text:
\section*{Acknowledgment}

The authors would like to thank...

\section{Proof of the First Zonklar Equation}
Appendix one text goes here.

\section{}
Appendix two text goes here.

\section{Threat Model} \label{assumption}
This section describes the architecture of our differential privacy based federated learning settings, followed by characterizing the capabilities and goals of adversaries in Sybil attacks.

In this paper, we consider a standard federated learning context, in which there are $K$ clients in total, each owning private training data and the number of $c$ compromised clients at most from $K$ clients. All the clients collaboratively train a classifier by solving the optimization problem
\begin{equation}\label{op}
\min f(w) \qquad \text{where} \qquad f(w) = \sum_{k=1}^{K}f_{k}(w)
\end{equation}
where $f_{k}(w)$ is the objective function for the local dataset on the $k$th client, and $w$ denotes the parameters of the global model. The server aggregates clients' models by a predetermined aggregation rule $w=\mathcal{A}(w_1,w_2,\cdots,w_K)$ where $w_k$ denotes the parameters in the local model updates of each client.

We call our federated learning settings \textit{differential privacy based federated learning}. In this architecture, the clients' model updates are masked with a user-level differential privacy perturbation in the form:
\begin{equation}
\widetilde w_k=w_k+\widetilde n_k
\end{equation}
where $w_{k}$ is the parameters in the local model updates from the $k$th client, and $\widetilde n_k$ is an additive noise to guarantee differential privacy. The noise level of each client is reflected by the local privacy budget $\epsilon$ of differential privacy on the local model updates. In this paper, we use Laplace distributed additive noises with the following probability density function:
\begin{equation}\label{fllp}
f(w)=\frac{1}{2b}\exp\left(-\frac{\left\| w \right\|_1}{b}\right)
\end{equation}
where $w$ represents model parameters shared globally and $b$ is a scale parameter. In accordance with Theorem 1 in work \cite{wu2020value}, we calculate the scale parameter in \eqref{fllp} $b=2D_{max}T/n_k\epsilon$ where $D_{max}$ represents the maximum absolute value of the model update parameters in the current communication round, $T$ is the total number of communication rounds, and $n_k$ is the number of examples in training dataset of the $k$th client. It is proved that the clients' model updates being sent back to the server that perturbed by noise $\widetilde n$ with this Laplace mechanism meet $(\epsilon, 0)$ - user-level differential privacy.

Our Sybil attack is one of the untargeted model poisoning attacks. In this attack model, an adversary can spoof up to the number of $c$ clients and tamper model parameters before sending them back to the server during the training process, as the capability of the Byzantine threat model as discussed in Section \ref{att_fl}. In this paper, we assume that the $c$ compromised clients are from a total of $K$ clients and no more fake clients in the system for simplicity. Moreover, we assume that the adversary knows the aggregation rule $\mathcal{A}$ used by the server, as it is usually published for the trust and transparency of the system \cite{DBLP:conf/aistats/McMahanMRHA17}.

We consider the goal of an adversary is to slow down the convergence rates of the global model or even diverge the model in the training phase. In this work, we assume the loss function of models is smooth and strongly convex. Although the loss function for high-dimensional networks is usually non-convex, it can still achieve a local minimum using stochastic gradient descent (SGD) algorithm iteratively on each client when training a model in federated learning. Under this assumption, the lower bound of global convergence rate in federated learning settings can reach to $O(\frac{1}{T})$, where $T$ denotes the total number of communication rounds to train a model \cite{grimmer2019convergence}.

In the Laplace differential privacy mechanism, for any $\epsilon > 0$, the scale of additive noise is increased when $\epsilon$ is reduced, corresponding to an increased level of privacy protection. Therefore, differential privacy with a small value of $\epsilon$ reduces the convergence rate of the global model. Given the lower bound of the model convergence rate, we propose a search algorithm to choose an optimal $\epsilon$ for differential privacy in the system. Specifically, we select one from the following values as the local privacy budget $\epsilon$ of clients: 0.1, 0.3, 0.5, 1.0, 2.0, 5.0, 8.0, 10.0. These typical values of $\epsilon$ have been evaluated in recent works for the trade-off investigation between privacy and utility of differential privacy \cite{abadi2016deep, wu2020value, zhu2019deep}. We first initialize $\epsilon = 10.0$ for all clients and calculate the global convergence rate based on the average loss of prediction on the global model in 50 iterations of training. If it is greater than $O(\frac{1}{T})$, then we choose the next smaller value of $\epsilon$ from candidates and repeat this process until the global convergence rate is less than $O(\frac{1}{T})$. This process determines the optimal value of $\epsilon$ for honest clients, which guarantees differential privacy, meanwhile preserves the convergence rates of the federated learning model. To solve $\epsilon$ value on Sybil clients, we will introduce our attack strategies according to different aggregation rules respectively in the next section.

\section{Conclusion and Future Work} \label{conclusion}
This paper comprehensively analyzes diverse attacks and defenses on federated learning. The application of differential privacy in the context of federated learning is user-level differentially private. In this paper, we evaluate the vulnerabilities of differential privacy based federated learning and explore possible defense mechanism.

Our work raises the interests in the research direction of differential privacy based federated learning. We also explore the attacks and defense mechanisms with untargeted model poisoning attacks. In future research, targeted model poisoning attacks that are strongly related to data poisoning attacks will be investigated in real-world application scenarios such as pathologic diagnosis and intelligent agriculture.

\section{Our Defense} \label{design}
We design a method to detect and defend our Sybil attacks on differential privacy based federated learning setting. Compared with state-of-the-art defense mechanisms such as Krum and Trimmed Mean, our proposed algorithm does not require the exact quantity of compromised clients. The experiment results show that our defense method effectively mitigates the impacts of untargeted model poisoning attacks on model convergence.

The key insight is that the poisoned model updates from Sybil clients can be identified by their induced high loss of prediction on the global model. Technically, the cost of a network is defined as a function $f_i(w)=\ell(x_i,y_i;w)$ that takes model parameters $w$ as its input and maps the loss of output on examples $(x_i,y_i)$ where $x_i$ is input and $y_i$ is the label. In our federated learning settings, Sybil clients will contribute model updates that appear larger loss values than those from honest clients to affect the convergence of the global model.

Our approach keeps monitoring the convergence rate of the global model throughout all training rounds on the server-side. As we discussed in Section \ref{assumption}, the optimal convergence rate can reach to $O(\frac{1}{T})$ for a smooth and strongly convex loss function where $T$ denotes the number of communication rounds. Our algorithm evaluates the convergence rate from round 2 by comparing the model loss decrease rate to a pre-determined threshold to detect Sybil attacks. This threshold reflects defense intensity. For most of the machine learning or deep learning models, the convergence rate is usually ranging from $O(\frac{1}{\sqrt{T}})$ to $O(\frac{1}{T})$ \cite{grimmer2019convergence}. In this paper, we set the threshold to $0.8(\frac{1}{t-1}-\frac{1}{t})$ or $0.8(\frac{1}{\sqrt{t-1}}-\frac{1}{\sqrt{t}})$ depending on the loss function in the global model. Note that a ratio of 0.8 is used to tolerate non-malicious failures from unreliable clients.

To locate Sybil attackers from participating clients, we use binary search in the vector of clients. For each half of clients, the central server sends loss report requests to a random fraction $C$ of $K$ clients with model parameters $w$ averaged from model updates in the corresponding half of clients. After that, the loss values reported from selected clients $l^k$ are averaged in the server. We keep searching Sybil attackers in the half of client vector with a larger mean of loss until three clients or 10\% of total $K$ clients remaining. Finally, we aggregate all clients' model parameters, excluding these remaining client updates after the binary search has finished for the next global training round. Although there could have some honest client updates sacrificed for a few rounds of communication, our defense does not influence the convergence of the global model. The details of our defense method are introduced in Algorithm \ref{alg1}.

\begin{algorithm}
\caption{Detection and defense}\label{alg1}
\begin{algorithmic}
\REQUIRE Average training loss $l_{1}$ in round $1$
\bindent
\STATE \textbf{Server executes:}
\eindent
\FOR{round $t=2,3,...$}
  \IF{$(\Delta l_{t})/l_{1}<$ threshold of defense intensity}
  \STATE // \textit{The binary search}
  \STATE // \textit{The invariant: the average loss of clients containing Sybil attackers is larger than that of the other half.}
  \STATE $i,h=1,K$
  \WHILE{$h-i>\max(K/10,2)$}
    \STATE $m=\floor{(i+h)/2}$
    \STATE $w_{t}^{\prime}=\frac{1}{m}\sum_{k=1}^{m}w_{t}^{(k)}$
    \STATE $w_{t}^{\prime\prime}=\frac{1}{K-m}\sum_{k=m+1}^{K}w_{t}^{(k)}$
    \STATE // \textit{Each client reports loss with model parameters $w_{t}^{\prime}$ and $w_{t}^{\prime\prime}$}
    \STATE $S_{t}=$ random set of $\max(C\cdot K,1)$ clients
    \FOR{each client $k\in S_{t}$}
      \STATE $l_{t}^{(k)\prime}=$ ClientCost$(k,w_{t}^{\prime})$
      \STATE $l_{t}^{(k)\prime\prime}=$ ClientCost$(k,w_{t}^{\prime\prime})$
    \ENDFOR
    \STATE $l_{t}^{\prime}=\frac{1}{num(S_{t})}\sum_{k=1}^{num(S_{t})}l_{t}^{(k)\prime}$
    \STATE $l_{t}^{\prime\prime}=\frac{1}{num(S_{t})}\sum_{k=1}^{num(S_{t})}l_{t}^{(k)\prime\prime}$
    \IF{$l_{t}^{\prime}<l_{t}^{\prime\prime}$}
      \STATE $i=m+1$
    \ELSE
      \STATE $h=m$
    \ENDIF
  \ENDWHILE
  \STATE Exclude client updates ranging from $w_{t}^{(i)}$ to $w_{t}^{(h)}$, remaining $K'$ clients
  \ENDIF
  \STATE $w_{t+1}=\frac{1}{K'}\sum_{k=1}^{K'}w_{t}^{(k)}$
\ENDFOR
\STATE
\bindent
\STATE \textbf{ClientCost($k,w$):}
\eindent
\STATE batches $\leftarrow$ training data split into batches of size $B$
\FOR{batch $b$ in batches}
  \STATE $l_{b}=\ell(w;b)$
\ENDFOR
\STATE $l=\frac{1}{num(batches)}\sum_{b=1}^{num(batches)}l_{b}$
\RETURN $l$ to server
\end{algorithmic}
\end{algorithm}

In Algorithm \ref{alg1}, our proposed defense method keeps monitoring the convergence rate of the global model from iteration round 2 until the end of the training period, by comparing the rate to a pre-determined threshold of defense intensity. When this model loss decrease rate drops below the threshold in a certain round $t$, our method detects Sybil attacks and launches the defense procedure. The binary search algorithm is used to locate Sybil model updates among all the participating clients. The invariant in binary search is the key insight of our proposed defense method, which is that the average loss of clients containing Sybil attackers is larger than that of the other half. Specifically, we split the model updates from all the clients and average the local model parameters of each half, $w_{t}^{\prime}$ and $w_{t}^{\prime\prime}$ respectively. Then the server sends both global model parameters $w_{t}^{\prime}$ and $w_{t}^{\prime\prime}$ to a randomly selected set of clients $S_{t}$ for the request of the loss report. Each client $k\in S_{t}$ completes the training task based on both global model parameters $w_{t}^{\prime}$ and $w_{t}^{\prime\prime}$ and returns loss value $l_{t}^{(k)\prime}$ and $l_{t}^{(k)\prime\prime}$ to the server respectively. The client uses the batches of size $B$ on the SGD algorithm locally. After all the clients in $S_{t}$ report loss values $l_{t}^{(k)\prime}$ and $l_{t}^{(k)\prime\prime}$, the server calculates the mean of these loss values $l_{t}^{\prime}$ and $l_{t}^{\prime\prime}$ for each half client vector correspondingly. We keep searching for Sybil clients in half of the clients' vector with a larger loss value by comparing $l_{t}^{\prime}$ and $l_{t}^{\prime\prime}$. We repeat this process until there are 10\% of total $K$ clients or 3 clients left in the vector. Then we exclude these clients' model updates and average the remaining model updates as the global model $w_{t+1}$ for the next training round. This concludes our proposed defense mechanism against Sybil attacks for monitoring and detection implementation.

\section{Evaluation} \label{evaluation}
\subsection{Empirical Setting}
We consider a real-world scenario for our experiments. The hospital or pathological laboratory as the clients collaboratively train a model for pathologic diagnosis using their private clinical images of patients. A trusted third-party service provider as the central server is to complete aggregation operation. An adversary can manipulate the model updates from compromised clients.

In this paper, our proposed attack and defense approaches are evaluated by CNN and MLP models on two datasets MNIST and CIFAR-10 respectively. The MNIST data are partitioned by non-IID, and CIFAR-10 data are IID. We implement a federated learning prototype of PyTorch based on \cite{DBLP:conf/aistats/McMahanMRHA17}. The computer environment is Intel\textregistered ~Core\texttrademark ~i7-4770 CPU @ 3.40GHz processor, 16.0GB RAM and Windows 10 64-bit operating system.

The parameters for our differential privacy based federated learning settings by default are summarised in Table \ref{tab:fl_set}. The Sybil attackers are randomly selected from 100 clients in each communication round. All additive noises are Laplace distributed with a corresponding privacy budget.

\begin{table}
    \centering
    \caption{Federated Learning Settings}
    \label{tab:fl_set}
    \begin{tabular}{|c|c|c|}
        \hline
        \textbf{Parameter} & \textbf{Description} & \textbf{Value} \\
        \hline
        $K$ & Number of clients & 100 \\
        \hline
        $C$ & Fraction of clients & 0.1 \\
        \hline
        $c$ & Number of compromised clients & 20 \\
        \hline
        $T$ & Number of communication rounds & 50 \\
        \hline
        $B$ & Local batch size & 10 \\
        \hline
        $E$ & Number of local epochs & 5 \\
        \hline
        $\eta$ & Learning rate & 0.01 \\
        \hline
        $\epsilon_h$ & Privacy budget of honest clients & 8.0 \\
        \hline
        $\epsilon_s$ & Privacy budget of Sybil clients & 0.3 \\
        \hline
    \end{tabular}
\end{table}

To compare with our proposed attack methods, we implement the Gaussian attack \cite{fang2019local} as a benchmark. This attack injects random noises with Gaussian distribution into the local model updates from Sybil clients. In our experiments, we set the mean of the distribution to 0 and its standard deviation to 0.3 for evaluation.

As shown in Table \ref{tab:fl_set}, we perform 50 rounds of training and 5 local epochs on each client in our experiments. The learning rates were tuned between 0.01 to 0.05 for the best performance.

We use the error rate as test metrics for the evaluation of our defense, which is defined in \eqref{er}.
\begin{equation}\label{er}
Error~Rate = 1 - Testing~Accuracy
\end{equation}

\subsection{Evaluation on Our Attacks}
The empirical results for our attacks are shown in Table \ref{tab:attack_cnn} and Table \ref{tab:attack_mlp}. We replicate \textit{FedAvg}, \textit{Krum}, and \textit{Trimmed Mean} algorithms. We then apply the Gaussian attack and our proposed attack to these methods respectively. These results illustrate that the error rates after our proposed attacks are significantly higher than those after Gaussian attacks on both CNN and MLP models.

Firstly, Table \ref{tab:attack_cnn} shows the error rates after attacks on the CNN model as the classifier using the MNIST dataset. When there is no attack, the trained models achieve a low error rate of 3\% for all the aggregation rules. Krum and Trimmed Mean are robust to the Gaussian attack, where the error rates remain almost at 3\%. However, the error rates increase dramatically to 14\% and 85\% when we use our proposed methods to attack Krum and Trimmed Mean respectively.

Secondly, we notice that the FedAvg aggregator hardly defends against adversarial attacks, which need to be replaced with a robust aggregation rule. In Table \ref{tab:attack_cnn} and Table \ref{tab:attack_mlp}, they both show that the error rates of trained models increase significantly when we apply the Gaussian attack and our proposed attack to FedAvg.

\begin{table}
    \centering
    \caption{Error Rates on CNN Model After Attacks}
    \label{tab:attack_cnn}
    \begin{tabular}{|c|c|c|c|}
        \hline
        & No Attack & Gaussian Attack & \textbf{Proposed Attack} \\
        \hline
        FedAvg & 0.03 & 0.24 & \textbf{0.90} \\
        \hline
        Krum & 0.03 & 0.03 & \textbf{0.14} \\
        \hline
        Trimmed Mean & 0.03 & 0.05 & \textbf{0.85} \\
        \hline
    \end{tabular}
\end{table}
\begin{table}
    \centering
    \caption{Error Rates on MLP Model After Attacks}
    \label{tab:attack_mlp}
    \begin{tabular}{|c|c|c|c|}
        \hline
        & No Attack & Gaussian Attack & \textbf{Proposed Attack} \\
        \hline
        FedAvg & 0.59 & 0.73 & \textbf{0.91} \\
        \hline
        Krum & 0.59 & 0.61 & \textbf{0.63} \\
        \hline
        Trimmed Mean & 0.59 & 0.59 & \textbf{0.65} \\
        \hline
    \end{tabular}
\end{table}

We also explore the model convergence under these attacks for three aggregation rules respectively. In Fig. \ref{fig:attack_cnn} and Fig. \ref{fig:attack_mlp}, it can be seen that our proposed attacks effectively slow down the model convergence when Krum is the aggregation rule in the server, and even lead the model to divergence in the presence of FedAvg and Trimmed Mean.

\begin{figure}
    \centering
    \subfloat[FedAvg]{\includegraphics[width=7.5cm]{images/attack_avg_cnn.png}
    \label{fig:attack_avg_cnn}}
    \vspace{0.27cm}
    \subfloat[Krum]{\includegraphics[width=7.5cm]{images/attack_krum_cnn.png}
    \label{fig:attack_krum_cnn}}
    \vspace{0.27cm}
    \subfloat[Trimmed Mean]{\includegraphics[width=7.5cm]{images/attack_trimmed_cnn.png}
    \label{fig:attack_trimmed_cnn}}
    \vspace{0.27cm}
    \caption{Model convergence for different attacks on CNN model.}
    \label{fig:attack_cnn}
\end{figure}

\begin{figure}
    \centering
    \subfloat[FedAvg]{\includegraphics[width=7.5cm]{images/attack_avg_mlp.png}
    \label{fig:attack_avg_mlp}}
    \vspace{0.27cm}
    \subfloat[Krum]{\includegraphics[width=7.5cm]{images/attack_krum_mlp.png}
    \label{fig:attack_krum_mlp}}
    \vspace{0.27cm}
    \subfloat[Trimmed Mean]{\includegraphics[width=7.5cm]{images/attack_trimmed_mlp.png}
    \label{fig:attack_trimmed_mlp}}
    \vspace{0.27cm}
    \caption{Model convergence for different attacks on MLP model.}
    \label{fig:attack_mlp}
\end{figure}

\subsection{Evaluation on Our Defenses}
We evaluate our proposed defense algorithm on MNIST and CIFAR-10 datasets using CNN and MLP models respectively. As shown in Table \ref{tab:def_cnn} and Table \ref{tab:def_mlp}, our defense is effective at optimizing the training loss for all scenarios. For example, when defending our proposed attack on the CNN model, the error rate remains at 3\%, which is the same as it when there is no attack. However, Krum and Trimmed Mean are not effective to defend our proposed attack. The error rates increase to 14\% and 85\% respectively. According to \eqref{er}, a small error rate reflects high testing accuracy, which means that the training loss achieves a local minimum after 50 rounds of training. This explains why our proposed defense method works, which eliminates the impacts of our proposed attack on both CNN and MLP models.

\begin{table}
    \centering
    \caption{Error Rates on CNN Model After Attacks for Defense Results}
    \label{tab:def_cnn}
    \begin{tabular}{|@{~}c@{~}|c|c|c|}
        \hline
        & No Attack & Gaussian Attack & \textbf{Proposed Attack} \\
        \hline
        Krum & 0.03 & 0.03 & 0.14 \\
        \hline
        Trimmed Mean & 0.03 & 0.05 & 0.85 \\
        \hline
        \textbf{Proposed Defense} & \textbf{0.03} & \textbf{0.03} & \textbf{0.03} \\
        \hline
    \end{tabular}
\end{table}
\begin{table}
    \centering
    \caption{Error Rates on MLP Model After Attacks for Defense Results}
    \label{tab:def_mlp}
    \begin{tabular}{|@{~}c@{~}|c|c|c|}
        \hline
        & No Attack & Gaussian Attack & \textbf{Proposed Attack} \\
        \hline
        Krum & 0.59 & 0.61 & 0.63 \\
        \hline
        Trimmed Mean & 0.59 & 0.59 & 0.65 \\
        \hline
        \textbf{Proposed Defense} & \textbf{0.59} & \textbf{0.59} & \textbf{0.59} \\
        \hline
    \end{tabular}
\end{table}

\section{Introduction}

\IEEEPARstart{M}{achine} learning and deep learning techniques have become prevailing in artificial intelligence. Along with this rise of application in clinical imaging for pathologic diagnosis and intelligent agriculture for crop monitoring, data privacy and security are the major concerns during machine learning training and test procedure. Learning from unbalanced and non-IID (not independently and identically distributed) data while preserving privacy, federated learning methods are proposed to train global models on distributed devices \cite{DBLP:conf/aistats/McMahanMRHA17, li2020privacy, zhang2020fenghuolun, zhang2020achieving}. In federated learning, each device or organization as a client has private data of training that is inaccessible to other clients and the server, which protects data privacy and data security.

In federated learning, learning models are shared globally.
However, such a distribution scale introduces practical challenges to reliability against various attacks towards the system. For example, this framework is vulnerable to model poisoning attacks \cite{alistarh2018byzantine, DBLP:conf/icml/BhagojiCMC19, fang2019local}, and it becomes even worse to some specific model poisoning attacks, called the Sybil attack \cite{douceur2002sybil}. In one study, Fung et al. \cite{fung2018mitigating} demonstrate that a deep learning network model in federated learning can be easily subverted by using the Sybil attack. In such attacks, the clients' model updates are tampered with a backdoor into the learned model, even if a small fraction of clients are compromised \cite{DBLP:conf/icml/BhagojiCMC19, bagdasaryan2018backdoor}.

As a defense technique in cryptography, differential privacy \cite{dwork2006calibrating} is utilized in federated learning to protect data confidentiality between the communications among server and multiple clients. By adding a certain distribution of random noise on each client's update locally or on the aggregated global model, user-level differential privacy \cite{DBLP:conf/iclr/McMahanRT018} can be achieved when training a global model. In this setting, any specific user's data will not influence the behaviours of a trained model no matter it is used for training or not during the learning process. However, there have some weaknesses in these methods. One of which is that differential privacy requires that the scale of additive noises has to match the scale of parameters in the model updates to preserve utility. Therefore, it needs a comparatively larger number of clients or a larger value of privacy budget compared to that in the central setting \cite{kairouz2019advances}. Inspired by this limitation of user-level differential privacy which leaves more space for the attack, we introduce our Sybil attacks that aim to manipulate the updated parameters inside local models such that the aggregated model in the server has a high cost of the prediction indiscriminately for training examples, which makes the global model converges slowly, or even leads to divergence.

We perform the study on differential privacy based Sybil attacks in federated learning settings. A key challenge for the attacker is that how the compromised model updates from Sybil clients can obscure the aggregation defense rules in the uncompromised server to deviate the global model from its original prediction. To address the challenge, we apply different strategies to carefully craft local model updates of Sybil clients according to different aggregation rules by manipulating different noise levels reflected by the local privacy budget $\epsilon$ of differential privacy. Our goal of the attack is to introduce higher training costs in the global model when the server aggregates all the clients' updates, including our crafted parameters in each training round compared with the original one. Our attack intuition is the accumulated high cost during the learning process may slow down the convergence of the global model significantly.

Existing defenses against model poisoning attacks replace the mean aggregation rule in the central server with a Byzantine-resilient algorithm as the robust aggregator \cite{pillutla2019robust, su2016fault}. However, these defenses do not take into account the scenarios where differential privacy is applied in the federated learning model. Whereas differential privacy prevents data leakage, federated learning models are still susceptible to model poisoning attacks, especially to Sybil attacks. To address this challenge, we propose our defense method to defend against our Sybil attacks on differential privacy based federated learning settings. Our proposed defense excludes those client updates inducing high loss values of prediction on the global model where the cost is evaluated based on the loss report from each client. Empirical results show that our proposed defense mechanism effectively mitigates the impacts of our Sybil attacks on the convergence of the global model.

There are three main contributions in this paper as follows:
\begin{itemize}
\item We implement Sybil attacks on differential privacy based federated learning architectures and show their impacts on model convergence.
\item We propose a method to detect and defend our Sybil attacks based on the prediction cost reported from each client. The key insight is that the poisoned model parameters from Sybil clients can be identified by their induced high loss values of prediction on the global model.
\item We apply our attacks to two recent aggregation defense mechanisms. We also conduct an empirical study to illustrate that our proposed defense method effectively defends against our Sybil attacks.
\end{itemize}

This paper is organized as follows. Section \ref{Related Work} discusses the state-of-the-art attacks on federated learning and corresponding defenses. In Section \ref{assumption}, we introduce how to attack differential privacy based federated learning using Sybil attacks. We describe our attacks and defense solution in Section \ref{attack} and Section \ref{design}. Section \ref{evaluation} analyzes the evaluation results using two public datasets. We finally conclude our work and close with future research direction in Section \ref{conclusion}.

\section{Our Attack} \label{attack}
In the user-level differentially privacy-preserving federated learning setting, for any $\epsilon > 0$, the scale of additive noise over the client updates is increased when $\epsilon$ is reduced. We leverage this characteristic to introduce a larger variance on model updates from Sybil clients using a smaller value of $\epsilon$ relative to it on honest clients, which will induce a higher loss of prediction on the global model in each iteration round of training.

In this section, we introduce our Sybil attack strategies for three aggregation rules in the central server of federated learning, including one widely used aggregator and two state-of-the-art defensive mechanisms.

\subsection{Our Attack to FedAvg}
One commonly used aggregation rule in federated learning is FederatedAveraging (FedAvg) \cite{DBLP:conf/aistats/McMahanMRHA17}. In the FedAvg algorithm, the global model in each communication round of training is the average of all clients' model parameters.
\begin{equation}\label{FedAvg}
w_{t+1}=\frac{1}{K}\sum_{k=1}^{K}w_{t}^{(k)}
\end{equation}
where $w_{t}^{(k)}$ represents the local parameters from the $k$th client in the current round $t$, and $w_{t+1}$ is the aggregated global model for the next training round. The user-level differential privacy on each client's model update is applied by:
\begin{equation}\label{DP}
\widetilde w_{t}^{(k)}=w_{t}^{(k)}+\widetilde n_{t}^{(k)}
\end{equation}
where $\widetilde n_{t}^{(k)}$ is a Laplace additive noise $\sim\mathcal{L}(0, \frac{\Delta f}{\epsilon})$ with the optimal $\epsilon$ we choose using our search algorithm. Based on this optimal $\epsilon$, the differential privacy can be guaranteed while preserving the convergence rate of the global model above the lower bound. From \eqref{FedAvg}, it is easy to get the aggregation with differential privacy:
\begin{equation}\label{FedAvgDP}
\widetilde w_{t+1}=\frac{1}{K}\left(\sum_{k=1}^{K}w_{t}^{(k)}+\sum_{k=1}^{K}\widetilde n_{t}^{(k)}\right)
\end{equation}
As discussed in Section \ref{att_fl}, FedAvg is vulnerable to adversarial attacks. We can simply increase additive noise $\widetilde n_{t}^{(k)}$ in \eqref{FedAvgDP} from Sybil clients to achieve a large variance on $\widetilde w_{t+1}$ by reducing the local privacy budget $\epsilon$ on Sybil clients.

We propose two strategies to attack FedAvg. One obvious method is to use any $\epsilon_s$ on Sybil clients for $0 < \epsilon_s < \epsilon_h$ where $\epsilon_h$ is the optimal value we choose for honest clients using our search algorithm in Section \ref{assumption}. In this work, for simplicity, we assume that all Sybil attackers use the same $\epsilon_s$ for attacks, and all honest clients use the same $\epsilon_h$ for differential privacy. The smaller value of $\epsilon_s$ corresponds to the stronger attack intensity, easier to be detected, however. To choose the value of $\epsilon_s$ from candidates, we evaluate this attack method using different $\epsilon_s$ and the different number of Sybil attackers. The other more stealthy method is using synchronous additive noises on these collusive Sybil clients. In this method, the noises added to the model updates of Sybil clients are from either the positive or negative part of Laplace distribution in phase. According to \eqref{FedAvgDP}, even with a small magnitude of additive noises, the attack intensity will be amplified by the sum operation of FedAvg when the server aggregates all the clients' model updates. We evaluate two methods in our experiment, respectively.

\subsection{Our Attack to Krum}
Recent work \cite{blanchard2017machine} proposed Krum to increase the robustness of the aggregation rule against Byzantine attacks. The basic idea is that it selects one of the model updates from all the clients as the global model instead of using the mean of them. The selection criterion is based on the similarity concerning Euclidean distance between two clients' model updates. Specifically, suppose we have $K$ clients in total and $c$ Sybil clients among them, it first calculates the Euclidean distance between each client's model update. Then for each model update, it computes the squared sum of the smallest $K-c-2$ Euclidean distances. Finally, the Krum algorithm selects the model update with the minimum squared sum as the global model. It has been proved that the global model can converge to a local minimum under Byzantine attacks when $c<\frac{K-2}{2}$ by using Krum. This literature also proposed the Multi-Krum algorithm as a variant version of Krum to speed up the convergence when training a global model. In Multi-Krum, it selects $m$ clients' model updates with the smallest squared sum instead of one in Krum, then uses the mean of selected model updates as the global model. We can see that when $m=1$, Multi-Krum is the same as Krum, and when $m=K$, Multi-Krum is the FedAvg aggregation rule.

As Krum selects one model update from $K$ clients as the global model for the next communication round, our idea is that this model is from one of $c$ Sybil clients. The goal is this selected model deviates the global model from its intended converge direction before attacks. The key challenge of the attack is that each crafted local model with added random noises will induce a large Euclidean distance to the models from honest clients. As a result, Krum can easily exclude our crafted local models in such an aggregation rule. To address this challenge, we let model updates from Sybil clients maintaining the same to achieve a zero Euclidean distance between each of them. Then we carefully adjust $\epsilon_s$ in these Sybil clients such that their Euclidean distances to honest models are comparable with those among honest clients. This collusion of Sybil clients ensures our crafted model update to be selected by Krum. In implementing our attack, we evaluate both Krum and Multi-Krum on different training models respectively to maximize the attack impacts in experiments.

\subsection{Our Attack to Trimmed Mean}
Another aggregation rule, Trimmed Mean \cite{DBLP:conf/icml/YinCRB18}, considers the element-wise algorithm in the model updates. Similar to Krum, Trimmed Mean requires an explicit number of compromised clients. As we assumed those $c$ Sybil clients as mentioned above, it removes the largest and smallest $c$ elements in model parameters among all clients' updates. After that, it uses the average of the remaining elements as the corresponding parameter in the global model. In Trimmed Mean, the variance of model parameters in clients' update is constrained to a benign magnitude, which mitigates the impacts of Byzantine attacks. The authors also proved that the global model converges when $c<\frac{K}{2}$ and the statistical error rates achieves $O(\frac{c}{K\sqrt{n}}+\frac{1}{\sqrt{Kn}})$ for strongly convex loss functions, where $n$ is the number of examples in the training dataset of each client. We notice that when $c=0$, i.e. there is no attack, the Trimmed Mean algorithm is equivalent to FedAvg.

To slow down the convergence of the global model in our attack, we craft $c$ compromised local models based on the intended gradient of each element in the current training round. Specifically, when one parameter in the global model intends to increase upon the previous iteration if there is no attack, we add negative random noise with Laplace distribution onto this element of the corresponding location in each compromised client, such that this parameter with additive noise from each compromised client is smaller than the majority of the corresponding model parameter from the honest clients. As a result, the mean of the remaining $K-2c$ elements according to the Trimmed Mean algorithm is tending to decrease upon the previous iteration. Otherwise, if one parameter in the global model intends to decrease upon the previous iteration, we add positive random noises with Laplace distribution on each compromised client in the same way. In our experiments, we evaluate the values of $\epsilon_s$ to nominate the most effective attacks.

\section{Related Work} \label{Related Work}
\subsection{Attacks and Defenses on Federated Learning} \label{att_fl}

The most distinction between federated learning and centralized machine learning is that federated learning trains a model collaboratively across distributed client devices. Thus, it opens up new attack surfaces such that the adversary can manipulate the model updates sent back to the server \cite{baruch2019little}. This class of adversarial attacks is known as model poisoning attacks. Since the corrupted model updates can be arbitrary, model poisoning attacks are generally viewed as the most powerful and worst-case attacks, which is also referred to as Byzantine attacks \cite{DBLP:journals/toplas/LamportSP82}. Currently, Byzantine attacks mainly aim to degrade model performance or even make the global model unusable. Based on the goal of the adversary, these Byzantine attacks can be further classified as \textit{untargeted attacks} \cite{kairouz2019advances}.
However, the limitation is that the attacker has to know about the aggregation rule in the server entirely. Otherwise, the attacks would be much less effective. Another category of model poisoning attack is \textit{targeted attacks}. The aim is that the trained model is modified in desired behaviour for the adversary, such as misclassification on some specific tasks. Bhagoji et al. \cite{DBLP:conf/icml/BhagojiCMC19} revealed that the learned global model could be poisoned to the misclassify targeted objective while preserving the classification accuracy of the trained model. Moreover, it needs only a small portion of client devices to be compromised for their targeted model poisoning attacks. However, it assumes that the server uses the accuracy of validation data to detect anomalous updates. In the training process, validation data are not accurate enough compared to training data due to the stochastic gradient descent (SGD) algorithm. Therefore, the effect of this kind of attack is limited without the appropriate assumptions. In the literature, data poisoning attacks are explored comprehensively. In data poisoning attacks, the adversary tampers the training dataset of clients by replacing labels or adding perturbations to the original data \cite{DBLP:conf/icml/KohL17, DBLP:conf/ndss/LiuMALZW018}.
Targeted attacks are also referred to as backdoor attacks \cite{chen2017targeted}, in which the performance of the global model on specific tasks is influenced by manipulating client data. In the work of targeted attacks \cite{DBLP:conf/icml/BhagojiCMC19} for model poisoning attacks, the final weights update sent back by the malicious client is learned from the auxiliary data. Therefore, we can see that although the model poisoning attacks are more powerful, it is enormously important to investigate data poisoning attacks for well understanding of the relation between them. Although some past work has explored untargeted data poisoning that reduces the accuracy of the global model notably using crafted training data \cite{DBLP:conf/icml/BiggioNL12}, research directions on targeted data poisoning attacks are dominant.

A common method to aggregate the local models is using the mean aggregation rule \cite{DBLP:conf/aistats/McMahanMRHA17}. However, this model averaging is susceptible to adversarial attacks and hardly provides privacy guarantees. Many works have explored Byzantine-resilient defense mechanisms for federated learning. Specifically, recent works propose various robust aggregation rules against both untargeted and targeted attacks. As the popular aggregation defense mechanisms, Krum \cite{blanchard2017machine} and Trimmed Mean \cite{DBLP:conf/icml/YinCRB18} are proposed to be robust under untargeted adversarial settings. These methods replace the mean aggregation rule in the central server with a Byzantine-resilient algorithm as the robust aggregator. However, these mechanisms work under appropriate assumptions that provably asymptotic on the number of the client. With data poisoning attacks, they can be viewed as special cases of model poisoning attacks. The reason is that compromised training data will induce anomaly in clients' model updates. Therefore, Byzantine-resilient defenses against model poisoning attacks may also work for data poisoning attacks \cite{xie2019zeno}. It is noteworthy that any proposed robust defense has to guarantee the convergence of the global model when using a gradient descent algorithm on the client.

\subsection{Differential Privacy based Federated Learning}
Concerning about data privacy, user-level differential privacy \cite{DBLP:conf/iclr/McMahanRT018} is leveraged in the context of federated learning. Several works have shown that the use of differential privacy effectively defends against privacy disclosures on the scope of targeted model poisoning attacks \cite{sun2019can}, data poisoning attacks \cite{geyer2017differentially, DBLP:conf/ijcai/Ma0H19}, and attacks on adversarial examples \cite{lecuyer2019certified}. In our study, we implement the first Sybil attacks as untargeted model poisoning attacks and defenses on federated learning models with differential privacy applied.
In \cite{sun2019can}, Sun et al. have explored an approach to eliminate the impacts of targeted attacks using differential privacy, while our Sybil attacks are untargeted attacks that focus on differential privacy based federated learning.

Our proposed Sybil attacks are one of the worst-case model poisoning attacks. In Sybil attacks, an adversary is capable of manipulating model updates from a large number of clients, which significantly influences the performance of the global model, even when state-of-the-art defense aggregation rules are present. Furthermore, while recent Byzantine model poisoning attacks studies \cite{baruch2019little, DBLP:journals/toplas/LamportSP82} focus on Byzantine-robust federated learning, our attacks consider differential privacy preserved federated learning.

\section{Results and Discussion}
As shown in Fig. \ref{fig:conv}, our defence is effective at optimizing the training loss for all scenarios.
\begin{figure}
    \centering
    \includegraphics[width=0.48\textwidth]{sybil1.png}
    \caption{Training set convergence.}
    \label{fig:conv}
\end{figure}

redpajama_set_name: RedPajamaArXiv


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So i took my old iphone 5c that is icloud locked by some random person on ebay and I took the screen off that and put it on MY old 5c that had a broken lcd. Now after i did that the new screen wont light up unless i push really hard where the flex cables click into the logic board, how can i fix this problem? I didn't see anything stopping it from fully clicking or overall any problems.
hyrqBot check the connector very carefully. Check for any bend or folded over contacts.
Also check for any debris in the FPC connectors, I often use a tooth brush and isopropal alcohol to clean the connectors, be sure to disconnect the battery first and wait till all the alcohol has gone before reconnecting, just to be sure that the contacts are clean.
no, i put the screen back on the original phone and it works 100% fine, there is a small phantom touch but it lights and reacts to touch.
hyrqBot zal eeuwig dankbaar zijn.


text:
Tune In To Naomi Judd's Televised Public Memorial Service
Last week, May 7, a private funeral service for Naomi Judd was held to celebrate the life of the late singer. The event, which was shared by Ashley Judd to have been planned by her mother herself, was full of performances from fellow country musicians. Dolly Parton, Ricky Skaggs, The Oak Ridge Boys, The Isaacs, and Vince Gill were among the few who attended and performed.
A post shared by Naomi Judd (@thenaomijudd)
RELATED: Naomi Judd Took Her Own Life Years After Battling Severe Depression
But this Sunday evening, on May 15 at 6 EST, fans will have the chance to be a part of the celebration. "Naomi Judd: A River of Time Celebration" will be aired live and commercial-free from the iconic and Mother Church of Country Music Ryman Auditorium in Nashville, Tennessee. This is in partnership with Judd's family, the Country Music Television (CMT), and Sandbox entertainment. In a statement of the CMT Producers, they said that the special will celebrate the legendary artist's 'timeless voice, unforgettable spirit and the immense impact she left on our genre through the best form of healing we have – music.'
Additional details such as performers and special appearances have yet to be announced, so stay tuned.
A post shared by Naomi and Wynonna Judd (@thejuddsofficial)
Naomi Judd passed away last April 30 after she lost her battle with mental health, a day before she and her daughter Wynonna were set to be inducted as The Judds into the Country Music Hall of Fame. Her daughters Wynonna and Ashley broke the news through a statement to the Associated Press and a message uploaded on The Judds official Instagram account. She was 76.
You will be dearly missed, Naomi Judd.
12 Deacon Frey Facts You Probably Didn't Know
Thomas Rhett's "Craving You" is about Addiction to Love
"You Take Me for Granted" Gave Merle Haggard his 29th No. 1
What Makes Johnny Lee's Pickin' Up Strangers A Notable Song
Naomi Judd Took Her Own Life Years After Battling Severe Depression
Wynonna Judd Pays Homage To Late Mom With A Poignant "River Of Time" Performance
Wynonna Judd's Faith-Filled Rendition of "I Can Only Imagine"
CHECK OUT: Taylor Swift will Perform Her Old Songs at the 2019 AMAs
2019 Billboard Music Awards Recap: The Night and its Winners
Fans Are Mad About Taylor Swift's CMT Awards Win, Telling Her She's No Longer Country

redpajama_set_name: RedPajamaCommonCrawl


text:
Q: Javascript - can console.log an object inside a function but when returned it is comes back as undefined I am trying to write a function to geocode a zipcode, and return the lat and lng to an object, and then return this object. I am then trying to assign the value of this geocode function to a variable to create an object.
When I console log the object created in this function, I get an object returned in my terminal as expected:
const geo = (zip) => {
    geocoder.geocode(zip, function(err, data) {
                let latLng = {};
                latLng.lat = data.results[0].geometry.location.lat;
                latLng.lng = data.results[0].geometry.location.lng;

                console.log(latLng); // Returns Object As Expected
        });
};

But when I instead replace the console.log(latLng) with return latLng I get an undefined value when assigning this function to a variable like so:
let newObj = geo(zip);
console.log(newObj) // Returns Undefined

I am confused because the initial console.log does show an object is being made in the geocode function, but when I use return it is coming back as undefined. I think it is an issue with how I am assigning it to a variable. I am misunderstanding something without doubt.

A: The problem is you are mixing async (via callbacks with sync), you could use promises to simplify this
const geo = (zip) => {
    return new Promise(resolve => {
       geocoder.geocode(zip, (err, data) => {
            let latLng = {};
            latLng.lat = data.results[0].geometry.location.lat;
            latLng.lng = data.results[0].geometry.location.lng;
            resolve(latLng);
       });
    })
};

you can utilise the function then like
geo(zip).then(latLng => {
  console.log(latLng) // or whatever
})

A: You didn't specify a return statement. Avoid the arrow in this case and just use a normal function. .geocode() method return value is not assigned in any way. You need to keep throwing the value back to its caller.
const geo = function(zip) {
        return geocoder.geocode(zip, function(err, data) {
                let latLng = {};
                latLng.lat = data.results[0].geometry.location.lat;
                latLng.lng = data.results[0].geometry.location.lng;

                console.log(latLng); // Returns Object As Expected
                return latLng;
        });

};

A: You can try async-await because geocode method is asynchronous.
Reference: How to make this method asynchronous?

redpajama_set_name: RedPajamaStackExchange


text:
University Boardrooms Need Corporate Governance-Style Reform
Universities are in many ways the worst run institutions in our society, with basically lousy governance.We see this in the way administrative bloat drives university tuition increases at well above inflation rates. We see it in the way the inmates are all too often allowed to run the asylum. We see it in how hiring emphasizes every kind of diversity except for ideological and intellectual diversity.
Maybe the answer has been staring us in the face; namely, bringing corporate governance reform to the university.
In today's Wall Street Journal, Paul S. Ivey explains that he resigned from the University of Pennsylvania's board of trustees mainly because it turned out that the board was a powerless, toothless watchdog. It sounds like administrators treated Penn's board the way farmers treat mushrooms (keep them in the dark and covered with manure).
Trustees and donors candidly admit in private that they do not want to jeopardize their children's chances for admission. Many serve out of genuine interest and affection for their alma mater, although they also enjoy the prestige, influence and perks, like access to the university's medical system, that go with the positions. There's no incentive to rock the boat, and universities make sure they don't get much opportunity. At the trustee level, the board is large and its formal meetings are entirely show and tell, with discussion severely limited and vote outcomes never in doubt. Penn Law overseers do not vote on anything. One Penn medical school board member told me he was dropped because he had asked too many questions.
The corporate world offers a parallel to trustees' abdication of their fiduciary duties. Reformers of the 1980s argued correctly that the interests of shareholders were too often subjugated to personal interest and small-group social dynamics on boards that compel unanimity. Just as the resulting realignment of interests between corporate boards and shareholders unleashed spectacular value for American investors, an activist response by the governing bodies of America's universities is now essential.
The problem is pervasive, as Michael DeBow explains:
On August 19 [2014] the intrepid American Council of Trustees and Alumni (ACTA) released a timely report on the governance of colleges and universities. "Governance for a New Era: A Blueprint for Higher Education Trustees" was the work of a blue-chip 22-member committee chaired by Benno Schmidt, a former president of Yale.
The 16-page report is available online, and deserves a wide readership, since most Americans have some stake in higher ed – as taxpayers, even if not as parents or students or employers.
As a matter of legal formality, the ultimate responsibility for a college or university is almost always in the hands of a board of trustees, responsible for selecting the institution's president, monitoring his or her performance, and making broad policy decisions. As a practical matter, however, the trustees' actual input into university governance is usually much less than that of the faculty – who call the shots as to the curriculum and other matters of academic policy, and the president and senior administrators – who run virtually everything else. This status quo is sometimes referred to, approvingly, as "shared governance."
Obviously, I'm not calling for the sort of Wild West activism we see in the corporate world. All I am saying is that higher education reform really ought to start with reforming the board of trustees so as to give them real power:
New board members should be selected by a committee of independent board members.
Board members should have the power to hire and fire the University President.
The University President's compensation should be set by the board.
Board members should be subject to term limits.
Board members should be committed to fostering a learning environment rather than safe spaces.
Board members should meet in executive session periodically without the presence of any university employees.
If the board chairman is a university employee, the board should appoint a lead independent member with power to call board meetings.
It should be made clear that the university's outside auditors work for the Board not the President.
Some of these reforms go beyond what I think is appropriate for corporations, but the reason should be obvious. Firms operate in host of markets that constrain managers and discipline poor ones. Imperfectly, to be sure, which is why governance is important, but surely better than the virtual absence of market constraints on universities.
Posted at 03:38 PM in Higher Education | Permalink | Comments (0)
Another item in the "I probably couldn't get a job as an academic these days" file
As an addition to the "I probably couldn't get a job as an academic these days" file comes this link (via @roddreher) to a University of California San Diego document requiring a written statement from faculty applicants:
The Contributions to Diversity Statement should describe your past efforts, as well as future plans to advance diversity, equity and inclusion. It should also demonstrate an understanding of the barriers facing women and underrepresented minorities and of UC San Diego's mission to meet the educational needs of our diverse student population.
Some faculty candidates may not have substantial past activities. If such cases, we recommend focusing on future plans in your statement. However, please note that a demonstrated record of past effort is given greater weight than articulating awareness of barriers or stating future plans. A more developed and substantial plan is expected for senior candidates.
I suspect "none" would not be an acceptable answer. I also expect a detailed discussion of how one has tried to promote intellectual diversity within the academy by resisting the left-liberal hegemony would be even less acceptable. Just as well I'm not trying to get a job at UCSD.
Bad Academic Writing
There is a new paper out on the corporation form and the concept of reification. My initial reaction to the paper was, how can you write on that topic and not cite the seminal work of my friend, co-author, and colleague William Klein. My second reaction was to have a bad headache caused by plowing through this sort of writing:
Combining the reified status of the corporate form with its convenience for the perpetuation of a particular kind of political economy, it can be argued that the highly problematic ontological and epistemological status of the corporate form may very well not be the result of simple theoretical and methodological aberration, and will probably not be solved by better theory formation.
As the NY Times observed all the way back in 1999:
The journal Philosophy and Literature has taken to holding an annual Bad Writing Contest, with prizes going to the work of some of the country's top scholars. There is even an Internet site that automatically creates a ''post-modern'' essay, replete with bloated jargon and incomprehensible sentence structure, every time someone logs onto it (www.cs.monash.edu.au/cgi-bin /postmodern). (''If one examines a post-dialectic conceptualist theory, one is faced with a choice: either reject post-dialectic conceptualist theory or conclude that culture is capable of truth,'' was a recent creation.)
Yet the debate has taken a new twist recently with a decision by Edward Said, the new president of the Modern Language Association, to use his first official column in the association's newsletter to denounce bad writing. In an essay on how science is growing at the expense of the humanities, he accused literature departments of fostering incomprehensible writing and factionalism, resulting even more in their ''diminishment and incoherence.''
It wasn't just Mr. Said's position as head of the largest and most influential organization of literary scholars that caught people's attention, however: Mr. Said himself is a progenitor of a new kind of literary and cultural criticism that has frequently used difficult language.
One of the country's most prominent literary critics, Mr. Said concedes that his own writing hasn't always been easily accessible, but he said in an interview: ''I moved away from that kind of thing many years ago, because I feel myself that it's terribly important as an intellectual to communicate as immediately and forcefully as possible.
Indeed, clarity ought to be the first goal of every academic. Bad writing is contrary to the very purpose of academic inquiry:
In 2006, Daniel Oppenheimer, then a professor of psychology and public affairs at Princeton University, published research arguing that the use of clear, simple words over needlessly complex ones can actually make authors appear more intelligent. ...
A disconnect between researchers and their audiences fuels the problem, according to Deborah S. Bosley, a clear-writing consultant and former University of North Carolina English professor. "Academics, in general, don't think about the public; they don't think about the average person, and they don't even think about their students when they write," she says. "Their intended audience is always their peers. That's who they have to impress to get tenure." But Bosley, who has a doctorate in rhetoric and writing, says that academic prose is often so riddled with professional jargon and needlessly complex syntax that even someone with a Ph.D. can't understand a fellow Ph.D.'s work unless he or she comes from the very same discipline.
Indeed. I have been thinking, reading, and writing about reification and the corporate form for almost 3 decades and I have no idea what point the author was trying to make. Interestingly, the corporate law sentence got a Gunning Fox index of 36, while the invented post-modern literary sentence only got an index score of 21. "An interpretation [of the index number] is that the text can be understood by someone who left full-time education at a later age than the index."
There are lots of explanations for why people write this way, but I think a lot of it has to do with obfuscation. How do you assess the merits of an idea if you can't parse it?
The Holes in Holistic Admissions
No sooner had Michigan voters passed Proposition 2, which bans affirmative action by state institutions in education, employment, and contracting, than University of Michigan President Mary Sue Coleman went to the University's famous Diag to issue a defiant blast. In a scene eerily mirroring Southern governors standing in school doors, Coleman announced a campaign of massive resistance to Proposition 2:
"I am deeply disappointed that the voters of our state have rejected affirmative action as a way to help build a community that is fair and equal for all. But we will not be deterred in the all-important work of creating a diverse, welcoming campus. We will not be deterred."[1]
Coleman plans a legal challenge to Proposition 2, but the odds are stacked against her. Michigan's Proposition 2 essentially tracks California's Proposition 209, which has been upheld by the courts against legal challenges.
Once the legal dust settles, the University of Michigan likely will look to the University of California's experience under Proposition 209 as a model.
The UC system has made no secret of its desire to preserve racial diversity on its campus. As UC President Richard Atkinson once explained:
"When race and ethnicity were disallowed, UC launched a greatly intensified program of outreach to public schools, working in partnership to improve academic performance and college eligibility in schools that traditionally sent few students to UC. We also made changes in our admissions process — such as granting UC eligibility to the top 4% of students in every California high school."[2]
The representation of certain minority groups in the UCLA student body dropped after 209 passed, especially African-Americans and Latinos, as it did at other UC campuses. Although Latino representation has increased somewhat in recent years, African-American enrollment at UCLA remains well below its pre-209 level. Clearly, the University administration is dissatisfied. According to and article in UCLA Today:
[Former UCLA Albert Chancellor] Carnesale ... calls the dearth of African-American faces among UCLA students "one of two critical challenges facing the university" alongside the need for increased financial resources.
"There is no question in my mind that my successor must be committed to meeting this challenge head-on," Carnesale said in a June 5 letter to members of the UCLA Afrikan Student Union, who met with the chancellor June 2 following a student demonstration.
It's with this background in mind that we must evaluate UCLA's recent decision to adopt a so-called holistic admission policy. UCLA's Janina Montero described the school's policy this way:
Under the new model, the review of each application will be an integrated process that will consider the full record of a student's achievements and experiences, as well as the challenges faced, and provide a more carefully individualized and qualitative assessment.[3]
And according to UCLA's News Office:
"Holistic review is another philosophical approach to implementing comprehensive review, and it will allow us the opportunity to have applications reviewed in their entirety so that all of a student's achievements — from academic performance to leadership skills and community services — can be looked at in the context of their life experiences," said Jenny Sharpe, chair of the faculty Committee on Undergraduate Admissions and Relations with Schools.[4]
It's clear that the continued low levels of African-Americans in the UCLA student body was the "catalyst" for this change.[5]
But why do UCLA administrators think holistic review will address that problem? If consideration of race is excluded from the process, do the life experiences and personal achievements of underrepresented minorities systematically differ from those of whites or Asian-Americans? To be sure, an African-American who excels despite growing up in an economically disadvantaged home has overcome important challenges relative to some privileged Beverly Hills High white student. But so has a white child from a family below the poverty line or an Asian-American from an immigrant family. In other words, if applied without regard to race, holistic admission should affect class differences in the student body rather than racial ones.
As commentator Lance Izumi observes, however, UC Berkeley's experience with its similar system of comprehensive review suggests that holistic admissions in fact will affect the racial composition of the student body:
Although race is not overtly mentioned as a factor in the comprehensive-review admissions process, the numbers indicate that it plays a significant role. A Los Angeles Times analysis shows that at UC Berkeley, low-scoring blacks and Hispanics were admitted at twice the rate of similarly scoring Asians and whites.[6]
A cynic thus might wonder whether the readers charged with holistically evaluating admission files share UM President Coleman's refusal to "stand by while the very heart and soul of this great university is threatened," and are taking into account the race of candidates despite Proposition 209's ban. Indeed, when Berkeley adopted comprehensive review, student activist Hoku Jeffrey noted that it gave "admissions officials the ability on paper to reverse the segregation ... and that's what must be done."[7]
The trouble with holistic admissions is that the readers don't have to explain why they make their admission decisions. They simply score the files. Whether consciously or unconsciously, the readers thus could systematically bias their scores so as to promote diversity, and no one would be the wiser. Even if required to offer an explanation for their scores, moreover, readers likely would point to some wrinkle other than race in each application that purportedly justified their decision.
It's a sure bet that UM officials will look to the UC as a model for dealing with Proposition 2. When they do so, Michigan voters should insist on a far greater degree of transparency and accountability than California voters have received.
[1] Mary Sue Cleman, Diversity Matters at Michigan (Nov. 8, 2006), available at http://www.umich.edu/pres/speeches/061103div.html
[2] Richard C. Atkinson, Efforts to Reflect Diversity Fall Short (2003), available at http://www.today.ucla.edu/2003/030513efforts.html
[3] Janina Montero, Holistic Evaluation Makes Admissions Fair, UCLA Today (Oct. 24, 2006), available at http://www.today.ucla.edu/voices/janina-montero_admissions/
[4] UCLA News Office Press Release, UCLA Adopts a Holistic Approach to Reviewing Freshman Applications; Change Is Most Sweeping Since Systemwide Revisions Five Years Ago (Sept. 28, 2006), available at http://newsroom.ucla.edu/page.asp?RelNum=7375
[6] Lance T. Izumi, Berkeley's "F": Unfair admissions game, Nat'l Rev. Online (Nov. 24, 2003), available at http://www.nationalreview.com/comment/izumi200311240932.asp
[7] Eric Ostrem, Regents Committee Passes Comprehensive Admissions, Daily Californian (Nov. 15, 2001), available at http://www.dailycal.org/sharticle.php?id=7076
Posted at 09:20 AM in Higher Education | Permalink | Comments (0)


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All My Pages
Melinda Moore was born in New Jersey, after which her family moved to South Florida. Living on a lake at the edge of the Everglades, within a mile of the Atlantic Ocean, gave her a unique opportunity to explore nature. Generations of family sailing enthusiasts fostered the love of all things nautical.
On a professional level, Melinda's caring nature motivated her to acquire her BS degree in Nursing from Florida State University, which was then followed by a career in Coronary Care in Boston and Yale in New Haven Ct. During this time Melinda discovered and studied film photography, which has become her lifelong passion, and her camera became her constant companion.
The intense five years of her life as a nurse in New England was then followed by a more peaceful style of living on a sailboat in Florida and the Bahamas. This is where Melinda spent time studying creatures under the sea and the vast sky above.
In the mid 1980's Melinda's adventurous family moved to Spain where she made her home for seventeen years, owned a property management company and raised her daughter Danielle. Living in Southern Spain exposed her to diverse cultures, and gave her an opportunity to travel throughout the British Isles, Europe and Africa. Visiting museums in Madrid, Paris and Rome offered a constant flow of inspiration and education in the arts. Studying Japanese Art, Ikebana, and Tea Ceremony for nine years with a Japanese Cultural Organization trained her eye for fine composition.
These wonderful life experiences have contributed to Melinda's unique world view and influenced her creative vision. Returning to her South Florida roots in 2001 she reunited with a long lost love from High School, William DaCamara and in 2003 they married, after this time she decided to focus even more intently on her art.
In 2008 she shared her photographic and digital talent with the world for the first time. She instantly gained recognition, winning various competitions, and receiving invitations to become a featured artist in galleries.
Melinda is very active in National Association of Women Artists, , Lighthouse ArtCenter, Jupiter Fl, Palm Beach Photographic Center, Artists of Palm Beach County, Armory Arts Salon . She volunteers time, both at Grassy Waters and Loxahatchee Preserves, and is an active board member of the Friends of John D MacArthur Beach State Park. Her images have been displayed in print and on large canvas at places like the Palm Beach International Airport, Coral Springs Art Museum, Palm Beach Photographic Center, Armory Arts Center, Southeast Gallery, and M&M Studio Gallery.
Dec 2010 to March 2011 she had her first Solo exhibit "Creative Nature" at the John D MacArthur Beach State Park followed by two more Solo shows in 2012, "Creative Focus" for GardensArt, City of Palm Beach Gardens, City Hall Lobby and"Creative Nature " Johnathan Dickinson State Park Hobe Sound, Fl and
First Public Art Installation at John D. MacArthur Beach State Park
Currently she gives presentations Art Schools , Florida Parks, Preserves, Audubon Society and Camera Clubs
Juror and Judge in local PSA Camera Clubs
In 2011 Melinda was invited to be represented commercially by Getty Images. She is being published globally and you may find her work in magazines, like Audubon or on the cover of a novel.
Her Fine Art is being collected privately and is signed and produced with archival process.
You can find her work at:
ArtfulHome.com (online Gallery curated by Michael Monroe who was curator for Smithsonian Renwick for 20 yrs)
Artisans on the Ave Gallery, Lake Worth, Fl Lighthouse Arts Center Guild Gallery, Tequesta, Fl
M&M Studio Gallery, Jupiter, Fl
John D MacArthur State Park, Singer Island Fl (if art purchased via location % donated to Nature Education Foundation)


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A Greener Le Châtelier's Principle Lab Mark as Favorite (33 Favorites)
LAB in Le Châtelier's Principle, Establishing Equilibrium. Last updated February 25, 2020.
In this lab, students will explore Le Châtelier's Principle using non-toxic materials, while still visualizing the equilibrium shifts through color changes. Traditionally, equilibrium experiments and Le Châtelier's Principle are illustrated using chemicals that undergo color changes as the equilibrium position shifts such as cobalt (IV) chloride and iron (III) thiocyanate. While these reactions effectively demonstrate Le Châtelier's Principle, they utilize reagents that are toxic.
NGSS Alignment
This lab will help prepare your students to meet the performance expectations in the following standards:
HS-PS1-6: Refine the design of a chemical system by specifying a change in conditions that would produce increased amounts of products at equilibrium.
Scientific and Engineering Practices:
Constructing Explanations and Designing Solutions
Engaging in Argument from Evidence
AP Chemistry Curriculum Framework
This lab supports the following units, topics and learning objective:
Unit 7: Equilibrium
Topic 7.9: Introduction to Le Châtelier's Principle
TRA-8.A: Identify the response of a system at equilibrium to an external stress, using Le Châtelier's principle.
By the end of this lab, students should be able to:
Explain the concept of chemical equilibrium.
Analyze how changing temperature can affect the equilibrium shift of a chemical reaction.
Interpret how changing concentration can affect the equilibrium shift of a chemical reaction.
Explain the "greenness" of the chemical reactions using the 12 principles of green chemistry.
This lab supports students' understanding of:
Le Châtelier's Principle
Reversible Reactions
Per group:
6 test tubes
Test tube rack
30 mL of butterfly pea tea
15 drops of vinegar or lemon juice
1 g of baking soda
3 drops of tincture of iodine
Hotplate/electric kettle
1 small (100-mL) beaker
2 larger (~400 mL) beakers (for hot and cold water baths)
Stirring rod
2 plastic pipettes
100 mL graduated cylinder
1 scoopula
When students complete the lab, instruct them how to clean up their materials and dispose of any chemicals.
Exercise caution when using a heat source. Hot plates should be turned off and unplugged as soon as they are no longer needed.
Iodine is a minor eye irritant and vinegar can cause skin irritation.
To learn even more about using this lab in your classroom, read the article, Designing a Greener Le Châtelier's Principle Lab , published in the March 2020 issue of Chemistry Solutions.
This lab is written for a general chemistry level but can be updated for AP Chemistry by including discussion of Q vs. K for each of the activities.
Starch solution: Spray starch into a 300 mL beaker (cover the bottom of the beaker with spray or use 5-10 drops of liquid starch). Fill the remainder of the beaker with water and stir. Be sure to check the ingredients list when purchasing spray starch to ensure starch is one of the primary ingredients.
Butterfly Pea tea: Prepare 1000 mL of butterfly pea tea—the darker, the better. Prepare with electric kettle and allow it to cool to room temperature.
Additional reagents that may be used for student inquiry include black tea (substitute for the butterfly pea tea) and lemon juice (substitute for the vinegar).
Activity 1 uses an iodine-starch complex to demonstrate the effect of temperature on equilibrium. The formation of the complex is an exothermic reaction and results in a deep purple color.
Iodine (aq) + Starch (aq) ↔ Starch-Iodine Complex (aq)
The Iodine-starch complex is colorless, and the starch-iodine complex is blue-black.
The soluble starch acts as an indicator of molecular iodine. In the above reaction, the "shifts" in equilibrium position produced by temperature changes are in accordance with Le Chatelier's principle. Enthalpy, or ΔH, is negative (exothermic) in the forward direction as written above because heat is being released. Cooling the system, or taking away heat, causes a "shift" to the right, resulting in the formation of the blue-black starch-iodine complex. Adding heat to the system causes a "shift" in the reverse direction (ΔH is positive) and the starch-iodine complex dissociates in an endothermic reaction, causing the system again to become clear and colorless.
In Activity 2, students evaluate the effect of pH on equilibrium using butterfly pea tea, baking soda, and vinegar. When vinegar is added to the tea, the color of the solution lightens and changes from blue to purple. When baking soda is added to the tea, the solution darkens and turns a blue/green color. This can be reversed, and students can change the tea solution in color to observe the shift in equilibrium. The lab presents this reaction in the format of:
Tea (aq) + H+ (aq) ↔ TeaH+(aq)
The Tea (aq) + H+ (aq) is blue/green, and the TeaH+(aq) is purple.
Butterfly Pea Tea can act as a base and react with an acidic solution (H +) to form the conjugate acid, a complex with the tea and hydrogen ion. In this lab, vinegar will be the source of the hydrogen ions. In addition you will observe how the color changes when a base, baking soda, NaHCO 3 is added. When NaHCO3 is added, it can accept a hydrogen ion, decreasing its concentration in solution.
Depending on the level of your students, you may prefer to share more information with them. Supplemental information is available for teacher download and use.
An Answer Key has been provided for teacher reference.
Special Acknowledgement: Thank you to a collaboration from a 2008 Beyond Benign Green Chemistry workshop for the original creation of this lab and all of the many edits made over the years from curriculum specialists, lead teachers, and workshop participants.
Chemical equilibrium is a state of dynamic balance where the rate of the forward reaction is the same as the rate of the reverse reaction. Examples of reactions in dynamic equilibrium are:
N2 (g) + 3H2 (g )↔ 2NH3 (g)
2NO2 (g) ↔ N2O4 (g)
If you look up "equilibrium," you will find it explained using words like "state of balance." A meter stick that is suspended at its center of gravity is said to be balanced or in equilibrium; it remains stationary, or static. Thus, this type of equilibrium is often referred to as static equilibrium. In other words, the entire system is not moving. Consider now the case of a man running on a treadmill. Overall, there is no change in his position. He is running forward at the same speed that the belt is moving in the opposite direction. The two opposing motions balance each other. This is an example of a type of equilibrium called dynamic equilibrium, as the word "dynamic" means "moving." Another example of a dynamic equilibrium is when you walk down an escalator at the same speed as it is moving up. There is no overall change in your position because the two opposing motions are balanced.
How can Le Châtelier's Principle be used to predict the direction in which a system at equilibrium will shift when conditions are altered? The equilibrium system that we will study in the Pre-Lab is:
CuCl2 (aq) ↔ Cu2+(aq)
+ 2Cl− (aq) + heat (∆H < 0)
Write out the balanced reaction between AgNO3 (aq) and CuCl2 (aq) and identify the precipitate formed.
For each change listed, predict the equilibrium shift using the reaction from Question 1 and your knowledge of Le Châtelier's Principle:
Direction of shift
(←, →, or no change)
Raise temperature
Add AgNO3 (aq)
Lower temperature
Add NaCl (aq)
Activity 1: Iodine and Starch
Starch solution
100 mL beaker
Plastic pipettes
Glass stirring rod
2 (300-400 mL) beakers for cold/hot water baths
Hot plate/electric kettle
Wash your hands thoroughly before leaving the lab.
Follow teacher instructions for clean up of materials and disposal of any chemicals.
Iodine is a minor eye irritant.
Measure 60 mL of starch solution using the graduated cylinder and pour into a small beaker.
Use the plastic pipette to add 3 drops of tincture of iodine to the starch solution and stir with a glass stirring rod. Note the color of the starch-iodine complex in data table.
Pour 20 mL of the starch/iodine solution into each of the three test tubes.
Prepare an ice bath with ice cubes and water in a 300 mL beaker.
Prepare a hot water bath by heating a 300 mL beaker filled with water on a hot plate until it reaches 800C; measure the temperature with a thermometer. Alternatively, obtain pre-heated water from your teacher.
Place one test tube in the ice bath, one in the hot water bath, and leave one as a control.
Observe and record the changes that occur.
Data and Observations
Iodine(aq) + Starch (aq)
↔ Starch-Iodine Complex (aq)
clear blue/black
Resulting color
What effect did heating the test tube have on the concentration of starch-iodine complex? Explain how you know this by using Le Châtelier's Principle.
What effect did cooling the test tube have on the concentration of starch-iodine complex? Explain how you know this by using Le Châtelier's Principle.
Which direction is exothermic? Which direction is endothermic? Explain your answer
Activity 2: Butterfly Pea Tea
Butterfly pea tea solution
1 plastic pipette
Scoopula
Follow teacher instructions for clean-up of materials and disposal of any chemicals.
Vinegar can cause skin irritation.
Measure 60 mL of butterfly pea tea solution into the graduated cylinder.
Pour 2 mL of tea into each of the three test tubes.
Use a plastic pipette to add 15 drops of vinegar to one of the test tubes, stir with a stirring rod.
Use a scoopula to weigh out 1 g of baking soda on a balance. Add 1 g of baking soda to the second test tube, stir with a stirring rod, and label.
Leave the third test tube as a control.
Observe and record the color changes that occur.
Tea (aq) + H+ ↔ TeaH+ (aq)
green/blue purple
Vinegar addition
Baking soda addition
For each reaction in Activity 2, demonstrate how each change can be explained by Le Châtelier's Principle. Be specific about where the chemical was added (the stress) and its impact on the other components of the Tea Equilibrium.
Traditionally, Le Châtelier's Principle might be demonstrated using cobalt ions. Explain why the activity you completed is a greener reaction. Cite at least one of the 12 green chemistry principles with your justification.
Find an accompanying article in the March 2020 issue of Chemistry Solutions.
Teacher Guide.docx
Student Activity.docx
Answer Key.docx
Answer Key.pdf
Cassidy Javner
Shakopee High School
Beyond Benign
Wilmington, MA


3rd ANNUAL WCN NATURAL & WILDLIFE PHOTOGRAPHY COMPETITIONS 2007 FOR AMATEURS.
WCN NATURAL & WILDLIFE PHOTOGRAPHY COMPETITIONS 2005 FOR AMATEURS.
This entry was posted on February 1, 2007 at 4:26 pm and is filed under Contest, Nepal Photography, Nepali Photographers, Promo.	You can follow any responses to this entry through the RSS 2.0 feed. You can leave a response, or trackback from your own site.


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Q: How many bit-flips are required to achieve random distribution? If I have a binary number W bits wide, initially all set to zero, and I repeatedly pick a random bit and toggle it from zero to one or vice versa, how many times would I need to do this to achieve maximum entropy?
I hope I am using the term "maximum entropy" correctly -- what I mean is  a point where the distribution of ones and zeros is as random as possible, and no amount of continued toggling will make the distribution any more random.

A: After $N=2T$ toggles, the average value of any bit is
$$\sum_{k=0}^{T-1}\frac{(W-1)^{2T-2k-1}}{W^{2T}}{2T\choose 2k+1}\\
=\frac12-\frac12\left(\frac{W-2}W\right)^{2T}$$
and the second term is roughly $\frac12e^{-2N/W}$.  Use enough toggles to make that difference small enough.

A: Starting with $N$ zero bits and performing $F$ flips.  Let's look at a single bit.
There are $(N-1)^{F-k}\binom{N}{k}$ ways that our bit can be flipped $k$ times.  There are $N^F$ ways the random choices can go, so define $p_k$:
$$p_k = \frac{(N-1)^{F-k}}{N^F}\binom{N}{k}$$
The generating function $G(x)$ for the number of flips is:
$$G(x) = \sum_k p_kx^k = \left(\frac{N-1}{N} + \frac{x}{N}\right)^F$$
We're interested in distinguishing the cases where the bit is flipped an even number of times and an odd number of times.  This is easy to do.  For any polynomial $p(x)$, $\frac{p(x) + p(-x)}{2}$ creates a new polynomial containing only the even terms of $p$, and $\frac{p(x) - p(-x)}{2}$ contains only the odd terms of p.
$$p_\text{even} = \frac{G(1) + G(-1)}{2}\\
p_\text{odd} = \frac{G(1) - G(-1)}{2}$$
Obviously, $G(1) = 1$.  But $G(-1) = \left(\frac{N-2}{N}\right)^F > 0$.  So you will always have:
$$p_\text{even} = \frac12 + \left(\frac{N-2}{N}\right)^F\\
p_\text{odd} = \frac12 - \left(\frac{N-2}{N}\right)^F\\
$$
$$ \lim_{N \rightarrow \infty}\left(\frac{N-2}{N}\right)^F = e^\frac{-2F}{N}$$


Baye and Jag's wedding was as trendy as it gets! Using Pantone's 2016 Colors of the Year (Serenity and Rose Quartz) as their guiding inspiration, this multi-day celebration was filled with marble, agate, and soft pastel hues. As the bride is an event planner, it was only natural that she would plan the day herself, with a little help from her friends at Laura & Co. Events. See more in the Summer/Fall 2018 issue of WedLuxe Magazine (Toronto & Eastern Canada edition), on newsstands now.


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From vineyards to bottle
By Visitors Guide
Paso Robles Paso Robles Wineries Wineries
Estrella Warbirds Museum and Woodland Auto Display, where history still flies
Paso Pizza Kitchen delivers fresh, tasty dishes
Spice up life at Naughty But Nice
Carnival rides free opening day of the Mid-State Fair
Enjoy some 'Liquid California' at Calwise Spirits Distillpub
Clavo Cellars showcases the best of Central Coast grapes
While he attended college at Fresno State, part of Neil Roberts' education was working in the vineyards with the field workers. "I was a green kid, and the guys in the field took me under their wing," Neil said. They gave him the nickname "Clavo," which means nail (sounds like Neil) in Spanish.
Neil has been managing and farming vineyards for most of his life. He grew up in California's Central Valley, where his family farmed grapes and row crops. After graduating from Fresno State, he started working as a viticulturalist for Robert Mondavi and traveled to San Luis Obispo, Monterey, and Santa Barbara counties. It was this time spent traveling, farming, and learning the ins and outs of the vineyard that piqued his interest in making wine.
In 2006, Neil started his own vineyard management company, Robert's Vineyard Services, and in 2004, he started making wine of his own – under the label Clavo, an homage to his nickname from the fields.
While Clavo Cellars does not have their own vineyard, per se, "We source the grapes for our wines off of the properties that we manage," says Neil. "We have lots of latitude in using these sites because we manage the grapes." This flexibility as well as hands-on work with the grapes allows Neil to make wines that express the individuality of each vineyard and see the process from beginning to end.
"I have a unique opportunity to make unique wines," Neil says, "We are always experimenting to get the best out of each vineyard."
The grapes that go into Clavo wines are sourced from several unique sites, from old vine zin at Peachy Canyon to mourvedre and grenache growing on steep West Side hillsides, to 870 acres of grapes on Santa Margarita Ranch.
Clavo Cellars' tasting room is located in downtown Templeton. "Templeton has its own character and uniqueness," says Neil, "It seems like a sleepy town, but we have great restaurants and community events – it's a truly unique destination. I would love to see more tasting rooms downtown."
One of Templeton's great community events is the summer Concerts in the Park series, which take place every Wednesday evening during the summer months. Each week, Clavo hosts a pre-concert party, where guests can come enjoy a glass of wine, catered hors d'oeuvres, and live music while they pick up a bottle of wine to enjoy in the park.
Clavo Cellars is located at 315 S. Main Street in Downtown Templeton. The tasting room is open from 12:00-5:00pm daily. For more information, please call (805) 226-0174 or visit clavocellars.com. They can also be found on Facebook: facebook.com/profile.php?id=1473375495.
— Laura E Zolnoski
Just the ticket for great wine in Templeton
Celebrating 20 years of Paso's heritage winegrape
The Central Coast Shakespeare Festival returns July 11
Celebrate Independence Day at Paso Pops
'Comedy For Musicians But Everyone Is Welcome' Review: Fred Armisen is hilariously funny
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In the 1920s and the 1930s, African-American artists, writers, dancers, scholars, singers, jazz musicians, gospel musicians, photographers, sculptors, actors, and the like initiated an outburst of creativity in Harlem, New York, which became known as the Harlem Renaissance.
Because the Harlem Renaissance was such a defining cultural and spiritual movement in America, Rodney and Reynita Blake used this idea as the theme of their nuptials. Bishop Sir Walter Mack Jr., pastor of Union Baptist Church, officiated with the assistance of Rev. Anita Wade.
Rodney and Reynita Blake were married at 1 p.m. on Saturday, March 10, at Saint John Christian Methodist Episcopal Church in the rich Harlem Renaissance tradition. More than 200 well-wishers attended. Cultural and spiritual aspects in their wedding included an invitation to discipleship, jumping the broom, the three-cord ceremony and the African libation.
Rodney Blake, the groom, is elated that their special day finally came.
The couple included the invitation to discipleship during their wedding.
"It is very fitting for Bishop Mack to open the doors of the church. This wedding is not just about us. It is also to bless the community," Rodney Blake said.
The musical selections were diverse. Baba Joseph of The Healing Force sang "Many Waters Cannot Quench Love" and "Think on These Things." Trumpet Maestro Dr. James Armstrong performed "Trumpet Voluntary" (Jeremiah Clark) and "Jesu, Joy of Man's Desiring" (J.S. Bach) accompanied by Rochelle L. Joyner, organ/piano maestro. Dana Powers sang "The Lord's Prayer" and "The One He Kept for Me" (Donald Hayes and Teresa Jones) as the bride made her entrance escorted by her father, Reynard McMillan.
The couple jumped the broom to connect with the African American tradition that marked matrimony for enslaved Africans.
Note: The Rev. Dr. Felecia Piggott-Long, the mother of the bride, performed the African libation and created the ancestor table to honor deceased family members and friends.


Join us for our popular breakfast discussion series designed to empower area business, educational and nonprofit leaders to be courageous. Simon Sinek, whose TED talks are among the most popular, tells us: "Leaders rush headlong into the unknown" and shows us how to become leaders others will follow. Come for one session, some sessions or all; each is a "stand alone" lesson drawn from this inspiring "how to" book.
LGH dues-paying members attend free! Need to register for or renew your membership? Click here.
Each session is "stand alone" and reading the book is not required. However we recommend that you read the book and attend as many sessions as possible to take full advantage of this popular series.
Questions? Please contact Jernelle Lavinier at 860.206.5074 or jernelle.lavinier@leadershipgh.org.


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Titanic Travels: Places with a link to the RMS Titanic
More than a hundred years after its tragic maiden voyage, the RMS Titanic still continues to fascinate people around the world. I've been lucky enough to visit a number of places that have a special link to that ill-fated ocean liner.
The Titanic embarked from Southampton on its maiden voyage on 10th April 2012. Many of the crew members came from that town, and more than five hundred households there lost a family member when the ship sank. The SeaCity Museum contains a collection of artefacts related to the Titanic, and there are two memorials in the town centre to staff on the Titanic. One is dedicated to the engineers and the other to the musicians. They all perished in the disaster.
Titanic's last port of call was Cobh in Ireland, which at that time was called Queenstown, a port close to the city of Cork. That was the last sight of land for those who perished, and the town still retains many old buildings. Most of the Irish immigrants boarded the ship there.
Titanic Experience is a museum in the former White Star Line ticket office, right beside the cruise terminal.
As you sail away from Cobh, you can see a memorial that was built to commemorate the hundredth anniversary of the tragedy.
Belfast in Northern Ireland is where the Titanic was built, at the Harland and Wolff shipyards. Now the Titanic Quarter is a major tourist attraction in the city. That includes Titanic Belfast, a huge immersive museum dedicated to RMS Titanic. In the same location is Harland and Wolff's drawing office and the historic slipway where the Titanic was built and launched.
At the same site, you can also visit the Nomadic, a tender built for the ship.
After the sinking, three ships were sent from Halifax, Nova Scotia to retrieve bodies. Fairview Lawn Cemetery is where 121 victims of the Titanic are buried. They represent a significant proportion of the 300 bodies retrieved from the icy waters at the site of the disaster. A small number are buried in two other local cemetries. 100 people were buried at sea and 59 others who came from wealthier families were transported home by train.
The Maritime Museum in the downtown area has an impressive collection of relics from the Titanic, including a deck chair. There is also a display about the retrieval of bodies and the tremendous impact this had on the city.
Thank you for stopping by. I'm an author who loves writing thrillers, and you can find out more about my books HERE. My latest release DEAD DRY HEART has been receiving great reviews at Amazon and Goodreads.
This is where you can find me online:
Text and Image copyright © 2018 Toni Pike – All Rights Reserved
This entry was posted in Blog on September 20, 2018 by Toni Pike.
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5 thoughts on "Titanic Travels: Places with a link to the RMS Titanic"
Terri Webster Schrandt October 22, 2018 at 12:27 am
The Titanic is fascinating and made more so by the movie years ago! Thanks for the historical tour,Toni!
Toni Pike Post author October 22, 2018 at 1:41 am
Thank you so much, Terri. I agree, the Titanic disaster is a fascinating story, and it continues to intrigue us even after more than a hundred years. I also loved the movie, I must admit. x
Lucinda E Clarke November 4, 2018 at 5:47 pm
There is a gruesome fascination with such a ship sinking.
Toni Pike Post author November 4, 2018 at 6:54 pm
There certainly is, Lucinda. A tragic event.
The Roman arena only at sea.


Located in the heart of George Street Sydney, 333 George Street is the latest 5 star Green Star premium office tower to join the harbour city's skyline.
Designed by international architects Grimshaw and Crone Partners, the 18 level commercial office tower is owned by one of Australia's leading property groups, Charter Hall Group.
As well as 12,500m² of contemporary office space, the building's lower three levels offer 2,100m² of retail space with tenants including Woolworths, NAB and HSBC.
The defining feature of the building is its five levels of cascading rooftop terraces that bring the outdoors in and build a sense of community for the building's tenants.
This sense of openness is enhanced by the use of floor-to-ceiling clear glazing.
333 George has been designed as a future-focused workplace, with a number of sustainable design features contributing to its 5 star Green Star Design rating.
These include smart metering and monitoring of energy and water use, the trial of new indoor environment quality (IEQ) technology and a 155kL water storage tank as the primary source of water. Additionally, rainwater and stormwater is collected and used to flush toilets and non-potable water hose taps.
The building is cooled by a water-cooled active chilled beam system.
Having been involved in the project from concept to completion, Automatic Heating is pleased to have had the opportunity to work with mechanical services consultant Arup and mechanical contractor Austral Air Conditioning.
Drawing on our experience and extensive range of hot water heating solutions, Automatic Heating has supplied four 260kW Moorea Condensing Water Heaters.
These met the challenges of tight spatial constraints in the rooftop plant enclosure, and will comfortably meet the building's substantial heating demands in an energy efficient manner.
Moorea condensing boilers represent a highly-evolved water heating system within the mid output range of condensing boilers. Their small footprint, improved modulation range and low operating noise are key features which make the Moorea an ideal fit for many projects.
Importantly, the Moorea units are also cascadable and designed for indoor or outdoor installations – as is the case at 333 George.
In addition, Automatic Heating supplied Duraflex EcoTrap dirt separators to the project, which remove impurities from the system. This contributes to higher system operation efficiencies while also reducing corrosion within the system.
330 George Street reached practical completion in late 2016.
Moorea is highly efficient in floor space as well energy usage.
Available in natural gas and liquid petroleum gas (LPG) options.
Units available to meet a power range of 125 to 600kW, with the option of cascading several boilers to build capacity as required.