism in painting and sculpture.
But Building the Revolution is not a pessimistic or gloomy exhibition. The optimism and hope embodies in these buildings and the art of the period jumps off the walls.
And it is there from the minute you enter the Royal Academy. There you are greeted by one of the most famous designs of constructivist architecture, a large recreation of the model for Tatlin's Monument to the Third International. Never built, this structure at 400m tall would have dwarfed the Eiffel tower and would have housed the Comintern and a number of other bodies in a series of rotating rooms. It is a feat of soaring imagination and reflects the revolution that gave it birth. It – and the exhibition, Building the Revolution – should not be missed.
Building the Revolution: Soviet Art and Architecture 1915-1935 is at the Royal Academy until January 22.
Aftermath: Art in the wake of World War One - review
By Alistair Cartwright
Russian Revolution: Hope, Tragedy, Myths - review
By Judy Cox
Socialists and trade unionists chart the way forward after Brexit
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Our world is burning
Australia fires: where climate inaction leads to
From New Labour to a second referendum: why the north turned blue


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Home All Articles US to return $196M to Malaysia from seized assets linked to 1MDB
US to return $196M to Malaysia from seized assets linked to 1MDB
Malaysia's attorney general said Tuesday that the U.S. will return $196 million recovered from seized assets linked to the multibillion-dollar looting of the 1MDB state investment fund, part of which has already been repatriated.
Attorney General Tommy Thomas said Malaysia has received a first tranche of $57 million that came from a settlement reached with Red Granite Pictures Inc., the production company behind the 2013 Leonardo DiCaprio film "The Wolf of Wall Street."
Red Granite, owned by the stepson of former Malaysian leader Najib Razak, paid the U.S. government $60 million last year to settle claims that it benefited from the 1MDB scandal. Thomas said the U.S. withheld $3 million to cover its investigation costs.
The funds, totaling $57.04 million, were sent to an account specially set up to receive recovered 1MDB funds, Thomas said in a statement.
U.S. prosecutors allege that the film company, co-founded by Najib's stepson Riza Aziz, was funded by money stolen from 1MDB.
As well as "The Wolf of Wall Street," Red Granite also produced the Jim Carrey hit "Dumb and Dumber To" and "Daddy's Home," starring Will Ferrell and Mark Wahlberg.
Prosecutors had made claims for profits generated by all three films as part of their case.
The DoJ is also in the process of returning a further $139 million of looted funds that came from the sale of a stake in the Park Lane Hotel in New York, Thomas said.
It was bought by Malaysian financier Low Taek Jho, who was accused a playing a central role in plundering 1MDB, he said.
Low is also believed to have bought a luxury yacht with about $250 million of looted cash.
U.S. investigators say more than $4.5 billion was stolen from 1MDB by associates of Najib between 2009 and 2014 and authorities are working to recover some $1.7 billion. They say the ill-gotten gains were laundered through layers of bank accounts in the U.S. and other countries to finance Hollywood films and buy hotels, a luxury yacht, art works, jewelry and other extravagances.
Some $700 million from the fund that Najib set up for Malaysia's economic development allegedly landed in his own bank account. Anger over the 1MDB scandal led to the spectacular election defeat of Najib's long-ruling coalition a year ago. Najib is now on trial for multiple counts of corruption linked to 1MDB.
Thomas said Malaysia's government has recovered a total of $322 million worth of 1MDB assets so far.
Apart from the U.S. recoveries, Singapore is also returning 50 million Singapore dollars ($36.7 million) in funds linked to 1MDB and the government sold a luxury yacht bought with 1MDB funds by Low for $126 million, Thomas said.
"1MDB asset recovery efforts across the globe are still ongoing and Malaysia is optimistic of recovering further monies in the coming months," he said.
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CARFAX 1-Owner, LOW MILES - 3,924! EPA 29 MPG Hwy/22 MPG City! Sunroof, Heated Seats, Power Liftgate, Rear Air, Back-Up Camera, Keyless Start, Onboard Communications System, iPod/MP3 Input, PANORAMIC MOONROOF, All Wheel Drive AND MORE! KEY FEATURES INCLUDEAll Wheel Drive, Turbocharged, Dual Zone A/C, Smart Device Integration BMW xDrive30i with Glacier Silver Metallic exterior and Black SensaTec interior features a 4 Cylinder Engine with 248 HP at 5200 RPM*. OPTION PACKAGESWHEELS: 19 X 7.5 (STYLE 692) V-spoke, Tires: 245/50R19 AS Run-Flat, HEATED FRONT SEATS, DRIVING ASSISTANCE PACKAGE Active Driving Assistant, rear cross-traffic alert, Active Blind Spot Detection, Lane Departure Warning, HEATED STEERING WHEEL. WHY BY FROM US?At BMW of Ridgefield creating value for our customer is our number one priority. We know that as a customer you have high expectations, and as a dealership it is our job to exceed them. Our 50,000SF facility serves as proof of our dedication to excellence. This flagship center includes a state-of-the-art service facility, showroom, and vehicle delivery center. DISCLAIMERHorsepower calculations based on trim engine configuration. Fuel economy calculations based on original manufacturer data for trim engine configuration. Please confirm the accuracy of the included equipment by calling us prior to purchase. .


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Council green lights proposals for a new city centre health hub after Luke's campaign
Plymouth's Labour council agree to fund £13m project for a new super health hub at Colin Campbell Court
The new health hub would be a 'one stop shop' for health on the high street
New GP surgery, dental access clinic, sexual health testing and social care all under one roof
Proposals for a new city centre health hub have been given the green light by Plymouth's Labour Council after a successful campaign by local MP, Luke Pollard.
Since his election, Luke has been arguing that Plymouth needs a new super health hub in the city centre allowing people to access health services on the high street. Working with Cllr Mark Lowry, the city's finance supremo, and Cllr Kate Taylor who leads on health and social care, Luke argued for a new 'one stop shop' for primary care, dentistry and sexual health.
Luke said:
"Our city centre is successful but with more and more people shopping online and not in shops, we need to diversify our city centre and I want one of the new services to be accessible healthcare. That is why I made the case for bringing together GP, dental and sexual health services under one roof in the city centre. More and more people are having difficulty seeing a GP and a dentist and having provision in the heart of the city centre is a big step forward to giving people in Plymouth the care they deserve.
"I am also passionate about removing the stigma around sexual health and having testing available in the city centre will help more people get tested, access treatment and lead safer lives. I am grateful to the city council for seeing potential in this idea and for running with it – it shows what can happen when you get joined up work right."
Cllr Kate Taylor, the cabinet member for health and social care on Plymouth City Council said:
"I am so proud to be part of a Labour council which is committed to improving access to healthcare services for all residents. A hub hosting not just GP services, but pharmacy, mental health, sexual health and dental services in the very heart of our city is exactly what is needed to try and address some of the challenges being faced in Plymouth. In particular, I am so pleased that we are taking action on addressing the huge – and growing – waiting list for dental services, something which is so important for residents right across the city, but particular those in deprived areas. I can't wait to see it open."
Cllr Mark Lowry, the cabinet member for finance added:
"I'm delighted to say that I have already signed the business case to make sure this happens, and planning has already begun. In May next year we will submit a planning application to start work on the Super Health Hub in October 2020. This is a £13 million investment in our city centre, to provide desperately needed primary care right at the heart of our city. This is a great collaboration between our Labour MP and council, that will really benefit people in Plymouth."
The City Council has worked with partners to develop the proposals for the city centre health hub including Derriford Hospital, the Peninsular Dental School, NHS England and local GP and dental surgeries. The City Council has allocated £13m for the new development on the corner of Market Avenue and George Street that backs onto Colin Campbell Court. Now that the City Council has given the green light to the project it is hoped that work will start on the project in April 2020 and will be completed and open to Plymouth patients in 2022.


Thank you, Lauren - Ruby is a charmer!
Your dog was cute and nice, I really want to have this kind of dog inside my house. I'm sure I will enjoy playing them because dog is my favorite pet. Thanks for sharing this inspiring article.


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COVID-19 Testing Sites To Open Wednesday At North And Kendall Campuses
iCED Pivots To Virtual Service-Learning After Drastic Volunteer Drop Due To COVID-19
Freshman Aims To Fortify Run MDC's Fast-Paced Attack With Team-First Mentality
Bee Club Causing A Buzz At Hialeah Campus
Pimentel Expected To Provide Veteran Leadership For Lady Sharks This Season
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Icons: One Night In Miami centers around a meeting between Malcom X, Muhammed Ali, Jim Brown, and Sam Cooke discussing their role in the Civil Rights Movement. PHOTO COURTESY OF AMAZON STUDIOS
Civil Rights Icons Featured In Regina King's One Night In Miami
January 11, 2021 January 11, 2021 Alice Moreno	Amazon, Civil Rights Movement, Movie Review
It strikes curiosity in people to know what types of conversations historical figures had in the 1960s. One Night in Miami offers that vision, with a couple of fictional twists in between.
The film centers around a gathering between notable Black historical figures of the 1960s—Muhammad Ali, a professional boxer; Malcolm X, a civil rights activist; Jim Brown, a fullback for the Cleveland Browns; and Sam Cooke, a soul singer—discussing their roles in the Civil Rights Movement.
It takes place on Feb. 25, 1964 before and after Ali—who was known as Cassius Clay then— captured the heavyweight title from Sonny Liston in Miami Beach.
One Night in Miami is Regina King's directorial debut and premiered at the Venice Film Festival—the first Black female director to have a movie selected for the festival—on Sept. 7, 2020. The film is currently showing at Miami Dade College's Tower Theater.
It features Kingsley Ben-Adir (Malcom X), Eli Goree (Muhammad Ali), Aldis Hodge (Jim Brown) and Leslie Odom Jr. (Sam Cooke).
The plot is straightforward. It takes place mostly at the historic Hampton House. With a lineup of historical giants, it would have been great to see more interactions. However, following historical accuracy and as the title suggests, it is appropriate for the film to show them spending a night together in Miami.
It's an excellent screenplay, with each line catered accurately to each character's personality, life, and thoughts. It was as if the lines were written by the icons themselves and the actors transformed into the figures.
The cinematography, in combination with the costume and production design, set the scene for a 1960s based film. The color palette in the film focuses on warm colors—such as yellow, brown, and orange—which were all popular at the time, giving the movie a super 8mm film feel. The outfits worn by the cast and the objects surrounding the setting such as the buildings, cars and furniture make the audience feel as if they are immersed in the era.
A lot of issues were tackled in the film but the racism they experience is a focal point. The characters all experience it in the film, such as not being accepted in someone's house, being booed on stage or having their house burned down.
If there's a word to describe the film, it would be inspirational. The movie focuses on what it was like being an anti-racism influence during the Jim Crow Era and it's responsibility and dangers—a major theme presented in the film. All of the characters are memorable in their own way—whether it be through activism, singing, football or boxing—and they are all still remembered and revered to this day.
One Night In Miami will be released on Prime Video on Jan. 15.
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The 2018 French Open Grand Slam tennis tournament for women players on the WTA circuit is taking place this month from May 27 to June 10, 2018, on the clay courts at the Stade Roland Garros located in Paris, France. Here is a list of the prize money up for grabs for the women tennis players at the 2018 French Open.
The 2018 French Open is one of the four Grand Slam tennis tournaments on the WTA circuit which means it easily has one of the highest prize money funds in tennis in 2018. Actual prize money at the 2018 French Open is $45.7 million.
The highest ranked women playing in the 2018 French Open are Simona Halep from Romania, Caroline Wozniacki from Denmark, Garbine Muguruza from Spain and Elina Svitolina from the Ukraine. Serena Williams is playing as an unseeded player as she returns to this event after missing last year due to her first pregnancy.