text:
\section{Introduction}
Among many neural network compression methodologies, quantization
is considered a promising direction because it can be easily supported by accelerator hardwares~\cite{tpu} than pruning~\cite{deepcompression} and is more lightweight than knowledge distillation~\cite{hinton2015distilling}.
However, quantization generally requires some form of adjustment (e.g., fine-tuning) using the original training data~\cite{dorefa, zhuang2018towards, robustnessdefensive, jacob2018quantization, shen2020q} to restore the accuracy drop due to the quantization errors.
Unfortunately, access to the original training data is not always possible, especially for deployment in the field, for many reasons such as privacy and security.
For example, the data could be medical images of patients, photos of confidential products, or pictures of military assets.

Therefore, data-free quantization is a natural direction to achieve a highly accurate quantized model without accessing any training data.
Among many excellent prior studies~\cite{aciq, dfq, choi2020data, outlier}, generative methods~\cite{gdfq, zaq, zeroq} have recently been drawing much attention due to their superior performance.
Generative methods successfully generate synthetic samples that resemble the distribution of the original dataset and achieve high accuracy using information from the pretrained full-precision network, such as batch-normalization statistics~\cite{zeroq, gdfq} or intermediate features~\cite{zaq}.

However, a significant gap still exists between data-free quantized models and quantized models fine-tuned with original data.
What is missing from the current generative data-free quantization schemes?
We hypothesize that the synthetic samples of conventional methods lack \emph{boundary supporting samples}~\cite{heo2019knowledge}, which lie on or near the decision boundary of the full-precision model and directly affect the model performance.
The generator designs are often based on conditional generative adversarial networks (CGANs)~\cite{cgan, acgan} that take class embeddings representing class-specific latent features.
Based on these embeddings as the centroid of each class distribution, generators rely on the input of random Gaussian noise vectors to gain diverse samples.
However, one can easily deduce that random noises have difficulty reflecting the complex class boundaries.
In addition, the weights and embeddings of the generators are trained with cross-entropy (CE) loss, further ensuring that these samples are well-separated from each other.

\JL{flat --> align , or cite stylegan here}

In this work, we propose Qimera\xspace, a method for data-free quantization employing superposed latent embeddings to create boundary supporting samples.
First, we conduct a motivational experiment to confirm our hypothesis that samples near the boundary can improve the quantized model performance.
Then, we propose a novel method based on inputting superposed latent embeddings into the generator to produce synthetic boundary supporting samples.
In addition, we provide two auxiliary schemes for flattening the latent embedding space so that superposed embeddings could contain adequate features.

Qimera\xspace achieves significant performance improvement over the existing techniques.
The experimental results indicate that Qimera\xspace sets new state-of-the-art performance for various datasets and model settings for the data-free quantization problem.
Our contributions are summarized as the following:
\begin{itemize}
    \item We identify that boundary supporting samples form an important missing piece of the current state-of-the-art data-free compression.
    \item We propose using superposed latent embeddings, which enables a generator to synthesize boundary supporting samples of the full-precision model.
    \item We propose disentanglement mapping and extracted embedding initialization that help train a better set of embeddings for the generator.
    \item We conduct an extensive set of experiments, showing that the proposed scheme outperforms the existing methods.
\end{itemize}

\JL{maybe itemized contributions}

\section{Related Work}
\subsection{Data-free Compression}
Early work on data-free compression has been led by knowledge distillation~\cite{hinton2015distilling},
which usually involves pseudo-data created from teacher network statistics~\cite{lopes2017data, nayak2019zero}.
Lopes et al.~\cite{lopes2017data} suggested generating pseudo-data from metadata collected from the teacher in the form of activation records.
Nayak et al.~\cite{nayak2019zero} proposed a similar scheme but with a zero-shot approach by modeling the output space of the teacher model as a Dirichlet distribution, which is taken from model weights.
More recent studies have employed generator architectures similar to GAN~\cite{gan} to generate synthetic samples replacing the original data~\cite{chen2019data, micaelli2019zero, fang2019data, yin2020dreaming}. \KH{deepdream is not using generator}
In the absence of the original data for training, DAFL~\cite{chen2019data} used the teacher model to replace the discriminator by encouraging the outputs to be close to a one-hot distribution and by maximizing the activation counts.
KegNet~\cite{kegnet} adopted a similar idea and used a low-rank decomposition to aid the compression.
Adversarial belief matching~\cite{micaelli2019zero} and data-free adversarial distillation~\cite{fang2019data} methods suggested adversarially training the generator, such that the generated samples become harder to train.
One other variant is to modify samples directly using logit maximization as in DeepInversion~\cite{yin2020dreaming}.
While this approach can generate images that appear natural to a human, it has the drawback of skyrocketing computational costs because each image must be modified using backpropagation.

Data-free quantization is similar to data-free knowledge distillation but is a more complex problem because quantization errors must be recovered.
The quantized model has the same architecture as the full-precision model; thus, the early methods of post-training quantization were focused on how to convert the full-precision weights into quantized weights by limiting the range of activations~\cite{aciq, outlier}, correcting biases~\cite{aciq, dfq}, and equalizing the weights~\cite{dfq, outlier}.
ZeroQ~\cite{zeroq} pioneered using synthetic data for data-free quantization employing statistics stored in batch-normalization layers.
GDFQ~\cite{gdfq} added a generator close to the ACGAN~\cite{acgan} to generate better synthetic samples,
\rev{and AutoReCon~\cite{autorecon} suggests a better performing generator found by neural architecture search.
In addition, DSG~\cite{zhang2021diversifying} further suggests relaxing the batch-normalization statistics alignment to generate more diverse samples.}
ZAQ~\cite{zaq} adopted adversarial training of the generator on the quantization problem and introduced intermediate feature matching between the full-precision and quantized model.
However, none of these considered aiming to synthesize boundary supporting samples of the full-precision model.
Adversarial training of generators have a similar purpose, but it is fundamentally different from generating boundary supporting samples.
In addition, adversarial training can be sensitive to hyperparameters and risks generating samples outside of the original distributions~\cite{outofmanifold}.
To the extent of our knowledge, this work is the first to propose using superposed latent embeddings to generate boundary supporting samples explicitly for data-free quantizations.

\subsection{Boundary Supporting Samples}
In the context of knowledge distillation, boundary supporting samples~\cite{heo2019knowledge} are defined as samples that lie near the decision boundary of the teacher models.
As these samples contain classification information about the teacher models, they can help the student model correctly mimic the teacher's behavior.
Heo et al.~\cite{heo2019knowledge} applied an adversarial attack~\cite{advatk} to generate boundary supporting samples and successfully demonstrated that they improve knowledge distillation performance.
In AMKD~\cite{dong2020adversarial}, triplet loss was used to aid the student in drawing a better boundary.
Later, DeepDig~\cite{karimi2019characterizing} devised a refined method for generating boundary supporting samples and analyzed their characteristics by defining new metrics.
Although boundary supporting samples have been successful for many problems, such as domain adaptation~\cite{saito2020universal} and open-set recognition~\cite{xu2020open}, they have not yet been considered for data-free compression.

\begin{table}[]
\begin{tabular}{l|c|c}
    \toprule
       Data & Accuracy & Data-free\\
       \midrule
       Fine-tuned with Original data & 68.28\%  & \xmark\\
       Synthetic samples & 63.39\% & \cmark\\
       Synthetic + unconfusing real samples  &  63.91\% (+0.52)& \xmark\\
       Synthetic + confusing real samples & 65.75\% (+2.36) & \xmark\\
       \midrule
       Qimera\xspace (This work) & 65.10\% (+1.71) & \cmark\\
    \bottomrule
    \end{tabular}
    \caption{Motivational Experiment\vspace{-3mm}}
    \label{tab:moti}
\end{table}

\section{Motivational Experiment}
To explain the discrepancy between the accuracy of the model fine-tuned with the original training data and the data-free quantized models, we hypothesized that the synthetic samples from generative data-free quantization methods lack samples near the decision boundary.
To validate this hypothesis, we designed an experiment using the CIFAR-100~\cite{cifar} dataset with the ResNet-20 network~\cite{resnet}.

First, we forwarded images in the dataset into the pre-trained full-precision ResNet-20.
Among these, we selected 1500 samples (3\% of the dataset, 15 samples per class) from samples where the highest confidence value was lower than 0.25, forming a group of 'confusing' samples.
Then, we combined the confusing samples with synthetic samples generated from a previous study~\cite{gdfq} to fine-tune the quantized model with 4-bit weights and 4-bit activations.
We also selected as a control group an equal number of random samples from the images classified as unconfusing and fine-tuned the quantized model using the same method.

The results are presented in \tablename~\ref{tab:moti}.
The quantized model with synthetic + unconfusing real samples exhibited only 0.52\%p increase in the accuracy from the baseline.
In contrast, adding confusing samples provided 2.36\%p improvement, filling almost half the gap towards the quantized model fine-tuned with the original data.
These results indirectly validate that the synthetic samples suffer from a lack of confusing samples (i.e., boundary supporting samples).
We aim to address the issue in this paper.
As we indicate in Section~\ref{sec:exp}, Qimera\xspace achieves a 1.71\%p \KH{CHECK!} performance gain for the same model in a data-free  setting, close to that of the addition of confusing synthetic samples.

\JL{somewhere: simple yet effective}

\section{Generating Boundary Supporting Samples with Superposed Latent Embeddings}
\label{sec:method}

\subsection{Baseline Generative Data-free Quantization}
\JL{maybe we will remove zaq from here}
Recent generative data-free quantization schemes~\cite{gdfq,zaq} employ a GAN-like generator to create synthetic samples.
In the absence of the original training samples, the generator $G$ attempts to generate synthetic samples so that the quantized model $Q$ can mimic the behavior of the full-precision model $P$.
For example, in GDFQ~\cite{gdfq}, the loss function $\mathcal{L}_{GDFQ}$ is
\begin{align}
    \mathcal{L}_{GDFQ}(G) = \mathcal{L}_{CE}^P(G) + \alpha\mathcal{L}_{BNS}^P(G),
    \label{eq:gdfq}
\end{align}
where the first term $\mathcal{L}_{CE}$ guides the generator to output clearly classifiable samples, and the second term $\mathcal{L}_{BNS}$ aligns the batch-normalization statistics of the synthetic samples with those of the batch-normalization layers in the full-precision model.
In another previous work ZAQ~\cite{zaq},
\begin{align}
    \mathcal{L}_{ZAQ}(G) = \mathcal{L}_{o}^{P,Q}(G) + \beta\mathcal{L}_{f}^{P,Q}(G) + \gamma\mathcal{L}_a^P(G),
    \label{eq:zaq}
\end{align}
where the first term $\mathcal{L}_o$ separates the prediction outputs of $P$ and $Q$, and the second term $\mathcal{L}_f$ separates the feature maps produced by $P$ and $Q$.
These two losses let the generator be adversarially trained and allow it to determine samples where $P$ cannot mimic $Q$ adequately.
Lastly, the third term $\mathcal{L}_a$ maximizes the activation map values of $P$ so that the created samples do not drift too far away from the original dataset.

The quantized model $Q$ is usually jointly trained with $G$, such that
\begin{align}
    \mathcal{L}_{GDFQ}(Q) = \mathcal{L}_{CE}^Q(Q) + \delta\mathcal{L}_{KD}^P(Q),
    \label{eq:gdfq_Q}
\end{align}
for GDFQ, and
\begin{align}
    \mathcal{L}_{ZAQ}(Q) = -\mathcal{L}_{o}^{P,Q}(G) - \beta\mathcal{L}_{f}^{P,Q}(G),
    \label{eq:zaq_Q}
\end{align}
for ZAQ, respectively, where $\mathcal{L}_{KD}$ from Eq.~\ref{eq:gdfq_Q} is the usual KD loss with Kullback-Leibler divergence.

\begin{figure}
\centering

	    \subcaptionbox{Synthetic Images generated \\with Gaussian noise.\label{fig:distr-small}}
	    {\includegraphics[height=0.27\textwidth]{figs/diagram1.pdf}}

	    \subcaptionbox{Synthetic boundary supporting samples with superposed latent embeddings (proposed).\label{fig:distr-qimera}}
	    {\includegraphics[height=0.27\textwidth]{figs/diagram2.pdf}}

	\caption{Diagram of synthetic samples in the feature space of the full-precision network. The black curves represent the decision boundary of the full-precision model, which are considered ideal for the quantized model (orange dotted curves) to mimic. When synthetic images are generated with per-class embeddings and noises as in (a), their features do not support the decision boundary, whereas the proposed approach in (b) generates the boundary supporting samples, helping the quantized model to set the near-ideal decision boundary. }
	\label{fig:distr}
\end{figure}

While these two methods exhibit great performance, they both model the distribution of per-class embeddings in the latent input space as a Gaussian distribution,
and generate diverse samples using random Gaussian noise inputs.
However, based on the Gaussian distribution, it is difficult to correctly reflect the boundary between the two different classes, especially when all samples have one-hot labels.
\figurename~\ref{fig:distr} visualizes such problems in a simplified two-dimensional space.
With samples generated from gaussian noise (\figurename~\ref{fig:distr-small}), the two per-class distributions are far away, and a void exists between the two classes (see \figurename~\ref{fig:pca_gdfq} for plots from experimental data).
This can cause the decision boundaries to be formed at nonideal regions.
One alternative is to use noise with higher variance, but the samples would overlap on a large region, resulting in too many samples with incorrect labels.

Furthermore, in the above approaches, the loss terms of Eqs.~\ref{eq:gdfq} and \ref{eq:zaq} such as $\mathcal{L}_{CE}$ and $\mathcal{L}_o$ encourage the class distributions to be separated from each other.
While this is beneficial for generating clean, well-classified samples, it also prevents generating samples near the decision boundary, which is necessary for training a good model~\cite{heo2019knowledge, dong2020adversarial}.
Therefore, in this paper, we focus on methods to generate synthetic samples near the boundary from the full-precision model $P$ (\figurename~\ref{fig:distr-qimera}).

\subsection{Superposed Latent Embeddings}
The overview of Qimera\xspace is presented in \figurename~\ref{fig:qimera}.
Often, generators use learned embeddings to create samples of multiple classes~\cite{acgan, stylegan}.
Given an input one-hot label $y$ representing one of $C$ classes and a random noise vector $\bf{z}$, a generator $G$ uses an embedding layer $E \in \mathbb{R}^{D\times C}$ to create a synthetic sample $\hat{x}$:
\begin{align}
    \hat x &= G({\bf z} + E_y), && {\bf z} \sim \mathcal{N}(0,1).
    \label{eq:onex}
\end{align}
To create boundary supporting samples, we superpose the class embeddings so that the generated samples
have features lying near the decision boundaries of $P$.
With two embeddings superposed, new synthetic sample $\hat{x}'$ becomes
\begin{align}
    \hat x' &= G({\bf z} + (\lambda E_{y_1}+(1-\lambda)E_{y_2})), && {\bf z} \sim \mathcal{N}(0,1),\ 0 \leq \lambda \leq 1.
    \label{eq:twox}
\end{align}
To avoid too many confusing samples from complicating the feature space, we also apply soft labels in the same manner as a regularizer:
\begin{align}
    \hat y' &= \lambda y_1 +(1-\lambda)y_2, && 0 \leq \lambda \leq 1.
    \label{eq:twoy}
\end{align}
Generalizing to $K$ embeddings, Eqs. \ref{eq:twox} and \ref{eq:twoy} become
\begin{align}
    \label{eq:manyx}
    (\hat x', \hat y') &= \Big( G\big(S(e)\big), \sum_k^K \lambda_k y_k\Big),

    \,\,S(e) = {\bf z} + \sum_k^K \lambda_k e_k, && {\bf z} \sim \mathcal{N}(0,1),\\
    \lambda_i &= Softmax(p_i) = \nicefrac{exp(p_i)}{\sum_k^K(exp(p_k))}, && p_i \sim \mathcal{N}(0,1).
\end{align}
where $e \in \mathbb{R}^{D\times K}$ has $K$ embeddings from $E$ as the column vectors
(i.e., $e =[E_{y_0},\dots,E_{y_{K-1}}]$),
and $S : \mathbb{R}^{D\times K} \rightarrow \mathbb{R}^D$ is a superposer function.
Applying this to existing methods is straightforward and incurs only a small amount of computational overhead.
Similar to knowledge distillation with boundary supporting samples~\cite{heo2019knowledge,dong2020adversarial}, the superposed embeddings are supposed to help transfer the decision boundary of the full precision (teacher) model to the quantized (student) model.

\begin{figure}
\centering
	\includegraphics[width=0.95\columnwidth]{figs/Qimera}

	\caption{An overview of the proposed method.
	Proposed components are denoted as colored shapes.}
	\label{fig:qimera}
\end{figure}

Although the superposed embedding scheme alone produces a substantial amount of performance gains, the generator embedding space is often not flat enough~\cite{oddity}; therefore, linearly interpolating them can result in unnatural samples~\cite{laine}.
To mitigate this, the embedding space used in Qimera\xspace should possess two characteristics.
First, the embedding space should be as flat as possible so that the samples generated from Eq. \ref{eq:manyx} reflect the intermediate points in the feature space.
Second, the individual embeddings should still be sufficiently distinct from each other, correctly representing the distance between each class distribution.
In the remaining two subsections, we describe our strategies for enforcing the embeddings to contain the above characteristics.

\subsection{Disentanglement Mapping}
To perform superposing in a flatter manifold, we added a learnable mapping function $M: \mathbb{R}^D \rightarrow \mathbb{R}^d$ before the embeddings are superposed, where $D$ is the embedding dimension and $d$ is the dimension of the target space.
Thus, $\hat{x}'$ from Eq.~\ref{eq:manyx} becomes $\hat x' = G\big(S(m)\big)$, where $m_k = M(e_k)$.
    %
    %

Although we do not add any specific loss that guides the output space of $M$ to be flat, training to match the output of the full-precision model (i.e., $\mathcal{L}_{CE}$) using the superposed $\hat y'$ encourages $M$ to map the input to a flatter space.
In practice, we modeled $M$ as a single-layer perceptron, which we call the disentanglement mapping layer.
The experimental results from Section~\ref{sec:exp} demonstrate that the disentanglement mapping provides a considerable amount of performance gain.

\subsection{Extracted Embedding Initialization}
For the embeddings to be flat, we want the distributions of the embeddings fused with noise to be similar to the feature space.
For this purpose, we utilize the fully connected layer of the full precision model $P$.
Given $f$, the output features of the full-precision model before the last fully connected layer, we minimize
    $\sum_y^C{dist\big( P(f|y), P(g|y)\big) }$,
where $C$ is the number of classes, $g$ is the input of the generator, and $dist$ is some distance metric.
We do not have knowledge of the distribution of $f$; thus, we modeled it as a Gaussian distribution, such that $f|y \sim \mathcal{N}(\mu_y, 1)$.
Therefore, solving it against Eq.~\ref{eq:onex} simply yields $ E(y) = \mu_y$.
In practice, we use the corresponding column from the weight of the last fully connected layer of the full-precision model because its weights represent the centroids of the activations.
If the weights of the last fully connected layer ${\bf W} = [{\bf w_1, w_2, ..., w_C}]$, we set ${\bf \mu_y = w_y}$.
Our experiments reveal that extracting the weights from the full-precision model and freezing them already works well \rev{(see Appendix B.2)}.
However, using them as initializations and jointly training them empirically works better.
We believe this is because fully connected layers have bias parameters in addition to weight parameters.
Because we do not extract these biases into the embeddings, a slight tuning is needed by training them.
This outcome aligns with the findings from the class prototype scheme used in self-supervised learning approaches~\cite{saito2019semi, saito2020universal}.

\section{Experimental Results}
\label{sec:exp}

\subsection{Experiment Implementation}
Our method is evaluated on CIFAR-10, CIFAR-100~\cite{cifar} and ImageNet (ILSVRC2012~\cite{imagenet}) datasets, which are well-known datasets for evaluating the performance of a model on the image classification task.
CIFAR10/100 datasets consist of 50k training sets and 10k evaluation sets with 10 classes and 100 classes, respectively, and is used for small-scale experiments in our evaluation.
ImageNet dataset is used for large-scale experiments.
It has 1.2 million training sets and 50k evaluation sets.
To keep the data-free environment, only the evaluation sets were used for test purposes in all experiments.

For the experiments, we chose ResNet-20~\cite{resnet} for CIFAR-10/100, and ResNet-18, ResNet-50, and MobileNetV2~\cite{mobilenetv2} for ImageNet.
We implemented all our techniques using PyTorch~\cite{pytorch} and ran the experiments using RTX3090 GPUs.
All the model implementations and pre-trained weights before quantization are from pytorchcv library~\cite{pytorchcv}.
For quantization, we quantized all the layers and activations using $n$-bit linear quantization, described by \cite{jacob2018quantization}, as below:
\begin{align}
    \label{eq:quant}
    \theta' =
        \bigg \lfloor\frac{\theta-\theta_{min}}{interval(n)}-2^{n-1} \bigg \rceil
\end{align}
\KH{Change notation to functional form}
where ${\theta}$ is the full-precision value, ${\theta'}$ is the quantized value, ${interval(n)}$ is calculated as ${\frac{\theta_{max}-\theta_{min}}{2^{n}-1}}$.
${\theta_{min}}$ and ${\theta_{max}}$ are per-channel minimum and maximum value of ${\theta}$. \KH{function notation}

To generate synthetic samples, we built a generator using the architecture of ACGAN~\cite{acgan} and added a disentanglement mapping layer after class embeddings followed by a superposing layer.
Among all batches, the superposing layer chooses between superposed embeddings and regular embeddings in ${p:1-p}$ ratio.
The dimension of latent embedding and random noise was set to be the same with the channel of the last fully connected layer of the target network.
For CIFAR, the intermediate embedding dimension after the disentanglement mapping layer was set as 64.
The generator was trained using Adam~\cite{adam} with a learning rate of 0.001.
For ImageNet, the intermediate embedding dimension was set to be 100.
To maintain label information among all layers of the generator, we apply conditional batch normalization~\cite{CBN} rather than regular batch normalization layer, following SN-GAN~\cite{sngan}.
The optimizer and learning rate were the same as that of CIFAR.

\JL{need a space before citation brackets! usually a unbreakable space (\textasciitilde) is preferred.}
To fine-tune the quantized model ${Q}$, we used SGD with Nesterov~\cite{nesterov} as an optimizer for ${Q}$ with a learning rate of 0.0001 while momentum and weight decay terms as 0.9 and 0.0001 respectively.
The generator $G$ and the quantized model ${Q}$ were jointly trained with 200 iterations for 400 epochs while decaying the learning rate by 0.1 per every 100 epochs. The batch size was 64 and 16 for CIFAR and ImageNet respectively.
While Qimera\xspace can be applied almost all generator-based approaches, we chose to adopt baseline loss functions for training from GDFQ~\cite{gdfq}, because it was stable and showed better results for large scale experiments.
Thus, loss functions $\mathcal{L}(G)$ and $\mathcal{L}(Q)$ are equal to Eq.~\ref{eq:gdfq} and Eq.~\ref{eq:gdfq_Q} with ${\alpha}=0.1$ and ${\delta=1.0}$, following the baseline.
\KH{Isn't it too similar with GDFQ?}\JL{I added `following the baseline'}
\JL{hyparm p and K?}

\newcommand{0.3\textwidth}{0.3\textwidth}
\subsection{Visualizations of Qimera\xspace-generated Samples on Feature Space}
\begin{figure*}[t]
    \centering
    \subcaptionbox{Original data\label{fig:pca_original}}{
        \centering
        \includegraphics[width=0.3\textwidth,trim={5mm 5mm 5mm 5mm},clip]{figs/pca/pca_original.png}
    }
    \subcaptionbox{GDFQ\label{fig:pca_gdfq}}{
        \centering
        \includegraphics[width=0.3\textwidth,trim={5mm 5mm 5mm 5mm},clip]{figs/pca/pca_gdfq.png}
    }
    \subcaptionbox{Qimera\xspace\label{fig:eyeriss}}{
        \centering
        \includegraphics[width=0.3\textwidth,trim={5mm 5mm 5mm 5mm},clip]{figs/pca/pca_qimera.png}
    }
    \caption{PCA plots of the features before the last layer of the full-precision model. In the plots of Qimera-generated samples (c), the black dots represent synthetic boundary supporting samples generated with the superposed latent embeddings, which fill the space between the clusters. GDFQ-generated samples (b) form clusters that are smaller than that of the original data (a) and lack samples in the mid-ground. Best viewed in color.}
    \label{fig:hardware}
\end{figure*}

To ensure that the superposed latent embedding creates boundary supporting samples, we conducted an experiment to compare generated synthetic samples on feature space visually.
The experimental results are based on the generators trained with ResNet-20 for CIFAR-10 dataset.
The features were extracted from the intermediate activation before the last fully connected layer of the full-precision model.
For the Qimera\xspace-based generator, we set K=2 during the sample generations for clarity.

After extracting the features, we projected the features into a two-dimensional space using Principal Component Analysis (PCA)~\cite{pca}.
The results are presented in \figurename~\ref{fig:hardware}.
Compared with the original training set data (\figurename~\ref{fig:pca_original}), the samples from the GDFQ-based generator (\figurename~\ref{fig:pca_gdfq}) show a lack of boundary supporting samples and the class distributions are confined to small regions around the centroid of each class.
A generator trained with Qimera\xspace, however, exhibits different characteristics (\figurename~\ref{fig:eyeriss}).
Samples that are generated from superposed latent embeddings are displayed as black dots.
The black dots are mostly located on the sparse regions between the class clusters.
This experimental result shows that our method, Qimera\xspace, successfully generates samples near the decision boundaries.
In other words, superposed latent embeddings are not only superposed on embedding space but also in the feature space, serving as synthetic boundary supporting samples.

\subsection{Quantization Results Comparison}

Table \ref{tab:exp} displays the classification accuracy on various datasets, target models, and bit-width settings.
Note that $n$w$m$a means ${n}$-bit quantization for weights and ${m}$-bit quantization for activations.
As baselines, we selected ZeroQ~\cite{zeroq}, ZAQ~\cite{zaq}, and GDFQ~\cite{gdfq} as the important previous works on generative data-free quantization.
In addition, we implemented Mixup~\cite{mixup} and Cutmix~\cite{cutmix} on top of GDFQ,
which are data augmentation schemes that mix input images.
To implement these schemes, we created synthetic images from GDFQ, and applied the augmentations to build training images and labels.
All baseline results are from official code of the authors, where a small amount of modifications have been made on the GDFQ baseline for applying Mixup and Cutmix.
We report top-1 accuracy for each experiment.
The numbers inside the parentheses of Qimera\xspace results are improvements over the highest baseline.

\JL{recheck below later for updated numbers}
The results demonstrate that Qimera\xspace outperforms the baselines at almost all settings.
Its performance improvement is especially large for low-bitwidth (4w4a) cases.
For 5w5a setting, the gain is still significant, and its performance reaches near that of the full-precision model,
which represents the upper bound.
In addition, Qimera\xspace is not limited to small datasets having a low spatial dimension.
The ImageNet results prove that Qimera\xspace performs beyond other baselines with considerable gaps, on a large-scale dataset with many classes.
Interestingly, the result of GDFQ+Mixup and GDFQ+Cutmix did not produce much noticeable improvement except for GDFQ+Mixup in 4w4a ResNet -18 and -50.
This implies that a mixture of generated synthetic samples of different classes in the sample space is not sufficient to represent boundary supporting samples.
In summary, Qimera\xspace achieves superior accuracy on various environments regardless of dataset or model scale.

\input{figs/perf}

\rev{
\subsection{Qimera\xspace on Top of Various Algorithms}

\input{figs/ontopof}

In this paper, we have applied GDFQ~\cite{gdfq} as a baseline.
However, our design does not particularly depend on a certain method, and can be adopted by many schemes.
To demonstrate this, we implemented Qimera\xspace on top of ZAQ~\cite{zaq} and AutoReCon~\cite{autorecon}.
The results are shown in \tablename~\ref{tab:ontopof}.

AutoReCon~\cite{autorecon} strengthens the generator architecture using a neural architecture search, and therefore applying this is no different from that of the original Qimera\xspace implemented on top of GDFQ.
However, original ZAQ does not use per-class sample generation.
To attach the techniques from Qimera\xspace, we extended the generator with an embedding layer, and added a cross-entropy loss into the loss function as the following:
\begin{align}
    \mathcal{L}(G) = \mathcal{L}_{o}^{P,Q}(G) + \beta\mathcal{L}_{f}^{P,Q}(G) + \gamma\mathcal{L}_a^P(G) +\rho\mathcal{L}_{CE}^P(G),
    \label{eq:zaqimera}
\end{align}
\begin{align}
    \mathcal{L}_(Q) = -\mathcal{L}_{o}^{P,Q}(G) - \beta\mathcal{L}_{f}^{P,Q}(G)+\rho\mathcal{L}_{CE}^Q(G),
    \label{eq:zaq_Q}
\end{align}
where $\rho$ was set to 0.1, and ${\mathcal{L}_{CE}^P(G)}$, ${\mathcal{L}_{CE}^Q(G)}$ are calculated based on Eq.~\ref{eq:manyx}.

For all cases, applying the techniques of Qimera\xspace improves the performance by a significant amount.
Especially for Qimera\xspace+ ZAQ, the performances obtained was better than those of our primary implementation Qimera\xspace+ GDFQ.
Unfortunately, it did not converge with ImageNet, and thus we did not consider this implementation as the primary version of Qimera\xspace.

}

\subsection{Further Experiments}
We discuss \rev{some} aspects of our method in this section, which are effect of each scheme introduced in Section~\ref{sec:method} on the accuracy of $Q$, a sensitivity study upon various $p$ and $K$ settings, \rev{and a closer investigation into the effect of disentanglement mapping (DM) and extracted embedding initialization (EEI) on the embedding space}.
All experiments in this subsection are under 4w4a setting.

\textbf{Ablation study.}
We conducted an ablation study by adding the proposed components one by one on the GDFQ baseline.
The results are presented in \tablename~\ref{tab:ab}.
As we can see, the superposed embedding (SE) alone brings a substantial amount of performance improvement for both Cifar-100 (1.16\%p) and ImageNet (11.98\%p).
On Cifar-100, addition of EEI provides much additional gain of 0.68\%p, while that of DM is marginal.
ImageNet, on the other hand, DM provides 1.97\%p additional gain, much larger than that of EEI.
When both of DM and EEI are used together with SE, the additional improvement over the best among (+SE, DM) and (+SE, EEI) is relatively small.
We believe this is because DM and EEI serve for the similar purposes.
However, applying both techniques can achieve near-best performance regardless of the dataset.

\textbf{Sensitivity Analysis.}
\tablename~\ref{tab:ab} also provides a sensitivity analysis for $K$ (number of classes for superposed embeddings) and $p$ (ratio of synthetic boundary supporting samples within the dataset).
As shown in the table, both parameter clearly have an impact on the performance.
For Cifar-100, the sweet spot values for $K$ and $p$ are both small, while those of the ImageNet were both large.
We believe is because ImageNet has more classes.
Because the number of class pairs (and the decision boundaries) is a quadratic function of number of classes, there needs more boundary supporting samples with more embeddings superposed to draw the correct border.

\input{figs/ablation}
\rev{
\begin{table}[]
\rev{
    \centering

    \caption{Embedding Distance Ratio and Intrusion Score}
    \label{tab:embedding}

    \begin{tabular}{ccccc}
    \toprule
    \multirow{2}{*}{Method} & \multicolumn{2}{c}{Cifar-10} & \multicolumn{2}{c}{Cifar-100} \\
    \cmidrule(lr){2-3}\cmidrule(lr){4-5}
    & Dist. Ratio & Intrusion & Dist. Ratio & Intrusion  \\
    \midrule
    Mixup & 2.44 & 0.800 & 3.14 & 0.400 \\
    SE Only & 1.58 & 0.00260 & 1.64 & 0.053 \\
    SE+DM & 1.67 & 0.00073 & 1.59 & 0.044 \\
    SE+DM+EEI & 1.57 & 0.00013 & 1.52 & 0.029 \\
      \bottomrule

    \end{tabular}
    }
\end{table}

\textbf{Investigation into the Embedding Space.}
To investigate how DM and EEI help shaping the embedding space friendly to SE, we conducted two additional experiments with ResNet-20 in \tablename~\ref{tab:embedding}.
First, setting $K$=2 (between two classes), we measured the ratio of the perceptual distance and Euclidean distance between the two embeddings in the classifier's feature space.
The perceptual distance is measured by sweeping $\lambda$ from Eqs.~\ref{eq:twox} and \ref{eq:twoy} from 0 to 1 by 0.01 and adding all the piecewise distances between points in the feature space (See \figurename~5 in Appendix).
We want the distance ratio to be close to 1.0 for the embedding space distribution to be similar to that of the feature space.
Second, we defined `intrusion score', the sum of logits that are outside the chosen pair of classes.
If the score is large, that means the synthetic boundary samples are regarded as samples of non-related classes. Therefore, lower scores are desired.
For comparison, we have also included the Mixup as a naive method and measured both metrics.
It shows that the DM and EEI are effective for in terms of both the distance ratio and the intrusion score, explaining how DM and EEI achieves better performances.

}

\section{Discussion}
\subsection{Does it Cause Invasion of Privacy?}
The motivation for data-free quantization is that the private original data might not be available.
Using a generator to reconstruct samples that follow the distribution of original data might indicate the invasion of privacy.
However, as displayed in \figurename~\ref{fig:samples}, at least with current technologies, there is no sign of privacy invasion.
The generated data are far from being human interpretable, which was also the case for previous works~\cite{zeroq, zaq, gdfq}.
\rev{See Appendix (\figurename~6) for more generated images}.

Geirhos et al.~\cite{texturebias} reveal an interesting property that CNNs are heavily biased by local textures, not global shapes.
BagNet~\cite{bagnet} supports this claim by confirming that images with distorted shapes but preserved textures can still mostly be correctly classified by CNNs.
In such regard, it is no wonder that the generated samples are non-interpretable, because there is only a few combinations that maintains the global shape out of all possibilities that preserve the textures.
Nonetheless, not being observed does not guarantee the privacy protection.
We believe it is a subject for further investigations.

\newcommand{0.23}{0.23}
\begin{figure*}[t]
    \centering
    \subcaptionbox{Original Cifar-10\label{fig:cifar_samples}}{
        \centering
        \includegraphics[height=0.23\textwidth]{figs/cifar_bright.png}
    }
    \subcaptionbox{Samples from GDFQ\label{fig:gdfq_samples}}{
        \centering
        \includegraphics[height=0.23\textwidth]{figs/gdfq_bright.png}
    }
     \subcaptionbox{Samples from ZAQ\label{fig:zaq_samples}}{
         \centering
         \includegraphics[height=0.23\textwidth]{figs/zaq_bright.png}
     }
    \subcaptionbox{Samples from Qimera\xspace\label{fig:qimera_samples}}{
        \centering
        \includegraphics[height=0.23\textwidth]{figs/qimera_bright.png}
    }
    \caption{Synthetic samples generated for Cifar-10 dataset. Each row represents one of the 10 classes, except for ZAQ which generates samples without labels.}
    \label{fig:samples}
\end{figure*}

\subsection{Limitations} \KH{CHECK!}
Even though Qimera\xspace achieves a superior performance, one drawback of this approach is the fact that it utilizes embeddings of multiple classes to generate the boundary supporting samples.
This restricts its application to classification tasks and its variants.
For example, generation of datasets such as image segmentation or object detection do not take class embeddings as the input.
However, generators for such data are not extensively studied yet, and we envision adding diversity to them would require inputting some form of labels or embeddings similar to paint-to-image~\cite{singan}, which would allow Qimera\xspace be easily applied.

\section{Conclusion}
In this paper, we have proposed Qimera\xspace, a simple yet powerful approach for data-free quantization by generating synthetic boundary supporting samples with superposed latent embeddings.
We show in our motivational experiment that current state-of-the-art generative data-free quantization can be greatly improved by a small set of boundary supporting samples.
Then, we show that superposing latent embeddings can close much of the gap.
Extensive experimental results on various environments shows that Qimera\xspace achieves state-of-the-art performance for many networks and datasets, especially for large-scale datasets.

\acksection
This work was supported by
National Research Foundation of Korea (NRF) grant funded by the Korea government(MSIT) (No.2021R1F1A1063670, No.2020R1F1A1074472)
and
Institute of Information \& communications Technology Planning \& Evaluation (IITP) grant funded by the Korea government (MSIT) (No.2020-0-01361, Artificial Intelligence Graduate School Program (Yonsei University))

\printbibliography

redpajama_set_name: RedPajamaArXiv


The last year has been a particularly busy one for David Walters. The UK based musician who records as The Echelon Effect released a couple singles in 2013, then the EP Cabrillo in July, before closing out the year with Atlantic, the first part of what will eventually be a double album with part two, Pacific, due for release in 2014. I have always enjoyed The Echelon Effect's music but Atlantic was a favorite upon first listen, just a beautiful, fully realized work all the way through.
As if all of this weren't enough, now Walters has collaborated with friend Aaron Gilbert to form a new project called Random Forest. And they've just put out a self-titled debut EP well worth your time if gorgeous, ambient, instrumental post-rock is your thing. There are four tracks on Random Forest with opener "See The Storm" and "Until The Shadows" heavier guitar based pieces, while "The Best Of Us" and "Shadows Fall" are focused more on piano as the main instrument. As expected, they're all captivating and leave us wanting to hear more from this duo.
Favorite tracks: "See The Storm", "Until The Shadows"
Random Forest was self-released as a name your price Digital Download January 10, 2014.


text:
Q: Asp.Net/Sitecore embedded ascx resources not picking up changes I'm not sure if this is a Sitecore 6 or Asp.net problem.
I have an assembly made up entirely of ascx user controls in which all of the necessary files are embedded resources (ascx, javascript, etc). I have been using the user controls in this assembly in a Sitecore web site for a few months.
Recently I tried to make changes to some of the user controls. I'm sure I made changes to the user controls in the past and it worked fine. But now when I make any changes to the ascx files they don't get picked up in the website. Changes to other files, including the code behind and javascript files are getting picked up. It appears only to be the ascx files that have a problem. It continues to use the old versions of the ascx files, from where I have no idea.
I know it's using the latest version of my assembly because I've stepped through the code behind, and used Fusion to check where the assemblies are loaded from. I've tried deleting all of the files in the Asp.Net cache. I've looked at the ascx files inside the assembly using a decompiler and it does have all of the changes I made. I turned off all caching in Sitecore just to see if that would fix it, but it didn't. I can't think of anything else to check. Any ideas?

A: Seems there is a new flag in web.config compilation settings called optimizeCompilations that is supposed to make things compile faster. Someone set this to true and that caused changes to user controls in other assemblies to not get compiled

redpajama_set_name: RedPajamaStackExchange


text:
Comfort-eating: People really do crave snacks and junk food when they are stressed, study finds
January 20, 2021 Dianne Reeves Online news
People really do crave snacks and junk food to eat when they are stressed out by events in their lives, a study has confirmed.
Researchers from Australia and New Zealand surveyed 137 adults about their eating habits, feelings of tension and food cravings over the course of one week.
The subjects reported craving more food — and eating both more junk food but also more overall — the more tension they were experiencing on a given day.
People really do crave snacks and junk food to eat when they are stressed out by their lives, a study has confirmed. Pictured: a man eats to cheer himself up (stock image)
The study was conducted by sport exercise and health researcher Shina Leow of the University of Western Australia, in Perth, and colleagues.
'Feelings of tension, anxiety, and stress may alter dietary behaviour,' the team wrote.
Stress, they added, also influences 'the types of foods that individuals consume—with both stressed individuals and emotional eaters often seeking palatable energy-dense food and drinks that are high in sugar and/or saturated and trans fats.'
Emotional eaters, they explained, are those who tend of overeat in response to negative emotions — in particular, when confronted with anxiety.
In the study, the researchers asked the participants to report their levels of tension and anxiety according to an established scale which considered states of feeling anxious, nervous, panicky and worried.
The team's analysis revealed that the participants reported greater cravings for carbohydrates, sweets and fast foods on days when they felt more tension.
Furthermore, the more tense the subjects were, the more sweets and fast food they reported consuming — along with greater volumes of food overall.
Researchers from Australia and New Zealand surveyed 142 adults about their eating habits, feelings of tension and food cravings over the course of one week (stock image)
'These findings encourage further investigation into the ways which emotion-induced food cravings lead to subsequent consumption,' the team wrote.
Future studies, they continued, should take 'into further consideration the role of individuals' eating habits and dietary preferences.'
'Given the high prevalence of tension and stress in our society, further research to determine the underlying mechanisms of emotion-induced eating is important if we are to attenuate it and its detrimental health implications.'
The full findings of the study were published in the journal Eating Behaviors.
OBESITY: ADULTS WITH A BMI OVER 30 ARE SEEN AS OBESE
Obesity is defined as an adult having a BMI of 30 or over.
A healthy person's BMI – calculated by dividing weight in kg by height in metres, and the answer by the height again – is between 18.5 and 24.9.
Among children, obesity is defined as being in the 95th percentile.
Percentiles compare youngsters to others their same age.
For example, if a three-month-old is in the 40th percentile for weight, that means that 40 per cent of three-month-olds weigh the same or less than that baby.
Around 58 per cent of women and 68 per cent of men in the UK are overweight or obese.
The condition costs the NHS around £6.1billion, out of its approximate £124.7 billion budget, every year.
This is due to obesity increasing a person's risk of a number of life-threatening conditions.
Such conditions include type 2 diabetes, which can cause kidney disease, blindness and even limb amputations.
Research suggests that at least one in six hospital beds in the UK are taken up by a diabetes patient.
Obesity also raises the risk of heart disease, which kills 315,000 people every year in the UK – making it the number one cause of death.
Carrying dangerous amounts of weight has also been linked to 12 different cancers.
This includes breast, which affects one in eight women at some point in their lives.
Among children, research suggests that 70 per cent of obese youngsters have high blood pressure or raised cholesterol, which puts them at risk of heart disease.
Obese children are also significantly more likely to become obese adults.
And if children are overweight, their obesity in adulthood is often more severe.
As many as one in five children start school in the UK being overweight or obese, which rises to one in three by the time they turn 10.
(Total views: 4 Time, 1 visits per day)
Christmas tree in Trafalgar Square is a gift on behalf of a Norwegian monarch
France will scrap British tourist ban as thousands protest over lockdown measures in Europe
Members of the Shrewsbury 24 win bid to clear their names at Court of Appeal
Indonesian motorcyclist narrowly avoids death at a level crossing
Moment two mountaineers ski down Yosemite's Half Dome
Four civilians and three police officers are injured as three-story home collapses in the Bronx
Mass testing 'will be a disaster', academic warns
Ireland showed their players anti-English video before their game at Wembley
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Simple Age Verification PopUp OpenCart extension is nice plugin to verify the age before entering the site. This is helpful for adult site, smoking or alcohol sites to verify the age before visitor enters your website.
Simple Age Verification Popup plugin opens popup in cool lightbox with image and check box with it. See the below screenshop of popup.
This is VQMOD extension of OpenCart. VQMOD helps in not overwriting the core files of OpenCart.
You can find more info on VQMOD and Download here.
Install VQMOD, if it is not installed.
Download the zip file, extract somewhere on your computer.
From extracted file, upload the the files/folder under "upload" folder to server using FTP, in root folder (where admin,catalog,system folder are available) of OpenCart installation and its done you do not have to do anything else to make it work.
Show Reward Point of Customer in Header plugin do exactly what it's name implies. In OpenCart, After login as Customer to view reward point customer have to go to My Account > Your Reward Points. This Free extension / plugin of OpenCart show Rewards points in Header under Login info.
Here is the screenshot how it looks like on default theme.
This is VQMOD extension of OpenCart. No Core file is updated using VQMOD.
New OpenCart Extension, Auto Add Reward Points add product rewards points to customer immediately after purchase finished. Usually in OpenCart website admin have to add reward point to customer manually by clicking on link as show in below screenshot.
Using Auto add reward point extension / plugin for OpenCart do not require to add rewards point manually. It does automatically for admin. It helps admin in reducing tedious task of adding rewards point for every product purchased.
This is VQMOD extension so you require VQMOD to be installed on OpenCart website. You can find the information of installing VQMOD here.
It is really easy to install this plugin.
Recently we released new Opencart plugin named "Product Buy Now button and Cart in Popup – Opencart – VQMOD". This is useful extension of Opencart for replacing the "Add to cart" button with "Buy Now" button. Clicking on Buy Now button opens shopping cart in popup. Popup shopping cart is fully functioned. User can change quantity, remove any product, Apply coupon code, etc. in popup itself.
Product Buy Now button and Cart in Popup – Opencart – VQMOD extension of Opencart makes your site User friendly and easy access to shopping cart. Shopping cart popup is displayed using Colorbox jQuery plugin popup. Colorbox jQuery comes bundled with Opencart installation so no extra file include.
This Opencart plugin is developed using vQmod so no core files will be changed. Installation is very easy.
This Opencart extension support multilanguage.
Here is the screenshot of extension.


text:
Q: takeEvery and takeLatest. Why? When to use? Use simultaneously? I am not clear in when to use takeEvery and when to use takeLatest ? in redux-saga.
I got a basic difference by reading the official documentation. But what is the use of creating concurrent actions in  takeEvery (for example, the user clicks on a Load User button 2 consecutive times at a rapid rate, the 2nd click will dispatch a USER_REQUESTED while the fetchUser fired on the first one hasn't yet terminated)
import { takeEvery } from `redux-saga/effects`

function* fetchUser(action) {
  ...
}

function* watchFetchUser() {
  yield takeEvery('USER_REQUESTED', fetchUser)
}

Can anyone please explain. As I am completely new to redux-saga.
Thanks in advance.

A: To summarize in a few words,

*

*takeEvery allows concurrent actions to be handled. For example, the user clicks on a Load User button 2 consecutive times at a rapid rate, the 2nd click will dispatch a USER_REQUESTED action while the fetchUser fired on the first one hasn't yet terminated.
takeEvery doesn't handle out of order responses from tasks. There is no guarantee that the tasks will terminate in the same order they were started. To handle out of order responses, you may consider takeLatest.

*takeLatest instead start a new fetchUser task on each dispatched USER_REQUESTED action. Since takeLatest cancels any pending task started previously, we ensure that if a user triggers multiple consecutive USER_REQUESTED actions rapidly, we'll only conclude with the latest action

Docu: https://redux-saga.js.org/docs/api/

A: Though @Martin Kadlec's solid answers covers the question, I want to elaborate a bit more on the details and differences on takeEvery and takeLatest and when they might be used so you can derive the possible use-cases of them.
TLDR:
You can use takeEvery when the returns of all previous tasks are needed. For example fetching temperature and humidity data from a weatherstation over a certain period to be stored in a database and displayed as a graph - in this case all previous sagas and their return-values are of interset and not only the latest.
You can use takeLatest if i.E. a internal/external instance or a user of an interface could trigger multiple consecutive actions and only the conclusion of the last value is desireable. A good example would be rapid calls to a broker-API for a live-ticker for stock-values where only the latest/most recent value is of interest.
DETAILED:
Think of takeEvery and takeLatest as helper funtions on top of the lower level API of  redux-saga which are wrapping internal operations such as spawning tasks when specific actions are dispatched to the Store. Calling them spawns a saga on each action dispatched to the Store that matches pattern.
takeEvery:
The most common takeEvery function is very similar to redux-thunk in its behaviour and methodology. It's basically a wrapper for yield take of a pattern or channel and yield fork.
The clue about takeEvery is that it allows multiple instances of a defined action/task (such as fetchSomeThing in the example below) to be started concurrently/simultaniously.
Unlike takeLatest you can start a new fetchSomeThing task while one or more previous instances of fetchSomeThing have not yet been completed/terminated, therefore are still pending. Keep in mind that there is no guarantee that the tasks will terminate/complete in the same order they were started. To handle out of order responses, you could use takeLatest.
From the official docs:
takeEvery(pattern, saga, ...args)
Spawns a saga on each action dispatched to the Store that matches pattern.

*

*pattern: String | Array | Function

*saga: Function - a Generator function

*args: Array - arguments to be passed to the started task. takeEvery will add the incoming action to the argument list (i.e. the action will be the last argument provided to saga)

You can also pass in a channel as argument instead of a pattern resulting in the same behaviour as takeEvery(pattern, saga, ...args).
takeLatest:
The takeLatest helper function in contrast only gets the response of the latest request that was fired and can be seen as a wrapper for yield take of a pattern or channel and an additional if-statement checking if a lastTask is present (a previous task that is still pending), which will then be terminated via a yield cancel and a subsequent yield fork that will spawn the current task/action.
From the official docs:
takeLatest(pattern, saga, ...args)
Will only get the response of the latest request fired.

*

*pattern: String | Array | Function

*saga: Function - a Generator function

*args: Array - arguments to be passed to the started task. takeLatest will add the incoming action to the argument list (i.e. the action will be the last argument provided to saga)

Similar to takeEvery you can also pass in a channel as argument instead of a pattern.

A: *

*takeEvery  - enables the use of several fetchData objects at the same time.
At a given moment, we can start a new fetchData task while there
are still one or more previous fetchData tasks which have not
yet terminated.

*takeLatest - Only one fetchData task can be active at any given moment. It
will also be the work that was started most recently. If a new
fetchData job is started while a previous task is still running,
the previous work will be terminated immediately.

A: This is something you really need to think about per use case.
These are some cases when you might use takeEvery.

*

*For sagas that are not asynchronous and so there is no reason to
cancel them. takeLatest would work here as well but it might give
false indication when reading code that there is something to
cancel.

*Sometimes when the action differs in some way each time. E.g.
imagine you have a movie and you are adding tags with genre of the
movie. Each time the action is triggered you get a different genre,
even though it is the same action type. The user can add genres
quicker than you get response from the server. But just because you
added multiple genres quickly doesn't mean you want to stop the saga
adding the previous one.

*Cheap implementation of when you have the same load action for
multiple different items.  E.g. You have list of movies and each has
"load detail" button. (Let's ignore the fact that you should
probably hide or disable the button once the loading starts). The
data are always the same for one movie but differs in between them.
When you click on load detail for movie 1 you don't want to cancel
loading of data for movie 2. Since it is a dynamic list of movies
the action type is the same every time, the difference will be
probably something like id of the movie in the action.
In ideal implementation you should probably cancel the previous load saga for
the same movie/id, but that will require more complicated code and
so if you are doing only some simple app you might decide to ignore
that and just allow to run the sagas for same movie multiple times.
That is why I call it "cheap implementation".


The site is located in a 'cluster' of buildings and institutions that provide care to people of Derbyshire with Special Needs, such as Morewood Centre. The relation of the proposals with this building will need to be considered carefully. The site includes a new Hydrotherapy Pool, in a separated building, completed a couple of years ago. This development was in preparation for the future expansion of the site and needs to be considered as an integral part of the site and School premises.


Thanks again guys! Art - you've got mail.
Well, I don't think this matters anymore, but just to let you know, I've tried the file you've sent me and I have no problems at all loading it in XL, so I'm not sure what the problem could be.
Hmm. I installed the new Xlobby simplicity -version at it worked here ok as well! Perhaps there's some adjustments made with the versions. Only one minor issue: a movie with two parts refused to play, but I guess the reason behind that can be anything. Thanks again art!
Re: Importing myhtpc-data into XLobby?
Art and TSWhite70 - would it be possible to get the source-code for the programs you have created?
My idea is to have a friend to adjust them and create me an importer from .my to YAMJ.


Corrie Ten Boom was bornon April 15, 1892 in Amsterdam, Netherlands. Author of the The Hiding Place, a 1971 memoir that details her family's efforts to hide Jews during the Holocaust and her imprisonment after being discovered by the Nazis.
Author of the The Hiding Place, a 1971 memoir that details her family's efforts to hide Jews during the Holocaust and her imprisonment after being discovered by the Nazis.
In 1924, she became the first licensed female watchmaker in the Netherlands.
After being released from a concentration camp where she had been imprisoned for hiding Jews, she established a rehabilitation center for Holocaust survivors.
She had two sisters, Betsie and Nollie, and a brother, Willem.
Her famous memoir was made into a film that featured Julie Harris in the role of Betsie Ten Boom.


When these came out, I know I was squinting at them trying to figure out the print (I cam up with Ballerinas and pairs of ballerinas, and feathers but couldn't see much more than that!) It turns out that there are swans, feathers, dots, ballerinas, ballet shoes, Angelic Pretty's name, ribbons, as well as what looks like some sort of gem, crest or egg with a crown on it.
It looks like a relatively small series; Two JSK, Two headbows, a skirt and socks. The strapless JSK is very reminiscent of the one from the Aqua Princess series. Very oddly, the back view for the strapless version is shown in Sax, but it's not listed as one of the colors on their blog, or the top of this sheet. They have used feathers in the headdress again like they did for the recent angel-themed print, Gloria. As someone who owns quite a few older pieces, I find this to be a slightly worrisome thing; as I can't see the feathers lasting too long (a pair of feather earrings I bought maybe 3 years ago totally disintegrated in my jewelry box recently).
All in all, it's a pretty series, and the dresses have lovely styling to go along with the theme, but it's probably not something I'd pick up.


Here begins the report of the court of Brandubh et Hildarun, Baron and Baroness of the Debatable Lands, at the Baronial Heavy Weapons and Fencing Champions Tournaments, Sunday, May 28, AS 52, Kieran MacRae, Comet Pursuivant, reporting.
Their Excellencies wished to thank everyone who participated in today's tournaments to determine who shall be BMDL Champion for Fencing and Heavy Weapons.
Their Excellencies called before them Drotinn Jorundr hinn rotinn and Lord Robert MacEwin of Thornhill before them. Jorundr was the winner and named champion, with Robert being named his second. Jorundr shall compete at the Seven Pearls Tournament in September representing the Barony. Prize scroll by Mistress Arianna of Wynthrope. Thanks were given to Lady Cairdha Eilis O'Coileain as outgoing champion.
Their Excellencies gave leave for Master Kieran MacRae as outgoing heavy weapons champion to call forth Traygor Greypanther of the Tuchux, winner and named new heavy weapons champion. Sir Maghnus an Chnoic na n'Iora was named second. Congratulations were given to Wulfgar Ronaldson of Sunderoak for coming in second. Prize scroll by Arianna.
For MOL Staff Baroness Anna Eisenkopf, Lady Edena the Red, and Lady Alyssa von Stahlgeist.
For Lady Zianna beguy urdina de Zabaleta for providing yummy refreshments!
For Mistress Arianna and her combat scribe skills making scrolls come out of thin air.
Congratulations were given to Master William de Montegilt, on this the day of his 42nd anniversary of being authorized to fight on the field of heavy combat.
Their Excellencies reminded all to preregister for Pennsic, and that preregistering is a must in order to camp with the Barony. Online prereg ends Saturday, June 17, 2017.
There being no further business, this court of Their Excellencies was closed.
Please report any errors to the BMDL Web Minister. This page was last revised on August 16, 2017. There have been views.


text:
The Supreme Court sided with science against Obama
By Will Happer and Rod Nichols
In his State of the Union Address, President Obama invited "anybody [who] wants to dispute the science around climate change . . . to have at it."
The Supreme Court's response? Thank you, Mr. President, for the offer. We will.
On Feb. 9, the court upheld a delay of Obama's war on fossil fuels, which is supposed to "stop climate change," in the form of new restrictions on factories' greenhouse-gas emissions. Apparently a majority of the court is less confident of the "science around climate change" than Obama is.
As well they should be. Obama's policies will have negligible effects on the climate and will be all pain with no gain.
Two critical points about "the science around climate change" stand out in a review recently completed by the CO2 Coalition, a new independent, nonpartisan scientific-educational group (CO2Coalition.org).
First, carbon dioxide, CO2, is emphatically NOT a "pollutant." All living things are built of carbon that comes from CO2. An increase in essential CO2 in the atmosphere will be a huge benefit to plants and agriculture. Satellite measurements show that the increase of CO2 over the last few decades has already caused a pronounced greening of the planet — especially in arid regions.
For tens of millions of years, plants have been coping with a "CO2 famine." Current CO2 concentrations of a few hundred parts per million (ppm) are close to starvation levels compared to the several thousand ppm that prevailed over most of history.
We support the cost-effective control of real pollutants associated with the use of fossil fuels — for example, fly ash, oxides of nitrogen and sulfur or smog-forming volatile hydrocarbons. But CO2 isn't a pollutant, and there's no reason to control it.
Second, the "warming" from CO2 — and yes, CO2 is a "greenhouse gas" — has been much less than predicted by the climate models Obama bases his policies on. For 20 years, the temperature has been virtually unchanged, in stark contrast to model predictions.
The war on fossil fuels isn't based on science but on unreliable climate models. Rather than trying to correct the models, Team Obama is trying to "dispute the science" by trying to manufacture scary warming trends.
A recent letter to the House Committee on Science, Space and Technology by more than 300 experts on data quality pointed out that the feds' attempt to erase or ignore evidence of the recent lack of global warming arguably violated the Data Quality Act of 2001, which requires that "highly influential scientific assessments," bearing the imprimatur of the federal government, be subject to rigorous external peer review.
It exposes the hollowness of the left's claim that "97 percent of scientists" support Team Obama's version of climate science.
Satellite measurements of atmospheric temperatures are the genuine gold standard — and they show negligible warming for the past two decades. Since the Obama administration can't read just satellite data (as they have tried to do with surface data), they have unleashed a campaign to discredit the satellite temperature record.
The observational record indicates that the temperature increase by the year 2100 will be less than 1 degree Celsius as a result of CO2 emissions. This small temperature increase, together with the robust benefits to plants and agriculture, will benefit the world.
How about other concerns? Some claim that more CO2 causes extreme weather, accelerating a rise in sea levels or other horrors. But extensive global measurements reveal no increase in extreme weather: The trends in tornadoes, droughts, floods and hurricanes are flat over the past generation. Sea levels are rising at about the same rate they did before the rising concentrations of CO2 during the past century.
Inexpensive, reliable energy from fossil fuels has raised living standards in the developed world to levels that only the wealthy could dream of a few centuries ago. Eliminating fossil fuels would do nothing to stop climate change, but it would keep much of the developing world in poverty.
Rising energy costs would hurt the less privileged populations of the developed world as well.
Bravo for the Supreme Court's vote for solid science!
Will Happer, professor of physics, emeritus at Princeton University and Rod Nichols, former president of the New York Academy of Sciences, are members of the CO2 Coalition (CO2Coalition.org).
This article appeared on the New York Post website at http://nypost.com/2016/02/15/the-supreme-court-sided-with-science-against-obama/


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A new study designed to test suspected links between genes and two immune disorders could open the door to better ways to diagnose and treat the conditions that affect a combined total of approximately 2.5 million people in the United States, report scientists at The University of Texas Health Science Center at Houston (UTHealth).
The genes are thought to be associated with ankylosing spondylitis, a form of arthritis that attacks the spine, and systemic sclerosis (scleroderma), a chronic, often progressive, disease of connective tissue. As many as 2.4 million people in the United States may be affected by ankylosing spondylitis and its related diseases. Systemic sclerosis impacts about 100,000 people in the United States.
The researchers plan to conduct an exhaustive analysis of these genes and others using a research technique called targeted resequencing. The study will involve more than 6,000 patients with ankylosing spondylitis or scleroderma from the United States, China and Spain and it will focus on an area of the genome linked to immunity issues called the major histocompatibility complex. The study will also include about 3,000 people without the conditions.
"This is the most complicated region of the human genome," said Xiaodong Zhou, M.D., a principal investigator and associate professor of internal medicine at the UTHealth Medical School. "The region contains genes linked to all types of immune diseases. In many instances, we don't know precisely where the linkage comes from. We want to find out if these are true associations."
Zhou, director of the UTHealth Research Laboratory in Rheumatology and Clinical Immunogenetics, said, "You need distinct ethnic populations for comparison purposes."
While the immune system's function is to protect the body, problems can occur. Immune systems can also induce inflammation, which is called autoinflammatory. Immune systems can also generate autoantibodies that can attack their own cells - a condition called autoimmune disease. Ankylosing spondylitis is an inflammatory disease and scleroderma is an autoimmune disorder.
Many genes linked to ankylosing spondylitis and scleroderma were identified with a research technique called a genome-wide association study in which scientists compare the genetic makeup of people with a certain disease to those without the disease. This technique shows researchers where to look for disease-associated genes in the human genome.
Several of the gene regions, each containing multiple genes, were discovered by the two other principal investigators in the study, John D. Reveille, M.D., and Maureen D. Mayes, M.D., M.P.H.
"We are taking our research to the next step," said Reveille, holder of the George S. Bruce, Jr. Professorship in Arthritis and Other Rheumatic Diseases and the Linda and Ronny Finger Foundation Distinguished Chair in Neuroimmunologic Disorders at the UTHealth Medical School. "These new tests will help us better understand the causes of these diseases."
Reveille, who is the chief of rheumatology at Memorial Hermann-Texas Medical Center, said researchers have identified many of the genetic components of ankylosing spondylitis.
Mayes, a professor of rheumatology at the UTHealth Medical School, said, "If we can find out which genes are responsible for these problems, we could potentially develop drugs to block their activity. These tests will also help us determine which combination of genes may be responsible for these illnesses."
Mayes, who is the principal investigator of the NIH's National Institute of Arthritis and Musculoskeletal and Skin Diseases-funded Genome-Wide Association Study in Systemic Sclerosis, said researchers are about a quarter of the way to finding the genes and pathways responsible for systemic sclerosis.
By comparing immune-related genes of different populations, the scientists think they will be able to zero in on the most critical genes tied to ankylosing spondylitis and scleroderma, Mayes said. "Genes can vary from continent to continent. For example, there is a gene variant in Africa that confers resistance to malaria. That is not as common in the United States," she said. "Where these genes overlap is of special interest to us."
Zhou, Reveille and Mayes already have access to the DNA information of patients with ankylosing spondylitis and scleroderma from established cohorts from their previous studies in both Spain and the United States.
Later this year, the researchers will travel to China to coordinate the recruitment of people with these immune disorders with the aid of Jiucun Wang, Ph.D., of Fudan University in Shanghai and Hejian Zou, M.D., Ph.D. of Huashan Hospital in Shanghai, who both will lead the Chinese effort.
Their research is titled "Studies of HLA Region Genomics in Systemic Sclerosis and Ankylosing Spondylitis." Their work is supported by a National Institutes of Health (NIH) grant covering $2 million in direct cost over five years.
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SELinux policy is customizable based on least access required. ftpd policy is extremely flexible and has several booleans that allow you to manipulate the policy and run ftpd with the tightest access possible.
If you want to allow ftp to read and write files in the user home directorie, you must turn on the ftp_home_dir boolean.
If you want to allow ftp servers to login to local users and read/write all files on the system, governed by DAC, you must turn on the allow_ftpd_full_access boolean.
If you want to allow ftp servers to use connect to mysql databas, you must turn on the ftpd_connect_db boolean.
If you want to allow ftp servers to use cifs used for public file transfer services, you must turn on the allow_ftpd_use_cifs boolean.
Allow ftpd servers to read the /var/ftpd directory by adding the public_content_t file type to the directory and by restoring the file type.
Allow ftpd servers to read and write /var/tmp/incoming by adding the public_content_rw_t type to the directory and by restoring the file type. This also requires the allow_ftpd_anon_write boolean to be set.
If you want to allow ftp servers to upload files, used for public file transfer services. Directories must be labeled public_content_rw_t., you must turn on the allow_ftpd_anon_write boolean.
You can see the context of a file using the -Z option to lsP Policy governs the access confined processes have to these files. SELinux ftpd policy is very flexible allowing users to setup their ftpd processes in as secure a method as possible.
- Set files with the ftpd_etc_t type, if you want to store ftpd files in the /etc directories.
- Set files with the ftpd_exec_t type, if you want to transition an executable to the ftpd_t domain.
- Set files with the ftpd_initrc_exec_t type, if you want to transition an executable to the ftpd_initrc_t domain.
- Set files with the ftpd_keytab_t type, if you want to treat the files as kerberos keytab files.
- Set files with the ftpd_tmp_t type, if you want to store ftpd temporary files in the /tmp directories.
- Set files with the ftpd_tmpfs_t type, if you want to store ftpd files on a tmpfs file system.
- Set files with the ftpd_var_run_t type, if you want to store the ftpd files under the /run directory.
Policy governs the access confined processes have to these ports. SELinux ftpd policy is very flexible allowing users to setup their ftpd processes in as secure a method as possible.
You can see the context of a process using the -Z option to psP Policy governs the access confined processes have to files. SELinux ftpd policy is very flexible allowing users to setup their ftpd processes in as secure a method as possible.


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HERBERT SMITH FREEHILLS' JUSTIN D'AGOSTINO NAMED AN OUTSTANDING LGBT+ EXECUTIVE ROLE MODEL IN 2019 LIST
30 October 2019 | Global
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As recognition of his ongoing leadership and commitment to the LGBT* community, Herbert Smith Freehills' Justin D'Agostino has been recognised as an OUTstanding 100 LGBT+ Executive, presented by Yahoo Finance, for the seventh consecutive year.
The annual OUTstanding rankings recognise the top 100 executives across the globe who have made a difference in gay, lesbian, bi-sexual and transgender (LGBT*) workplaces.
Justin D'Agostino is Executive Partner for the Global Dispute Resolution practice, the Asia region and the US at Herbert Smith Freehills. He is also the Executive Sponsor of the firm's global IRIS Network for LGBT* staff and allies, which has grown from the network he first launched in 2007. Externally, Justin is a Top Table Stonewall Ambassador, a mentor with the Queer Straight Alliance in Hong Kong, a proud supporter of and mentor with Pink Dot, and recently hosted a CEO roundtable in Hong Kong with Out Leadership. Justin has also personally supported events in favour of marriage equality across Australia and supported the firm's involvement in the marriage equality campaign in Northern Ireland, and in L'Autre Circle in France.
"I am once again very proud to be included in this prestigious list" said Justin. " I take great pride in the work we are doing at Herbert Smith Freehills to build an ever-growing inclusive culture. LGBT+ is an integral part of our wider strategic drive on diversity and inclusion and it is great to receive recognition for the work we do in driving cultural change right across the network, both as an individual but also as a firm.
This is the seventh year that OUTstanding has published The Leading LBGT+ & Ally Executives and LGBT+ Future Leaders Lists, and the first year it has partnered with Yahoo Finance. Justin has been listed since its inception. OUTstanding is a professional membership organisation for global businesses. It works directly with LGBT+ and ally leaders to promote inclusion in the workplace, drive change, challenge stereotypes and inspire future generations. OUTstanding is part of Involve, a membership organisation championing diversity and inclusion in business.
Founder & CEO of INvolve, Suki Sandhu OBE, says,
"We're delighted to be celebrating another fantastic group of people from across the globe who are collectively driving cultural change and creating workplaces where everyone can succeed. LGBT+ discrimination is still prevalent in a lot of the world's biggest economies, but highlighting the work of successful role models changes people's perceptions. They demonstrate that you can be successful and out at work, but most importantly inspire the next generation of LGBT+ leaders."
For further information on this news article, please contact:
Claire Watts, Communications Manager
claire.watts@hsf.com
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The Volunteer Management Audit is a unique tool for analyzing the effectiveness of an organization's approach to and procedures for involving volunteers. It's designed to be administered by a team of staff and volunteers. The Audit provides a step-by-step guide to planning, conducting, and following-up the assessment process, with complete Scoresheets and instructions.
The Volunteer Management Audit is applicable to any type of agency seeking to assess whether its volunteer management practices are up to the most effective standards. Its value lies in the recommendations made as a result of the Audit process and in any actions taken subsequently. It is above all a discussion starter, focusing attention on issues affecting volunteers.
Complete instructions for every phase of the process.
Suggestions for winning the support of agency executives for examining volunteer management.
Full Scoresheets for either a complete or an abbreviated version of the Audit (price includes permission to make as many copies as necessary for one organization's use).
Analysis and strategy planning questions.
Permits printing and copying of Audit Scoresheets and handouts as necessary for in-house use only.
Permits buyer to administer the Audit repeatedly in different agencies by copying the Audit Scoresheets and handouts in Section II as often as necessary. License does not include permission to copy Section I (instructions for Audit administrator) and the buyer may not make any electronic copies of the Audit to give, sell, or loan to any other organization.
Licensing Agreement for a national organization or any group needing to distribute the Audit to multiple staff members, chapters or sites: Please contact info@energizeinc.com with the number of users you wish to arrange and we will give you an individual quote for the price of distributing multiple copies of the Audit to your network.
The Audit has been produced with a top-level audience in mind, so its look and tone are professional. When someone reads this material, its relevance to decision makers ought to be evident. But the challenge is to get the attention of executives, since one of the concerns in the volunteer field is the relatively low-level management position many volunteer program leaders hold. One not-so-hidden agenda of the Audit is to demonstrate why executives need to take a greater interest in volunteer involvement.
Consider requesting the chance to make a formal presentation at a face-to-face meeting with your agency's executive director or other top managers. Present your case for conducting the Volunteer Management Audit and—perhaps even more important—for implementing the findings afterwards. Explain the time involved and your need for access to representative staff members. Do not [expect] resistance. Even if there is no precedence for something like this, approach your request with a positive attitude. After all, it is good management practice to want to assess a program. You may find that the request is greeted with some surprise at first, but that your initiative ultimately brings you increased respect for wanting to do the Audit.
Remember to stress that the purpose of the Audit is to strengthen the way the agency involves volunteers so that services can thrive as you move into the future. This is not a "report card"; it's a strategic planning tool.
If you have a volunteer program advisory committee, have them present the idea to the executive director on behalf of the program. If there is no formal advisory group, you can still enlist one or two long-time volunteers to accompany you to the meeting with the executive.
If your agency conducts regular or periodic evaluations of other aspects of its service delivery, use that momentum to suggest this more intensive look at the volunteer program in particular.
If it is your immediate supervisor who must first be convinced, use the same techniques at the middle management level. Try to be the one to make the presentation higher up, or at least to accompany your supervisor. If this is not workable, give the person who will represent you a "talking points" list in preparation for being an advocate for the Audit concept.


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Bartomeu: "FC Barcelona wants to create a women's soccer team in the United States"
President expresses interest in women's football as a way of boosting the club's profile in North America
11:07AM Wednesday 07 Sep
Josep Maria Bartomeu attended a meeting of Spanish press correspondents based in the United States, which was held after the opening ceremony of the new offices in New York and at which he was accompanied by first vice-president Jordi Cardoner and vice-presidents Jordi Mestre and Manel Arroyo.
Afterwards, in an interview with Barça TV, Bartomeu described the club's interest in women's soccer, a very important sport in this region. This is a circumstance that FC Barcelona would like to exploit in order to boost its profile in the United States. He explained that one of the club's targets is to "create a team that can take part in the National Women's Soccer League" in the form of a franchise "that will help us to promote the club". But that's not the only sports project that FC Barcelona has in mind for North America. The president added that in the future Barça is also proposing "the creation of a futsal team to compete in the local leagues".
Search for sponsors and expansion of FCB Escola
Apart from strictly sporting matters, Bartomeu also stressed the club's focus on charity and education. "One of the goals is to set up projects with the Foundation and to establish a broad network of FCB Escola soccer schools to help to project the club's name around the territory". These centers will play a key role in expanding the club in the country, whereby the "growth plan is mainly based on the search for sponsors and our FCB Escola schools". The club currently has one FCBEscola in Florida, and another is to be opened over the next few days in Charlotte. All of these projects "will help us to get even closer to our fans" said the president.
As for FC Barcelona's relations with other sports organizations, Bartomeu cited the existing synergies with Major League Soccer (MLS) "to help to promote the game in this territory". In parallel, the club is also in contact with LaLiga for the same reasons, and in this regard "we have engaged in talks with Raúl González, the ambassador for LaLiga in the United States and with its president, Javier Tebas, in order to undertake this partnership".
The meeting with the Spanish media was held after the cocktail reception at the Mad46 restaurant on the rooftop of the Roosevelt Hotel in New York, with its splendid views of Manhattan. The reception was attended by over a hundred guests, including former Barça players Ronaldinho, Hristo Stoichkov and David Villa, and prestigious Catalan oncologist Josep Baselga.


POLICE TELL INJURED "MOPED ENABLED CRIMINAL" TO F**K OFF WHEN HE COMPLAINED!
04/12/2018 Bullshire Online Reporter Crime, News Comments Off on POLICE TELL INJURED "MOPED ENABLED CRIMINAL" TO F**K OFF WHEN HE COMPLAINED!
After a 'MoPed Enabled' Criminal was nudged from his stolen chicken chaser recently, he sustained injuries and complained.
But, in a further controversial move, Bullshire Police have responded to the complaint in a rather unusual manner.
Bullshire Online can exclusively reveal that we have had sight of the Microsoft Word document the Bullshire Police Social Media Ambassador has just typed!
Do you like our nonsense? Why not choose to to browse our 'Stop'?
Next Post:HERE'S WHY IT'S ILLEGAL TO PUT BANNERS UP ON ROUNDABOUTS!


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'Tom Sawyer' on stage this weekend in Willmar, Minn.
"Tom Sawyer" will be presented at 7 p.m. Friday and at 1 p.m. Saturday at the Willmar Education and Arts Center in downtown Willmar. The play is being presented as part of a week-long youth enrichment program offered through a partnership with Wi...
Cast members in the Prairie Fire Children's Theatre production of "Tom Sawyer" rehearse Tuesday at the Willmar Education and Arts Center. The production will be on stage for the public Friday evening and Saturday afternoon in Willmar. (Tribune photos by Gary Miller)
By Tribune Newsroom
"Tom Sawyer" will be presented at 7 p.m. Friday and at 1 p.m. Saturday at the Willmar Education and Arts Center in downtown Willmar. The play is being presented as part of a week-long youth enrichment program offered through a partnership with Willmar Community Education and Recreation and the Prairie Fire Theatre. The staff from Prairie Fire Theatre bring all the props, costumes and makeup. Up to 84 youth, grades 1-12, may be involved in this week's program. Tickets are $4 for adults, $3 for students, kindergarten and under will be admitted free; tickets are available at the door.
Minneapolis artist Gregory Rose visits Ridgewater College to talk about trauma, community and art
Minneapolis artist Gregory Rose in a visit to Willmar shared how art can be a way to confront trauma, build community and look different at the world.
By Shelby Lindrud
Classes and workshops calendar published Jan. 25, 2023
Art classes and meetings in the area
By Donna Middleton
Museums and historic sites published Jan. 25, 2023
Museum hours and special events in the next month
SMAC calendar published Jan. 25, 2023
Southwest Minnesota Arts Council events in the next month


The fat in sheep milk is in the form of small fat globules, 1.5 12.0 mm in diameter, present as an emulsion in the aqueous phase. The average fat globule size in sheep milk is slightly smaller than that of cow milk and varies significantly in size during lactation. Sheep milk fat is characterized by a higher content of short-chain fatty acids (C4 C12) than cow's milk fat (20 25% to 10 12%, respectively).1-2-1 The short-chain fatty acids contribute many of the distinctive flavors observed in sheep milk and sheep milk products. Sheep milk fat also contains some volatile branched-chain fatty acids that have been associated with the flavor of lamb and some sheep milk cheeses. Milk fat of sheep is white in color since it does not contain carotenoids, e.g., ß-carotene, like cow milk fat. Milk produced in the early lactation (<30 days) in a mixed management system where ewes are milked once a day and the lambs are allowed to nurse the other half day results in milk with over 50% less milk fat than ewes milked twice a day since lambing. In the postwean period of the lactation, fat content is comparable for all management systems.


This past weekend was truly a two-wheeled one. On Saturday I covered the Grays Chapel Lions annual Tour de Lions bicycle ride. It was a beautiful day and over 400 riders took part to raise money for the visually impaired.


Happy Father's Day to all of you fathers!
Now more than ever, our world is in great need of strong, loving dads who are present to their family with a loving heart. God's grace is abundant and He is longing to give it to all who ask!
Fatherhood is a holy responsibility, because it is a reflection of God the Father. Men are called to lead and serve their families, and to pray for them—an important and beautiful task!
Here is a prayer that a father can say daily for his family.
Heavenly Father, I thank you for the gift of my family, for whom I now pray and upon whom I now ask you to shower your blessings. With Saint Joseph as my guide, may I always be ready to spend my life for them.
Bless my wife whom you have given to me as my spouse, sharing in your wondrous work of creation. May I see her as my equal and treat her with the love of Christ for his Church. May Mary be her guide and help her to find your peace and your grace.
Bless my children with your life and presence. May the example of your Son be the foundation upon which their lives are built, that the Gospel may always be their hope and support.
I ask you, Father, to protect and bless my family. Watch over it so that in the strength of your love its members may enjoy prosperity, possess the gift of your peace and, as the Church alive in this home, always bear witness to your glory in the world.