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\section{Introduction}

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\section{Introduction}
Deep neural network quantization~\cite{binaryconnect,hwang2014fixed,dorefa, lin2016fixed} is a powerful tool for improving the computational efficiency of deep neural networks (DNNs).
When being accompanied with the \newdel{right}\new{low-bitwidth} hardware design\newdel{(of low-bitwidth)}~\cite{outliernpu, bitfusion, tpu}, \newdel{both }the latency and energy consumption of \newdel{deep neural networks}\new{DNNs} can be greatly reduced.

One problem of quantized models is, however, that they often suffer from the significant drop in accuracy, mainly due to quantization errors~\cite{lin2016fixed}.
A popular way to address the problem is to further train or calibrate the model with training data~\cite{choi2018pact,jacob2018quantization,rastegari2016xnor,zhang2018lq,zhou2017incremental,dorefa}.
During the fine-tuning procedure, the forward pass is performed with quantized values whereas the backpropagation is done with floating-point values to recover the accuracy loss in the initial quantization.

Unfortunately, such fine-tuning methods, which assume the full availability of training data at the time of quantization, are often not feasible in reality.
Many models are disclosed to public only with their trained weights, and the dataset may contain proprietary, confidential, or sensitive data that fundamentally prohibit sharing.

\JL{too long. get to the point}
\emph{Zero-shot quantization} (or data-free quantization)~\cite{outlier,aciq,dfq,choi2020data,dsg,gdfq,zeroq,qimera,autorecon} is therefore a necessary technique for quantization.
It assumes that only the architecture and the pre-trained weights are available at the time of quantization.
Current successful approaches are mainly led by generative approaches~\cite{zeroq,gdfq,zaq,dsg,autorecon,qimera}.
Using synthetic samples from generators, knowledge distillation~\cite{hinton2015distilling} is applied against full-precision models.
It is known that the state-of-the-art methodology achieves almost similar performance to that of the data-driven approaches (i.e., quantization with real samples) for 5-bit fixed-point quantization, and comparable performance on 4-bit fixed-point setting.~\cite{qimera}.

\JL{hey, what happened to CE label smoothing? sensitivity on CE? (maybe that has some meaning in cifar100?)}

However, the recipe of the fine-tuning in zero-shot quantization is mainly adopted from common knowledge distillation problems~\cite{hinton2015distilling,haroush2020knowledge,cho2019efficacy} that consider neither quantization nor synthetic samples.
As in the knowledge distillation, the loss function of the zero-shot quantization is \emph{habitually} built as a combination of the cross-entropy (CE) against the hard label and the Kullback–Leibler (KL) divergence against the full-precision network's output.\footnote{In the remainder of this paper, we refer to \emph{CE} as the cross-entropy against the hard label and \emph{KL} as the KL divergence against the full-precision network unless otherwise stated. \JL{would this CE/KL definitions be okay?}}
It works well in practice, but there \newdel{@@do not exist any explicit }\new{are no detailed }studies to introspect the appropriateness of the loss in the context of zero-shot quantization.
\newdel{Therefore, there is an urgent need for more analyses on whether those solutions are appropriate.}
\new{Therefore, more analyses on those solutions are needed.}
Moreover, the distribution of synthetic samples can be different from that of the original data.
In such a case, they can be considered a type of adversarial samples (also see \cref{fig:samples} for examples) and thus, the quantized network produces a huge generalization gap.

To our knowledge,
we for the first time perform in-depth analyses on the loss surface of the zero-shot quantization problem.
Through the analyses, we find several key observations for better quantization.
First, quantized models often have difficulty optimizing multiple loss terms, and the loss terms fail to cooperate --- in other words, the angle between the gradients of CE and KL is quite large in many cases.
Second, KL usually has a much flatter loss surface than that of CE, having a better potential for generalizability.

To this end, we propose a method to address such problems of the zero-shot quantization, called \textit{AIT\xspace (All In the Teacher)}.
While pursuing a flatter surface of the loss curve, AIT\xspace lets the quantized student model get closer to the full-precision teacher model.
To be more specific, we exclude CE from the loss, and apply our proposed \emph{gradient inundation\xspace} with KL only.
In addition, gradient inundation\xspace is designed to grow the gradients of KL in such a manner that a certain portion of weights are guaranteed to be updated in each layer.
As a result, the quantized model approaches closer to the full-precision teacher, and our method takes over the state-of-the-art position for various datasets.
Our contributions can be summarized as follows: \JL{are there too many?}
\begin{itemize}
    \item We analyze the first and second-order loss surfaces, i.e., \newdel{Jacobian}\new{gradient} and Hessian, of the zero-shot quantization problem. To the best of our knowledge, we are the first to closely investigate the loss function in the zero-shot quantization problem.

    \item We identify that the gradients from CE and KL form a large angle from the beginning to the end of the fine-tuning. This implies that the quantized network is \newdel{making a trade-off between the two instead of benefiting from them}\new{suffering from their trade-off instead of benefiting from them working in harmony}.

    \item We analyze the local curvature of the loss surface and observe that the two losses of our interest exhibit a great amount of curvature difference.

    \item We observe that the quantized student suffers from infrequent updates, where only a few layers are changing their integer weights and the remaining layers are stuck below rounding thresholds.

    \item Based on these findings, we propose AIT\xspace which excludes the cross-entropy loss, and manipulates the gradients using our proposed gradient inundation\xspace method such that the quantized student model can faithfully resemble the full-precision teacher model.

    \item We perform a thorough evaluation of AIT\xspace. The results show that AIT\xspace outperforms the existing algorithms by a great margin, showing the state-of-the-art performance on the zero-shot quantization problem.
\end{itemize}

\section{Background and Related Work}
\subsection{Quantization}
Quantization of neural networks have been studied for a while, and there are numerous methods~\cite{han2015deep, park2017weighted, cai2017deep, choi2017towards, faraone2018syq, jung2019learning}.
In this work, we consider symmetric, uniform quantization that is known to be much easier to build hardware architectures for.
With $n$ bits, a weight parameter $\theta$ is represented by one of $2^n$ ranges.
We use $\theta^q$ to denote the quantized weights as outputs of a quantization function $Quant()$.
For $Quant()$, we use a simple yet efficient function as the following \cite{jacob2018quantization}:
\begin{align}

    \theta^q&=Quant(\theta) = \lfloor \theta \times S - z \rceil,\\
    S &= \frac{2^n -1}{\theta_{max}-\theta_{min}}, \\
    z &= S \times \theta_{min} + 2^{n-1},
\end{align}
where $S$ is the scaling factor to convert the range of $\theta$ to $n$ bit, and $z$ decides which quantized value zero is mapped to.
After quantization, the quantized integer value represents $\theta'\in\mathbb{R}$ obtained by dequantization:
\begin{align}
    \theta' = \frac{(\theta^q+z)}{S}.
    \label{eq:quant_fake}
\end{align}
The procedure is the same for activation values, except that the minimum and the maximum are obtained from observing activations from a few batches and taking a moving average.

\JL{KH: chk. can i change this to $\theta^q$?}

\subsection{Zero-shot Quantization}
\label{sec:zq}
Even though quantization has been shown to be effective even for extremely low bits~\cite{binaryconnect, dorefa, liu2018bi, rastegari2016xnor}, they usually require training data to fine-tuning or calibration.
Zero-shot quantization is a method to relax the privacy or confidentiality problem of the training data.

Earlier methods for zero-shot quantization were focused on how to build a good quantization function $Quant()$, by using schemes such as weight equalization, bias correction, or range adjustments~\cite{aciq,outlier,dfq}.
Among them, ZeroQ~\cite{zeroq} was the first work to introduce the notion of distilled data that is designed to match the batch-norm stats of the original full-precision network.
\newdel{@@}With this scheme, choosing the adequate mixed-precision quantization for each layer has been proposed together.
On top of ZeroQ, DSG~\cite{dsg} added diverse sample generation to improve the performance.

Later, GDFQ~\cite{gdfq} adopted generative models~\cite{cgan,acgan} to create better samples.
The generator $G$ and the quantized model $Q$ are jointly trained with the following loss functions:
\begin{align}
    \mathcal{L}_{GDFQ}(G) &= (1-\alpha)\mathcal{L}_{CE}^P(G) + \alpha\mathcal{L}_{BNS}^P(G),
    \label{eq:gdfq_G} \\
    \mathcal{L}_{GDFQ}(Q) &= (1-\delta) \mathcal{L}_{CE}^Q(Q) + \delta\mathcal{L}_{KL}^P(Q),
    \label{eq:gdfq_Q}
\end{align}
where both utilizes cross-entropy ($L_{CE}$), while the generator matches the batch normalization stats from the full-precision model ($L_{BNS}$) and the quantized network optimizes KL divergence ($L_{KL}$).
Variants of GDFQ are currently forming state-of-the-art family of the zero-shot quantization, by adopting better generator~\cite{autorecon}, adversarial training~\cite{zaq}, or boundary-supporting sample generations~\cite{qimera}.
In this work, we provide an in-depth analysis of the loss function $\mathcal{L}_{GDFQ}(Q)$, and a novel scheme to improve its performance.

\begin{figure}
\centering
\centering
\includegraphics[width=0.9\columnwidth]{figs/cos/cosim.pdf}
\vspace{-2mm}
\caption{Plot of cosine similarity between cross-entropy on hard labels KL divergence on the full-precision model. At the bottom are snapshots of the relative angle between KL and CE of knowledge distillation (KD, bottom left) and zero-shot quantization (ZQ, bottom right) at epoch 200.}
\vspace{-3mm}
\label{fig:cos}
\end{figure}

\newcommand{0.45\textwidth}{0.45\textwidth}

\begin{figure*}[t]
\centering
\begin{subfigure}[t]{0.45\textwidth}
\centering
\includegraphics[width=0.9\textwidth]{figs/concept_modified.pdf}
\caption{A conceptual diagram showing the relation between local curvature and generalizability.}
\label{fig:concept}
\end{subfigure}
\hspace{5mm}
\begin{subfigure}[t]{0.45\textwidth}
    \input{figs/hessian/hessianKD}

\caption{Value of the trace of the Hessian matrix for real-data driven knowledge distillation (top) and GDFQ (bottom).\JL{to 400 epochs}}
\label{fig:hessian}
\end{subfigure}
\begin{subfigure}[t]{0.45\textwidth}
\centering
\includegraphics[width=0.9\textwidth]{figs/hessian/loss_visual_zoom.png}
\caption{Visualization of the loss surface along the largest eigenvector.}
\label{fig:visual}
\end{subfigure}
\hspace{5mm}
\begin{subfigure}[t]{0.45\textwidth}
\centering
\includegraphics[width=1.0\textwidth]{figs/hessian/eigenv_density.png}
\caption{Distribution of the eigenvalues.}
\label{fig:eigenvalue}
\end{subfigure}
\vspace{-1mm}
\caption{Analysis of the loss surface.}
\vspace{-3mm}
\label{fig:surface}
\end{figure*}

\section{Analyses on the Zero-shot Quantization}
\label{sec:diagnosis}
In this section, we provide in-depth analyses on the first/second-order loss surface of the state-of-the-art zero-shot quantization method.
We studied the impact of CE and KL on the loss function of zero-shot quantization setting, and reveal that they are not cooperative but hinder each other (\cref{sec:cos}).
Then, we investigate the difference of the local curvature with the lens of Hessian.
Because the zero-shot quantization suffers from larger generalization gaps, finding the solution of the flatter minima is critical (\cref{sec:generalization}).

\subsection{Gradient Cosine Similarity}
\label{sec:cos}

In this subsection, we attempt to find a partial answer to the question: \emph{are CE and KL cooperative in quantization?} \JL{'zero shot' temporarily taken off}
As discussed in \cref{sec:zq}, loss functions from current techniques for zero-shot quantization~\cite{gdfq,autorecon,qimera} are mainly composed of the CE against the hard labels, and KL divergence against the full-precision teacher.

\JL{put Quant cause before synthetic}
However, it has been discussed that better models do not necessarily make good teachers when the student has limited capacity.
In such cases, the student often has to make trade-off between KL and CE~\cite{cho2019efficacy}.
As quantized models have much lower representational capability~\cite{nahshan2019loss, jacob2018quantization}, it could be difficult for them to optimize both terms.
\JL{toned down a bit}
Furthermore, with synthetic samples not exactly matching the distribution of the real samples,
the labels associated to each sample via hard labels and teacher outputs can be distinct, contributing to the difficulty of addressing both losses.

In such regard, \cite{auxloss} suggested using cosine similarity of the two gradients as a metric for determining whether an auxiliary can contribute toward a single main task.
The authors suggest that the two losses should be used together only in steps where their cosine similarity is larger than zero (when they form an acute angle).

Thus, inspired by the proposals of \cite{auxloss}, we analyzed the training of the zero-shot quantization as the following.
Using GDFQ~\cite{gdfq} as a representative for zero-shot quantization, we measured the cosine similarity of the $g_{KL}$ and $g_{CE}$ while training a quantized ResNet-20 model with synthetic data.
The full-precision teacher is pre-trained, and the generator is jointly trained with the quantized student using \cref{eq:gdfq_G} and \cref{eq:gdfq_Q} with \newdel{$\alpha=0.5$ and  $\delta=0.5$}\new{$\alpha=\delta=0.5$}, respectively.
For comparison, we have also measured the same metric from a common real data (CIFAR-10) based knowledge distillation with pre-trained ResNet-20 as a teacher and random initialized ResNet-20 as a student (self-distillation) using the same loss function.

The results are presented in \cref{fig:cos}.
Using the real samples denoted as `KD', the KL and CE terms induce gradients of the similar direction, supporting the common wisdom that the combination of them works well in practice.
However, with the zero-shot quantization setting using synthetic samples denoted as `ZQ', the cosine distance between them take negative values.
The bottom two plots visualize the angle of the gradients.
In both plots, the \newdel{CE gradient vector }\new{$g_{CE}$} is set to (1,0), and \newdel{KL gradient }\new{$g_{KL}$} is plotted to preserve the relative angle to the \newdel{CE}\new{$g_{CE}$}.

The trend persists throughout the training, as shown in the plot.
Right after the training begins,
the cosine distance of ZQ becomes negative and it is maintained until the end of training, while that of the KD is positive.
This implies that combinations of the two losses do not cooperate well with each other, and using them together could potentially harm the model performance.
Although we display only one case for clarity,
the same trend was observed across many models and datasets.
Refer to the Appendix for further results.

\subsection{Generalizability}
\label{sec:generalization}

\JL{chk this later}
The observation in \cref{sec:cos} suggest that using only one of the two losses --- KL divergence against the full-precision teacher or CE against the hard label --- could be better for the problem.
Some work~\cite{cho2019efficacy} suggest modifying the teacher for distillation, but such method is unavailable in a zero-shot setting \newdel{because we only have access to the weights, and not the training data of the full-precision teacher.}\new{because we have no access to the training data}

In such regard, we examine the generalizability of the \newdel{two }loss terms\newdel{ of our interest}.
Including zero-shot quantization, diverse applications relying on synthetic samples~\cite{park2018data,shankarampeta2021few,choi2021dual,van2021class,liu2020generative,yin2020dreaming,lee2021invertible} usually suffer from huge generalization gap, coming from the discrepancy in the data distributions.
One can easily infer that quantization requires stronger generalization when performed under a zero-shot environment.

To evaluate the generalizability, we measure local curvature of the loss surface.
Popularly measured with the Hessian matrix $\bold{H}$ ($\frac{\partial^2 L}{\partial \theta^2} \in \mathbb{R}^{n\times n}$, where $\theta$ is a vector of $n$ weight parameters), local curvature of the loss surface is a metric that is drawing much attention from the field, and it is thought to hold a key to better generalization~\cite{keskar2016large,jastrzebski2020break,jastrzkebski2018relation,jastrzkebski2017three,chaudhari2019entropy, achille2017critical, jastrzebski2021catastrophic}.
As illustrated in \cref{fig:concept}, if the optimizer settles at a sharp minima, the performance at test time is likely to incur a larger degradation compared that of a flat minima.
Such gap would be much larger with synthetic data incorrectly modeling the validation data distribution.
In line with this finding, \newdel{there are numerous pieces of literature that supports}\new{many literatures support} the claim of smaller local curvature improving generalization~\cite{keskar2016large,jastrzebski2020break,jastrzkebski2018relation,jastrzkebski2017three,chaudhari2019entropy, achille2017critical, jastrzebski2021catastrophic}.

\newcommand{0.3\textwidth}{0.3\textwidth}
\begin{figure*}[t]
\centering
\begin{subfigure}[t]{0.3\textwidth}
    \input{figs/disparity/disparity}

 \vspace{-4mm}
\caption{Cosine similarity of the gradients between epochs.\label{fig:cossim}}
\end{subfigure}
\hspace{3mm}
\begin{subfigure}[t]{0.3\textwidth}
\centering
\includegraphics[width=\textwidth, trim={0 1.5cm 0 0},clip=true]{figs/weight_delta/GDFQ_ep_60}
\caption{Number of quantized parameters updated per each layer of ResNet-18 at epoch 60. \label{fig:count_before}}
\end{subfigure}
\hspace{3mm}
\begin{subfigure}[t]{0.3\textwidth}
\centering
\includegraphics[width=\textwidth, trim={0 1.7cm 0 0},clip=true]{figs/weight_delta/GDFQ_ep_350}
\caption{Number of quantized parameters updated per each layer ResNet-18 at epoch 350.\label{fig:count_after}}
\end{subfigure}
\vspace{-1mm}
\caption{Analysis on the naive KL-only zero-shot quantization.}
\vspace{-3mm}
\label{fig:disparity}
\end{figure*}

\cref{fig:hessian} plots $Tr(\bold{H})$, the trace of the Hessian matrix, approximated by PyHessian~\cite{yao2020pyhessian} implementing Lanczos algorithm~\cite{lanczos1950iteration}.
We separate Hessian calculation for each of CE and KL.
The trace values are significantly different, where that of the KL \newdel{divergence }is much smaller than the CE.
The gap is notably larger in zero-shot quantization (right, \emph{ZQ}) than on real-data knowledge distillation (left, \emph{KD}).
In addition, the distribution of the eigenvalues displayed in \cref{fig:eigenvalue} also show a huge difference in the local curvature of the \newdel{two }loss terms.
\JL{KH: chk the following, too}
While CE has longer tail for high eigenvalues, those of KL has more concentration to lower \new{eigen}values\newdel{ with a higher peak near zero}.
\JL{chk later}

This could potentially lead to two conclusions: the loss surface of the KL divergence is much flatter, or the model has converged to the minima in the loss surface of the KL divergence.
However, in our case, we believe it advocates for the former\newdel{ of flatter loss surface}, based on an auxiliary experiment.
It is commonly observed that near the minima, the disparity between gradients start to arise~\cite{thiran2020early, mahsereci2017early}.
Following the same regard,
we measure the cosine distance of the gradient averaged within an epoch, compared to that of the previous epoch (\emph{inter-epoch cosine similarity}).
As displayed in \cref{fig:cossim}, the gradients of KL in the zero-shot quantization setting (ZQ) points to a consistent direction (large cosine similarity) compared to that of the real data distillation (KD),
indicating that it has not reached the minima yet.

\cref{fig:visual} illustrates a more direct visualization of the loss surface.
From the Hessian matrix, we took the largest eigenvector $e$ at each epoch, we plotted the value of CE and KL on the left plot by calculating $L(\theta(t) + k \cdot e \cdot \hat{g}(t))$ for $k \in [-0.5,0.5]$, where
$\hat{g}$ is the average gradient along $e$.
The left plot presents the CE in red color scheme and KL in blue color scheme.
It is clearly shown that the surface is flatter in the KL surface especially near the end of training\newdel{ (epoch 400)}.

\subsection{Summary}
\JL{remove this one?}
Summarizing the studies in this section,
we first observed that the CE and KL form a large angle in the gradient space,
and the quantized model has difficulty optimizing both directions.
Furthermore, by measuring the stats from Hessian matrices,
we conclude that KL has a much flatter loss surface for potentially better generalization, which is an important issue for generative zero-shot quantization methods.

\section{AIT Method}
In this section, we describe our \emph{AIT (All In the Teacher)} method in detail.
From the observations in \cref{sec:diagnosis}, we first drop CE term from the loss, and apply a novel \emph{gradient inundation\xspace} to bring the quantized model as close as possible to the full-precision teacher.

\subsection{KL-only Zero-shot Quantization}
\label{sec:kl}
Motivated by the experiments from \cref{sec:generalization}, we ran GDFQ~\cite{gdfq} with KL-only loss (i.e., $\delta=1$).
However, as will be shown later in \cref{tab:ablation}, the performance severely degrades in all settings. \KH{check degradation.}
We find an explanation from the experiments of \cref{fig:cossim}.
Even toward the end of the training, the direction of the \newdel{gradients from the KL divergence}\new{$g_{KL}$} remains consistent, and training for more epochs did not solve the problem.
This indicates that the model did not converge at the minima of the KL surface.

Another set of experiments shown in \cref{fig:count_before,fig:count_after} gives a closer look at the phenomenon.
We count the average number of weight parameters that cross the rounding threshold (parameters whose quantized values have changed from the previous step).
We make two observations:
First, the portion of quantized values crossing the rounding threshold is extremely small.
Even when training has not stabilized (epoch 60), only 0.0011\% of weights are being updated each step.
At a later epoch (350), the phenomenon becomes worse that only four values are updated in a whole epoch, which is only 1.8e-7\% of weight updates per step during the epoch.
In addition, the changes are extremely unbalanced,
where all the updates are only occurring in just three layers.

We posit that this is from the quantized training process that constrains integer value updates.
During the training, the quantized network internally stores its full-precision values.
The parameters are quantized for the forward pass of the backpropagation, and the gradients are applied to the internal full precision values.
As the gradient values become smaller after a few epochs of training, the change in the parameters are usually not large enough to cross the threshold, and only a few layers are continuously making changes,
stopping the model from moving towards a lower point in the loss surface.

\input{tables/perf}

\subsection{Gradient Inundation\xspace}

To address the problem of KL-only method, we propose gradient inundation\xspace (GI\xspace).
Overall, we attempt to dynamically manipulate the gradients \new{$g_l$} of each layer \new{$l$}, such that certain number of parameters are guaranteed to update in its integer value.
\new{With stochastic gradient descent, consider the update rule of parameter $\theta_{l,k}$ at step $k$ with learning rate $\eta$:}
\begin{align}
    \theta_{l,k+1} &= \theta_{l,k} - \eta \cdot g_{l,k}.
\end{align}
with gradient inundation\xspace, the modified rule is as the following:
For the parameters $\theta_{l,k}$, quantized parameters $\theta_{l,k}^q$ and the corresponding gradients $g_{l,k}$ from layer $l$,
\begin{align}
    \theta_{l,k+1} &= \theta_{l,k} - \eta \cdot g'_{l,k}, \\
    g'_{l,k} &= \kappa_{l} \cdot g_{l,k}, \\
    \kappa_{l} &= \text{arg} \min\limits_{\kappa_l} \lVert \Delta \theta^q_{l,k} - T \rVert, \label{eq:GI_objective} \\
    \Delta \theta^q_{l,k} &= \sum \mathbb{I}(\theta^q_{l,k} \neq \theta^q_{l,k+1}), \\
    T &= \rho \cdot dim(\theta_l),
\end{align}
where $\rho \in [0,1]$ is a predetermined proportion that exceeds the quantization threshold, $\mathbb{I()}$ is the indicator function, and $dim(\theta_l)$ is the number of elements in $\theta_l$. \new{Our goal is to find $\kappa_l$ that guarantees the number of parameter updates on a quantized layer $\Delta \theta^q_{l,k}$ exceeds a certain ratio $T$.

\newdel{Finding the exact solution of $\kappa_l$ for each layer can be time-consuming because it requires analyzing the distributions of $g_{l,k}$.}
To quickly find an approximate solution, we applied a simple two-step heuristic.
Firstly, starting from 1.0, $\kappa_l$ is doubled until $\Delta \theta^q_{l,k} > T$.
Then, to satisfy Eq. \ref{eq:GI_objective}, $\kappa_l$ is adjusted between $\nicefrac{\kappa}{2}$ and $\kappa$ by binary search.
For the sake of computation efficiency, the total number of search steps is limited to five.
In addition, to assure early phase stability of the training, we added a warm-up phase for the GI\xspace method.
In the warm-up phase, the maximum \newdel{adjustment }of $\kappa$ is limited to 128\newdel{ times larger than the original magnitude} for more accurate solutions.
When the generator requires a separate warm-up, the GI\xspace warm-up phase starts after the generator warm-up ends. \JL{KH:chk this entire paragraph}
Similar to learning rate exponential decay scheduling, we apply the exponential decay to $\rho$.
We discuss the sensitivity to this in \cref{sec:sensitivity}.

\section{Experimental Results}
\label{sec:exp}
\subsection{Experimental Environments}
We evaluate AIT\xspace on three datasets, CIFAR-10, CIFAR-100~\cite{cifar}, and ImageNet (ILSVRC2012~\cite{imagenet}).
CIFAR-10 and CIFAR-100 contain 10 and 100 classes of images, respectively, and represent small-scale datasets.
ImageNet has 1000 classes of images with 1.2M training samples and 50K validation samples, which represent large-scale dataset\newdel{ in our experiments}.

For CIFAR-10\newdel{ and CIFAR-}\new{/}100, we use commonly used ResNet-20~\cite{resnet} model.
For ImageNet, we use ResNet-18 and ResNet-50 to represent popular medium- and large-sized models, and MobileNetV2~\cite{mobilenetv2} to represent a lightweight model.
All \new{pre-trained }models\newdel{ and full-precision weight parameters} are from pytorchcv library~\cite{pytorchcv}.
For more results on various models, please refer to the Appendix.

For baselines, we use the official code provided by the authors of ZeroQ~\cite{zeroq}, GDFQ~\cite{gdfq}, ARC~\cite{autorecon} and Qimera~\cite{qimera} with the identical settings.
AIT\xspace is implemented using PyTorch~\cite{pytorch} version 1.10.0\newdel{, which is installed in Ubuntu 20.04.3 LTS with CUDA version 11.1}.
\new{
All experiments are conducted using NVIDIA RTX3090 and A6000 GPUs.
}

The generator is trained with the loss function \cref{eq:gdfq_G} with $\alpha=0.5$ using Adam optimizer~\cite{adam} with learning rate of 0.001.
For training the quantized student model, SGD with Nesterov~\cite{nesterov} was used with momentum 0.9\JL{did we remove w decay?}.
For AIT\xspace, the hyperparameter $\rho$ was set to 0.001 and 0.0001 for CIFAR and ImageNet respectively, which both were decayed by 0.1 every 100 epochs.
\newdel{Experiments on CIFAR-10 and CIFAR-100 were run for 400 epochs with 200 batches on learning rate $\eta=$1e-4, and experiments on ImageNet were run for 400 epochs with 16 batches on learning rate $\eta=$1e-4.}
\new{Experiments on CIFAR and ImageNet were run for 400 epochs on learning rate $\eta=$1e-4, with batchsize of 200 and 16, respectively.}

\label{sec:environment}
\subsection{Performance Comparison}
\label{sec:perf}
AIT\xspace can be applied to most generative zero-shot quantization methods.
In this section, we apply our method to three: GDFQ~\cite{gdfq}, the first method to suggest such approach, ARC\cite{autorecon}, which improves the generator, and Qimera~\cite{qimera}, the SOTA technique in the same family.
We also include ZeroQ~\cite{zeroq} for comparison.
\newdel{For the baselines, we use the official code repository provided by the authors.}
We report top-1 accuracies.

Overall, AIT\xspace achieves significant performance improvements in most settings tested, whether implemented on top of GDFQ, ARC or Qimera.
Notably large improvements have been observed on ImageNet datasets, especially in 4w4a settings, because there still exists a large gap towards the full-precision (32bit) model.
The largest gain was found for 4w4a ResNet-50 on top of GDFQ, with the gain of 12.12\%p that seems to mainly come from the huge gap (25.61\%p) GDFQ originally had between the full-precision model.
\new{For the results of lower-bit settings, refer to Appendix.}

An interesting trend is that for the other two methods with better generators (Qimera, ARC),
the performance gain on 4w4a setting is larger for smaller models (ResNet-50\textrightarrow ResNet-18\textrightarrow MobileNetV2).
The improvements are ($+$1.38\%p, $+$2.99\%p, $+$5.19\%p) for Qimera and ($+$3.90\%p, $+$4.41\%p, $+$6.34\%p) for ARC in \newdel{the order of descending}\new{a descending order of} model size.
This indirectly supports our claim that quantized networks with smaller capacity have difficulties optimizing for multiple loss terms, and AIT\xspace can alleviate such effect.

In addition, the performance of ARC+AIT\xspace is better than Qimera+AIT\xspace for all ImageNet settings except one, even though \newdel{ baseline Qimera outperforms baseline ARC.}\new{ Qimera outperforms ARC in their default settings.}
We find the reason from generator model size of ARC.
While Qimera uses the exact same generator from GDFQ, ARC uses a larger generator model found by neural architecture search.
The result demonstrate that AIT\xspace is making better use of the potential of the generator network.
Small performance degradations were observed for CIFAR-10 on Qimera by 0.03\%p.
Since CIFAR-10 is a small dataset and the performance is already close to the fp32 model, we believe this is because there is not much room left to improve.

\input{tables/ablation.tex}

\subsection{Ablation Study}
\label{sec:ablation}
\cref{tab:ablation} shows an ablation study performed over GDFQ.
\new{The ResNet family and MobileNet are denoted as `RN' and `MB' respectively.}
`KL-only' drops CE from the original loss function of GDFQ, and let the quantized model optimize only on the KL divergence against the full-precision teacher.
However, this results in a huge degradation in all settings.
As analyzed in \cref{sec:kl}, this is due to the scarce, unbalanced quantized weight updates.
By applying gradient inundation\xspace, the lost performance is more than recovered and the superior gain over the baseline is obtained (KL-only+GI\xspace).

`Baseline+GI\xspace' represents the gradient inundation\xspace applied on top of GDFQ without dropping the CE term,
and `CE only+GI\xspace' represents the same with KL term dropped from the loss.
Unfortunately, they only result in performance degradation, because the baseline GDFQ with CE+KL loss or CE loss does not suffer from the aforementioned scarce weight update problems.
Therefore, gradient inundation\xspace only makes detrimental changes to the quantized weights.
\newcommand{0.48\columnwidth}{0.48\columnwidth}
\begin{figure*}
\centering
\begin{subfigure}[t]{0.45\columnwidth}
\includegraphics[width=\textwidth, trim={0 35mm 0 0}, clip=true]{figs/weight_delta/highlr_ep_60}
\caption{}
\label{fig:highlr_60}
\end{subfigure}
\begin{subfigure}[t]{0.45\columnwidth}
\includegraphics[width=\textwidth, trim={0 35mm 0 0},  clip=true]{figs/weight_delta/highlr_ep_350}
\caption{}
\label{fig:highlr_350}
\end{subfigure}
\begin{subfigure}[t]{0.45\columnwidth}
\includegraphics[width=\textwidth, trim={0 35mm 0 0},  clip=true]{figs/weight_delta/ait_ep_60}
\caption{}
\label{fig:gi_60}
\end{subfigure}
\begin{subfigure}[t]{0.45\columnwidth}
\includegraphics[width=\textwidth, trim={0 35mm 0 0},  clip=true]{figs/weight_delta/ait_ep_350}
\caption{}
\label{fig:gi_350}
\end{subfigure}
\vspace{-3mm}
\caption{Distribution of the updated quantized values with $\times$100 learning rate at an earlier epoch (a), later epoch (b) and with gradient inundation\xspace at an earlier epoch (c), later epoch (d).\vspace{-3mm}}
\label{fig:lr_updates}
\end{figure*}

Seeing the effect of `KL-only + GI\xspace', one might wonder if the weight update problem can be solved by simply increasing the learning rate.
`KL-only (high lr)' row shows the results of such experiments conducted with $\times$100 learning rate.
In addition, \cref{fig:lr_updates} shows the distribution of the updates in each layer, in comparison to \cref{fig:count_after}.
\newdel{Increasing the learning rate }\new{`KL-only (high lr)'} achieves a small gain \newdel{.
However, increasing the learning rate}\new{ but} does not entirely solve the problem.
First, increasing the learning rate incurs too frequent updates in a few layer which was already getting enough updates as shown in \cref{fig:highlr_60,fig:highlr_350}, and many layers still not being updated.
Moreover, further increasing the learning rate results in divergence of the model.
\cref{fig:gi_60,fig:gi_350} presents the number of updates with gradient inundation\xspace, where $\rho$ is depicted in dotted red lines. Gradient inundation\xspace tunes the gradients to the right level, leading to a better performance.

\begin{figure*}
\centering
\begin{subfigure}[t]{0.3\textwidth}
\centering
\includegraphics[width=\textwidth]{figs/fig5/klloss.pdf}
\caption{}\label{fig:after_kl}
\end{subfigure}
\hspace{3mm}
\begin{subfigure}[t]{0.3\textwidth}
    \includegraphics[width=\columnwidth]{figs/fig5/cosimil.pdf}
    \caption{}\label{fig:after_cossim}

\end{subfigure}
\hspace{3mm}
\begin{subfigure}[t]{0.3\textwidth}
\centering
\includegraphics[width=\textwidth]{figs/fig5/valid_ce.pdf}
\caption{}\label{fig:after_ce}
\end{subfigure}
\vspace{-3mm}
\caption{Further experiments. (a) KL divergence. (b) Cosine similarity of KL and CE for GDFQ, KL-only, and AIT\xspace.  (c) Cross-entropy against the validation samples. }
\vspace{-2mm}
\label{fig:ait_detail}
\end{figure*}

\newcommand{0.22\columnwidth}{0.22\columnwidth}
\begin{figure}[t]
    \centering
    \subcaptionbox{\label{fig:cifar_samples}}{
        \centering
        \includegraphics[height=0.22\columnwidth]{figs/synth/cifar_bright}
    }
     \subcaptionbox{\label{fig:ait_samples}}{
         \centering
         \includegraphics[height=0.22\columnwidth]{figs/synth/AIT_CIFAR10}
     }
     %
    \subcaptionbox{\label{fig:imgnet_samples}}{
        \centering
        \includegraphics[height=0.22\columnwidth]{figs/synth/imagenet_original}
    }
     \subcaptionbox{\label{fig:ait_imgnet}}{
         \centering
         \includegraphics[height=0.22\columnwidth]{figs/synth/imagenet_generated_AIT}
     }
    \caption{Comparison of samples. (a) Real CIFAR-10 samples (b) synthetic CIFAR-10 samples (c) Real ImageNet samples (d) synthetic ImageNet samples. Each row represents one of the 10 classes for CIFAR-10, and 10 randomly chosen classes of ImageNet.}
    \vspace{-4mm}
    \label{fig:samples}
\end{figure}

\new{

\input{tables/optimizer}

For a comprehensive comparison, we also tested adaptive optimizers such as Adam~\cite{adam}, RMSProp~\cite{tieleman2012lecture}, and especially LARS~\cite{you2017large}, which adjusts learning rates per layer.
The results of these optimizers for the quantized model are shown in \cref{tab:optimizer}.
`Baseline-' denotes the existing methods, and `GI-' denotes modified optimizer with GI.
The results show that `Baseline-Adam' and `Baseline-RMSProp' suffer from noticeable accuracy degradation, especially on the large models.
`Baseline-LARS' survived from such trend, it does not make significant differences compared to `Baseline-SGD'.
We further expand our research by applying GI to Adam and RMSProp, written as `GI-Adam' and `GI-RMSProp'.
The results verify that our method has solid performance on various optimizers and still outperforms existing methods.

}

\subsection{Sensitivity Analysis}
\label{sec:sensitivity}
\input{tables/sense_t.tex}
\input{tables/lrsense}
The $\rho$ value controls the portion of quantized weights guaranteed to get updates in each layer.
In \cref{tab:sense}, a sensitivity study of $\rho$ on top of GDFQ+AIT has been performed.\newdel{ on a range of $\rho$ values.}
\newdel{As displayed in \cref{tab:sense},}\new{The results show that} AIT\xspace is not very sensitive to $\rho$, although there is some effect.
\new{Refer to the Appendix for more results.}

\new{
Tab 5. shows the learning rate sensitivity of our method with comparison to the GDFQ.
The results show that the GI method is robust to the learning rate changes on both datasets while steadily outperforms the baseline method.
}

\subsection{Further Analysis}
In this section, we present more details of AIT\xspace.
\cref{fig:after_kl} shows the KL divergence over the training in the baseline GDFQ, `KL-only' and AIT\xspace.
AIT\xspace is able to reach a lower KL distance.
This supports our analysis from \cref{sec:kl} that there is still room for KL to be optimized further.

Another observation can be found from \cref{fig:after_cossim},
where we have measured the angle between the CE and KL.
For `KL-only' and AIT\xspace, CE is calculated only for measurements, and did not affect the SGD updates.
It is now shown that `KL-only' and AIT\xspace, both have positive cosine similarities between the losses.
This represents that, although they are difficult to optimize concurrently in the beginning, as they get near the global minima, they become to share the same direction for optimization.
AIT\xspace sacrifices a small amount of directional alignment, but outperforms `KL-only' by guaranteeing quantized weight updates with gradient inundation\xspace.
Lastly, \cref{fig:after_ce} shows that AIT\xspace achieves lower CE in validation against the hard label, even though it has not seen the real data, and not optimized for CE.

\JL{maybe talk a bit more about update distributions}

\section{Discussion}
\textbf{Removing Cross-Entropy} against the hard label from the loss function as done in AIT\xspace \newdel{removes the cross-entropy against the hard label from the loss function.}
\newdel{At a glance, this} could be a penalty, because some methods~\cite{qimera} \newdel{seem to }rely on the sample labels.
However, as we demonstrated in \cref{sec:exp}, AIT\xspace applied to Qimera was able to obtain significantly better performance despite the exclusion of the mixed labels.
In addition, our method does not depend on \newdel{whether the predicted label is ground truth or not}\new{per-image hard label}, so it can be widely used for segmentation ~\cite{ronneberger2015u, long2015fully} or object detection~\cite{redmon2016you, ren2015faster}.

\textbf{Privacy Leak} is one societal concern \newdel{One societal concern} of zero-shot quantization \newdel{is privacy leak} because the generator creates synthetic samples that \newdel{closely }follow the distribution of real data.
As several input reconstruction techniques point out~\cite{dosovitskiy2016inverting, mahendran2015understanding}, \newdel{it could be a privacy leak if} \new{it could be that} the synthetic samples can reconstruct the private training data.
However, to the extent of our observation, there is no sign that AIT\xspace reconstructs the real data
as shown in \cref{fig:samples}.
Training method for the generator in AIT\xspace is no different from its baselines, since it does not alter the generator loss in \cref{eq:gdfq_G}, thus does not contribute any further to the privacy leak.

\JL{other regularizer such as label smoothing?}

\section{Conclusion}
In this work, we analyzed the SOTA family of solutions for zero-shot quantization.
Through a series of experiments and analyses, we found that current solutions can be improved through pursuing a flatter minima and guaranteeing weight updates during fine-tuning.
We achieve the goal by bringing the quantized model closer to the full-precision model in terms of the KL divergence and designing AIT\xspace --- before our method, people habitually have used a combination of CE and KL as the main loss of the zero-shot quantization.
Experimental results show that AIT\xspace is effective and can be easily applied to existing algorithms.

\section*{Acknowledgements}
\footnotesize{
This work has been supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIT) (2022R1C1C1008131,
2022R1C1C1011307),
Year 2022 Copyright Technology R\&D Program by Ministry of Culture, Sports and Tourism and Korea Creative Content Agency(Project Name: Development of artificial intelligence-based copyright infringement suspicious element detection and alternative material content recommendation technology for educational content, Project Number: CR202104003, Contribution Rate: 30\%),
and
Institute of Information \& communications Technology Planning \& Evaluation
(IITP) grant funded by the Korea government (MSIT)
(2020-0-01361,
Artificial Intelligence Graduate School Program (Yonsei University
).
}

{\small
\bibliographystyle{ieee_fullname}

redpajama_set_name: RedPajamaArXiv


text:
State's home sales off in June
By JOSH SALMAN
Florida home sales took a step backward in June, as seasonal markets throughout the state lost traffic from snowbirds and tourists.
But the pace of real estate changing hands and the price of those properties remained well above levels from a year ago, according to a report Tuesday from Florida Realtors, delayed one day from its scheduled release as a result of processing errors.
The dip was mirrored throughout Southwest Florida, with markets from Parrish to Punta Gorda falling somewhat.
"This is sort of what we're used to seeing in the summer time, with families going on vacation and seasonal residents returning north," said Lee Forbes, broker and owner of the Forbes Property Group in Bradenton.
"We slowed down some, and I expect that to spill into July and early August, but I don't think we'll fall off the pace too much."
Home buyers in the North Port-Bradenton-Sarasota metropolitan area purchased 1,198 single-family homes in June, down 9 percent from May but up 13.8 percent from the same month a year ago. The market's median sales price held relatively steady over the month at $198,000 -- also up 14 percent from last year, according to Florida Realtors.
The delayed statewide report comes a day after a lackluster release from the National Association of Realtors saw U.S. home sales plunge more than expected. A local slowdown caused median prices to slide $20,000 in the territory covered by the Sarasota Association in Realtors.
In Manatee County, Realtors last month sold 298 single-family dwellings, a modest dip from May and a 12.5 percent gain from June 2012.
As the pace of distressed short sales and foreclosures eases, putting less downward pressure on property values, the median price in Manatee has climbed 11 percent over the year to reach $196,770.
Nearly one in four Manatee County homebuyers paid cash, and inventory continues to shrink to just a 3.8-month supply, the report shows.
In smaller Charlotte County, agents in June closed on 367 home purchases, a 7 percent annual gain. While the area continues to carry the lowest prices at $145,000, that median has leapt 26 percent over the year.
Agents attribute the uptick to pent-up demand from baby boomers. Intuitional investors, which have been on a real estate buying spree across Southwest Florida, also have pushed up prices by outbidding other buyers for acquisitions.
Realtors also pegged the June slowdown to lingering supply constraints.
"The only reason we're slowing down is because there just isn't any inventory," said Gloria Weed, managing broker of the Michael Saunders & Co. office in Lakewood Ranch.
"The buyers are here, my conference rooms are full, but we're not making the sales we could make if there were more homes to sell."
Across the Sunshine State, sales of existing single-family homes totaled 20,403 in June, an 8 percent decline from May but a 9 percent gain compared with the year-ago figure, according to the Florida Realtors data.
Meanwhile, pending sales -- contracts that are signed but not yet completed or closed -- for existing single-family homes last month rose 23.1 percent over the previous June.
The statewide median sales price for single-family existing homes last month was $175,000, up 15.1 percent over the year.
Economists now hope a modest uptick in new seller listings this summer will help stabilize the market to more normal conditions.
"For the past two years, we've been up month-over-month in closed sales," Florida Realtors chief economist John Tuccillo said.
"The significant part of this month's numbers is the growth in new listings, suggesting that the inventory crunch will ease in the second half of this year."

redpajama_set_name: RedPajamaCommonCrawl


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text:
Ottawa 67's je kanadský juniorský klub ledního hokeje, který sídlí v Ottawě v provincii Ontario. Od roku 1967 působí v juniorské soutěži Ontario Hockey League (dříve Ontario Hockey Association). Své domácí zápasy odehrává v hale TD Place Arena s kapacitou 9 500 diváků. Klubové barvy jsou červená, bílá a černá.

Nejznámější hráči, kteří prošli týmem, byli např.: Martin Hamrlík, Lukáš Mensator, Lukáš Kašpar, Zenon Konopka, Nick Boynton, Radim Ostrčil, Denis Potvin, Bobby Smith, Corey Locke, Jakub Petružálek, Brian Campbell, Petr Mrázek, Bryan Bickell, Tyler Toffoli, Sean Monahan nebo Logan Couture.

Úspěchy

 Vítěz Memorial Cupu ( 2× )
 1984, 1999
 Vítěz OHL ( 3× )
 1976/77, 1983/84, 2000/01

Přehled ligové účasti

Zdroj:

 1967–1975: Ontario Hockey Association
 1975–1980: Ontario Hockey Association (Leydenova divize)
 1980–1994: Ontario Hockey League (Leydenova divize)
 1994– : Ontario Hockey League (Východní divize)

Odkazy

Reference

Externí odkazy

 Oficiální stránky
 Profil klubu na hockeydb.com

Kanadské kluby ledního hokeje
Kluby ledního hokeje založené v roce 1967
Ontario Hockey League
Sportovní kluby v Ontariu
Sport v Ottawě

redpajama_set_name: RedPajamaWikipedia


We love welcoming your new arrivals in the Daily Liberal babies gallery. Here are some of the bundles of joy born recently in Dubbo and the wider Orana region, shared with us by local families. We will also feature baby photos in the print version of the Daily Liberal periodically. If you have a new addition to your family, you're invited to send us a photo and some details and we will add them to the gallery. Send us a message on Facebook or email your baby's photo to faye.wheeler@fairfaxmedia.com.au It's a special way to capture a moment and create a keepsake to share with your son or daughter in the future. This year the first baby born at Dubbo Hospital was baby Robert at 7.10pm on January 1.
Bryleigh Claire Carlton: Born December 11 at Dubbo Hospital. Parents: Brett Carlton & Ella Short of Dubbo. Sister: Faith. Grandparents: Jo & Greg Carlton of Dubbo and Glenn & Trish Short of Narromine. Photo: Sam Rummans Photography.
Reuben Jack Mackey: Born December 19 at Dubbo Hospital. Parents: Bradley and Megan Mackey. Weight 6 pound 11 oz. Grandparents: Desmond and Nancy Mackey, Ann-Maree Kelly and Craig Hugo. Photo contributed.
Adaline Joan Jackson: Born December 10 at Dubbo Hospital. Parents: Cameron and Rebecca Jackson of Dubbo. Weight: 10 pounds 10 ounces. Brother: Bayne, 4. Grandparents: Julie Moran and Helen & Ron Higgins. Photo contributed.
Rory Morris O'Sullivan: Born September 22 at Dubbo Hospital. Parents: Yvonne & Mark O'Sullivan. Weight: 2.765kg. Grandparents: Mary and Billy Gilmour of Kilmarnock, Scotland and Paddy and Cherry O'Sullivan of Dunfermline, Scotland. Photo contributed.
Archie Jackson Boyd: Born November 26 at Dubbo Hospital. Parents: Belinda McGuire and Jesse Winkle of Dubbo. Weight: 7 pound 5 ounces. Sister: Tatum. Photo contributed.
Layla Anne Allatt: Born November 10 at Dubbo Hospital. Parents: Rebecca and Nicholas Allatt of Dubbo. Weight: 7.05 pounds. Grandparents: Kylie Hull and Greg Hull, Debbie Allatt and Dennis Allatt. Photo contributed.
Becky Janine Hutchison: Born November 2 at Dubbo Hospital. Parents: Tom & Nadine Hutchison of Gilgandra. Weight: 8lb 2oz L 56cm. Sibling: Kellie. Grandparents: Jason & Elizabeth McGaw of Dubbo and Greg Hutchison of Gilgandra. Photo: Danielle Fiddes.
Kiyaan Bhetwal: Born: November 13 at Dubbo Base Hospital. Parents: Kishor and Yashoda Bhetwal, who are from Nepal. Photo contributed.
Lola Clare Houghton: Born October 25 at Dubbo Hospital. Parents: Teneille & Josh Houghton of Dubbo. Weight: 5.13 pounds Grandparents: Shirley & Dave Bloomfield and Kim & Tim Houghton both of Dubbo. Photo contributed.
Hugo Oz Keinert: Born: October 17. Parents: Renee and Tom Keinert of Ames, Iowa USA. Weight: 7lb 4oz. Grandparents: Graham and Judi Phipps of Dubbo, Fritz and Rea Keinert. Photo contributed.
Sophie Rose Weber: Born: October 21, at Dubbo Hospital. Parents: Jared and Rebekah Weber. Weight: 3800 grams. Siblings: Harry. Grandparents: David and Kim Weber, Robert and Bernadette Pattie. Great-grandparents: Ken (dec) and Barbara Weber. Photo contributed.
Archer William Bateup: Born: October 21. Parents: Andrew Bateup and Brianna Snare. Weight: 7 pound 6 oz. Siblings: Xander Bateup. Grandparents: Natalie and Peter Snare and Kathy and Grahame Bateup. Photo contributed.
Aurelia Matisse Amos: Born October 20. Parents: Scott and Amy Amos of Dubbo. Weight: 7lb 12oz. Siblings: Orlando, 4 and Sawyer, 2. Grandparents: Ian and Tracey Robinson & Bill & Debbie Amos. Great Grandma: Berry Timmer. Photo contributed.
Hallie Rae Baker: Born August 30 at Dubbo Hospital. Parents: Holly Willoughby and Joel Baker. Weight: 7pound 7oz. Grand parents: Vicki Everingham, Lindsay Baker, Amanda Willoughby and Mark Willoughby. Photo contributed.
Brodie Thomas Hutchins: Born August 1. Parents: Anthony Hutchins and Hayley Leggatt. Weight: 8lb 7oz. Siblings: Kye and Maddy. Grandparents: Ron and Vicky Leggatt and Ken and Elaine Hutchins. Photo contributed.
Joshua Phillip Gregory Sweeney: Born July 13. Parents: Phillip and Jessica Sweeney. Weight: 4220g. Grandparents: Kevin and Gwen Sweeney and Gregory and Jennifer Salmon. Photo contributed.
Kristin Melinda Cran. Born: September 28. Parents: Tanika & Douglas of Orange. Orange. Weight: 1800 kg. Photo contributed.
Archie Brian Peckham. Born: September 14 at Dubbo Hospital. Mother: Emily Ling. Father: Thomas Peckham. Weight: 4160g. Siblings: Gerald Peckham, Graham Peckham, Hope Butler and Declan Butler. Grandparents: Brian Ling, Caroline Ling and Sandra Peckham. Photo contributed.
Ayla Grace Lane: Born: September 8 at Dubbo Hospital. Parents: Katrina Carlton & Justin Lane of Dubbo. Weight: 3040g. Sibling: Codie Lane. Grandparents: Jo & Greg Carlton, Kelly Rowe & Chris Lane.
Jayden Nigel Knight: Born: August 28 at Dubbo Hospital. Parents: Zamiah Knight and Brian Smith of Bourke. Grandmothers: Annette Mackay of Dubbo and Nora Smith of Bourke. Photo contributed.
Jayce Bennett: Born August 23. Mother: Aleaha Bennett. Father: Hayden Greenaway. Weight 5.5 pounds. Photo contributed.
Noah Leslie Davis: Born August 19 at Dubbo Hospital. Father: Justin Davis, mother Sarah Durham of Coonamble. Weight: 6.1 pounds. Siblings: Jack, Aylessa, Nathan, Jayden, Maeson, Sofia. Grandparents: Joanne Durham, Steven Durham, Carol Davis and Leslie Davis. Photo contributed.
Rory James Egan: Born July 7 at Dubbo Hospital. Parents: Shei and Lyndall Egan of Dubbo. Brother: Noah. Grandparents: Lance and Kerrie Woodhouse, Greg and Rachel Egan. Photo: Sam Rummans Photography.
Mia Jane Benton. Born September 3 - Father's Day - at Dubbo Hospital. Parents: Rachael & Mark Benton of Dubbo. Weight: 3880gm. Siblings: Alex, 7. Photo contributed.
Joziaha Michael Kennedy. Born: August 30. Parents: Michael Kennedy and Melody McEwan of Walgett. Siblings: Ziarlah 2. Photo contributed.
Zeke Henry Howarth: Born: August 5. Parents: Lee schellnegger & Paul Howarth. Weight: 3290g. Siblings: Shanayah, 15, Jye, 13, Tyran, 7, Mitchell, 4 and Sienna, 1. Photo contributed.
Riley Michael James Hayden: Born: July 28 at Dubbo Hospital. Parents: Kadeisha Harley and Carlewis Hayden of Dubbo. Weight: 4015g. Grandparents: Kim and Michael Harley, Allicen and Roger Hayden. Photo contributed.
Sealey Robert Neave: Born: July 29 at Dubbo Hospital. Parents: Jeremy Neave & Sarah Bloomfield. Weight: 4kg. Siblings: Bianka. Photo contributed.
Kyra Fox. Born: August 2 at Dubbo. Parents: Natalie and Scott fox. Sibling: Paityn, 3. Photo contributed.
Hannah Elizabeth Ray. Born: July 21 at Dubbo Hospital. Parents: Jeremy & Erin Ray of Dubbo. Weight: 3040g. Brother: Mitchell, 3. Grandparents: Tom & Colleen Gablonski, Stephen Ray, Gaye Jones. Photo: Georgie Newton Photography.
Elsie Florence Whittaker Gain. Born: July 14 at Dubbo Hospital. Parents: Patrick and Carmen Gain of Geurie. Weight: 8lb 10oz. Siblings: Hugh, 6, and Harry, 2. Grandparents: Rodney and Dianne Whittaker of Yeoval and Peter and Robyn Gain of Gundagai. Photo contributed.
Edward James Robertson. Born July 29. Parents: Paul and Emma Robertson of Warren. Brother: George. Grandson number three for Brian and Jenny Robertson of Warren and grandchild number 20 for Jim and Ann Rice of Bourke. Photo contributed.
Alexander Edward Williams. Born: July 25 at Dubbo Hospital. Parents: Sarah Winkle and Sean Williams. Sister: Lilly, 3. Photo: Georgie Newton Photography.
Jack Andrew Munro. Born: July 20. Parents: Maddie Carolan & James Munro of Dubbo. Siblings: Hunter and Oliver. Grandparents: Gerard & Lucy Carolan, Kevin & Lea Munro. Photo: Sam Rummans Photography.
Maliah Kate Corby. Born: July 11. Parents. Haylee Rees & Jared Corby. Weight: 3.6 kg. Siblings: Hudson, 3 and Archie, 1. Grandparents: Donna and Dave Rees, Bronwyn Smyth of Dubbo, and Clinton & Raelene Corby of Oberon. Photo: Georgie Newton Photography.
Grace Anne James. Born: July 1. Parents: Hayley Ellis and Ben James of Dubbo. Weight: 6 pounds 9 ounces. Grandparents: Pieta and Ursula Lindner-Ellis and Vickie James. Photo: Sam Rummans Photography.
Charlie Patrick Coyle. Born: June 29. Parents: Jason and Sam Coyle of Dubbo. Weight: 3.7kg (8 pound 1 oz). Siblings: Jack (19 months). Grandparents: Norman and Anne O'Neill, David and Debbie Coyle. Great-grandparents: Connie and (the late) Joe Snare, Jodie and (the late) Bill O'Neill, Ella Barber, Reg Barber, Joe and Phyllis Coyle. Photo contributed.
Thea Kate Davis. Born: June 18 at Dubbo Hospital. Parents: Becky-Rae Holland and Jordan Davis. Weight: 8.7 pound. Sibling: Roman, 3. Grandparents: Dave Holland, Karen Fullerton (dec), Steve Davis, Louise Davis. Photo: Lisa Weber Photographer.
Sophie Mae Zumbo. Born: June 15 at Dubbo Hospital. Parents: Sebastian & Kristi Zumbo. Weight: 4.28kg (9lb 7oz). Sibling: Mia, 3. Grandparents: Tony & Carrie Zumbo of Coonamble and Greg & Therese Lummis of Curban. Photo: Sophie Li/ Portraits by Sophie.
Riley James Chapman. Born: June 13 at Dubbo Hospital. Mother: Stacey Chapman. Weight: 6 pounds, 4 ounces. Grandfather: James Chapman. Grandmother: Belinda Butcher. Grandmother: Jodi Cooper. Grandfather: Peter Cooper.
Jessica Fay Smith. Born: April 10. Parents: Rebbecca and Adam Smith. Weight: 3260g. Grandparents: Roland and Helen Williams and Mark Sladden. Photo contributed.
Audrey Annette Buchanan. Born: April 13 at Dubbo Hospital. Parents: Jessica Leigh Smith and Derek Buchanan. Weight: 7.25 lbs 51 cm. Photo: Wild Lily Photography.
Eadie Ataahua Charman. Born: May 23 at Dubbo Hospital. Parents: Andrew and Jade Charman of Wellington. Siblings: Matilda and Alby Charman. Grandparents: Rebecca and Peter Larsen.
Skylah Maree-Helen Veech. Born: May 27. Parents: Megan Veech & Sonny Amatto. Weight: 3410g. Siblings: Keely, Charlotte,Ellah, Malakai, Sherry. Grandparents: Susan & Geoff Veech, Tina & Wayne Hammond, Kerry Amatto.
Zaylah Michelle Rose Binge. Parents: Monique Binge and Colin Briggs. Weight 7 pounds. From Boggabilla but lived in Forbes. Grandparents: Shelly Binge, Mathew Sharman, and Karen Barber and Andrew Briggs from Forbes.
Reuben Shane Taylor. Born: April 7. Parents: Jessica Reed & Brayden Taylor . Weight: 7 pound 13. Sibling: Nate Reed. Grandparents: Shane Reed & Annette Carter, Amanda Leslie & Matthew Taylor.
Darby Jack Owens. Born: March 3, 2017. Parents: Ian and Amanda Owens (formerly of Dubbo). Weight: 8.2 pounds. Brothers: Bailey, 12 and Colby, 10. Grandparents: Phil and Bev Owens of Dubbo, Brett and Debbie Kelly of Ormeau, QLD.
Willow Amarlie London. Born: April 11. Parents: Crystal Gray and Josh London. Weight: 1.820kg/ 3.9 pounds. Twin of stillborn Amarlie Nevaeh London. Grandparents: Susan Shelton, Trudy London and Simon London. Great-grandmother: Patsy Clarke. Photo contributed.
Thomas Michael Mackander. Born: February 7 at Dubbo Hospital. Parents: Michael and Melissa Mackander of Dubbo. Siblings: Emily and Maddison. Grandparents: Graham & Sue Bender, Graham & Lynne Mackander.
Paisley Samantha Hyde. Born: April 9 at Dubbo Hospital. Parents: Shanelle and Jake Hyde. Weight: 7.4 pounds. Siblings: Kyrra and Malakye. Grandparents: Samantha Tucker from Stuart Town and Leslie Hyde from Koorawatha. Photo contributed.
Landon Austin Roberts. Born: March 4 at Dubbo Hospital. Parents: Phillip and Bernadette Roberts of Dubbo. Weight: 3020g. Grandparents: Geoff and Judy Bray of Dubbo, Joy Roberts of Gilgrandra and Ken Roberts of Peterborough South Australia. Photo contributed.
Madeline Estelle Clark. Born: April 4. Parents: Nikita Munro and Thomas Clark of Geurie. Weight: 5.1 pounds. Sheridan Baker and Neil Baker of Geurie, and Jane and Stephen Clark of Dubbo.
Summah Elizabeth Payne. Born: March 19, 2017 at Dubbo Hospital. Parents: Jarrah & Aaron Payne of Dubbo. Brothers: Blake, Hunter & Callum. Grandparents are Stephen & Deborah Payne, Wendy Rawson, Reece Rutherford.
Adalyn Kate Powyer. Born: March 21 at Dubbo Hospital. Parents: Mark & Meg Powyer of Wellington. Weight: 1.66kg. Grandparents: Bill & Fiona MacCallum, Geoff Powyer & Denise Clark.
Peighton Ann Margaret Seymour. Born: January 7, 2017 at Dubbo Hospital. Parents: Geramy Seymour & Jessica Harding. Weight: 3.82kg (8pounds 6ounces). Brothers: Zakory, 8, & Kobe, 5. Grandparents: Melissa Williams, Ronald Seymour, Karen Seymour, Sally Harding.
Amelia Jennine McAnally. Born: March 18, 2017. Parents: Michael McAnally & Jessica Healand. Weight: 7 pounds. Brother: Benjamin, 5. Sister: Imogen, 3. Grandparents: Jenny, Brett, Jason, Kim, Joanne and Chris.
Heidi Lyn Robin Burns. Born: March 17, 2017. Parents: Shai Copelin and Aaron Burns of Dubbo. Weight: 6.1 pounds. Grandparents: Hugh Burns and Lyn Cresswell, Glen Copelin and Roz McKenzie and Jayne Copelin.
Zahra Deanne Lorraine Kemp. Born: March 10, 2017 at Dubbo Hospital. Parents: Darrell Kemp & Tori Towney. Grandparents: Deanne Towney, Shane Pennell & Kerry Kemp & Craig Clarke.
Lola Claire Kennedy. Born: March 9,2017 at Dubbo. Parents: Dominic and Annabelle Kennedy of Nyngan. Weight: 7.6lbs. Brother: Max, 4. Grandparents: Michael and Leonie Kennedy, Nyngan and Ken and Rene Buckley, Batemans Bay.
Huxley Kale Bock. Born: February 28, 2017 at Dubbo Hospital. Parents: Kale Bock and Lanae Barbary of Narromine. Weight: 3800g. Grandparents: Kathie Williams, Brett Barbary, Tony and Debbie Bock.
Jaxon Phoenix Brown. Born February 26 at Dubbo Hospital. Parents: Kristen Brown and Kala Ohlsen. Weight: 7 pound 9ounces. Brother: Harleigh, 5. Sister: Azalia, 4. Photo: Gotcha Kids Photographers.
Charlie Jack Bower. Born: February 24, 2017. Parents: Mitch & Ash Bower of Dubbo. Weight: 3960g. Brother: Tommy. Grandparents: Mick & Jo Evans, Scott Vaughn & Cassie Bower-Vaughn.
Harrison Adam Baker. Born: February 20 at Dubbo Hospital. Parents: Angela Shannon and Matthew Baker. Weight: 3.520kg. Brother: William Baker, 4. Grandparents: Shane Baker (deceased), Jamie Cummings (Nan) and Adam Shannon (Pop).
Amelia Isobel Weldon. Born: February 13, 2017 at Dubbo Hospital. Parents: Jadi & Simon Weldon. Weight: 2870g. Siblings: Blake,11, Holly, 10, Harrison, 8, William, 7. Grandparents: Vicki & Mitchell Crooks, Ted & Gina Weldon.
Marlee Jade Barrett. Born: January 26, 2017 Parents: Naomi-Jade Triplett & Sam Barrett of Dubbo Weight: 3.360kg Grandparents: Corina and Anthony Triplett, Fiona & James Rayner, Phil Barrett.
Imogen Grace Ryan. Born: January 13, 2017. Mother: Brooke Dorin. Father: Rory Ryan. Siblings: James, Seth, Isabelle, Ebony. (From Yeoval). Grandparents: Beth Dorin from Wellington, Les and Gillian Ryan from Yeoval. Great-grandparent: James Cassidy from Yeoval.
Chad Reign Kelleher. Born January 11 at Dubbo Hospital. Parents: Shelley Conway-Davis and Darren Kelleher of Binnaway. Weight: 8lbs 13 oz. Brothers: Darren, 5, Blake, 2, and Lucas, 1. Grandparents: Darren and Sarah Kelleher and Susan Davis and Graham Cross.
Ignatius Zachary Jacob Cameron Stanley: Born January 10 at Dubbo Hospital. Parents: Bianca Linnett and Zachary Stanley of Trangie. Weight: 3130 grams. Grandmothers: Gillian Stanley, and also the late Susan Connell, and grandfather: Kym Linnett. Photo contributed.
Hannah Michele Southwell. Born: January 9. Parents: Greg and Sarah Southwell. Weight: 3.510kg. Grandparents: Roland and Helen Williams and Michele Southwell.
Raven Joan Martin. Born: January 5, 2017. Parents: Nathan Martin and Jessica Watson. Weight: 6 pounds 8 ounces. Grandparents: Sue Watson, Ron Watson and Alison Martin. Great-grandparents: Ted (Edward) Martin and Barbara Martin.
Billie Janet Jean Toomey. Born New Year's Eve December 31, 2016. Last baby born in Dubbo Base for 2016. Weight: 6pd 13oz. Daughter to Bronte Ross and Barry Toomey. Grandparents Wayne & Deanne Ross of Gilgandra, Janet Toomey of Dubbo & Wayne Bates of Broken Hill.
We love welcoming your new arrivals in the Daily Liberal babies gallery.
Here are some of the bundles of joy born recently in Dubbo and the wider Orana region, shared with us by local families.
We will also feature baby photos in the print version of the Daily Liberal periodically.
If you have a new addition to your family, you're invited to send us a photo and some details and we will add them to the gallery.
It's a special way to capture a moment and create a keepsake to share with your son or daughter in the future.
This year the first baby born at Dubbo Hospital was baby Robert at 7.10pm on January 1.
Discuss "Welcome to the World: Babies 2017 | Photos"


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Braids Return with 'In Kind // Amends' 12-inch, Open Up About Lineup Change and New Electronic Direction
By Alex Hudson
Braids have been plotting the follow-up to their 2011 debut album Native Speaker for some time now, and now the group are finally heading back our way with a new release. The Montreal-based band will release the 12-inch single In Kind // Amends on June 11 through Flemish Eye Records in Canada. This will lead up to the band's sophomore full-length, which is due out this fall.
In Kind // Amends includes two songs from the forthcoming as-yet-untitled album — "In Kind" and "Amends" — plus two exclusive cuts, "Near Enough" and "A Dawn in Me." That's the 12-inch's cover art above.
Speaking with Exclaim!, singer Raphaelle Standell-Preston confirms prior reports that the band are moving in a more electronic direction. "We've totally ditched the guitars," she says. "'In Kind,' on the record, is the only song that has guitars."
Having abandoned their rock band setup, Braids have been using keyboards and building tracks on the computer. "All of us, over the two years that we toured Native Speaker, became really heavily into and inspired by electronic music. We listened to so much of it," the singer recalls. "I still haven't really gone back to more traditional music."
She elaborates on her love of electronic music, explaining, "It's strikingly beautiful. It's very amazing, and to me, it reflects where we have gone to as a human race in 2013 and our involvement with the iPhone and laptops and everybody being interconnected. That's one of the reasons I think that electronic music excites something inside of me."
Throughout the conversation, she name-checks favourite artists like Aphex Twin, Stephan Bodzin, Max Cooper, Pantha du Prince and Burial.
Not only did Braids change their sound, but they experienced a lineup change when keyboardist Katie Lee left the group during the self-produced recording session.
Standell-Preston candidly admits, "We're a lot more focused now on being really honest and open with each other. I feel like when we did Native Speaker and we were doing a lot of interviews, we were always talking about friendship, but we never really felt it that much. I kind of just felt like a bunch of mumbo. It felt like a bunch of talk. [It was] something to talk about in interviews, and we kind of realized at the end of the touring cycle that we weren't friends anymore. We were going on totally different creative paths. One of the dynamics that we try to uphold is being really real, and having a lot of realness in the band and just being straightforward. Creatively, we were just butting heads so much."
She doesn't blame Lee — who appears extensively on the new album — for the struggles, but notes that the band have begun working much much productively on new material. This emphasis on "realness" also comes across in Standell-Preston's lyrics.
"When we released Native Speaker, I was really kind of embarrassed about what I had written," she confesses. "I felt really far away from that person. Since that time, since I was 17 until now — I'm 23 — I definitely became a woman and had a lot of mind-opening experiences. The poems, the lyrics are beautiful and forgiving and honest and more vulnerable. I feel like, with Native Speaker, I tried to project this strong, maybe somewhat bitchy person, and I'm not really like that."
For a taste of the new sound, stream the electronic-leaning "Amends" at the bottom of this page. Also below, check out the band's upcoming tour schedule, which includes Canadian shows in Montreal and Toronto. Except more information about the full-length to be announced in the coming months.
6/11 Montreal, QC - Casa Del Popolo
6/13 Toronto, ON - Arbutus Showcase (NXNE)
6/15 Brooklyn, NY - Cameo Gallery (Northside Festival)
More Braids


The main principle of a wood-fired oven is very simple. A wood fire is lit in the back, side or centre of the oven to heat up the inner dome and hearth area (mass). The oven will retain heat to create optimum cooking temperatures long after the flame is exhausted. An Authentic Oven is designed to retain its heat evenly between the temperatures of 300-400 Degree C for long periods. You can start cooking at any stage and the best way is to just try it! Its that easy. We have done the hard work now you can relax and enjoy.
Your oven needs to heat quickly! The larger the oven the longer it will take to heat so in fact it may get used less than a smaller oven. Residential Authentic Ovens heat quickly and retain the heat perfectly for hours of enjoyment. Finding the right combination has only been achieved by experience. Keep in mind having enough space in the oven for your fire and multiple cooking trays. Our 750 oven is the smallest we recommend before things get too crammed but its heats quickly so you can use it whenever you feel like it. The 900 oven is larger, will take longer than the 750 to heat and will hold its heat longer. Heating times to peak temp vary with wood type, water content and also wind.
Ready to cook, move your hot ashes to the sides and use the retaining heat to cook virtually anything. The use of tinfoil assists required cooking temps when cooking multiple items at one time.
A well balanced oven is critical. This means when you place your pizza in the hot oven hearth surface it will cook both sides evenly. Authentic Oven's are balanced to ensure even cooking over a variety of foods.
Residential ovens are much different to commercial ovens, always use the correct design of oven for the job. Residential ovens are used in short bursts and heated from cold where commercial ovens are heated over the first week and mostly stay hot for their entire lifetime. You will find that most DIY online designs don't take this into account and there is no real performance in mind when building one for the first time. Authentic Oven have built thousands of ovens and had years of experience and the opportunity to perfect our designs which has made us New Zealand's prominent oven manufacturer.
A fire is lit inside the brick oven with wood or in some cases gas.
The heat generated by the source of energy used heats up the internal mass of the oven. The wood fired (or gas fired) brick oven is designed to retain heat and radiate it over an even cooking area. Heat must be even top and bottom of a pizza to cook properly. Your fire can be completely burnt out or ideally just hot ashes and embers left.
Pizzas should be cooked directly on the hearth and roasts in cooking trays. The ideal hearth temperature for cooking pizza is around 350-425 degrees centigrade. The pizza dough should rise quickly but should not burst. Water content in your dough is important and relevant to the temperature of the oven.
The best pizza ovens retain their even heat for long periods and don't require much additional heating. To achieve the best cooking results you require products designed to RETAIN heat at required cooking temperatures…say 350 to 425 degrees centigrade on the hearth and surrounding dome. We call this 'hover temperature' (The period which the internal temperature stays the same or similar without adding additional energy for a period of time). The heat must be even under and on top of the pizza while cooking. Even density & mass also insulation. There is no text book to show you this……. 100's of ovens later you may understand it like we do.
Authentic Ovens have a long even hover temperature, which makes us stand out from the rest. An even heat throughout from a solid one piece hearth and fire brick dome. Each internal brick in the oven is adhered to each other making it one item so the heat is transferred evenly. The insulation is 100% ceramic fibre rated to 1670 Degree C.
To achieve this we use compressed high alumina fire bricks. Authentic Oven has invested large amounts of time and money on research to develop the correct hover temperature, by adjusting the levels of density to make long hover and adjusting the alumina content and thickness of each brick to adjust how quickly the heat is transferred. No kit-set product will achieve heat retention like this. Kit-set and cast ovens cannot achieve this as they do not have the quality and detail in their design.
Authentic Ovens internal dome utilizes custom compressed fire bricks to achieve this effective formula. 2.5g/cm3 pressed and 42% alumina on EVERY OVEN. We are on a mission to be the world leaders in brick oven manufacturing and design by looks and performance.
Different designs of wood ovens work better for different types of cooking so consider the type of cooking you would like to do. In essence the more density in the internal structure of the oven the more versatile it will be but the initial heating will take longer. Authentic Oven will design and manufacture residential or commercial ovens to suit your needs to perfection. We believe New Zealand made quality with high performance products is the right way to go.
The defining difference between an Authentic Oven and our competitor's ovens is the compressed high alumina fire brick used. Our ovens hold heat like no other product on the market. Many ovens are poured into molds with products with no alumina and no heat retaining properties. Authentic Ovens also contain 50mm of 100% ceramic fibre insulation surrounding the fire bricks and hearth creating an evenly heated cooking surface and baking oven.
The insulation is so effective that while internal temperatures can reach 800 deg C, the outside cladding only reaches 55-75 deg c over several hours on continuous heating. Authentic wood fired ovens are made from the best products available. Absolutely no compromise in quality is our objective with over 2000 happy customers worldwide to date.
The Authentic Oven traditional external front flue system has been designed to work indoors and out. Perfectly sucking up smoke with minimal heat loss. Ovens with the flue system inside the dome area or back are simply not as effective at retaining heat as the heat escapes out the flue without transferring to the dome mass. See illustration above of correct flue system construction.
Once your order has been placed, you are welcome to drop by the factory and see how your ovens construction is progressing. You will appreciate the work of art that is your Authentic Oven.
Weekends please call for an appointment.


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La Zellik-Galmaarden fou una cursa ciclista belga que es disputava entre Zellik i Galmaarden, al Brabant Flamenc. La primera edició es disputà el 1983 i la darrera el 2012. Des del 2005 i fins a la seva desaparició formà part de l'UCI Europa Tour.

Palmarès

Referències

Enllaços externs
 Palmarès a sitiodeciclismo.net

Competicions ciclistes belgues
UCI Europa Tour


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European Raptors: Biology and Conservation
Raptors Species
Owl Species
Raptor Interviews
Impressum/Legal Notice
Data protection / Datenschutz
Conservation, Eagles
Sea Eagles could return to England in the near future
September 25, 2009 Markus Jais
The White-tailed Eagle (or Sea Eagle) is the largest eagle in Europe. In the UK, the species was persecuted to extinction. England lost the large raptor in the 19th century and in the 20th century, the last Sea Eagle was killed in Scotland.
Since then, the impressive bird was reintroduced successfully to western Scotland with more than 40 territories occupied in 2009. A new project for eastern Scotland started releasing birds in 2007.
Fore more about the project's progress, see the Sea eagle project newsletter 2008 .
Now, the White-tailed Eagle could also return to England. The RSPB and other organisations hope to reintroduce the species to England at the Suffolk coast. According to the RSPB, it could take decades or even centuries until the birds recolonise England on their own from Scotland.
A study found the area at the Suffolk coast to be suitable for Sea Eagles. Now, the next step is to start an open debate on how to proceed. The project can only be successful of the local people support it, including farmers and hunters.
For more information, check out the following article on the RSBP website:
The RSPB: Suffolk coast could be home to eagles
Previous PostAlmost 60 hectares of habitat for the Lesser Spotted Eagle passed to NABU/Birdlife Germany in Northern GermanyNext PostNew record year for White-tailed Eagles in Scotland
European Raptors
Harriers
Sparrowhawks


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After being brutally rejected on The Bachelor Australia, it might be surprising to learn Abbie Chatfield is back on reality television again.
Abbie Chatfield: 'I was the most hated person in Australia'
by Adrienne Tam
21st Mar 2020 2:43 PM
When astrophysicist Matt Agnew picked Chelsie McLeod to be his partner in the series finale of The Bachelor Australia, fans of the Network 10 dating show sent social media into meltdown.
But it wasn't so much the (short-lived) couple who whipped viewers into a frenzy as much as his rejection of runner-up Abbie Chatfield - to say nothing of the dramatic way she handled it.
Spurned and angry, Chatfield did not follow her predecessors' lead by graciously wishing Agnew the best before quietly slinking away. Instead, she stared Agnew down and assertively demanded "Why?" before - fed up with his mealy-mouthed apology - she stormed off.
It may have been unprecedented behaviour from a runner-up, but Chatfield's feistiness was evident throughout the series, garnering love, and quite a bit of hate, from the audience.
In an exclusive interview with Stellar, Chatfield discusses how she dealt with the barrage of negativity, the reason she is willing to risk it all again on sister reality television show Bachelor In Paradise - and why she still believes in love.
Chatfield is soon to appear on Bachelor In Paradise. (Picture: Cara O'Dowd for Stellar)
Chatfield with her dog Rosie. (Picture: Instagram/@abbiechatfield)
It's been six months since the finale of The Bachelor Australia. How are you feeling about it now?
I'm much better. The weekend before the finale aired was really rough. I was getting cyber-bullied intensely, and had to be reminded I wasn't with Matt. But ever since then I've been received well in all of my social media, and by people who didn't like me before - now they like me a bit more.
Are you and Matt in contact?
No. Matt hasn't contacted me. I think he could have called me, but whatever. I mean, what can we do?
How did you deal with online trolling happening while the show aired?
Probably not very well, to be honest. I had my friend say to me, "Don't look at your DMs [direct messages]." A lot of my friends, we talk only through Instagram, so I would go onto the app when I wanted to talk to friends and I would see hundreds of comments about how I was a sociopath or manipulative.
It got to a point near the finale where I was having very serious suicidal thoughts because your online experience becomes your reality.
And it isn't realistic when people say, "Just don't go on social..."
Particularly when all of Australia's speaking about you! To say just don't use Instagram at all is unrealistic for a 24-year-old woman.
Whether or not you know it's negative, you want to try and find that one good comment, or one nice DM, which would kind of get me through to the next day.
So if I just left it and just thought about what people were saying about me, I would go insane.
I was getting all of these death threats - they're still in my inbox. People were just relentless. I was never bullied in the flesh, so to go from being pretty well-liked from my friends and family, to feeling like I'm the most hated person in Australia is really hard to deal with.
"I would go onto the app when I wanted to talk to friends and I would see hundreds of comments about how I was a sociopath or manipulative." (Picture: Cara O'Dowd for Stellar)
S ince the show ended, you have been very open about everything from having an abortion to the toll that being body-shamed took on you. Why is speaking out important to you?
After the show, I had the opportunity to say things in my own words, which is a huge relief. But I think the reason I was able to speak out about so many things - abortion, depression and anxiety, and being body-shamed - is because everyone hated me anyway.
So if I made one person feel a bit better if they had been slut-shamed, or told they're fat, ugly, or had an abortion, I'm fine to be a sacrificial lamb.
Everyone says I'm so real and so open, but I literally just don't have a filter. If something is annoying me, then I'm going to post about it.
Given what you went through, do you still believe in love?
Yeah... yes. [Laughs.] I mean, I'm a little bit scarred from Matt. It's confusing when you believe everything someone's saying, and then you turn around, and you're like, well, what was true or wasn't true?
So you just don't really trust your own instincts anymore or believe what anyone says. I believe in love. But I'm a bit more cautious.
MORE STELLAR
Sam Armytage admits she doesn't have a clue
'Nicole Kidman made me the woman I am today'
You're in the new season of Bachelor In Paradise. Why do this again?
It can't get any worse. I already went through the worst of it. It's another experience, another opportunity and there are positive things that can come from it. And I genuinely did fall in love on The Bachelor.
When you go through that experience when you're in an environment where you can fall in love with someone and you don't think you can - because I didn't believe I would - I'm just like, "Why not?"
Abbie Chatfield features in this Sunday's Stellar.
Who are you excited to see in Paradise?
The only person I'm really excited to see is probably Ciarran Stott from Angie [Kent]'s season. Ciarran's hot. He's funny, he's cool, he's got banter. I don't have anyone else on my hit list. I'm just going to go in and see what happens.
You have a degree in property economics and are obviously very articulate. So do you find it funny or annoying that the joke you made on The Bachelor about being a Gemini [her response when Agnew told her he was an astrophysicist] went viral?
It's all right. It's funny. I have friends in America who I speak to and they're like, "Oh, you're the Gemini girl."
I mean, it's a mark of some sort, but I'm just glad I didn't go home the first episode and have that be my legacy. There's a lot more to me.
Bachelor In Paradise is coming soon to Network 10.
Originally published as Abbie Chatfield: 'I was the most hated person in Australia'
Chatfield with Matt Agnew in The Bachelor Finale. (Picture: Network 10)
abbie chatfield the bachelor australia