text:
Dorottya Faluvégi (n. 31 martie 1998, în Budapesta) este o handbalistă maghiară care joacă pentru clubul FTC-Rail Cargo Hungaria și echipa națională a Ungariei. Faluvégi evoluează pe postul de extremă dreapta.

Palmares
Magyar Kupa:
 Câștigătoare: 2017

Liga Campionilor EHF:
Sfert-finalistă: 2015

Cupa Cupelor EHF:
Semifinalistă: 2015

Referințe

Jucătoare de handbal din Ungaria
Nașteri în 1998
Sportivi maghiari în viață
Oameni din Budapesta
Handbaliste ale Ferencvárosi TC


text:
Lets Go! - On A Plane
Get ready to fly into the sky for an amazing adventure! Past a shiny moon and twinkling stars and a "Good day" from the pilot, you'll soon find out where you're going! There's no end to the excitement of this airplane ride.
"Let's go on an airplane, Up into the sky, Where buzzy bees and butterflies, Like to fly so high."
This fun, rhyming book for babies and toddlers is vibrantly illustrated and includes a diverse set of characters. With lots to spot and see on every page, this is a perfect introduction to transport for 0 to 3-year-olds. You'll want to read this book again and again!
"The books are written in rhyme and are great fun to read aloud. What particularly struck me was the lovely language and the inclusion of some pretty advanced vocabulary. It's so important for children to hear words like this from an early age."--getkidsintobooks
"This book will make a delightful accompaniment when little M finally takes his first real flight."--baby.m.reads
"We can all still dream of trips to come and this is a great book before bedtime to dream of flying away to exciting locations."--kids.books.we.love

redpajama_set_name: RedPajamaCommonCrawl


We offer the most exclusive ties for men that get noticed by all the right people a true sample is if you wear our designs.
We sell floral ties that are are among some of the most exclusive on the internet for the money, and as soon as they arrive and you put it round your collar you will know you made the right choice. We offer a rare opportunity to purchase a fancy black and silver tie.
If you bought this design here you can make a good saving online against the high prices taht they are sold at in the retail shops. Our most exclusive floral ties are all handmade with a wool & silk lining that can help create a very good knot and we have used the best silk in this black and silver tie.


The Africa-India Mobility Fund is accepting applications for travel grants from researchers from Africa and India.
The programme, which is a partnership of African Academy of Sciences, the Wellcome Trust/DBT India Alliance and Wellcome, funds short-term visits of up to three months for researchers to travel in either direction.
Funding will be given to nationals of India and African countries to support collaboration in research in HIV/AIDS, tuberculosis, dengue, malaria, vector-borne diseases, parasitic infections, emerging infections, cancer, diabetes, hypertension, health systems research, antimicrobial resistance, microbiome, drug development and general biomedical sciences.
The AIMF initiative by the African Academy of Sciences and the Wellcome Trust/DBT India Alliance (India Alliance) intends to encourage South-South collaborations and learning between the two ecosystems.
This is in recognition of the fact that Africa and India face similar challenges, both in the diseases that affect their populations and socio-political issues as well as the leadership required to address these.
The exchanges are expected to enhance their skills and contribute to the growth of knowledge and leadership towards common health challenges.
At the AAS, the programme is implemented through the Alliance for Accelerating Excellence in Science for Africa (AESA), an initiative of the African Academy of Sciences (AAS) and the New Partnership for Africa's Development (NEPAD) Agency.
Applications broadly focused on infectious and non-communicable diseases of relevance to local, national, or global health will be accepted every month.
The scope of the collaborative opportunity may include but is not limited to HIV/AIDS, TB, dengue, malaria, vector-borne diseases, parasitic infections, emerging infections, cancer, diabetes, hypertension, health systems research, antimicrobial resistance, drug development, microbiome and general biomedical sciences.


text:
A London Summer with Huntley Dent / Music
BBC Prom 63: Berliner Philharmoniker and Sir Simon Rattle
September 1, 2012 September 2, 2012 Huntley Dent 2 Comments
Sir Simon Rattle conducts the Berliner Philharmoniker at the BBC Proms
Ligeti – Atmosphères
Wagner – Lohengrin – Prelude (Act I)
Sibelius – Symphony No. 4 in A minor
Debussy – Jeux
Ravel – Daphnis et Chloé – Suite No. 2
Sir Simon Rattle conductor
Three-star chef. Life may not be a box of chocolates, but the Berlin Philharmonic is, and during their first visit to Royal Albert Hall this Proms season, Sir Simon Rattle made gestures that were like plucking nougatine from a dazzling array of sweets. He beams as he conducts, looking seraphic in a nimbus of snow white hair, and why not? At his disposal is an instrument of unparalleled virtuosity, and he sometimes puts his hands down at his side, gazing in rapt appreciation. If his interpretation should falter, the orchestra will carry the music along as if on a flying carpet. I can imagine someone tiring of London without tiring of life. No one who loves music could tire of the Berliners.
The program was dazzling but also didactic, displaying Rattle's streak as television educator on the BBC. It began with a clever device. Ligeti's Atmospheres was played to its hushed conclusion, then Rattle beat a measure of silence before proceeding seamlessly into the Prelude to Act 1 of Lohengrin. Two examples of light-as-air orchestration were thus linked, with the additional suggestion that Wagner's ethereal evocation of a holy knight descending to earth was paralleled by a modernist attempt to portray light as evanescent sound. Still Ligeti's most popular orchestral work, due to its inclusion in Stanley Kubrick's 2001: A Space Odyssey, Atmospheres reminds one that the composer had the most adventurous ear of anyone back in the Fifties and Sixties, no small claim. As a study in what he called "micropolyphony," Atmospheres is textured like fine lace, and at times the string playing verges on silence, making you lean forward to detect the faintest glow of sound – at those times son et lumiere are one.
Going to Berlin turned Rattle into a Wagnerian – also a Brahmsian – which was all but written into his contract. His predecessor, Claudio Abbado, had to swallow the same pill, and neither was given any leeway. Their personal inclinations didn't matter in the wake of Furtwangler and Karajan, who had made Wagner and Brahms twin peaks in their monumental careers. This night's Lohengrin Prelude was an exercise in sublime sound more than anything else, and since the music portrays a single celestial mood, that was more than good enough.
The first half ended with the enigmatic Sibelius Fourth, a symphony so loosely attached to formal theme-development-recapitulation-coda that only the Scherzo pays attention to it. The rest seems more like three tone poems strung together, and even then the themes are brief mottoes that creep in and out of the forest light. As an extension of Rattle's teaching, the Sibelius worked very well after the Ligeti and the Wagner. This was a chillier light being portrayed but sacred in its own way; Sibelius's reputation rises and falls depending on whether Nature worship and the wisdom of trees are taken seriously. The Berliners learned to play this music on a vast scale under Karajan, unlike the more restrained approach of Colin Davis over the years. Such sumptuousness both hurt and helped on Thursday night. The symphony's orchestration became a deep, resonant palette, while the privacy of Sibelius's reverie was made into a huge public utterance. When you were engulfed in beautiful sounds, nothing else much mattered.
Clarity of purpose matters a lot in Debussy's Jeux, however, a work that entirely baffles me. Is it a masterpiece or a mess, the glittering climax of impressionism or a shower of confetti and ticker tape for a parade that never appears? Rattle brought out snippets of dance – a hint of Spanish castanets, a carousel waltz at the end – and clarified all the textures. But for me, Jeux is embroidery in thin air, and during its twenty minutes I tried, in futility, to imagine how Nijinsky ever choreographed it as a tennis game. Ghosts doing rhythmic gymnastics would be closer to home. My companion suggested that the music tells a romantic story, moving from shy, skittering flirtation to warm embrace. If you say so.
The evening ended with a warhorse, Ravel's Daphnis et Chloe Suite no. 2, done impeccably. As with everything else, Rattle's approach could have used more rigor and spine, less mesmerizing beauty of sound. Such beauty becomes narcotic when delivered for its own sake. An extraordinary moment occurred as the audience erupted in ovations for the Ravel. Rattle bounded off the podium and rushed through the orchestra to embrace first flutist Emmanuel Pahud Andreas Blau in a bear hug. Blau looked pleased, and the crowd was thrilled. Rattle has introduced a new egalitarianism between conductor and orchestra. Maybe this was an extreme example – what about the musicians who weren't hugged? Do they pout? But Blau stood out, even in a galaxy of first-magnitude stars, and the Berliners left the stage confident in their position as the brightest constellation in the evening sky.
BBC Promenade Concerts, Berlin Philharmonic, London 2012, Sir Simon Rattle
A Tanglewood Conducting Master Class — My Bird's Eye View
Philadelphia, Here I Come! By Brian Friel at Donmar Warehouse
2 thoughts on "BBC Prom 63: Berliner Philharmoniker and Sir Simon Rattle"
Jonathan Kelly says:
The flautist was Andreas Blau, and not Emmanuel Pahud.
Thank you for the correction.


Join us for the 1st annual Big Superhero Run on August 18th, 2018!
This superhero themed fun run offers a 10km & 5km Big Superhero Run/Walk (2pm), as well as a 1km Sidekick Run/Walk (1pm). The Big Superhero Run tours Richmond's picturesque Middle Arm Trail.
The course winds its way along trails lining the inlets of the Fraser River and the shores of the Strait of Georgia, with views of Vancouver Island, the North Shore mountains and local wildlife.
With mild inclines, paved trails and gravel tracks, everyone is assured a beautiful and stress-free run or walk. The route is suitable for all ages and abilities. Leashed dogs and strollers are absolutely welcome.
Did you know inflammatory bowel disease (IBD) often starts in childhood and that Canada has the highest prevalence of IBD in the world?
The Gastrointestinal Society invites you to participate in a family-friendly art project led by renowned Vancouver artist, Foster Eastman.
We welcome children, teens, and adults whose lives have been affected by IBD, together with their family and friends, to paint one of 90 toilet seats that will comprise a large art installation.
Liminal Motion examines the role of the Sacred Tree, as witness, as anchor, as compass, as guide in creating paths through time and across cultures. The Sacred Tree teaches that we have inner resources, and that the seed of possibility becomes transformative.
Alicia Marie Lawrence studied visual arts at Emily Carr University of Art + Design and the Vancouver Island School of Art.
Safe & Sound Music Fest 2018 - Emily Carr Student Discount!
Join the Museum of Vancouver for a special screening of locally produced film, c̓əsnaʔəm, the city before the city (2017, 75 min, Canada), in addition to a discussion with the film's director Elle-Máijá Tailfeathers and Musqueam Members.
grunt gallery has been given the reigns to program content on a new non-commercial urban media screen over a five-year period in Mount Pleasant.
The urban screen is being constructed on the second floor of the RIZE building on the western side of Kingsway Avenue facing the street. The Mount Pleasant Urban Screen will provide a wide range of artwork representing different parts of the Mount Pleasant community. One of our first initiatives for the screen is a series of digital stories workshops. We are inviting you to become part of grunt gallery's new Urban Screen project by participating in a one-day Digital Stories workshop!
Want to know the processes for roasting coffee? Want to try several different varieties? Head along to the cafeteria on Wednesday March 21st between 11am-1pm to enjoy free samples of the brilliant Pallet coffee and help choose the new roast for Loafe Cafe.
Please be advised that extended hours are now in progress for Spring 2018.
Between March 12 - 29, Monday through Thursday, the campus will remain open until 1am. Friday - Sunday, regular hours will be in effect.
The Caetani Cultural Centre offers artists and writers creative living and working spaces in an independent community atmosphere for self-directed residencies, providing time, support and room for creativity, at affordable rates.
Open to emerging and established visual artists in any medium. Writers, musicians, curators, cultural researchers, musicians and performing artists are also welcome and encouraged to apply. Self-directed residencies are of varying lengths of time, from 2 weeks to 3 months, to suit the individual needs of the applicant. Longer terms may be negotiated if needed. Please specify the preferred time period for the residency and length of stay for project in your application form.


Exhibition Area for Cross-border E-Commerce Logistics -- solve some problems encountered by cross-border E-commerce, like long-term distribution, non-full tracing of goods, unavailable changing or refunding, customs clearance issues, goods damage and missing.
Exhibition Area for Cold Chain Logistics -- provide proposals so as to have an intelligent supervision on the whole course and add technology of Internet of Things into cold chain logistics in terms of its raw material procurement, goods storage, goods transportation and goods sales.
Exhibition Area for Logistics Auto -- highly efficient equipment like transportation tools, industrial robots, warehousing facilities and communications are to be exhibited. These equipment will be controlled by computer systems so that an integrated system with specific function will be formed, then the work efficiency will be improved and labor cost will be reduced.


Philosophy Society Research Seminar, Autumn 2013, Fridays 1:00pm–3:00pm in Arts A108. All welcome.
Virtuous Interventions: A Way Out for Virtue Epistemology?
What's 'Looks' Got To Do With It?
For details of previous years' Philosophy Society Research Seminars, see here.
See what people are saying about Sussex Philosophy Society on Twitter.


text:
\section{Introduction}
Generating high-quality samples from complex distribution is one of the fundamental problems in machine learning. One of the approaches is by generative adversarial networks (GAN)~\cite{goodfellow2014generative}. Inside the GAN, there is a pair of generator and discriminator trained in some adversarial manner. GAN is recognized as an efficient method to sample from high-dimensional, complex distribution given limited examples, though it is not easy to converge stably. Major failure modes include gradient vanishing and mode collapsing.A long line of research is conducted to combat instability during training, e.g.~\cite{mao2017least,arjovsky2017wasserstein,gulrajani2017improved,miyato2018spectral,zhang2018self,brock2018large}. Notably, WGAN~\cite{arjovsky2017wasserstein} and WGAN-GP~\cite{gulrajani2017improved} propose to replace the KL-divergence metric with Wasserstein distance, which relieve the gradient vanishing problem; SNGAN~\cite{miyato2018spectral} takes a step further by replacing the gradient penalty term with spectral normalization layers, improving both the training stability and sample quality over the prior art. Since then, many successful architectures, including SAGAN~\cite{zhang2018self} and BigGAN~\cite{brock2018large}, utilize the spectral normalization layer to stabilize training. The core idea behind the WGAN or SNGAN is to constrain the smoothness of discriminator. Examples include weight clipping, gradient norm regularization, or normalizing the weight matrix by the largest singular value. However, all of them incur non-negligible computational overhead (higher-order gradient or power iteration).
\par
Apart from spectral normalization, a bundle of other techniques are proposed to aid the training further; among them, essential techniques are two time-scale update rule (TTUR)~\cite{heusel2017gans}, self attention~\cite{zhang2018self} and large batch training~\cite{brock2018large}. However, both of them improve the Inception score(IS)~\cite{salimans2016improved} and Frechet Inception Distance(FID)~\cite{heusel2017gans} at the cost of slowing down the per iteration time or requiring careful tuning of two learning rates. Therefore, it would be useful to seek some alternative ways to reach the same goal but with less overhead. In particular, we answer the following question:
\begin{quote}\textit{Can we train a plain ResNet generator and discriminator without architectural innovations, spectral normalization, TTUR or big batch size?}
\end{quote}
\par
In this paper, we give an affirmative answer to the question above. We will show that all we need is adding another adversarial training loop to the discriminator. Our contributions can be summarized as follows
\vspace{-5pt}
\begin{itemize}[leftmargin=*,noitemsep]
    \item We show a simple training algorithm that accelerates the training speed and improves the image quality all at the same time.

    \item We show that with the training algorithm, we could match or beat the strong baselines (SNGAN and SAGAN) with plain neural networks (the raw ResNet without spectral normalization or self-attention).
    \item Our algorithm is widely applicable: experimental results show that the algorithm works on from tiny data (CIFAR10) to large data (1000-class ImageNet) using $4$ or less GPUs.
\end{itemize}
\paragraph{Notations} Throughout this paper, we use $G$ to denote the generator network and $D$ to denote the discriminator network (and the parameters therein). $x\in\mathbb{R}^d$ is the data in $d$ dimensional image space; $z$ is Gaussian random variable; $y$ is categorical variable indicating the class id. Subscripts $r$ and $f$ indicates real and fake data. For brevity, we denote the  Euclidean 2-norm as $\|\cdot\|$. $\mathcal{L}$ is the loss function of generator or discriminator depending on the subscripts.
\vspace{-5pt}
\section{Related Work}
\subsection{Efforts to Stabilize GAN Training}\label{GAN_background}
Generative Adversarial Network (GAN) can be seen as a min-max game between two players: the generator ($G$) and discriminator ($D$). At training time, the generator transforms Gaussian noise to synthetic images to fool the discriminator, while the discriminator learns to distinguish the fake data from the real. The loss function is formulated as
\begin{equation} \label{eq:orig_GAN}
\min_G \max_D \mathop{\mathbb{E}}_{x\sim \mathbb{P}_{\text{real}}}\big[\log D(x)\big]+\mathop{\mathbb{E}}_{x \sim G(z)}\Big[\log\big(1-D(x)\big)\Big],
\end{equation}
where $\mathbb{P}_{\text{real}}$ is the distribution of real data, and $z$ is sampled from the standard normal distribution. Our desired solution of \eqref{eq:orig_GAN} is $G(z)=\mathbb{P}_{\text{real}}$ and $D(x)=0.5$; however, in practice we can rarely get this solution. Early findings show that training $G$ and $D$ with multi-layer CNNs on GAN loss~\eqref{eq:orig_GAN} does not always generate sensible images even on simple CIFAR10 data. Researchers tackle this problem by having better theoretical understandings and intensive empirical experiments. Theoretical works mainly focus on the local convergence to the Nash equilibrium~\cite{mescheder2017numerics,mescheder2018training,wang2019solving} by analyzing the eigenvalues of the Jacobian matrix derived from gradient descent ascent (GDA). Intuitively, if the eigenvalues $\lambda_i$ (maybe complex) lie inside the unit sphere , i.e. $\|\lambda_i\| \le 1$, and the ratio $\frac{Im(\lambda_i)}{Re(\lambda_i)}$ is small, then the convergence to local equilibrium will be fast.
\begin{table}[htb]
    \centering
    \caption{\label{tab:compare-GANs}Comparing the key features and training tricks in GAN models. Our model is a simple combination of ResNet backbone and adversarial training. The model size is calculated for the ImageNet task.}
    \begin{tabular}{ccccc}
    \toprule
      & SNGAN & SAGAN & BigGAN & \textbf{FastGAN (ours)} \\
    \midrule
    ResNet $G/D$ & \Yes & \Yes & \Yes & \Yes \\
    Wider $D$ & \No & \Yes & \Yes & \No \\
    Shared embedding \& skip-z & \No & \No & \Yes & \No \\

    Self attention & \No & \Yes & \Yes & \No\\
    TTUR & \No & \Yes & \Yes & \No\\
    Spectral normalization & \Yes & \Yes & \Yes & \No\\
    Orthogonal regularization & \No & \No & \Yes & \No \\
    Adversarial training & \No & \No & \No & \Yes \\
    Model size & 72.0M & 81.5M & 158.3M & 72.0M \\
    \bottomrule
    \end{tabular}
\end{table}
Empirical findings mainly focus on regularization techniques, evolving from weight clipping~\cite{arjovsky2017wasserstein}, gradient norm regularization~\cite{gulrajani2017improved}, spectral normalization~\cite{miyato2018spectral}, regularized optimal transport~\cite{sanjabi2018convergence} and Lipschitz regularization~\cite{zhou2019lipschitz}, among others. Our method could also be categorized as a regularizer~\cite{DBLP:journals/corr/abs-1906-01527}, in which we train the discriminator to be robust to adversarial perturbation on either real or fake images.

\subsection{Follow the Ridge (FR) algorithm}
A recent correction to the traditional gradient descent ascent (GDA) solver for \eqref{eq:orig_GAN} is proposed~\cite{wang2019solving}. Inside FR algorithm, the authors add a second-order term that drags the iterates back to the ``ridge'' of the loss surface $\mathcal{L}(G, D)$, specifically,
\begin{equation}
    \label{eq:FR-iter}
    \begin{aligned}
    w_G &= w_G-\eta_G\nabla_{w_G}\mathcal{L},\\
    w_D &= w_D+\eta_D\nabla_{w_D}\mathcal{L}+\eta_GH^{-1}_{w_Dw_D}H_{w_Dw_G}\nabla_{w_G}\mathcal{L},
    \end{aligned}
\end{equation}
where we denote $\mathcal{L}$ as the GAN loss in \eqref{eq:orig_GAN}, $H_{w_Dw_D}=\frac{\partial^2\mathcal{L}}{\partial w_D^2}$ and $H_{w_Dw_G}=\frac{\partial^2\mathcal{L}}{\partial w_D\partial w_G}$. With the new update rule, the authors show less rotational behavior in training and an improved convergence to the local minimax. The update rule~\eqref{eq:FR-iter} involves Hessian vector products and Hessian inverse. Comparing with the simple GDA updates, this is computationally expensive even by solving a linear system. Interestingly, under certain assumptions, we can see our adversarial training scheme could as an approximation of \eqref{eq:FR-iter} with no overhead. As a result, our FastGAN converges to the local minimax with fewer iterations (and wall-clock time), even though we dropped the bag of tricks (Table~\ref{tab:compare-GANs}) commonly used in GAN training.
\vspace{-5pt}
\subsection{\label{adv_training}Adversarial training}
Adversarial training~\cite{goodfellow2014explaining} is initially proposed to enhance the robustness of the neural network. The central idea is to find the adversarial examples that maximize the loss and feed them as synthetic training data. Instances of which include the FGSM~\cite{goodfellow2014explaining}, PGD~\cite{madry2017towards}, and free adversarial training~\cite{shafahi2019adversarial}. All of them are approximately solving the following problem
\begin{equation} \label{eq:adv_training}
\begin{split}
\min_{w} \frac{1}{N}\sum_{i=1}^N \Big[\max_{\|\epsilon\|\leq c_{\max}}\mathcal{L}(f(x_i+\epsilon;w),y_i)\Big],
\end{split}
\end{equation}
where $\{(x_i,y_i)\}_{i\in[N]}$ are the training data; $f$ is the model parameterized by $w$; $\mathcal{L}$ is the loss function; $\epsilon$ is the perturbation with norm constraint $c_{\max}$. It is nontrivial to solve \eqref{eq:adv_training} efficiently, previous methods solve it with alternative gradient descent-ascent: on each training batch $(x_i, y_i)$, they launch projected gradient ascent for a few steps and then run one step gradient descent~\cite{madry2017towards}. In this paper, we study how adversarial training helps GAN convergence. To the best of our knowledge, only \cite{zhou2018don} and \cite{liu2019rob} correlate to our idea. However, \cite{zhou2018don} is about unconditional GAN, and none of them can scale to ImageNet. In our experiments, we will include \cite{liu2019rob} as a strong baseline in smaller datasets such as CIFAR10 and subset of ImageNet.
\vspace{-7pt}
\section{FastGAN -- Free AdverSarial Training for GAN}
Our new GAN objective is a three-level min-max-min problem:
\begin{equation} \label{eq:faster-rob-GAN}
    \min_G \max_D \mathop{\mathbb{E}}_{x\sim \mathbb{P}_{\text{real}}}\big[\min_{\|\epsilon\|\le c_{\max}}\log D(x+\epsilon)\big]+\mathop{\mathbb{E}}_{x \sim G(z)}\Big[\min_{\|\epsilon\|\le c_{\max}}\log\big(1-D(x+\epsilon)\big)\Big].
\end{equation}
Note that this objective function is similar to the one proposed in RobGAN~\cite{liu2019rob}. However, RobGAN cannot scale to large datasets due to the bottleneck introduced by adversarial training (see our experimental results), and no satisfactory explanations are provided to justify why adversarial training improves GAN.
We made two improvements: First of all, we employ the recent free adversarial training technique~\cite{shafahi2019adversarial}, which simultaneously updates the adversaries and the model weights. Second, in the label-conditioned GAN setting, we improved the loss function used in RobGAN (both are based on cross-entropy rather than hinge loss).
In the following parts, we first show the connection to follow-the-ridge update rules~\eqref{eq:FR-iter} and then elaborate more on the algorithmic details as well as a better loss function.

\subsection{From \textit{Follow-the-Ridge} (FR) to FastGAN}
Recently \cite{wang2019solving} showed that the Follow-the-Ridge (FR) optimizer improves GAN training, but FR relies on Hessian-vector products and cannot scale to large datasets.
We show that solving \eqref{eq:faster-rob-GAN} by one-step adversarial training can be regarded as an efficient simplification of FR, which partially explains why the proposed algorithm can stabilize and speed up GAN training.
To show this, we first simplify the GAN training problem as follows:
\begin{itemize}[leftmargin=*,noitemsep]
\item Inside the minimax game, we replace the generator network by its output $x_f$. This dramatically simplifies the notations as we no longer need to calculate the gradient through G.
\item The standard GAN loss on fake images~\cite{goodfellow2014generative} is written as $\log\big(1-D(G(z)\big)$. However, we often modify it to $-\log D(G(z))$ to mitigate the gradient vanishing problem.
\end{itemize}
We first consider the original GAN loss:
\begin{equation}
    \label{eq:minmax-problem}
    \min_{G}\max_{D}\mathcal{L}(G, D)
\triangleq\mathop{\mathbb{E}}_{x_r\sim \mathbb{P}_{\text{real}}}\big[\log D(x_r)\big]-\mathop{\mathbb{E}}_{x_f\sim G}\big[\log D(x_f)\big].
\end{equation}
With FR algorithm, the update rule for $D$ (parameterized by $w$) can be written as\footnote{In this equation, we simplify the FR update ($H_{yy}^{-1}$ is dropped). The details are shown in the appendix.}
\begin{equation}
    \label{eq:FR-update}
   w^+ \xleftarrow{\text{FR}} w+\eta_D\Big(\underbrace{\frac{\nabla_wD(x_r)}{D(x_r)}-\frac{\textcolor{blue}{\nabla_w D(x_f)}}{D(x_f)}}_{\text{Gradient ascent}}\Big)-\eta_G\underbrace{H_{wx}\nabla_{x_f}\mathcal{L}(w, x_f)}_{\text{off-the-ridge correction}},
\end{equation}
where $H_{wx}=\frac{\partial^2 \mathcal{L}}{\partial w\partial x_f}$. The last term is regarded as a correction for ``off-the-ridge move'' by $G$-update. If we decompose it further, we will get
\begin{equation}
    \label{eq:FR-correction-term}
    H_{wx}\nabla_{x_f}\mathcal{L}=\frac{\nabla^2_{w, x_f} D(x_f)\nabla_{x_f}D(x_f)}{D(x_f)^2}-\frac{\|\nabla_{x_f} D(x_f)\|^2\textcolor{blue}{\nabla_w D(x_f)}}{D(x_f)^3}.
\end{equation}
Since the first-order term in \eqref{eq:FR-correction-term} is already accounted for in \eqref{eq:FR-update} (both are highlighted in blue), the second-order term in \eqref{eq:FR-correction-term} plays the key role for fast convergence in FR algorithm. However, the algorithm involves Hessian-vector products in each iteration and not very efficient for large data such as ImageNet. Next, we show that our adversarial training could be regarded as a Hessian-free way to perform almost the same functionality. Recall in the adversarial training of GAN, the loss (Eq.~\eqref{eq:faster-rob-GAN}) becomes (fixing $c_{\max}=1$ for brevity)
\begin{equation}
    \label{eq:RobGAN}
    \begin{aligned}
\mathcal{L}^{\text{adv}}(G, D)
&\triangleq\mathop{\mathbb{E}}_{x_r\sim \mathbb{P}_{\text{real}}}\big[\min_{\|\epsilon\|_2\le 1}\log D(x_r+\epsilon)\big]-\mathop{\mathbb{E}}_{x_f\sim G}\max_{\|\epsilon\|_2\le 1}\log D(x_f+\epsilon)\\
&\approx \mathop{\mathbb{E}}_{x_r\sim\mathbb{P}_{\text{real}}}\Big[\log D(x_r)-\frac{\|\nabla_{x_r}D(x_r)\|}{D(x_r)}\Big]-\mathop{\mathbb{E}}_{x_f\sim G}\Big[\log D(x_f)+\frac{\|\nabla_{x_f}D(x_f)\|}{D(x_f)}\Big],
    \end{aligned}
\end{equation}
here we assume the inner minimizer only conducts one gradient descent update (similar to the algorithm proposed in the next section), and use first-order Taylor expansion to approximate the inner minimization problem.
So the gradient descent/ascent updates will be
\begin{equation}
\resizebox{.5\linewidth}{!}{$
    w^+ \xleftarrow{\text{GDA}} w+\eta_D\Big(\underbrace{\frac{\nabla_wD(x_r)}{D(x_r)}-\frac{\nabla_w D(x_f)}{D(x_f)}}_{\text{Gradient ascent}}\Big)-\overrightarrow{\bm{\bigtriangledown}}$.
}
\end{equation}
Here we define $\overrightarrow{\bm{\bigtriangledown}}$ as the gradient correction term introduced by adversarial training, i.e.
\begin{equation}
    \label{eq:gradient-correction-from-ADV}
    \resizebox{.93\linewidth}{!}{$
    \overrightarrow{\bm{\bigtriangledown}}=\sum\limits_{x\in\{x_r, x_f\}}\nabla_w\Big(\frac{\|\nabla_{x}D(x)\|}{D(x)}\Big)=\sum\limits_{x\in\{x_r, x_f\}}\frac{\nabla^2_{w,x}D(x)\nabla_{x}D(x)}{\|\nabla_{x}D(x)\|D(x)}-\frac{\|\nabla_{x}D(x)\|^2\nabla_wD(x)}{D(x)^2}.$}
\end{equation}
Comparing~\eqref{eq:gradient-correction-from-ADV} with~\eqref{eq:FR-correction-term}, we can see both of them are a linear combination of second-order term and first-order term, except that the FR is calculated on fake images $x_f$ while our adversarial training can be done on $x_r$ or $x_f$. The two becomes the same up to a scalar $D(x)$ if 1-Lipschitz constraint is enforced on the discriminator $D$, in which case $\|\nabla_x D(x)\|=1$. The constraint is commonly seen in previous works, including WGAN, WGAN-GP, SNGAN, SAGAN, BigGAN, etc. However, we do not put this constraint explicitly in our FastGAN.

\subsection{Training algorithm}
Our algorithm is described in Algorithm~\ref{alg:fastrobgan}, the main difference from classic GAN training is the extra for-loop inside in updating the discriminator. In each iteration, we do $\texttt{MAX\_ADV\_STEP}$ steps of adversarial training to the discriminator network. Inside the adversarial training loop, the gradients over input images (denoted as $g_x$) and over the discriminator weights (denoted as $g_{w_D}$) can be obtained with just one backpropagation. We train the discriminator with fake data $(x_f, y_f)$ immediately after one adversarial training step. A handy trick is applied by reusing the same fake images (generated at the beginning of each $\texttt{D\_step}$) multiple times -- we found no performance degradation and faster wall-clock time by avoiding some unnecessary propagations through $G$.
\begin{algorithm}[htb]
\DontPrintSemicolon
\SetAlgoLined
\caption{\label{alg:fastrobgan}FastGAN training algorithm}
\KwData{Training set $\{(x_i,y_i)\}$, max perturbation $c_{\max}$, learning rate $\eta$.}
Initialize: generator $G$, discriminator $D$, and perturbation $\epsilon$.\\
\While{not converged}{
  \tcc{Train discriminator for MAX\_D\_STEP steps}
\For{\texttt{D\_step=1..MAX\_D\_STEP}}{
\tcc{Prepare real and fake data, same data will be used multiple times in the nested for-loop.}
$z\gets \mathcal{N}(0, 1)$; $y_f\gets \texttt{Categorical}(1..C)$\\
$x_f \gets G(z, y_f)$ \\
$x_r, y_r \gets \texttt{sample}(\{x_i,y_i\}, i\in[N])$ \\

\For{\texttt{adv\_step=1..MAX\_ADV\_STEP}}{
\tcc{Conduct free adversarial training on real data}
$g_{x},\ g_{w_D} \gets \nabla_{x_{r}} \mathcal{L}\big(D(x_r+\epsilon),y_r\big)$, $\nabla_{w_D} \mathcal{L}\big(D(x_r+\epsilon),y_r\big)$\\
$w_D \gets w_D + \eta \cdot g_{w_D}$\\
$\epsilon \gets \proj_{\|\cdot\|\le c_{\max}}\big(\epsilon - c_{\max} \cdot \sign(g_{x})\big)$\\
\tcc{Reuse the fake data during adversarial training for best speed}
$g_{w_D}\gets \nabla_{w_D}\mathcal{L}\big(D(x_f), y_f)\big)$\\
$w_D\gets w_D+\eta\cdot g_{w_D}$\\
}
}
\tcc{Train generator for one step}
$z\gets \mathcal{N}(0, 1)$; $y_f\gets \texttt{Categorical}(1..C)$\\
$x_{f} \gets G(z, y_f)$ \\
$g_{w_G} \gets \nabla_{w_G}\mathcal{L}\big(D(x_f),y_f\big)$ \\
$w_G \gets w_G - \eta \cdot g_{w_G}$
}
\end{algorithm}

Contrary to the common beliefs~\cite{heusel2017gans,zhang2018self} that it would be beneficial (for stability, performance and convergence rate, etc.) to have different learning rates for generator and discriminator, our empirical results show that once the discriminator undergoes robust training, it is no longer necessary to tune learning rates for two networks.
\subsection{\label{sec:improved_objective_loss}Improved loss function}
Projection-based loss function~\cite{miyato2018cgans} is dominating current state-of-the-art GANs (e.g., \cite{wu2019logan,brock2018large}) considering the stability in training; nevertheless, it does not imply that traditional cross-entropy loss is an inferior choice. In parallel to other works~\cite{gong2019twin}, we believe ACGAN loss can be as good as projection loss after slight modifications. First of all, consider the objective of discriminator $\max_D \mathcal{L}_D\coloneqq\mathcal{L}_D^r+\mathcal{L}_D^f$ where $\mathcal{L}_D^r$ and $\mathcal{L}_D^f$ are the likelihoods on real and fake minibatch, respectively. For instance, in ACGAN we have
\begin{equation}
    \label{eq:AC-GAN-loss}
        \mathcal{L}_D^r=\mathbb{E}_{(x_r, y_r)\sim \mathbb{P}_{\text{real}}}\big[\log \Pr\big(\text{real}\wedge y_r|D(x_r)\big)\big],   \ \ \
        \mathcal{L}_D^f=\mathbb{E}_{(x_f, y_f)\sim G}\big[\log \Pr\big(\text{fake}\wedge y_f|D(x_f)\big)\big].

\end{equation}
We remark that in the class-conditioning case, the discriminator contains two output branches: one is for binary classification of real or fake, the other is for multi-class classification of different class labels. The log-likelihood should be interpreted under the joint distribution of the two. However, as pointed out in~\cite{gong2019twin,liu2019rob}, the loss~\eqref{eq:AC-GAN-loss} encourages a degenerate solution featuring a mode collapse behavior. The solution of TwinGAN~\cite{gong2019twin} is to incorporate another classifier (namely ``twin-classifier'') to help generator $G$ promoting its diversity; while RobGAN~\cite{liu2019rob} removes the classification branch on fake data:
\begin{equation}
    \label{eq:RobGAN-loss}
    \mathcal{L}_D^f=\mathbb{E}_{(x_f,y_f)\sim G}\big[\log \Pr\big(\text{fake}|D(x_f)\big)\big].
\end{equation}

Overall, our FastGAN uses a similar loss function as RobGAN~\eqref{eq:RobGAN-loss}, except we changed the adversarial part from probability to hinge loss~\cite{lim2017geometric,miyato2018spectral}, which reduces the gradient vanishing and instability problem. As to the class-conditional branch, FastGAN inherits the auxiliary classifier from ACGAN as it is more suitable for adversarial training. However, as reported in prior works~\cite{odena2017conditional,miyato2018cgans}, training a GAN with auxiliary classification loss
has no good intra-class diversity. To tackle this problem, we added a KL term to $\mathcal{L}_D^f$. Therefore, $\mathcal{L}_D^r$ and $\mathcal{L}_D^f$ become:
\begin{equation}
    \label{eq:FastGAN-Ld}
    \begin{aligned}
    &\mathcal{L}_D^r=\mathbb{E}_{(x_r,y_r)\sim \mathbb{P}_\text{real}}\big[-\max\big(0, 1-D(x_r)\big)+\Pr\big(y_r|D(x_r)\big)\big],\\
    &\mathcal{L}_D^f=\mathbb{E}_{(x_f,y_f)\sim G}\big[-\max\big(0, 1+D(x_f)\big)\big]-\alpha_c^f\cdot\mathsf{KL}(\Pr(y_f|D(x_f)), \mathcal{U})\big].
    \end{aligned}
\end{equation}
where $\alpha_c^f\in(0, 1)$ is a coefficient, $\mathcal{U}=(1/C, 1/C, \dots, 1/C)^T$ is a uniform categorical distribution among all $C$-classes. Our loss~\eqref{eq:FastGAN-Ld} is in sharp difference to ACGAN loss in~\eqref{eq:AC-GAN-loss}: in ACGAN, the discriminator gets rewarded by assigning high probability to $y_f$; while our FastGAN is encouraged to assign a uniform probability to all labels in order to enhance the intra-class diversity of the generated samples. Additionally, we found that it is worth to add another factor $\alpha_c^g$ to $\mathcal{L}_G$ to balance between image diversity and fidelity, so the generator loss in FastGAN is defined as
\begin{equation}
    \label{eq:FastGAN-Lg}
     \mathcal{L}_G = \mathbb{E}_{(x_f,y_f)\sim G}\big[-\mathop{\mathbb{E}}[ D(x_f)] - \alpha_c^g\cdot\log \Pr(y_f|D(x_f))\big].
\end{equation}
Overall, in the improved objectives of FastGAN, $G$ is trained to minimize  $\mathcal{L}_G$ while $D$ is trained to maximize $\mathcal{L}_D^r+\mathcal{L}_D^f$.
\vspace{-5pt}
\section{\label{experiments}Experiments}
\vspace{-5pt}
In this section, we test the performance of FastGAN on a variety of datasets. For the baselines, we choose SNGAN~\cite{miyato2018spectral} with projection discriminator, RobGAN~\cite{liu2019rob}, and SAGAN~\cite{zhang2018self}. Although better GAN models could be found, such as the BigGAN~\cite{brock2018large} and the LOGAN~\cite{wu2019logan}, we do not include them because they require large batch training (batch size~$>$~1k) on much larger networks (see Table~\ref{tab:compare-GANs} for model sizes).

\textbf{Datasets.}\quad We test on following datasets: CIFAR10, CIFAR100~\cite{krizhevsky2009learning}, ImageNet-143~\cite{miyato2018spectral,liu2019rob}, and the full ImageNet~\cite{russakovsky2015imagenet}. Notablly the ImageNet-143 dataset is an $143$-class subset of ImageNet~\cite{russakovsky2015imagenet}, first seen in SNGAN~\cite{miyato2018spectral}. We use both 64x64 and 128x128 resolutions in our expriments.

\textbf{Choice of architecture.}\quad As our focus is not on architectural innovations, for a fair comparison, we did the layer-by-layer copy of the ResNet backbone from SNGAN (spectral normalization layers are removed). So our FastGAN, SNGAN, and RobGAN are directly comparable, whereas SAGAN is bigger in model size. For experiments on CIFAR, we follow the architecture in WGAN-GP~\cite{gulrajani2017improved}, which is also used in SNGAN~\cite{miyato2018spectral,miyato2018cgans}.

\textbf{Optimizer.}\quad We use Adam~\cite{kingma2014adam} with learning rate $\eta_0=0.0002$ and momentum $\beta_1=0.0$, $\beta_2=0.9$ (CIFAR/ImageNet-143) or $\beta_2=0.99$ (ImageNet) for both $D$ and $G$. We use exponential decaying learning rate scheduler: $\eta_t = \eta_0 \cdot e^{t/\kappa }$
where $t$ is the iteration number.

Other hyperparameters are attached in appendix.
\subsection{CIFAR10 and CIFAR100}
We report the experimental results in Table~\ref{tab:cifar-exp}. We remind that SAGAN does not contain official results on CIFAR, so we exclude it form this experiment. The metrics are measured after all GANs stop improving, which took $\sim 2.5\times 10^4$ seconds. As we can see, our FastGAN is better than SNGAN and RobGAN at CIFAR dataset in terms of both IS score and FID score. Furthermore, to compare the convergence speed, we exhibit the learning curves of all results in Figure~\ref{fig:conv_cifar}. From this figure, we can observe a consistent acceleration effect from FastGAN.
\begin{table}[htb]
  \caption{\label{tab:cifar-exp}Results on CIFAR10 and CIFAR100. IS indicates the inception score (higher better), FID is the Fréchet Inception Distance (lower better). Time is measured in seconds. Wining results are displayed in \textbf{bold}, collapsed results are displayed in \textcolor{cyan}{blue}.}
  \centering
  \scalebox{0.9}{
  \begin{tabular}{lcccccc}
    \toprule
    & \multicolumn{3}{c}{CIFAR10} & \multicolumn{3}{c}{CIFAR100}  \\
     \cmidrule(r){2-4} \cmidrule(l){5-7}
         & IS $\uparrow$ & FID $\downarrow$ & Time $\downarrow$  & IS $\uparrow$ & FID $\downarrow$ & Time $\downarrow$ \\
    \midrule
    Real data & $11.24\pm.10$ & $5.30$ & -- & $14.79\pm .15$ & $5.91$ & -- \\
    \midrule
    SNGAN & $7.47\pm.13$ & $14.59$ & $2.45\times 10^4$ & $7.86\pm .21$ & $18.29$  & $2.56\times 10^4$ \\
    RobGAN & $6.95\pm .06$ & $20.75$ & $2.50\times 10^4$ & $7.24\pm.21$  & $28.27$ & $2.6\times 10^4$ \\
    FastGAN &  $\bm{7.76\pm .12}$ &  $\bm{12.97}$ &  $\bm{2.28\times 10^4}$ &  $\bm{8.87\pm .06}$ & $\bm{17.27}$  & $\bm{2.31\times 10^4}$\\
    \bottomrule
    + Revert to RobGAN loss & $7.14\pm.18$  &  $20.90$  & $2.28\times 10^4$  & $\textcolor{cyan}{6.26\pm .19}$  & $\textcolor{cyan}{43.69}$   & $\textcolor{cyan}{2.31\times 10^4}$\\
    + Disable adv. training &  $7.79\pm .12$  & $13.93$  &  $2.28\times 10^4$ & $8.38\pm .21$   & $19.43$  & $2.31\times 10^4$\\
    + Constant lr. &   $\bm{8.09\pm.11}$ &  $14.67$ &  $2.28\times 10^4$  &  $\bm{8.95\pm.31}$ & $19.53$ & $2.31\times 10^4$\\
    + Disable KL-term~\eqref{eq:FastGAN-Ld} &  $7.37\pm .17$ & $16.26$  &  $2.28\times 10^4$ & $\textcolor{cyan}{7.42\pm.18}$  & $\textcolor{cyan}{24.02}$ & $\textcolor{cyan}{2.31\times 10^4}$\\
    \bottomrule
  \end{tabular}}
\end{table}
\begin{figure}
\begin{center}
\includegraphics[width=1.0\linewidth]{figs/cifar.pdf}
\end{center}
\vspace{-7pt}
   \caption{Comparing the convergence of GANs on CIFAR10 (a, b) and CIFAR100 (c, d) datasets.}
\label{fig:conv_cifar}
\end{figure}
\par
Next, we study which parts of our FastGAN attribute to performance improvement. To this end, we disable some essential modifications of FastGAN (last four rows in Table~\ref{tab:cifar-exp}). We also try different $\alpha_c^g$ in the experiments of CIFAR100 in Figure~\ref{fig:alpha_cg} and show it effectively controls the tradeoff between diversity and fidelity.
\begin{figure}[htb]
    \centering
    \includegraphics[width=0.9\linewidth]{./figs/alpha_g.pdf}
    \caption{The effect of $\alpha_c^g$ on the diversity - fidelity tradeoff in CIFAR100 experiments. Specifically in the last row, when $\alpha_c^g\in [0.8, 1.0]$ the distributions are unimodal (roses all in the same color: either dark red or peach); when $\alpha_c^g\in [0.2, 0.4]$, distributions are multimodal so we could see roses of more colors. If the coefficient is as small as $0.1$, the distributions become more complex, but we could no longer recognize the images.}
    \label{fig:alpha_cg}
\end{figure}
\subsection{ImageNet-143}
ImageNet-143 is a dataset first appeared in SNGAN. As a test suite, the distribution complexity stands between CIFAR and full ImageNet as it has 143 classes and 64x64 or 128x128 pixels. We perform experiments on this dataset in a similar way as in CIFAR. The experimental results are shown in Table~\ref{Tab:imgnet143gq}, in which we can see FastGAN overtakes SNGAN and RobGAN in both IS and FID metrics. As to the convergence rate, as shown in Figure~\ref{fig:conv_catdog}, FastGAN often requires $2{\sim}3$ times less training time compared with SNGAN and RobGAN to achieve the same scores.
 \begin{table}[htb]
  \caption{Results on ImageNet-143.}
  \label{Tab:imgnet143gq}
  \centering
  \resizebox{0.95\textwidth}{!}{
  \begin{tabular}{lcccccc}
    \toprule
    & \multicolumn{3}{c}{64x64 pixels} & \multicolumn{3}{c}{128x128 pixels}  \\
    \cmidrule(r){2-4}\cmidrule(l){5-7}
 & IS $\uparrow$ & FID $\downarrow$ & Time $\downarrow$  & IS $\uparrow$ & FID $\downarrow$ & Time $\downarrow$ \\
    \midrule
Real data & $27.9\pm 0.42$  & $0.48$ & -- & $53.01\pm 0.56$  & $0.42$ & -- \\
\midrule
SNGAN & $10.70\pm 0.17$ &  $29.70$ & $2.2\times 10^5$ & $26.12\pm 0.29$ &  $30.83$ & $5.9\times 10^5$ \\
RobGAN & $24.62\pm0.31$ & $14.64$ & $1.8\times 10^5$ & $33.81\pm 0.47$ & $33.98$ & $1.8\times 10^5$ \\
FastGAN & $22.23\pm 0.35$ & $\bm{12.04}$  &  $\bm{3.6\times 10^4}$ & $40.41\pm 0.49$ &  $\bm{14.48}$ & $\bm{7.1\times 10^4}$ \\
\midrule
+ Revert to RobGAN loss & $25.49\pm0.29$ &  $14.03$ &  $3.6\times 10^4$ & $\bm{45.94\pm0.52}$ &  $25.38$ & $7.1\times 10^4$\\
+ Disable KL-term~\eqref{eq:FastGAN-Ld} & $\bm{25.61\pm0.47}$ &  $13.94$ &  $3.6\times 10^4$ & $42.54\pm 0.40$ &  $17.51$ & $7.1\times 10^4$ \\
    \bottomrule
  \end{tabular}
  }
\end{table}
\begin{figure}[htb]
\begin{center}
\includegraphics[width=1.0\linewidth]{figs/catdog.pdf}
\end{center}
\vspace{-7pt}
   \caption{Comparing the convergence of GANs on 64x64 resolution (in (a) and (b)) and 128x128 resolution (in (c) and (d)) of the ImageNet-143 dataset.}
\label{fig:conv_catdog}
\end{figure}
\subsection{ImageNet}
\begin{table}[h!]
  \caption{Results on full ImageNet.}
  \label{Tab:imgnetgq}
  \centering
  \scalebox{0.83}{
  \begin{tabular}{lccc|lccc}
    \toprule
         & IS $\uparrow$ & FID $\downarrow$ & Time $\downarrow$ &  & IS $\uparrow$ & FID $\downarrow$ & Time $\downarrow$ \\
    \midrule
Real data & $217.43\pm 5.17$  & $1.55$ & -- & & & \\
\midrule
\multicolumn{4}{l}{\textit{Trained with batch size $=64$}} & \multicolumn{4}{l}{\textit{Trained with batch size $=256$}}\\
SNGAN & $36.8$  &  $27.62$ & $2.98\times 10^6$ & SAGAN & $52.52$ &  \bm{$18.65$} & $1.79\times 10^6$ \\
FastGAN & \bm{$51.43\pm 2.51$} & \bm{$22.60$}  &  \bm{$1.34\times 10^6$} &  FastGAN & \bm{$65.57\pm2.65$} &  $19.41$ &  \bm{$1.10\times 10^6$}\\
    \bottomrule
  \end{tabular}}
\end{table}
In this experiment, we set SNGAN and SAGAN as the baselines. RobGAN is not included because no ImageNet experiment is seen in the original paper, nor can we scale RobGAN to ImageNet with the official implementation. The images are scaled and cropped to 128x128 pixels. A notable fact is that SNGAN is trained with batch size $=64$, while SAGAN is trained with batch size $=256$. To make a fair comparison, we train our FastGAN with both batch sizes and compare them with the corresponding official results. From Table~\ref{Tab:imgnetgq}, we can generally find the FastGAN better at both metrics, only the FID score is slightly worse than SAGAN. Considering that our model contains no self-attention block, the FastGAN is $13\%$ smaller and $57\%$ faster than SAGAN in training.
\vspace{-10pt}
\section{Conclusion}
\vspace{-10pt}
In this work, we propose the FastGAN, which incorporates free adversarial training strategies to reduce the overall training time of GAN. Furthermore, we further modify the loss function to improve generation quality. We test FastGAN from small to large scale datasets and compare it with strong prior works. Our FastGAN demonstrates better generation quality with faster convergence speed in most cases.

redpajama_set_name: RedPajamaArXiv


Equipoise® is the popularly referenced brand name for the veterinary injectable steroid boldenone undecylenate. Specifically it is a derivative of testosterone, which exhibits strong anabolic and moderately androgenic properties. The undecylenate ester greatly extends the activity of the drug (the undecylenate ester is only one carbon atom longer than decanoate), so that clinically injections would need to be repeated every three or four weeks. In veterinary medicine Equipoise© is most commonly used on horses, exhibiting a pronounced effect on lean bodyweight, appetite and general disposition of the animal. This compound is also said to shows a marked ability for increasing red blood cell production, although there should be no confusion that this is an effect characteristic of newly all anabolic/androgenic steroids. The favorable properties of this drug are greatly appreciated by athletes, Equipoise® being a very popular injectable in recent years. It is considered by many to be a stronger, slightly more androgenic Deca-Durabolin®. It is generally cheaper, and could replace Deca in most cycles without greatly changing the end result.
The side effects associated with Equipoise® are generally mild. The structure of boldenone does allow it to convert into estrogen, but it does not have an extremely high affinity to do so. To try and quantify this we can look toward aromatization studies, which suggest that its rate of estrogen conversion should be roughly half that of testosterone's. The tendency to develop a noticeable amount of water retention with this drug would therefore be slightly higher than that with Deca-DurabolinO (with an estimated 20°/a conversion), but much less than what would be expected with a stronger agent such as Testosterone. While one does still have a chance of encountering an estrogen related side effect as such when using this substance, it is not a common problem when taken at a moderate dosage level. Gynecomastia might theoretically become a concern, but is usually only heaved of with very sensitive individuals or (again) those venturing high in dosage. Should estrogenic effects become troublesome, the addition of Nolvadex® and/or Proviron® should of course make the cycle more tolerable. An antiaromatase such as Cytadren® or Arimidex® would be stronger options, however probably not indicated with a mild drug as such.
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text:
Kayhan Kalhor: The Wind
By BUDD KOPMAN
In an exquisite moment on Kayhan Kalhor's last recorded collaboration, The Rain (ECM, 2003), tabla player Sandeep Das hits an "out" note on his tuned drum. This momentarily shocks Kalhor and sitarist Shujaat Khan, but soon brings a smiling murmur when he repeats it as if to say, "I meant that." The two soloists, Kalhor (from Iran) and Khan (from Northern India) had been blending their related but distinct musical traditions, battling while creating long, mystical and spiritual waves of sound. This one note caused the men to step back, before they plunged forward again.
The Wind is Kalhor's second musical journey outside of his native land, this time to Turkey to meet and learn from the premier baglama (saz) master, Erdal Erzincan. Kalhor plays kamanche (Iranian spike fiddle), which is usually bowed, and thus contrasts with the plucked sounds of both the sitar and oud-like baglama. Kalhor explains, as the proactive member of the project, that while he wishes to learn a new tradition, he also desires to bring a new aesthetic to his partner. Contemporary Turkish music tends to be based on song and vocals and thus is more limited than what Kalhor wanted to do or Erzincan was used to. However, Erzincan did not hesitate to jump whole-heartedly into the void, resulting in this exquisite record.
Made up of twelve unnamed "Parts" that run into each other, The Wind is really one long improvisation that rises and falls, inhales and exhales as Kalhor and Erzincan become one musician with one mind. Kalhor explains that he told Erzincan, "I'm looking for something that departs from nothing and then goes into developing material and then goes into something else really improvised. Maybe we'll go for a climax in terms of melody and energy and keep it there.... And I'm looking at this for a form for maybe an hour of music."
As with "The Rain," Kalhor seems to follow as much as he leads, which only means that his partners took his desire for a new form to heart. Indeed, Erzincan sounds on fire and totally at ease. Not only did he respond to Kalhor's statement of purpose with, "I haven't done that before, but I would like to do this," but he rose to the occasion, displaying not only virtuosity in the uncommon finger-style that he developed, but a command of the long improvised form that "surprised and delighted" Kalhor.
The record feels like one giant meditation, and an extremely strong feeling of tension is held for a long time, which might strain some listeners' attentive abilities. However, if you can let go and allow the music to wash over you, the hour will fly by.
Part I; Part II; Part III; Part IV; Part V; Part VI; Part VII; Part VIII; Part IX; Part X; Part XI; Part XII.
Kayhan Kalhor: kamancheh (Iranian spike fiddle); Erdal Erzincan: baglama (Turkish saz); Ulaş Özdemir: divan baglama (Turkish bass saz).
Title: The Wind | Year Released: 2006 | Record Label: ECM Records
About Kayhan Kalhor
Kayhan Kalhor CD/LP/Track Review Budd Kopman ECM Records United States The Wind
Nathan Sanders


While NTU has many partner universities, there are other universities appealing to students that do not have an agreement with NTU; or, the agreement quota of some of our partner universities is simply not enough to meet the demand. In light of this situation, the Visiting Student Program is introduced.
The Visiting Program is a fee-paying program that requires students to apply either through the Exchange Student Internal Screening Process or directly to the university. Students will study at the desired universities for one semester or one academic year as a short-term/non-degree-seeking student. The right and obligation of a visiting student is, in most ways, the same as an exchange student except for the tuition fee. Visiting students will have to pay the tuition fee of both NTU and host university.
Other than our partner universities, there are students who have successfully applied to top-notch universities like Harvard University and Stanford University through the Visiting Student Program, providing students with more options.
Application Process: Add the universities that provide visiting student programs to the wish list of Exchange Student Internal Screening Process and submit it.
Note: For both exchange and visiting program: upon being nominated through the Exchange Student Internal Screening Process, students are eligible to apply for scholarships provided by NTU.
Application Process: Students can reach out to the foreign university on their own and apply for the respective visiting program within the application period. After receiving the admission letter, please submit the letter to the OIA and we will assist with the administrative process.
Attention: Fees for each program are charged according to Guidelines for NTU Students Studying Abroad (in Chinese only).


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Fleming, Ian
Book - 1961 (1983 rinting)
"The girl looked him up and down. He had dark, rather cruel good looks and very clear, blue-grey eyes. He was wearing a very dark-blue lightweight single-breasted suit over a cream silk shirt and a black knitted silk tie. Despite the heat, he looked cool and clean. 'And who might you be?' she asked sharply. 'My name's Bond, James Bond ...'" When a stranger arrives in the Bahamas, the locals barely turn their heads, seeing another ex-pat with money to burn at the casino tables. But James Bond has more than money on his mind: he's got less than a week to find two stolen atom bombs hidden among the coral reefs. While acting the playboy, Bond meets Domino, sultry plaything of secretive treasure hunter Emilio Largo. In getting close to this gorgeous Italian girl, Bond hopes to learn more about Largo's hidden operation ...
Publisher: London : Jonathan Cape, 1961 (1983 printing)
Alternative Title: Thunder ball
Read more reviews of Thunderball at iDreamBooks.com
StarGladiator Aug 07, 2017
When I was very young and these books first came out, I would borrow them or sneak them from the Jesuit brothers at the Catholic orphanage I grew up in and was always tremendously impressed with Fleming's biographical profiles of the bad guys - - especially Blofeld in this book, Thunderball. Really superb! I assumed years later Fleming had picked up much from his military service and days in journalism, but then later still I learned that he came from a really rich banker family [their Flemings Bank would be sold for $7 billion to Chase in 2000], and there were probably a lot more conduits of information flowing to Ian Fleming. [His ancestor originally made their fortune during the American Civil War {they were and still are British} when they sold sandbags to the armies during that war.]
Bond, James — (Fictitious Character) — Fiction.
Intelligence Service — Great Britain — Fiction.


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World premiere at boot 2019
The new J/99 is the latest addition to the J/Sport series and combines headroom and comfortable interior with the responsiveness of a sports boat.
The sail and deck plan of the J/99 is optimized for easy handling with the smallest crew and takes into account the latest developments of the award-winning J/121 and the new offshore sailing world champion, the J/112E. Equipped with either single or double rudder, the J/99 offers the best sailing characteristics and is easy to manoeuvre.
"The J/99 opens up a wide range of sailing opportunities," comments designer Alan Johnstone. "The versatile sailing plan, balanced hull shape and efficient cockpit will be suitable for both short distance offshore sailing and weekend cruising with friends. The J/99 combines power and versatility in a manageable size and budget".
More information about J/Boats in the boot Düsseldorf exhibitor database
J/99 in pictures
For regatta use and family cruises
J/99 is the result of in-depth studies based on the vast experience of J/Boats and J/Composites. It is just as suitable for use in regattas as it is for a leisurely trip with the family. The interior has two aft cabins, a forward facing navigation corner, an L-shaped galley and a small forward bath. With its two double-bed aft cabins, it offers a comfortable and highly functional layout with sufficient headroom and, thanks to its kitchen and bathroom, is also suitable for multi-day trips.
Length over all: 9,97 m
Draught: 1,99 m
Displacement: 3,800 kg
Engine power: 19 HP
More sailing yachts up to 10m...
Astus 22.5 - Premiere at boot 2020
Astus 22.5
Following the great success of the Astus 16.5 and Astus 20.5, Astus is expanding its product range. Premiere at boot 2020.
All about sailing
Discover all about sailing: news, yachts & trends!


This lovely button is made from Czech glass. The button has a pretty blue swirl design around a small metallic gold flower. The back of the glass is coated with a pretty metallic AB finish which changes colour as you move it in the light. The back has a metal shank. This pretty button will make a lovely addition to any project.
Please Note – Products may appear bigger due to close up photography. It is advisable to double check the size in the listing. Colours may vary due to different monitors and ambient lighting.


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Podcast: How Can We Change Corporate Policies That Exploit?
Ep. 20 Changing culture by changing major companies.
We live in an age where many consumers demand transparency from companies they trust. We want to know the working conditions of those who made our clothing, and if pesticides were used to grow our food.
Now, thanks to The Dirty Dozen List, we can see which well-known entities promote and profit from forms of sexual exploitation.
The Dirty Dozen List is an activism tool that has instigated tremendous changes, leading to policy improvements at Google, Hilton Worldwide, Verizon, Walmart, and the Department of Defense.
This episode of the "Sexploitation?" podcast gives you a behind-the-scene look at the origin and momentum of the Dirty Dozen List.
1) SUBSCRIBE to our podcast. It's available on iTunes and on Google Play, and this episode is available for direct download.
2) DOWNLOAD the existing episodes.
3) RATE it (we'd love 5 stars!).
4) SHARE this with your friends, networks, social media, etc.
Haley McNamara (Halverson)
Vice President & Director, International Centre on Sexual Exploitation
Haley McNamara (formerly Halverson) is the Director of the International Centre on Sexual Exploitation in the UK, and a Vice President at the U.S. based National Center on Sexual Exploitation. She leads international efforts and joint campaigns to improve policies and education among global governing bodies, citizenry, and corporations regarding the full web of sexual exploitation issues. Her advocacy work has contributed to policy improvements in social media, online advertising, retail, and hotel industries. She has advocated at the United Nations, led international coalition campaigns, presented to Danish, Croatian and Rwandan government officials, and more
She is a former member of the Washington DC Mayor's Advisory Committee on Child Abuse and Neglect. This Committee advises DC Mayor Muriel Bowser on the multi-faceted continuum of the District of Columbia's child welfare services, including prevention, early intervention, treatment, and sources of permanency.
Haley regularly speaks and writes on topics including child sexual abuse, sex trafficking, prostitution, sexual objectification, the exploitation of males, and more. She has presented before officials at the United Nations, as well as at several national symposia before influencers from the Department of Justice, Department of Health and Human Services, the Federal Bureau of Investigation, and Croatian government officials. She has provided training to Arlington County Child & Family Services on the social media grooming, recruitment, and advertising for sex trafficking. She has a Master of Arts in Government from Johns Hopkins University where she received honors for her thesis regarding the online commercial sexual exploitation marketplace.
Previously, Haley served for two years as Director of Communications for the National Center on Sexual Exploitation where she oversaw strategic messaging development, press outreach, email marketing, and social media marketing.
Prior to working at NCOSE, Haley wrote for a cultural media outlet. Haley graduated from Hillsdale College (summa cum laude) with a double major, and conducted a senior thesis on the abolitionist argument regarding prostitution. During her studies, she studied abroad at Oxford University and established a background in policy research through several internships in the DC area.
Haley has appeared on, or been quoted in, several outlets including the New York Times, NBC's The Today Show, BBC News, New York Post, USA Today, Chicago Tribune, Fox News, San Francisco Chronicle, the Washington Post, Yahoo News, Voice of America, Dr. Drew Midday Live, The DeMaio Report, the New York Daily News, the Washington Examiner, USA Radio Network, the Washington Times, CBC News, The Rod Arquette Show, The Detroit News, Lifezette, The Christian Post, Lifeline with Neil Boron, EWTN News Nightly, KCBS San Francisco Radio, LifeSiteNews, The Drew Mariano Show on Relevant Radio, News Talk KGVO, and American Family News.
She has written op-eds for the Washington Post, the Huffington Post, FoxNews.com, Washington Examiner, Townhall.com, Darling Magazine, the Daytona-Beach News Journal, and has been published in the Journal of Internet Law and the journal Dignity: A Journal on Sexual Exploitation and Violence. She has also contributed to a digital middle school curriculum regarding the links between sex trafficking and pornography as well as the public health impacts of sex trafficking.
VICTORY: TikTok's New Privacy and Safety Features for Children
Rose Kalemba and Pornhub: How MindGeek Exploited the Sexual Assault of a 14-Year-Old
Pornhub Falling: Visa and Mastercard Cut Ties with MindGeek's Cash Cow
corporations | Dirty Dozen List | podcast | Sexploitation
Learn more about this related project:
Lawsuit Taking on the State of Nevada for Enabling Slavery Through Brothels
Netflix Promotes Sexualization of Children, Racist Stereotypes with New Film 'Cuties'
7th Grade Girl Exposed to Porn on School Device
Tweets by @Porn_Harms


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Life in the Colonies
The New England colonies, including Connecticut, Massachusetts, New Hampshire, Rhode Island, and Vermont, were among the earliest colonies settled but life there wasn't easy. Even today, New Englanders are known for their tough, no-nonsense attitudes. If you were an early colonist in New England, you'd need such an attitude to survive. New England's poor soil and cold climate made farming hard. Many farmers kept dairy cows because crops didn't grow well. Farmers moved into towns to learn trades. They became carpenters, dressmakers, cobblers and tradesmen.
New Englanders also made their livelihood on the sea. Towns, such as Boston, grew and became prosperous because of shipbuilding, fishing, whaling, and trading.
Life in the Colonies- Image of King Charles II
The Middle Colonies
Further south, colonists in New York, New Jersey, Delaware, and Pennsylvania enjoyed greater prosperity. These colonies were known as the "bread colonies" because the mild climate and rich soil was good for farming. The food grown in these colonies helped feed all the colonies.
New York was originally settled by the Dutch. In 1664, King Charles II gave a large portion of land to his brother James. Included in this land was New Netherland, now known as New York. James sent three warships to New Amsterdam's harbor and demanded that the city surrender.
Charles II gave William Penn a colony as payment for a debt. Penn named this colony Pennsylvania. William Penn belonged to the Society of Friends, or Quakers, a religious group that was persecuted in England. He called for religious freedom for all in his colony. He paid Indians for their land and tried to treat them fairly. His city, Philadelphia, was known as one of the most modern and beautiful cities in the colonies.
Most people in the South lived on small farms, but the large plantations wielded enormous power. Plantations were like small villages. Each had its own blacksmith shop, church, and pottery kiln. Wealthy plantation owners had political and military power. Tobacco was a major crop, as was the indigo plant, which was used to make blue dye.
These large plantations needed cheap labor to be successful. Originally, plantation owners brought over poor Europeans. The plantation owners paid for the workers' travel expenses. Once in America, the indentured servants agreed to work for a certain period of time – typically seven years. After their debt was paid, they were given land, money, and freedom.
Declan, Tobin. " Fun Facts about Colonial America for Kids - ." American History for Kids, Jan 2021. Web. 20 Jan 2021. < https://www.americanhistoryforkids.com/life-in-the-colonies/ >.
Tobin, Declan. (2021). Fun Facts about Colonial America for Kids -. American History for Kids. Retrieved from https://www.americanhistoryforkids.com/life-in-the-colonies/


Tales of Shiva - Amar Chitra Katha Comics by iRemedi Corp.
From his snowy abode on Mount Kailasa Lord Shiva travelled far and wide, answering the prayers of his devotees. But he was often in disguise to ensure that his favours went only to the deserving. And so it was that he battled with the dauntless Pandava Arjuna, cast his net into the sea as he mingled with humble fisherfolk, and emerged from the sand to kick aside even the mighty Yama, god of death.


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Home > Indian > Is CAB cherry-picking of people in distress?
Is CAB cherry-picking of people in distress?
Citizenship (Amendment) Bill (CAB) in House likely next week
New Delhi, November 30: The big question is – is CAB cherry-picking of people in distress? After dealing with the state of Jammu and Kashmir and Article 370 apparently to restore rights of Kashmiris, Home Minister Amit Shah and Prime Minister Narendra Modi now seem set to fulfil another election promise they have twice made in 2014 and again in 2019 – that of amending the Citizenship Act 1955.
Narendra Modi's government worked on the Bill in its previous term and got it through the Lok Sabha but did not manage to get it though the upper house, the Rajya Sabha.
The Bill was passed in the Lok Sabha on 8 January 2019, after it was introduced by Home Minister Rajnath Singh. After the Lok Sabha's term ended on 3 June 2019 having been dissolved for 2019 election, that bill stands now lapsed and the work has to start afresh.
And if all indications are accurate, the controversial Citizenship (Amendment) Bill, or CAB, is likely to be placed before Parliament soon, senior government officials aware of the development have said.
CAB is controversial as it looks like cherry-picking of people in distress and proposes to provide Indian citizenship to members of religious minorities except Muslims, from three Muslim-dominated countries – Afghanistan, Bangladesh and Pakistan.
Speaking on the condition of anonymity, some officials in the Home Ministry have said that the Union home minister Amit Shah is holding a series of meetings with chief ministers of North Eastern states and leaders of social and cultural bodies, students' organisations and political parties starting on Friday on plans to amend the Citizenship Act.
CAB seeks to amend the Citizenship Act, 1955, in order to fast-track grant of Indian nationality to religious minorities – Hindus, Sikhs, Buddhists, Jains, Parsis and Christians — from Afghanistan, Bangladesh and Pakistan.
"This was an electoral promise made by the Bharatiya Janata Party in the 2014 and the 2019 Lok Sabha polls and they need to deliver on that", says a friend of the BJP living in Melbourne.
CAB faces widespread opposition, especially in n the northeast. There have been protests in Tripura and even Arunachal Pradesh against the proposed bill.
In Mizoram CAB would make Buddhist Chakma refugees Indian citizens. In Assam the proposed amendment has raised concerns that it could nullify the 1985 Assam Accord. The Assam Accord had fixed the cut-off date for deportation of all illegal immigrants irrespective of religion as March 24, 1971.
The BJP and its affiliates believe that minorities, including a significant number of Hindus from the three countries, should be granted citizenship as refugees because of the persecution they face there.
Opposition parties like the Congress, Trinamool Congress and CPI (Marxist) oppose the bill saying that the Indian Constitution does not allow granting of citizenship on the basis of religion. Their argument is by making special provisions for citizenship on the grounds of religious persecution the Bill will introduce religion as a new principle into the citizenship law, indeed by stipulating and hard-coding Muslims out.
With the routing of the Congress at the hands of the BJP in the last two elections, the BJP is brimmjing with confidence and is set to cintinue moving forward to fulfill promises made the elections.
Also read: Is it the end of Rahul's dynasty appeal?
Regardless, Amit Shah's engagement with socio-cultural, student and political groups is taking place. Shah's efforts are directed at understanding their objections to and apprehensions about the proposed amendment.
Shah is meeting the North East Students' Organisation, the All Bodo Students' Union and student bodies from Meghalaya, Nagaland and Arunachal Pradesh. He will also meet the Chief Ministers of North Eastern states to get their take on the issue.
Although the government is confident of steering the bill through both Houses, it has apprehensions of possible public protests in some of the North Eastern states and thus earlier this week the National Security Council Secretariat (NSCS) headed by National Security Advisor Ajit Doval took stock of the situation in the region.
Whatever the sentiment, CAB does look like cherry-picking people in distress driven by the Hindutva. The ideologues at the heart of it must be mindful that it is certain to give the critics ammunition that the country under Modi is turning into a nation of fanatics.
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3 thoughts on "Is CAB cherry-picking of people in distress?"
Pingback: No, CAB is not India's descent into theocracy CAB is not India's descent
this is a disgrace
Nitin Kumar says:
CAB has been passed and awaiting President's assent. When are you going to update your take on it?
sixteen − eleven =
No, CAB is not India's descent into theocracy CAB is not India's descent on Is CAB cherry-picking of people in distress?


In terms of size, Argentina is only second to Brazil in South America. The country is bordered by Paraguay, Bolivia, Brazil, Uruguay and Chile. It is the location of South America's lowest and highest points. The capital city is Buenos Aires and the country has a population of over 41 million people. Some of its tourist attractions include the Perito Moreno Glacier, Iguazu Falls and the capital Buenos Aires.
Since the turn of the century, Argentina has seen its economy suffer due to mismanagement under the regime of former president Christina Fernandez de Kirchner.
While Argentina experienced fast economic growth in the 1990s, the period of prosperity was followed by an alarming economic collapse, reports of grand corruption and economic mismanagement. Unemployment remains high, as the economy struggles with stagnation with inflation reaching unprecedented levels.
The official currency in Argentina is the Argentine peso. One peso has 100 centavos. Centavos come in coins of one, five, ten, 25 and 50, although the one centavo coin is no longer being minted. The denominations used for the banknotes are two, five, ten, 20, 50 and 100.
If you are travelling to Argentina, you can find pesos in banks and exchange houses, commonly known as "cambios". Most towns also have ATMs, so accessing your cash should not be a problem. As a visitor in Argentina, you will find good value for money as exchanging the pound or the dollar for the local currency gives you a great advantage. One Argentine peso is the equivalent of 0.065 dollars and 0.050 British pounds.
The announcement in late 2015 that Argentina was lifting its currency controls came as a relief to many. The controls had been imposed four years earlier in a bid to stabilise the peso. Although the move had been expected to lead to a devaluation of the peso, the hope was that it would boost exports and be a catalyst for economic growth. Under the new regulations, the maximum currency that could be acquired to be used for investments, domestic or foreign, was increased to $2 million every month.
Any foreign remittance handled by local commercial banks is required to be followed by a confirmation to the central bank that the services were actually fully rendered.
Any foreigner working in Argentina can buy foreign currency or wire funds to another country. At the same time, foreign institutions that pay monies on behalf of host locations can ask Argentina to reimburse their expenses.
The $500,000 monthly restriction for investment portfolios of non-residents is also no longer applicable. The holding period was also brought down to 120 days from 365 days.
One of the requirements before completing bank wire transfers in Argentina is that you have to have a bank account in the country. Your foreign bank then sends money in via the central bank. After the central bank converts those funds into pesos, you can now access them through your bank account. Deposits to Argentinian banks take a number of days to reflect in the accounts.
When depositing funds into Argentinian bank accounts, you need to have the following details about your recipient; bank name, unique banking key, account type, account currency, full name on the recipient's bank account, valid phone number and complete address. The address should include the province and the city.
There are limits imposed on how much you can send or receive every year. If you are sending an amount exceeding 15,000 pesos, your recipient will be required to present a compliance form and a government issued ID. If the amount is over 120,000 pesos, a proof of income will also be required. Once received, the approval will be valid for one full year. The documentation can either be presented directly at the bank or sent by mail.
Travellers' cheques are accepted in some of the high-end establishments but the preferred method of payment is credit cards or cash.
Cash pickup is a very commonly used means of payment in Argentina. There are MoneyGram and Western Union offices throughout the country. Cash pickups are made popular by the fact that the recipient receives the money within the same day. In fact, it only takes a matter of less than one hour.
When accepting the transfer, the recipient needs to get the transaction number from the sender. The recipient's name alone is not enough for him or her to be handed the money.
Cash pickups are known to be highly convenient. At the same time, they are also very secure. As long as there is a cash pickup point near where you live, this is one of the easiest ways to send or receive cash. However, make sure that you are only using reputable agents who have a positive track record. You don't want to be conned out of your hard earned cash.


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Did a Zeppelin Fly over Giza in the 1930s?
While airships may now invoke visions of fiery disasters, dirigibles once circumnavigated the globe.
A zeppelin airship flew over Giza in Egypt in 1931.
A viral photograph supposedly showing a zeppelin airship flying above the Great Sphinx of Giza in Egypt is frequently circulated on social media:
We have not been able to locate the original source of this image, but the story relayed in this tweet does reference an actual event that we could corroborate via news stories and genuine photographs.
The image shown above likely depicts the LZ 127 Graf Zeppelin, an airship built in Germany in 1928. That dirigible's most famous flight occurred in 1929, when it circumnavigated the globe in 21 days, and this representation probably depicts a flight from a few years later when the airship traveled across the Middle East in April 1931.
Karl Henry von Wiegand, a journalist who was aboard the Graf Zeppelin during its flight over Egypt, described the journey in an article published by the San Francisco Examiner in June 1931. In addition to documenting the airship's journey, von Wiegand's article is of note because it argued that airships were "steadily convincing a hard-headed and skeptical world of the practicability and safety of dirigibles as an outstanding factor in long-distance air transportation." While this sentiment may have been true at the time, airship travel was all but grounded a few years later after the infamous Hindenberg disaster of 1937.
We've transcribed a few relevant portions of that 1931 news article below:
Sun, Jul 12, 1931 – 41 · The San Francisco Examiner (San Francisco, California) · Newspapers.com
The appalling disaster to the British R-101 — which I claim was avoidable and in the main due to three things: national pride, certain details of design and construction and inexperience in handling such ships — has been used to renew, even intensify, skepticism and opposition to airships. Nevertheless, the Graf Zeppelin, in all probability the last of hydrogen-filled airships, has just demonstrated again the commercial practicability of dirigibles. With sixty-six persons on board, including twenty-five passengers and a quantity of mail, we flew from Friedrichshafen to Alexandria, Egypt in thirty hours and forty-five minutes.
Southward into the teeth of a stiff wind we turned and flew over the Nile delta, probably the richest soil in teh world. Below us soon the Nile, like a ribbon, with scores of picturesque lateen-sailed feluccas. Sailing boats, donkeys, camels, motor-cars, railway trains, slow plodding natives – every mode of transportation was represented there.
"The Pyramids in sight!" came a shout from the bridge. A rush to the windows. In the distance, silhouette-like against the sun, loomed the world's most ancient monuments. We circled them and the Sphinx and twice over the city of Cairo with the domes and needle-like minarets of the many mosques.
The image popularized on social media might be a symbolic representation created by manipulating pictures after the fact rather than an actual photograph of the event, but even if so it's a reasonably accurate depiction. We located a number of real photographs showing the Graf Zeppelin over the pyramids in Egypt, along with one additional image of the airship specifically journeying over the Great Spinx of Giza.
The following photograph, available via the Library of Congress and attributed to the Keystone View Company, shows three men watching the Graf Zeppelin as it approached the pyramids:
Title: The Graf Zeppelin's rendezvous with the eternal desert and the more than 4,000 year old pyramids of Gizeh, Egypt
Summary: Three men sitting on a hill near one of the great pyramids with desert in background and the Graf Zeppelin in the sky.
The Giza Project at Harvard University has collected several additional photographs of the airship above the pyramids in Egypt, and a photograph showing an airship flying over the Sphinx of Giza is available via Getty Images:
The German airship Zeppelin flies over the Sphinx and Giza pyramids, Egypt, from L'Illustrazione Italiana, year LVIII, n 19, May 10, 1931.
Henry von Wiegand, Karl. "Zep Proving Worth of Dirigibles."
The San Francisco Examiner. 12 July 1931.
Airships.net. "Graf Zeppelin History."
Retrieved 28 March 2019.