text:
739 DWI arrests made during Drive Sober campaign
Mike Randall Times Herald-Record @MikeRandall845
State police made 739 arrests for driving while intoxicated during the Drive Sober or Get Pulled Over campaign, which ran from Aug. 19 to Labor Day.
Of those arrests, 219 took place during Labor Day weekend.
In the lower Hudson Valley, a total of 4,230 tickets were issued during that period: 92 for drunk driving, 1,644 for speeding, 225 for child restraint or seat belt violations, 101 for distracted driving and 68 for violations of the "move over" law.
Statewide, troopers issued 43,704 tickets, including 17,555 for speeding, 2,306 for child restraint and seat belt violations, 1,413 for distracted driving and 531 for failing to move over when approaching a stopped police car or other emergency vehicle. The total number of tickets also includes those written for all other violations.
Troopers investigated 3,243 crashes during the campaign, up from 2,973 last year. There were 1,006 people hurt and 11 killed in those crashes.
"Impaired and reckless drivers are a danger to themselves and others on New York's roadways, and we have zero tolerance for this dangerous and selfish behavior," Gov. Andrew Cuomo said.


The second panel, focused on integrating transportation planning and economic development, began with a presentation by Ken Livingston, of Fitzgerald & Halliday, and Mark Paquette, of the Windham Regional Council of Governments, on creating a regional sustainability plan for eastern Connecticut. The project is located in one of the most rural parts of the I-95 corridor and the eastern seaboard, with a population of 430,331 that is somewhat underserved. A Sustainable Communities Regional Planning Grant administered through the U.S. Department of Housing and Urban Development, the U.S. Department of Transportation, and the U.S. Environmental Protection Agency supported the project.
Livingston and Paquette explained how they engaged both traditional and nontraditional partners in the grant process, allowing them to reach the population base that is a key component of the Sustainable Communities grant focusing on housing and jobs. Consortium members also included a wealth of people who would not be involved in a more traditional transportation project led by the DOT. The project expanded the realm of who was involved in the planning process and functioned as a fully integrated plan. In the context of home rule municipalities in Connecticut, where county governments are not dominant, this level of regional thinking is often considered "almost taboo," and Paquette suggested might have been easier in other states.
The goals of the plan center around improving quality of life in the region, and all of its components (mobility, employment, and housing) return to this point. A large variety of jobs are available in the region, and a total of 113,000 people both live and work in the region, with a larger outflow than inflow of workers. This information was important for studying the demands on the transportation network and how mobility and access could be improved with more options.
Livingston and Paquette emphasized that transportation is a means to an end. The goal should be to increase transportation options and create linkages between housing, jobs, and retail, needs that are not always best served by transit. In promoting affordable transportation, it is important to consider cost to both the user and the provider—sustainable solutions must be innovative and built on partnerships. Another goal of this plan has been to connect the three rural transit districts in eastern Connecticut. The speakers cited a lack of awareness of what resources are available, resulting in some duplication of services. The plan aims to increase understanding of what agencies are already providing service to facilitate further collaboration.
As the project continues, Livingston and Paquette will continue to reach out to constituents to determine their concerns and needs. They plan to create a video as part of the visioning session required by the Sustainable Communities grant to help "tell the story of what's actually happening in the region." For more information, visit http://sustaineasternct.org (http://sustaineasternct NULL.org).
A participant asked Livingston and Paquette if they experienced any political pushback in eastern Connecticut for the sustainability grant. They replied that they made a consistent effort to frame the plan as a way to promote quality of life and access to economic opportunities and did not encounter any notable opposition. Paquette also responded to a question about engaging youth in the planning process. He praised the local community college for being very adaptable by offering new training programs, such as one for brownfields remediation technicians.
This entry was posted in Past Conferences, Presentations, Regional and Community Planning, Sustainable Development, Training, Training, Transportation. Bookmark the permalink.


We have a top level site with a list that contains information (site name, owner, URL, etc.) about subsites that have been created. The scenario I'm trying to figure out is how to change a subsite's URL and Title if a user changes the name of the subsite in the list at the top level. I'm basically trying to use a workflow to accomplish changing the subsite URL and Title name.
I was able to do this on premise using the PowerShell action for Nintex Workflow that Aaron Labiosa created (NTX PowerShell Action - Stable Release). But I'm striking out so far as to how to do this in O365 via Nintex Workflow. Any ideas if this is even possible and if there are any approaches?
Eric, this should be possible, using the Call HTTP Web Service Action. (Don't have time to go into outline it out), but you'll have to come up with a method of know what the current Site Title / URL are and the new Site Title / URL are to be at the top level list. That way you can send a HTTP POST to the current site url web api to replace the Title and URL.
Will also need to make sure that your workflow has admin workflow permissions to do this.
I do this exact same mechanism (not with subsites but with document sets in a subsite) where the master data is controlled at a top level list.
You can do this using SharePoint REST API (How to execute a SP2013 REST API request with Nintex Workflow).
To use this method you need the current subsite url.
and you need to populate a username and password.
Then you can use the Join Items in Collection action to get all this content in a workflow variable (and reference this workflow variable in the body/content part of the web request).
Don't hesitate if you need help.
Caroline, can you post a link to your blog? I searched around but was unable to find it. Thanks!
Thank you Caroline. Your guidance helped me figure this out.
Caroline Jung I really appreciate you taking the time to provide a workaround for this wf variable bug in web-request actions, that saved me quite some time!!
Frank Field what's the best way to inquire about a timeline for a fix on this one? I'm working with a single variable in my web-request body, and this bug cost me 5 actions since the below isn't possible. That's quite painful when trying to development within the action count limitations of O365 (especially with more variables or header values like Caroline Jung's example).


text:
\section{Introduction}
Federated learning (FL)~\cite{mcmahan2017communicationefficient} has emerged as a promising learning paradigm which allows multiple clients to collaboratively train a better global model without exposing their private training data. In FL, each client trains a local model on its own data and needs to periodically share its high-dimensional local model parameters (gradients) with a (central) server, this incurs a high communication cost and limits its practicality in real-world applications.

For example, in model market scenarios, we can only buy the final trained model from the market and are unable to involve the model we bought in a FL system~\cite{li2020practical}.
Moreover, frequently sharing information is at a high risk of being attacked. For instance, frequent communication can be easily intercepted by attackers, who can launch man-in-the-middle attacks~\cite{wang2020man} or even reconstruct the training data from gradients~\cite{yin2021see}.

A promising solution to the above problem is one-shot FL~\cite{guha2019one}.
In one-shot FL, clients only need to upload their local models in a single round, which largely reduces the communication cost, and makes the method more practical in real-world applications (e.g.,\xspace model market).
In addition, compared with the multi-round FL, one-shot FL also reduces the probability of being intercepted by malicious attackers due to the one round property~\cite{lyu2020threats,lyu2020privacy,lyu2021novel}

However, existing one-shot FL studies~\cite{guha2019one,li2020practical,zhou2020distilled,dennis2021heterogeneity} are still hard to apply in real-world applications, due to impractical settings, or unsatisfactory performance.
\cite{guha2019one} and \cite{li2020practical} involved a public dataset for training, which may be impractical in very sensitive scenarios such as the biomedical domains~\cite{lyu2020towards,lyu2020democratise}.
~\cite{zhou2020distilled} adopted dataset distillation~\cite{wang2020dataset} in one-shot FL, but could hardly achieve satisfactory performance. \cite{dennis2021heterogeneity} utilized cluster-based method in one-shot FL,
which required to upload the cluster means to the server, causing additional communication cost.
In addition,
none of these methods considered model heterogeneity, i.e.,\xspace different clients have different model architectures~\cite{li2020federated}, which is very common in practical scenarios. For instance, in model market scenarios, models sold by different sellers are likely to be heterogeneous.
Therefore, developing a practical one-shot FL method is in urgent need.

\begin{figure*}[t]

    \centering
    \includegraphics[width=14cm]{figs/architecture.pdf}
    \caption{An illustration of training process of FedSyn on the server, which consists of two stages:
    (1) In data generation stage, we train an auxiliary generator that considers similarity, stability, and transferability at the same time; (2) In model distillation stage, we distill the knowledge of the ensemble models and transfer to global model.}

    \label{fig:illustration}

\end{figure*}

In this work, we are motivated to propose a novel two-stage one-shot FL framework named FedSyn. In the first stage, we utilize the ensemble models (i.e.,\xspace ensemble of local models uploaded by clients) to train a generator, which can generate high-quality unlabeled data for training in the second stage.
In the second stage, we distill the knowledge of the ensemble models to the (server's) global model.
Our model distillation-based method
can deal with statistical heterogeneity, i.e.,\xspace the clients' data are not independent and identically distributed (non-iid).
Moreover, since no aggregation operation is needed, our framework can handle model heterogeneity, i.e.,\xspace clients can have different model architectures. In summary, our main contributions include:

\begin{itemize}
    \item We present a practical
    data-free approach to one-shot federated learning framework named FedSyn, which trains the global model by a data generation stage and a model distillation stage. In the first stage, we train a generator that considers \emph{similarity}, \emph{stability}, and \emph{transferability} at the same time. In the second stage, we use the ensemble models and the data generated by the generator to train a global model.
    \item The setting of FedSyn is practical in the following aspects.
    First,
    FedSyn requires no additional information (except the model parameters) to be transferred between clients and the server;
    Second, FedSyn does not require any auxiliary dataset for training;
    Third, FedSyn is the first to consider both model and statistical heterogeneities, i.e.,\xspace the clients' data are non-iid and different clients may have different model architectures.
    To the best of our knowledge, FedSyn is the first method that can handle all the above practical issues.
    \item FedSyn is a compatible approach, which can be combined with any local training techniques to further improve the performance of the global model. For instance, we can adopt LDAM~\cite{cao2019learning} to train the clients' local models, and improve the accuracy of the global model (refer to Section~\ref{sec:model_dis} and Section~\ref{sec:exp_results}).
    \item Extensive experiments on various datasets verify the effectiveness of our proposed FedSyn. For example, FedSyn outperforms the best baseline method Fed-ADI~\cite{yin2020dreaming} by 5.08\% on CIFAR10 dataset when data are non-iid.
\end{itemize}

\section{Notation and preliminaries}
\subsection{Notation}
We use $\mathcal{C}$ to denote the set of clients and $m=|\mathcal{C}|$ to denote the cardinality of set $\mathcal{C}$.
We use superscript $k$ to denote the elements of $k$-th client, $f^k()$ to denote the model of $k$-th client, and $\bs{\theta}^k$ to denote the parameter of $f^k()$.
We use subscript $S$ to denote the elements of the global model, $f_S()$ to denote the global model, $\bs{\theta}_S$ to denote the parameter of $f_S()$, and $\eta_S$ to denote the learning rate of the global model.
We use subscript $G$ to denote elements of the auxiliary generator. $G(\cdot)$ denotes the generator, $\bs{\theta}_G$ denotes the parameter of $G(\cdot)$, and $\eta_G$ denotes the learning rate of the generator.
We use subscript $i$ to denote elements of the $i$-th sample. $\mb{z}_i$, $\hat{\mb{x}}_i$, and $\mb{y}_i$ denote the random noise, synthetic data, and corresponding label of the $i$-th sample.

\subsection{Federated Learning}
Suppose there are $m$ clients in a FL system, and each client $k$ has its own dataset $\mathcal{D}_k=\{\mb{x}_i,\mb{y}_i\}_{i=1}^{n_k}$ with $n_{k}=\abs{\mathcal{D}_k}$ being the size of local data.
Each client $k$ optimizes its local model by minimizing the following objective function.
\begin{equation}
    \min_{\bs{\theta}^k}\frac{1}{n_k}\sum_{i=1}^{n_k}\ell(f^k(\mb{x}_i),\mb{y}_i),
\end{equation}
where $f^k()$ and $\bs{\theta}^k$ are the local model and local model parameter of client $k$ respectively.
Then, client $k$ uploads $\bs{\theta}^k$ to the server for aggregation.
After receiving the uploaded model parameters from the $m$ clients, the server computes the aggregated global model parameter as follows.
\begin{equation}
    \bs{\theta}_S=\sum_{k=1}^m\frac{n_k}{n}\bs{\theta}^k,
\end{equation}
where $n=\sum_{k=1}^m n_k$ is the total size of training data (of all clients) and $\bs{\theta}_S$ is the server's global model parameter.
Afterwards, the server distributes $\bs{\theta}_S$ to all clients for training in the next round.
However, such a training procedure needs frequent communication between the clients and the server, thus incurs a high communication cost, which may be intolerable in practice~\cite{li2020practical}.

\subsection{One-shot Federated Learning}
A promising solution to reduce the communication cost in FL is one-shot FL, which is first introduced by~\cite{guha2019one}.
In one-shot FL, each client only uploads its local model parameter to the server once.
After obtaining the global model, the server does not need to distribute the global model to the clients for further training.
There is only one unidirectional communication between clients and the server, thus it highly reduces the communication cost and makes it more practical in reality. Moreover, one-shot FL also reduces the risk of being attacked, since the communication happens only once.
However, the main problem of one-shot FL is the
difficult convergence of the global model and it is hard to achieve a satisfactory performance especially when the data on clients are non-iid.

\cite{guha2019one} and \cite{li2020practical} used ensemble distillation to improve the performance of one-shot FL.
However, they introduced a public dataset to enhance training, which is not practical.
In addition to model distillation, dataset distillation~\cite{wang2020dataset} is also an alternative prevailing approach. \cite{zhou2020distilled} and \cite{kasturi2020fusion} proposed to apply dataset distillation to one-shot FL, where each client distills its private dataset and transmits distilled data to the server.
However, data distillation methods
fail to offer satisfactory performance.
\cite{dennis2021heterogeneity} utilized cluster-based method in one-shot FL, but they required to upload the cluster means to the server, which incurs additional communication cost.

Overall, none of the above methods
can be practically applied. In addition, none of these studies consider
both model and statistical heterogeneities,
which are two main challenges in FL~\cite{li2019federated}.
This leads to a fundamental yet so far unresolved question: \emph{"Is it possible to conduct one-shot FL without the need to share additional information or rely on any auxiliary dataset, while
compatible with both model and statistical heterogeneities?"}

\section{Practical One-shot Federated Learning}
In this section, we first
introduce our proposed two-stage one-shot federated learning framework named FedSyn, then we present each stage in more detail.

\subsection{System Overview}
An illustration of the learning procedure is demonstrated in Figure~\ref{fig:illustration}, and the whole training process of FedSyn is shown in Algorithm~\ref{alg:fedsyn}.
After clients upload their local models to the server, the server trains a global model with FedSyn in two stages.
In the data generation stage (first stage), we train an auxiliary generator that can generate high-quality unlabeled data by the ensemble models, i.e.,\xspace ensemble of local models uploaded by clients. In the model distillation stage (second stage), we use the ensemble models and the synthetic data (generated by the generator) to train a global model.

To the best of our knowledge, FedSyn is the first practical framework that can conduct one-shot FL without the need to share additional information or rely on any auxiliary dataset, while considers both model and statistical heterogeneities.

\begin{algorithm}[t]

    \caption{Training process of FedSyn}

    \textbf{Input:} Number of client $m$, clients' local models $\{f^1(),\cdots,f^m()\}$,
    generator $G(\cdot)$ with parameter $\bs{\theta}_G$, learning rate of the generator $\eta_G$, number of training rounds $T_G$ for generator in each epoch,
    global model $f_S()$ with parameter $\bs{\theta}_S$, learning rate of the global model $\eta_S$,

    global model training epochs $T$, and batch size $b$.
    \\
    \textbf{Output:} Trained parameter of the global model $\bs{\theta}_S$.
	\begin{algorithmic}[1]

		\For{ each client $k \in \mathcal{C}$ \textbf{in parallel} }
		\State{ $\bs{\theta}^k \leftarrow \textbf{LocalUpdate}(k)$}

        \EndFor
        \State Initialize parameter $\bs{\theta}_G$ and $\bs{\theta}_S$
        \For {epoch=$1,\cdots,T$}

		\State Sample a batch of noises and labels $\{\mb{z}_i, \mb{y}_i\}_{i=1}^b$
		\State // data generation stage
		\For {$j=1,\cdots,T_G$}

        \State Generate $\{\hat{\mb{x}}_i\}_{i=1}^b$ with $\{\mb{z}_i\}_{i=1}^b$ and $G(\cdot)$
        \State $\bs{\theta}_G\leftarrow \bs{\theta}_G-\eta_G\frac{1}{b}\sum_{i=1}^b\nabla_{\bs{\theta}_G}\ell_{gen}(\hat{\mb{x}}_i,\mb{y}_i;\bs{\theta}_G)$

        \EndFor

        \State // model distillation stage

        \State Generate $\{\hat{\mb{x}}_i\}_{i=1}^b$ with $\{\mb{z}_i\}_{i=1}^b$ and $G(\cdot)$
        \State $\bs{\theta}_S=\bs{\theta}_S-\eta_S \frac{1}{b}\sum_{i=1}^b\nabla_{\bs{\theta}_S} \ell_{dis}(\hat{\mb{x}}_i;\bs{\theta}_S)$
		\EndFor
		\State{\Return $\bs{\theta}_S$}
\end{algorithmic}

	\label{alg:fedsyn}
\end{algorithm}

\subsection{Data Generation}
In the first stage, we aim to train a generator to generate unlabeled data.
Specifically, given the ensemble of well-trained models uploaded by clients, our goal is to train a generator that can generate high-quality data that are similar to the training data of clients, i.e.,\xspace share the same distribution with the training data of clients.
Recent work~\cite{lin2021ensemble} generated data by utilizing a pre-trained generative adversarial network (GAN).
However, such a method is unable to generate high-quality data
as the pre-trained GAN is trained on public datasets, which is likely to have different data distribution
from the training data of clients.
Moreover, we need to consider both model and statistical heterogeneities, which makes the problem more complicated.

To solve these issues, we propose to train a generator that considers \emph{similarity}, \emph{stability}, and \emph{transferability}.
The
data generation process is shown in line 8 to 11 in Algorithm~\ref{alg:fedsyn}.
In particular, given a random noise $\mb{z}$ (generated from a standard Gaussian distribution) and a random one-hot label $\mb{y}$ (generated from a uniform distribution), the generator $G(\cdot)$ aims to generate a synthetic data $\hat{\mb{x}}=G(\mb{z})$ such that $\hat{\mb{x}}$ is similar to the training data (with label $\mb{y}$) of clients.

\paragraph{Similarity}
First, we need to consider the similarity between synthetic data $\hat{\mb{x}}$ and the training data. Since we are unable to access the training data of clients, we cannot compute the similarity between the synthetic data and the training data directly.
Instead, we first compute the average logits (i.e.,\xspace outputs of the last fully connected layer) of $\hat{\mb{x}}$ computed by the ensemble models.
\begin{equation}
\label{eq:avg_logit}
    D(\hat{\mb{x}};\{\bs{\theta}^k\}_{k=1}^m)=\frac{1}{m} \sum_{k \in \mathcal{C}} f^k\left(\hat{\mb{x}};\bs{\theta}^{k} \right),
\end{equation}
where $m=|\mathcal{C}|$, and $D(\hat{\mb{x}};\{\bs{\theta}^k\}_{k=1}^m)$ is the average logits of $\hat{\mb{x}}$,
$\bs{\theta}^{k}$ is the parameter of the $k$-th client. And $f^k\left(\hat{\mathbf{x}};{\bs{\theta}}^{k} \right)$ is the prediction function of client $k$ that outputs the logits of $\hat{\mb{x}}$ given parameter $\bs{\theta}^{k}$. For simplicity, we use $D(\hat{\mb{x}})$ denote $D(\hat{\mb{x}};\{\bs{\theta}^k\}_{k=1}^m)$ for the rest of the paper.

Then, we minimize the average logits and the random label $y$ with the following cross-entropy (CE) loss.
\begin{equation}
\label{eq:ce_loss}
    \ell_{CE}(\hat{\mb{x}},\mb{y};\bs{\theta}_G)=CE(D(\hat{\mb{x}}), \mb{y}),
\end{equation}
It is expected that the synthetic images can be classified into one particular class with a high probability by the ensemble models. In fact, during the training phase, the loss between $D(\hat{\mb{x}})$ and $\mb{y}$ can easily reduce to almost 0,
which indicates the synthetic data matches the ensemble models perfectly.
However, by utilizing only the CE loss, we cannot achieve a high performance (please refer to Section~\ref{sec:exp_results} for detail).
We conjecture this is because the ensemble models are trained on non-iid data, the generator may be unstable and trapped into sub-optimal local minima or overfit to the synthetic data~\cite{wang2020tackling,li2020fedbn}.

\paragraph{Stability}
Second, to improve the stability of the generator, we propose to add an additional regularization to stabilize the training.
In particular, we utilize the Batch Normalization (BN) loss to make the synthetic data conform with the batch normalization statistics~\cite{yin2020dreaming}.
\begin{equation}
    \label{eq:bn_loss}
    \ell_{BN}(\hat{\mb{x}};\bs{\theta}_G)=\frac{1}{m}\sum_{k\in\mathcal{C}}\sum_{l}\left(\norm{\mu_{l}(\hat{\mb{x}})-\mu_{k,l}}+\norm{\sigma^2_{l}(\hat{\mb{x}})-\sigma_{k,l}^2}\right),
\end{equation}
where $\mu_{l}(\hat{\mb{x}})$ and $\sigma^2_{l}(\hat{\mb{x}})$ are the batch-wise mean and variance estimates of feature maps corresponding to the $l$-th BN layer of the generator $G(\cdot)$\footnote{We assume the input is a batch of data.}, $\mu_{k,l}$ and $\sigma_{k,l}^2$ are the mean and variance of the $l$-th BN layer~\cite{ioffe2015batch} of $f^k(\cdot)$.
The BN loss minimizes the distance between the feature map statistics of the synthetic data and the training data of clients. As a result, the synthetic data can have a similar distribution to the training data of clients.

\begin{figure}[t]

    \centering
    \includegraphics[width=9.5cm]{figs/one-shot.pdf}

    \caption{The illustration of generated data and decision boundary of ensemble models (teachers) and global model (student). \textbf{Left panel:} Synthetic data (red circles) are far away from the decision boundary, which is unhelpful to the transfer of knowledge. \textbf{Right panel:} By utilizing our boundary support loss, we can generate more synthetic data
    near the decision boundaries (black circles), which can help the global model better learn the decision boundary of the ensemble models. }
    \label{illustration}

\end{figure}

\paragraph{Transferability}
By utilizing the CE loss and BN loss, we can train a generator that can generate high-quality synthetic data, but
we observed that the synthetic data are likely to be far away from the decision boundary (of the ensemble models), which makes the ensemble models (teachers) hard to transfer their knowledge to the global model (student).
We illustrate the observation in the left panel of Figure~\ref{illustration}. S and T are the decision boundaries of the global model (the detail of the global model is introduced in Section~\ref{sec:model_dis}) and ensemble models respectively.
The essence of knowledge distillation is transferring the information of decision boundary from the teacher model to the student model~\cite{heo2019knowledge}. We aim to learn the decision boundary of global model and have a high classification accuracy on the real test data (blue diamonds).
However, the generated synthetic data (red circles) are likely to be on the same side of the two decision boundaries and unhelpful to the transfer of knowledge~\cite{heo2019knowledge}.
To solve this problem, we argue to generate more synthetic data that fall between the decision boundaries of the ensemble models and the global model. We illustrate our idea in the right panel of Figure~\ref{illustration}.
Red circles are synthetic data on the same side of the decision boundary, which are less helpful in learning the global model. Black circles are synthetic data between the decision boundaries, i.e.,\xspace the global model and the ensemble models have different predictions on these data. Black circles can help the global model better learn the decision boundary of the ensemble models.

Motivated by the above observations, we introduce a new boundary support loss, which urges the generator to generate more synthetic data between the decision boundaries of the ensemble models and the global model.
We divide the synthetic data into 2 sets: (1) the global model and the ensemble models have the same predictions on data in the first set ($\argmax_c D^{(c)}(\hat{\mb{x}})=\argmax_c f_S^{(c)}(\hat{\mb{x}};\bs{\theta}_S)$); (2) different predictions on data in the second set ($\argmax_c D^{(c)}(\hat{\mb{x}})\neq\argmax_c f_S^{(c)}(\hat{\mb{x}};\bs{\theta}_S)$), where $D^{(c)}(\hat{\mb{x}})$ and $f_S^{(c)}(\hat{\mb{x}};\bs{\theta}_S)$ are the logits for the $c$-th label of the ensemble models and the global model respectively. The data in the first set are on the same side of those two decision boundaries (red circles in Figure~\ref{illustration}) while the data in the second set (black circles in Figure~\ref{illustration}) are between the decision boundaries of the ensemble models and the global model. We maximize the differences of predictions of the global model and the ensemble models on data in the second set with Kullback-Leibler divergence loss as follows.
\begin{align}
\label{eq:diversity}

    \ell_{div}(\hat{\mb{x}};\bs{\theta}_G)=-\omega KL\left(D(\hat{\mb{x}}), f_S(\hat{\mb{x}};\bs{\theta}_S)\right),
\end{align}
where $KL(\cdot,\cdot)$ denotes the Kullback-Leibler (KL) divergence loss, $\omega=\mathbbm{1}(\argmax_c D^{(c)}(\hat{\mb{x}}) \neq\argmax_c f_S^{(c)}(\hat{\mb{x}};\bs{\theta}_S))$ outputs 0 for data in the first set and 1 for data in the second set, and $\mathbbm{1}(a)$ is the indicator function that outputs 1 if $a$ is true and outputs 0 if $a$ is false.
By maximizing the KL divergence loss, the generator can generate more synthetic data that are more helpful to the model distillation stage (refer to Section~\ref{sec:model_dis} for detail) and further improve the transferability of the ensemble models.

By combining the above losses, we can obtain the generator loss as follows
\begin{equation}
    \label{eq:gen_loss}
    \begin{split}
    \ell_{gen}(\hat{\mb{x}},\mb{y};\bs{\theta}_G)=\ell_{CE}(\hat{\mb{x}},\mb{y};\bs{\theta}_G)&+\lambda_1 \ell_{BN}(\hat{\mb{x}};\bs{\theta}_G)\\
    &+\lambda_2  \ell_{div}(\hat{\mb{x}};\bs{\theta}_G),
    \end{split}
\end{equation}
where $\lambda_1$ and $\lambda_2$ are scaling factors for the losses.

Last, we optimize the parameter of the generator by gradient descent.
\begin{equation}
    \label{eq:gen_GD}
    \bs{\theta}_G=\bs{\theta}_G-\eta_G \nabla_{\bs{\theta}_G} \ell_{gen}(\hat{\mb{x}},\mb{y};\bs{\theta}_G),

\end{equation}
where $\eta_G$ is the learning rate of the generator.

\begin{table*}[t]
\centering
\caption{Accuracy of different
methods across $\alpha=\{0.1, 0.3, 0.5\}$ on different datasets. Best results are in bold.}
\scalebox{0.64}{
\begin{tabular}{c|ccc|ccc|ccc|ccc|ccc|ccc}
\toprule
Dataset & \multicolumn{3}{c|}{MNIST} & \multicolumn{3}{c|}{FMNIST} & \multicolumn{3}{c|}{CIFAR10} & \multicolumn{3}{c|}{SVHN} & \multicolumn{3}{c|}{CIFAR100} & \multicolumn{3}{c}{Tiny-ImageNet} \\
\midrule
Method & $\alpha$=0.1 & $\alpha$=0.3 & $\alpha$=0.5 & $\alpha$=0.1 & $\alpha$=0.3 & $\alpha$=0.5 & $\alpha$=0.1 & $\alpha$=0.3 & $\alpha$=0.5 & $\alpha$=0.1 & $\alpha$=0.3 & $\alpha$=0.5 & $\alpha$=0.1 & $\alpha$=0.3 & $\alpha$=0.5 & $\alpha$=0.1 & $\alpha$=0.3 & $\alpha$=0.5 \\
\midrule
FedAvg & 48.24 & 72.94 & 90.55 & 41.69 & 82.96 & 83.72 & 23.93 & 27.72 & 43.67 & 31.65 & 61.51 & 56.09 & 4.58 & 11.61 & 12.11 & 3.12 & 10.46 & 11.89 \\
FedDF & 60.15 & 74.01 & 92.18 & 43.58 & 80.67 & 84.67 & 40.58 & 46.78 & 53.56 & 49.13 & 73.34 & 73.98 & 28.17 & 30.28 & 36.35 & 15.34 & 18.22 & 27.43 \\
Fed-DAFL & 64.38 & 74.18 & 93.01 & 47.14 & 80.59 & 84.02 & 47.34 & 53.89 & 58.59 & 53.23 & 76.56 & 78.03 & 28.89 & 34.89 & 38.19 & 18.38 & 22.18 & 28.22 \\
Fed-ADI & 64.13 & 75.03 & 93.49 & 48.49 & 81.15 & 84.19 & 48.59 & 54.68 & 59.34 & 53.45 & 77.45 & 78.85 & 30.13 & 35.18 & 40.28 & 19.59 & 25.34 & 30.21 \\
\midrule
FedSyn (ours) & \textbf{66.61} & \textbf{76.48} & \textbf{95.82} & \textbf{50.29} & \textbf{83.96} & \textbf{85.94} & \textbf{50.26} & \textbf{59.76} & \textbf{62.19} & \textbf{55.34} & \textbf{79.59} & \textbf{80.03} & \textbf{32.03} & \textbf{37.32} & \textbf{42.07} & \textbf{22.44} & \textbf{28.14} & \textbf{32.34} \\
\bottomrule
\end{tabular}}
\label{all_methods}
\end{table*}

\subsection{Model Distillation}
\label{sec:model_dis}
In the second stage, we train the global model with the generator (discussed in the previous section) and the ensemble models.
Previous research~\cite{zhang2020diversified,lin2021ensemble} showed that model ensemble provides a general method for improving the accuracy and stability of learning models.
Motivated by~\cite{zhang2020diversified}, we propose to use the ensemble models as a teacher to train a student (global) model.
A straightforward method is to obtain the global model by aggregating the parameters of all client models (e.g.,\xspace by FedAvg~\cite{mcmahan2017communicationefficient}). However, in real-world applications, clients are likely to have different model architectures~\cite{sun2020federated}, making FedAvg useless.
Moreover, since the data in different clients are non-iid,
FedAvg cannot deliver a good performance or even diverge~\cite{li2020fedbn}.

To this end, we follow~\cite{lin2021ensemble}
to distill the knowledge of the ensemble models to the global model by minimizing the predictions between the ensemble models and the student global model on the same synthetic data.
The
model distillation process is shown in line 13 to 14 in Algorithm~\ref{alg:fedsyn}.
First, we compute the average logits of the synthetic data according to Eq. \eqref{eq:avg_logit}, i.e.,\xspace $D(\hat{\mb{x}})=\frac{1}{m} \sum_{k \in \mathcal{C}} f^k\left(\hat{\mb{x}};\bs{\theta}^{k} \right)$.
In contrast to traditional aggregation methods (e.g.,\xspace FedAvg) that are
unable to aggregate heterogeneous models, averaging logits can easily be applied to both heterogeneous and homogeneous FL systems.

Then, we use the average logits to distill the knowledge of the ensemble models by minimizing the following objective function.
\begin{equation}
    \label{eq:distill_loss}

    \ell_{dis}(\hat{\mb{x}};\bs{\theta}_S) = {K L}\left(D(\hat{\mb{x}}), f_S(\hat{\mathbf{x}};\mathbf{\bs{\theta}}_S)\right).
\end{equation}
By minimizing the KL loss, we can train a global model with the knowledge of the ensemble models and the synthetic data regardless of data and model heterogeneity.

Last, we optimize the parameter of global model by gradient descent.
\begin{equation}
    \label{eq:dis_GD}
    \bs{\theta}_S=\bs{\theta}_S-\eta_S \nabla_{\bs{\theta}_S} \ell_{dis}(\hat{\mb{x}};\bs{\theta}_S),

\end{equation}
where $\eta_S$ is the learning rate of the global model.

Note that FedSyn has no restriction on the clients' local models, i.e.,\xspace clients can train models with arbitrary techniques.
Thus, FedSyn is a compatible approach, which can be combined with any local training techniques to further improve the performance of the global model. We further discuss the combination of local training techniques in Section~\ref{sec:exp_results}.

\begin{figure}[t]

    \centering
    \includegraphics[width=11.5cm]{figs/plt-1.pdf}

    \caption{ \textbf{Left panel:} Accuracy of FedAvg and clients' local models across different local training epochs $E=\{20,40,60,\cdots, 400\}$.
    \textbf{Right panel:} The accuracy curve for local training. The dotted lines represent the best results of two one-shot FL methods (FedAvg and FedSyn).

    Our FedSyn outperforms FedAvg and local models consistently.

    }

    \label{fig2}

\end{figure}

\begin{table}[t]
    \centering
    \caption{Performance analysis of FedSyn+LDAM.

    }
    \scalebox{0.999}{
    \begin{tabular}{c|ccc|ccc}
    \toprule
    Dataset & \multicolumn{3}{c|}{CIFAR10} & \multicolumn{3}{c}{SVHN} \\
    \midrule
    Method  & $\alpha$=0.1   & $\alpha$=0.3   & $\alpha$=0.5   & $\alpha$=0.1  & $\alpha$=0.3  & $\alpha$=0.5  \\
    \midrule
    FedSyn    & 50.26   & 59.76   & 62.19   & 55.34  & 79.59  & 80.03  \\
    \midrule
    FedSyn+LDAM & \textbf{57.24} & \textbf{63.13} & \textbf{64.76} & \textbf{58.04} & \textbf{81.28} & \textbf{81.77} \\
    \bottomrule
    \end{tabular}}
    \label{tb4}

\end{table}

\begin{figure}[t]

    \centering
    \includegraphics[width=11.1cm]{figs/ldam.pdf}
    \caption
    \textbf{Left panel:} Accuracy curves of FedSyn and FedSyn+LDAM. \textbf{Right panel:} Data distribution of different clients for CIFAR10 ($\alpha$=0.1). }

    \label{fig4}

\end{figure}

\section{Experiments}
\label{exp}

\subsection{Experimental setup}
\paragraph{Datasets and non-iid data partition} We perform an extensive empirical analysis using several benchmark datasets containing various degrees of heterogeneity. Specifically, we use the following datasets: MNIST~\cite{lecun1998gradient}, FMNIST~\cite{xiao2017fashionmnist}, SVHN~\cite{netzer2011reading}, CIFAR10~\cite{krizhevsky2009learning}, CIFAR100~\cite{krizhevsky2009learning}, and Tiny-ImageNet~\cite{le2015tiny}.
To simulate real-world statistical heterogeneity, we use Dirichlet distribution to generate non-iid data partition among clients~\cite{yurochkin2019bayesian, li2021federated}.
In particular, we sample $p_{k} \sim Dir(\alpha)$ and allocate a $p_k^i$ proportion of the data of class $k$ to client $i$.
By varying the parameter $\alpha$, we can change the degree of imbalance. A small $\alpha$ generates highly skewed data. We set $\alpha=0.5$ as default.

\paragraph{Baselines} To ensure fair comparisons, we
neglect the comparison with methods that require downloading auxiliary models or datasets, such as FedBE~\cite{chen2021fedbe} and FedGen~\cite{zhu2021datafree}.
Moreover, since there is only one communication round, aggregation methods that are based on regularization have no effect. Thus, we also
omit the comparison with these regularization-based methods, e.g.,\xspace
FedProx~\cite{li2020federated}, FedNova~\cite{wang2020tackling}, and Scaffold~\cite{karimireddy2020scaffold}.
Instead, we compare our proposed FedSyn with FedAvg~\cite{mcmahan2017communicationefficient} and FedDF~\cite{lin2021ensemble}.
Furthermore, since FedSyn is a data-free method, we derive some baselines from prevailing data-free knowledge distillation methods, including: 1) DAFL~\cite{DAFL}, a novel data-free learning framework based on generative adversarial networks; 2) ADI~\cite{yin2020dreaming}, an image synthesizing method that utilizes the image distribution to train a deep neural network without real data. We apply these methods to one-shot FL, and name these two baselines as Fed-DAFL and Fed-ADI.

\begin{table*}[t]
    \centering

    \caption
    Accuracy comparisons across heterogeneous client models on CIFAR10. There are five clients in total, and each client has a personalized model. Best results are in bold.}
    \scalebox{0.93}{
    \begin{tabular}{c|ccccc|cccc}
        \toprule
        \multirow{2}{*}{Model} & \multicolumn{5}{c|}{Client} & \multicolumn{4}{c}{Server (ResNet-18)}                                                                                \\

                               & ResNet-18                    & CNN1                                  & CNN2  & WRN-16-1 & WRN-40-1 & FedDF & Fed-DAFL & Fed-ADI & \textbf{FedSyn (ours)}            \\
        \midrule
        $\alpha$=0.1           & 40.83                       & 33.67                                 & 35.21 & 27.73      & 32.93      & 42.35 & 43.12    & 44.63   & \textbf{49.76} \\
        \midrule
        $\alpha$=0.3           & 51.49                       & 52.78                                 & 44.96 & 47.35      & 37.24      & 52.72 & 57.72    & 58.96   & \textbf{63.25} \\
        \midrule
        $\alpha$=0.5           & 59.96                       & 58.67                                 & 54.28 & 53.39      & 58.14      & 60.05 & 61.56    & 63.24   & \textbf{67.42} \\
        \bottomrule
    \end{tabular}
    \label{tb2}}
\end{table*}

\begin{figure*}[t]

    \centering
    \includegraphics[width=16.5cm]{figs/x.pdf}

    \caption{Visualization of the test accuracy and data distribution for CIFAR10 dataset with $\alpha=\{0.3, 0.5\}$.}

    \label{fig6}

\end{figure*}

\begin{table*}[t]
    \centering
    \caption{Accuracy across different number of clients $m=\{5, 10,20,50,100\}$ on CIFAR10 and SVHN datasets. Best results are in bold.}

    \scalebox{0.82}{
    \begin{tabular}{c|ccccc|ccccc}
        \toprule
        Dataset    & \multicolumn{5}{c|}{CIFAR10} & \multicolumn{5}{c}{SVHN}                                                                                \\
        \midrule
        $m$ & FedAvg                       & FedDF                    & Fed-DAFL & Fed-ADI & \textbf{FedSyn (ours)}            & FedAvg & FedDF & Fed-DAFL & Fed-ADI & \textbf{FedSyn (ours)}            \\
        \midrule
        5          & 43.67                        & 53.56                    & 55.46    & 58.59   & \textbf{62.19} & 56.09  & 73.98 & 78.03    & 78.85   & \textbf{80.03} \\
        \midrule
        10         & 38.29                        & 54.44                    & 56.34    & 57.13   & \textbf{61.42} & 45.34  & 62.12 & 63.34    & 65.45   & \textbf{67.57} \\
        \midrule
        20         & 36.03                        & 43.15                    & 45.98    & 46.45   & \textbf{52.71} & 47.79  & 60.45 & 62.19    & 63.98   & \textbf{66.42} \\
        \midrule
        50         & 37.03                        & 40.89                    & 43.02    & 44.47   & \textbf{48.47} & 36.53  & 51.44 & 54.23    & 57.35   & \textbf{59.27} \\
        \midrule
        100         & 33.54 & 36.89 & 37.55 & 36.98  & \textbf{43.28} & 30.18  & 46.58 & 47.19 & 48.33 & \textbf{52.48} \\
        \bottomrule
    \end{tabular}}
\label{tb3}
\end{table*}

\begin{table}[t]
\centering
\caption{Accuracy on multiple communication rounds of FedSyn.}
\scalebox{0.999}{
\begin{tabular}{c|ccc|ccc}
\toprule
Dataset & \multicolumn{3}{c|}{CIFAR10} & \multicolumn{3}{c}{SVHN} \\
\midrule
 Communication rounds & $\alpha$=0.1 & $\alpha$=0.3 & $\alpha$=0.5 & $\alpha$=0.1 & $\alpha$=0.3 & $\alpha$=0.5 \\
\midrule
$T_c=1$ & 50.72 & 59.41 & 63.89 & 54.344 & 79.87 & 80.14 \\
$T_c=2$ & 63.08 & 65.90 & 71.16 & 56.13 & 79.75 & 85.18 \\
$T_c=3$ & 61.61 & 69.73 & 73.91 & 74.41 & \textbf{86.42} & 86.18 \\
$T_c=4$ & 66.26 & 69.40 & 74.39 & 78.67 & 86.36 & 86.43 \\
$T_c=5$ & \textbf{67.65} & \textbf{71.42} & \textbf{76.01} & \textbf{80.28} & 86.25 & \textbf{86.55} \\
 \bottomrule
\end{tabular}}
\label{multi-round}
\end{table}

\begin{table}[t]
    \centering
    \caption{Impact of loss functions in data generation.}
    \scalebox{1.1}{
    \begin{tabular}{cccc}
        \toprule
    Dataset & CIFAR10 & SVHN & CIFAR100 \\
    \midrule
    FedSyn & \textbf{62.19} & \textbf{80.03} & \textbf{42.07} \\
    \midrule
    w/ $\ell_{CE}$ & 53.12 & 73.11 & 36.47 \\
    \midrule
    w/o $\ell_{BN}$ & 61.05 & 78.36 & 39.89 \\
    \midrule
    w/o $\ell_{div}$ & 59.18 & 77.59 & 39.14 \\
    \bottomrule
    \end{tabular}}
    \label{abl}

\end{table}

\paragraph{Settings}
For clients' local training, we use the SGD optimizer with momentum=0.9 and learning rate=$0.01$. We set the batch size $b=128$, the number of local epochs $E=200$, and the client number $m=5$.

Following the setting of~\cite{DAFL}, we train the auxiliary generator $G(\cdot)$ with a deep convolutional network. We use Adam optimizer with learning rate $\eta_G=0.001$. We set the number of training rounds in each epoch as $T_G=30$, and set the scaling factor $\lambda_1=1$ and $\lambda_2=0.5$.

For the training of the server model $f_S()$, we use the SGD optimizer with learning rate $\eta_S=0.01$ and momentum=0.9. The number of epochs for distillation $T=200$.
All baseline methods use the same setting as ours.

\subsection{Results}
\label{sec:exp_results}

\paragraph{Evaluation on real-world datasets}
To evaluate the effectiveness of our method, we conduct experiments under different non-iid  settings by varying $\alpha=\{0.1, 0.3, 0.5\}$ and report the performance on different datasets and different methods in Table~\ref{all_methods}.
The results show that:
(1) Our FedSyn achieves the highest accuracy across all datasets.
In particular, FedSyn outperforms the best baseline method Fed-ADI~\cite{yin2020dreaming} by 5.08\% when $\alpha=0.3$ on CIFAR10 dataset.
(2) FedAvg has the worst performance, which implies that directly averaging the model parameters cannot achieve a good performance under non-iid setting in one-shot FL.
(3)
As $\alpha$ becomes smaller (i.e.,\xspace data become more imbalanced), the performance of all methods decrease significantly, which shows that all methods suffer from highly skewed data.
Even under highly skewed setting, FedSyn still significantly outperforms other methods, which further demonstrates the superiority of our proposed method.

\paragraph{Impact of model distillation}
We show the impact of model distillation by comparing
with FedAvg. We first conduct one-shot FL and use FedAvg to aggregate the local models. We show the results of the global model and clients' local models across different
local training epochs $E=\{20,40,60,\cdots, 400\}$ in the left panel of Figure~\ref{fig2}.
The global model achieves the best performance (test accuracy=34\%) when $E=40$, while a larger value of $E$ can cause the model to degrade or even collapse. This result can be attributed to the inconsistent optimization objectives with non-iid data~\cite{wang2020tackling}, which leads to weight divergence~\cite{zhao2018federated}.
Then, we show the results of one-shot FL when $E=400$ and report the performance of FedAvg and FedSyn in the right panel of Figure~\ref{fig2}. We also plot the performance of clients' local models.
FedSyn outperforms each client's local model while FedAvg underperforms each client's local model.
This validates that model distillation can enhance training while directly aggregating is harmful to the training under non-iid setting in one-shot FL.

\paragraph{Combination with imbalanced learning}
The accuracy of federated learning reduces significantly with non-iid data, which has been broadly discussed in recent studies~\cite{li2021federated,wang2020tackling}. Additionally, previous studies~\cite{cao2019learning,Kim_2020_CVPR} have demonstrated their superiority on imbalanced data.  The combination of our method with these techniques to address imbalanced local data can lead to a more effective FL system.
For example, by using LDAM~\cite{cao2019learning} in clients' local training, we can mitigate the impact of data imbalance, and thereby build a more powerful ensemble model.
We compare the performance of the original FedSyn and FedSyn combined with LDAM (FedSyn+LDAM) across $\alpha=\{0.1, 0.3, 0.5\}$ on CIFAR10 and SVHN datasets.
As demonstrated in Table~\ref{tb4},
FedSyn+LDAM can significantly improve the performance, especially for highly skewed non-iid data (i.e. $\alpha=0.1$).
To help understand the performance gap and data skewness, in Figure~\ref{fig4}, we visualize the accuracy curve and data distribution of CIFAR10 ($\alpha$=0.1) in the left panel and right panel respectively.
The number in the right panel stands for the number of examples associated with the corresponding label in one particular client.
These figures imply that significant improvement can be achieved by combining FedSyn with LDAM on highly skewed data.

\paragraph{Results in heterogeneous FL} Note that our proposed FedSyn can support heterogeneous models. We apply five different CNN models on CIFAR10 dataset with Dirichlet distribution $\alpha=\{0.1,0.3,0.5\}$. The heterogeneous models include: 1) one ResNet-18~\cite{he2015deep}, 2) two small CNNs: CNN1 and CNN2; 3) two Wide-ResNets (WRN)~\cite{zagoruyko2017wide}: WRN-16-1 and WRN-40-1. For knowledge distillation, we use ResNet-18 as the server's global model. Detailed architecture information of the given deep networks can be found in Appendix.
Table~\ref{tb2} evaluates all methods in heterogeneous one-shot FL under practical non-iid data settings. We
omit the results for FedAvg as FedAvg does not support heterogeneous models.
We remark that FL under both the non-iid data distribution and different model architecture setting is a quite challenging task. Even under this setting,
our FedSyn still significantly outperforms the other baselines.
In addition, we report the accuracy curve of global distillation. As shown in Figure~\ref{fig6}, our method outperforms other baselines by a large margin.

\paragraph{Impact of the number of clients} Furthermore, we evaluate the performance of these methods on CIFAR10 and SVHN datasets by varying the number of clients $m=\{5,10,20,50, 100\}$. According to~\cite{lian2017decentralized}, the server can become a bottleneck when the number of clients is very large, we are also concerned with the model performance when $m$ increases. Table~\ref{tb3} shows the results of different methods across different $m$. The accuracy of all methods decreases as the number of clients $m$ increases, which is consistent with observations in~\cite{lian2017decentralized,mcmahan2017communicationefficient}. Even though the number of clients can affect the performance of one-shot FL, our method still outperforms other baselines. The increasing number of clients can pose new challenges for ensemble distillation,
which we leave for future investigation.

\paragraph{Extend to multiple rounds}
To further show the effectiveness of FedSyn, we extend FedSyn to multi-round FL, i.e.,\xspace there are multiple communication rounds between clients and server. Table~\ref{multi-round} demonstrates the results of FedSyn across different communication rounds $T_c=\{1,2,3,4,5\}$ on CIFAR10 and SVHN datasets. The local training epoch is fixed as $E=10$. The performance of FedSyn improves as $T_c$ increases, and FedSyn achieves the best performance when $T_c=5$. This shows that FedSyn can be extended to multi-round FL and the performance can be further enhanced by increasing the communication rounds.

\paragraph{Contribution of $\ell_{BN}$ and $\ell_{div}$}
We are interested in exploring one question: during data generation, which loss function in Eq.~\ref{eq:gen_loss} contributes more? To answer this question,
we conduct leave-one-out testing and show the results by removing $\ell_{div}$ (w/o $\ell_{div}$), and removing $\ell_{BN}$ (w/o $\ell_{BN}$). Additionally, we report the result by removing both $\ell_{div}$  and $\ell_{BN}$, i.e.,\xspace using only $\ell_{CE}$ (w/ $\ell_{CE}$).
As illustrated in Table~\ref{abl}, using only $\ell_{CE}$ to train the generator leads to poor performance.
Besides, removing either the $\ell_{BN}$ loss or $\ell_{div}$ loss also affects the accuracy of the global model.
A combination of these loss functions leads to a high performance of global model, which shows that each part of the loss function plays an important role in enhancing the generator.

\section{Conclusion} In this paper, we
propose an effective two-stage one-shot learning method named FedSyn. FedSyn is a practical method that can be used in real-world scenarios due to the following advantages: (1) In FedSyn, no additional information (except the model parameters) is required to transfer between clients and the server; (2) FedSyn does not require any auxiliary dataset for training; (3) FedSyn is the first to consider both model and statistical heterogeneities.
Extensive experiments across various settings validate the efficacy of our proposed FedSyn. Overall, FedSyn is the first practical framework that can conduct data-free one-shot FL, while considers both model and statistical heterogeneities.

\bibliographystyle{ieee_fullname}

redpajama_set_name: RedPajamaArXiv


Long range shooting, whether you like it or not, is here to stay, and I am happy about that. The shooting industry has made huge technical advancements in optics, laser rangefinders, and the general overall accuracy of most brands of rifles.
We now can hit something in a different zip code. What seemed impossible a short time ago can now be done consistently. With that being said, I personally want to take advantage of knowing what can and will help me to achieve my long range shooting goals.


text:
Q: React Native Cannot find supported Android SDK My goal here is to learn how to debug in VSCode. After checking this article the author mentions running npx react-native doctor to check your environment is set up for debugging
This question is similar to the SO question here however I have some different issues.
Firstly, I'm familiar with VSCode and do not want to use the android studio editor.
In regards to the problem it self. React-native is finding ANDROID_HOME and all the versions inside, however it doesn't like 31.0.0.

Is 31.0.0 meant to be the same as .b31.0.0? If not I cannot see an obvious .b31.0.0 version..

redpajama_set_name: RedPajamaStackExchange


text:
Factor unleashes new production superbikes – meet the One and O2
UCI-legal, David Millar-tested road machines and a fully integrated tri bike to follow
By Jamie Beach
The new Factor One, ready to rumble
Joolze Dymond Photography
ONE Pro Cycling's Matt Prior (left), David Millar and Baden Cooke
The integrated cockpit on the Factor One – incredibly thin
Neat cable routing on the split down tube of the Factor One
Baden Cooke and BikeRadar's Jamie Beach riding the new Factor One
Aero seat tube and post on the Factor One
The modified – but still beefy – head tube on the Factor One
Check out the scalloping on the top tube
Those skinny rear seat stays should deliver some comfort
One Pro Cycling have picked InfoCranks as their power meter of choice
From this angle you can see the split down tube
The Black Inc wheels run on their own carbon hubs
Deep-section 80mm wheels for this bike
Lovely day for a ride
ONE Pro Cycling getting acquainted with their new bikes
The new Factor O2
She's a beaut
The Factor O2 is available both with and without the integrated cockpit
Plenty of stiffness here
The Factor O2 runs on 30mm deep Black Inc wheels
If you haven't yet heard of Factor Bikes, best lock away your credit card before you read any further. This small British brand, born out of F1 technology, has three new bikes it wants you to know about, and they're seriously impressive. So good in fact that ONE Pro Cycling has already ordered a complete fleet of the bikes for the 2016 season, which the team will be racing under a Pro Continental licence.
BikeRadar was invited out to ONE Pro's training camp in Andalucia to see the new Factor One, so for those who want to know how it compares to the 'old' Vis Vires – head on down the page. But first some backstory: Factor Bikes was formed back in 2007 as a showcase for the engineering expertise of Norfolk-based motorsports company bf1 systems, and it quickly blew people away with the superbikes it produced.
First there came the 001, which incorporated advanced electronics, hydraulic brakes, integrated LEDs and an onboard battery system into a superbike carbon frame. This was refined and evolved into the limited edition One-77, developed in partnership with Aston Martin and costing a whopping £25,000 (about $37,500 / AU$52,000. But it wasn't until the advent of the Vis Vires that we saw a production bike with wider market appeal. BikeRadar rode the Vis Vires back in 2013, and described it as a "production race bike with superbike credentials", costing £10,000 (about $15,000 / AU$20,800).
Related: Factor Bikes Vis Vires first ride review
Trouble was, it wasn't UCI-legal. The split down tube and chunky front end, which gave the incredible aero gains and stiffness that impressed us so much, meant it couldn't be raced in anger. So Factor went back to the drawing board, this time with new owners at the helm. One of those owners is former pro cyclist Baden Cooke, winner of the Tour de France green jersey back in 2003, and the other is Rob Gitelis, an industry stalwart who owns a bike manufacturing plant out in Taiwan.
Together, the new owners have come up with three new models, with input from former pro cyclist David Millar, who's been acting as a test pilot. The manufacturing facilities in Taiwan mean that Factor has been able to act very quickly on the feedback that Millar has given them, tweaking the designs until they were ready to be raced. And pass UCI scrutiny, which was important as ONE Pro Cycling wanted the bikes, and quickly.
"Baden and I knew right away that we wanted to be associated with a pro team," said Gitelis. "The whole discussion started, and we realised that where ONE Pro Cycling were going and where we going was a very similar place. They wanted a partner who could develop the product for them and with them. We're not just expecting them to ride the bike we give them."
That's echoed by Matt Prior, ONE Pro's owner: "What was really appealing to me was the fact that we can go to a guy who owns a factory, who has the ability to get feedback from the riders and make the changes we need," he told us.
"Factor want to create the best bike in the world. We want to be riding the best bike in the world. And the exciting thing is that it's only going to get better."
So what are these new bikes? Read on…
Factor One
ONE Pro Cycling's Matt Prior, David Millar and Baden Cooke with the new Factor One
Top of three is the Factor One, the new aero race bike that's the most similar to the Vis Vires. The front end has been reshaped and the fork is no longer twin-bladed, but it retains the distinctive 'Twin-Vane' down tube to help funnel wash from the front wheel through the frame rather than around.
"It definitely cleans up the wind that's coming up off the wheel," Gitelis told BikeRadar. "It's very easy to prove this. When you're out on the bike you can see the dust coming up." The twin vanes aren't connected in the middle, which is very difficult to make – and around 40 percent more expensive to create the carbon moulds – but also very difficult to replicate.
It keeps the wheels from Black Inc, which Cooke also runs. These will be spinning on Ceramic Speed bearings, and in the configuration that BikeRadar rode (of which more later), sported the 80mm-deep carbon rims. Tyres are Hutchinson's handmade Pro Tour tubulars, 24mm wide with dual-compound tread and polycotton casing.
There's a new handlebar design, too. "We're pretty confident that our new design has greatly reduced the frontal area of this bike," added Gitelis. "You can see that we've hidden the Di2 box away from the handlebar, so we've been able to go very thin. Our Di2 junction box is underneath the water bottle on the down tube."
Seating duties are taken care of by Prologo's Nago Evo CPC saddle. It will come with three widths of handlebars and five lengths of stems.
The Factor One runs on Shimano's Dura-Ace Di2 groupset, and uses standard Dura-Ace caliper brakes rather than the TRP mini V-brakes that the Vis Vires used. The rear brake is hidden out of the wind under the bottom bracket, while the front brake is mounted to the front of the fork rather than the rear.
Complete build weight for the new Factor One is 7.4kg, and it will cost just shy of $10,000 (we'll add UK and Australian prices as we have them). There'll also be a Factor One S, which loses the integrated fork and handlebars, for riders who want to spec their own handlebars. It will cost $7,650.
Factor O2
The Factor O2 is an all-rounder bike
Next up is Factor's all-rounder bike, the O2. This loses the split down tube and deep-section 80mm wheels, but keeps the integrated handlebars and comes in a little lighter than the Factor One at 6.9kg for the complete build. It will be available in Dura-Ace and Ultegra Di2 models costing $9,000 and $7,650 respectively.
"When we designed the O2, we designed it knowing that we need a disc-brake version, so we are making a racing disc-brake bike," said Gitelis. "It's our O2 model frame, designed for disc brakes. We're not doing anything that's going to change the ride characteristics. It will be a racing disc brake frame, available in March 2016. The price point will be the same [as the O2] at Dura-Ace and Ultegra Di2."
There will also be a Factor O2 that loses the integrated handlebars and stem, for riders who want to spec their own.
Finally, there will also be a tri bike to come, which will be a non-UCI compliant, very integrated machine. Gitelis told us it will be competitive with the Scott Plasma and Canyon Speedmax CF SLX in terms of integration. According to Gitelis, this will be coming in 2016; we'll update with more details as we have them.
Tour ambitions
So will we be seeing these bikes at the Tour de France any time soon? "Make no mistake, it's going to be a massive challenge making the step up to Pro Continental," Prior said. "We both have the ambition to put this team in the Tour de France. We've made one step, a big step, but this is just a step in the right direction."
Jamie Beach

redpajama_set_name: RedPajamaCommonCrawl


Dr Stanley Okoro offers customized plastic cosmetic procedures at his private practice locations Nigeria (Abuja & Lagos), and Atlanta, Georgia. He has an extensive experience in cosmetic & reconstructive surgery of the breast, body, and face. His priorities when performing cosmetic procedures are patient safety, superior results, and absolute confidentiality. Dr. Okoro is the Plastic Surgeon for many celebrities & VIP both in Nigeria and United States. Find out why!
He has extensive experience and special interest in Facial rejuvenation, breast enhancement, breast augmentation, body contouring such as tummy tuck, (Abdominoplasty) and Laser Liposuction with SmartLipo. His Navy experience gives him an edge in facial, body contouring & reconstruction. He is a well rounded plastic surgeon with a world of experience. He was recently voted the 2011 & 2012 "Compassionate Doctor Award" by US based Patient Choice and he was listed in the "Guide to America's Top Surgeon" 2011 edition by the Consumer's Research Council of America.
Dr Okoro is honest, sincere and easy to get along with. In Plastic surgery, it's all on the details. Dr Okoro is a hard working and diligent Plastic Surgeon. He is certified by both the American Board of Plastic Surgery and the American Board of Surgery. He is a member of many prestigious & exclusive organizations including the International Society of Aesthetic Plastic Surgery (ISAPS), American Society for Aesthetic Plastic Surgery (ASAPS), American Society of Plastic Surgeons (ASPS), and American College of Surgeons (ACS).
Dr Okoro became a surgeon at Emory University in Atlanta Georgia. He completed a rigorous five year training program in Surgery at Emory University after graduating with honors from Meharry Medical College. After his training at Emory, he joined the US Navy and served as the Chief of General Surgery at the US Naval Hospital Great Lakes for four years. While at Great Lakes, he established and served as the director of the Breast Care Center receiving many accolades for his service to breast cancer patients. He also established and served as the director of the endoscopic and minimally invasive surgery unit.
After Navy General surgery, he proceeded to complete an additional three year training program in Plastic & Reconstructive Surgery at the University of Texas Health Science Center, San Antonio. While are UT San Antonio, Dr Okoro was specially trained by many famous surgeons in the most current cosmetic surgery and non-surgical procedures, which he has mastered.
Dr Okoro returned to active duty Navy after successfully completing his plastic surgery training. Achieving the rank of Commander, he served as the Chief of Plastic and Reconstructive Surgery at our "President's Hospital", the world famous National Naval Medical Center, Bethesda Maryland. While at Bethesda, received the majority of Marine and Navy casualties from the Iraq and Afghanistan conflicts. While also at Bethesda, the headquarters of the National Capital Area Breast Care Center, he established and directed the micro-vascular breast reconstruction surgery. Dr Okoro has performed countless primary and revision cosmetic procedures for these men and women. He single handedly sustained reconstruction of war injured service men and women, while providing both aesthetic and reconstructive service to eligible dependents. He served as an Assistant Professor of Surgery at Uniformed Services University of the Health Sciences, Bethesda MD.
He has received multiple awards, authored many topics in plastic surgery journals, and presented at several local, regional and national plastic surgery conferences.
In addition to his academic accomplishments, Dr Okoro is a philanthropist and an international Plastic Surgeon. He serves as the Executive Director of Imo Medical Mission, Inc, a non-profit charitable organization. He organizes and participates in an ongoing volunteer medical and surgical mission to undeveloped countries twice yearly since 2003.
Dr Okoro is a dedicated family man and has been married for 20 years. He has 4 wonderful children.
Dr. Okoro's looks may be deceiving. He may look young, but he has a world wealth of unmatched knowledge, experience, skills, and surgical wisdom. Dr Okoro brings this vast experience with him to Nigeria.
Dr. Okoro is a member of the Cobb County Medical Society, Medical Association of Georgia, American Society for Aesthetic Plastic Surgery, American Society of Plastic Surgeons, American College of Surgeons.


text:
Q: How to remove ad countdown timer in google-ima SDK? I'm using video.js and videojs-ima plugin in order to create ad pre-rolls on videos, however; Google's Ads SDK creates an ad countdown timer through the videoAdUi and it appends it through a iframe which means I cannot affect or change the styles within the iframe.
I also found this, but obviously I'm trying to achieve the opposite.
Here's a picture of my problem.
I'm trying to get rid of the Ad: (00:14) and keep the one I already have. Like I've already mentioned, I followed the trail to figure out it was in a iframe with the class of videoAdUiAttribution in a container of videoAdUiBottomBar.
I found this as well, but again. Their documentation isn't the best and I can't seem to find anything that tells me how to go about disabling/hiding it.
Thanks in advance!

A: As far as I know the IMA SDK does not allow you to customize the Ad user interface to use your own countdown timer. You can use their API to disable the countdown timer and attribution but doing more than that might violate their Ad Serving Guidelines, at least according to this post on the IMA SDK forum.
Assuming you can get a reference to the adsRenderingSetting object, you should be able to remove the countdown and have it show only the Ad Attribution, I believe, as follows (from the same post above):
 //DISPLAY COUNTDOWN & AD_ATTRIBUTION
  adsRenderingSettings.uiElements = [google.ima.UiElements.AD_ATTRIBUTION, google.ima.UiElements.COUNTDOWN];

  //DISPLAY  ONLY AD_ATTRIBUTION
  adsRenderingSettings.uiElements = [google.ima.UiElements.AD_ATTRIBUTION];

  //THIS DOES NOTHING - IT'S NOT POSSIBLE TO ONLY SHOW THE COUNTDOWN
  adsRenderingSettings.uiElements = [google.ima.UiElements.COUNTDOWN];


text:
Israel's concessions to Hamas are just the prelude
by Trumpsmin | Mar 31, 2019 | Uncategorized
Six rockets were fired from Gaza at Israel on Sunday. First, a bit after midnight, five rockets landed in open areas in the western Negev. Then the sirens sounded again in the northern Negev, but it turned out that the rocket fired had landed in Palestinian territory.
Despite the fire – Islamic Jihad was apparently responsible for the first volley – Israel on Sunday morning opened the Erez Crossing (for Gaza residents with exit permits), as well as the Kerem Shalom crossing (for the movement of goods), and allowed fishing to resume off the Gaza coast.
skip – Haaretz Weekly Episode 20
Haaretz Weekly Episode 20Haaretz
The government is prepared to go far to achieve quiet in the south until the April 9 election. Not a single official announcement – by the Israel Defense Forces spokesman or the Prime Minister's Office – has been issued about easing conditions in the Strip. The Defense Ministry wouldn't even answer a question about how large the fishing limits off Gaza are.
>> Gaza protests: Israel will reward Hamas for its restraint, but any incident could reignite the flames | Analysis ■ Hamas' troubles don't stop at the Isralei border | Analysis
Israeli voters will have to make do with the general statements made by Prime Minister Benjamin Netanyahu himself, who said Israel is "prepared for any scenario" in the south, or the slightly more detailed remarks by the Likud campaign spokesman, who told Army Radio, "If there's no choice, we may launch a military operation."
The operation, one assumes, will be launched by the State of Israel and the IDF, not Likud headquarters. But the trend is clear; chances of a military operation before the elections don't seem great unless Hamas insists on dragging Netanyahu into one. Netanyahu seems to believe that a conflagration on the Gaza border could jeopardize his success in the elections. Plenty has been written in recent years about his lack of faith in a large-scale military operation in Gaza.
A masked Palestinian boy during a protest marking Land Day and the first anniversary of a surge of border protests, at the Israel-Gaza border fence east of Gaza City, March 30, 2019.\ MOHAMMED SALEM/ REUTERS
The Israeli easing of restrictions was a response to Hamas' relative restraint in the demonstrations along the border fence on Saturday, during which three young men were nevertheless killed by army fire, during incidents that included attempts to damage the fence and cross it. Hamas, for its part, is meant to stop the violent nightly disturbances along the border and reduce the throwing of grenades, explosive devices and the launching of incendiary balloons and kites. Hamas leader Yahya Sinwar declared, however, that the March of Return would continue every Friday as planned.
But the main issue on the agenda relates to the package of economic steps that will be declared for the Strip after the election. Here the ideas will have to be combined with "the deal of the century" that the U.S. administration is meant to propose for the Israeli-Palestinian track. It seems like one of the ideas being considered is to take the plan's Gaza appendix and implement it first.
Many of the proposals being mentioned now to rehabilitate Gaza were raised more than two years ago, in what was dubbed the "Poly plan," after former Israeli Coordinator of Activities in the Territories Maj. Gen. Yoav ("Poly") Mordechai.
At the time there was talk of injecting about $1.5 billion into Gaza, mostly from the Gulf States, but with American and European support. Among the proposals were: Establishing industrial zones for Palestinian labor in Egyptian Rafah; building solar farms along the Gaza border; the upgrading of the Gaza's gas and electricity infrastructure with Israeli assistance; the construction of desalination plants; the construction of a short railway connecting the Erez checkpoint with northern Gaza City (which would allow swifter passage of goods through Ashdod Port), and possibly constructing a dock for the Palestinians at the El-Arish Port in Sinai.
Now these ideas are again on the agenda, since, after nearly two years of delays, it seems the Trump administration is close to actually submitting its peace plan. This, even though the Palestinian Authority has declared from every possible platform, including PA President Mahmoud Abbas' speech to the Arab League conference in Morocco on Sunday, that the plan is dangerous, it favors Israel, and cannot be accepted.
Palestinians demonstrate to mark the first anniversary of the March of Return protests, Israel-Gaza border, March 30, 2019.AFP
However, if the administration is determined to try, it could influence the Israeli political arena. Netanyahu may now be labeling the Kahol Lavan party as "the weak left" and declared over the weekend that he wouldn't appoint Benny Gantz defense minister in his government. But the circumstances could change after the election. Just as Gideon Sa'ar was once enemy No. 1 in the Prime Minister's Residence and now appears at Netanyahu's side in Likud election posters, Netanyahu may need to seek a more moderate coalition if U.S. President Donald Trump is serious about his peace proposals.
Meanwhile, it's becoming clearer why Egypt is so willing to go out on a limb to reach an agreement in Gaza that could help Netanyahu safely get through the elections. Trump announced that he has invited Egyptian President Abdel-Fattah al-Sissi to visit the United States soon. Their meeting will take place only a few days before the Egyptian parliament is meant to vote on controversial changes to the country's constitution that would allow Sissi to remain president until 2034.
Sissi's opponents were hoping for international pressure to foil this process but Trump, by inviting Sissi to Washington, is doing the opposite. And who is Egypt's biggest advocate in the United States? Why Netanyahu, of course; just as the American media reported that the Israeli premier helped reduce the tensions between the Trump administration and the Saudi royal family after it was revealed that Crown Prince Mohammed bin Salman was involved in the murder of journalist Jamal Khashoggi in the Saudi consulate in Istanbul.
When we add to this the many reports in foreign media about Israeli air strikes in Sinai in the service of the Egyptian struggle with the Islamic State there, one can only guess at the depth of the alliance between Jerusalem and Cairo and understand why Egypt is investing so much in achieving a long-term truce between Israel and Hamas.
File Photo: Iron Dome intercepts a rocket from Gaza, March 28, 2019.Ilan Assayag
National Union of Israeli Students objects to deepening gender segregation
The National Union of Israeli Students on Sunday came out against a new plan to extend the segregation of men and women in academic institutions.
The plan calls for changes that would ease the criteria that allow gender-based segregation, with the opening of separate graduate programs and a dramatic reduction in the oversight of the Council for Higher Education on programs for ultra-Orthodox students.
>> Israel underfunds special-ed preschools in ultra-Orthodox communities ■ Does racism now define Jewish identity in Israel? | Opinion
According to the union's head, Shlomi Yehiav, "The proposal crosses red lines on its way to funding separate education systems, based on gender and sectors."
Students are joining the harsh criticism of the proposal voiced by senior officials at the finance and justice ministries. The new plan is being spearheaded by the head of the council's Planning and Budgeting Committee, Prof. Yaffa Zilbershats.
In recent discussions, Deputy Attorney General Dina Zilber has warned that the significance of the proposed changes is "the granting of extensive and far-reaching permission to exclude women," which will be demanded not just by the ultra-Orthodox, but by other groups that prefer segregated studies, such as national-religious Jews. According to different sources, Zilbershats announced that she did not accepted the justice ministry's position and was asking for a meeting with Attorney General Avichai Mendelblit, apparently in an attempt to bypass Zilber. The main points of the new plan were revealed in Haaretz last Thursday.
A steering committee at the Council for Higher Education that is dealing with increasing the number of ultra-Orthodox students was expected to hold its first discussion of the new plan on Sunday. A document explaining the opposition of the student union says that in contrast to current policies, the new plan "does not provide a balance between the need to provide equal opportunities for ultra-Orthodox men and women and the extensive infringement of equality and human dignity, leading to gender segregation and the exclusion of women in public spaces used by male and female students, as well as by administrative and academic staff."
"This proposal disrupts the balance between ultra-Orthodox students and the rest of the student body" says Yehiav. "Its principles are contrary to agreements and balances we reached after lengthy negotiations on this topic only two years ago, in May 2017. We have no choice but to object to the majority of the proposal's clauses."
Student representatives claim that "there has been much pressure over the last year, demanding to widen the current definition of people entitled to study in the segregated ultra-Orthodox programs, adding secular people who have adopted religion and graduates of religious institutions who have taken core subjects [such as English and math]. This will lead to expanded gender segregation and to a different mix of students, which will negatively impact the original plan of increasing accessibility for ultra-Orthodox students."
Including a new category of students will leave the ultra-Orthodox at a disadvantage, say student representatives, leading to a larger number of dropouts. They say that there is no support for Zilbershats' claim that many groups are left out by the current definition of suitability, which is based on the high school students attended.
Student reps warn that "recognizing segregation as a cultural need will make it difficult for graduates to integrate into the job market after graduation. The program could actually become a hindrance to finding employment, missing the realization of its intended objective."
Student representatives add that opening these programs to "born-again" students, could "remove any obstacle to demands for gender segregation by anyone claiming cultural considerations. This opens the door to future demands for segregation in academic institutions, followed by state institutions and the workplace."
Senior officials in the treasury and justice ministry are critical of Zilbershats' plan. Treasury representative Ido Sofer said that changing the definition of who is suitable for these programs, such that only cultural considerations remain, will lead to other sectors demanding special educational programs, depending on origin, nationality and gender. Zilber said, "You can't artificially increase the number of people entitled to join these programs by turning to populations that don't meet the criteria."
'MAGA' Hat-Wearing Assailant Attacks Man With Sword
A man in a pirate costume wearing a red "Make America Great Again" slashed another man's hand with a sword Friday night in San Francisco after the pair got into a verbal dispute, reports the San Francisco Chronicle.
The incident happened around 9:50 p.m. outside a popular skating rink in the city's Western Addition. Police originally said the victim was wearing a MAGA hat but changed their statement Sunday to say it was the suspect who was wearing the hat.
Scott Sweeney, 24, told the Chronicle the assailant was shouting homophobic slurs at him.
"In my mind I didn't think it was a real sword until we came out later and police were on the scene and there was blood and the hat on ground," he said.
Robert Rueca, a police spokesperson for the SFPD, said the victim was stabbed after he approached the suspect.
The MAGA hat has become a source of contention since President Donald Trump won the 2016 presidential election.
Just last month an 81-year-old New Jersey man was assaulted during an attack stemming over his MAGA hat.
The man was shopping at a supermarket when the scuffle took place.
Donald Trump Jr. Obliterates Biased Media Over Russian Investigation Coverage
Ashley (Kimber)
As we've said many times before, the implications of the Russian investigation are far-reaching and nefarious.
No one knows this better than Don Jr.
When news broke that he had allegedly met with some Russians to commit evil, dastardly deeds, the media and internet LIT UP with speculation about him – and some pretty awful things were said.
He is now speaking out against the media that took joy in not only attacking our President with some entirely made-up narrative, but everyone around him as well.
According to Daily Wire:
Trump Jr.'s interview came after Attorney General William Barr sent a letter to Congress over a week ago that revealed that Special Counsel Robert Mueller's Russia investigation did not find that the Trump campaign colluded with Russia and that there was not enough evidence to pursue charging the president with obstruction of justice.
Speaking on whether he thought Mueller's investigation wanted to find President Donald Trump guilty of crimes, Trump Jr. said: "I think so because they didn't find anything, but the reality is there was nothing there to find. Now I don't believe for one second that there's not one person on that team whose dream it would not have been to take down Donald Trump — I stand by that 100 percent."
Trump Jr. wasted no time going after the media over its non-stop negative coverage of the investigation in which they often ignored the ties the investigation had to people on the political Left and what triggered the investigation to begin with.
In fact, Judge Jeanine Pirro went so far to say that those who triggered the investigation should now be investigated themselves.
He does, however, feel that some in the media were trying their best to be objective in a sea of biased information:
"I think there were some people that tried to actually be fair — when they were actually fair you'd see the other side just try to just obliterate them, right, even their own following. So you know, I think there were a couple of people that did good reporting, and I don't mean you know conservative reporters," Trump Jr. said. "I mean actually people on the other side that were at least unbiased or try to look at it objectively… They lost their following, they get ratioed to heck, you know, it became a business model for most media to attack Donald Trump and to buy into this narrative. And if you didn't you upset a lot of people, you risked your career, and you risked other things."
"And so, the people who just bought into this hook, line, and sinker, who were selling it every day… They lied to the American public for two years," Trump Jr. added. "They did a terrible disservice to this country, to journalism as a profession. I think they've done irreparable damage to the faith that the average American is going to have in terms of mainstream journalism. I think it's a blight on our republic on democracy and on our Constitution — that's not going to come undone very quickly."
Meanwhile, people like Jake Tapper don't think networks like CNN did "anything wrong."
The state of American media an absolute disgrace.
Israel Police dismiss left-wing activist's complaint because of his political views
A police officer has been recorded dismissing a complaint by a left-wing activist due to the activist's political orientation.
"He can report as many incidents as he likes," the policeman said in a telephone call two weeks ago, but "there's no point in going there." He also called the complainant, Rabbi Arik Ascherman, "the biggest anarchist in Judea and Samaria," referring to the West Bank.
Ascherman, a well-known left-wing activist, called the Binyamin police station on March 18 to report that settlers had trespassed on private Palestinian land near the West Bank settlement of Rimonim. The station said it was sending someone to the scene.
Sometime later, a policewoman who identified herself as Keren called from the station to ask Ascherman for directions to the site. She connected him via conference call to Avi Illouz, the policeman en route to the site.
When Ascherman directed Illouz to a road "that bypasses the settlement to the east," Illouz replied, "I don't know of any settlement near Rimonim." Ascherman said he meant Rimonim itself, and Illouz responded, "Oh, that's not a settlement, that's a community."
Ascherman said he wouldn't dispute the point and continued giving Illouz directions. He also described the truck that had trespassed on the Palestinian lands and said he had photographed it.
Illouz then asked Ascherman to file a complaint, saying that was the only remaining course of action, "because the vehicle has already left the site." But that conclusion was based solely on Ascherman saying he could no longer see it, and Ascherman himself wasn't at the site, only nearby.
Toward the end of the conference call, when Illouz thought Ascherman had already hung up, he began talking to his colleague. "Keren, my oh my, don't you know him? Don't you know who he is? He's the biggest anarchist in Judea and Samaria."
"We know him, so what?" Keren replied. "He's allowed to report incidents."
"No problem, he can report as many incidents as he pleases," Illouz said. "As far as I'm concerned, the vehicle has left the site and he was told to file a complaint. There's no point in going there."
At that point Ascherman, who was still on the line, interrupted, saying, "Avi, it's embarrassing that you're talking about me like that." Illouz replied, "Heaven forbid, I'm not talking about you like that. I simply know you," and hung up the phone.
Ascherman never did file the complaint, saying he was later told that only the Palestinian landowner could do so. Nevertheless, he said, had Illouz arrived at the scene quickly, he could have seen the vehicle for himself and run its license plate. Moreover, the law requires the police to open an investigation any time they know a crime has been committed, even if no formal complaint is filed.


text:
Q: Read TF Record file takes very long Trying to go through a tensorflow tutorial here; I build a tf record file with ~100 images, and now when I try the following, the kernel hangs; Why is this happening? The tf record file is not large only 30MB+ or so, it shouldn't take long to read them in:
import tensorflow as tf
import os

print(os.path.exists("../carmakesorter/train-00000-of-00001"))

filenameQ = tf.train.string_input_producer(["../carmakesorter/train-00000-of-00001"],num_epochs=None)

# object to read records
recordReader = tf.TFRecordReader()

# read the full set of features for a single example
key, fullExample = recordReader.read(filenameQ)

# parse the full example into its' component features.
features = tf.parse_single_example(
    fullExample,
    features={
        'image/height': tf.FixedLenFeature([], tf.int64),
        'image/width': tf.FixedLenFeature([], tf.int64),
        'image/colorspace': tf.FixedLenFeature([], dtype=tf.string,default_value=''),
        'image/channels':  tf.FixedLenFeature([], tf.int64),
        'image/class/label': tf.FixedLenFeature([],tf.int64),
        'image/class/text': tf.FixedLenFeature([], dtype=tf.string,default_value=''),
        'image/format': tf.FixedLenFeature([], dtype=tf.string,default_value=''),
        'image/filename': tf.FixedLenFeature([], dtype=tf.string,default_value=''),
        'image/encoded': tf.FixedLenFeature([], dtype=tf.string, default_value='')
    })

label = features['image/class/label']
with tf.Session() as sess:
    print('start ...')
    print(sess.run(label))               # I want to check the label here
    print('end ...')

It prints:
True
start ...

My notebook kernel hangs for 10 minutes already and I don't see there will be an ending. Can somebody point out what I am doing wrong?

A: You forget to run queue runners with 'tf.train.start_queue_runners(sess)'


Hedgelands Nursery celebrated its 20 birthday with a party for the children this month.
Nursery owner Denise Akindulureni has seen Hedgelands progress over the years.
She was an RAF wife and ran the nursery from RAF Stanbridge but when it closed she found new premises at Hornbeam Community hall and Eggington Village hall and opened in March 1995.
Mrs Akindulureni said: "I am very proud of how far the nursery has come and it is great to celebrate our 20th birthday.
"I would like to take this opportunity to thank everyone for their continued support and recommending Hedgelands to their families and friends.
"There has been lots of changes during the last 20 years due to the 15 hours free funding for three-year-olds and more recently for two-year-olds.
"We have made many changes and amendments to the Early Years Foundation Stage.
The children made bunting and party hats and they had a cake to celebrate the 20th birthday.


Chile is divided into 15 districts with the catchy names one – fifteen; in order from North to South. We traveled today through the Fifteenth District, which is the largest geographically, but only houses one percent of the country's population.
Today we travelled through beautiful fjords on the way to Punta Arenas. The glaciers just don't seem to quit.
Check out the seal in the picture that is totally relaxed on the ice. He couldn't care less about us in his territory.
We watched a great presentation on looted art during WWII, and the U.S. work that went into returning it to its legitimate owners. It turns out that Russia kept whatever they could steal back from the Nazis, and they have it to this day.
We were supposed to arrive in Punta Arenas at 7:00 p.m., which we did, but the harbor is closed because of 60 mph winds. We are going to try again at 4:00 a.m.
It is impressive how stable the ship is.


text:
Doctors' Climate Blockade
By Tess De La Mare, Theecologist.org
Doctors' Climate Blockade2019-09-282019-09-27https://popularresistance-uploads.s3.amazonaws.com/uploads/2017/12/popres-shorter.pngPopularResistance.Orghttps://popularresistance-uploads.s3.amazonaws.com/uploads/2019/09/22070228-1.jpg200px200px
Above Photo: From Theecologist.org
Thirty doctors from across UK join protest calling on government to take rapid action to create a carbon neutral health service.
@DrChrisNewman, thank you 🙏🏽 pic.twitter.com/GYfjx1WDHW
— DoctorsForXR (@DoctorsXr) September 25, 2019
Doctors backing the Extinction Rebellion movement have blockaded the Department of Business, Energy and Industrial Strategy to highlight the impact of climate change on public health.
Thirty doctors from all over the UK joined the protest on Wednesday to call on the Government to take rapid action to create a carbon neutral health service.
Two doctors scaled the building to glue themselves to the glass porch over the entrance, while several others held a banner reading "government inaction will cost thousands of lives".
The protesters told police they would not move until a representative from the department came to speak with them.
Rachel Cottam, a GP from Brighton, told the PA news agency: "The NHS is the biggest public sector emitter, equally climate change is a public health emergency and seven million people die globally because of air pollution.
"The last four years were the hottest on record, this July was the hottest July on record and it's no surprise we saw the highest level of A&E admissions. For every one degree of warming there is a 3.4 percent increase in cardiovascular disease mortality and a 3.6 percent increase in respiratory mortality.
"The wonderful thing is that what we need to do to combat climate change is incredibly good for our health. Stop eating meat, it's the biggest cause of colon cancer; stop driving our cars and walk, tackling diabetes and obesity.
"The climate emergency is intimately linked with every aspect of our health."
Chris Newman, one of the two men glued to the building, said in a statement: "Climate and ecological breakdown poses one of the greatest threats to public health the world has ever faced. Yet the Government is failing to take meaningful steps to protect its citizens.
"Non-violent peaceful protests like today are essential public health interventions for getting the government to take immediate action."
Climate Change Protests Reform Solidarity United Kingdom Workers
Greeley Miklashek
Population density stress is killing us now through ALL of our myriad and rapidly increasing "diseases of civilization" and I can pretty much guarantee that these well-meaning fellow physicians are clueless, as drug and biomedical corporations control their med school curricula and journals. They wouldn't know the truth of human diseases if they glued themselves to it. Stress R Us
Don't hange the subject, which is: does civilization itself survive?


text:
Mum lands top comedy role
AN ACTRESS has won a role in a new British comedy. Mother-of-two Sue Parker-Nutley, 57, of Melody Road, Biggin Hill, secured the part in
AN ACTRESS has won a role in a new British comedy.
Mother-of-two Sue Parker-Nutley, 57, of Melody Road, Biggin Hill, secured the part in The Flirting Club after auditioning through a website set up to choose the cast.
The film, set to hit UK cinemas later this year, will be shot by the Academy Award nominated director, Alex Jovy.
Ms Parker-Nutley, said: "I am so excited about the role and really looking forward to the start of filming. Rehearsals have begun and it looks like being a very funny film.
"I'm absolutely ecstatic that I got the part. Although I have been a stage actress for 20 years, this is my first film so I have been learning a lot.
"I am getting on very well with everyone. Alex Jovy is excellent. I am the oldest woman in the cast, there are a lot of young people but everyone is very pleasant."
To land the role, Mrs Parker-Nutley had to download the script, choose the character she wanted to play and attend a filmed audition.
She will play the part of the lead man Jamie's mum who has remarried and returned to England from Australia.
Hoping the film will be her big break, she believes its launch will change the lives of all its actors.
The actress said: "We're all fairly new actors, some have travelled from as far as Australia and Holland to be in this ground breaking movie and I just hope I'll be one of the people whose performance stands out. I am really looking forward to it.


text:
Q: How I can define an equivalence relation? Let $f:ℝ→ℝ$ be a real analytic function. Assume that $f$ has infinitely may zeros. Let $D$ be the set values of the function $f$. I want to construct a partition of $D$ as follow:  $D=D₁∪D₂$ where $D₁= \{ x∈D:f(x)=0 \} $ and $D₂= \{ x∈D:f(x)≠0 \} $. One way to do this is to define an equivalence relation. My question is: How I can define this relation?

A: A partition is the same as an equivalence relation: Given any partition $P=\{D_i:i\in I\}$ of any set $D$, you can define the equivalence relation $\sim_P$ by $$x\sim_P y \Longleftrightarrow \exists i\in I: x,y\in D_i.$$ Conversely, any equivalence relation gives you a partition of $D$ into equivalence classes.


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My heartfelt sympathy to you and your family. I am sorry for the past time your father suffered, but knowing his loving family was by his side I am sure brought great comfort.
Carry your special thoughts of your Dad with you throughout your life, and the memories will be carried down through the generations of your family.
I am sorry for your loss. bc really ticks me off.
But, your heartfelt remembrence of your dad was a beautiful tribute to him. He would (and is) proud of what you have become. Trying to emulate him is the most sincere form of love. He fought the battle, and there is no shame in losing the fight. he gave it his all.
Having you remember him so fondly is a wonderful thing. Take that foundation he gave to you and make him proud indeed.
Very moving and heartfelt words about your father. It's just so sad when someone has to go through so much, and you as a loved one feel so helpless in the face of it all. My husband and I were with both my mother and with his mother when they passed away, and it was a much more peaceful and less frightening experience than we would have thought. It's good you were all able to be with your father in his last days, and I'm sure he was aware of your presence even when he was unable to respond. When my mother was dying, the wonderful CNA who was with us in the room told us to keep talking to my mother, that hearing is the last sense to go and that people who appeared to be unconscious could very likely hear,understand, and be comforted. May you and your family find peace and comfort down the road.
Thank you for sharing! My condolences. It is never easy. It is brutal and emotionally draining what you have been through. Remember the good times also before all of these struggles happened. You must certainly be a loving and caring husband, brother and son as you hoped and wrote about! Be proud.
I too had the difficult decision but also a privilege to be at my Dad's bedside as he passed a few years ago at age 88 while he was semi comatose from a heart attack. Being there while not fun I learned many can still hear and communicate if only their eyes. I could tell him that his kids and grandkids were OK and that all was taken care of. I tried to soothe as much as possible with singing, talking and stroking his head and hands. Cherish the fact that you helped in a moment of need!
Then just a month ago a sister passed from a severe stroke. Same chapter and verse at the end. Unpleasant-you bet but, again a privilege.
Some handle passing differently which is also OK. There is no right or wrong. You did what you felt was best. I applaud you. You will reflect positively upon your experience as painful as it is. I have never looked back. Also, kudo's to hospice, they are a neat organization!
Last edit: 4 years 7 months ago by Alan.
I am so sorry that this horrible disease has taken another loved one from us.
I know that you will be glad that you were able to share those last days and moments with your dad...He knew you were there for him and that was all you could do. It is so hard, but you should be proud and grateful that you were able to do this for him.
Hospice is a wonderful thing. People do wait too long to avail themselves of all it has to offer.
I am so, so sorry for the loss of your Dad to this terrible disease. I am also very sorry for all the pain and suffering he had to endure, and for your family's heartbreak as you stayed by his side throughout the course of treatment. Your words are a beautiful tribute to him and what a wonderful father and human being he was to have touched so many over his lifetime.
This is a good place to share your grief because you can say here what you don't want to say to your loved ones because they have their own grief to carry. You weathered this as a family and I know your Dad was greatly comforted by all the love surrounding him.
I am glad you had hospice to help get through the last days. Sharing this information will undoubtedly guide others if they ever find that they are facing the loss of a loved one.
Please visit the forum as often as you need to help work through your grief and sadness. There are many here who can be understanding sounding boards for what you are going through. Again... I am very sorry for your loss.
Last edit: 4 years 7 months ago by CatherineH.


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