For those who doubt the impact of global targets, you only need to look at how funds and policy attention were directed towards the Millennium Development Goals (MDGs) focus areas to see the potential impact these targets can have in terms of policy making, funding and service delivery.
Palliative care is a cross-cutting issue, many palliative care developments were supported under MDG 6, in relation to mitigating the impact of HIV. As we move into a new era, palliative care cuts across many SDG health targets, but it is particularly relevant to Universal Health Coverage (UHC).
The attention to UHC is mounting at all levels and it is not just about policy. Look at the increased focus on the International Health Partnership which is being revamped to look at how UHC will be funded.
So, the question is, are palliative care advocates, researchers and practitioners in a position to maximise the opportunities to get universal coverage of palliative care and do they know what this would look like?
There are few people who would not agree that they want palliative care available to all those who need it, without putting households into financial hardship.
We want to see the end of avoidable and unnecessary pain and hardships caused by life-limiting illness – whether these are physical, psychosocial, emotional, spiritual OR – critically in this debate – financial. But how will this be achieved and what is needed?
If we are to persuade policy makers about the need to invest in palliative care services we need strong evidence to support our case.
Where are the coverage gaps at present both in terms of people not receiving good quality palliative care and/or experiencing financial hardship in accessing services?
Where and how is palliative care currently included in Universal Health Coverage plans?
What should be in an essential package of palliative care?
How much does it cost to provide palliative care?
What is the cost implication of not providing palliative care?
What is the impact on households of palliative care being included in UHC schemes?
What is the impact of only including one aspect of palliative care into a UHC scheme e.g. palliative care for certain cancers but not other conditions?
To what extent are civil society organisations currently providing free palliative care services and is this sustainable?
What political factors should be taken into account when promoting palliative care services within a UHC service package?
Palliative care advocates, researchers and practitioners at all levels need to be on top of the UHC debate and understand how and where palliative care fits in and how they can contribute to moving the agenda forward.
In addition, those working on overall UHC strategies need to recognise the particular issues relating to palliative care and work together to address them.
Understanding, education and awareness-raising are critical. As countries look to implement all-encompassing UHC reforms and allocate large sums of public financing, it is critical that those working in palliative care develop strong, collaborative partnership across sectors and issues.
A clear and very particular issue is around monitoring and evaluation. As WHO and its member states propose indicators on monitoring UHC as part of the SDGs, there is no inclusion of a palliative care indicator.
This poses a major threat to the palliative care agenda receiving sufficient political commitment and public financing.
The indicators civil society have proposed in the past to measure palliative care coverage within UHC have not been accepted for various, largely valid, reasons.
This is partly because of the shortage of palliative care indicators which are tested, measured and fully described. But, also, because there are not currently palliative care indicators and routine data collection.
The WHO Indicator and Measurement Registry now has three indicators relating to palliative care (and NCDs) but the extent to which they are routinely measured or utilised is unclear.
The Demographic and Health Surveys Program (DHS) has no data collection on palliative care. Therefore, any additional palliative care data collection or indicators proposed at the global level are adding an additional burden to already overburdened national governments' systems.
Nobody wants additional data burdens on countries, but, at the same time, this needs to be measured, and it needs to be done. So, what is the solution?
We need evaluated test projects and pilots. What is the impact on people living with life-limiting illness? What is the impact on households? What is the impact on quality of life? What is the impact on the health system? What projects can we do and test to get palliative care included into UHC coverage schemes?
Discussions are taking place at the global level on when and how UHC will be delivered but the real frontline for UHC reforms is what happens at the country level.
It is the health and finance ministries and politicians that will be working out what Universal Health Coverage means for their country – and each country will be different.
Take a look at the different extent to which palliative care is or is not included in countries which have or are progressing to UHC and the challenges in accessing that information.
Thailand for instance is recognised as a UHC success story and palliative care for end stage patients has earmarked budget within the Universal Coverage Scheme.
While in Mexico, Human Rights Watch identified two challenges with the availability and accessibility of palliative care as a component of UHC.
One challenge was the inclusion of appropriate and needed palliative care entitlements under the insurance packages – it was only partially included.
The second challenge was the actual availability of hospice and palliative care services through hospitals, clinics and in the home in the networks of insurers,.
National and local level evidence, information and advocacy is going to be absolutely critical to move forward this agenda.
Governments have a responsibility to ensure access to essential healthcare services for their populations, including the poorest and most vulnerable, without households suffering financial hardship.
Palliative care is recognised as an essential and basic healthcare service within UHC.
When governments plan their UHC strategies, a basic package of palliative care must be included as a critical and essential service with a mechanism for measuring coverage.
Whilst it is government's responsibility, if palliative care advocates, researchers and practitioners at all levels do not pay the appropriate attention and commit sufficient resources to UHC as it unfolds, we are potentially doing a massive disservice to the poorest and most vulnerable who require palliative care.
Palliative care is not a luxury and the holistic and comprehensive partnership approach it takes could have a huge impact on health service delivery.
A collaborative programmatic approach will be required to address the current gaps at all levels and show the potential of the UHC movement to meet the palliative care needs of the poorest and most vulnerable.
Rob Yates will be speaking at an event at Hospice House, London on 22 November from 12 noon – 2pm looking at the role of community based and palliative care within UHC.
He will be joined by Bev Sebastian from Island Hospice and Health Care Service, Jo Ecclestone Ford from True Colours Trust and Simon Wright from Save the Children.
 Human Rights Watch (2014). Care when there is no cure: ensuring the right to palliative care in Mexico.


text:
PEOPLE v. TOWNER
The PEOPLE, Plaintiff and Respondent v. Ellen Lee TOWNER, Defendant and Appellant.
Daniel L. Dintzer, Los Angeles, under appointment by the Court of Appeal, for defendant and appellant. Thomas C. Lynch, Atty. Gen., William E. James, Asst. Atty. Gen., and Megan A. Geffeney, Deputy Atty. Gen., for plaintiff and respondent.
Defendant Towner and codefendant Watson were charged by information with violation of Health and Safety Code, section 11911 (possession of a dangerous drug for sale). Defendant pleaded not guilty and waived her right to trial by jury. The matter was submitted on the testimony contained in the transcript from the preliminary hearing, subject to the court's rulings, with each side reserving the right to present additional testimony. At the conclusion of the trial the court found the defendant guilty of violating section 11910 of the Health and Safety Code (possession of a dangerous drug), a lesser but necessarily included offense. Defendant was sentenced to one year in the county jail. She appeals from the judgment.
The sole contention on this appeal is that the arresting officers did not have probable cause to arrest defendant and therefore the contraband seized incident to the arrest is not admissible into evidence.
Officer White of the Los Angeles Police Department, one of the officers participating in the arrest, testified in substance that he was experienced in narcotic control police work; that he had made numerous arrests for narcotics violations at the same location and on previous occasions had seen the defendant there; he had information from businessmen and residents of the area, as well as from police undercover agents concerning the activities of the defendant and codefendant; he also had been in contact with codefendant and had reviewed codefendant's police department records concerning dangerous drug activity. He further testified that on June 4, 1966, at about 9:30 p.m., he was observing an area of the 5900 block of Avalon Boulevard in Los Angeles. The area under observation was illuminated by the lights of a liquor store. He was on the roof of a mattress factory on the other side of the street from the store. The roof location was approximately 100 feet across from the observed area, and 35 feet up from the ground. At about 9:45 p.m. he observed a man walk up to the defendant and then hand her something. She placed it in her pocket. She then turned to the south, placed her hand in the area of her breast, and then appeared to hand something to the man. At that time codefendant walked up to defendant and the two walked over to a car; the defendant got in and the car was driven away. Codefendant remained at the location, and about two minutes later, a man approached him. Codefendant pointed to another lady, and the man walked over to her and appeared to hand her something. The man walked back to codefendant, who bent over and removed a shiny object from the area of his shoe or sock, and handed it to the man. Officer White related that codefendant went through similar procedures with three more men who approached him. About 10:40 p.m., the defendant returned, and within five minutes, another man approached her and appeared to give her something which she placed in her jacket pocket. Defendant was observed to reach into the area of her breast, then appeared to hand something to the man. Officer White testified that he then formed the opinion that the defendant and codefendant were engaged in the sale of contraband, and he and his partner left the roof area to arrest them. The defendant was placed under arrest. Officer White thereafter entered a hotel doorway just to the north of the liquor store, checked the area, and found six foil-wrapped packages of pills. Codefendant was then arrested. Officer White advised both defendant and codefendant as to their constitutional rights, and each defendant stated he understood. Then a search of Watson was conducted. The search produced 17 foil-wrapped rolls of benzedrine pills from codefendant's sock. The defendant was transported to the Georgia Street juvenile facility, where she was searched by two policewomen. The search revealed 23 foil-wrapped packages of pills.
The defendant's sole contention on this appeal, that the officers lacked probable cause for the arrest, is unsupported by the evidence. It is unnecessary to consider this case as one involving an informer plus corroborative data, as is suggested in the briefs. The subject matter of the information conveyed concerning the defendant does not appear in the record, nor is there more than general reference made to informants. We conclude that the entire pattern of suspicious activity of defendant and codefendant, when coupled with previous contact with codefendant, plus the officer's experience with illegal traffic of drugs at the same location, provided ample probable cause for the arrest. 'Reasonable or probable cause is shown if a man of ordinary care and prudence would be led to believe and conscientiously entertain an honest and strong suspicion that the accused is guilty.' (People v. Cedeno, 218 Cal.App.2d 213, 219, 32 Cal.Rptr. 246, 250.) When considering the reaction of the ordinary man to a set of facts, one may take into account the expertise of the arresting officer in matters of narcotic detection in determining if his observations would arouse the requisite belief. (People v. Williams, 196 Cal.App.2d 726, 728, 16 Cal.Rptr. 836.) Here the officer testified that, based on all the facts and his knowledge, he concluded that the defendant was selling dangerous drugs. We concur that this was a reasonable conclusion. Though each case involving the question of probable cause must be decided on its own facts (People v. Ingle, 53 Cal.2d 407, 412, 2 Cal.Rptr. 14, 348 P.2d 577), People v. Brown, 147 Cal.App.2d 352, 305 P.2d 126 is quite similar to the one before us as it involves suspicious acts and a pattern of exchange activity. There, probable cause was found to exist, though repetition of activity, as in our case, was not present, and the police officers therein involved had no particular reason to be looking for narcotics violations at the time they viewed the activity. (See also People v. Garcia, 171 Cal.App.2d 757, 341 P.2d 351.)
We do not consider comments made by the trial judge indicating his reasoning processes in reaching his decision on probable cause. The inquiry on appeal is whether, in fact, the decision of the trial court was correct, not the reasons he may have stated for making it. (People v. Grana, 1 Cal.2d 565, 571, 36 P.2d 375.)
STEPHENS, Associate Justice.
KAUS, P.J., and AISO, J. pro tem.,* concur.

redpajama_set_name: RedPajamaCommonCrawl


At National Hearing Care, we're proud to be able to provide the most advanced hearing aid technology available and we're driven by the desire to provide excellent experiences for our clients. To accomplish that, we make sure to provide an excellent experience for our team – by celebrating our achievements, encouraging a healthy work-life balance and providing opportunities to grow and develop as individuals and as a team.
National Hearing Care began its operations in 1997 with just three clinics. Since then, we've grown to become the leading provider of hearing healthcare services and products in Australia. In 2010, we joined the Amplifon Group – the world's leader in hearing services, present in over 20 countries. Being part of Amplifon gives us access to world-class devices, advice and service, but just as importantly gives our team access to world-class benefits.
A career with National Hearing Care means working for a company that values you as a member of the team, wants to see you grow as a person and who shares your commitment to your clients. And for us, that's the most rewarding part.
We invest time, effort and resources into our team because we know that without them, we wouldn't be able to do what we do best.
Our team is made up of a diverse range of individuals – each with their own unique set of skills, strengths and distinct personalities.
We want to see you grow into the person you want to be, so we're proud to offer you career development opportunities, mentoring, coaching and additional training.
Do you want to make a difference to people's lives? National Hearing Care currently has exciting new opportunities for Customer Service Officers to join our teams around Australia. Working closely with our customers you can help make a difference by conducting free hearing tests. Learn new skills and be part of a fantastic growing organization!
Since 2010, National Hearing Care has been part of the Amplifon Group, the global leader in hearing solutions and services for retail expertise, customization and consumer care active in 22 countries across the world. Browse the slider below and our global careers website to find out more.
There's so much going on at Amplifon all around the world. Filter the latest news and initiatives happening globally using the drop down menu.
Jacy speaks about the career opportunities she received with National Hearing Care.


When Google removed our reliance on keyword data in Analytics, we were forced to look elsewhere to drive content strategy. One such place was the page content reports in Google Analytics. I segmented the traffic and activity to each individual page on my website. This helped me to determine what types of content on my site performed best in search engines, which types of content users were more engaged with, etc.
Of course, I still wanted to conduct keyword research. Instead of starting with the search keywords identified in GA, I started with keywords that drove impressions and clicks in Webmaster Tools.
The amazing thing about this tool is the insight you get from comparing impressions to clicks. Why did certain keywords draw more impressions than clicks? Was I not optimizing my pages correctly? Or was I just not writing the right content for those highly searched terms? I answered these questions with generating more content and then gauging the response.
Though search rankings have always been important, they've gained momentum in the world post (not provided). Similar to how I analyzed impressions vs. clicks in Google Webmaster Tools for individual keywords, I analyzed my search rankings for these keywords that were or were not driving impressions and clicks. Where opportunities arose, I would develop a content strategy to improve search rankings, impressions, and clicks for that self-identified keyword group.
Ultimately, I haven't looked back since Google encrypted Keyword data, and neither should you. Changes like this give us the opportunity to look forward, reanalyze, and improve as digital marketers. Staying dynamic means staying ahead.


text:
\section{EXPERIMENTAL RESULTS}

The proposed framework is applicable to a wide range of multi-label learning tasks, where the students can learn from the teachers owning sensitive private data. Note that the privacy budget of a complete training procedure greatly depends on the noise injected to each training step and the number of training epochs. To prove the generality, we here employ three widely-adopted datasets, MNIST, SVNH, CIFAR, to evaluate the proposed framework. We compare our results with the state-of-art existing works in \cite{papernot2016semi, wang2019private} using the reported data of \cite{papernot2016semi} on MNIST and SVHN, and the reported data of \cite{wang2019private} on CIFAR-10, respectively.

\subsection{Experiment Setup}
Here we briefly describe our experiment setup. We implement our learning strategy based on Tensorflow.  The experiments are based on MNIST, SVHN and CIFAR-10 classification tasks. We first pre-train a teacher model on the entire dataset (with separate training and testing), and treat it as the private teacher. Then we randomly select the public data  from the training dataset and assume that those data are unlabelled, which is used to train the student model through the proposed learning strategy in Sec. 4.

Three datasets of MNIST, SVNH and CIFAR-10 are used in our experiments with the following details:

\textbf{MNIST.} The MNIST dataset  \cite{lecun1998gradient} has 60000 grayscale images (50000 for training and 10000 for testing) with 10 different label classes. Teacher, student and discriminator are implemented using a LeNet, an MLP and a LeNet. When trained on the entire dataset, the teacher model has a 99.40\% test accuracy. We vary the number of unlabelled training instances in [100, 10000] to train the student.

\textbf{SVHN.} The SVHN dataset \cite{netzer2011reading} consists of $32\times32$ colored digit images, each digit representing one class. The training and testing sets contain 604388 and 26032 images, respectively. Teacher, student and discriminator are implemented using a ResNet, a LeNet and a LeNet. The teacher has a 95.30\% test accuracy after training. The number of unlabelled training instances to train the student is varied in [500, 50000].

\textbf{CIFAR.} The CIFAR-10 dataset \cite{krizhevsky2009learning} contains colored natural images with a size of $32\times32$, with 10 classes. The training and testing sets contain 50000 and 10000 images, respectively. The three networks and training setup are the same as the SVHN dataset. The teacher can reach a 89.4\% accuracy after training.

\begin{figure*}[htp]
	 \caption{Training curves (blue) of teachers on the private dataset, and training curves (red) of students on a public subset with 10000 training instances for the three datasets: (a) MNIST; (b) SVHN; (c) CIFAR-10.}
	\label{fig:trainingcurves}
    \begin{minipage}[t]{0.33\linewidth}
    \centering
    \includegraphics[width=6cm]{figure/1.pdf}
    \caption*{(a) Training curves for MNIST}
    \label{fig:curves:a}
    \end{minipage}
    \begin{minipage}[t]{0.33\linewidth}
    \centering
    \includegraphics[width=6cm]{figure/2.pdf}
    \caption*{(b) Training curves for SVHN.}
    \label{fig:curves:b}
    \end{minipage}
    \begin{minipage}[t]{0.33\linewidth}
    \centering
    \includegraphics[width=6cm]{figure/3.pdf}
    \caption*{(c) Training curves for CIFAR-10.}
    \label{fig:curves:c}
    \end{minipage}
\end{figure*}

\begin{table*}[tbp]
\newcommand{\tabincell}[2]{\begin{tabular}{@{}#1@{}}#2\end{tabular}}\vspace{-0.3cm}
    \caption{Comparison on average accuracy over 20 runs between the proposed joint learning strategy (combining KD and GAN) and the KD-only optimization for different number of training instances (denoted as $n$) for students.}\vspace{-0.3cm}
    \label{table:train:size}
    \begin{center}
        \begin{tabular*}{17cm}{@{\extracolsep{\fill}}cccccccccc}
            \hline
            \multirow{2}*{Method} & \multicolumn{3}{c}{MNIST} & \multicolumn{3}{c}{SVHN} & \multicolumn{3}{c}{CIFAR} \\
            \cline{2-4} \cline{5-7} \cline{8-10}
 			& n=100 & n=1000 & n=10000 & n=500 & n=5000 & n=50000  & n=500 & n=5000 & n=50000 \\ \hline
 KD-only (\%) & 66.61 & 93.57 & 98.38& 33.24 & 86.28 & 92.49 & 19.71 & 64.59 & 81.18 \\
\hline
Joint (\%) & 67.99 & 96.46 & 98.52& 56.23 & 87.74 & 96.62 & 46.45 & 66.9 & 80.06 \\
 \hline
        \end{tabular*}
    \end{center}\vspace{-0.3cm}
\end{table*}

\begin{figure}\vspace{-0.3cm}
    \centerline{\includegraphics[width=8cm]{figure/acc_bound.pdf}}
    \caption{Student model accuracy $v.s.$ privacy bound $\epsilon$ for the three datasets.}
    \label{fig:acc:bound}\vspace{-0.3cm}
\end{figure}

\subsection{Results and Analyses}

The typical application of knowledge distillation is to transfer from a powerful and large network or an ensemble of networks to a small network (also known as deep model compression), which can reduce the network complexity and capacity to improve the deployability of the deep models \cite{wang2019private,wang2018kdgan,xu2017training}. In this paper, instead of focusing on the model compression performance, \textbf{the goal is to achieve a good student accuracy under a tight privacy bound, $i.e.$, optimal trade-off between utility and privacy.} We conduct experiments with different number of unlabelled data to explore how utility and privacy budget vary against the number of training instances and training epochs. This is aligned with the aforementioned motivation of this paper and the practical demands from mobile/edge scenarios.

In the following, we thoroughly study the impacts from training size, epochs, and privacy bounds on the framework performance. For the other hyper-parameters in Algorithm \ref{algorithm} ($e.g.$, batch size, distillation weight, clipping threshold, noise multiplier, $etc.$), the optimal values within the range are searched and pre-determined. It is noted that, even with such practical constraints of tight privacy bound, fewer training epochs and limited public data, the proposed framework still can exhibit higher model utility than prior works, with different network typologies employed to teachers and students ($e.g.$, a MLP student and a LeNet teacher for MNIST). This indicates the overall superiority of the framework and its applicability to real-world mobile/edge scenarios with limited resources.

\textbf{Training speed.} We first investigate the training speed of the framework on the aforementioned three datasets. The learning curves of both teachers and students are plotted in Figure~\ref{fig:trainingcurves}, where the students are trained with a subset of 10000 public unlabelled training instances. It is intuitively understandable that, in knowledge distillation, the student model accuracy learned from the teachers is sub-optimal to the teacher. Thus, we observe in the figure that the student models for MNIST, SVHN, CIFAR can quickly reduce the accuracy loss to 0.89\% after 20 training epochs, 2.34\% after 20 training epochs, and 13.69\% after 30 training epochs, respectively. As discussed in Sec. 4, Figure~\ref{fig:trainingcurves} also validates that our student models can reach the convergence with a small number of training epochs (20-30). Thus, the proposed learning strategy that combines KD with GAN greatly \textbf{speeds up the training procedure by reducing the number of training epochs for convergence}. As a result, the fewer accesses to the private teacher induces a smaller privacy bound and cost to facilitate a meaningful utility .

\textbf{Training size.} The proposed strategy combines the advantages of both KD and GAN: 1) KD requires a small number of training instances to distill knowledge; 2) GAN enforces the student to learn the true distribution in the min-max game. We compare the proposed joint optimization with the KD-only optimization to show the efficiency of the proposal, the results of which are summarized in Table~\ref{table:train:size}. We vary the size of training sets on MNIST, SVHN and CIFAR-10 and compute the average accuracy over 20 runs. It is observed that the joint optimization of KD and GAN consistently outperforms the KD-only optimization, especially when only a small number of training images are available. For example, there are a 22.99\% accuracy improvement for SVHN and a 26.74\% accuracy improvement for CIFAR-10 when with 500 training instances. Thus, the joint optimization of KD and GAN \textbf{ not only reduces the required number of training instances but no longer need to purposely select the training instances }to achieve equally effective student model. This is especially beneficial to healthcare and finance applications where local agents (students) typically have very few data for training.

\textbf{Utility $v.s.$ privacy.} To study the trade-off between utility ($i.e.$, student model accuracy or accuracy loss $w.r.t.$ the teacher) and privacy ($i.e.$, differential privacy bound $\epsilon$), we track the values of $\epsilon$ for a given failure probability $\delta$ in the proposed differential privacy mechanism through the RDP accountant. We set the experiments with training size of 10000, batch size $B$ of 50, noise multiplier $m$ of 1.1. In Figure~\ref{fig:acc:bound}, we report the values of the $\epsilon$-differential privacy bounds ($\delta$ is $10^{-5}$ on MNIST, $10^{-6}$ on SVHN and CIFAR-10) for student training and testing. It is found that with a smaller $\epsilon$ guarantee, our MNIST student can still achieve a high accuracy ($i.e.$ ($1.93$, $10^{-5}$)-differential privacy for a $98.52\%$ accuracy). For SVHN and CIFAR-10, student accuracies generally increase with growing privacy bound but quickly converge after certain privacy bound thresholds ($i.e.$, SVHN student's accuracy stays almost the same for privacy bound $\epsilon > 3.5$, and CIFAR-10 student's accuracy for a bound $\epsilon > 8.5$).
Thus, the proposed framework is capable to \textbf{effectively make trade-off between utility and privacy, allowing the student to achieve an optimal accuracy within a small privacy bound}.

\subsection{Comparisons and Discussions}

We compare the proposed framework with two state-of-art related works, PATE \cite{papernot2016semi} and RONA \cite{wang2019private}. As is discussed in the previous sections, the major differences among the three methods are: for PATE, the student receives knowledge from an ensemble of teachers, whereas for RONA, the student is trained with cross entropy loss supervised from labelled data in addition of distillation loss. As in Sec. 5.2, the proposed framework has already been proved to achieve fast training speed with fewer training instances (that can be unlabelled), exhibiting superiority for privacy-concerned applications in mobile/edge scenarios. Thus, the focus of comparison here is placed upon the utility and privacy trade-off.

Table~\ref{table:comparison } compares the performance of the three methods, which reports the student accuracies and the accuracy losses $w.r.t.$ teachers for a given privacy bound. For fair comparison, the reported data of PATE and RONA in Table~\ref{table:comparison } are directly taken from the papers \cite{papernot2016semi,wang2019private} to prevent any re-implementation issues. Note that RONA requires all the public data are labelled \cite{wang2019private}, which is different from the setup of PATE and the proposed framework. However, since PATE does not have the results for CIFAR-10, we compare with RONA on CIFAR-10 to demonstrate the scalability of the proposed framework (even under unfavorable conditions). It is observed that the proposed framework can offer better trade-offs between privacy ( privacy bound $\epsilon$) and utility (student model accuracy) than the other two methods for all the datasets.
Moreover, with the relaxation of privacy bound $\epsilon$, the proposed framework can achieve more significant accuracy improvement than the other works. For examples, when $\epsilon$ is increased from 2 to 8 for MNIST, the accuracy loss of our student is reduced from 0.89\% to 0.20\%, while the change for PATE is very small, from 1.21\% to 1.11\%, indicating an 82\% and 26\% \textit{accuracy loss improvements} (defined as the difference between the accuracy losses for two methods over the loss for the reference method) for the two privacy bounds.

Such performance difference is due to the fact that the the accuracy improvement of PATE requires a massive number of queries, while its privacy budget sharply grows with increasing queries \cite{papernot2016semi}.
On the other hand, the proposed mechanism applies Gaussian noise with random sampling to the loss function for DP guarantee and is shown with good scalability, as the privacy cost of single query is quadratically reduced with the sampling rate.
Such scalability limitation of PATE can be amplified when with more complex dataset. For example, for SVHN, with an $\epsilon$ of 5.04, the student model from PATE has an accuracy of 82.70\% $w.r.t.$ a 92.80\% teacher accuracy, resulting in significant accuracy loss. Unlike PATE, with a similar $\epsilon$ of 5.02, our framework can achieve 93.12\% student model accuracy for a 95.30\% teacher accuracy ($i.e.$, 79\% accuracy loss improvement from PATE), making it feasible to achieve a desired accuracy and privacy trade-off.

For the comparison with RONA, with $\epsilon$ bounds of 4.21 and 8.81, our student can achieve accuracies of 81.76\% and 84.79\%, which is slightly better than the performance of RONA\cite{wang2019private} ($i.e.$, 5\% accuracy loss improvement from RONA). However, as is discussed earlier, RONA relies on both transfer and self learning where all the public samples must be labelled. Such scenarios can be limited in practice. Unlike that, our framework can achieve good performance even with training data unlabelled, which is a more challenging but practical scenario, indicating a broader applicability.

Note that the proposed framework can be used as a baseline backbone for private knowledge transfer even with limited quality public data, which is well aligned with the goal of this paper to develop the transfer framework instead of extensive optimizations. In other words, many other optimization techniques, such as well-designed sample selection for querying, carefully-analyzed sensitivity, can be seamlessly integrated into this framework to further improve model utility or trade-off between utility and privacy. For example, the selective aggregation mechanism in improved PATE \cite{papernot2018scalable} can reduce the added noise and achieve an accuracy loss of 0.71\% for a (1.97, $10^{-5}$) bound on MNIST. Such optimization techniques are orthogonal to the proposed privacy knowledge transfer framework and hence can be always employed at the cost of complexity, which is not the focus of the paper, but can be explored in the future.

 \begin{table}
\newcommand{\tabincell}[2]{\begin{tabular}{@{}#1@{}}#2\end{tabular}}\vspace{-0.1cm}
    \caption{Utility and privacy trade-off comparisons among the proposed framework (Proposed), PATE \cite{papernot2016semi} and RONA \cite{wang2019private}}.\vspace{-0.3cm}
    \label{table:comparison }
    \begin{center}
        \begin{tabular}{|c|c|c|c|c|c|}
            \hline
            \multirow{2}*{Dataset} & \multirow{2}*{Framework} & Privacy & \multicolumn{2}{c|}{Accuracy} & Accuracy \\
            \cline{4-5} {} & {} & Bound $\epsilon$ & Student & Teacher & loss  \\
            \hline\hline
            \multirow{4}*{MNIST} & \multirow{2}*{PATE} & 2.04 & 98.0\% & \multirow{2}*{99.20\%} & 1.21\% \\
            & {} & 8.03 & 98.1\% & {} & 1.11\% \\
            \cline{2-6}
            & \multirow{2}*{Proposed} & 1.93 & 98.52\% & \multirow{2}*{99.40\%} & 0.89\% \\
            & {} & 8.00 & 99.20\% & {} & 0.20\% \\
            \hline\hline
            \multirow{4}*{SVHN} & \multirow{2}*{PATE}  & 5.04 & 82.70\% & \multirow{2}*{92.80\%} & 10.88\% \\
            & {} & 8.19 & 90.70\% & {} & 2.26\% \\

            \cline{2-6}
            & \multirow{2}*{Proposed} & 5.02 & 93.12\% & \multirow{2}*{95.30\%} & 2.29\% \\
            & {} & 8.18 & 93.71\% & {} & 1.68\% \\
            \hline\hline
             \multirow{4}*{CIFAR} & \multirow{2}*{RONA}  & 4.20 &  78.6 \% & \multirow{2}*{86.35\%} & 8.98\% \\
            & {} & 8.87 & 81.69\% & {} & 5.40\% \\
            \cline{2-6}
            & \multirow{2}*{Proposed}  & 4.21 &   81.76\% & \multirow{2}*{89.40\%} & 8.54\% \\
            & {} & 8.81 & 84.79\% & {} & 5.16\%  \\
			\hline
        \end{tabular}\vspace{-0.5cm}
    \end{center}
\end{table}

\section{INTRODUCTION}

At the era of big data, the recent breakthroughs of computing infrastructures and neural network algorithms have facilitated the adoption of deep learning in various domains from facial recognition to health care management. The successful deep learning applications in real-world services depend on not only the high-performance inference models but also the quantity and the quality of training data.

Such training data often contains private and sensitive information, $e.g.$, facial features, financial records, health history, $etc.$, inevitably causing the risk of privacy leakage for the data owners. Thus, there have been increasing privacy concerns with the growing deployment of deep learning applications. Since deep neural network itself can function as an encoder by translating the individual data into model parameters, many prior works \cite{fredrikson2015model,shokri2017membership} have demonstrated the 'hacking' of sensitive information from the neural network, the performance of which can be notably improved if the attacker can repeatedly query outputs of the model even in a 'black-box' manner \cite{tramer2016stealing}. Thus, it is highly desired to {have privacy-preserving techniques for deep learning applications that can ensure model utility while protect sensitive information}.

Recent researches \cite{abadi2016deep,shokri2015privacy,yu2019differentially} have investigated privacy protection from various aspects. The concept of privacy-preserving deep learning was first proposed in \cite{shokri2015privacy}, in which multiple private participants jointly trained a model by updating the sanitized model parameters, while the training data were kept at local. A more general approach was then proposed by \cite{abadi2016deep} that applied differential privacy (DP) mechanisms to perturb the gradients of each iteration and employed a privacy accountant to track the privacy loss during training. Thus, by limiting the impact of one single data on model parameters, the privacy can hardly be reverse-engineered.

In practice, the aforementioned protections are prone to the worst case privacy guarantees assuming attackers have access to the internal model parameters, $i.e.$, very large noise can be injected at the cost of model utility degradation \cite{abadi2016deep}. Thus, the applicability of such mechanisms are questionable. A more promising alternative is to only expose a learned model obtained through transfer learning instead of the private model directly learned on the sensitive data. The training procedure with access to the private model is prudently privacy-bounded. For example, recent works \cite{papernot2016semi, papernot2018scalable} proposed a two-player (teacher-student) framework to train a student model on the differentially private aggregated outputs of an ensemble of teachers. Such privacy protection is achieved through the noisy voting by teachers but requires many teachers to compensate for the noise added to each query to ensure model utility. Although such a private transfer learning mitigates the exposure of private models associated with sensitive data, it still remains\textit{ a challenging task to balance between model utility and privacy when the training data is limited}. This is not a trivial problem and needs to resolve the following issues:
\begin{itemize}
    \item \textbf{Model performance bottleneck}. Differential privacy enforces a certain level of privacy budget given the composition theorem, inevitably restricting the number of training epochs that makes it hard to further improve model performance.
    \item \textbf{Availability of training resources}. For privacy purpose, it is desired to have only public data fed to all the networks. However, it is difficult for the student ($e.g.$, local reservoir of a hospital) to collect sufficient quality data for training ($e.g.$, lack of labels, limited quantity, $etc.$).
    \item \textbf{Overhead of noisy queries}. The standard approach to DP is to inject noise to the output while the noise scale is proportional to the sensitivity of the query function. A higher sensitivity inevitably results in excessive noise, completely masking the knowledge to be transferred. For example, PATE proposes to reduce the sensitivity of voting results by ensembling 250 teacher models \cite{papernot2016semi}. Such an approach brings significant overhead to computational resources and hence is not scalable to large-scale tasks.
\end{itemize}

In this paper, we propose a novel three-player (teacher-student-discriminator) framework that combines the state-of-art knowledge transfer techniques with the advanced privacy-preserving mechanisms.
In the framework, the target model is treated as the $student$ in the conventional \textit{teacher-student} learning paradigm and the generator in {generative adversarial networks} (GAN). The $student$ is not only trained by the $teacher$ through {Knowledge Distillation} (KD), but also adversarially trained with the $discriminator$ through GAN to generate similar outputs as the teacher. When with limited quality training data, which is often the case in practice, we enforce the discriminator to train the student to mimic the 'true' learned distribution of the teacher. The teacher in the framework is pre-trained on sensitive data and frozen during the training. Thus, unlike the two-way transfer learning as in \cite{wang2018kdgan}, the knowledge transfer in our framework is unidirectional, $i.e.$ only the teacher distills the knowledge to the student without privacy breach. Then, to enforce privacy guarantee during training, a privacy protection mechanism is introduced that provides insights into the vulnerabilities of the framework and applies DP to protect the private teacher. Finally, the proposed learning strategy achieves the joint utility and privacy optimization that transfers private knowledge from a protected teacher to a public student.

The proposed learning strategy for private knowledge transfer exhibits three important advantages:
\begin{itemize}
    \item The student network that learns the distilled knowledge with the discriminator is better optimized than the conventional teacher-student paradigm;
    \item Faster convergence can be achieved even with limited training epochs, while the performance is not overly bounded by the number of training instances;
    \item With the well-designed privacy-preserving mechanism, the framework is able to achieve excellent model utility and rigorous privacy guarantee.
\end{itemize}
The contributions of this work can be briefly summarized as below:
\begin{itemize}
\item We propose a three-player (teacher-discriminator-student) framework to transfer private knowledge to the student. With the discriminator, the student can accurately and efficiently mimic the teacher even with limited quality training data, which enables excellent model utility and a strong privacy guarantee.
\item We integrate a differential privacy mechanism into the learning procedure that allows student to be trained with a rigorous privacy budget, in which the privacy accountant provides a theoretical basis for trade-off between utility and privacy.
\item We evaluate the proposed framework on MNIST, SVHN and CIFAR-10 datasets. It is found that the proposed framework offers a good utility and privacy trade-off even with very few training epochs and unlabelled training instances.
In addition,  Our students achieve accuracies of 98.52\%, 93.12\% and 84.79\% with DP bounds of (1.93, $10^{-5}$) for MNIST, (5.02, $10^{-6}$) for SVHN and (8.81, $10^{-6}$) for CIFAR-10, respectively.
Compared with the existing works, our framework consistently ensures a lower student accuracy loss ($w.r.t.$ the teacher). In particular, the students have only 0.89\%, 2.29\% and 5.16\% accuracy losses for the three datasets, while the reported student accuracy losses from other works are 1.21\%, 10.88\%, and 5.40\%, respectively \cite{papernot2016semi, wang2019private}.
\end{itemize}

\section{Method}

In this section, we formulate the proposed learning strategy with a cohort of the three networks (as plotted in Figure~\ref{fig_learning}). Given a private dataset, we can always pre-train the teacher network $\mathcal{T}$ using the cross-entropy error as the objective function. On the other hand, with {a nonsensitive public dataset $X$}, the student $\mathcal{S}$ is trained by not only the teacher $\mathcal{T}$ to minimize the perturbed distillation loss $\tilde{L_{DS}}$, but also the discriminator $\mathcal{D}$ to minimize the adversarial loss $L_{AD}$. In the following, We first discuss the knowledge transfer path of KD and its training loss. Then, we introduce the proposed privacy protection mechanism. After that we describe the knowledge transfer path of GAN and the adversarial learning loss. Finally, we present the joint learning procedure for the framework.

\subsection{Knowledge Transfer Path with KD}

\subsubsection{Knowledge distillation}
Given a sample set of $N$ samples $X = \{x_i\}_{i=1}^N$ from $M$ classes, we denote the corresponding label set as $Y = \{y_i\}_{i=1}^N$ with $y_i = \{1,2,...,M\}$. The categorical distributions of the two networks $\mathcal{T}$ and $\mathcal{S}$ are probability values of $M$ classes, denoted by $p^\mathcal{T}$ and $p^\mathcal{S}$, respectively. For a sample $x_i$, the two probabilities are:
\begin{eqnarray}
    p^\mathcal{T}(x_i) = softmax(z_i^\mathcal{T}) \quad \textrm{and} \quad
    p^\mathcal{S}(x_i) = softmax(z_i^\mathcal{S})
\label{equ_cp}
\end{eqnarray}
where the logits $z^\mathcal{T}$ and $z^\mathcal{S}$ are the outputs of the last fully connected layer of the two networks $\mathcal{T}$ and $\mathcal{S}$, respectively.

Kullback Leibler (KL) Divergence is a measure of how one probability distribution differs from another. Here we adopt KL divergence as the distillation loss to distill knowledge from network $T$ to network $S$. Then we have the following loss function:
\begin{equation}
    L_{DS} =
    \sum_{i=1}^{N} p_{\boldsymbol{\tau}}^\mathcal{T}(x_i){\rm log}(\frac{p_{\boldsymbol{\tau}}^\mathcal{S}(x_i)}{p_{\boldsymbol{\tau}}^\mathcal{T}(x_i)})
\label{equ_ds}
\end{equation}
where the probability distribution $p_{\boldsymbol{\tau}}(x_i)=softmax(z_i/{\boldsymbol{\tau}})$ is the softmax temperature function with ${\boldsymbol{\tau}}$ as the temperature parameter. The temperature parameter ${\boldsymbol{\tau}}>0$ controls how much we want to soften or smooth the class probability predictions from $p^\mathcal{T}$. A higher temperature indicates a softer probability distribution generated by the teacher network with privileged knowledge of the differences among the classes.

\subsubsection{Differential privacy protection}
The proposed privacy protection is built upon a general approach in \cite{mcmahan2018general}, which can allocate privacy budget to each step and compute the total privacy cost over iterations.

Given a probability sample $q$, clipping threshold $C$, noise multiplier $m$,  in \cite{mcmahan2018general}, the standard procedure of adding Gaussian noise ($i.e.$, applying DP) to a vector to be protected is:
\begin{enumerate}[\indent 1.]
    \item Select a subset of records (or samples) $R_i$, $i\subseteq[1,...,N]$, with probability $q$ to choose each record. The result of each query for the record is a vector $v^i\in{R^D}$;
    \item Clip each $v^i$ by threshold $C$ and $L_2$ norm: $\pi_{C}(v^i)=v^i{\cdot}{\rm min}(1,C/||v^i||_2)$;
    \item The sum with noise (or DP) added is then computed by: $\tilde{v}=\frac{1}{qN}(\sum\pi_{C}(v^i)+\mathcal{N}(0;\sigma^2I))$.
\end {enumerate}
where $\sigma=m\cdot{C}$ and can be intuitively understood as the scale of the injected noise.

Unlike the general approach in \cite{mcmahan2018general} that applys DP to the gradients during training, the primary privacy concerns of the proposed strategy (as briefly discussed in Sec. 3) is only associated with the private teacher network, in particular, the distillation loss to be queried. Thus, in the proposed framework, we do not need to protect the gradient vector as \cite{mcmahan2018general}. Instead, we can choose a batch of samples, then protect a vector of batch distillation loss that is queried from the teacher. We can use the following to compute the batch distillation loss:
 \begin{equation}
     v^i = p_{\boldsymbol{\tau}}^\mathcal{T}(x_i){\rm log}(\frac{{p_{\boldsymbol{\tau}}^\mathcal{S}(x_i)}}{{p_{\boldsymbol{\tau}}^\mathcal{T}(x_i)}})
     \label{equ_ds}
 \end{equation}
To protect the vector, we can then inject Gaussian noise with mean of 0 and standard deviation of $m\cdot{C}$ to the norm-bounded batch distillation loss. As a result, the batch of differentially-private distillation loss can be defined as:
\begin{equation}
    \tilde{L_{DS}} =\frac{1}{qN}  (L_{DS}   / {\rm max}(1,\frac{||L_{DS}||_2}{C} ) + \mathcal{N}(0; \sigma^2I))
\label{equ_dp_ds}
\end{equation}
With such a differentially-private loss $\tilde{L_{DS}}$ for each batch of samples, we can bound the privacy cost of each query to the teacher. In addition, it is necessary to keep track of the privacy cost during the entire training procedure and then derive the final privacy budget for a desired model utility. Such a relationship acts as the theoretical basis for our trade-off between utility and privacy.
We employ the concept of R{\'e}nyi divergence and R{\'e}nyi differential privacy (RDP) accountant \cite{mironov2017renyi, mironov2019r} as the measure. Per the definitions of DP and RDP, the privacy loss can be considered as a random variable dependent on the injected random noise.
RDP accountant then generalizes the pure $\epsilon$-differential privacy and achieves a more compact composition theorem than the standard ($\epsilon$, $\delta$)-DP \cite{mironov2017renyi, mironov2019r}. In particular, RDP ensures that a randomized mechanism can be bounded by a smaller $\epsilon$ through R{\'e}nyi divergence of two adjacent inputs, and then tracks privacy bounds on the moments of the privacy loss. In the proposed DP mechanism, RDP is applied to track the bound on the batch distillation loss with sampled Gaussian noise.

\subsection{Knowledge Transfer Path with GAN}
With the pre-trained teacher network $\mathcal{T}$, the student network $\mathcal{S}$ can be trained adversarially against the discriminator network $\mathcal{D}$. The student and the discriminator play a min-max game. While the network $\mathcal{S}$ attempts to generate a probability $p^\mathcal{S}$ mimicking the distribution of the teacher network $p^\mathcal{T}$, the discriminator model tries to distinguish the 'true' label predicted by $\mathcal{T}$ from the pseudo label by $\mathcal{S}$. We then can define the objective function $L_{AD}$ for the min-max game:
\begin{eqnarray}
    \mathop{\rm min}\limits_s\mathop{\rm max}\limits_dL_{AD} =
    E_{y\sim{p^\mathcal{T}}}[{\rm log}\,p^\mathcal{D}(y) + \nonumber\\
    E_{y\sim{p^\mathcal{S}}}[{\rm log}\,(1-p^\mathcal{D}(y)]
\label{equ_gan}
\end{eqnarray}
where $y\sim{p^\mathcal{T}}$ and $y\sim{p^\mathcal{S}}$ are the continuous samples generated from the discrete probability distributions of $p^\mathcal{T}$ and $p^\mathcal{S}$, respectively; $p^\mathcal{D}(y)$ is the probability generated by the discriminator network for a label $y$.

In the proposed framework, the network $\mathcal{D}$ gets updated by maximizing the objective function $L_{AD}$ in Eq.~\eqref{equ_gan}, while $\mathcal{S}$ attempts minimizing $L_{AD}$, thereby making $\mathcal{D}$ unable to differentiate whether a given label is predicted by $S$ or not. Such a min-max game updates $\mathcal{S}$ and $\mathcal{D}$ alternatively until the equilibrium is reached, $i.e.$, $\mathcal{S}$ learns the distribution of $\mathcal{T}$ given the discrimination of $\mathcal{D}$. As shown in Figure~\ref{fig_learning}, the network $D$ cannot directly take the discrete probabilities from $\mathcal{T}$ and $\mathcal{S}$, which may inevitably result in high variances in the gradients. To reduce the variances for $\mathcal{D}$, we use the Gumbel-Max trick \cite{maddison2014sampling} to re-parameterize the generation of the discrete samples to a continuous space. Then, we can conduct sampling approximation to obtain the continuous samples $y$ and formulate $L_{AD}$ with lower-variance gradients.

\subsection{Joint Learning Procedure}
Based on the two knowledge transfer paths and the proposed privacy protection mechanism, we incorporate the differentially private distillation loss in Eq.~\eqref{equ_dp_ds} and adversarial loss in Eq.~\eqref{equ_gan} into the final objective function for the target student as below:
\begin{eqnarray}
   L = \alpha\tilde{L_{DS}} + (1 - \alpha)L_{AD}
\label{equ_total}
\end{eqnarray}
where $\alpha$ is a distillation weight set between 0 and 1.
We achieve the joint utility and privacy optimization with the proposed privacy protection mechanism that transfers knowledge from a protected private teacher to a public student.
The overall learning procedure is summarized in Algorithm~\ref{algorithm}.

Our learning strategy has its unique advantages: when combined with the adversarial learning, the joint optimization of distillation loss and adversarial loss can greatly save training epochs, which results in a stronger privacy guarantee. In addition, with RDP accountant, by searching the optimal values for the hyper-parameters ($i.e.$, $q$, $C$, $\sigma$) and the distillation weight $\alpha$, we can ensure an optimal model utility with a tight privacy bound.

\begin{algorithm}
	\caption{Privacy-Preserving Learning Procedure.}
    \label{algorithm}
    \KwIn{a pre-trained network $\mathcal{T}$, public training samples $N$, training epochs $T$, training epochs for the discriminator $T_\mathcal{D}$, training epochs for the student $T_\mathcal{S}$, batch size $B$, clipping threshold $C$, the noise multiplier $m$.}
    \KwOut{a student network $\mathcal{S}$}
    \For{$t=0$ to $T-1$}
    {
        \For{$i=0$ to $(T_D-1)*(N/B)$}
        {
            Sample a batch $x$ of size $B$;\\
            Sample $y$ from discrete probabilities $p^\mathcal{T}$ and $p^\mathcal{S}$;\\
            Compute adversarial loss $L_{AD}$;\\
            Update $\mathcal{D}$ by ascending along its gradients of $L_{AD}$ ;
        }
        \For{$j=0$ to $(T_S-1)*(N/B)$}
        {
        	Sample a batch $x$ of size $B$;\\
            Compute distillation loss $L_{DS}$;\\
            Apply differential privacy mechanism: $\tilde{L_{DS}} \gets L_{DS}  / {\rm max}(1,\frac{||L_{DS}||_2}{C} ) + N(0; \sigma^2I)$;\\
            Sample $y$ from discrete probabilities $p^\mathcal{T}$ and $p^\mathcal{S}$;\\
            Compute adversarial loss $L_{AD}$;\\
            Compute weighted sum $L$;\\
            Update $\mathcal{S}$ by desending along its gradients of $L$;
        }
    }
\end{algorithm}

\section{CONCLUSION}
This paper presented a three-player (teacher-student-discriminator) framework that transfers private knowledge and improves privacy and utility trade-off. The proposed framework combines KD from a teacher with GAN involving a student and a discriminator. A key insight in the proposed learning strategy is that the combination of KD and GAN provides additional quality supervision in addition to the distilled knowledge. Then, with a differential privacy mechanism, the proposed framework can establish a precise privacy guarantee of the training procedure while the training convergence can be quickly achieved even with limited training epochs and unlabelled training instances. Experimental results show that the proposed framework can act as the baseline framework for private knowledge transfer and achieve excellent utility and privacy trade-off on the datasets of MNIST, SVHN and CIFAR-10 for multi-label classification tasks.

\section{ACKNOWLEDGEMENTS}
 This work was supported in part by the National Key R\&D Program of China (Grant No. 2018YFE0126300) and the National Science Foundation of China (Grant No. 61974133).

\input{main.bbl}
\end{document}

\section{OVERVIEW}

This paper proposes a private knowledge transfer strategy, where the private teacher trained on sensitive data is not publicly accessible but can be used to teach a student model to be released. The target student is not only taught by the distilled knowledge from the teacher, but also trained against a discriminator to mimic the behavior of the teacher. There are two critical questions to be answered to implement the aforementioned strategy: (1) How to combine KD with GAN to train the target student model? (2) How the teacher response is privacy-guaranteed during the training procedure?

An overview of the proposed framework to implement such a strategy is demonstrated in Figure~\ref{fig_overview}. The framework involves three players (teacher-student-discriminator) and two domains (non-public and public).

There are two \emph{knowledge transfer paths} for the target student. KD acts as a unidirectional path from teacher to student, and GAN is a two-way path between discriminator and student:
\begin{itemize}
    \item\textbf{Distillation learning by KD} {(detailed in Sec. 4.1):} For a given input instance, we adopt the Kullback Leibler (KL) divergence as the distillation loss to measure the distance between two categorical distributions of teacher and student, $i.e.$, class probabilities from teacher and student. The effectiveness of distillation stems from the additional supervision and regularization of higher entropy soft targets.
    \item \textbf{Adversarial learning by GAN} {(detailed in Sec. 4.2):} Student (generator) and discriminator play a min-max game between one another. The discriminator tries to distinguish the student's output from the teacher's, while the student tries to generate similar output as the teacher that makes the discriminator can no longer differentiate. Both student and discriminator are adversarially trained epoch by epoch until the equilibrium.
\end{itemize}

Due to the lack of high quality training data for the student, it is typically challenging for the student to use the cross-entropy error as the objective function, which makes the training without an effective supervision.
However, the combination of knowledge distillation and adversarial learning results in more effective optimization that resolves the issue.
As discussed in \cite{wang2018kdgan}, a weighted sum of the distillation loss (from the teacher) and the adversarial loss (from the discriminator) may reduce the gradient variance, thereby accelerating the student training convergence with fewer training epochs. Motivated by that, we propose a joint optimization of distillation and adversarial losses (as detailed in Sec. 4.3) to cover the \textit{first question}, which can enforce the student to accurately mimic the teacher and ensure GAN to quickly reach the equilibrium.

To protect the privacy of sensitive data, as shown in Figure~\ref{fig_overview}, we keep the pre-trained teacher model (on the sensitive data) inaccessible to the public (or attackers), $i.e.$, either internal model parameters or outputs of the model are not available to the public. The only accessible part in the framework is the student model, which can take nonsensitive public data. The connection between non-public and public domains are the two knowledge transfer paths without any direct access to private data. Since the student itself cannot access the private data, the remaining privacy protection problem for the framework is how to limit privacy loss for querying the private teacher during the student training.

Intuitively, the excessive memorization of the private teacher may expose the private and sensitive data under attacks. Thus, we propose to integrate a differential privacy (DP) mechanism into the training procedure for privacy guarantee while ensuring model utility (as detailed in Sec. 4.1), which answers the \textit{second question} for the strategy implementation. The mechanism injects the Gaussian noise to each query to the teacher, and then track the bound of privacy loss based on the Composition Theorem of DP \cite{dwork2011differential}. In other words, we sanitize the teacher's distillation loss through clipping the batch loss with the global norm bound and perturbing it with appropriate Gaussian noise. Since the discriminator in the framework is not accessible to attackers after the training, the discriminator itself and its adversarial training procedure does not incur additional privacy violations. Finally, the mechanism employs \textit{RDP accountant} to keep track of privacy loss during training, enabling a theoretical basis for trade-off between utility and privacy.

\section{BACKGROUND AND RELATED WORK}
Since the emergence of AlexNet, it is found that deeper neural networks require more training data to ensure convergence and robustness. In the healthcare, financial, or privacy-related domains, such training data is typically collected in a centralized manner and inevitably undergoes privacy breach risk if the trained models are exposed to the public. Thus, it is crucial to protect the privacy of training data as well as its use in deep neural networks. Recently various attacks further aggravate such privacy concerns in training and releasing deep learning models, as attackers can excessively analyze the model responses to recover the sensitive information. As a consequence, privacy protection in deep learning has been a concerning research area.

\textbf{Differential privacy} (DP) is a widely-adopted approach to privacy protection in deep learning models. The theoretical study \cite{dwork2006calibrating} provides provable privacy guarantees for differential privacy that is achieved through adding noise to mask the output differences for the two different inputs. The very first proposal of deep learning with DP was presented in \cite{abadi2016deep}, in which the gradients in stochastic gradient descent (SGD) algorithm were perturbed and the privacy budgets were accordingly tracked using the \textit{moment accountant}. Its successful development has promoted several following studies \cite{mcmahan2017learning, yu2019differentially} on differentially private deep learning.

Instead of directly applying DP to model training, references \cite{papernot2016semi, papernot2018scalable} implemented a private transfer learning framework (PATE) that transferred the knowledge of an ensemble of teacher models to a student model. Intuitively, its privacy is guaranteed by training teachers on disjoint datasets and aggregating the outputs with noise. However, PATE requires a large number of teacher models to compensate for the noise injected to the individual query responses to ensure a desired trade-off between utility and privacy. In addition, the efficiency and effectiveness of PATE heavily relies on the correctness of voting results of the sample query as well as the appropriate noise added. This is actually hard in practice to select a voting label that is helpful to improve the training while does not reveal privacy when the student only has access to limited public (even unlabelled) data. Another representative work is \cite{wang2019private}, which introduced a private model compression framework with the conventional transfer learning and supervised learning techniques. However, the efficiency and applicability of the work \cite{wang2019private} may be significantly impacted when the student only can use limited quality data.

\textbf{Privacy accountant} is an indispensable part to DP, which can track the accumulated privacy loss and enforce the applicable privacy policy \cite{abadi2016deep}. It has been noted that the privacy loss radically comes from the number of queries responded by the private teacher, $i.e.$, the number of iterations during student training. A formal definition of differential privacy \cite{dwork2011differential} is given as below :
\begin{Def}
A randomized mechanism $\mathcal{M}$ with domain $\mathcal{D}$ and range $\mathcal{R}$ satisfies ($\varepsilon,\delta$)-differential privacy if for any two adjacent inputs $D$, $D'\in\mathcal{D}$ and for any subset of outputs $\mathcal{S}\subseteq\mathcal{R}$ it holds that:
\begin{equation}
\begin{aligned}
Pr[\mathcal{M}(D)\in\mathcal{S}]\leq{e}^{\varepsilon}\cdot{Pr}[\mathcal{M}(D'\in\mathcal{S}]+\delta.
\label{def:dp}
\end{aligned}
\end{equation}
\end{Def}
In Eq.~\eqref{def:dp}, the parameter $\epsilon$ is an upper bound for privacy loss, and parameter $\delta$ is a failure probability for this privacy guarantee. A smaller privacy bound $\epsilon$ enforces a stronger privacy guarantee but inevitably incurs more significant accuracy loss. Based on the composition theorem of DP, \cite{abadi2016deep} proposed the concept of \textit{moment accountant} to quantitatively capture the privacy budget. The extended work in \cite{mcmahan2018general, mironov2019r} further proposed the application of R{\'e}nyi differential privacy (RDP) accountant by analyzing R{\'e}nyi divergence to enable tighter bounds of privacy loss. In this paper, we employ \textit{RDP accountant} to measure the privacy budget of the proposed training procedure, and balance the trade-off between utility and privacy.

It is noted that direct applications of DP to the exposed models may incur too much utility loss.
Other than transfer learning via an ensemble of teachers, \textbf{Knowledge distillation} (KD) is considered as an effective alternative to transfer knowledge from a private teacher to a student network to protect the privacy of training data. In the teacher-student paradigm, the teacher teaches the student through a softened distribution of its output, $a.k.a.$, dark knowledge \cite{xu2017training}. It is easier to mimic the teacher than directly learning the target function, as the output distribution from the teacher embodies additional information beyond the ground-truth distribution. A recent study in \cite{lopez2015unifying} provided theoretical insights that an intelligent teacher can transfer helpful privileged information only available at the teacher's training stage. Thus, the performance of the published student model is actually associated with the distilled knowledge from the teacher network trained on private data. Such findings motivate us to investigate how to design knowledge transfer to balance between utility and privacy.

\textbf{Generative adversarial network} (GAN) is another alternative to enable the model to learn the true data distribution \cite{isola2017image,yu2017seqgan,zhang2017adversarial}. GAN may employ a generator to synthesize student features and a discriminator to distinguish student outputs from teacher outputs. Recently GAN has been successfully applied to generate discrete data ($e.g.$, sequence, text) \cite{yu2017seqgan, zhang2017adversarial}. This is consistent with our purpose of generating/learning a discrete classification distribution for the student.

Thus, in this paper, we explore the idea of integrating KD and GAN, where \textit{a discriminator trains the student to learn the distribution over the pseudo labels created by the teacher while the teacher distills dark knowledge to the student.} An earlier work of \cite{wang2018kdgan} also proposed to combine KD with GAN to help speed up the training procedure, where both the student and the teacher are trained against the discriminator to learn the true distribution and then distill knowledge to each other. Such two-way transfer learning inevitably undergoes the risk of privacy breach and hence cannot be simply applied to the private knowledge transfer.

redpajama_set_name: RedPajamaArXiv


text:
Jack Osbourne's 'Strongest Man' Post About Dad Ozzy Gives Fans All the Feels
By Stephanie Downs - January 26, 2020 03:59 pm EST
On Jan. 21, it was reported that famed rocker Ozzy Osbourne had been diagnosed with Parkinson's disease. Following the news, plenty have voiced their support for Ozzy, particularly as he deals with yet another difficult health journey. Ozzy's son, Jack Osbourne, has also taken to social media to share some lovely words for his father, and fans and celebrities alike couldn't help but gush over the post.
"Dad, Bear and myself around 87'. Just with all that been said this week I figured I'd show some love to my father," Jack wrote on Instagram on Jan. 23 alongside a throwback photo of himself and his father. "He's the strongest man I know and what he has been facing will only make him and stronger/better man."
Fans of the rocker, and even a few celebrities, all flocked to Jack's post to share their own kind words for Ozzy.
A post shared by Jack Osbourne (@jackosbourne) on Jan 23, 2020 at 11:30am PST
"Oz is a rock. I love you and your family," Wilson wrote. "Strength is all our STRENGTHS!!!! I'm always here to hug you, love you all, bake for you - be a backbone. You can count on me forever."
"sorry to hear about your dad Jack," one fan wrote. "He is strong like you and will keep fighting it."
"Great pic, always loved your dad, he will be brave and classy throughout this battle," another fan wrote.
"Ozzy is the best! I wish him and his family/ friends the very best!!! You got this Ozzy," yet another fan commented, adding an all-too-appropriate rock on emoji to boot.
Jack's sweet Instagram post came only days after Ozzy, and Sharon Osbourne, sat down on Good Morning America to discuss his Parkinson's diagnosis.
On Tuesday, Jan. 21, Ozzy and Sharon both appeared on GMA in order to explain the rocker's recent diagnosis, which is being treated with medication. The Black Sabbath singer received the diagnosis in February, amid his treatments for a staph infection and a scary fall he experienced in his home.
"I found out I have a mild form of … what is it?" Ozzy said, before handing the reins over to Sharon, who added, "PRKN 2, which is a form of Parkinson's."
"There's so many different types of Parkinson's," Sharon continued to explain. "It's not a death sentence by any stretch of the imagination, but it does affect certain nerves in your body. It's like you have a good day, a good day and then a really bad day."
Ozzy added that he's been experiencing some symptoms that may or may not be related to Parkinson's (the rocker also fell in his home in April, an incident which dislodged metal rods in his back that were placed there after a 2003 motorbike accident).
"I got a numbness down this arm from the surgery; my legs keep going cold," he said. "I don't know if that's the Parkinson's or what, you know, but that's — see, that's the problem. Because they cut nerves when they did the surgery. I'd never heard of nerve pain, and it's a weird feeling."
Despite feeling these symptoms, Ozzy went on to relate that he's glad that this information has been revealed as it's been a sort of weight lifted off of his shoulders.
"I feel better now that I've owned up to the fact that I have a case of Parkinson's," he said. "And I just hope [my fans] hang on and they're there for me because I need them."
Photo Credit: Karwai Tang/WireImage/Getty
11 Kardashian-Jenner Family Photoshop Fails You Can't Unsee
Amy Schumer Reveals She Underwent Plastic Surgery Procedure, Endometriosis Surgery
Andre Leon Talley, 'Vogue' Fashion Icon, Dead at 73
Sidney Poitier's Cause of Death Released Alongside Death Certificate
Black Sabbath's Tony Iommi Mourns Death of Dear Friend
'This Is Us' Star Mandy Moore Announces Next Project Is 'Coming Very Soon'
Jamie Lynn Spears Reveals Britney Spears' Reaction to Justin Timberlake's 'Cry Me a River'


text:
RELIEF FUND 7
New Russian Bill Seeks to Take Biological Children Away From Gays and Lesbians
Posted on September 6, 2013 September 6, 2013 by Melanie Nathan
Lesbian Activist Speaks about Russia's Continued Defiance with worsening New Laws Against Gays and asylum being the only answer
By Melanie Nathan, September 06, 2013.
A Russian lawmaker has proposed a bill that would deny gay parents custody over their children. The draft bill, published on parliament's website on Thursday, would make the "fact of nontraditional sexual orientation" a basis for denying custody. Other grounds include alcoholism, drug use, and abuse. The proposal follows heightened anti-gay sentiment in Russia and the the June 30 legislation banning homosexual "propaganda" among minors. The motive behind the bill is the myth that gays are harmful to children, and the justification is that it is aimed at so called protection of children, rather than the oppression of the LGBT community. Alexei Zhuravlev, the author of the bill, referred to the earlier law and said that homosexual "propaganda" had to be banned not only in the public space "but also in the family."
Michelangelo Signorile wrote an article today featuring his interview with lesbian activist, Masha Gessen, who was a Moscow-based writer journalist, and has been speaking out in recent months on Russia's anti-gay propaganda law. An American citizen, Gessen who has lived in Russia for many years, and is raising three children with her lesbian partner, a Russian citizen, reacted to the new Bill.
The article notes that "Gessen hoped Western pressure in recent months would help change the course of Russia's crackdown on its LGBT citizens, but now she believes that that's not going to happen, and that it's time to for LGBT Russians to flee the country to escape what she says has now become "all-out war" against LGBT people in Russia. And she's calling on the United States to allow political asylum for LGBT Russians, and for LGBT activists here to focus on making that happen."
This newer Bill indicates that the Russians are becoming more aggressive in their anti-gay stance.
Important factors to elucidate:-
1. Gessen's call for such should extend to all LGBT asylum seekers around the world,to include those who live in countries that criminalize homosexuality, making it a crime, which is not the case in Russia. This call should also be a wake up scream for imperative changes that must occur in our asylum laws – the subject of another article; and
2. We cannot sit back in silence. The only worthy big noise, whether successful or not, will be one where we call even louder for the boycotts of all things Russian, whether it be vodka, or the removal of the Winter Olympics from Sochi.
Gessen told Signoreli that the crackdown on gays in Russia has intensified, despite international outcry, and that LGBT people are "living through an all-out hatred campaign that's been unleashed by the Kremlin."
Gessen speaks of this all out war against gays, by referring to the heightened violence where gays are kidnapped, beaten and police do not intervene. She sets the blame squarely on the Kremlin and hence Putin and refers to old familiar tactics used in this war against gays, where even LGBT activists have been compromised:
"It's a situation in which even longtime gay activists may be corrupted or coerced through old Soviet tactics designed to destroy their reputations and diminish their influence. In recent weeks activists in Russia and the West have been discussing the bizarre behavior of the well-known Russian gay activist Nikolai Alexeyev, who had what was called a Twitter meltdown, sending out tweets attacking other activists and making ugly anti-Semitic remarks, among other strange pronouncements (including that he was quitting activism). At the same time, his home had been raided by the police."
"I have great respect for some of the work Nikolai Alexeyev has done in the past," Gessen said, offering her beliefs on what might have happened to him. "And I have had disagreements with him on many occasions in the past. What I do know is that he's given every sign of working for the Kremlin right now. Whether he was coerced or blackmailed into doing that through threat of arrest, which exists, through the search of his apartment, which occurred, or seduced by money, at this point he's being used as a spokesperson for the Kremlin."
SIgnoreli notes that Alexeyev, who recently went on a Nationalistic, Anti-Asylee, Anti-semitic rant, declined a request for an interview. Some sources ,however, report that Alekseev's meltdown can be attributed merely to drug use. Who knows – he is compromised and any way one looks at that such compromise serves Putin and not the LGBT community.
Gessen was extremely negative about anything changing for the better for LGBTI people in Russia and suggested the only option is that of asylum:
"At this point, with the fact that they're proposing this law during the G20 Summit, it shows that no Western pressure is going to keep Russia from passing anti-gay laws, from endangering the lives of lesbian and gay people, from endangering our families," she said. Gessen is fortunate that, as an American citizen, with the Defense of Marriage Act now struck down, she can move to the U.S. with her partner, whom she can sponsor for a green card. But she knows that that's not the case for the vast majority of LGBT Russians. "It's high time to talk about asylum," she said. "The only way at this point that the U.S. can help Russian gays and lesbians is get us the hell out of here."
I would agree on the necessity of asylum, but not the exclusion of boycotts. I still believe, regardless of immediate impact, that it is imperative we do not let up on worldwide boycotts of vodka, the call for removal of the Winter Olympic Games from Sochi, the call to move Donald Trump's Miss Universe from Russia, and also an outright ban of Russian Arts, Culture and sport from international soil. Even if it takes years and years for the Russians to get it, that is the only way to drive the message home. I still believe that the South African boycotts eventually brought the self-righteous Afrikaner Government to its knees. Perhaps the Russians need similar punitive measures as 88% of them agree with the idea that their LGBT neighbors should not be afforded very basic human rights.
It is important to note that until the asylum laws change in the U.S.A. ( and other countries) and we start advocating for a special VISA to the USA for persecuted and criminalized LGBT people, who cannot afford to get here on the pretense of being a visitor, a student, a conference attendee, or 'other business' so they can seek asylum once in the USA, we are doing very little to help this cause.
By Melanie Nathan
@melanienathan1
PLEASE JOIN ME at 7:00 PM at the San Francisco Opening of Opera Season for a Vigil for LGBT Russians:
READ MORE-
Opera Couple Patricia Racette and Beth Clayton to Support LGBT Vigil for Russia
by Melanie Nathan on September 5, 2013 i
Press Release: Contact Melanie Nathan: Dated: September 05, 2013. https://www.facebook.com/events/433652160084761/ Tomorrow night, Friday, September 6 at 7:00 pm, the coalition, including members of Gays Without Borders, BoycottRussianVodka.com and PCI-Justice, will stage an informational vigil, outside the San Francisco Opera's gala opening night at the War Memorial Opera House, located at Van Ness and Grove. […]
Vigil to be held at San Francisco Opera Opening for solidarity with Russian Gays
by Melanie Nathan on September 4, 2013
By Melanie Nathan, September 04, 2013. A group of LGBT activists will stage an informational vigil including members of Gays Without Borders, BoycottRussianVodka.com will be holding a fun informational vigil on Friday, September 6 at 7:00 pm outside the San Francisco Opera's gala opening night at the War Memorial Opera House, located at Van Ness […]
Posted in Adoption, Advocacy & Activism, EVENTS, FAMILY, Law, LGBT, NEWS, Parenting, Rally, Russia, WORLDTagged Alexei Zhuravlev, Asylum, Gay, LGBT, Masha Gessen, melanie Nathan, Michelangelo Signorile, Nikolai Alekseev, Olympic Games, Russia, Russian, Sochi
Shame on President Obama's Jon Carson for not getting the ENDA Picture
Vigil for LGBT Russia Greets San Francisco Opera Opening
4 thoughts on "New Russian Bill Seeks to Take Biological Children Away From Gays and Lesbians"
Dr. Rex says:
Reblog: http://hrexach.wordpress.com/
Reblogged this on It Is What It Is and commented:
Russia's laws …. getting more illogical. Definitely discriminatory to the LGBT community!! Something needs to be done!!! SMH …..
Nicolai Dominic says:
Dear God, that law was applicable under the South-African Apartheid regime. It is a law with disastrous consequences. A mistake that cant be rectified. I know! This article stirs up bad memories!
Pingback: 10 Actions you can take against Russia | shocking video of Russian Gay Teen torture | O-blog-dee-o-blog-da
Categories Select Category Abortion Academic Adoption Advocacy & Activism Africa AIDS/HIV American Angola Animals Anti Semitism Argentina ART Art Art Culture Entertainment Asia Auction Australia Awards Belgiam Belize Books Botswana Boycotts Brazil Brunei BUSINESS CALIFORNIA California Cameroon Canada Central America Conferences Congress Court Cases CRIME CULTURE Debates DRC Education Egypt Elections Entertainment Environment Ethiopia Ethiopia Europe EVENTS FAMILY FASHION Film Film Festivals Florida Fundraisers Gambia Games gay Ghana GUNS ha ha ha Hawaii Hawaii HEALTH HEALTH History Homeless Youth Iceland Immigration India Indonesia Intersex IRAN Iraq Ireland Israel Jamaica Kenya Latin America Law LGBT LGBTI Malawi Malaysia Marin MARRIAGE EQUALITY Massachusetts MEDIA Melanie Nathan Memorial Middle East Military Mississippi Namibia New York New Zealand New Zealnd NEWS Nigeria North Carolina Norway Olympics Organizations Parenting Parliament The Gambia Parliament Uganda Pennsylvania Petitions PODCASTS POLITICAL VIEWS Pride Protests Racism Rally RELIGION Ruhakana Rugunda Russia San Francisco SCOTUS Senegal Serbia Sierra Leone South Africa South America Sport SPORTS Surveys Sweden Syria Texas The Netherlands TOURISM Transgender U.S.A. Uganda Ukraine United Kingdom United Nations USA VIDEO Vietnam Weddings WORLD Zambia Zimbabwe
Archives Select Month October 2016 September 2016 August 2016 July 2016 June 2016 May 2016 April 2016 March 2016 February 2016 January 2016 December 2015 November 2015 October 2015 September 2015 August 2015 July 2015 June 2015 May 2015 April 2015 March 2015 February 2015 January 2015 December 2014 November 2014 October 2014 September 2014 August 2014 July 2014 June 2014 May 2014 April 2014 March 2014 February 2014 January 2014 December 2013 November 2013 October 2013 September 2013 August 2013 July 2013 June 2013 May 2013 April 2013 March 2013 February 2013 January 2013 December 2012 November 2012 October 2012 September 2012 August 2012 July 2012 June 2012 May 2012 April 2012 March 2012 February 2012 January 2012 December 2011 November 2011 October 2011 August 2011 July 2011 June 2011 May 2011 May 2010 April 2010 March 2010 February 2010 January 2010 December 2009 November 2009 October 2009 September 2009 August 2009 July 2009 June 2009 May 2009 April 2009 March 2009 February 2009
I aced this can you? fb.me/51sEwT5bQThat said... 4 hours ago
Cindi Creager CreagerCole Communications Please sned us nice pics after the event.... fb.me/63oN3rVtvThat said... 14 hours ago
Wow fb.me/8Fcz87F1VThat said... 14 hours ago
Revelation .... #JillStein She inherited money is with many millions ... and is heavily invested in the very... fb.me/2klQOUeZVThat said... 1 day ago
Melanie Nathan on South Africa Releases Draft Bi…
Denis LeBlanc on South Africa Releases Draft Bi…
Melanie Nathan on Shortage of Injectable Estroge…
Beverly Cosgrove on Shortage of Injectable Estroge…
Dr. Rex on Hillary Clinton addresses LGBT…


text:
Android team to be independent of Motorola
The Motorola you know today will be the same Motorola even though it's now owned by Google…well, more or less.
Google has already made a few drastic changes since acquiring Motorola Mobility last year, like kicking out the company's existing CEO Sanjay Jha for someone on Google's advertising team. But for the most part, the search giant is going to let the Motorola team run independently of the Android team.
At the Mobile World Congress, Google's Android head Andy Rubin was quoted as saying he has "nothing to do with" the Motorola team. "I don't even know who's running it."
"They're going to continue building Motorola branded devices and it's going to be the same team doing it," he added.
The decision to keep the Motorola side of things separate from the rest of Google's mobile division is no doubt a response to concerns that Motorola would become the favored manufacturer, and other Android device makers would get the short end of the stick.
Just imagine if Microsoft started making its own branded computers. Those devices would come with a kind of consumer guarantee that other Windows PC manufacturers simply can't offer, and they probably wouldn't like that very much.
Google has already faced this issue on some level, with the Nexus brand of Android phones, which Google has an active hand in developing. The Nexus phones are always the first to get Android software updates and get a level of support unmatched by other handsets. Google, however, has not chosen a specific manufacturer to be the face of the Nexus brand. Different Nexus phones have been made by different companies.
It's "physically difficult for me to advantage somebody," Rubin said.
Rich people more likely to take candy from a baby
RIM says Blackberry apps are on the way


Go diving in Australia and meet the local sharks like Hammerheads, Bull Sharks and Reef Sharks, in Osprey Reef which is 350 kilometers north of Cairns. Some of the most dangerous sharks like Gray Reef, Silvertip, Silky, and Hammerheads are often spotted here. One can be extremely lucky to view the giant whale shark or dolphins swim throughout the waters. One of the best features of Osprey Reef is its clear visibility and the opportunity to view some of the largest mammals in the sea.
Zorbing is one of the craziest activities a traveller can experience in Australia, where travelers experience life in a bubble. Just get strapped in an 11-foot sphere, where a person inside is itself a ball and land is his playground. Zorbers(people inside that ball) can roll around in Victoria and the Gold Coast. A place called Pimpama in the Gold Coast offers "Hydro Wet Zorb," where 40 liters of water are placed into the globe. This is not all one can enjoy with their friends and family together, a town called Coomera allows groups to roll together to up and down the hills.
The duration of the tour is approximately 90 minutes with lots of scenery within a six kilometer run. Breathe the flowers in the Royal Botanical garden or turn into an Opera singer when passing the Sydney Opera House.
Fraser Island, one of the most beautiful, adventurous and isolated islands located off the Queensland coast. A traveller cruise, 4WD the coastline that is around the island's coast along with Dodge dingoes and oncoming waves would fascinate them. There will be Camping, hiking or hot spring, but be very careful of the shark-infested waters where sharks are visible in knee-deep water. Therefore, swimming is not allowed in the ocean as it has the most aquatic life. One can experience breathtaking Lake McKenzie where blue water and white mineral sand is featured for an exfoliating dirt bath which is considered as nature's best skin care product.
Indiana Jones was the most adventurous movie, which was shot in Darwin, especially the last scene with lots of crocodiles. Australia is known for its most ferocious species of crocodiles. And that is not all the people are in bold spirits, could dive into the Cage of Death, "Australia's only crocodile diving area, also known as 'Crocosaurus Cove, "where divers are placed in the glass cage while watching the croc swim and feed on lunch.
Australians love to experience adventure in every way and they enjoy the wildlife in their natural habitat. In Perth, one can swim with the dolphins in the Rockingham's sheltered bays and islands where it is home to over 180 dolphins. The tour operators are friends with the local marine mammals and have over 20 years' experience. Book the tour and one will get a wetsuit, mask and snorkel for much more fun. You might also like to view our volunteer programs in Australia where you can help dolphins.
Do whatever from Hiking, bushwalking or walkabout Ayers Rock and the Olgas. Ranger guided walks occur year round which is completely free of cost, but are plenty of self-guided options. Walking around the base of Uluru is a popular option, which takes three to four hours to complete. Few rocks are considered as spiritual and Ayers Rock is one of its kind and due to its spiritual significance and climbing is not allowed.
Last but not least, no Aussie adventure is complete without taking a surf lesson. These lessons are popular throughout coastal cities for those who need to brush up on their skills especially In Queensland. Surfers Paradise on the Gold Coast is popular for those who enjoy warm weather and aqua-blue water or head up toward the Sunshine Coast for a more secluded surfing experience.
Australia is one of the most spectacular nations in the world and why not escape the mainland and go sailing. There are lots of excursions available including trips to explore the incredible WhitSunday islands off the East Coast.
The Great Ocean Road is an incredible journey to take - it often gets recognised as one of the best road trips in the world! Think spectacular scenery along the coast from Melbourne to Sydney.
Whether you would like to plan a gap year to Australia, discover the country on an adventure holiday, or stay longer we have some fantastic trips available. You might want to search Australia jobs and be more than just a tourist.


2 God spoke to Moses and said to him, 'I am Yahweh.
3 To Abraham, Isaac and Jacob I appeared as El Shaddai, but I did not make my name Yahweh known to them.
4 I also made my covenant with them to give them the land of Canaan, the country in which they were living as aliens.
5 Furthermore, I have heard the groaning of the Israelites, enslaved by the Egyptians, and have remembered my covenant.
6 So say to the Israelites, "I am Yahweh. I shall free you from the forced labour of the Egyptians; I shall rescue you from their slavery and I shall redeem you with outstretched arm and mighty acts of judgement.
7 I shall take you as my people and I shall be your God. And you will know that I am Yahweh your God, who have freed you from the forced labour of the Egyptians.
9 And Moses repeated this to the Israelites, but they would not listen to Moses, so crushed was their spirit and so cruel their slavery.
13 Yahweh spoke to Moses and Aaron and sent them to Pharaoh king of Egypt, to lead the Israelites out of Egypt.
14 These were their heads of families: The sons of Reuben, Israel's first-born: Hanoch, Pallu, Hezron and Carmi: these are the clans of Reuben.
15 The sons of Simeon: Jemuel, Jamin, Ohad, Jachin, Zohar, and Shaul son of the Canaanite woman: these are the clans of Simeon.
16 These were the names of the sons of Levi with their descendants: Gershon, Kohath and Merari. Levi lived for a hundred and thirty-seven years.
17 The sons of Gershon: Libni and Shimei, with their clans.
18 The sons of Kohath: Amram, Izhar, Hebron and Uzziel. Kohath lived for a hundred and thirty-three years.
19 The sons of Merari: Mahli and Mushi. These are the clans of Levi with their descendants.
20 Amram married Jochebed, his aunt, who bore him Aaron and Moses. Amram lived for a hundred and thirty-seven years.
21 The sons of Izhar were: Korah, Nepheg and Zichri.
22 And the sons of Uzziel: Mishael, Elzaphan and Sithri.
23 Aaron married Elisheba daughter of Amminadab and sister of Nahshon, and she bore him Nadab, Abihu, Eleazar and Ithamar.
24 The sons of Korah: Assir, Elkanah and Abiasaph. These are the clans of the Korahites.
25 Eleazar, son of Aaron, married one of Putiel's daughters who bore him Phinehas. These were the Levitical heads of families, according to clan.
27 It was they who spoke to Pharaoh, king of Egypt, to lead the Israelites out of Egypt -- namely Moses and Aaron.


RealPlayer for Linux allows you to enjoy streaming audio and video on networks and the Internet. It provides high sound quality and newscast-quality video to systems with 28.8kbps or faster modems. RealPlayer includes the Buffered Play feature for playing content that is encoded for higher bit rates than your connection provides. RealPlayer for Linux has the ability to pinpoint the A/V programming you want with new features, including the integrated Real.com Media Guide, a new, integrated Tuner that makes it easy to choose your favorites from among 2,500 Internet radio stations, new 3D effects, and visualizations.


Nursing homes are responsibility for the safety of their residents. Poor wages, under staffing, inadequate job screening and lack of supervision can create a dangerous environment in a nursing home. As a result residents often suffer physical abuse, mental abuse and/or sexual abuse. This abuse may come at the hands of a staff member, another resident or a visitor.


Reusable magic hook is suitable for using on smooth surface like glass, tile, stainless steel, fridge, ect. With the advantages of eco-friendly, non-toxic, removable, reusable and waterproof. If the hook get dirty, you just need to wash it by water and dry it. Then it can be reused for many times.
1, No mucilage glue, and washable for reuse.
3, Removable, no stain and marks when removable.
4, Various customized printing design are available.
5, Loading capacity can reach 2--5kgs.
How to customized a private custom logo self adhesive hooks?
1. Design your company's logo and shape, or ask our company design for you.
Smooth and tough sticking surface like: wooden furniture and partition, metal, stainless, plastic, glass surface of house applicants. Columns, Ceramic tile, etc.
1. Clay wall, papered wall, clothed or water-based painted wall.
1. Do not stick the hook to the oiled, dusted, waxed and moist surface.
2. Do not stick the hook near the hot place like stove.
3. Hanging after 12 hours from first sticking, overweight hanging is not suggested.
4. Please heat the glutinous area with heater before sticking under extremely cold weather.
5. Do not hang the fragile items.
Wenzhou samanl warehouse Co., Ltd was set up in 2015, which located in the famous City —Wenzhou, Zhejiang Province.
We have our own experienced research and development team(R&D TEAM), keep on creating the most perfect product with the newest perspective, to make life surprised by every small detail !
1.How can I place order?
3.Will you offer free sample?
Yes, sample of hooks is free, the express fee paid by buyer.
The samples will be sent to you via express and arrived in 3-5 workdays after the artwork and payment.
4.Do you accept customized order?
Yes, We provide products of customized printing and size to meet your requirement.
5.Can I make my customized color and print my logo?
Yes, your customized color and logo will be welcome to do.
6.Can we change the color of the product?
Yes, any colors do you want can be acceptable as long as you tell us the pantone color.


Webcam Spy by WebCam Spy is about to start. You can choose the download location from 4 different links. If not you will be redirected to the first link on the list. Home security software, that constantly monitor wabcamera for motion, and starts video recording in sace is alarm. Program may also optionally playback alarm sound, run external programs and much more.


text:
Денислав Харалампиев Калчев е български плувец, роден на 1 юли 1973 г. в Силистра.

Започва да тренира плуване при Веселин Суров, който го превръща в един от най-добрите български плувци за всички времена.

Печели титла в спринтовия шампионат на Европа (Ставангер-94) на 100 м съчетано плуване и е първият ни финалист на световното първенство (Рим-84) на 100 м бътерфлай. Също така има едно трето място на 100 м бътерфлай на Европейско първенство на 25-метров басейн и едно 8-о място на ЕП на 50-метров басейн.

Националният му рекорд от 1994 г. оцелява 14 години, като е подобрен от Георги Палазов през 2008 г.

Резултати

Бътерфлай
50 м. басейн
 100 м – 54,37 София 1994 г.

Източници
 Профил в Olympic sports

Български плувци
Родени в Силистра

redpajama_set_name: RedPajamaWikipedia