text:
\section{Introduction}
\noindent Transformers are on the rise---they are now the de-facto standard architecture
for language tasks
\cite{vaswani2017attention,Radford2018ImprovingLU,Radford2019LanguageMA,brown2020language}
and are increasingly adapted in other areas such as audio
\cite{child2019generating} and vision \cite{chen2020generative,
anonymous2021an}. In contrast to the predominant vision architecture,
convolutional neural networks (CNNs), the transformer architecture contains no
built-in inductive prior on the locality of interactions and is therefore free
to learn complex relationships among its inputs.
\enlargethispage{1\baselineskip}
However, this generality also implies that it \emph{has to} learn all
relationships, whereas CNNs have been designed to exploit
prior knowledge about strong local correlations within images.
Thus, the increased expressivity of transformers comes with quadratically
increasing computational costs, because all pairwise interactions are taken
into account. The resulting energy and time requirements of state-of-the-art
transformer models thus pose fundamental problems for scaling them to
high-resolution images with millions of pixels.

Observations that transformers tend to learn convolutional structures
\cite{anonymous2021an} thus beg the question: Do we have to re-learn everything
we know about the local structure and regularity of images from scratch each time we train a vision model, or can we efficiently encode inductive image biases while still retaining the flexibility of
transformers? We hypothesize that low-level image structure is well
described by a local connectivity, i.e.\ a convolutional architecture,
whereas this structural assumption ceases to be effective on higher semantic levels.
Moreover, CNNs not only exhibit a strong locality bias, but also a bias towards
spatial invariance through the use of shared weights across all positions. This
makes them ineffective if a more holistic understanding of the input is
required.

Our key insight to obtain an effective and expressive model is that, \emph{taken
together, convolutional and transformer architectures can model
the compositional nature of our visual world \cite{ommer2007learning}}: We use a
convolutional approach to efficiently learn a codebook of context-rich visual parts
and, subsequently, learn a model of their global compositions.
The long-range interactions within these compositions require
an expressive transformer architecture to model distributions over their consituent
visual parts. Furthermore, we utilize an adversarial approach to ensure that the dictionary of local parts captures perceptually important local structure to alleviate the need for modeling low-level
statistics with the transformer architecture.
Allowing transformers to concentrate on their unique strength --- modeling
long-range relations --- enables them
to generate high-resolution images as in
Fig.~\ref{fig:frontpage}, a
feat which previously has been out of reach. Our formulation directly
gives control over the generated images by means of conditioning information
regarding desired object classes or spatial layouts. Finally, experiments
demonstrate that our approach retains the advantages of transformers by
outperforming previous codebook-based state-of-the-art approaches based on convolutional
architectures.

\section{Related Work}
\newcommand{n}{n}
\paragraph{The Transformer Family}
The defining characteristic of the transformer architecture
\cite{vaswani2017attention} is that it models interactions between its inputs
solely through attention
\cite{bahdanau2016neural,kim2017structured,parikh2016decomposable} which
enables them to faithfully handle interactions between inputs regardless of
their relative position to one another.
Originally applied to language tasks, inputs to the transformer were given by tokens,
but other signals, such as those obtained from audio \cite{li2019neural} or images
\cite{chen2020generative},
can be used. Each layer of the transformer then consists of an attention
mechanism, which allows for interaction between inputs at different positions,
followed by a position-wise fully connected network, which is applied to all positions
independently. More specifically, the (self-)attention mechanism can be described by mapping an intermediate representation with three position-wise linear layers into three representations, query $Q \in \mathbb{R}^{N\times d_k}$, key $K \in \mathbb{R}^{N\times d_k}$
and value $V \in \mathbb{R}^{N\times d_v}$, to compute the output as
\newcommand{\operatorname{Attn}}{\operatorname{Attn}}
\newcommand{\operatorname{softmax}}{\operatorname{softmax}}
\begin{equation}
  \operatorname{Attn}(Q,K,V) = \operatorname{softmax}\Big(\frac{QK^t}{\sqrt{d_k}}\Big)V \in
  \mathbb{R}^{N\times d_v}.
\end{equation}

When performing autoregressive maximum-likelihood learning, non-causal entries
of $QK^t$, i.e. all entries below its diagonal, are set to $-\infty$ and the
final output of the transformer is given after a linear, point-wise
transformation to predict logits of the next sequence element.
Since the attention mechanism relies on the computation of inner products
between all pairs of elements in the sequence, its computational complexity
increases quadratically with the sequence length.  While the ability to
consider interactions between \emph{all} elements is the reason transformers
efficiently learn long-range interactions, it is also the reason transformers
quickly become infeasible, especially on images, where the sequence length
itself scales quadratically with the resolution.
Different approaches have been proposed to reduce the computational
requirements to make transformers feasible for longer sequences.
\cite{parmar2018image} and \cite{weissenborn2020scaling} restrict the receptive
fields of the attention modules, which reduces the expressivity and, especially
for high-resolution images, introduces unjustified assumptions on the
independence of pixels.
\cite{child2019generating} and \cite{ho2019axial} retain the full receptive
field but can reduce costs for a sequence of length $n$ only from $n^2$ to $n \sqrt{n}$, which makes
resolutions beyond $64$ pixels still prohibitively expensive.
\modelfigure
\paragraph{Convolutional Approaches}
The two-dimensional structure of images suggests that local
interactions are particularly important. CNNs exploit this structure by
restricting interactions between input variables to a local neighborhood
defined by the kernel size of the convolutional kernel. Applying a kernel thus
results in costs that scale linearly with the overall sequence length (the
number of pixels in the case of images) and quadratically in the kernel size,
which, in modern CNN architectures, is often fixed to a small constant such as
$3\times3$. This inductive bias towards local interactions thus leads to
efficient computations, but the wide range of specialized layers which are
introduced into CNNs to handle different synthesis tasks
\cite{park2019semantic,zhang2020cross,Siarohin_2019_NeurIPS,zhu2019sean,zhou2017view}
suggest that this bias is often too restrictive.

Convolutional architectures have been used for
autoregressive modeling of images \cite{oord2016pixel,oord2016conditional,chen2018pixelsnail} but, for
low-resolution images,
previous works \cite{parmar2018image,child2019generating,ho2019axial}
demonstrated that transformers consistently outperform their convolutional
counterparts.
Our approach allows us to efficiently model high-resolution images with
transformers while retaining their advantages over state-of-the-art
convolutional approaches.

\paragraph{Two-Stage Approaches}
Closest to ours are two-stage approaches which first learn an encoding of data
and afterwards learn, in a second stage, a probabilistic model of this
encoding. \cite{dai2019diagnosing} demonstrated both theoretical and empirical
evidence on the advantages of first learning a data representation with a
Variational Autoencoder (VAE) \cite{vae,vae2}, and then again learning its
distribution with a VAE. \cite{esser2020invertible,xiao2019generative} demonstrate similar gains when using an
unconditional normalizing flow for the second stage, and \cite{rombach2020making}
when using a conditional normalizing flow. To improve training efficiency of
Generative Adversarial Networks (GANs), \cite{liu2019acceleration} learns a
GAN \cite{gan} on representations of an
autoencoder and \cite{han2020notsobiggan} on low-resolution wavelet coefficients
which are then decoded to images with a learned generator.

\cite{oord2018neural} presents the Vector Quantised Variational Autoencoder (VQVAE), an
approach to learn discrete representations of images,
and models their distribution autoregressively with a convolutional
architecture. \cite{razavi2019generating} extends this approach to use a hierarchy of
learned representations. However, these methods still rely on
convolutional density estimation, which makes it difficult to capture
long-range interactions in high-resolution images.
\cite{chen2020generative} models images autoregressively with transformers in order to
evaluate the suitability of generative pretraining to learn image representations for downstream tasks. Since input resolutions of $32\times 32$ pixels
are still quite computationally expensive \cite{chen2020generative}, a VQVAE is used to
encode images up to a resolution of $192\times 192$. In an effort to keep the
learned discrete representation as spatially invariant as possible with respect
to the pixels, a shallow VQVAE with small receptive field is employed.
In contrast, we demonstrate that a powerful first stage, which captures as much
context as possible in the learned representation, is critical to enable
efficient high-resolution image synthesis with transformers.

\section{Approach}
\label{sec:approach}
Our goal is to exploit the highly promising learning capabilities of
transformer models \cite{vaswani2017attention} and introduce them to high-resolution image
synthesis up to the megapixel range.
Previous work \cite{parmar2018image, chen2020generative} which applied transformers to
image generation demonstrated promising results for images up to a size of $64
\times 64$ pixels but, due to the quadratically increasing cost in sequence
length, cannot simply be scaled to higher resolutions.

High-resolution image synthesis requires a model
that understands the global composition of images, enabling it to
generate locally realistic as well as globally consistent patterns.
Therefore, instead of representing an image with pixels, we represent it
as a composition of perceptually rich image constituents from a codebook.  By
learning an effective code, as described in Sec.~\ref{sec:vqgan}, we can
significantly reduce the description length of compositions, which allows us to
efficiently model their global interrelations within images with a transformer
architecture as described in
Sec.~\ref{sec:trafo}. This approach, summarized in Fig.~\ref{fig:model}, is
able to generate realistic and consistent high resolution
images both in an unconditional and a conditional setting.

\newcommand{n_z}{n_z}
\subsection{Learning an Effective Codebook of Image Constituents for Use in Transformers}
\label{sec:vqgan}
\noindent To utilize the highly expressive transformer architecture for image synthesis, we need to
express the constituents of an image in the form of a \emph{sequence}.
Instead of building on individual pixels, complexity necessitates
an approach that uses a discrete codebook of learned representations,
such that any image $x \in \mathbb{R}^{H \times W \times 3}$ can be represented by a
spatial collection of codebook entries $z_{\mathbf{q}} \in \mathbb{R}^{h \times w
\times n_z}$, where $n_z$ is the dimensionality of codes.
An equivalent representation is a sequence of $h\cdot w$ indices which specify
the respective entries in the learned codebook.
To effectively learn such a discrete spatial codebook, we propose to directly
incorporate the inductive biases of
CNNs and incorporate ideas from neural discrete representation learning \cite{oord2018neural}.
First, we learn a convolutional model
consisting of an encoder $E$ and a decoder $G$, such that taken
together, they learn to represent images with codes from a
learned, discrete codebook $\mathcal{Z} = \{z_k\}_{k=1}^K \subset
\mathbb{R}^n_z$ (see Fig.~\ref{fig:model} for an overview).
More precisely,
we approximate a given image $x$ by $\hat{x}=G(z_{\mathbf{q}})$. We
obtain $z_{\mathbf{q}}$ using the encoding $ \hat{z} = E(x) \in \mathbb{R}^{h\times w \times
n_z}$ and a subsequent element-wise quantization $\mathbf{q}(\cdot)$ of each spatial code
$\hat{z}_{ij} \in \mathbb{R}^n_z$ onto its closest codebook entry $z_k$:
\begin{equation}
  z_{\mathbf{q}} = \mathbf{q}(\hat{z}) \coloneqq
  \left(\argmin_{z_k \in \mathcal{Z}} \Vert \hat{z}_{ij} - z_k \Vert\right)
  \in \mathbb{R}^{h\times w \times n_z}.
\end{equation}
The reconstruction $\hat{x} \approx x$ is then given by
\begin{equation}
  \hat{x} = G(z_{\mathbf{q}} ) = G\left(
    \mathbf{q}(E(x)) \right).
\label{eq:vqrec}
\end{equation}
Backpropagation through the non-differentiable quantization operation in
Eq.~\eqref{eq:vqrec} is achieved by a straight-through gradient estimator,
which simply copies the gradients from the decoder to the encoder
\cite{bengio2013estimating}, such that the model and codebook can be trained end-to-end
via the loss function
\begin{align}
\mathcal{L}_{\text{VQ}}(E, G, \mathcal{Z}) = \Vert x - \hat{x} \Vert_2^2
  &+ \phantom{\beta} \Vert \text{sg}[E(x)] - z_{\mathbf{q}} \Vert_2^2 \nonumber \\ &+ \beta \Vert \text{sg}[z_{\mathbf{q}}] - E(x) \Vert_2^2.
\label{eq:origvqloss}
\end{align}
Here, $\Vert x - \hat{x} \Vert_2^2$ is a reconstruction loss,
$\text{sg}[\cdot]$ denotes the stop-gradient operation, and
$\Vert \text{sg}[z_{\mathbf{q}}] - E(x) \Vert_2^2$ is the so-called
``commitment loss'' with weighting factor $\beta$ \cite{oord2018neural}.

\paragraph{Learning a Perceptually Rich Codebook}
Using transformers to represent images as a distribution over latent image constituents requires us to push the limits of compression and learn a rich codebook.
To do so, we propose \emph{VQGAN}, a variant of the original VQVAE,
and use a discriminator and perceptual loss
\cite{larsen2015autoencoding,johnson2016perceptual,lamb2016discriminative,dosovitskiy2016generating} to keep good perceptual quality at
increased compression rate. Note that this is in contrast to previous works which applied pixel-based
\cite{oord2016conditional,razavi2019generating} and transformer-based
autoregressive models \cite{chen2020generative} on top of only a shallow quantization model.
More specifically, we replace the $L_2$ reconstruction in
Eq.~\eqref{eq:origvqloss} by a perceptual loss and introduce an adversarial
training procedure with a patch-based discriminator $D$
\cite{isola2017image} that aims to differentiate between real and reconstructed images:
\begin{align}
\mathcal{L}_{\text{GAN}}(\{E, G, \mathcal{Z} \}, D) =
  \left[ \log D(x) + \log (1 - D(\hat{x}))\right]
\label{eq:ourvqloss}
\end{align}
The complete objective for finding the optimal compression model $\mathcal{Q}^* = \{E^*, G^*, \mathcal{Z}^* \}$ then reads
\begin{align}
\mathcal{Q}^* = \argmin_{E, G, \mathcal{Z}} \max_{D}
  \mathbb{E}_{x\sim p(x)} \Big[
    &\mathcal{L}_{\text{VQ}}(E, G, \mathcal{Z}) \nonumber \\
    + \lambda &\mathcal{L}_{\text{GAN}}(\{E, G, \mathcal{Z} \}, D)
    \Big],
\end{align}
where we compute the adaptive weight $\lambda$ according to
\begin{equation}
\lambda = \frac{\nabla_{G_L}[ \mathcal{L}_{\text{rec}} ]}{\nabla_{G_L} [\mathcal{L}_{\text{GAN}}] + \delta}
\end{equation}
where $\mathcal{L}_{\text{rec}}$ is the perceptual reconstruction loss
\cite{zhang2018perceptual}, $\nabla_{G_L}[\cdot]$ denotes the gradient of its input
w.r.t.\ the last layer $L$ of the decoder, and $\delta = 10^{-6}$ is used for numerical stability.
To aggregate context from everywhere, we apply a single attention layer on the
lowest resolution.
This training procedure significantly reduces the sequence length when
unrolling the latent code and thereby enables the application of powerful
transformer models.

\subsection{Learning the Composition of Images with Transformers}
\label{sec:trafo}

\paragraph{Latent Transformers} With $E$ and $G$ available, we can now represent images in terms of the codebook-indices of
their encodings. More precisely, the quantized encoding of an image $x$ is given by
$z_{\mathbf{q}} = \mathbf{q}(E(x)) \in \mathbb{R}^{h \times w \times
n_z}$ and is equivalent to
a sequence $s \in \{0, \dots, \vert \mathcal{Z} \vert - 1 \}^{h \times w}$ of indices from the codebook,
which is obtained
by replacing each code by its index in the codebook $\mathcal{Z}$:
\begin{equation}
  %
  s_{ij} = k \text{ such that } \left( z_{\mathbf{q}} \right)_{ij} = z_k.
\label{eq:indexmap}
\end{equation}
By mapping indices of a sequence $s$ back to their corresponding codebook entries,
$z_{\mathbf{q}} = \left( z_{s_{ij}} \right)$ is readily recovered and decoded to an image $\hat{x} =
G(z_{\mathbf{q}})$.

Thus, after choosing some ordering of the indices in $s$, image-generation can be formulated as
autoregressive next-index prediction: Given indices
$s_{<i}$, the transformer learns to predict the distribution of possible next
indices, i.e. $p(s_i \vert s_{<i})$ to compute the likelihood of the full representation as
$p(s)=\prod_i p(s_i \vert s_{<i})$.
This allows us to directly maximize the log-likelihood of the data
representations:
\begin{equation}
  \mathcal{L}_{\text{Transformer}} = \mathbb{E}_{x \sim p(x)} \left[ -\log p(s) \right].
\end{equation}

\paragraph{Conditioned Synthesis}
In many image synthesis tasks a user demands control over the generation
process by providing additional information from which an example shall be
synthesized.
This information, which we will call $c$, could be a single label describing the
overall image class or even another image itself.
The task is then to learn the likelihood of the sequence given this information $c$:
\begin{equation}
  \label{eq:conddensity}
  p(s|c) = \prod_i p(s_i|s_{<i}, c).
\end{equation}
If the conditioning information $c$ has spatial extent, we first learn another \emph{VQGAN} to obtain
again an index-based representation $r \in \{0, \dots, \vert \mathcal{Z}_c \vert
-1 \}^{h_c \times w_c}$ with the newly obtained codebook $\mathcal{Z}_c$
\footnote{Note that this is completely analogous to the procedure that
generates the data representation $s$.}. Due to the autoregressive structure of the transformer, we can then simply prepend $r$ to $s$ and
restrict the computation of the negative log-likelihood to entries
$p(s_i \vert s_{<i}, r)$. This ``decoder-only'' strategy has also been
successfully used for text-summarization tasks \cite{liu2018generating}.

\paragraph{Generating High-Resolution Images}
\attentionslide
The attention mechanism of the transformer puts limits on the sequence length
$h \cdot w$ of its inputs $s$. While we can adapt the number of downsampling
blocks $m$ of our \emph{VQGAN} to reduce images of size $H\times W$ to $h=H/2^m
\times w=W/2^m$, we observe degradation of the reconstruction quality beyond a
critical value of $m$, which depends on the considered dataset. To generate
images in the megapixel regime, we therefore have to work patch-wise and crop images to restrict the length of $s$ to a maximally
feasible size during training. To sample images, we then use the transformer in a
sliding-window manner as illustrated in Fig.~\ref{fig:attentionslide}.
Our \emph{VQGAN} ensures that the available context is still
sufficient to faithfully model images, as long as either the statistics of the
dataset are approximately spatially invariant or spatial conditioning
information is available. In practice, this is not a restrictive requirement,
because when it is violated, \ie unconditional image synthesis on
aligned data, we can simply condition on image coordinates, similar to
\cite{lin2019cocogan}.

\section{Experiments}
\label{sec:experiments}

\noindent This section evaluates the ability of our approach to retain the
advantages of transformers over their convolutional counterparts
(Sec.~\ref{sec:psnailvstrafo}) while integrating the effectiveness of
convolutional architectures to enable high-resolution image synthesis
(Sec.~\ref{sec:four}). Furthermore, we investigate how codebook quality
(Sec.~\ref{sec:wurst}) and codebook orderings (Sec.~\ref{sec:three})
affect our approach.

Based on initial experiments, we choose the size of the codebook as $\vert
\mathcal{Z} \vert=1024$ and train all subsequent transformer models to predict
sequences of length $16\cdot 16$, as this is the maximum feasible length to
train a GPT2-medium architecture (307 M parameters)
\cite{Radford2019LanguageMA} on a GPU with 12GB VRAM. More details on architectures and hyperparameters can be found in the appendix (Tab.~\ref{tab:vqaearchitecture} and Tab.~\ref{tab:hyperparameters}).

\subsection{Attention Is All You Need in the Latent Space}
\label{sec:psnailvstrafo}
\pixelsnailvstransformersmall
\noindent Transformers show state-of-the-art results on a wide variety of tasks,
including autoregressive image modeling. However, evaluations of previous works
were limited to transformers working directly on (low-resolution) pixels
\cite{parmar2018image,child2019generating,ho2019axial}, or to deliberately shallow pixel encodings \cite{chen2020generative}.
This raises the question if our approach retains
the advantages of transformers over convolutional approaches.

To answer this question, we use a variety of conditional and unconditional
tasks and compare the performance between our transformer-based approach and a
convolutional approach. For each task, we train a \emph{VQGAN} with $m=4$
downsampling blocks, and, if needed,
another one for the conditioning information,
and then train both a transformer and a PixelSNAIL \cite{chen2018pixelsnail} model on the same
representations, as the latter has been used
in previous state-of-the-art two-stage approaches \cite{razavi2019generating}.
For a thorough comparison, we vary the model capacities between
85M and 310M parameters and adjust the number of layers in each model to match
one another. We observe that PixelSNAIL trains roughly twice as fast as the
transformer and thus, for a fair comparison, report the negative log-likelihood
both for the same amount of training time (\emph{P-SNAIL time}) and for the same amount of training
steps (\emph{P-SNAIL steps}).
\vspace{-1.1em}
\paragraph{Results} Tab.~\ref{tab:gptvspixelsnail} reports results
for unconditional image modeling on \emph{ImageNet} (IN) \cite{deng2009imagenet}, \emph{Restricted ImageNet} (RIN) \cite{santurkar2019computer},
consisting of a subset of animal classes from ImageNet, \emph{LSUN Churches and
Towers} (LSUN-CT) \cite{yu15lsun}, and for conditional image modeling of RIN
conditioned on depth maps obtained with the approach of \cite{Ranftl2020} (D-RIN) and of landscape images
collected from Flickr conditioned on semantic layouts (S-FLCKR) obtained with the
approach of \cite{chen2017deeplab}. Note that for the semantic layouts, we train the
first-stage using a cross-entropy reconstruction loss due to their discrete
nature.
The results shows that the transformer consistently
outperforms PixelSNAIL across all tasks when trained for the same amount of
time and the gap increases even further when trained for the same number of
steps. These results demonstrate that gains of transformers carry over to our
proposed two-stage setting.

\subsection{A Unified Model for Image Synthesis Tasks}
\label{sec:four}
\firstpagefigure
The versatility and generality of the transformer architecture makes it a
promising candidate for image synthesis. In the conditional case, additional
information $c$ such as class labels or segmentation maps are used and the goal
is to learn the distribution of images as described in
Eq.~\eqref{eq:conddensity}. Using the same setting as in
Sec.~\ref{sec:psnailvstrafo} (i.e.\ image size $256 \times 256$, latent size
$16 \times 16$), we perform various conditional image synthesis experiments:

\noindent (i): \textbf{Semantic image synthesis}, where we condition on semantic
segmentation masks of ADE20K \cite{zhou2016semantic}, a web-scraped
landscapes dataset (S-FLCKR) and COCO-Stuff \cite{caesar2018cvpr}.
Results are depicted in Figure~\ref{fig:firstpage},~\ref{fig:bigsamples} and Fig.~\ref{fig:convcondplot}.

\noindent (ii): \textbf{Structure-to-image}, where we use either depth or edge
information to synthesize images from both RIN and IN (see
Sec.~\ref{sec:psnailvstrafo}). The resulting depth-to-image and edge-to-image translations
are visualized in Fig.~\ref{fig:firstpage} and Fig.~\ref{fig:convcondplot}.

\noindent (iii): \textbf{Pose-guided synthesis:} Instead of using the semantically
rich information of either segmentation or depth maps, Fig.~\ref{fig:firstpage}
shows that the same approach as for the previous experiments can be used to
build a shape-conditional generative model on the DeepFashion
\cite{liuLQWTcvpr16DeepFashion} dataset.

\noindent (iv): \textbf{Stochastic superresolution}, where low-resolution images
serve as the conditioning information and are thereby upsampled. We train our model for an
upsampling factor of 8 on ImageNet and show results in
Fig.~\ref{fig:convcondplot}.

\noindent (v): \textbf{Class-conditional image synthesis:} Here, the conditioning
information $c$ is a single index describing the class label of interest.
Results on conditional sampling for the RIN dataset are
demonstrated in Fig.~\ref{fig:firstpage}. %

\noindent All of these examples make use of the same methodology. Instead of requiring
task specific architectures or modules, the flexibility of the transformer
allows us to learn appropriate interactions for each task, while the
\emph{VQGAN} --- which can be \emph{reused}
across different tasks --- leads to short sequence lengths. In combination, the
presented approach can be understood as an efficient, general purpose mechanism
for conditional image synthesis. Note that additional results for each experiment can be found in the appendix, Sec.~\ref{sec:addition}.
\paragraph{High-Resolution Synthesis} The sliding window approach introduced in Sec.~\ref{sec:trafo}
enables image synthesis beyond a resolution of $256 \times 256$ pixels.
We evaluate this approach on unconditional image generation on LSUN-CT and
FacesHQ (see Sec.~\ref{sec:wurst}) and  conditional synthesis on D-RIN,
COCO-Stuff and S-FLCKR, where we show results in
Fig.~\ref{fig:frontpage},~\ref{fig:convcondplot} and the supplementary (Fig.~\ref{fig:bigsamplesbig}-\ref{fig:unconditionalchurches}). Note that this approach can in principle be used to generate images of arbitrary ratio and size, given that the image statistics of the dataset of interest are approximately spatially invariant or spatial information is available.
Impressive results can be achieved by applying this method to image generation from semantic layouts on S-FLCKR, where a strong \emph{VQGAN} can be learned with $m=5$, so that its codebook together with the conditioning information provides the transformer with enough context for image generation in the megapixel regime.
\bigsamples
\convcondplot
\subsection{Building Context-Rich Vocabularies}
\label{sec:wurst}
\fstudyfaces
How important are context-rich vocabularies? To investigate this question, we
ran experiments where the transformer architecture is kept fixed while the
amount of context encoded into the representation of the first stage is varied
through the number of downsampling blocks of our \emph{VQGAN}.
We specify the amount of context encoded in terms of reduction factor in the
side-length between image inputs and the resulting representations, \ie a first
stage encoding images of size $H \times W$ into discrete codes of size $H/f \times W/f$ is denoted by a factor $f$. For $f=1$, we reproduce the
approach of \cite{chen2020generative} and replace our \emph{VQGAN} by a k-means clustering of RGB
values with $k=512$. \\
During training, we always crop images to obtain inputs of size $16 \times 16$
for the transformer, \ie when modeling images with a
factor $f$ in the first stage, we use crops of size $16f \times 16f$.
To sample from the models, we
always apply them in a sliding window manner as described in
Sec.~\ref{sec:approach}. \\
\newline
\noindent \textbf{Results} Fig.~\ref{fig:fstudyfaces} shows results for unconditional
synthesis of faces on \emph{FacesHQ}, the combination of \emph{CelebA-HQ}
\cite{karras2018progressive} and \emph{FFHQ} \cite{karras2019stylebased}. It clearly demonstrates the benefits of
powerful \emph{VQGAN}s by increasing the effective receptive field of the
transformer. For small receptive fields, or equivalently small $f$, the model
cannot capture coherent structures. For an intermediate value of $f=8$, the
overall structure of images can be approximated, but inconsistencies of facial
features such as a half-bearded face and of viewpoints in different parts of
the image arise. Only our full setting of $f=16$ can synthesize high-fidelity
samples. For analogous results in the conditional setting on S-FLCKR, we refer
to the appendix (Fig.~\ref{fig:rfwfstudy} and Sec.~\ref{supp:contextrich}).

To additionally assess the effectiveness of our approach quantitatively, we compare results between
training a transformer directly on pixels, and training it on top of a
\emph{VQGAN}'s
latent code with $f=2$, given a fixed computational budget. Again, we follow
\cite{chen2020generative} and learn a dictionary of $512$ RGB values on
CIFAR10 to operate directly on pixel space and
additionally train the same transformer architecture on top of our
\emph{VQGAN} with a latent code of size $16\times16=256$. We
observe improvements of $18.63\%$ for FID scores and $14.08$ times faster
sampling of images.

\subsection{On the Ordering of Image Representations}
\label{sec:three}
\noindent For the ``classical'' domain of transformer models, NLP, the order
of tokens is defined by the language at hand. For images and their discrete
representations, in contrast, it is not clear which linear ordering to use.
In particular, our sliding-window approach depends on a row-major ordering and
we thus investigate the performance of the following five different
permutations of the input sequence of codebook indices: (i) \textbf{row
major}, or \emph{raster scan order}, where the image representation
is unrolled from top left to bottom right.
(ii) \textbf{spiral out}, which incorporates the prior assumption that most images show a
\emph{centered} object.
(iii) \textbf{z-curve}, also known as \emph{z-order} or
\emph{morton curve}, which introduces the
prior of \emph{preserved locality} when mapping a 2D image
representation onto a 1D sequence.
(iv) \textbf{subsample}, where prefixes correspond to subsampled
representations.
(v) \textbf{alternate}, which is related to \emph{row major}, but alternates the
direction of unrolling every row.
(vi) \textbf{spiral in}, a reversed version of \emph{spiral out} which provides
the most context for predicting the center of the image.
A graphical visualization of these permutation variants is shown in the
supplement.
Given a \emph{VQGAN} trained on ImageNet,
we train a transformer for each permutation in a controlled setting, i.e.\
we fix initialization and computational budget.

\noindent \textbf{Results} Sec.~\ref{sec:orderingsupp} contains
the evolution of negative log-likelihood for
each variant as a function of training iterations, with final values given by
(i) 4.767, (ii) 4.889, (iii) 4.810, (iv) 5.015, (v) 4.812, (vi) 4.901.
Interestingly, \emph{row
major} performs best in terms of this metric, whereas the more hierarchical
\emph{subsample} prior does not induce any helpful bias.
We also include qualitative samples in the appendix (Fig.~\ref{fig:permutationsbatches}) and
observe that the two worst performing models in terms of NLL (\emph{subsample} and \emph{spiral in}) tend to produce more
textural samples, while the other variants synthesize samples with much more
recognizable structures. Overall, we can conclude that the autoregressive codebook modeling
is \emph{not} permutation-invariant, but the common \emph{row major}
ordering \cite{oord2016conditional,chen2020generative} outperforms other
orderings.

\section{Conclusion}
\noindent This paper adressed the fundamental challenges that previously confined transformers to
low-resolution images. We
proposed an approach which represents images as a composition of perceptually
rich image constituents and thereby overcomes the infeasible quadratic
complexity when modeling images directly in pixel space. Modeling constituents
with a CNN architecture and their compositions with a transformer architecture
taps into the full potential of their complementary strengths and thereby
allowed us to represent the first results on high-resolution image synthesis
with a transformer-based architecture. In experiments, our approach
demonstrates the efficiency of convolutional inductive biases and the
expressivity of transformers by synthesizing images in the megapixel range and
outperforming state-of-the-art convolutional approaches. Equipped with a
general mechanism for conditional synthesis, it offers many
opportunities for novel neural rendering approaches.

\newpage

redpajama_set_name: RedPajamaArXiv


text:
Boating While Intoxicated (BWI)
Dallas Boating While Intoxicated Lawyers
Just as individuals must take proper precautions while driving along Texas roadways, the same must be done while boating on Texas waters. Boating is popular hobby for many lake-goers in the Dallas area, but what many don't realize is that a boating while intoxicated (BWI) charge is equally as serious as a DWI charge.
The Dallas BWI lawyers at the Law Offices of Mark T. Lassiter encourage all individuals to practice the highest standards of safety while driving motorized water vehicles. Always have your boating license on hand and always be sure to have enough life vests on board for yourself and other passengers. Boat drivers should also be aware of their alcohol intake, making sure to stay at least below the legal limit of impairment for the sake of their own life and the lives of other passengers.
At the Law Offices of Mark T. Lassiter, our Dallas lawyers understand that boating security and safety officers are sometimes over-zealous when targeting drunk boat drivers and may heighten one's boating while intoxicated offense. Sometimes, boaters can have the physical signs of intoxication, such as redness, fatigue, or instability because of "sea legs," even when they haven't consumed any alcohol or intoxicating substances.
Penalties for Boating While Intoxicated
At the Law Offices of Mark T. Lassiter, we understand that your BWI charge may seem extremely ambiguous or over-zealous, but it is imperative to understand the penalties that may come from boating while intoxicated. Such penalties may include the following:
Revocation of your boating license
A $2,000 fine
Up to 180 days in state jail
The penalties listed above are those of a first offense, but the consequences may be heightened after a second or third offense.
Contact The Dallas Boating While Intoxicated Lawyers
A boating while intoxicated charge can have detrimental effects on your future professional and personal life. As such, if you are facing a BWI conviction in the Dallas area, it would likely be wise to get in touch with the experienced BWI lawyers at the Law Offices of Mark T. Lassiter today. Call us at at (214) 845-7007 for quality legal assistance.
Boating While Intoxicated FAQs
Can I lose my boating license for a BWI charge?
Yes, the revocation of a boating license is a possible consequence of a boating under the influence conviction. Under Texas law, all boaters who operate a watercraft while intoxicated face license suspension. For a first time offense, a boating license can be suspended for up to six months. A repeat offender is likely to have their license suspended for a minimum of one year. First-time BWI penalties in Texas also usually include a jail sentence between 72 hours and 180 days, as well as a monetary fine up to $2,000 If a BWI incident resulted in any serious damage or bodily injuries, the consequences will be much harsher. Because the penalties for a BWI conviction can be very serious, it is important to find strong legal representation.
Are boating while intoxicated charges handled the same way as DUI charges?
Boating while intoxicated laws are very similar to DUI and DWI laws. Just like a DUI incident, law enforcement officials can stop a boat if they suspect the driver is under the influence of drugs or alcohol. In both BWI and DUI/DWI cases, the driver in question is considered legally intoxicated and therefore incapable of operating a boat or motor vehicle if their blood alcohol content is over .08, or if they are under the influence of drugs and do not have "normal use of mental or physical faculties." Penalties of a BWI conviction are also similar to a DUI conviction, as both may include fines, jail time, and the revocation of a license.
What kind of boats can BWI charges affect?
Texas law states that it is illegal to operate any watercraft while under the influence of drugs or alcohol. The state laws define "watercraft" as any water vehicle used to carry or transport a person. Many BWI offenders have found themselves facing these charges because they were simply unaware of the illegality of operating certain watercrafts while under the influence. For example, many jet skiers do not realize they can be arrested for operating their jet ski while under the influence of drugs or alcohol.

redpajama_set_name: RedPajamaCommonCrawl


Outspoken Chelsea boss Jose Mourinho has been hit with a suspended one-match stadium ban and fined £50,000 by the Football Association.
Mourinho admitted a breach of FA Rules in relation to post-match media comments he made after the champions were beaten by Southampton on October 3.
It was alleged the Blues manager's remarks constituted improper conduct in that they alleged and/or implied bias on the part of a match official or match officials and/or brought the game into disrepute.
Mourinho's suspended stadium ban will be immediately invoked if he commits a further breach of FA Rule E3 for any comment or statement to or through the media before October next year.


How to change the urlbar richtext results colors?
I'd like to change the urlbar colors especially the ugly blue highlight which doesn't fit well with the colors I have in my theme. The url's are also blue, which I don't like.
What I would really like to know now is how can I use the the color that's given by the system (windows 8). AKA the color of the titlebar, which changes when my desktop wallpaper changes.
I'm hoping there's a special -moz-Something color for this (like -moz-Dialog).
I could work around this when I was toying with the sidebar and tabs by by going up the hierarchy and setting the parents' colors' to transparent until I reached this color. But apparently whatever is on top here is colored black or something dark colored.
Well, I ran out of ideas.
Okay, now it works. But I think there's a misunderstanding, I have problems with changing change the the highlight/background colors of the results.
This works also. (I see this way it also affects other selected text, which might be good) Though My main problem.
Okay, sorry I wasn't clear enough in my first post.
The colors of the urlbar [url]results[/url] are my main problem.
I want to base the colors on the system color or whatever the name of the color of the titlebar is in windows. Which the default theme uses on some level. I could also set other UI elements (tabs, sidbar) to depend on this color by making them and their parents transparent. But this approach didn't work with the urlbar results, they just got black.
So the problem I can't solve is how get this "titlebar color" or whatever to the urlbar results.
Yep, that's how it works. I started by coding it to be url/searchbar specific and then realised that if an OS selected background/text colour is crap (most are) then really they should all be changed, so that's what I did.
However, I can see now that wasn't your main problem, next time I'm in Firefox I'll have a look at how I've done all that, it's not hard to do, as I recall.
But, I don't really *do* native OS effects in my stuff, I override everything. So a more 'native' look themer will probably be able to advise you better on your particular requirements.
avada wrote: I want to base the colors on the system color or whatever the name of the color of the titlebar is in windows.
There you are out of luck. There is a very old bug out on getting "Aero" colors which no Mozilla folks (or anyone else apparently judging by online discussions) could figure out. I figured out how to do the maths to extract the colors a few years ago, but now no-one can be bothered to build a patch for it (because below).
avada wrote: Which the default theme uses on some level.
Nope. What the default theme does is apply transparencies on top of the titlebar itself. Because they think that's especially clever, they won't bother to to actually extract the real color.
patrickjdempsey wrote: There you are out of luck. There is a very old bug out on getting "Aero" colors which no Mozilla folks (or anyone else apparently judging by online discussions) could figure out. I figured out how to do the maths to extract the colors a few years ago, but now no-one can be bothered to build a patch for it (because below).
So I'm guessing you can channel the color to the urlbar results is because they're not "on" the window but overlaid over other ui elements.
I use for the titlebar the native appearance on Windows. Have a look on one of my themes (I'm at work now, can't figure out the code).


text:
Global N-methyl-N-nitro-N-nitrosoguanidine (CAS 26305-08-8) Market
Global N-methyl-N-nitro-N-nitrosoguanidine (CAS 26305-08-8) Market Report 2019 - market Size, Share, Price, Trend and Forecast
The global market size of N-methyl-N-nitro-N-nitrosoguanidine is $XX million in 2018 with XX CAGR from 2014 to 2018, and it is expected to reach $XX million by the end of 2024 with a CAGR of XX% from 2019 to 2024.
Global N-methyl-N-nitro-N-nitrosoguanidine Market Report 2019 - Market Size, Share, Price, Trend and Forecast is a professional and in-depth study on the current state of the global N-methyl-N-nitro-N-nitrosoguanidine industry. The key insights of the report:
1.The report provides key statistics on the market status of the N-methyl-N-nitro-N-nitrosoguanidine manufacturers and is a valuable source of guidance and direction for companies and individuals interested in the industry.
5.The report estimates 2019-2024 market development trends of N-methyl-N-nitro-N-nitrosoguanidine industry.
7.The report makes some important proposals for a new project of N-methyl-N-nitro-N-nitrosoguanidine Industry before evaluating its feasibility.
For competitor segment, the report includes global key players of N-methyl-N-nitro-N-nitrosoguanidine as well as some small players.
For product type segment, this report listed main product type of N-methyl-N-nitro-N-nitrosoguanidine market
6.2 N-methyl-N-nitro-N-nitrosoguanidine Analysis
8.1 Export of N-methyl-N-nitro-N-nitrosoguanidine by Region
8.2 Import of N-methyl-N-nitro-N-nitrosoguanidine by Region
Chapter 9 Historical and Current N-methyl-N-nitro-N-nitrosoguanidine in North America (2013-2018)
9.1 N-methyl-N-nitro-N-nitrosoguanidine Supply
9.2 N-methyl-N-nitro-N-nitrosoguanidine Demand by End Use
Chapter 10 Historical and Current N-methyl-N-nitro-N-nitrosoguanidine in South America (2013-2018)
10.1 N-methyl-N-nitro-N-nitrosoguanidine Supply
10.2 N-methyl-N-nitro-N-nitrosoguanidine Demand by End Use
Chapter 11 Historical and Current N-methyl-N-nitro-N-nitrosoguanidine in Asia & Pacific (2013-2018)
Chapter 12 Historical and Current N-methyl-N-nitro-N-nitrosoguanidine in Europe (2013-2018)
Chapter 13 Historical and Current N-methyl-N-nitro-N-nitrosoguanidine in MEA (2013-2018)
Chapter 14 Summary for Global N-methyl-N-nitro-N-nitrosoguanidine (2013-2018)
Chapter 15 Global N-methyl-N-nitro-N-nitrosoguanidine Forecast (2019-2023)
15.1 N-methyl-N-nitro-N-nitrosoguanidine Supply Forecast
15.2 N-methyl-N-nitro-N-nitrosoguanidine Demand Forecast
16.1.2 Main Business and N-methyl-N-nitro-N-nitrosoguanidine Information
16.1.4 Company A N-methyl-N-nitro-N-nitrosoguanidine Sales, Revenue, Price and Gross Margin (2014-2019)
16.2.4 Company B N-methyl-N-nitro-N-nitrosoguanidine Sales, Revenue, Price and Gross Margin (2014-2019)
16.3.4 Company C N-methyl-N-nitro-N-nitrosoguanidine Sales, Revenue, Price and Gross Margin (2014-2019)
16.4.4 Company D N-methyl-N-nitro-N-nitrosoguanidine Sales, Revenue, Price and Gross Margin (2014-2019)
16.5.4 Company E N-methyl-N-nitro-N-nitrosoguanidine Sales, Revenue, Price and Gross Margin (2014-2019)
16.6.4 Company F N-methyl-N-nitro-N-nitrosoguanidine Sales, Revenue, Price and Gross Margin (2014-2019)
16.7.4 Company G N-methyl-N-nitro-N-nitrosoguanidine Sales, Revenue, Price and Gross Margin (2014-2019)
Table Research Scope of N-methyl-N-nitro-N-nitrosoguanidine Report
Table Primary Sources of N-methyl-N-nitro-N-nitrosoguanidine Report
Table Secondary Sources of N-methyl-N-nitro-N-nitrosoguanidine Report
Table Major Assumptions of N-methyl-N-nitro-N-nitrosoguanidine Report
Figure N-methyl-N-nitro-N-nitrosoguanidine Picture
Table N-methyl-N-nitro-N-nitrosoguanidine Classification
Table N-methyl-N-nitro-N-nitrosoguanidine Applications List
Table Drivers of N-methyl-N-nitro-N-nitrosoguanidine Market
Table Restraints of N-methyl-N-nitro-N-nitrosoguanidine Market
Table Opportunities of N-methyl-N-nitro-N-nitrosoguanidine Market
Table Threats of N-methyl-N-nitro-N-nitrosoguanidine Market
Table Different Production Methods of N-methyl-N-nitro-N-nitrosoguanidine
Table Cost Structure Analysis of N-methyl-N-nitro-N-nitrosoguanidine
Table Latest News of N-methyl-N-nitro-N-nitrosoguanidine Market
Table Planned/Future Project of N-methyl-N-nitro-N-nitrosoguanidine Market
Table Policy of N-methyl-N-nitro-N-nitrosoguanidine Market
Table 2014-2024 Regional Export of N-methyl-N-nitro-N-nitrosoguanidine
Table 2014-2024 Regional Import of N-methyl-N-nitro-N-nitrosoguanidine
Table 2014-2024 North America N-methyl-N-nitro-N-nitrosoguanidine Market Size (M USD) and Market Volume (Tons) List
Figure 2014-2024 North America N-methyl-N-nitro-N-nitrosoguanidine Market Size (M USD) and CAGR
Figure 2014-2024 North America N-methyl-N-nitro-N-nitrosoguanidine Market Volume (Tons) and CAGR
Table 2014-2024 North America N-methyl-N-nitro-N-nitrosoguanidine Demand (Tons) List by Application
Table 2014-2019 North America N-methyl-N-nitro-N-nitrosoguanidine Key Players Sales (Tons) List
Table 2014-2019 North America N-methyl-N-nitro-N-nitrosoguanidine Key Players Market Share List
Table 2014-2024 North America N-methyl-N-nitro-N-nitrosoguanidine Demand (Tons) List by Type
Table 2014-2019 North America N-methyl-N-nitro-N-nitrosoguanidine Price (USD/Ton) List by Type
Table 2014-2024 US N-methyl-N-nitro-N-nitrosoguanidine Market Size (M USD) and Market Volume (Tons) List
Table 2014-2024 US N-methyl-N-nitro-N-nitrosoguanidine Import & Export (Tons) List
Table 2014-2024 Canada N-methyl-N-nitro-N-nitrosoguanidine Market Size (M USD) and Market Volume (Tons) List
Table 2014-2024 Canada N-methyl-N-nitro-N-nitrosoguanidine Import & Export (Tons) List
Table 2014-2024 Mexico N-methyl-N-nitro-N-nitrosoguanidine Market Size (M USD) and Market Volume (Tons) List
Table 2014-2024 Mexico N-methyl-N-nitro-N-nitrosoguanidine Import & Export (Tons) List
Table 2014-2024 South America N-methyl-N-nitro-N-nitrosoguanidine Market Size (M USD) and Market Volume (Tons) List
Figure 2014-2024 South America N-methyl-N-nitro-N-nitrosoguanidine Market Size (M USD) and CAGR
Figure 2014-2024 South America N-methyl-N-nitro-N-nitrosoguanidine Market Volume (Tons) and CAGR
Table 2014-2024 South America N-methyl-N-nitro-N-nitrosoguanidine Demand (Tons) List by Application
Table 2014-2019 South America N-methyl-N-nitro-N-nitrosoguanidine Key Players Sales (Tons) List
Table 2014-2019 South America N-methyl-N-nitro-N-nitrosoguanidine Key Players Market Share List
Table 2014-2024 South America N-methyl-N-nitro-N-nitrosoguanidine Demand (Tons) List by Type
Table 2014-2019 South America N-methyl-N-nitro-N-nitrosoguanidine Price (USD/Ton) List by Type
Table 2014-2024 Brazil N-methyl-N-nitro-N-nitrosoguanidine Market Size (M USD) and Market Volume (Tons) List
Table 2014-2024 Brazil N-methyl-N-nitro-N-nitrosoguanidine Import & Export (Tons) List
Table 2014-2024 Argentina N-methyl-N-nitro-N-nitrosoguanidine Market Size (M USD) and Market Volume (Tons) List
Table 2014-2024 Argentina N-methyl-N-nitro-N-nitrosoguanidine Import & Export (Tons) List
Table 2014-2024 Chile N-methyl-N-nitro-N-nitrosoguanidine Market Size (M USD) and Market Volume (Tons) List
Table 2014-2024 Chile N-methyl-N-nitro-N-nitrosoguanidine Import & Export (Tons) List
Table 2014-2024 Peru N-methyl-N-nitro-N-nitrosoguanidine Market Size (M USD) and Market Volume (Tons) List
Table 2014-2024 Peru N-methyl-N-nitro-N-nitrosoguanidine Import & Export (Tons) List
Table 2014-2024 Asia & Pacific N-methyl-N-nitro-N-nitrosoguanidine Market Size (M USD) and Market Volume (Tons) List
Figure 2014-2024 Asia & Pacific N-methyl-N-nitro-N-nitrosoguanidine Market Size (M USD) and CAGR
Figure 2014-2024 Asia & Pacific N-methyl-N-nitro-N-nitrosoguanidine Market Volume (Tons) and CAGR
Table 2014-2024 Asia & Pacific N-methyl-N-nitro-N-nitrosoguanidine Demand (Tons) List by Application
Table 2014-2019 Asia & Pacific N-methyl-N-nitro-N-nitrosoguanidine Key Players Sales (Tons) List
Table 2014-2019 Asia & Pacific N-methyl-N-nitro-N-nitrosoguanidine Key Players Market Share List
Table 2014-2024 Asia & Pacific N-methyl-N-nitro-N-nitrosoguanidine Demand (Tons) List by Type
Table 2014-2019 Asia & Pacific N-methyl-N-nitro-N-nitrosoguanidine Price (USD/Ton) List by Type
Table 2014-2024 China N-methyl-N-nitro-N-nitrosoguanidine Market Size (M USD) and Market Volume (Tons) List
Table 2014-2024 China N-methyl-N-nitro-N-nitrosoguanidine Import & Export (Tons) List
Table 2014-2024 India N-methyl-N-nitro-N-nitrosoguanidine Market Size (M USD) and Market Volume (Tons) List
Table 2014-2024 India N-methyl-N-nitro-N-nitrosoguanidine Import & Export (Tons) List
Table 2014-2024 Japan N-methyl-N-nitro-N-nitrosoguanidine Market Size (M USD) and Market Volume (Tons) List
Table 2014-2024 Japan N-methyl-N-nitro-N-nitrosoguanidine Import & Export (Tons) List
Table 2014-2024 South Korea N-methyl-N-nitro-N-nitrosoguanidine Market Size (M USD) and Market Volume (Tons) List
Table 2014-2024 South Korea N-methyl-N-nitro-N-nitrosoguanidine Import & Export (Tons) List
Table 2014-2024 ASEAN N-methyl-N-nitro-N-nitrosoguanidine Market Size (M USD) List
Table 2014-2024 ASEAN N-methyl-N-nitro-N-nitrosoguanidine Market Volume (Tons) List
Table 2014-2024 ASEAN N-methyl-N-nitro-N-nitrosoguanidine Import (Tons) List
Table 2014-2024 ASEAN N-methyl-N-nitro-N-nitrosoguanidine Export (Tons) List
Table 2014-2024 Australia N-methyl-N-nitro-N-nitrosoguanidine Market Size (M USD) and Market Volume (Tons) List
Table 2014-2024 Australia N-methyl-N-nitro-N-nitrosoguanidine Import & Export (Tons) List
Table 2014-2024 Europe N-methyl-N-nitro-N-nitrosoguanidine Market Size (M USD) and Market Volume (Tons) List
Figure 2014-2024 Europe N-methyl-N-nitro-N-nitrosoguanidine Market Size (M USD) and CAGR
Figure 2014-2024 Europe N-methyl-N-nitro-N-nitrosoguanidine Market Volume (Tons) and CAGR
Table 2014-2024 Europe N-methyl-N-nitro-N-nitrosoguanidine Demand (Tons) List by Application
Table 2014-2019 Europe N-methyl-N-nitro-N-nitrosoguanidine Key Players Sales (Tons) List
Table 2014-2019 Europe N-methyl-N-nitro-N-nitrosoguanidine Key Players Market Share List
Table 2014-2024 Europe N-methyl-N-nitro-N-nitrosoguanidine Demand (Tons) List by Type
Table 2014-2019 Europe N-methyl-N-nitro-N-nitrosoguanidine Price (USD/Ton) List by Type
Table 2014-2024 Germany N-methyl-N-nitro-N-nitrosoguanidine Market Size (M USD) and Market Volume (Tons) List
Table 2014-2024 Germany N-methyl-N-nitro-N-nitrosoguanidine Import & Export (Tons) List
Table 2014-2024 France N-methyl-N-nitro-N-nitrosoguanidine Market Size (M USD) and Market Volume (Tons) List
Table 2014-2024 France N-methyl-N-nitro-N-nitrosoguanidine Import & Export (Tons) List
Table 2014-2024 UK N-methyl-N-nitro-N-nitrosoguanidine Market Size (M USD) and Market Volume (Tons) List
Table 2014-2024 UK N-methyl-N-nitro-N-nitrosoguanidine Import & Export (Tons) List
Table 2014-2024 Italy N-methyl-N-nitro-N-nitrosoguanidine Market Size (M USD) and Market Volume (Tons) List
Table 2014-2024 Italy N-methyl-N-nitro-N-nitrosoguanidine Import & Export (Tons) List
Table 2014-2024 Spain N-methyl-N-nitro-N-nitrosoguanidine Market Size (M USD) and Market Volume (Tons) List
Table 2014-2024 Spain N-methyl-N-nitro-N-nitrosoguanidine Import & Export (Tons) List
Table 2014-2024 Belgium N-methyl-N-nitro-N-nitrosoguanidine Market Size (M USD) and Market Volume (Tons) List
Table 2014-2024 Belgium N-methyl-N-nitro-N-nitrosoguanidine Import & Export (Tons) List
Table 2014-2024 Netherlands N-methyl-N-nitro-N-nitrosoguanidine Market Size (M USD) and Market Volume (Tons) List
Table 2014-2024 Netherlands N-methyl-N-nitro-N-nitrosoguanidine Import & Export (Tons) List
Table 2014-2024 Austria N-methyl-N-nitro-N-nitrosoguanidine Market Size (M USD) and Market Volume (Tons) List
Table 2014-2024 Austria N-methyl-N-nitro-N-nitrosoguanidine Import & Export (Tons) List
Table 2014-2024 Poland N-methyl-N-nitro-N-nitrosoguanidine Market Size (M USD) and Market Volume (Tons) List
Table 2014-2024 Poland N-methyl-N-nitro-N-nitrosoguanidine Import & Export (Tons) List
Table 2014-2024 Russia N-methyl-N-nitro-N-nitrosoguanidine Market Size (M USD) and Market Volume (Tons) List
Table 2014-2024 Russia N-methyl-N-nitro-N-nitrosoguanidine Import & Export (Tons) List
Table 2014-2024 MEA N-methyl-N-nitro-N-nitrosoguanidine Market Size (M USD) and Market Volume (Tons) List
Figure 2014-2024 MEA N-methyl-N-nitro-N-nitrosoguanidine Market Size (M USD) and CAGR
Figure 2014-2024 MEA N-methyl-N-nitro-N-nitrosoguanidine Market Volume (Tons) and CAGR
Table 2014-2024 MEA N-methyl-N-nitro-N-nitrosoguanidine Demand (Tons) List by Application
Table 2014-2019 MEA N-methyl-N-nitro-N-nitrosoguanidine Key Players Sales (Tons) List
Table 2014-2019 MEA N-methyl-N-nitro-N-nitrosoguanidine Key Players Market Share List
Table 2014-2024 MEA N-methyl-N-nitro-N-nitrosoguanidine Demand (Tons) List by Type
Table 2014-2019 MEA N-methyl-N-nitro-N-nitrosoguanidine Price (USD/Ton) List by Type
Table 2014-2024 Egypt N-methyl-N-nitro-N-nitrosoguanidine Market Size (M USD) and Market Volume (Tons) List
Table 2014-2024 Egypt N-methyl-N-nitro-N-nitrosoguanidine Import & Export (Tons) List
Table 2014-2024 Iran N-methyl-N-nitro-N-nitrosoguanidine Market Size (M USD) and Market Volume (Tons) List
Table 2014-2024 Iran N-methyl-N-nitro-N-nitrosoguanidine Import & Export (Tons) List
Table 2014-2024 Israel N-methyl-N-nitro-N-nitrosoguanidine Market Size (M USD) and Market Volume (Tons) List
Table 2014-2024 Israel N-methyl-N-nitro-N-nitrosoguanidine Import & Export (Tons) List
Table 2014-2024 South Africa N-methyl-N-nitro-N-nitrosoguanidine Market Size (M USD) and Market Volume (Tons) List
Table 2014-2024 South Africa N-methyl-N-nitro-N-nitrosoguanidine Import & Export (Tons) List
Table 2014-2024 GCC N-methyl-N-nitro-N-nitrosoguanidine Market Size (M USD) and Market Volume (Tons) List
Table 2014-2024 GCC N-methyl-N-nitro-N-nitrosoguanidine Import & Export (Tons) List
Table 2014-2024 Turkey N-methyl-N-nitro-N-nitrosoguanidine Market Size (M USD) and Market Volume (Tons) List
Table 2014-2024 Turkey N-methyl-N-nitro-N-nitrosoguanidine Import & Export (Tons) List
Table 2014-2019 Global N-methyl-N-nitro-N-nitrosoguanidine Market Size (M USD) List by Region
Table 2014-2019 Global N-methyl-N-nitro-N-nitrosoguanidine Market Size Share List by Region
Table 2014-2019 Global N-methyl-N-nitro-N-nitrosoguanidine Market Volume (Tons) List by Region
Table 2014-2019 Global N-methyl-N-nitro-N-nitrosoguanidine Market Volume Share List by Region
Table 2014-2019 Global N-methyl-N-nitro-N-nitrosoguanidine Demand (Tons) List by Application
Table 2014-2019 Global N-methyl-N-nitro-N-nitrosoguanidine Demand Market Share List by Application
Table 2014-2019 Global N-methyl-N-nitro-N-nitrosoguanidine Capacity (Tons) List
Table 2014-2019 Global N-methyl-N-nitro-N-nitrosoguanidine Key Vendors Capacity Share List
Table 2014-2019 Global N-methyl-N-nitro-N-nitrosoguanidine Key Vendors Production (Tons) List
Table 2014-2019 Global N-methyl-N-nitro-N-nitrosoguanidine Key Vendors Production Share List
Figure 2014-2019 Global N-methyl-N-nitro-N-nitrosoguanidine Capacity Production and Growth Rate
Table 2014-2019 Global N-methyl-N-nitro-N-nitrosoguanidine Key Vendors Production Value (M USD) List
Figure 2014-2019 Global N-methyl-N-nitro-N-nitrosoguanidine Production Value (M USD) and Growth Rate
Table 2014-2019 Global N-methyl-N-nitro-N-nitrosoguanidine Key Vendors Production Value Share List
Table 2014-2019 Global N-methyl-N-nitro-N-nitrosoguanidine Demand (Tons) List by Type
Table 2014-2019 Global N-methyl-N-nitro-N-nitrosoguanidine Demand Market Share List by Type
Table 2014-2019 Regional N-methyl-N-nitro-N-nitrosoguanidine Price (USD/Ton) List
Table 2014-2019 Company A N-methyl-N-nitro-N-nitrosoguanidine Product Capacity Production (Tons) Price Cost (USD/Ton) Production Value (M USD) List
Figure 2014-2019 Company A N-methyl-N-nitro-N-nitrosoguanidine Capacity Production (Tons) and Growth Rate
Figure 2014-2019 Company A N-methyl-N-nitro-N-nitrosoguanidine Market Share
Table 2014-2019 Company B N-methyl-N-nitro-N-nitrosoguanidine Product Capacity Production (Tons) Price Cost (USD/Ton) Production Value (M USD) List
Figure 2014-2019 Company B N-methyl-N-nitro-N-nitrosoguanidine Capacity Production (Tons) and Growth Rate
Figure 2014-2019 Company B N-methyl-N-nitro-N-nitrosoguanidine Market Share
Table 2014-2019 Company C N-methyl-N-nitro-N-nitrosoguanidine Product Capacity Production (Tons) Price Cost (USD/Ton) Production Value (M USD) List
Figure 2014-2019 Company C N-methyl-N-nitro-N-nitrosoguanidine Capacity Production (Tons) and Growth Rate
Figure 2014-2019 Company C N-methyl-N-nitro-N-nitrosoguanidine Market Share
Table 2014-2019 Company D N-methyl-N-nitro-N-nitrosoguanidine Product Capacity Production (Tons) Price Cost (USD/Ton) Production Value (M USD) List
Figure 2014-2019 Company D N-methyl-N-nitro-N-nitrosoguanidine Capacity Production (Tons) and Growth Rate
Figure 2014-2019 Company D N-methyl-N-nitro-N-nitrosoguanidine Market Share
Table 2014-2019 Company E N-methyl-N-nitro-N-nitrosoguanidine Product Capacity Production (Tons) Price Cost (USD/Ton) Production Value (M USD) List
Figure 2014-2019 Company E N-methyl-N-nitro-N-nitrosoguanidine Capacity Production (Tons) and Growth Rate
Figure 2014-2019 Company E N-methyl-N-nitro-N-nitrosoguanidine Market Share
Table 2014-2019 Company F N-methyl-N-nitro-N-nitrosoguanidine Product Capacity Production (Tons) Price Cost (USD/Ton) Production Value (M USD) List
Figure 2014-2019 Company F N-methyl-N-nitro-N-nitrosoguanidine Capacity Production (Tons) and Growth Rate
Figure 2014-2019 Company F N-methyl-N-nitro-N-nitrosoguanidine Market Share
Table 2014-2019 Company G N-methyl-N-nitro-N-nitrosoguanidine Product Capacity Production (Tons) Price Cost (USD/Ton) Production Value (M USD) List
Figure 2014-2019 Company G N-methyl-N-nitro-N-nitrosoguanidine Capacity Production (Tons) and Growth Rate
Figure 2014-2019 Company G N-methyl-N-nitro-N-nitrosoguanidine Market Share


Initially a T. Rex/David Bowie-inspired glam rock act, Yellow Monkey soon expanded the range of their influence to include styles like hard rock, disco, and progressive rock, and rose to be one of Japan's top bands of the '90s. The group, shortened to YeMon by its fans, began in 1988, formed by Yoshii "Lovin" Kazuya, Hirose "Heesey" Youichi, and Kikuchi "Annie" Eiji and joined later by Kikuchi "Emma" Hideaki (the brother of Eiji). Initially a bassist, Yoshii soon switched to guitar and took over the vocal duties. The lineup was stabilized — and remained unchanged thereafter — and so Yellow Monkey began to build up their rock cred by playing underground clubs. It took them three years to release their first EP (Bunched Birth, 1991), but the wait was worth it, because their indie offering got them a deal with Columbia. Over the course of four albums released in the early '90s, Yellow Monkey gradually realized their commercial potential, their success becoming obvious by 1995 when they recorded the best-selling single "Love Communication," featured on the album Smile, and finished their big Japanese tour with a Budokan show, sneaking another album Four Seasons into their schedule before 1995's end.


text:
A barna gödény (Pelecanus occidentalis) a madarak (Aves) osztályának gödényalakúak (Pelecaniformes) rendjébe, ezen belül a gödényfélék (Pelecanidae) családjába tartozó faj.

Saint Kitts és Nevis nemzeti madara.

Rendszerezése
A fajt Carl von Linné svéd természettudós írta le 1766-ban.

Alfajai
Pelecanus occidentalis californicus Ridgway, 1884Pelecanus occidentalis carolinensis Gmelin, 1789
Pelecanus occidentalis murphyi Wetmore, 1945Pelecanus occidentalis occidentalis Linnaeus, 1766
''Pelecanus occidentalis urinator Wetmore, 1945

Előfordulása
Kalifornia partvonalától Ecuadorig, nyugati irányban a Galápagos-szigetekig, a keleti partvonalon Carolinától a Mexikói-öblön át egészen Venezueláig.

Természetes élőhelyei a tengerpartok, part közeli vizek. Vonuló faj.

Megjelenése
Testhossza 152 centiméter, szárnyfesztávolsága 203-228 centiméter, testtömege 6500-9000 gramm. A legfeltűnőbb ismertetőjegye a hatalmas csőr, amely felülről lapított. Az alsó csőrkáváról alácsüngő torokzsákba tíz liter vizet is képes felvenni. Tollazata barna, feje világosabb és fehéres.

Életmódja
A madarak a meleg vizű tengerek partján élnek, és az idő nagy részét sziklákon, zátonyokon töltik. Kerülik a szárazföld belsejét és a nyílt tengert. E gödény az egyetlen gödényfaj, mely bukórepüléssel szerzi meg táplálékát. Túlnyomórészt halak, olykor-olykor dögök teszik ki táplálékát. A barna gödény általában 15-25 évet él.

Szaporodása
A barna gödény társas lény, hagyományos fészkelőhelyein kolóniákban költ. Ivarérettségüket 2-3 évesen érik el, a költési időszak a szélességi foktól különbözik. Az USA nyugati partvonalán tavasszal költ. Rendszerint 3 fehér tojást rak a tojó, melyen 39 napig kotlik. A fiatal gödények körülbelül 9 hetesen repülnek ki.

Természetvédelmi helyzete
Az elterjedési területe rendkívül nagy, egyedszáma pedig növekszik. A Természetvédelmi Világszövetség Vörös listáján nem fenyegetett fajként szerepel.

Jegyzetek

Források

  - magyar neve

További információk
 Képek az interneten a fajról
 Ibc.lynxeds.com - videók a fajról

Gödényfélék
Madárfajok
A Galápagos-szigetek madarai
Kanada madarai
Az Amerikai Egyesült Államok madarai
Mexikó madarai
Trinidad és Tobago madarai
Puerto Rico madarai
Dél-Amerika madarai

redpajama_set_name: RedPajamaWikipedia


text:
A solid understanding of the foreign tax credit can help minimize your investment-related taxes. What's the foreign tax credit? The IRS explains it this way:
"If you paid or accrued foreign taxes to a foreign country on foreign source income and are subject to U.S. tax on the same income, you may be able to take a credit for those taxes."
In short, the idea of the credit is to eliminate double taxation on foreign income.
Example: You earn $500 in foreign dividend income over the course of the year and you pay $100 in foreign taxes on that income. You can claim a $100 credit for foreign taxes paid, thereby reducing your U.S. income tax obligation by $100.
Important note: Unless you meet three requirements, there is a limit to the credit you can take (please see IRS Publication 514 for details on the limit). The three requirements are as follows:
Your only foreign income is passive income (passive income being things like dividends, interest, and rents),
Your qualified foreign taxes for the tax year are not more than $300 ($600 if married filing jointly), and
All of your gross foreign income and the foreign taxes are reported to you on a payee statement such as a Form 1099-DIV or 1099-INT.
And now the fun part: How can you put this knowledge to use?
Use it to help determine your asset location.
Funds held in a retirement account–like an IRA or 401(k)–do not qualify for the credit even if they're funds that would qualify were they held in a taxable account.
Does this mean that you should only buy international stock funds in taxable accounts rather than retirement accounts? No. Not at all. The benefit from tax-deferred or tax-free growth far outweighs this little credit.
If, however, you're simply deciding which fund to hold in your retirement account (domestic stock fund vs. international stock fund) and which fund to hold in your taxable account, then it's probably best to tax-shelter your domestic stock funds before tax-sheltering your international stock funds.
Claiming the Credit
Generally, you have to file Form 1116 to claim the foreign tax credit. If, however, you meet a few requirements (the same as the requirements listed above to avoid the limitation on the credit), you can simply enter your foreign taxes directly on Form 1040, line 47.
For More Information, See My Related Book:
The difference between deductions and credits,
Itemized deductions vs. the standard deduction,
Several money-saving deductions and credits and how to make sure you qualify for them,
"Very easy to read and is a perfect introduction for learning how to do your own taxes. Mike Piper does an excellent job of demystifying complex tax sections and he presents them in an enjoyable and easy to understand way. Highly recommended!"
Are We Qualified to Invest on Our Own?
A recent Get Rich Slowly post asked whether one should stop investing for retirement in order to pay off debt. It's an important question, and one that I've attempted to tackle before. But what I really want to talk about are the comments that were left on the GRS post.
They're frightening. And I'm not saying this just to be snarky (though, admittedly, I do partake in a little snark from time to time).
Some people were attempting to mathematically justify paying down debt rather than taking advantage of a fully vested, 100% employer match. Now, if you gain a valuable psychological benefit from paying down debt, that's fine. Go for it. But how somebody could argue that a 20% return is mathematically superior to a 100% return escapes me.
Other commenters argued that, if an investor is young, it's better mathematically to invest for retirement, rather than to pay down debt–even if there's no employer match to be gained, and even if the interest rate on the debt is higher than the rate of return from investing for retirement.
"The Horsepower to Do the Math"
This all reminded me of an article William Bernstein wrote a few months back, where he argues that most people just aren't qualified to invest on their own. Bernstein estimates that less than 10% of the population has "the horsepower to do the math." He elaborates,
"Fractions are a stretch for 90% of the population. The Discounted Dividend Model, or at least the Gordon Equation? Geometric versus arithmetic return? Standard deviation? Correlation, for God's sake? Fuggedaboudit!"
I'm inclined to think that his estimate is overly pessimistic. (Is it really that hard to explain correlation?) And I've always thought investing mistakes aren't caused by a lack of math skills so much as by a decision process that's not based on math at all. ("I'll just hold this stock until it gets back to where I bought it," for example.)
But I may be wrong. Thoughts?
Vanguard Investors Outperform Fidelity Investors
Russell Kinnel, director of mutual fund research at Morningstar, recently compared investor returns at Vanguard to investor returns at Fidelity. His study is interesting because it looks at investor returns (aka "dollar-weighted returns") rather than investment returns (aka "time-weighted returns").
A brief example of dollar-weighted returns
If Mutual Fund ABC earned a 25% return in Year 1, then lost 20% in Year 2, its effective annual return over the two years would have been 0% (because it would be back exactly where it started).
If, however, the fund had doubled in size at the end of Year 1–due to investors chasing performance and buying the fund after a great year–its dollar-weighted return would be significantly below 0%, because the performance in Year 2 would be weighted twice as heavily in the calculation. In short, dollar-weighted returns measure how the investors performed rather than how the investments performed.
What did Morningstar's research show?
The study showed that Vanguard investors earned greater returns than Fidelity investors over the last 10 years. But that doesn't really mean a great deal to me. It could simply be the result of Vanguard's larger funds being in more successful asset classes than Fidelity's.
What does interest me, however, are the two following facts:
As usual, mutual fund investors underperformed their own investments across the board.
Vanguard's investors underperformed their investments by a smaller margin than Fidelity's investors.
Why do we underperform?
We underperform because we try to time the market, and we (usually) fail. We chase performance–both in terms of hot asset classes and in terms of hot funds–and it destroys our returns.
Why do Vanguard investors perform better?
My hypothesis is that it has to do with the core philosophies of the two companies. As a company whose success has been based on index funds, Vanguard's core tenets are minimizing costs, diversifying, and buying and holding.
In contrast, Fidelity, at its core, is about active investment. Many of their own fund managers turn over their portfolios more than once per year. It wouldn't be terribly surprising to learn that their investors do something similar.
Kinnel has a similar opinion:
"It's possible that the performance gap also has something to do with each firm's message to investors. Vanguard preaches long-term investing and goes so far as to warn investors away from hot-performing funds…Fidelity also preaches long-term investing, but it sometimes nudges people to invest based on short-term results."
What can we learn here?
Surely, some people will look at this study and see it as evidence of an opportunity to outperform the market. They'll draw the conclusion that we must learn to "be fearful when others are greedy and greedy when others are fearful," as Warren Buffett would say.
To me, the lesson is slightly different. I see it as another piece of evidence that our predictive abilities are decidedly lacking. After all, nearly every single one of those people who underperformed sincerely believed that he was going to outperform.
"I am above average!" is the battle cry of underperformance.
The Oblivious Investor is 1 Year Old
Today (10/26/09) is The Oblivious Investor's first birthday. 🙂
A few statistics of interest:
285 posts,
~ 600 subscribers,
1,359 comments,
Currently getting ~ 230 first-time visitors each day via search engines, and
2 new books launched.
On growth and goals:
I'm happy with that level of growth. I can't say whether or not it exceeded my expectations, because I didn't have any. This being my first blog, I had no clue what to expect. Similarly, I don't know what to expect by the end of year two. 1,000 subscribers? 2,000? 3,000? No idea really.
I'm comfortable with the fact that this blog will never be comparable to Get Rich Slowly or The Simple Dollar in terms of readership. It focuses on too narrow a niche for that to happen. That is, it only appeals to people with a serious interest in investing. And within that group, it only appeals to people who don't mind the fact that I openly criticize activities such as stock picking, market timing, etc.
Giving thanks where due:
The blog has been an absolute blast to write so far. And with a growing group of people reading and participating in the discussion, it's becoming more fun as time passes. So thanks to all of you for reading, and thanks to everyone who has participated in the discussion whether via comments, email, or replies on your own blogs.
Also, to everyone who has helped to share this blog (and its ideas) with others, whether by linking to it, tweeting about it, stumbling posts, or good old-fashioned word of mouth: Thanks!
Similarly, I'm quite grateful to everybody who has helped with the two books, whether linking to them, reviewing them on Amazon, or, of course, buying them. 😀
Where we go from here:
For my part, I'll be continuing to write on a regular schedule, most likely on a range of similar topics. But a large part of where the blog goes from here is up to you. For example:
How quickly it spreads/grows is primarily a function of how many of you are compelled to share it with others.
In terms of topics, if you have something you'd like me to cover or questions you'd like me to address, please let me know.
Thanks again, everyone. I'm looking forward to year #2. 😀
Review: The Bogleheads' Guide to Retirement Planning
I've read books that are intended to be "all you need to know about personal finance." And I've read books that are intended to be "all you need to know about investing." But The Bogleheads' Guide to Retirement Planning is the first book I've read that's "all you need to know about planning for retirement."
Note the distinction: This book is not about saving/investing for retirement. It's about planning for retirement (and everything that's a part of such planning). The best part about the book, in my opinion, is the breadth of topics covered. The chapter-by-chapter table of contents should give you an idea of the scope:
The Retirement Planning Process
Individual Taxable Accounts
Defined Benefit Plans (written by fellow blogger, The Finance Buff)
Single-Premium Immediate Annuities
Basic Investing Principles
Funding Your Retirement Accounts
Understanding Social Security
Withdrawal Strategies
Income Replacement
Essentials of Estate Planning
Estate and Gift Taxes
Seeking Help from Professionals
Divorce and Other Financial Disasters
Meet the Bogleheads
The chapters average 16 pages in length, so you get a little more than just a high-level introduction to each topic, while at the same time avoiding details that are unlikely to be important to more than a small number of readers. Also, the authors conclude each chapter with suggestions for additional reading, making it easy to research a particular topic further should you so desire.
A Few Discussions I Particularly Enjoyed
(In chapter 11) Social Security strategies: By intelligently planning when to begin taking Social Security payments, you can significantly increase your expected lifetime payout. (This goes double for married couples, as the planning opportunities increase when there are two spouses involved.)
(In chapter 9) How asset allocation should be affected by your other assets: For example, if you have a pension that pays out a fixed amount every year, that's roughly equivalent to a large bond holding, so perhaps you should have a heavier stock allocation than other investors. Also, if your pension is not indexed to inflation, perhaps your other bond holdings should be in TIPS so as to provide some inflation protection.
(In chapter 7) How to shop for Single Premium Immediate Annuities: SPIAs can be used to offset the risk of outliving your income, but shopping for annuities is tricky business. Being an informed customer helps you avoid getting ripped off.
Would I Recommend the Book?
It depends. If you're still quite young, there's a lot of material here that you don't need to worry about just yet. I'd suggest starting with something different–perhaps some combination of the following:
The Four Pillars of Investing,
How a Second Grader Beats Wall Street,
The Bogleheads' Guide to Investing,
The New CoffeeHouse Investor, or
my latest: Investing Made Simple.
If, however, you're starting to think seriously about getting all the retirement planning specifics nailed down, then I can't recommend The Bogleheads' Guide to Retirement Planning highly enough. It's the only book I've seen so far that covers all of the different topics involved in planning for retirement.
ETF List: The Best (Low-Cost) ETFs
Quick note: This article focuses on ETFs from the perspective of a buy & hold investor. If you're looking for information for more active investors, I'd suggest signing up for a Morningstar account. (It's free.)
Last week's post on the best/lowest-cost index funds went company-by-company. One of the nice things about ETFs is that all you need is a discount brokerage account somewhere, and you can pick and choose between ETF providers–no need to stick with just one company.
In that vein, this week's comparison of ETFs is sorted by asset class rather than by company. Really, it's just a shopping list of sorts: The lowest-cost ETFs that I've found for each asset class. If you know of others that should be added (or any that should be replaced with something else), please feel free to share. 🙂
Update: Because of the fact that Vanguard now allows investors to trade Vanguard ETFs with no commissions in a Vanguard brokerage account, I've used Vanguard ETFs wherever possible. (In most cases, they have the lowest expense ratios as well.)
Domestic Stock ETFs
Asset Class Name Ticker ER
Total Stock Market Vanguard Total Stock Market ETF VTI 0.07%
Large-Cap Blend Vanguard S&P 500 Index ETF VOO 0.06%
Large-Cap Value Vanguard Value ETF VTV 0.14%
Large-Cap Growth Vanguard Growth ETF VUG 0.14%
Small-Cap Blend Vanguard Small-Cap ETF VB 0.14%
Small-Cap Value Vanguard Small-Cap Value ETF VBR 0.14%
Small-Cap Growth Vanguard Small-Cap Growth ETF VBK 0.14%
International Stock ETFs
Developed Markets:
Large-Cap Blend Vanguard Europe Pacific ETF VEA 0.15%
Large-Cap Value iShares MSCI EAFE Value Index EFV 0.40%
Mid-Cap Value WisdomTree Interntnl SmallCap Div DLS 0.58%
Emerging Markets:
Large-Cap Blend Vanguard Emrg Mkts ETF VWO 0.27%
Emrg & Devlpd Markets:
Large-Cap Blend Vngrd FTSE All-World Ex-U.S. ETF VEU 0.25%
Mid-Cap Blend Vngrd FTSE AW ex-US Sm-Cap ETF VSS 0.40%
Bond ETFs
Short-Term Bond Vanguard Short-Term Bond ETF BSV 0.12%
Interm-Term Bond Vanguard Total Bond Market ETF BND 0.12%
Interm-Term Gov't Vanguard Inter-Term Gov't Bond ETF VGIT 0.15%
Long-Term Gov't Vanguard Long-Term Gov't Bond ETF VGLT 0.15%
Long-Term Bond Vanguard Long-Term Bond ETF BLV 0.12%
TIPS iShares Barclays TIPS Bond TIP 0.20%
Alternative Asset Class ETFs
REITs Vanguard REIT ETF VNQ 0.13%
Precious Metals (Gold) SPDR Gold Trust ETF GLD 0.40%
In my opinion, that very same flexibility–the ability to buy 800 different ETFs from one discount brokerage account–is also the danger of ETF investing. It's easy to get carried away tinkering with your portfolio, when the reality is that most of us would be better served by simply buying a handful of ETFs and holding them as long as we can.


Are you wondering the Reason as to why you need to respond to online reviews? This is given the fact that a good percentage of your prospects actually read online reviews and you know what an impact this creates and has on your business. The other reason is online reviews is just but free advertising.
The fact is, as we have highlighted above, that a lot of the search engine users actually depend on the business reviews they find online to finally rest their need to decide to purchase or not to purchase from a given entity for their needs for services or goods. Nonetheless, the 5 star rated entities don't so obviously receive the deal anyway. For this reason,it is just necessary that you know how you can respond to Google reviews. Thus we will take a look at some of the tips.
We may still have to ask ourselves one more time, the significance of responding to Google reviews. The response is given for two reasons- it is just good for the business and it is as well a savvy SEO strategy. These are the reasons why it happens to be so.
The first reason is that it creates a valuable bond. Consumers will only continue using your products or services if they indeed feel that they are being listened to. Do not forget the fact of business and the competition out there which proves that the task and cost of attracting new customers is often a lot more costlier and harder than the task of retaining the current customer base.
Going forward you will realize that this is even an investment into the future which will pay off in due course. This is due to the fact that the owner responses are not only read by the reviewers but as well people who may turn out to be your future clients. In fact, a good percentage of the prospect population, up to 88%, actually trust the reviews more than they would personal recommendation.
So how do you respond to the Google Reviews anyway? Respond to all the classes and types of reviews, both negative and positive without any bias. For those entities in a similar practice as that of Julie Johnson Law, response to reviews becomes even more serious than you ever think it could be. If you happen to have fewer reviews then it is a sure way o run out of competition as your competitors will have a fair share of the market courtesy of the reviews that they have and ratings.


Innoson Nigeria Limited obtained a writ of Fifa from the Federal High Court in Awka, Anambra State, against Guaranty Trust Bank (GTB) to effect the judgment given by the Federal High Court in Ibadan and upheld by the Supreme Court of Nigeria. The Judgment debt of N2.4bn has an accrued interest as at today of about N6,717,909,849.96 which results in about N8.8 billion.
Thus, the Court of Appeal affirmed the judgment of the Federal High Court, Ibadan Division which ordered GTB by way of Garnishee order absolute – to pay N2.4 Billion to Innoson with a 22 percent interest, per annum, on the judgment sum until the final liquidation of the judgment debt.
A statement by the Innoson group's Head of Corporate Communications, Cornel Osigwe disclosed that "The Judgment debt of N2.4B has an accrued interest as at today of about N6,717,909,849.96 which results in about N8.8 Billion.


November 1st, All Saints' Day, Day of the Dead; most people spend it visiting the cemeteries and lighting candles. We went hiking instead. At 1453 meters above sea level, Kašna planina mountain offered great views as always, despite the smoggy and cloudless day.
The Kamnik Alps from Kašna planina after sunset. From left to right: Mts. Kočna, Grintovec, Skuta, Turska gora, Brana, Planjava, Ojstrica.


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"Lend Me a Tenor" Launches the Mainstage Season at Bay Street Theatre
May 31, 2013 by Pat Rogers EVENTS, HAMPTONS, Hamptons & East End, LIFESTYLE
DISPATCH - MAY 31, 2013 (9:20 a.m.)
SAG HARBOR, NY-
Preview audiences had the first look and the first laughs this week during "Lend Me a Tenor," the classic Ken Ludwig farce launching the mainstage season featuring three zany comedies in a row at Bay Street Theatre in Sag Harbor. Tonight is the final preview with the show officially opening tomorrow (Saturday, June 1). Performances continue through June 23.
Set in 1934, "Lend Me a Tenor" tells the story of a struggling opera company in Cleveland putting up its special guest star, the renowned singer Tito Merelli, at a local hotel on the eve of a major production.
Along with a big voice, the temperamental Tito, also known as "Il Stupendo," turns out to have a big ego, a roving eye, and a very jealous wife. When he is accidentally given a double dose of tranquilizers, the side effects include a chain reaction of mistaken identity, double-entendres and a merry-go-round of ambition and hysterical romantic mayhem.
Nancy Johnston, Betsy Dilellio, Steve Rosen, and Scott Cote in "Lend Me a Tenor." Photos by Jerry Lamonica. Courtesy Bay Street Theatre.
Judy Blazer and Scott Cote in "Lend Me a Tenor." Photos by Jerry Lamonica. Courtesy Bay Street Theatre.
Broadway productions of "Lend Me a Tenor" in 1989 and 2010 garnered multiple Tony nominations and rave reviews. The New York Times said the show "achieves true comic delirium." Variety described it as "… the classic farce situation—disaster; denial; recovery; complication; catastrophe—and it's a joy to watch!"
Directed by Don Stephenson, the cast for the Bay Street production of "Lend Me a Tenor"—all of whom boast a range of Broadway, Off-Broadway and regional credits. The cast features Judy Blazer, Scott Cote, Betsy DiLellio, Donna English, Nancy Johnston, Noah Plomgren, Steve Rosen and Roland Rusinek.
Noah Plomgren, Steve Rosen, and Roland Rusinek in "Lend Me a Tenor." Photos by Jerry Lamonica. Courtesy Bay Street Theatre.
Steve Rosen and Nancy Johnston in "Lend Me a Tenor." Photos by Jerry Lamonica. Courtesy Bay Street Theatre..
"Lend Me a Tenor" was written by Ken Ludwig. Ludwig is an internationally acclaimed playwright whose work has been performed in at least 30 countries in over 20 languages. He has had six shows on Broadway and six in the West End.
Awards include two Laurence Olivier Awards, three Tony Award nominations and two Helen Hayes Awards. His best known plays include "Lend Me A Tenor," "Crazy For You," "Moon Over Buffalo," "Leading Ladies," "Twentieth Century," "The Adventures of Tom Sawyer," "The Game's Afoot," "The Fox on the Fairway," "Midsummer/Jersey," "The Three Musketeers," "Treasure Island" and "The Beaux' Stratagem."
His book, How to Teach Your Children Shakespeare, will be published in June 2013 by Crown Publishing. Ludwig studied music at Harvard with Leonard Bernstein and theatre history at Cambridge University in England.
Steve Rosen, Judy Blazer, Noah Plomgren, and Roland Rusinek in "Lend Me a Tenor." Photos by Jerry Lamonica. Courtesy Bay Street Theatre.
The Bay Street production is directed by Stephenson, whose credits include critically acclaimed productions of "The 39 Steps," "Titanic," "Cat on a Hot Tin Roof," "Oleanna" and "The Great Unknown. Broadway acting credits include "The Producers," "Dracula," "Titanic," "Parade," "By Jeeves," "Wonderful Town," "Private Lives," "Rock of Ages" and "Pardon My English."
The production team includes Ken Goldstein (Set Design), Mike Billings (Lighting Design), Wade Laboissonniere (Costume Design), Matt Kraus (Sound Design), Tom Helm (Music Coordinator), Kathy Fabian/Propstar (Properties Design) and April Ann Kline (Production Stage Manager).
BASIC FACTS: "Lend Me a Tenor" is presented at Bay Street Theatre through June 23. Ticket prices are $59.50 and $69.50 with discounts for seniors, students, veterans and first responders. For more information or to purchase tickets, call the box office—now open seven days a week from 11 a.m. till show time—at 631-725-9500, or visit www.baystreet.org.
The Bay Street Theater is located at 1 Bay St, Sag Harbor, NY 11963. www.baystreet.org
For the complete lineup at Bay Street for summer 2013, log on to www.baystreet.org or call the box office at 631-725-9500.
© 2013 Hamptons Art Hub LLC. All rights reserved.
NYC Things To Do – Through September 11, 2016
Weekend: Best Bets for Events in The Hamptons and East End Oct 6 – 10, 2016
Mary Montes Solo Exhibition "de Kooning and Friends" Closing Reception & Final Weekend


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, född 12 augusti 1997 i Chiba prefektur, är en japansk fotbollsspelare.

Kida började sin karriär 2020 i V-Varen Nagasaki. 2021 flyttade han till Vegalta Sendai.

Källor

Noter

Japanska fotbollsspelare
Spelare i V-Varen Nagasaki
Spelare i Vegalta Sendai
Födda 1997
Levande personer
Män
Personer från Chiba prefektur
Japanska idrottare under 2000-talet


The National Commercial Bank in a statement on its Facebook page advised customers that due to a major system challenge affecting its service provider LIME several of its services are down. NCB says its ATM Services (Off-Site), POS Services (Dial-up & Mobile), Internet Banking Service, Email & Blackberry Service and Call Centre Service have been affected. Last night thieves made off with a major cable from LIME, which links Kingston to Portmore, and this has affected several customers and businesses in the Corporate Area, St. Catherine and Clarendon.


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jenni@jennirochelle.com
LoveLetter Blog
"Well, when I think about from when I started to where I'm at now, I do feel like a different person. Just so many things come to my mind, like slowing down is my superpower, and this might not be exactly tied to week one, but just overall, and really connecting to my body and paying attention to it, and valuing it, because I do take information in so many other ways than just in my head, and getting out of my head, and I feel much stronger."
— Julie M.
"The validation I received in telling my story in group brought so much healing. It is comforting to know that women warriors have blazed the path for me and I am not alone! Jenni Rochelle creates a beautiful space that encourages me to grow at my pace and accepts me where I am when I don't feel ready. She let's me have space to figure out when I am making decisions that don't serve me. If you are thinking about joining and having your own Big Sexy Comeback, don't wait! You are so worth it!"
— Cindy
"I just feel like it just brought it all home for me that I really shut this part of me down in my life for way too many years. I closed myself off from sex, sexuality, open-heartedness, any of it. And I see now I thought, 'Well I don't need it. I can live without it. It's not that big a deal. I've got too many other things in my life,' but I realize now how important it is to me and that I want to keep exploring it."
— Ellyn H.
"I've been very grateful for this group for the opportunity to learn how to embrace pleasure again in my life."
— Jenn M.
"There was a shift in me to realize that, first of all I realized how much even in my marriage I really shut down, and I didn't even realize it. So now I feel like there's a sizzle back, and I do believe there could be a good man and I could fall in love again, and get to enjoy intimacy, but even if that doesn't happen, that sizzle is still in me, and I am a sexual being and that's perfectly fine."
"Women who have been through betrayal trauma understand the complexities of the process of healing from this painful discovery. You need connection. You need a tribe to help hold you up. You need a safe place.
That is just what I found from Jenni Rochelle and the Beauty After Betrayal community: my safe place.
The gifts that I have received are beyond my expectations. I received care, deep support, belly laughs, and celebrations of successes every step of the way. I am truly grateful."
— Jenn
"I was afraid, confused and in recovery from trauma related to spousal sex addiction when I started working with Jenni Rochelle.
Through intuitive listening, shared experiences, honesty and insight, I quickly learned that I am not alone and that there are steps to healing.
Jenni is a gem for those who have been struggling with disloyal partners and trauma. I am now strong and have hope that I will heal and that my future is bright. "
"A fire in my belly has been lit again. It has everything to do with sexuality, womanhood, desire and pleasure."
"Jenni's energy, knowledge, compassion, and spirit are infectious; I cannot imagine a better guide to help explore yourself in order to heal and thrive. She has helped me identify my own strength and softness, and to manage the tension of nourishing both. I am more grounded, confident, free, and whole than I've ever been."
— Erika S.
"I wanted to move beyond surviving betrayal trauma and into rediscovering myself and living my best life! I knew I had an inner light inside me, but it was dim and I needed help making it bright.
Through my work with Jenni, I uncovered who I am, what my values are, what I believe in, what I want more of (and less of), and I now feel more confident making decisions and listening to my three wise women–head, heart, and gut. I have peace and calmness.
I know there are so many amazing things out there for me–more love, friendship, happiness, and satisfaction..and they are all starting to happen."
— Sandy
"Jenni has this really quirky, spiritual, creative presence, and she's so empathetic. She can feel what you're feeling, even in the virtual meetings, which at first I thought, 'Oh my gosh, we have to do this virtually. Is this really going to help me? I need an actual person I need somebody who can give me a hug. But Jenni's presence is like a virtual hug, seriously!"
"I found out that my husband of twelve years was having an affair and throughout the next months of trauma, indecision and ultimately divorce, Jenni was the one person I could count on.
She was there when I needed someone who understands the trauma a situation like this causes. She was there when I was lonely, sad, mad, terrorized, and questioning.
She made me laugh, helped me understand this (shitty) process and reminded me that I was strong even when I felt like everything was crumbling.
The best part about Jenni? She listens without judgement.
I can say whatever needed to be said and trust that she will be there again the next time I need someone to be present in a conversation. What a gift."
— Amy M.
"Jenni is present, trustworthy, loving, and empowered. She is one of my most trusted people. I know sex addiction is still a relatively taboo subject…if you are experiencing this in your life, I encourage you to reach out to her for validation and support."
— Angie K.
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The zinc and nickel miner says the proposed new plant would provide 150 permanent jobs and give Finland a leg-up in the electric and hybrid motor supplies market.
The north-eastern municipality of Sotkamo has decided to green-light a building permit for the state-owned mining company Terrafame to build a battery chemical plant there. The company says the new plant would employ 150 people permanently and give Finland a leg-up in the growing market for materials used in the manufacture of electric and hybrid motor batteries.
"We've secured a building permit and now we are carrying out an environmental impact assessment. We hope to receive a decision by the end of the year," says Terrafame's CEO Joni Lukkaroinen.
The nickel and zinc miner is still awaiting an environmental permit, however. This might be more difficult to procure, due to the mine's extensive track record of serious environmental offenses back when it was still known as Talvivaara.
The government of Prime Minister Juha Sipilä made a controversial decision in 2016 to bail the bankrupt mine out to tune of 100 million euros. The name of the operation was subsequently changed from Talvivaara to Terrafame.
The Finnish Association for Nature Conservation has been highly critical of the mine's treatment solutions for the waste rock that is produced in the mining process. At present, this rejected host rock material is deposited in a waste repository at the facility.
The group's Kainuu region chair Antti Lankinen says the extractive waste at Terrafame contains large amounts of black schist that crumbles easily and can cause acidicification in the surrounding environment.
Lankinen predicts that the waste rock repository at the mine will expand to hundred of hectares in size within the next few years.
"The mine operations as planned would create over one billion tonnes of waste rock," he says.
Terrafame CEO Joni Lukkaroinen maintains that the mine's environmental solutions are sound. He also points out that the new battery chemicals plant would not increase the mine's output.
"The battery chemicals plant would be a refining stage added on to the current production, not an expansion of the mining operations," he says.
His company says that current forecasts predict that the battery industry's demand for nickel will increase five-fold by the year 2030. New large-scale nickel mines are also being established in Australia and Canada.
"We believe that every fourth vehicle on the road will have an electric or hybrid motor by the year 2030. This means that 80 million cars a year would be manufactured and 20 million of those would need a lithium battery," Lukkaroinen says.