text:
Finance Property Renters face eviction, homelessness as pandemic support falls away
6:00am, May 9, 2021 Updated: 6:44am, May 9
Renters face eviction, homelessness as pandemic support falls away
Rents are "less affordable than ever", a new report has warned. Image: TND
Isabelle Lane Journalist @isabellelane
Renters across Australia are facing eviction and homelessness following the end of pandemic support, a new report has warned, with advocates urging the federal government to address the crisis in next week's Budget.
The grim findings came as the Morrison government announced a scheme to allow 10,000 single parents to buy a home with a deposit of just 2 per cent, ahead of Tuesday's Budget.
Titled 'A perfect storm', the Anglicare Australia report, released on Sunday, reveals a surge in the number of people turning to the organisation's homelessness services for help with rental arrears.
"Rents are less affordable than ever. Yet the support that had been given to renters at the height of the pandemic is falling away," the report said.
"Government actions, which seemed so promising at the beginning of the COVID-19 pandemic, have not tackled the twin crises of rental stress and poverty."
Median national house rents have jumped by four per cent since the start of the pandemic, according to data released in April, and are increasing about three times faster than wages.
Cuts to JobSeeker payments and the end of pandemic rent deferrals and eviction moratoriums, which expired in March in most states and territories, have pushed many renters to "the brink", Anglicare Australia executive director Kasy Chambers said.
"We're seeing renters with bigger and bigger debts. Some have already been given notice to leave their homes," Ms Chambers said.
"People are turning to homelessness services to keep a roof over their head. Many are also going to payday lenders. That will only lead to more hardship and debt."
Ms Chambers called on the Morrison government to urgently boost social welfare payments to mitigate the affordable housing crisis.
"We need action in next week's Budget. We need to lift JobSeeker and other payments above the poverty line," she said.
"If we don't, people out of work will be pushed deeper into housing stress and even homelessness."
Sunday's report followed the release of Anglicare's Rental Affordability Snapshot in April, which surveyed 74,266 rental listings across Australia.
The snapshot found that just 1.2 percent of rentals were affordable for a person earning the minimum wage, 0.5 percent were affordable for a person on the Age Pension, and 0.3 percent were affordable for a person on the Disability Support Pension.
Just three rentals across the nation, all sharehouses, were affordable for a person on JobSeeker, while none were affordable for a person on Youth Allowance, the survey revealed.
Kate Colvin, national spokesperson for the Everybody's Home campaign, urged the government to use the Budget to "fire the starter's gun on building half a million new social and affordable rental homes cross Australia".
"There is no better public investment than social and affordable housing. We have a rapidly accelerating homelessness problem," Ms Colvin said.
"The government should also rethink planned cuts to homelessness services. Next week's Budget is due to cut $56.7 million from this critical support right when we need it most."
Australian Politics Property Real Estate
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As we continue to wait and watch, I wanted to note a few sites that are collecting online reporters' resources linked to the life and work of Pope John Paul II. Our friends over at Poynter.org have a special edition of Al's Morning Meeting online, in which online researcher Al Tompkins pulls together a mountain of links and background resources. There is no way I can compete with that, so just click here.
Similar materials will continue to be updated at the ReligionLink site operated by the Religion Newswriters Association. (Here is a direct link to the RNA's fast-developing collection of links on Pope John Paul II and the Vatican.
Those seeking materials from a traditionalist Catholic perspective can head to Catholic World News and its Off the Record blog. Christianity Today's team is hard at work, so click here.
This list will keep growing in the hours and days ahead. However, may I also be so bold as to point you toward a column that I wrote recently for the Scripps Howard News Service, at the time of the first real crisis in this threat to the life of the pope and the media panic that ensued.
Reporters are trying to cover their bases. The panic also may have been fueled by another reality. This pope's life is impossible to capture in a few dramatic images, a three-minute sound-bite blitz and a sentence or two about the length of his tenure (second longest ever) and the number of nations he has visited (125 so far). Journalists must ask: What is the lead on this story?
Please let us know of the best, and the worst, articles that you see in the mainstream press. Also, pass along good sites for research on the story. Once again, please know that we are interested in a wide range of materials, from a variety of viewpoints. I would also be interested in hearing from journalists evaluating the, well, doctrinal balance of some of these resource sites.
While we all face our personal reactions to this story, we must remember that people have work to do. It's called journalism.


text:
Ustnik ciemnopręgi (Chaetodon melannotus) – gatunek morskiej ryby okoniokształtnej z rodziny chetonikowatych (Chaetodontidae).

Występowanie
Gatunek ten występuje na głębokości od 1 do 25 m w południowo-zachodnim Pacyfiku, od Morza Czerwonego przez Ocean Indyjski po Wyspy Samoa. Północna granica zasięgu obejmuje południową Japonię, południowa Nową Południową Walię i wyspę Lord Howe. Chaetodon melannotus zamieszkuje bogate w koralowce obszary lagun i raf morskich.

Filatelistyka
Poczta Polska wyemitowała 1 kwietnia 1967 r. znaczek pocztowy o nominale 5 gr, przedstawiający Chaetodon melannotus, w serii Ryby egzotyczne. Autorem projektu znaczka był Jerzy Desselberger. W wydrukowanym na znaczku opisie ryby słowo melannotus zapisano przez jedno n. Znaczek pozostawał w obiegu do 31 grudnia 1994 r..

Przypisy

Linki zewnętrzne

 Film z osobnikiem tego gatunku

Chaetodon
Fauna i flora w akwarium morskim
Gatunki i podgatunki zwierząt nazwane w 1801 roku

redpajama_set_name: RedPajamaWikipedia


Justin Blackburn,( Greenville , SC ) began laughing at himself around the age of six and has not looked back. In collaboration with Kenny Norsworthy, Blackburn created a controversial stir with the US Government with their powerful novel Gifted Disabilities, (2005). This novel caused quite an uproar in the underground world especially with lead singer of the acclaimed Fat Possum Record's band The Company, Brian Hannon who published a collection of Blackburn and Norsworthy's early poetry It's Hard To Get There When You Are Already There which led to Justin Blackburn being voted Upstate Poet Of The Year in The Beat Magazine in 2007. In 2009 Blackburn had two chapbooks of poetry published the hilariously wild Farting Fire by Virgogray Press and the emotional cleansing Female Human Whispers Of Strong Masculine Gentleness by Shadow Archer Press and became the highest seller for both publications. Blackburn has had hundreds of poems published through the literary world in magazines, literary journals, zines, and anthologies including Fissure Magazine, Semantics, Left Behind Literary Journal, Speed Poets, Open Mind's Quarterly, Decanto, Angel Voices, A &U's America AIDS Magazine etc. He has featured and performed in numerous venues/colleges/high schools across the country including Brick City Poetry, Verse Works, Witsend, TRAM Arts Festival, Converse College, Leaf Festival, Louder Arts etc; In 2010 Blackburn became a member of the New Danger Slam Team as well as having his words featured throughout the music world due to the success of Coma Cinema who took lines and based songs on his poems. Also Blackburn formally an Intuitive Inner Healer at Kimah Healing Arts Center in Greenville, South Carolina, now holding various workshops on Learning To Love Yourself, Understanding Your Intuition, Flying Through The Astral Plane, and Stepping Into Your Feminine and Masculine Power. Justin Blackburn loves you.


Computer Tutor Trouble Shooter - CT2S is committed to bringing you a consistently high level of service. We are an important resource for local individuals and businesses.
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Content copyright . Pat Nugent. All rights reserved.


text:
Q: Linux Mint 17-18 freezing randomly I am totally desperate and just a tiny hair far from using some other OS instead of Linux because it is giving me a headache.
So the thing is, I am using Linux Mint from version 17 and at the moment I have the version 18 installed and nothing have changed. So after fresh install (not using swap partition but only home and root), I am getting random freezes all the time. Sometimes it works for a day, sometimes it freezes multiple times per day and so on.
Freeze means -> keyboard and mouse is not responding and that's it. The only way is to shut it down with a power button hold. (CTRL + ALT + F1... doesn't work obviously). Sometimes I can even reboot with (ALT + PRINTSCREEN) + R E I U S B combination but very rarely. Then on different computers there are different behaviours. On some computer the Linux doesn't boot properly for 3 times in a row because I get some errors with nouveau issues constantly popping in the console.
I tried these bumblebees, proprietary drivers and so on and nothing helps. Actually there is not a way that I haven't tried. I went through all the solutions until year 2011 or even less but not even one solution helped me.
Since that issue is present on 5th computer in a row, I am really not sure what am I doing wrong or what is wrong with the distribution.
Have anyone had such experience with it? I also tried raw Debian and I also experienced these issues, mostly with graphic card drivers.
It would be really good if someone finally clarify that what is happening there actually.
Thank you very much in advance!

redpajama_set_name: RedPajamaStackExchange


Ng'eny integrated primary school has over a half a century of existence. The school stands out from the rest and has positively influenced many learners with disabilities in the country.
The teachers in the school are unique in many virtuous ways. They are specialised in different fields of dissabilities having graduated from Kenya Institute Of Special Education (KISE). They tend to go extra miles in everything that they do to help the learners wiyh special needs in educatioon. The teachers are dedicated, proud and enthusiastic; virtues that are rarely seen in many schools.
The school has made a major difference to learners with various disabilities and therefore it serves as a centre of excellence in special needs education in the country.
The quality of education is commensurate with that of the school. A complete institution of an inclussive education.Besides covering the convensional curriculum, teachers tend to turn the school into a centre of excellence in special needs in education.
The buildings and facilities especially the small home within the school, have served the school for long and are now run down, worn out, dilapidated, small, unfit ande some pose agreat danger to the pupils and the staff.
I call upon on all the stakeholders to jeolously guard the future of this school against infrastructure decay and looming collapse of the buildings.


text:
\section{Introduction}
\label{sec:introduction}
\input{sections/introduction}

\section{Background and Related Work}
\label{sec:background_and_related_work}
\input{sections/background}

\section{Tabular Leakage}
\label{sec:methods}
\input{sections/methods}

\section{Experimental evaluation}
\label{sec:results}
\input{sections/results}

\section{Conclusion}
\label{sec:conclusion}
\input{sections/conclusion}

\section{Ethics Statement}
\input{sections/ethics_statement}

\section{Accuracy Metric}
\label{appendix:accuracy_metric}
To ease the understanding, we start by repeating our accuracy metric here, where we measure the reconstruction accuracy between the retrieved sample $\hat{x}$ and the ground truth $x$ as:
\begin{equation}
    \text{accuracy}(x, \,\hat{x}) \coloneqq \frac{1}{K+L} \left (\sum_{i=1}^{K} \mathbb{I}\{x^D_i = \hat{x}^D_i\}+ \sum_{i=1}^{L} \mathbb{I}\{\hat{x}^C_i \in [x^C_i-\epsilon_i,\, x^C_i+\epsilon_i]\}\right).
\end{equation}
Note that the binary treatment of continuous features in our accuracy metric enables the combined measurement of the accuracy on both the discrete and the continuous features. From an intuitive point of view, this measure closely resembles how one would judge the correctness of numerical guesses. For example, guessing the age of a $25$ year old, one would deem the guess good if it is within $3$ to $4$ years of the true value, but the guesses $65$ and $87$ would be both qualitatively incorrect. In order to facilitate scalability of our experiments, we chose the $\left \{ \epsilon_i \right \}_{i=1}^L$ error-tolerance-bounds based on the global standard deviation if the given continuous feature $\sigma^C_i$ and multiplied it by a constant, concretely, we used $\epsilon_i = 0.319 \, \sigma^C_i$ for all our experiments. Note that $Pr[\mu - 0.319\,\sigma < x < \mu + 0.319\, \sigma] \approx 0.25$ for a Gaussian random variable $x$ with mean $\mu$ and variance $\sigma^2$. For our metric this means that assuming Gaussian zero-mean error in the reconstruction around the true value, we accept our reconstruction as privacy leakage as long as we fall into the $25\%$ error-probability range around the correct value. In \cref{table:tolerance_bounds} we list the tolerance bounds $\epsilon_i$ for the continuous features of the Adult dataset produced by this method. We would like to remark here, that we fixed our metric parameters before conducting any experiments, and did not adjust them based on any obtained results. Note also that in \cref{appendix:further_experiments} we provide results where the continuous feature reconstruction accuracy is measured using the commonly used regression metric of root mean squared error (RMSE), where \OurMethod{} also achieves the best results, signaling that the success of our method is independent of our chosen metric.

\begin{table}[H]
	\caption{Resulting tolerance bounds on the Adult dataset when using $\epsilon_i = 0.319\,\sigma_i^C$, as used by us for our experiments.}
	\label{table:tolerance_bounds}
	\centering
	\resizebox{0.97\columnwidth}{!}{
	\begin{tabular}{lcccccc}
		\toprule
		 feature & \texttt{age} & \texttt{fnlwgt} & \texttt{education-num} & \texttt{capital-gain} & \texttt{capital-loss} & \texttt{hours-per-week}\\
		\midrule
		tolerance & 4.2 & 33699 & 0.8 & 2395 & 129 & 3.8 \\
		\bottomrule
	\end{tabular}
	}
\end{table}

\section{Further Experimental Details}
\label{appendix:further_experimental_details}
Here we give an extended description to our experimental details provided in \cref{sec:results}. For all attacks, we use the Adam optimizer~\citep{Kingma2014} with learning rate $0.06$ for $1\,500$ iterations and without a learning rate schedule. We chose the learning rate based on our experiments on the baseline attack where it performed best. In line with \citet{Geiping2020}, we modify the update step of the optimizer by reducing the update gradient to its element-wise sign. We attack a fully connected neural network with two hidden layers of $100$ neurons each at initialization. However, we provide a network-size ablation in \cref{fig:network_size_all}, where we evaluate our attack against the baseline method for $5$ different network architectures. For each reported metric we conduct $50$ independent runs on 50 different batches to estimate their statistics. For all FedSGD experiments we clamp the continuous features to their valid ranges before measuring the reconstruction accuracy, both for our attacks and the baseline methods. We ran each of our experiments on single cores of Intel(R) Xeon(R) CPU E5-2690 v4 @ 2.60GHz.

\paragraph{Federated Averaging Experiments} For experiments on attacking the FedAvg training algorithm, we fix the clients' local dataset size at $32$ and conduct an attack after local training with learning rate $0.01$ on the initialized network described above. We use the FedAvg attack-framework of \citet{Dimitrov22}, where for each local training epoch we initialize an independent mini-dataset matching the size of the client dataset, and simulate the local training of the client. At each reconstruction update, we use the mean squared error between the different epoch data means ($D_\text{inv} =\ell_2$ and $g=\text{mean}$ in \citet{Dimitrov22}) as the permutation invariant epoch prior required by the framework, ensuring the consistency of the reconstructed dataset. For the full technical details, please refer to the manuscript of \citet{Dimitrov22}. For choosing the prior parameter $\lambda_\text{inv}$, we conduct line-search on each setup and attack method pair individually on the parameters $[0.0, 0.5, 0.1, 0.05, 0.01, 0.005, 0.001]$, and pick the ones providing the best results. Further, to reduce computational overhead, we reduce the ensemble size of \OurMethod{} from $30$ to $15$ for these experiments on all datasets.
\section{Further Experiments}
\label{appendix:further_experiments}
In this subsection, we present three further experiments:
\begin{itemize}
	\item Results of attacking neural networks defended using differentially private noisy gradients in \cref{appendix:attack_against_gaussian_dp}.
	\item Ablation study on the impacts of the neural network's size on the reconstruction difficulty in \cref{appendix:varying_network_size}.
	\item Measuring the Root Mean Squared Error (RMSE) of the reconstruction of continuous features in \cref{appendix:continuous_feature_recosntruction_measured_by_rmse}.
\end{itemize}

\subsection{Attack against Gaussian DP}
\label{appendix:attack_against_gaussian_dp}
Differential privacy (DP) has recently gained popularity, as a way to prevent privacy violations in FL~\citep{Abadi2016,Zhu2019}. Unlike, empirical defenses which are often broken by specifically crafted adversaries~\citep{Balunovic2021}, DP provides guarantees on the amount of data leaked by a FL model, in terms of the magnitude of random noise the clients add to their gradients prior to sharing them with the server~\citep{Abadi2016,Zhu2019}. Naturally, DP methods balance privacy concerns with the accuracy of the produced model, since bigger noise results in worse models that are more private. In this subsection, we evaluate \OurMethod{}, and the Cosine baseline against DP defended gradient updates, where zero-mean Gaussian noise is added with standard deviations $0.001$, $0.01$, and $0.1$ to the client gradients. We present our results on the Adult, German Credit, Lawschool Admissions, and Health Heritage datasets in \cref{fig:dp_adult}, \cref{fig:dp_german}, \cref{fig:dp_lawschool}, and \cref{fig:dp_health}, respectively. Although both methods are affected by the defense, our method consistently produces better reconstructions than the baseline method. However, for high noise level ($\sigma = 0.1$) and larger batch size both attacks break, advocating for the use of DP defenses in tabular FL to prevent the high vulnerability exposed by this work.
\begin{figure}
	\centering
	\begin{subfigure}{.33\textwidth}
		\centering
		\includegraphics[width=0.95\textwidth]{figures/ADULT_128_All_0,6_0001.pdf}
		\subcaption{$\sigma = 0.001$}
	\end{subfigure}%
	\begin{subfigure}{.33\textwidth}
		\centering
		\includegraphics[width=0.95\textwidth]{figures/ADULT_128_All_0,6_001.pdf}
		\subcaption{$\sigma = 0.01$}
	\end{subfigure}
	\begin{subfigure}{.33\textwidth}
		\centering
		\includegraphics[width=0.95\textwidth]{figures/ADULT_128_All_0,6_01.pdf}
		\subcaption{$\sigma = 0.1$}
	\end{subfigure}
	\caption{Mean and standard deviation accuracy [\%] curves over batch size at varying Gaussian noise level $\sigma$ added to the client gradients for differential privacy on the \textbf{Adult} dataset.}
	\label{fig:dp_adult}
\end{figure}

\begin{figure}
	\centering
	\begin{subfigure}{.33\textwidth}
		\centering
		\includegraphics[width=0.95\textwidth]{figures/German_128_All_0,6_0001.pdf}
		\subcaption{$\sigma = 0.001$}
	\end{subfigure}%
	\begin{subfigure}{.33\textwidth}
		\centering
		\includegraphics[width=0.95\textwidth]{figures/German_128_All_0,6_001.pdf}
		\subcaption{$\sigma = 0.01$}
	\end{subfigure}
	\begin{subfigure}{.33\textwidth}
		\centering
		\includegraphics[width=0.95\textwidth]{figures/German_128_All_0,6_01.pdf}
		\subcaption{$\sigma = 0.1$}
	\end{subfigure}
	\caption{Mean and standard deviation accuracy [\%] curves over batch size at varying Gaussian noise level $\sigma$ added to the client gradients for differential privacy on the \textbf{German Credit} dataset.}
	\label{fig:dp_german}
\end{figure}

\begin{figure}
	\centering
	\begin{subfigure}{.33\textwidth}
		\centering
		\includegraphics[width=0.95\textwidth]{figures/Lawschool_128_All_0,6_0001.pdf}
		\subcaption{$\sigma = 0.001$}
	\end{subfigure}%
	\begin{subfigure}{.33\textwidth}
		\centering
		\includegraphics[width=0.95\textwidth]{figures/Lawschool_128_All_0,6_001.pdf}
		\subcaption{$\sigma = 0.01$}
	\end{subfigure}
	\begin{subfigure}{.33\textwidth}
		\centering
		\includegraphics[width=0.95\textwidth]{figures/Lawschool_128_All_0,6_01.pdf}
		\subcaption{$\sigma = 0.1$}
	\end{subfigure}
	\caption{Mean and standard deviation accuracy [\%] curves over batch size at varying Gaussian noise level $\sigma$ added to the client gradients for differential privacy on the \textbf{Lawschool Admissions} dataset.}
	\label{fig:dp_lawschool}
\end{figure}

\begin{figure}
	\centering
	\begin{subfigure}{.33\textwidth}
		\centering
		\includegraphics[width=0.95\textwidth]{figures/HealthHeritage_128_All_0,6_0001.pdf}
		\subcaption{$\sigma = 0.001$}
	\end{subfigure}%
	\begin{subfigure}{.33\textwidth}
		\centering
		\includegraphics[width=0.95\textwidth]{figures/HealthHeritage_128_All_0,6_001.pdf}
		\subcaption{$\sigma = 0.01$}
	\end{subfigure}
	\begin{subfigure}{.33\textwidth}
		\centering
		\includegraphics[width=0.95\textwidth]{figures/HealthHeritage_128_All_0,6_01.pdf}
		\subcaption{$\sigma = 0.1$}
	\end{subfigure}
	\caption{Mean and standard deviation accuracy [\%] curves over batch size at varying Gaussian noise level $\sigma$ added to the client gradients for differential privacy on the \textbf{Health Heritage} dataset.}
	\label{fig:dp_health}
\end{figure}

\pagebreak
\subsection{Varying Network Size}
\label{appendix:varying_network_size}
To understand the effect the choice of the network has on the obtained reconstruction results, we defined $4$ additional fully connected networks, two smaller, and two bigger ones to evaluate \OurMethod{} on. Concretely, we examined the following five architectures for our attack:
\begin{enumerate}
	\item a single hidden layer with $50$ neurons,
	\item a single hidden layer with $100$ neurons,
	\item two hidden layers with $100$ neurons each (network used in main body),
	\item three hidden layers with $200$ neurons each,
	\item and three hidden layers with $400$ neurons each.
\end{enumerate}
We attack the above networks, aiming to reconstruct a batch of size $32$. We plot the accuracy of \OurMethod{} and the cosine baseline as a function of the number of parameters in the network in \cref{fig:network_size_all} for all four datasets. We can observe that with increasing number of parameters in the network, the reconstruction accuracy significantly increases on all datasets, and rather surprsingly, allowing for near perfect reconstruction of a batch as large as $32$ in some cases. Observe that on both ends of the presented parameter scale the differences between the methods degrade, \textit{i.e. } they either both converge to near-perfect reconstruction (large networks) or to random guessing (small networks). Therefore, the choice of our network for conducting the experiments was instructive in examining the differences between the methods.

\begin{figure}
	\centering
	\begin{subfigure}{.5\textwidth}
		\centering
		\includegraphics[width=0.9\textwidth]{figures/ADULT_all_0,6.pdf}
		\subcaption{Adult}
	\end{subfigure}%
	\begin{subfigure}{.5\textwidth}
		\centering
		\includegraphics[width=0.9\textwidth]{figures/German_all_0,6.pdf}
		\subcaption{German Credit}
	\end{subfigure}
	\begin{subfigure}{.5\textwidth}
		\centering
		\includegraphics[width=0.9\textwidth]{figures/Lawschool_all_0,6.pdf}
		\subcaption{Lawschool Admissions}
	\end{subfigure}%
	\begin{subfigure}{.5\textwidth}
		\centering
		\includegraphics[width=0.9\textwidth]{figures/HealthHeritage_all_0,6.pdf}
		\subcaption{Health Heritage}
	\end{subfigure}
	\caption{Mean attack accuracy curves with standard deviation for batch size $32$ over varying network size (measured in number of parameters, \#Params, log scale) on all four datasets. We mark the network we used for our other experiments with a dashed vertical line.}
	\label{fig:network_size_all}
\end{figure}

\subsection{Continuous Feature Reconstruction Measured by RMSE}
\label{appendix:continuous_feature_recosntruction_measured_by_rmse}
In order to examine the potential influence of our choice of reconstruction metric on the obtained results, we further measured the reconstruction quality of continuous features on the widely used Root Mean Squared Error (RMSE) metric as well. Concretely, we calculate the RMSE between the $L$ continuous features of our reconstruction $\hat{x}^C$ and the ground truth $x$ in a batch of size $n$ as:
\begin{equation}
	\text{RMSE}(x^C,\hat{x}^C)= \frac{1}{L} \sum_{i=1}^L \sqrt{\frac{1}{n} \sum_{j=1}^n ( x_{ij}^C - \hat{x}_{ij}^C)^2 }.
\end{equation}
As our results in \cref{fig:rmse_cont} demonstrate, \OurMethod{} achieves significantly lower RMSE than the Cosine baseline on large batch sizes, for all four datasets examined. This indicates that the strong results obtained by \OurMethod{} in the rest of the paper are not a consequence of our evaluation metric.

\begin{figure}
	\centering
	\begin{subfigure}{.5\textwidth}
		\centering
		\includegraphics[width=0.9\textwidth]{figures/ADULT_128_cot_rmse.pdf}
		\subcaption{Adult}
	\end{subfigure}%
	\begin{subfigure}{.5\textwidth}
		\centering
		\includegraphics[width=0.9\textwidth]{figures/German_128_cot_rmse.pdf}
		\subcaption{German Credit}
	\end{subfigure}
	\begin{subfigure}{.5\textwidth}
		\centering
		\includegraphics[width=0.9\textwidth]{figures/Lawschool_128_cot_rmse.pdf}
		\subcaption{Lawschool Admissions}
	\end{subfigure}%
	\begin{subfigure}{.5\textwidth}
		\centering
		\includegraphics[width=0.9\textwidth]{figures/HealthHeritage_128_cot_rmse.pdf}
		\subcaption{Health Heritage}
	\end{subfigure}
	\caption{The mean and standard deviation of the Root Mean Square Error (RMSE) of the reconstructions of the continuous features on all four datasets over batch sizes.}
	\label{fig:rmse_cont}
\end{figure}

\section{All Main Results}
\label{appendix:all_main_results}
In this subsection, we include all the results presented in the main part of this paper for the Adult dataset alongside with the corresponding additional results on the German Credit, Lawschool Admissions, and the Health Heritage datasets.

\subsection{Full FedSGD Results on all Datasets}
\label{appendix:full_fedsgd_results_on_all_datasets}
In \cref{table:adult_results_all}, \cref{table:german_results_all}, \cref{table:lawschool_results_all}, and \cref{table:health_results_all} we provide the full attack results of our method compared to the Cosine and random baselines on the Adult, German Credit, Lawschool Admissions, and Health Heritage datasets, respectively. Looking at the results for all datasets, we can confirm the observations made in \cref{sec:results}, \textit{i.e. } (i) the lower batch sizes are vulnerable to any non-trivial attack, (ii) not knowing the ground truth labels does not significantly disadvantage the attacker for larger batch sizes, and (iii) \OurMethod{} provides a strong improvement over the baselines for practically relevant batch sizes over all datasets examined.

\begin{table}
	\caption{The mean inversion accuracy [\%] and standard deviation of different methods over varying batch sizes with given true labels (top) and with reconstructed labels (bottom) on the \textbf{Adult} dataset.}
	\label{table:adult_results_all}
	\centering
	\resizebox{.99\columnwidth}{!}{\input{sections/tables_appendix/table_adult_all.tex}}
\end{table}

\begin{table}
	\caption{The mean inversion accuracy [\%] and standard deviation of different methods over varying batch sizes with given true labels (top) and with reconstructed labels (bottom) on the \textbf{German Credit} dataset.}
	\label{table:german_results_all}
	\centering
	\resizebox{.99\columnwidth}{!}{\input{sections/tables_appendix/table_german_all.tex}}
\end{table}

\begin{table}
	\caption{The mean inversion accuracy [\%] and standard deviation of different methods over varying batch sizes with given true labels (top) and with reconstructed labels (bottom) on the \textbf{Lawschool Admissions} dataset.}
	\label{table:lawschool_results_all}
	\centering
	\resizebox{.99\columnwidth}{!}{\input{sections/tables_appendix/lawschool_table_all.tex}}
\end{table}

\begin{table}
	\caption{The mean inversion accuracy [\%] and standard deviation of different methods over varying batch sizes with given true labels (top) and with reconstructed labels (bottom) on the \textbf{Health Heritage} dataset.}
	\label{table:health_results_all}
	\centering
	\resizebox{.99\columnwidth}{!}{\input{sections/tables_appendix/health_table_all.tex}}
\end{table}

\subsection{Categorical vs. Continuous Features on all Datasets}
\label{appendix:categorical_vs_continuous_features_on_all_datasets}
In \cref{fig:cat_vs_cont_all}, we compare the reconstruction accuracy of the continuous and the discrete features on all four datasets. We confirm our observations, shown in \cref{fig:attack_errors_adult_cat_cont} in the main text, that a strong dichotomy between continuous and discrete feature reconstruction accuracy exists on all 4 datasets.

\begin{figure}
	\centering
	\begin{subfigure}{.5\textwidth}
		\centering
		\includegraphics[width=0.9\textwidth]{figures/ADULT_128_catvscont_0,6.pdf}
		\subcaption{Adult}
	\end{subfigure}%
	\begin{subfigure}{.5\textwidth}
		\centering
		\includegraphics[width=0.9\textwidth]{figures/German_128_catvscont_0,6.pdf}
		\subcaption{German Credit}
	\end{subfigure}
	\begin{subfigure}{.5\textwidth}
		\centering
		\includegraphics[width=0.9\textwidth]{figures/Lawschool_128_catvscont_0,6.pdf}
		\subcaption{Lawschool Admissions}
	\end{subfigure}%
	\begin{subfigure}{.5\textwidth}
		\centering
		\includegraphics[width=0.9\textwidth]{figures/HealthHeritage_128_catvscont_0,6.pdf}
		\subcaption{Health Heritage}
	\end{subfigure}
\caption{Mean reconstruction accuracy curves with corresponding standard deviations over varying batch size, separately for the discrete and the continuous features on all four datasets.}
\label{fig:cat_vs_cont_all}
\end{figure}

\subsection{Federated Averaging Results on all Datasets}
\label{appendix:federated_averaging_results_on_all_datasets}
In \cref{table:rec_fedavg_adult}, \cref{table:rec_fedavg_german}, \cref{table:rec_fedavg_lawschool}, and \cref{table:rec_fedavg_health} we present our results on attacking the clients in FedAvg training on the Adult, German Credit, Lawschool Submissions, and Health Heritage datasets, respectively. We described the details of the experiment in \cref{appendix:further_experimental_details} above. Confirming our conclusions drawn in the main part of this manuscript, we observe that \OurMethod{} achieves non-trivial reconstruction accuracy over all settings and even for large numbers of updates, while the baseline attack often fails to outperform random guessing, when the number of local updates is increased.

\begin{table}
	\caption{Mean and standard deviation of the inversion accuracy [\%] with local dataset size of $32$ in FedAvg training on the \textbf{Adult} dataset. The accuracy of the random baseline for $32$ datapoints is $58.0\pm 2.9$.}
	\label{table:rec_fedavg_adult}
	\centering
	\resizebox{0.97\columnwidth}{!}{
	\begin{tabular}{lcccccc}
		\toprule
		 & \multicolumn{3}{c}{\OurMethod{}} & \multicolumn{3}{c}{Cosine}\\
		\cmidrule[1pt](l{5pt}r{5pt}){2-4}\cmidrule[1pt](l{5pt}r{5pt}){5-7}
		 \#batches & 1 epoch & 5 epochs & 10 epochs & 1 epoch & 5 epochs & 10 epochs \\
		\midrule

		 1 & $\mathbf{77.4 \pm 4.5}$ & $\mathbf{71.1 \pm 2.9}$ & $\mathbf{67.6 \pm 3.7}$ & $65.2 \pm 2.7$ & $56.1 \pm 4.1$ & $53.2 \pm 4.2$ \\
		 2 & $\mathbf{75.7 \pm 5.0}$ & $\mathbf{71.7 \pm 3.9}$  & $\mathbf{67.7 \pm 4.2}$ & $64.8 \pm 3.3$ & $56.4 \pm 4.8$ & $56.2 \pm 4.8$ \\
		 4 & $\mathbf{75.9 \pm 4.4}$ & $\mathbf{71.0 \pm 3.2}$ & $\mathbf{67.4 \pm 3.4}$ & $64.8 \pm 3.4$ & $58.7 \pm 4.6$ & $56.6 \pm 5.0$ \\
		\bottomrule
	\end{tabular}
	}
\end{table}

\begin{table}
	\caption{Mean and standard deviation of the inversion accuracy [\%] with local dataset size of $32$ in FedAvg training on the \textbf{German Credit} dataset. The accuracy of the random baseline for $32$ datapoints is $56.9 \pm 2.1$.}
	\label{table:rec_fedavg_german}
	\centering
	\resizebox{0.97\columnwidth}{!}{
	\begin{tabular}{lcccccc}
		\toprule
		 & \multicolumn{3}{c}{\OurMethod{}} & \multicolumn{3}{c}{Cosine}\\
		\cmidrule[1pt](l{5pt}r{5pt}){2-4}\cmidrule[1pt](l{5pt}r{5pt}){5-7}
		 \#batches & 1 epoch & 5 epochs & 10 epochs & 1 epoch & 5 epochs & 10 epochs \\
		\midrule

		 1 & $\mathbf{95.2 \pm 3.8}$ & $\mathbf{87.9 \pm 6.2}$ & $\mathbf{83.4 \pm 4.6}$ & $78.2 \pm 4.6$ & $65.4 \pm 6.2$ & $62.5 \pm 6.1$ \\
		 2 & $\mathbf{95.5 \pm 3.9}$ & $\mathbf{88.2 \pm 5.2}$  & $\mathbf{84.0 \pm 6.6}$ & $78.3 \pm 5.8$ & $68.8 \pm 6.6$ & $63.4 \pm 4.8$ \\
		 4 & $\mathbf{95.6 \pm 3.6}$ & $\mathbf{85.5 \pm 6.0}$ & $\mathbf{81.0 \pm 6.1}$ & $79.2 \pm 4.9$ & $67.4 \pm 4.8$ & $62.6 \pm 6.5$ \\
		\bottomrule
	\end{tabular}
	}
\end{table}

\begin{table}
	\caption{Mean and standard deviation of the inversion accuracy [\%] with local dataset size of $32$ in FedAvg training on the \textbf{Lawschool Admissions} dataset. The accuracy of the random baseline for $32$ datapoints is $57.8 \pm 2.3$.}
	\label{table:rec_fedavg_lawschool}
	\centering
	\resizebox{0.97\columnwidth}{!}{
	\begin{tabular}{lcccccc}
		\toprule
		 & \multicolumn{3}{c}{\OurMethod{}} & \multicolumn{3}{c}{Cosine}\\
		\cmidrule[1pt](l{5pt}r{5pt}){2-4}\cmidrule[1pt](l{5pt}r{5pt}){5-7}
		 \#batches & 1 epoch & 5 epochs & 10 epochs & 1 epoch & 5 epochs & 10 epochs \\
		\midrule

		 1 & $\mathbf{85.6 \pm 3.8}$ & $\mathbf{83.3 \pm 2.9}$ & $\mathbf{80.7 \pm 4.1}$ & $72.2 \pm 2.6$ & $68.1 \pm 3.1$ & $65.2 \pm 2.8$ \\
		 2 & $\mathbf{86.0 \pm 3.8}$ & $\mathbf{83.0 \pm 3.2}$  & $\mathbf{79.8 \pm 3.5}$ & $72.5 \pm 1.9$ & $68.3 \pm 4.4$ & $66.2 \pm 2.8$ \\
		 4 & $\mathbf{85.8 \pm 3.5}$ & $\mathbf{81.7 \pm 3.8}$ & $\mathbf{79.3 \pm 4.3}$ & $72.5 \pm 2.4$ & $69.4 \pm 3.9$ & $67.9 \pm 3.8$ \\
		\bottomrule
	\end{tabular}
	}
\end{table}

\begin{table}
	\caption{Mean and standard deviation of the inversion accuracy [\%] with local dataset size of $32$ in FedAvg training on the \textbf{Health Heritage} dataset. The accuracy of the random baseline for $32$ datapoints is $43.4 \pm 3.5$.}
	\label{table:rec_fedavg_health}
	\centering
	\resizebox{0.97\columnwidth}{!}{
	\begin{tabular}{lcccccc}
		\toprule
		 & \multicolumn{3}{c}{\OurMethod{}} & \multicolumn{3}{c}{Cosine}\\
		\cmidrule[1pt](l{5pt}r{5pt}){2-4}\cmidrule[1pt](l{5pt}r{5pt}){5-7}
		 \#batches & 1 epoch & 5 epochs & 10 epochs & 1 epoch & 5 epochs & 10 epochs \\
		\midrule

		 1 & $\mathbf{68.5 \pm 5.0}$ & $\mathbf{62.2 \pm 3.5}$ & $\mathbf{57.4 \pm 3.0}$ & $53.8 \pm 5.5$ & $41.4 \pm 3.6$ & $41.1 \pm 3.4$ \\
		 2 & $\mathbf{68.1 \pm 4.9}$ & $\mathbf{62.4 \pm 4.1}$  & $\mathbf{57.0 \pm 2.8}$ & $52.4 \pm 5.7$ & $43.4 \pm 4.28$ & $44.4 \pm 4.3$ \\
		 4 & $\mathbf{67.3 \pm 5.8}$ & $\mathbf{62.0 \pm 3.5}$ & $\mathbf{57.0 \pm 3.0}$ & $52.5 \pm 6.6$ & $43.4 \pm 5.7$ & $44.8 \pm 4.4$ \\
		\bottomrule
	\end{tabular}
	}
\end{table}

\subsection{Full Results on Entropy on all Datasets}
\label{appendix:full_results_on_entropy_on_all_datasets}
In \cref{table:global_entropy_adult_full}, \cref{table:global_entropy_german_full}, \cref{table:global_entropy_lawschool_full}, and \cref{table:global_entropy_health_full} we provide the mean and standard deviation of the reconstruction accuracy and the entropy of the continuous and the categorical features over increasing batch size for attacking with \OurMethod{} on the four datasets. In support of \cref{sec:results}, we can observe on all datasets a trend of increasing entropy over decreasing reconstruction accuracy as the batch size is increased; and as such providing a signal to the attacker about their overall reconstruction success.

To generalize our results on the local information contained in the entropy, we show the mean reconstruction accuracy of both the discrete and the continuous features with respect to bucketing them based on their entropy in a batch of size $128$ in \cref{table:local_entropy_adult_full}, \cref{table:local_entropy_german_full}, \cref{table:local_entropy_lawschool_full}, and \cref{table:local_entropy_health_full} for all four datasets, respectively. We can see that with the help of this bucketing, we can identify subsets of the reconstructed features that have been retrieved with a (sometimes significantly \textit{e.g., } up to 24\%) higher accuracy than the overall batch.

\begin{table}
	\caption{The mean accuracy [\%] and entropies with the corresponding standard deviations over batch sizes of the categorical and the continuous features on the \textbf{Adult} dataset.}
	\label{table:global_entropy_adult_full}
	\centering
	\resizebox{0.7\columnwidth}{!}{
	\begin{tabular}{lcccc}
		\toprule
			& \multicolumn{2}{c}{Discrete} & \multicolumn{2}{c}{Continuous}\\
		\cmidrule[1pt](l{5pt}r{5pt}){2-3}\cmidrule[1pt](l{5pt}r{5pt}){4-5}
			& Accuracy & Entropy & Accuracy & Entropy \\
		\midrule
			1 & $100.0 \pm 0.0$ & $0.01 \pm 0.04$ & $98.7 \pm 6.5$ & $-3.1 \pm 0.26$ \\
			2 & $99.5\pm 3.5$ & $0.01 \pm 0.06$ & $98.8 \pm 8.2$ & $-2.82 \pm 0.57$\\
			4 & $99.5 \pm 1.4$ & $0.08 \pm 0.11$ & $95.8 \pm 9.9$ & $-1.89 \pm 0.99$ \\
			8 & $98.5 \pm 5.6$ & $0.15 \pm 0.13$ & $90.9 \pm 14.7$ & $-1.11 \pm 0.95$ \\
			16 & $97.2 \pm 4.3$ & $0.26 \pm 0.11$ & $78.8 \pm 13.5$ & $-0.11 \pm 0.63$ \\
			32 & $91.0 \pm 4.4$ & $0.40 \pm 0.06$ & $59.2 \pm 6.9$ & $0.77 \pm 0.30$ \\
			64 & $83.2 \pm 3.6$ & $0.48 \pm 0.04$ & $55.1 \pm 3.0$ & $1.21 \pm 0.19$ \\
			128 & $78.5 \pm 1.8$ & $0.53 \pm 0.03$ & $55.7 \pm 2.0$ & $1.48 \pm 0.10$ \\
		\bottomrule
	\end{tabular}
	}
\end{table}

\begin{table}
	\caption{The mean accuracy [\%] and entropies with the corresponding standard deviations over batch sizes of the categorical and the continuous features on the \textbf{German Credit} dataset.}
	\label{table:global_entropy_german_full}
	\centering
	\resizebox{0.7\columnwidth}{!}{
	\begin{tabular}{lcccc}
		\toprule
			& \multicolumn{2}{c}{Discrete} & \multicolumn{2}{c}{Continuous}\\
		\cmidrule[1pt](l{5pt}r{5pt}){2-3}\cmidrule[1pt](l{5pt}r{5pt}){4-5}
			& Accuracy & Entropy & Accuracy & Entropy \\
		\midrule
			1 & $100.0 \pm 0.0$ & $0.00 \pm 0.01$ & $100.0 \pm 0.0$ & $-3.10 \pm 0.18$ \\
			2 & $100.0 \pm 0.0$ & $0.03 \pm 0.05$ & $100.0 \pm 0.0$ & $-2.41 \pm 0.97$\\
			4 & $100.0 \pm 0.0$ & $0.07 \pm 0.05$ & $99.8 \pm 1.1$ & $-1.66 \pm 0.80$ \\
			8 & $100.0 \pm 0.0$ & $0.10 \pm 0.07$ & $99.1 \pm 3.1$ & $-1.38 \pm 0.54$ \\
			16 & $99.5 \pm 1.4$ & $0.25 \pm 0.07$ & $89.1 \pm 8.1$ & $-0.35 \pm 0.22$ \\
			32 & $93.0 \pm 2.1$ & $0.43 \pm 0.04$ & $66.0 \pm 4.9$ & $0.60 \pm 0.13$ \\
			64 & $81.9 \pm 1.8$ & $0.56 \pm 0.02$ & $57.5 \pm 2.2$ & $1.08 \pm 0.06$ \\
			128 & $78.2 \pm 1.1$ & $0.59 \pm 0.02$ & $58.4 \pm 1.7$ & $1.30 \pm 0.05$ \\
		\bottomrule
	\end{tabular}
	}
\end{table}

\begin{table}
	\caption{The mean accuracy [\%] and entropies with the corresponding standard deviations over batch sizes of the categorical and the continuous features on the \textbf{Lawschool Admissions} dataset.}
	\label{table:global_entropy_lawschool_full}
	\centering
	\resizebox{0.7\columnwidth}{!}{
	\begin{tabular}{lcccc}
		\toprule
			& \multicolumn{2}{c}{Discrete} & \multicolumn{2}{c}{Continuous}\\
		\cmidrule[1pt](l{5pt}r{5pt}){2-3}\cmidrule[1pt](l{5pt}r{5pt}){4-5}
			& Accuracy & Entropy & Accuracy & Entropy \\
		\midrule
			1 & $100.0 \pm 0.0$ & $0.01 \pm 0.03$ & $100.0 \pm 0.0$ & $-3.28 \pm 0.29$ \\
			2 & $100.0 \pm 0.0$ & $0.02 \pm 0.05$ & $100.0 \pm 0.0$ & $-2.85 \pm 0.87$\\
			4 & $100.0 \pm 0.0$ & $0.03 \pm 0.04$ & $100.0 \pm 0.0$ & $-2.45 \pm 0.78$ \\
			8 & $99.8 \pm 1.1$ & $0.11 \pm 0.10$ & $96.4 \pm 11.1$ & $-1.77 \pm 0.62$ \\
			16 & $98.1 \pm 2.8$ & $0.24 \pm 0.11$ & $87.1 \pm 13.4$ & $-0.65 \pm 0.49$ \\
			32 & $93.4 \pm 3.0$ & $0.41 \pm 0.08$ & $65.1 \pm 8.0$ & $0.18 \pm 0.20$ \\
			64 & $87.0 \pm 2.4$ & $0.55 \pm 0.05$ & $57.7 \pm 4.8$ & $0.78 \pm 0.12$ \\
			128 & $83.5 \pm 1.5$ & $0.60 \pm 0.03$ & $62.6 \pm 3.4$ & $1.07 \pm 0.11$ \\
		\bottomrule
	\end{tabular}
	}
\end{table}

\begin{table}
	\caption{The mean accuracy [\%] and entropies with the corresponding standard deviations over batch sizes of the categorical and the continuous features on the \textbf{Health Heritage} dataset.}
	\label{table:global_entropy_health_full}
	\centering
	\resizebox{0.7\columnwidth}{!}{
	\begin{tabular}{lcccc}
		\toprule
			& \multicolumn{2}{c}{Discrete} & \multicolumn{2}{c}{Continuous}\\
		\cmidrule[1pt](l{5pt}r{5pt}){2-3}\cmidrule[1pt](l{5pt}r{5pt}){4-5}
			& Accuracy & Entropy & Accuracy & Entropy \\
		\midrule
			1 & $100.0 \pm 0.0$ & $0.02 \pm 0.05$ & $99.6 \pm 2.5$ & $-2.97 \pm 0.33$ \\
			2 & $100.0 \pm 0.0$ & $0.05 \pm 0.09$ & $96.5 \pm 12.9$ & $-2.55 \pm 0.80$\\
			4 & $99.7 \pm 1.8$ & $0.08 \pm 0.10$ & $97.5 \pm 9.0$ & $-1.71 \pm 0.79$ \\
			8 & $99.1 \pm 3.7$ & $0.13 \pm 0.11$ & $94.3 \pm 11.3$ & $-1.06 \pm 0.64$ \\
			16 & $96.6 \pm 7.5$ & $0.26 \pm 0.10$ & $80.2 \pm 11.2$ & $-0.11 \pm 0.42$ \\
			32 & $85.1 \pm 6.4$ & $0.43 \pm 0.06$ & $61.7 \pm 3.8$ & $0.72 \pm 0.23$ \\
			64 & $73.1 \pm 4.7$ & $0.52 \pm 0.03$ & $59.6 \pm 2.5$ & $1.13 \pm 0.20$ \\
			128 & $66.1 \pm 2.4$ & $0.57 \pm 0.02$ & $60.7 \pm 1.6$ & $1.44 \pm 0.13$ \\
		\bottomrule
	\end{tabular}
	}
\end{table}

\begin{table}
	\caption{The mean accuracy [\%] and the share of data [\%] in each entropy bucket for batch size 128 on the \textbf{Adult} dataset.}
	\centering
	\begin{tabular}{lcclcc}
		\toprule
		 Entropy & \multicolumn{2}{c}{Categorical Features} & Entropy & \multicolumn{2}{c}{Continuous Features}\\
		\cmidrule[1pt](l{5pt}r{5pt}){2-3}\cmidrule[1pt](l{5pt}r{5pt}){5-6}
		Bucket  & Accuracy [\%] & Data [\%] & Bucket & Accuracy [\%] & Data [\%] \\
		\midrule
		 0.0-0.2 & $95.7$ & $8.1$ & $\infty$-0.72 & 72.7 & 1.2 \\
		 0.2-0.4 & $90.5$ & $23.4$ & 0.72-1.16 & 65.5 & 13.6 \\
		 0.4-0.6 & $79.8$ & $27.7$ & 1.16-1.6 & 56.4 & 50 \\
		 0.6-0.8 & $69.8$ & $29.2$ & 1.6-2.04 & 51.1 & 32.4 \\
		 0.8-1.0 & $61.2$ & $11.6$ & 2.04-$\infty$ & 41.8 & 2.9 \\
		\midrule
		Overall & $78.5$ & $100$ & Overall & 55.7 & $100$\\
		\midrule
		Random & $73.8$ & $100$ & Random & 44.4 & $100$\\
		\bottomrule
	\end{tabular}
	\label{table:local_entropy_adult_full}
\end{table}

\begin{table}
	\caption{The mean accuracy [\%] and the share of data [\%] in each entropy bucket for batch size 128 on the \textbf{German Credit} dataset.}
	\centering
	\begin{tabular}{lcclcc}
		\toprule
		 Entropy & \multicolumn{2}{c}{Categorical Features} & Entropy & \multicolumn{2}{c}{Continuous Features}\\
		\cmidrule[1pt](l{5pt}r{5pt}){2-3}\cmidrule[1pt](l{5pt}r{5pt}){5-6}
		Bucket  & Accuracy [\%] & Data [\%] & Bucket & Accuracy [\%] & Data [\%] \\
		\midrule
		 0.0-0.2 & $98.1$ & $7.4$ & $\infty$-0.72 & 55.7 & 1.2 \\
		 0.2-0.4 & $92.5$ & $15.3$ & 0.72-1.16 & 62.3 & 28.1 \\
		 0.4-0.6 & $83.3$ & $22.2$ & 1.16-1.6 & 57.7 & 56.4 \\
		 0.6-0.8 & $71.6$ & $33.7$ & 1.6-2.04 & 53.4 & 13.2 \\
		 0.8-1.0 & $66.0$ & $21.3$ & 2.04-$\infty$ & 48.2 & 0.2 \\
		\midrule
		Overall & $78.2$ & $100$ & Overall & $58.4$ & $100$\\
		\midrule
		Random & $73.5$ & $100$ & Random & $37.8$ & $100$\\
		\bottomrule
	\end{tabular}
	\label{table:local_entropy_german_full}
\end{table}

\begin{table}
	\caption{The mean accuracy [\%] and the share of data [\%] in each entropy bucket for batch size 128 on the \textbf{Lawschool Admissions} dataset.}
	\centering
	\begin{tabular}{lcclcc}
		\toprule
		 Entropy & \multicolumn{2}{c}{Categorical Features} & Entropy & \multicolumn{2}{c}{Continuous Features}\\
		\cmidrule[1pt](l{5pt}r{5pt}){2-3}\cmidrule[1pt](l{5pt}r{5pt}){5-6}
		Bucket  & Accuracy [\%] & Data [\%] & Bucket & Accuracy [\%] & Data [\%] \\
		\midrule
		 0.0-0.2 & $95.5$ & $3.4$ & $\infty$-0.72 & 69.5 & 20.7 \\
		 0.2-0.4 & $92.1$ & $14.2$ & 0.72-1.16 & 63.3 & 35.1 \\
		 0.4-0.6 & $88.0$ & $32.7$ & 1.16-1.6 & 60.1 & 32.9 \\
		 0.6-0.8 & $81.2$ & $30.4$ & 1.6-2.04 & 55.5 & 10.8 \\
		 0.8-1.0 & $70.7$ & $19.3$ & 2.04-$\infty$ & 54.1 & 0.5 \\
		\midrule
		Overall & $83.5$ & $100$ & Overall & $62.6$ & $100$\\
		\midrule
		Random & $81.1$ & $100$ & Random & $19.1$ & $100$\\
		\bottomrule
	\end{tabular}
	\label{table:local_entropy_lawschool_full}
\end{table}

\begin{table}
	\caption{The mean accuracy [\%] and the share of data [\%] in each entropy bucket for batch size 128 on the \textbf{Health Heritage} dataset.}
	\centering
	\begin{tabular}{lcclcc}
		\toprule
		 Entropy & \multicolumn{2}{c}{Categorical Features} & Entropy & \multicolumn{2}{c}{Continuous Features}\\
		\cmidrule[1pt](l{5pt}r{5pt}){2-3}\cmidrule[1pt](l{5pt}r{5pt}){5-6}
		Bucket  & Accuracy [\%] & Data [\%] & Bucket & Accuracy [\%] & Data [\%] \\
		\midrule
		 0.0-0.2 & $90.7$ & $6.2$ & $\infty$-0.72 & 69.1 & 1.1 \\
		 0.2-0.4 & $84.7$ & $22.1$ & 0.72-1.16 & 65.6 & 17.0 \\
		 0.4-0.6 & $70.5$ & $21.2$ & 1.16-1.6 & 61.8 & 52.9 \\
		 0.6-0.8 & $54.8$ & $32.3$ & 1.6-2.04 & 55.9 & 26.5 \\
		 0.8-1.0 & $50.3$ & $18.4$ & 2.04-$\infty$ & 49.4 & 2.5 \\
		\midrule
		Overall & $66.1$ & $100$ & Overall & $60.7$ & $100$\\
		\midrule
		Random & $69.8$ & $100$ & Random & $34.2$ & $100$\\
		\bottomrule
	\end{tabular}
	\label{table:local_entropy_health_full}
\end{table}

\subsection{The Softmax Structural Prior}
\label{subsec:softmax_trick}
We now discuss our solution to challenge (i) -- we introduce the softmax structural prior, which turns the hard mixed discrete-continuous optimization problem into a fully continuous one. This drastically reduces its complexity, while still facilitating the recovery of correct discrete structures.

To start, notice that the recovery of one-hot encodings can be enforced by ensuring that all entries of the recovered vector are either zero or one, and exactly one of the entries equals to one. However, these constraints enforce integer properties, \textit{i.e. } they are non-differentiable and can not be used in combination with the powerful continuous optimization tools used for gradient leakage attacks. Relaxing the integer constraint by allowing the reconstructed entries to take real values in $[0, 1]$, we are still left with a constrained optimization problem not well suited for popular continuous optimization tools, such as Adam~\citep{Kingma2014}. Therefore, we are looking for a method that can implicitly enforce the constraints introduced above.

Let $z \in \mathbb{R}^d$ be our approximate intermediate solution for the true one-hot encoded data $c(x)$ at some optimization step. Then, we are looking for a differentiable function $\sigma: \mathbb{R}^{D_i} \rightarrow [0, 1]$, such that:
\begin{equation}
\label{eq:one_hot_continuous_relaxation}
    \sum_{j=1}^{D_i} \sigma(z_i^D)[j] = 1 \qquad \text{and} \qquad \sigma(z_i^D)[j] \in [0, 1] \quad \forall j \in \mathcal{D}_i.
\end{equation}
Notice that the two conditions in \cref{eq:one_hot_continuous_relaxation} can be fulfilled by applying a \textit{softmax} to $z_i^D$, \textit{i.e. } define:
\begin{equation}
\label{eq:softmax_definition}
    \sigma(z_i^D)[j] \coloneqq \frac{\text{exp}(z^{D}_i[j])}{\sum_{k=1}^{D_i}\text{exp}(z^D_i[k])}\qquad \forall j \in \mathcal{D}_i.
\end{equation}
Note that it is easy to show that \cref{eq:softmax_definition} fulfills both conditions in \cref{eq:one_hot_continuous_relaxation} and that it is differentiable. Putting this together, in each round of optimization we will have the following approximation of the true data point: $c(x) \approx \sigma(z) = \left [ \sigma(z^{D}_1), \, \dots,\, \sigma(z^{D}_K), \, z^C_1, \, \dots, \, z^C_L \right]$. In order to preserve notational simplicity, we write $\sigma(z)$ to mean the application of softmax to each group of entries representing a given categorical variable separately.

\subsection{Pooled Ensembling}
\label{subsec:pooled_ensembling}
As mentioned earlier, the mix of categorical and continuous features introduces further variance in the difficult reconstruction problem which already has multiple local minima and high sensitivity to initialization~\citep{Zhu2020} (challenge (ii)). Concretely, as the one-hot encodings of the categorical features are orthogonal to each other, a change in the encoded category can drastically change the optimization trajectory. We alleviate this problem by adapting an established method of variance reduction in noisy processes~\citep{hastie2009elements}, \textit{i.e. } we run independent optimization processes with different initializations and ensemble their results through feature-wise pooling.

Note that the features in tabular data are tied to a certain position in the recovered data vector, thereby we can combine independent reconstructions to obtain an improved and more robust final estimate of the true data by applying feature-wise pooling. Formally, we run $N$ independent rounds of optimization with $i.i.d.$ initializations recovering potentially different reconstructions $\left \{ \sigma(z_j) \right \}_{j=1}^N$. Then, we obtain a final estimate of the true encoded data, denoted as $\sigma^D_i(\hat{z})$, by pooling them:
\begin{equation}
\label{eq:pooling_non_projected}
    \sigma^D_i(\hat{z}) = \text{pool}\left(\left \{ \sigma(z^D_{ji}) \right \}_{j=1}^N \right)\quad \forall i \in [K] \quad \text{and} \quad \hat{z}_i^C = \text{pool}\left (\left \{ (z^C_{ji}) \right \}_{j=1}^N \right)\quad \forall i \in [L].
\end{equation}
Where the $\text{pool}(\cdot)$ operation can be any permutation invariant mapping that maps to the same structure as its inputs. In our attack, we use median pooling for both continuous and categorical features.

\begin{wrapfigure}{r}{0.5\textwidth}
	\centering
	\includegraphics[trim={0 5.5cm 0 0},clip, width=0.48\textwidth]{figures/batch_matching_png.pdf}
	\caption[short]{Maximum similarity matching of a sample from the collection of reconstructions to the best-loss sample $\hat{x}^{best}$.}
	\label{fig:matching_figure}
\end{wrapfigure}

Notice that because a batch-gradient is invariant to permutations of the datapoints in the corresponding batch, when reconstructing from such a gradient we may retrieve the batch-points in a different order at every optimization instance. Hence, we need to reorder each batch such that their lines match to each other, and only then we can conduct the pooling. We reorder by first selecting the sample that produced the best reconstruction loss at the end of optimization $\hat{z}^{best}$, with projection $\hat{x}^{best}$. Then, we match the lines of every other sample in the collection with respect to $\hat{x}^{best}$. Concretely, we calculate the similarity (described in detail in \cref{sec:results}) between each pair of lines of $\hat{x}^{best}$ and another sample $\hat{x}_{i}$ in the collection and find the maximum similarity reordering of the lines with the help of bipartite matching solved by the Hungarian algorithm~\citep{Kuhn1955}. This process is depicted in \cref{fig:matching_figure}. Repeating this for each sample, we reorder the entire collection with respect to the best-loss sample, effectively reversing the permutation differences in the independent reconstructions. Therefore, after this process we can directly apply feature-wise pooling for each line over the collection.

\subsection{Entropy-based Uncertainty Estimation}
\label{subsec:entropy_uncertainty}
We now address challenge (iii) above. To recap, it is significantly harder for an adversary to asses the quality of an obtained reconstruction when it comes to tabular data, as almost any reconstruction may constitute a credible datapoint when projected back to mixed discrete-continuous space. Note that this challenge does not arise as prominently in the image (or text) domain, because by looking at a picture one can easily judge if it is just noise or an actual image. To address this issue, we propose to estimate the reconstruction uncertainty by looking at the level of agreement over a certain feature for different reconstructions. Concretely, given a collection of reconstructions as in \cref{subsec:pooled_ensembling}, we can observe the distribution of each feature over the reconstructions. Intuitively, if this distribution is "peaky", \textit{i.e. } concentrates the mass heavily on a certain value, then we can assume that the feature has been reconstructed correctly, whereas if there is high disagreement between the reconstructed samples, we can assume that this feature's recovered final value should not be trusted. We can quantify this by measuring the entropy of the feature distributions induced by the recovered samples.

\paragraph{Categorical Features}
Let $p(\hat{x}_i^D)_m \coloneqq \frac{1}{N}\, \text{Count}_j(\hat{x}_{ji}^C=m)$ be the relative frequency of projected reconstructions of the $i$-th discrete feature of value $m$ in the ensemble. Then, we can calculate the normalized entropy of the feature as $\Bar{H}^D_i = \frac{-1}{\log\,|\mathcal{D}_i|}\,\sum_{m=1}^{D_i} p(\hat{x}_i^D)_m\,\log\,p(\hat{x}_i^D)_m$. Note that the normalization allows for comparing features with supports of different size, \textit{i.e. } it ensures that $\Bar{H}^D_i \in [0, 1]$, as $0\leq H(k) \leq \log\,|\mathcal{K}|$ for any discrete random variable $k \in \mathcal{K}$ of finite support.

\paragraph{Continuous Features}
In case of the continuous features, we calculate the entropy assuming that errors of the reconstructed samples follow a Gaussian distribution. As such, we first estimate the sample variance $\hat{\sigma}^2_i$ for the $i$-th continuous feature and then plug it in to calculate the entropy of the corresponding Gaussian: $H^C_i = \frac{1}{2} + \frac{1}{2}\,\log\, 2 \pi \hat{\sigma}^2_i$. As this approach is universal over all continuous features, it is enough to simply scale the features themselves to make their entropy comparable. For example, this can be achieved by working only with standardized features.

Note that as the categorical and the continuous features are fundamentally different from an information theoretic perspective, we have no robust means to combine them in a way that would allow for equal treatment. Therefore, when assessing the credibility of recovered features, we will always distinguish between categorical and continuous features.

\subsection{Combined Attack}
\label{subsec:combined_attack}
\begin{figure}[t]
\begin{algorithm}[H]
    \caption{Our combined attack against training by FedSGD}
    \label{algorithm:comb_attack}
    \begin{algorithmic}[1]
        \Function{SingleInversion}{Neural Network: $f_{\theta}$, Client Gradient: $\nabla_{\theta} \,\mathcal{L}(f_{\theta}(c(x)), y)$, Reconstructed Labels: $\hat{y}$, Initial Reconstruction: $z_i^0$, Iterations: $T$, N. of Discrete Features: $K$}
        \For{$t$ \textbf{in} $1, 2, \dots, T$}
            \For{$k$ \textbf{in} $1, 2, \dots, K$}
                \State $\sigma(z_{ik}^D) \leftarrow$ \Call{softmax}{$z_{ik}^D$}\label{algline:softmax_prior}
            \EndFor
            \State $z_i^{t+1} \leftarrow z_i^{t} - \eta \, \nabla_{z} \mathcal{E}_{CS}(\nabla_{\theta}\, \mathcal{L}(f_{\theta}(c(x)), y), \nabla_{\theta}\, \mathcal{L}(f_{\theta}(\sigma(z_i^t)), \hat{y}))$\label{algline:update}
        \EndFor
        \State\Return $z_i^T$
        \EndFunction
        \State
        \Function{TabLeak}{Neural Network: $f_{\theta}$, Client Gradient: $\nabla_{\theta} \,\mathcal{L}(f_{\theta}(c(x)), y)$, Reconstructed Labels: $\hat{y}$, Ensemble Size: $N$, Iterations: $T$, N. of Discrete Features: $K$}
        \State $\left \{ z_i^0 \right \}_{i=1}^N \sim \mathcal{U}_{[0, 1]^d}$\label{algline:initialization}
        \For{$i$ \textbf{in} $1, 2, \dots, N$}\label{algline:samples}
            \State $z_i^T \leftarrow$ \Call{SingleInversion}{$f_{\theta}$, $\nabla_{\theta} \,\mathcal{L}(f_{\theta}(c(x)), y)$, $\hat{y}$, $z_i^0$, $T$, $K$}
        \EndFor\label{algline:end_optim}
        \State $\hat{z}^{best} \leftarrow \argmin_{z_j^T} \, \mathcal{E}_{CS}(\nabla_{\theta}\, \mathcal{L}(f_{\theta}(c(x)), y), \nabla_{\theta}\, \mathcal{L}(f_{\theta}(\sigma(z_j^T)), \hat{y}))$\label{algline:select_lowest_loss}
        \State $\sigma(\hat{z}) \leftarrow$ \Call{MatchAndPool}{$\left \{ \sigma(z_i^T) \right \}_{i=1}^N, \sigma(\hat{z}^{best})$}\label{algline:match_and_pool}
        \State $\Bar{H}^D, H^C \leftarrow$ \Call{CalculateEntropy}{$\left \{ \sigma(z_i^T) \right \}_{i=1}^N$}\label{algline:entropy_calc}
        \State $\hat{x} \leftarrow$ \Call{Project}{$\sigma(\hat{z})$}\label{algline:project}
        \State\Return $\hat{x}$, $\bar{H}^D$, $H^C$
        \EndFunction
    \end{algorithmic}
\end{algorithm}
\end{figure}

Now we provide the description of our end-to-end attack, \OurMethod{}. Following \citet{Geiping2020}, we use the cosine similarity loss as our reconstruction loss, defined as:
\begin{equation}
    \mathcal{E}_{CS}(\nabla_{\theta_{t}}\, \mathcal{L}(f_{\theta_t}(c(x)), y), \nabla_{\theta_{t}}\, \mathcal{L}(f_{\theta_t}(\sigma(z)), \hat{y})), \quad \text{with} \quad \mathcal{E}_{CS}(l, g) \coloneqq 1- \frac{\langle l, \,g \rangle}{\| l\|_2\, \| g \|_2},
\end{equation}
where $(x, y)$ are the true data, $\hat{y}$ are the labels reconstructed beforehand, and we optimize for $z$.
Our algorithm is shown in~\cref{algorithm:comb_attack}.
First, we reconstruct the labels using the label reconstruction method of \citet{Geng2021} and provide them as an input to our attack. Then, we initialize $N$ independent dummy samples for an ensemble of size $N$ (\cref{algline:initialization}). Starting from each initial sample we optimize independently (\cref{algline:samples}-\ref{algline:end_optim}) via the \textsc{SingleInversion} function. In each optimization step, we apply the softmax structural prior of \cref{subsec:softmax_trick}, and let the optimizer differentiate through it (\cref{algline:softmax_prior}). After the optimization processes have converged or have reached the maximum number of allowed iterations $T$, we identify the sample $\hat{z}^{best}$ producing the best reconstruction loss  (\cref{algline:select_lowest_loss}). Using this sample, we match and median pool to obtain the final encoded reconstruction $\sigma(\hat{z})$ in \cref{algline:match_and_pool} as described in \cref{subsec:pooled_ensembling}. Finally, we return the projected reconstruction $\hat{x}$ and the corresponding feature-entropies $\bar{H}^D$ and $H^C$, quantifying the uncertainty in the reconstruction.

redpajama_set_name: RedPajamaArXiv


Kaylynn joined ESC in 2018 as a HR Business Partner. She began her HR career in 2012 as a recruiting manager for an Health Care company. Kaylynn went on to hone her recruiting, interviewing and onboarding skills through subsequent positions. She is also proficient in employee relations, working with employees on multiple engagement levels including training and development, performance management, and employee appreciation programs. She has handled employee relations issues in a multi-state environment and has managed a team of customer service representatives. Kaylynn has Bachelor's Degree in Marketing and an Associate's Degree, Concentration in Communications.


text:
Yosemite Outsmarts its Food-Stealing Bears
March 4, 2014, 2:15 PM UTC / Source: LiveScience
By Becky Oskin, Senior Writer
Remembering to lock up everything from baby wipes to bratwurst may be irksome for Yosemite National Park visitors, but a new study finds the park's stringent food storage rules slashed the amount of human food eaten by black bears by 63 percent.
Yosemite National Park is home to hundreds of black bears — no one keeps an accurate count — only some of which seek out human food and garbage. After a record 1,584 bear incidents in 1998, park officials enacted new food storage requirements to stop bears from stealing food and garbage. These measures help prevent bear deaths and human-bear interactions, because a food-reliant bear often becomes an aggressive bear, according to the park. With more than 4 million visitors yearly, Yosemite now spends about $500,000 annually on supplies, outreach and activities meant to prevent bears from getting into human food. [ Image Gallery: Beastly Bears ]
According to the new study, which tracked the diets of nearly 200 bears by analyzing hair samples, the massive effort is working. Chemical signatures in the hair differentiate between human and wild food sources. The findings were published in the March issue of the journal Frontiers in Ecology and the Environment.
"What we found was that the diets of bears changed dramatically after 1999, when the park got funding to implement a proactive management strategy to keep human food off the landscape," Jack Hopkins, lead study author and a wildlife ecologist at the University of California, Santa Cruz. "This suggests that the bear's diets are likely going back to their natural diet."
Among bears that eat human food, only 13 percent of their diet came from anthropogenic, or human sources, between 2001 and 2007, Hopkins found. That's the same as in the park's early years, between 1915 and 1919, when only a few thousand visitors made the long journey into Yosemite Valley. Hopkins examined the early diet of Yosemite's black bears by snipping hair from museum specimens.
Established in 1890, Yosemite National Park hasn't always had a hands-off approach to feeding its bears. Between 1923 and 1971, the park operated artificial feeding areas, to attract bears for visitors who wanted to view the local wildlife. A trout hatchery in the park, open between 1927 and 1956, also offered bears a spot for easy gorging.
The bear's diet reflects these changing food sources, the study shows. The proportion of human food was 27 percent between 1928 to 1939, and 35 percent between 1975 to 1985, among bears who ate a non-natural diet. Throughout the study, Hopkins also found bears in the park who subsist only on their natural diet of berries, nuts, insects and meat.
Prevention is best
The findings support the management approach of preventing bear access to food. Brown metal food storage lockers for campers, cylindrical plastic storage containers for backcountry hikers and funding for inspections by rangers were added in 1999 in Yosemite. Everything must go into storage, even toothpaste. Nothing should remain in sight in cars, where the sight of a cooler or storage bin might tempt a hungry bear to bust open a window.
"Reducing the amount of food on the ground and making sure visitors are compliant with food storage has led to this management success," Hopkins told Live Science. "It appears that management that is related to preventing bears from becoming too conditioned to food in the first place is one of the best things to put money into."
Previous research by Hopkins and his colleagues also found that stopping bears from getting that first taste of human food can break the chain of picnic-basket stealers. For example, bears that are raised by moms who snack on human food also strike out in search of campgrounds and other human sources once they're on their own. "They carry that information with them throughout their lives," Hopkins said. And problem bears that are moved away from people eventually make their way back to their favorite food sites, another study found. The reward? Bears that eat human food are bigger and bear more cubs than those who subsist solely on their traditional diets, though they have a shorter life span. That's because they're eventually killed as a nuisance or by hunters, because the bears are near developed areas.
Email Becky Oskin or follow her. Follow us, Facebook &. Original article on Live Science.
Smile! Rocky Mountain Critters Caught on Camera
7 Iconic American Animals
Stunning Stargazing In Yosemite National Park | Video
Becky Oskin, Senior Writer

redpajama_set_name: RedPajamaCommonCrawl


text:
Danny Carey: Tool Don't Have a Record Label Anymore
Travis Shinn
Tool drummer Danny Carey recently revealed that the band is now without a record label, which one could take to mean that Fear Inoculum was Tool's final album on a major label contract. He also said he still hopes the group will release an EP this year, in place of planned tours that have come to a halt.
The proposed EP effort would supplant the majority of the band's 2020 concerts after the coronavirus (COVID-19) pandemic curbed most live shows, including Tool's spring North American tour.
Speaking to Skinny Puppy's Cevin Key on the musician's "Sunday Live Chat" Aug. 9, Carey admitted that Tool are currently taking a bit of a break. But it's one he hopes won't last for long — the percussionist seemed eager to get back into the recording studio with his bandmates.
"Tool hasn't been jamming," Carey explains, as pointed out by ThePRP. "We've been just kind [of] on hiatus. Nothing's really been happening. But I think it's time. We kept hoping that we were going to get back out, so we were just kind of, 'Hey, enjoy this while it lasts.' But now it's looking like it could go to the rest of the year, so we need to get the lead out and start functioning. Maybe knock out another EP, at least, or something like that."
He continues, "We've never really done that since our first release — done an EP — so I thought it would be kind of nice to do something like that. We don't have a record deal anymore — we're free agents — so we can kind of release whatever we want to release now, which is a good feeling."
Elsewhere in the chat, Carey underscored that Tool were about to "start the biggest American tour we've ever done. So that was a heartbreaker when all that hit the fan. So we're just kind of in suspension now. We have dates held in November and December, but it's highly unlikely … to pan out."
It's not the first time that Carey has set his sights on a new EP from Tool this year. The drummer also floated the idea in April, near the pandemic's start, when he was a guest on a music school webinar.
"I'm hoping, during this downtime, as soon as we're able, maybe we'll get together — Justin [Chancellor, bass] and I, and Adam [Jones, guitar] — maybe start hashing out some new Tool stuff in the meantime, maybe write another EP since we're down and we can't do anything else," Carey said at the time.
Tool's last EP was 1992's Opiate, issued ahead of their debut studio album Undertow. It wasn't available on major streaming services until the group finally acquiesced to the digital domain in 2019.
Tool's Danny Carey Talks to Skinny Puppy's Cevin Key - Aug. 9, 2020
See Tool in the Most Performed Songs by 50 of Metal's Biggest Bands
Source: Danny Carey: Tool Don't Have a Record Label Anymore
Filed Under: Danny Carey, Tool


US defence secretary James Mattis said on Wednesday that the Syrian government of president Bashar al-Assad appeared so far to have heeded a warning this week from Washington not to carry out a chemical weapons attack.
But Russia, the Syrian government's main backer, said the US assertions that Mr Assad's forces may have been planning a chemical attack complicated peace talks on ending Syria's six-year-old civil war.
The White House said on Monday it appeared the Syrian military was preparing to conduct a chemical weapons attack and Mr Assad and his forces would "pay a heavy price" if it did so.
US officials later said the warning was based on intelligence that indicated preparations for such a strike were under way at Syria's Shayrat airfield.
"It appears that they took the warning seriously," Mr Mattis said. "They didn't do it," he told reporters flying with him to Brussels for a meeting of Nato defence ministers. He offered no evidence other than the fact that an attack had not taken place.
Washington accused Syrian forces of using the Shayrat airfield for a chemical weapons attack in April. Syria denies this. However, Mr Mattis said Syria's chemical weapons threat was larger than any single location. "I think that Assad's chemical programme goes far beyond one airfield," he said.
US and allied intelligence officers had for some time identified several sites where they suspected Mr Assad's government may have been hiding newly made chemical weapons from inspectors, a US official familiar with the intelligence said.
The US launched a cruise missile strike on Shayrat in April following the deaths of 87 people in what Washington said was a poison gas attack in rebel-held territory.
The intelligence that prompted the administration's warning to Syria this week was "far from conclusive", said another US official familiar with the intelligence. "It did not come close to saying that a chemical weapons attack was coming," the official said.
The US conveyed the warning to Russia via the "deconfliction channel" the two countries use to avoid clashes in Syrian air space, but there is no evidence indicating that this deterred a chemical attack, the official said.
The Syrian military and foreign ministry did not comment on the White House warning, although the state-run al-Ikhbariya television station said the allegations were fabricated.
Russia denounced the warning and dismissed White House assertions that a strike was being prepared as "unacceptable".
Russian deputy foreign minister Gennady Gatilov on Wednesday told the US not to take unilateral actions in Syria.
He said the US assertions complicated peace talks on Syria, according to RIA news agency.
Russian officials have described the war in Syria as the biggest source of tension between Moscow and Washington and say the April cruise missile strike ordered by US president Donald Trump raised the risk of confrontation between them.
The US supports the YPG in the fight against Islamic State in Syria, while Nato ally Turkey regards them as terrorists indistinguishable from militants from the outlawed PKK (Kurdistan Workers Party), which is carrying out an insurgency in southeast Turkey.


text:
Melvyn Bragg
Love Without End: A Story of Heloise and Abelard
Trade Paperback Mar 12, 2019 | 9781473690936 | RRP $32.99 Buy Now
Hardback Mar 7, 2019 | 9781473690929 | RRP $45.00 Buy Now
e-Book Mar 7, 2019 | 9781473690950 | RRP $14.99 Buy Now
A profoundly thought-provoking, moving novel that breathes fresh life into one of history's most remarkable and enduring love stories.
A classic love story, retold for our times.
Heloise, a young scholar reputed to be the cleverest woman in 12th-century France, arrives in Paris set on entering the city's masculine world of learning. Frustrated in her wishes, she is stunned when the brilliant, radical philosopher, Peter Abelard, consents to be her tutor in exchange for lodgings with her uncle. But what starts out as a meeting of minds turns into a passionate, dangerous love affair, which sends shockwaves throughout the country and incurs terrible retribution.
Nine centuries later, Arthur, an English academic, is in Paris attempting to recreate Heloise and Abelard's story in a novel. When his daughter Julia comes to visit, she agrees to help, interrogating his portraits of a couple who seem often inscrutable, sometimes strikingly modern. As she spars with her father, it becomes evident that Julia is on her own quest is to discover more about her parents' fractured relationship - and that Arthur's connection to his subject is more emotional than he cares to admit.
In this profoundly thought-provoking, moving novel, Melvyn Bragg brings the 12th-century into the 21st as he breathes fresh life into one of history's most remarkable and enduring love stories.
More books by Melvyn Bragg
Grace and Mary
Crossing The Lines
The Book of Books: The Radical Impact of the King James Bible
The South Bank Show: Final Cut
In Our Time: The companion to the Radio 4 series
Remember Me...
12 Books That Changed The World: How words and wisdom have shaped our lives
Rich: The Life of Richard Burton
A Son of War
The Hired Man
A Place in England
The Soldier's Return
On Giants' Shoulders: Great Scientists and Their Discoveries from Archimedes to DNA
Crystal Rooms
Autumn Manoeuvres
The Silken Net
Praise for Love Without End: A Story of Heloise and Abelard
Bragg brilliantly re-imagines the legendary love story of Heloise and Abelard, uniting the middle ages and today in this thrilling novel. - Antonia Fraser
Melvyn Bragg brings a fascinated attention to the moral complexities of a love story we all thought we knew, but perhaps did not understand well enough. His compassion for Abelard and Heloise makes brilliantly real and present to us their anguished journey from erotic excess towards the mystical sublime. - Rose Tremain
A tour de force - a moving, poignant, compelling tale, wonderfully told. I have never read such true and compellingly depicted accounts of sexual desire and encounter, and Paris, both medieval and modern, comes vividly before one. - A.C. Grayling
Bragg has mastered his sources, chiefly the letters of Abelard and Heloise and Abelard's autobiographical
Historia Calamitaturn. By the pen of Arthur the novelist, Bragg with his own flair and perceptive imagination tells their story . . . Bragg's ability to live inside the minds of these two mighty philosophical and theological intellectuals. He understands their agonies, their manipulation . . . and persecution . . . Bragg writes his version of this life-long love with ease and confidence. It is a pleasure to read; and to be reminded of Chaucer's fastidious Prioress whose shining gold brooch declares: "Amor vincit omnia." - Spectator
Melvyn Bragg is a writer and broadcaster whose first novel, For Want of a Nail, was published in 1965. His novels since include The Maid of Buttermere, The Soldier's Return, Credo and Now is the Time, which won the Parliamentary Book Award for fiction in 2016. His books have also been awarded the Time/Life Silver Pen Award, the John Llewellyn Rhys Prize and the WHSmith Literary Award, and have been longlisted three times for the Booker Prize (including the Lost Man Booker Prize).
He has also written several works of non-fiction, including The Adventure of English and The Book of Books about the King James Bible. He lives in London and Cumbria.
Blood Royal: A True Tale of Crime and Detection in Medieval Paris
Eric Jager
The Spring Madness of Mr Sermon
R. F. Delderfield
Stand The Storm
Breena Clarke
The Vagrant King
E. V. Thompson
Gold for Prince Charlie: MacGregor Trilogy 3
Nigel Tranter
True Thomas
Past Master: Master of Gray trilogy 3
The Bruce Trilogy: The thrilling story of Scotland s great hero, Robert the Bruce
The Unicorn Rampant
Stewart Trilogy
Wisest Fool
The Wallace
Macbeth the King
Margaret the Queen
God is an Englishman
The Young Montrose: Montrose 1
Theirs Was the Kingdom
Black Douglas
A Family Madness
Victim of the Aurora


text:
Wisconsin & Local Law
Wisconsin & Local Government
Wisconsin Legal Reference Library
International and Foreign Law
Constitution Finder (University of Richmond T.C. Williams School of Law)
"This index offers constitutions, charters, amendments, and other related documents. Nations of the world are listed alphabetically, and each is linked to its constitutional text posted somewhere on the Internet." This is a special faculty project at the law school.
Financial Action Task Force on Money Laundering
"The Financial Action Task Force (FATF) is an inter-governmental body whose purpose is the development and promotion of policies, both at national and international levels, to combat money laundering and terrorist financing."
French Codes in English (Legifrance)
International Humanitarian Law (International Committee of the Red Cross)
International Law (United States Mission to the United Nations)
Includes War Crimes, International Criminal Court, Terrorism, Law of the Sea, Human Cloning, Treaties, and General topic headings.
International Money Laundering Information Network (United Nations Office on Drugs and Crime, Anti-Money Laundering Unit, Global Programme against Money Laundering)
A "network assisting governments, organizations and individuals in the fight against money laundering. IMoLIN has been developed with the cooperation of the world's leading anti-money laundering organizations. Include[s] a database on legislation and regulation throughout the world (AMLID), an electronic library, and a calendar of events in the anti-money laundering field."
Laws of Hong Kong, including Hong Kong Bilingual Laws Information System (Hong Kong Special Administrative Region Government, Department of Justice)
Laws of Hong Kong, the Basic Law, database of the laws of Hong Kong, including statutory law, selected constitutional documents, treaties, and bilateral agreements, agreements and arrangements with international organizations, and consular agreements, and glossary.
"One of the very first law sites on the Web, Lex Mercatoria was started by the Law Faculty of the University of Tromsø, Norway."
LexinfoSys: Law Reform in Transition States
"The gtz project office "Law Reform in Transition States" at the University of Bremen offers a free data base which consists of more than 4500 legal acts. A network of coordinators in the countries provides for the actuality of the legal information. The main focus is on private and economic law. Recently, legal texts in the field of administrative and criminal law have also been added. The legal acts are published in Russian or in the language of the respective country. Some texts are available in German or English.
Coverage includes: Armenia, Azerbaijan, Georgia, Kazakhstan, Kyrgyzstan, Moldova, Mongolia, Tajikistan, Turkmenistan, and Uzbekistan.
Organization of American States Department of International Law (Organization of American States)
Wex: Foreign Relations (Legal Information Institute)
Wex: International Law (Legal Information Institute)
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text:
Q: How to unit testing with jest in vue composition api component? I'm writing a unit test with jest, for my composition API component in vue.js.
But I can't access to functions in composition API's setup().
Indicator.vue
<template>

      step
       {{ current }}
      from
       {{ steps }}

</template>

Indicator.test.ts
import Indicator from '@/views/components/Indicator.vue';
import { shallowMount } from '@vue/test-utils';

describe('@/views/components/Indicator.vue', () => {
  let wrapper: any;
  beforeEach(() => {
    wrapper = shallowMount(Indicator, {
      propsData: {
        steps: 4,
        current: 2
      }
    });
  });
  it('should return "current" for values (2,2)', () => {
    expect(wrapper.vm.activeClass(2, 2)).toBe('current');
  });
});

And I got this Error, in running test command:

TypeError: Cannot read property 'vm' of undefined

A: I would also suggest to use jest.config.js file to initialize it by default:
setupFiles: ['<rootDir>/tests/helpers/foo.js']

then in your tests/ folder create helpers/ with file foo.js
and in file:
import Vue from 'vue'
import CompositionApi from '@vue/composition-api'
Vue.use(CompositionApi)

by this way it's available for each test file :)

A: I think simply importing CompositionApi should solve your issue.
import CompositionApi from '@vue/composition-api'

Vue.use(CompositionApi)

A: In nuxt.js version 2 @andrej-gaspar solution almost worked but it throws Cannot read property install of undefined error, so to fix that error do like below:
jest.config.js
setupFiles: ['<rootDir>/tests/helpers/foo.js']

foo.js
import Vue from 'vue'
import * as CompositionApi from '@vue/composition-api'
Vue.use(CompositionApi)

Or if you're using @nuxtjs/composition-api:
import Vue from 'vue'
import * as CompositionApi from '@nuxtjs/composition-api'
Vue.use(CompositionApi)


The Wild Idea Preserve will be about more than science, but humanity. This includes interdisciplinary investigations of art, among other media of human expression.
Here are some teaser pics of three dimensional cellular automata, simulated in C++ and rendered using the POVRay ray tracer. In the future this collection will be broken down into individual exhibits, as part of member projects.


text:
Q: Remove leading '0's from all strings in a list My code
l=['99','08','096']
for i in l:
    if i.startswith('0'):
        i.replace('0','')
print(l)

Output
l=['99','08','096']

I just want to remove the leading '0's from all the strings:
l=['99','8','96']

A: You can use str.lstrip() for this in a list comprehension - to remove all leading '0's from each string.
>>> l = ['99', '08', '096']
>>> [x.lstrip('0') for x in l]
['99', '8', '96']

This has the added benefit of not removing instances of '0' from within the string, only from the front.
>>> l=['99', '08', '096', '0102']
>>> [x.lstrip('0') for x in l]
['99', '8', '96', '102']

A: If I understood you correctly, you want to remove the string from the array if it starts with 0, but from the code you showed, you are just replacing the 0 in the string with nothing. i.replace('0','')
one way to filter out is:
new_list = [el for el in l if not el.startswith('0')]

A: you can use list comprehension to correct your strings by using str.lstrip:
[e.lstrip('0') for e in l]

or you can use a for loop:
for i in range(len(l)):
    if l[i].startswith('0'):
        l[i] = l[i][1:]


text:
Q: Vector in row space and null space of a matrix is necessarily 0 This fact was proven here, but only for $$A\in\mathbb{R}^{m\times n}$$
Is there a proof for a matrix over a general field?

A: This is not true. Consider the matrix $\begin{pmatrix} 1 & 1 \end{pmatrix}$ in the field of integers modulo 2. The only row of the matrix is in its null space.


text:
Cortes, officially the Municipality of Cortes (Surigaonon: Lungsod nan Cortes; ), is a 4th class municipality in the province of Surigao del Sur, Philippines. According to the 2020 census, it has a population of 17,924 people.

Cortes faces the Philippine Sea on the eastern part. It has a protected marine sanctuary located in barangay Balibadon, Capandan, Mabahin, Poblacion, Tigao, Uba and Tag-anongan. It is known in the province of Surigao del Sur for its rich source of sea foods in the area. You can find different kinds of fish, crabs, etc. It has beaches with white sands and water falls. It also has a bird sanctuary located in barangay Burgos. It also has a wide area for agriculture. It has two season, rainy and sunny.

History

Cortes was originally known as "Kagyunod". It cannot be traced who changed Kagyunod into Cortes but many believed that Cortes derived its name after Governor General Cortes who was assigned to the Philippines by the Spanish King.

Cortes was once part of Tandag. It gained its independence on October 1, 1953, by virtue of Executive Order No. 642 series of 1953 creating Cortes into an independent municipal or corporation. It was not until March 19, 1954, however, that the new local government unit formally started to function with the assumption into office of the first set of municipal officials appointed by then President Elpidio R. Quirino.

Geography

Cortes lies between 9 deg. 08'00 N to 9 deg. 19'00 North latitude and 126 deg. 03'00 to 126 deg. 13'00 East longitude or lies in the mid-eastern portion of Surigao del Sur along the Pacific Coast. It is 28 kilometers from Tandag, the capital of the province. Cortes is bounded on the east by the vast Pacific Ocean, on the north by the Municipality of Lanuza and Lanuza Bay, on the south by the municipality of Tandag, and on the west by the municipalities of Lanuza and Tandag.

Cortes has a land area of 13,509.00 hectares. It comprises 12 barangays with Balibadon and Tag-anongan as the biggest and smallest barangays respectively.

Cortes has numerous rolling hills and uneven distribution of lowlands. Mabahin, Tigao and Burgos have a large area of rice and swamplands, the rest of the barangays are generally hilly. The south-western portion of the town is generally mountainous and covered with second growth forest.

Barangays

Cortes is politically subdivided into 12 barangays. In 1955, the sitios of Uba, Mabahin, Tag-anongan and Manlico were converted into barrios.

 Balibadon
 Burgos
 Capandan
 Mabahin
 Madrelino
 Manlico
 Matho
 Poblacion
 Tag-anongan
 Tigao
 Tuboran
 Uba

Climate

Demographics

Economy

Agriculture is the major source of living in Cortes. It is also known for its rich source of sea foods. Fishing is one source of living. However, there are limitations that been lay down by the local government to protect their sea against abuse. Farming is the second source of living.

Tourism
Attractions include:
 Laswitan Lagoon (a 20-foot rock formation with three lagoons; name derives from the word "laswit" which means splash; when the water is high, especially during Amihan season, it creates huge waves that will splash on the rocks and creates a waterfall effect)
 Bakwitan Cave
 Lubcon Falls
 Bugsay Beach Resort
 Sihagan Beach
 Buybuyan Beach
 La Soledad Beach

Culture

Festivals
Kadagatan Festival – Every 21st to 24th day of July, the town of Cortes celebrates Kadagatan Festival. This festival is celebrated because of the abundance of fishes and other seafoods that Cortes have. The Rabita Statue symbolize this festival. You can see the statue in Cortes Commercial Triangle.

Araw ng Cortes – Every 29th day of September up to 1st day of October, Cortes celebrates Araw ng Cortes. This is because Cortes have its freedom from being separated from Tandag City.

References

External links
 Cortes Profile at PhilAtlas.com
   Cortes Profile at the DTI Cities and Municipalities Competitive Index
 [ Philippine Standard Geographic Code]
 Philippine Census Information
 Local Governance Performance Management System

Municipalities of Surigao del Sur
Establishments by Philippine executive order


Permission to podcast/stream the music in this service obtained from One License with license #A-710913. Portions of the liturgy are from Sundays and Seasons.com. © 2018 Augsburg Fortress. All rights reserved. Reprinted by permission under Augsburg Fortress Liturgies Annual License #23803. New Revised Standard Version Bible, © 1989, Division of Christian Education of the National Council of the Churches of Christ in the United States of America. Used by permission. All rights reserved. "All Earth is Hopeful." Words: Madeleine Forell Marshall, ©1995 Augsburg Fortress Publishers. Music (Toda la Tierra): Alberto Taulé, ©1993 OCP Publications. Reprinted with permission under ONE LICENSE #A-710931. All rights reserved. "Awake! Awake! And Greet the New Morn." Words and music: Marty Haugen, ©1983 GIA Publications, Inc. All rights reserved. Reprinted with permission under ONE LICENSE #A-710931. All rights reserved. "Canticle of the Turning." Words and music: Rory Cooney, ©1990 GIA Publications, Inc. All rights reserved. Reprinted with permission under ONE LICENSE #A-710931. All rights reserved. "This is the Truth Sent From Above." Words and music: ©St. James Music Press, 2015 – www.sjmp.com – used by permission. "Watchman, Tell Us of the Night." Words: Public Domain. "The Angel Gabriel from Heaven Came." Words and music: Public Domain. "He Comes to Us as One Unknown." Music: Public Domain. Words: Timothy Dudley-Smith, ©1973 Hope Publishing Company. Reprinted with permission under ONE LICENSE #A-710913. All rights reserved. "People, Look East." We don't have copyright permission on the text.


text:
Listen/Buy Music
Press Kit/Meet Eileen
Peak and Valley Song Coaching
Double Intrigue
Meet Gregtape
Eileen Bio
Award winning singer-songwriter Eileen Bernstein takes you up and down the peaks and valleys of life with "songs [that] make you want to tap your toes, along with touching melodies that grip your heart with their expressive lyrics," - Baltimore Jewish Times. Her distinctive writing and vocal style developed among lifelong influences as varied as Evanescence, Journey, Miranda Lambert, Katy Perry, Foreigner, Sara Bareilles, Keith Urban, Sheryl Crow, Gretchen Wilson, etc. She recently played The Great Frederick Fair with her Double Intrigue duo partner, Gregtape, who played acoustic, electric, and bass guitar on her latest project, "No Place But Up." She is delighted that people come up to her at shows and tell her how much her songs meant to them. Playing a two-hour set at the Havre de Grace Seafood Festival shortly before Three Dog Night headlined in 2014 was a treasured highlight. The long-time resident of Howard County, Maryland -- born and raised in New Jersey -- has also appeared at area venues such as More Than Java Cafe, Leadbetters, Poolesville Music and Arts Festival, McGintys, Frederick Coffee Company, Takoma Porch Festival and The Electric Maid in Takoma Park, MD, Artomatic Arts Festival in Washington DC, the Dorchester Arts Festival, Columbia Lakefront Festival, JVs Restaurant in Arlington, VA, College Perk Coffeehouse, Caribou Coffee in Olney, The Coffee Beanery in Annapolis, Red Rocks Café in Centreville, VA, Oakland Manor and Great Sage in Columbia, and in the Music Matters Showcase series.


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Featuring a fleet of warm and luxurious ships, Oceania Cruises offers an unrivaled vacation experience renowned for its gourmet culinary program inspired by Master Chef Jacques Pépin and its array of destination-rich itineraries spanning the globe. Seasoned world travellers are drawn to Oceania Cruises' diverse voyages, which call on more than 330 ports across Europe, Asia, Africa, Australia, New Zealand, the South Pacific and the Americas. Spacious and elegant accommodations aboard the 684-guest Regatta, Insignia and Nautica and the 1,250-guest Marina and Riviera invite guests to explore the world in unparalleled comfort and style. Oceania Cruises added the Sirena in 2016, a fourth 684-guest ship.


The Uppababy Cozy Ganoosh Footmuff is a perfect fit with the VISTA and CRUZ. The Uppababy Cozy Ganoosh Footmuff keeps your child warm so now your little one can enjoy the outdoors even on the colder days.
Compatible with all Vista, Cruz and G-Luxe models.
The Uppababy CozyGanoosh Footmuff features a hood which keeps baby warm and comfortable but also folds over the back out the seat to keep it out of the way when not in use.
The UppababyCozy Ganoosh Footmuff from Tony Kealys is available in Jake (black) and Pascal (Grey).


Ep. 2 Do research and trust your Stylist!
Before we get started I want to start with this..
For those who have dated or are currently dating, remember that first date? Maybe it was a blind date, maybe you knew each other, maybe similar friends...The anticipation, the stress around what to wear, how the hair was supposed to be….When you're out on the date, and it doens't matter what exactly what you did on the date, you could tell the date was bad at some point during the night or day and that there was not going to be another date.
It was easy to tell you know? Maybe he talked too much about himself, maybe she had a tooth picking habit right, gross! Maybe he or she was too handsy and presumptuous, maybe he was not chivalrous...Whatever it was, it was easy to tell a bad date black and white….But those grey dates...he was kind, she was nice, there was good conversation, you laughed, the food was good, the dancing was great, there were some physical sparks eh…..ehehe….
Seeing a new stylist is the same thing. As much as we want to eliminate all the previous bad experiences, stylists or partners that we "wasted time with." But it's impossible...I don't care what tinder, bumble, eharmony, whatever other single dating service says...Most of us take a few tries to figure it if it's a match or not….The same with your stylist. It takes a stylist, mostly 3 times cutting your hair to really get it down, get to know you..
If you think about how much we trust it's pretty incredible...We trust that the food at a restaurant is fresh if they say it is and no one did anything gross to it, that the meat is sourced the way, the veggies are clean, they say and that it's real meat...what about candy...we trust they are cleaning those machines...We trust dentists to poke and prod our teeth for the good of our oral health. The hardest one for me...trusting car guys...I am always on the defense with a chip on my shoulder with car places...but once you find that person, wow….you just want to hold on to them forever, right!?
It's hard to trust but it's so freeing when we do, and I dug up an article on the huffington post website about the writer, named Anya Strzmien who at the time was an editor for the magazine, talk about how she was always a control freak with her hair, telling the stylist exactly what to do, even though she wasn't a stylist...she decided to just let a stylist go and do what was best for her...I'll post a link to the story if you want to read it in full and see her before and after pictures.
Trusting someone is always the hardest part, especially when it comes to your hair! But, if you do research you can avoid pitfalls so here are some things to keep in mind when looking for a salon or stylist to trust and things that will be red flags.
Manage your expectations. If you are showing pictures of an actress or famous blogger and you don't look like that person, trust the stylist when they recommend a different route.
Listen to their recommendations on hair upkeep, you're not going to wake up with fabulous hair, it takes effort!
If it feels weird, it's probably weird...go with your gut. We have had many clients say they walked in and they knew it wasn't going to go well and it did not, then we were fixing the issues.
If they are not upfront with how much they are going to charge...Too often I hear salons and stylists not talk about money, then charge whatever...that's a terrible feeling to leave with clients and again shows amateurism instead of professionalism.
One of the harder red flags to discern is if the stylist is doing things in your best interest or for theirs. I have hard of stylists ignoring what they client was wanting, because they didn't like the look, the stylist did what they wanted to and the client didn't like it...A great stylist has a preference, but can change it up when the client asks for something specific.
They overcurl your hair, and you shower your hair and hate the haircut a couple days later..it means they weren't as good as they may have stated.
Now you hairstylists, don't break that trust by mailing it in on an appointment and taking clients for granted. Solve their hair problems, educate yourself, make yourself visible online, and communicate clearly with them, add value to your services, and work to be better each day! If you are a stylist that is already doing that, keep it up!


West Midlands Police have confirmed they were called to attend an incident in Selly Oak Park this afternoon (Wednesday 18th July).
Officers were called at around 1.30pm to reports of a woman with stab injuries, although they stress that they are NOT seeking anyone else in relation to the incident.
The woman was taken to hospital where she is being treated for life threatening injuries.
Our thoughts are with the woman and her family and friends at this time.


Recently, I saw a Tweet about an article called "How to Do. One. Thing. At. A. Time" and it made me think about The Minimalists. Sometimes life moves on and I don't spend the same time I used to in terms of consuming content from my favorite sources. But that's ok.
Then I saw the email with that podcast episode called Simpleliving. I must say these guys have continued to improve how they deliver their message of living a meaningful life and spreading the word about what it means to be a minimalist.
I recently found another video by Louie Schwartzberg, who gave a TED Talk that became almost my favorite one as well as a favorite inspirational video.
Then back in 2015, and to my surprise, I found Louie in my favorite place to be in the world: The Tetons.
He was sitting down on one of the couches in the Jenny Lake Lodge, where there is an area where there is a huge window from which you can see the majestic Tetons. I couldn't help but go and talk to him. We had quite a good, long and meaningful conversation.
I remember sharing and talking about that video so many times, that I posted this video with a list of my favorite parts of the video on my main website.