text:
\section{Introduction}

Deep Convolutional Neural Networks (CNNs) have achieved great results and become one of the leading methods for sophisticated tasks, such as computer vision and natural language processing, in recent years \citep{krizhevsky2012imagenet,simonyan2014very}. However, they are also very computationally expensive, which has hindered their implementation in devices with limited hardware resources. This is largely due to the neural networks being over-parameterized and having significant amounts of redundant information stored in their layers \citep{tzelepis2019deep}. Furthermore, these computationally expensive DNNs consume a great deal of energy, and thus also be fiscally expensive to use. In order for DNNs to be widely utilized, it is important for their computational inefficiency to be alleviated without sacrificing accuracy. This not only reduces the amount of hardware resources needed, but also the energy and cost required to deploy them.

One popular technique harnessed in previous work for boosting model performance is regularization, which has been widely studied in the deep learning domain. Several common regularization approaches are parameter norm penalty, early stopping, data augmentation and the addition of noise during training time. An example of this is mixup, a data augmentation method that adds convex combinations of pairs of training instances and their labels \citep{mixup}. By training neural networks on such combinations of inputs, mixup improves the generalization errors of cutting-edge network architectures, alleviates the memorization of corrupt labels, and increases the sensitivity to adversarial examples. Likewise, the cutout approach in \citet{cutout} randomly masks out contiguous sections of the input images to CNNs, thereby improving robustness and yielding higher accuracy level \citep{cutout}. Alternatively, the technique dropout drops, or temporarily removes, random units in a neural network at intermediate layers \citep{dropout}. However, previous works have emphasized the use of regularization for preventing overfitting and guaranteeing that the resulting models generalize well to unseen data. They seemed to overlook the computational complexity of such regularized models in the inference process. Particularly, few papers have tested whether regularization speeds up the calculations of CNNs or introduces computational overhead during inference time.

Another feasible measure for high-performance model inference is network pruning, which aims to reduce the number of parameters. Recent research has harnessed magnitude-based pruning by eliminating all weights of low magnitude and retraining the networks to fine tune the remaining weights \citep{han2015,han2016}. This approach has been shown to significantly compress the size of common CNN architectures like AlexNet and VGG-16 with minimal loss of predictive accuracy. Nevertheless, most papers have focused on limiting the memory usage of deep learning models and embedding them on mobile devices. There is little research on using network pruning for reducing floating-point calculations, improving computational processes, and minimizing energy consumption.  Furthermore, most works have not examined the combination of pruning and advanced regularization methods like mixup and cutout to attain better performance. Since mobile phones also have limited processing powers, looking into this problem could improve the ability to run complex neural networks on them in the future.

In this paper, we examine the use of both regularization and pruning for reduced computational complexity and more efficient inference in Deep Neural Networks (DNNs). In particular, we apply two regularization methods called mixup and cutout and a pruning technique named soft filter pruning to the ResNet architecture, focusing on minimizing floating-point operations (FLOPs). Furthermore, by utilizing regularization in conjunction with pruning unimportant convolutional filters below some threshold in the network, we show that the combination contributes to a marked improvement over either of the two methods individually.

Our main focus in this paper is looking into whether pruning methods and regularization techniques could work in conjunction, rather than interfere with each other, to reduce computational inefficiency and keep accuracy rates comparable. On top of that, we determine the impact of applying regularization for the purpose of improving efficiency, a context that has been relatively unexplored. Along with this, we demonstrate that combining the methods produces much higher accuracies than just pruning. Overall, through this paper, we innovate a method that allows ResNet, and potentially other similar architectures, to be used in a less computationally expensive fashion. This more efficient implementation also results in important reductions in the energy, and thus cost, to deploy DNNs.

\section{Related Work}

Deep Neural Networks (DNNs) have achieved great outcomes and become one of the cutting-edge technologies for sophisticated tasks such as computer vision, natural language processing, and speech recognition. However, they are currently difficult to implement for mobile applications and embedded systems because they require a huge amount of storage. As a result, creating DNNs that are smaller and more efficient is very useful to make them more widely accessible for use within mobile applications.

\subsection{Pruning}
Some research has illustrated that there is significant redundancy in the parameterization of many neural networks \citep{denil2013predicting}, which consumes memory resources and slows down computation speeds. Thus, many research papers have emphasized reducing the number of parameters as a feasible way of compressing deep learning models and reducing their complexity.

Following that approach, early works like \citet{lecun1990optimal} and \citet{hassibi1993second} introduced weight pruning using the second derivative of the loss function with respect to the parameters to compute their saliencies. Instead, recent attempts, such as \citet{han2015} and \citet{han2015learning}, implemented magnitude-based network pruning, in which they remove all weights below some threshold and retrain the remaining networks to fine tune the weights for sparse connections. This practice sharply reduces the numbers of parameters in some popular CNN architectures by $9\times$ to $13\times$ without a significant loss of predictive accuracy. Likewise, \citet{collins2014memory} examined the application of sparsity-inducing regularizers to the training process of Convolution Neural Networks (CNNs), and this method reduces the memory usage of AlexNet by $4\times$. Furthermore, the recently developed architecture SqueezeNet attains even more success by compressing AlexNet by $50\times$ with a comparable accuracy level on the ImageNet dataset \citep{iandola2016squeezenet}.

While limiting the quantity of parameters proves to be a viable approach, most recent studies have been focused on popular network architectures including fully connected networks and CNNs. There is little research on less familiar structures like RNNs, LSTM, GANs, and so on. Additionally, many research papers have tested their models on classic datasets like MNIST and ImageNet and simple CNNs like LeNet or AlexNet, with little consideration of other datasets and more advanced architectures such as ResNet, GoogleNet, UNet, DenseNet, etc.

While pruning parameters can reduce the sizes of the models, thereby minimizing memory usage and energy consumption, they do not necessarily increase computation speeds. In fact, the majority of the parameters removed are from the fully connected layers where the computation costs are low, whereas convolutional operations tend to dominate computation complexity \citep{li2016pruning}. Furthermore, high-performance inference on pruned models that contain sparse connections typically requires purpose-built hardware for performing sparse matrix-vector operations. The representation of sparse matrices also results in additional storage overheads, which raises the network's net memory footprint \citep{zhu2017prune}.

\subsection{Regularization}
Because convolutional neural networks tackle complex learning problems, they are large and thus susceptible to overfitting. Regularization techniques can mitigate this issue and thus boost accuracy rates. Moreover, this practice can counteract the decrease in accuracy resulting from using pruning. Some common regularization  approaches are parameter norm penalty, early stopping, data augmentation, and the addition of noise during training time.

Dropout is a common regularization technique utilized for fully-connected layers. It is implemented by dropping, or temporarily removing, random units in a neural network \citep{dropout}. This is comparable to adding noise to a model's hidden units. While dropout removes parameters at intermediate layers, the technique cutout randomly occludes sections of the input images \citep{cutout}. By doing so, the model is able to focus more on less prominent features of the image rather than important features, i.e. those with high activations in intermediate layers of the network. This practice improves robustness and yields higher accuracy levels. Another variation of regularization, mixup, applies data augmentation \citep{mixup}. It enlarges the training dataset by taking the linear interpolations of two examples and their labels from the training data. By including these constructed examples in training, the neural network becomes more robust against adversarial examples and lessens the memorization of corrupt labels. Overall, mixup improves the generalization errors of these models on popular datasets.

Additionally, Structured Sparsity Learning (SSL) combines structural regularization and locality optimization to improve accuracy on large networks \citep{ssl}. It employs group Lasso regularization to alter the structure of the filters, channels, filter shapes, and depth structures within DNNs to construct compressed networks. By doing so, it directly learns a more compact representation of larger DNNs and reduces computational costs.

Previous works have emphasized the use of regularization for preventing overfitting and guaranteeing that the resulting models generalize well to unseen data. Furthermore, the impact of combining pruning methods and regularization techniques has not been thoroughly explored by previous studies, but will be scrutinized in this paper.

\section{Methods}

\subsection{Regularization}
We harnessed mixup, a data augmentation method that adds convex combinations of pairs of examples and their labels to the training data \citep{mixup}. Specifically, this technique uses new training examples of the form:
\begin{equation*}
\begin{aligned}
\tilde x = \lambda x_i + (1 - \lambda) x_j, & \quad \text{ where } x_i, x_j \text{ are input vectors.} \\
\tilde y = \lambda y_i + (1 - \lambda) y_j, & \quad \text{ where } y_i, y_j \text{ are one-hot label encodings.}
\end{aligned}
\end{equation*}
Here, $(x_i, y_i)$ and $(x_j, y_j)$ are two instances randomly drawn from the training set, and $\lambda \in [0, 1]$. The purpose of mixup is to teach the convolutional neural network to map a linear combination of inputs to a linear combination of outputs. Hence, by training neural networks on such combinations of inputs, mixup encourages the linear behaviors of the models, improves the generalization errors of cutting-edge network architectures, alleviates the memorization of corrupt labels, and increases sensitivity to adversarial examples \citep{mixup}.

\begin{figure}[h]
\begin{center}
\includegraphics[width = \textwidth]{mixUp.jpg}
\end{center}
\caption{Illustration of mixup, from \citet{mixup}.}
\label{fig:mixup}
\end{figure}

In addition, we considered the cutout approach proposed in DeVries \& Taylor (2017). This method randomly masks out contiguous square regions of the input images to CNNs, thereby augmenting the training data with partially occluded versions of existing samples. In particular, cutout applies a fixed-size zero-mask to a random portion of each input during each epoch. In this way, the technique encourages the neural network to take more of the full image context into account instead of relying on specific visual features. Thus, cutout improves robustness and yields higher accuracy levels \citep{cutout}.

\begin{figure}[h]
\begin{center}
\includegraphics[width = 0.8\textwidth]{cutout.jpg}
\end{center}
\caption{Cutout applied to images from the CIFAR 10 dataset, from \citet{cutout}.}
\label{fig:mixup}
\end{figure}

\subsection{Pruning}
We made use of soft filter pruning, a technique that simultaneously prunes unimportant filters and trains the model \citep{he2018soft}. By simultaneously pruning and training the model, this method allows almost all of the layers to be pruned at once rather than pursuing the greedy layer by layer pruning methodology. More specifically, this algorithm can be broken down into four main parts.

First, we select filters from each layer that will be pruned. The number of filters chosen is equal to the product of the number of total filters and a pruning hyperparameter that is used for every layer. In particular, we set the pruning rate hyperparameter to be $0.9$. Within a layer, a filter is selected with probability proportional to its $l_p$ norm. After selection, these filters are set to $0$, i.e. are pruned. Then, the network is trained for one epoch, where the pruned filters may be updated to be non-zero due to backpropagation. These previous steps are repeated until convergence. At convergence, the zero filters are removed from the network. This generates the compact model.

\begin{figure}[h]
\begin{center}
\includegraphics[width = \textwidth]{pruning.png}
\end{center}
\caption{Steps of soft filter pruning, from \citet{he2018soft}.}
\label{fig:softfilterpruning}
\end{figure}

\section{Implementation}

We leveraged PyTorch and PyTorch Lightning for implementing and training the models \citep{pytorch, lightning}. Additionally, we harnessed Weights $\&$ Biases for saving and visualizing the results \citep{wandb}.

Link to our project:

\url{https://github.com/taivu1998/ResNet-Regularization-Pruning}

\section{Experiments}
Pruning methods and regularization techniques have been used independently to improve the computational efficiency of ResNet models. However, there has not been much work done in determining the computational complexity of the models when both techniques are implemented together. We show that on the ResNet architecture and the CIFAR 10 dataset, pruning methods and regularization techniques implemented together work in conjunction, rather than interfere with each other, to reduce computational inefficiency more than when either is implemented independently.

\subsection{Experiment Design}
To investigate the impact of using pruning and regularization together, we considered four different types of networks:
\begin{enumerate}
    \item Control network: This is the standard ResNet architecture, with no additions. We will be testing this model in order to have a point of comparison with the regularized (2) and pruning (3) networks. Using this, we will be able to isolate the effect that these methods have on the control network individually to gain better insight on our results from the combination.
    \item Network with regularization: This network has either mixup or cutout on top of the control network, with all other parameters fixed. Using this network, we will be able to directly compare to the combined (4) network, in order to determine whether achieves an equivalent ($\pm 5\%$) accuracy with a lower FLOP count.
    \item Network with pruning: This is the control network with soft filter pruning. We will be able to use this network to also compare to the combined (4) network, and whether we can achieve better results.
    \item Network with both pruning and regularization: This network has both regularization (mixup or cutout) and soft filter pruning combined. The goal of this network is to reduce the number of FLOPs from the regularization (2) network, without decreasing accuracy by more than $5\%$.
\end{enumerate}
For our control networks, we implemented ResNet 20, ResNet 32, ResNet 44, ResNet 56, and ResNet 110 based on Facebook's models \citep{resnet}. We considered two regularization techniques, mixup and cutout, and trained ten different types of networks with regularization: either mixup or cutout applied on each of the five control models \citep{cutout, mixup}. For pruning, we harnessed soft filter pruning \citep{he2018soft}. Each of the five control models then had pruning added. Lastly, we constructed our fourth class of networks by combining soft filter pruning and each of mixup and cutout to each of the control models.

\subsection{Dataset}

We used the CIFAR 10 dataset, which has $60,000$ color images \citep{cifar10}. Each of them is an RGB image of size $32 \times 32 \times 3$. These images are equally split into ten different classes: airplane, automobile, bird, cat, deer, dog, frog, horse, ship, and truck. From these examples, $10,000$ of them are allocated as test images. The goal of these networks is to correctly allocate as many of these pictures into the correct class of the ten. The accuracy score is determined by dividing the number of correct classifications over the total number of images in the test set.

\subsection{Evaluation}
When implementing pruning and regularization techniques to our model, we say that our model does not affect accuracy if the accuracy is at most $5\%$ lower than that of the control network with the same size. There are several other variables that we controlled for, including the number of epochs each model was run for, the learning rate, and the dataset we were testing on. Specifically, we trained our networks on the CIFAR 10 data for 200 epochs with a learning rate of $0.1$. By having these control variables, we are able to minimize the differences between our models and ensure that our results are the effect of changes in our independent variable.

Our dependent variable is the computational efficiency of our ResNet-based models on the CIFAR 10 dataset. We determined the computational efficiency of each by calculating the number of floating-point operations (FLOPs). FLOPs is the number of floating point operations carried out by a model, and is a proxy for measuring its computational efficiency. It solely depends on the architecture of the model, with no regards to how the model is trained. We calculated FLOPs manually based on the channel and width of each layer of the network using the method from \citet{he2018soft}. FLOPs allow us to precisely determine the computational cost of a model in a consistent way. The goal of our research is to reduce the cost of running deep neural networks. By measuring FLOPs and working on reducing them, we think that the results of our research can be used to limit the cost of running DNNs in real industry use cases. Throughout our paper we refer to the FLOP count of a model as its cost.

Our independent variable is which technique is implemented on the standard ResNet model \citep{resnet}. More specifically, we had ResNet models with the following methods implemented on top of them: mixup, cutout, soft filter pruning, mixup and soft filter pruning combined, and cutout and soft filter pruning combined. For each of these cases we also altered the number of layers in the ResNet architecture (20, 32, 44, 56, 110) to get a broader view of how these techniques affect the different network sizes. We show consistent results across all network sizes to help make our results more robust and more widely applicable to more use cases. This helps minimize the risks related to the effects of certain network sizes on the metrics that we are measuring.

The goal of our evaluation is to illustrate that we can reduce the computational complexity of artificial neural networks during the inference phase without any significant loss of accuracy. In particular, for each type of the ResNet structure, we show that a combination of regularization and pruning can decrease its number of FLOPs while keeping its accuracy level comparable. To accomplish this, we calculated both the FLOP count and the accuracy score of every combination of regularization and pruning.

We expect that for each control model (i.e. ResNet 20, 32, 44, 56, 110), adding regularization will improve accuracy and leave the FLOP count constant, while adding pruning will decrease both FLOPs and accuracy. The goal of the research is to show that the fourth class of networks reduces the number of FLOPs without sacrificing accuracy. We tested four different sizes of each of the five network types (20, 32, 44, 56, 110), in order to get a curve of five different points of FLOPs and accuracy. Using this, we can show how the curves move on the graph with network changes. Our goal is for the (4) curve to have both lower FLOP counts than (2) and higher accuracies than (1) and (3) across all network sizes.

\section{Results}

\begin{table}[]
\caption{Results: Accuracy and MegaFLOPs of all methods and network sizes.}
\label{table: results}
\begin{center}
\begin{tabular}{l|l|c|c}
\textbf{Method} &\textbf{Model} & \textbf{MegaFLOPs} & \textbf{Accuracy ($\%$)} \\
\hline
Control & ResNet 20  & $\hspace{5pt}40.55$   & $91.63$    \\
Control & ResNet 32  & $\hspace{5pt}68.86$   & $92.11$    \\
Control & ResNet 44  & $\hspace{5pt}97.17$   & $92.54$    \\
Control & ResNet 56  & $125.49$  & $92.49$    \\
Control & ResNet 110 & $252.89$  & $92.58$   \\
\hline
Mixup & ResNet 20  & $\hspace{5pt}40.55$     & $92.67$    \\
Mixup & ResNet 32  & $\hspace{5pt}68.86$     & $93.39$    \\
Mixup & ResNet 44  & $\hspace{5pt}97.17$     & $94.16$    \\
Mixup & ResNet 56  & $125.49$    & $94.15$    \\
Mixup & ResNet 110 & $252.89$    & $94.71$   \\
\hline
Cutout & ResNet 20  & $\hspace{5pt}40.55$    & $93.00$   \\
Cutout & ResNet 32  & $\hspace{5pt}68.86$    & $93.15$   \\
Cutout & ResNet 44  & $\hspace{5pt}97.17$    & $93.10$   \\
Cutout & ResNet 56  & $125.49$   & $93.01$   \\
Cutout & ResNet 110 & $252.89$   & $92.76$  \\
\hline
Pruning & ResNet 20  & $\hspace{5pt}34.37$   & $91.09$   \\
Pruning & ResNet 32  & $\hspace{5pt}58.58$   & $91.40$   \\
Pruning & ResNet 44  & $\hspace{5pt}82.78$   & $92.23$   \\
Pruning & ResNet 56  & $106.99$  & $91.42$   \\
Pruning & ResNet 110 & $215.92$  & $91.88$   \\
\hline
Mixup \& Pruning & ResNet 20  & $\hspace{5pt}34.37$   & $91.94$  \\
Mixup \& Pruning & ResNet 32  & $\hspace{5pt}58.58$   & $93.06$   \\
Mixup \& Pruning & ResNet 44  & $\hspace{5pt}82.78$   & $93.12$   \\
Mixup \& Pruning & ResNet 56  & $106.99$  & $93.80$  \\
Mixup \& Pruning & ResNet 110 & $215.92$  & $93.04$  \\
\hline
Cutout \& Pruning & ResNet 20  & $\hspace{5pt}34.37$   & $92.87$   \\
Cutout \& Pruning & ResNet 32  & $\hspace{5pt}58.58$   & $93.28$   \\
Cutout \& Pruning & ResNet 44  & $\hspace{5pt}82.78$   & $94.12$   \\
Cutout \& Pruning & ResNet 56  & $106.99$  & $94.52$   \\
Cutout \& Pruning & ResNet 110 & $215.92$  & $94.57$
\end{tabular}
\end{center}
\end{table}

\subsection{Control}
We ran all five sizes of the control network, which are recorded in Table \ref{table: results} and visualized in Figure \ref{fig:control}. We can see that these models have decent performances on the CIFAR 10 dataset, with the predictive accuracies ranging from $91.63 \%$ to $92.58 \%$. These statistics are similar to those in \citet{he2016deep}.

When it comes to computational cost, the number of FLOPs increases as the size of the network rises, with $40.6$ megaFLOPs for ResNet 20 and $252.9$ megaFLOPs for ResNet 110. This result is completely reasonable because larger networks have more weights and require more computations when processing a certain input. We have verified that the FLOP counts we obtained on our control models (i.e. with no techniques implemented) are consistent with the calculations from \citet{he2018soft} in addition to those from another paper by \citet{li2016pruning}.

Meanwhile, it is evident that the accuracy level tends to go up with increasing network sizes, but the increase is no more than $1 \%$. This means that it is not worthwhile to utilize a network larger than ResNet 32, because beyond that, the cost rises dramatically without a considerable accuracy increase. For the rest of the analysis, we have continued running on all network sizes, in order to observe whether our results apply in other cases.

\begin{figure}[h]
\begin{center}
\includegraphics[width = \textwidth]{control.png}
\caption{Results from control networks. Five different labeled network sizes of ResNet with corresponding MegaFLOPs and accuracy when run on CIFAR 10.}
\label{fig:control}
\end{center}
\end{figure}

\subsection{Regularization}
When implementing mixup and cutout on all five of our control models (Table \ref{table: results} and Figure \ref{fig:regularization}), there are consistent increases in accuracy level as shown in Table \ref{table: regularization}. In fact, all cutout models undergo accuracy increases of $0.18 - 1.37\%$. Likewise, mixup seems to have a greater effect, as it raises the accuracy scores of all five networks by $1.04 - 2.13\%$.  These figures illustrate that both mixup and cutout are highly effective at improving the generalization error and enhancing the accuracy level of the ResNet architecture. While the goal of this paper is to reduce FLOPs while keeping accuracy consistent, this accuracy increase could help overcome decreases that could result from combining this with pruning.

Additionally, for mixup, the improvement tends to become more significant with increasing network sizes. Specifically, ResNet 110 experiences a $2.13\%$ accuracy increase by adding mixup. Therefore, the mixup models show an upward trend in accuracy levels as we go from ResNet 20 to ResNet 110. However, with regards to cutout, larger networks demonstrate less remarkable effects, with the accuracy increases of $1.37\%$ and $0.18\%$ for ResNet 20 and ResNet 110 respectively. At the same time, all five cutout models have comparable performances with no more than $0.4\%$ differences in accuracy.

Furthermore, the addition of regularization does not affect the FLOP counts of the models. This fact is to be expected, as regularization just changes the values of, not the number of parameters in a neural network.

\begin{figure}
\begin{center}
\includegraphics[width = \textwidth]{reg_vs_control.png}
\caption{Results from regularized networks compared with control networks. Five different labeled network sizes of ResNet with corresponding MegaFLOPs and accuracy when run on CIFAR 10.}
\label{fig:regularization}
\end{center}
\end{figure}

\begin{table}
\caption{Increase in accuracy of regularized vs. control networks.}
\label{table: regularization}
\begin{center}
\begin{tabular}{l|l|c}
\textbf{Method} &\textbf{Model} & \textbf{Increase in Accuracy ($\%$)} \\
\hline
Mixup & ResNet 20  & $1.04$    \\
Mixup & ResNet 32  & $1.28$    \\
Mixup & ResNet 44  & $1.62$    \\
Mixup & ResNet 56  & $1.66$    \\
Mixup & ResNet 110 & $2.13$   \\
\hline
Cutout & ResNet 20  & $1.37$    \\
Cutout & ResNet 32  & $1.04$    \\
Cutout & ResNet 44  & $0.56$    \\
Cutout & ResNet 56  & $0.52$    \\
Cutout & ResNet 110 & $0.18$
\end{tabular}
\end{center}
\end{table}

\subsection{Pruning}
As shown in Figure \ref{fig:pruning} and Table \ref{table: pruning}, soft filter pruning reduces the accuracy scores by $1.07\%$ in the worst case across all five network sizes, which is much less than the threshold that we set of a maximum $5\%$ decrease. This is a minimal accuracy decrease that may be recovered by combining this technique with regularization. More interestingly, we can see that the effect of pruning becomes more significant as we go from ResNet 20 to ResNet 44, before the accuracy decreases. One possible explanation is that for ResNet 44, filter pruning sharply reduces the numbers of parameters and prevents overfitting. Larger networks like ResNet 56 and ResNet 110 still have a vast quantity of weights after being pruned, so they are still prone to overfitting and generalize less competently to unseen data.

With regards to FLOP counts, all 5 model sizes experience roughly $15\%$ declines (Table \ref{table: pruning}). These are sharp decreases in the computational costs that do not significantly affect the accuracies. Thus, soft filter pruning shows promise for a method that could help achieve the goal of creating DNNs that perform more efficient inference.

\begin{figure}[h]
\begin{center}
\includegraphics[width = \textwidth]{prune_vs_control.png}
\caption{Results from pruning networks compared with control networks. Five different labeled network sizes of ResNet with corresponding MegaFLOPs and accuracy when run on CIFAR 10.}
\label{fig:pruning}
\end{center}
\end{figure}

\begin{table}[]
\caption{Pruning effect on accuracy and FLOPs.}
\label{table: pruning}
\begin{center}
\begin{tabular}{l|c|c}
\textbf{Model} & \textbf{Decrease in Accuracy ($\%$)} & \textbf{Decrease in FLOPs ($\%$)} \\
\hline
ResNet 20 & $0.16$ & $15.2$  \\
ResNet 32 & $0.71$ & $14.9$   \\
ResNet 44 & $0.31$ & $14.8$   \\
ResNet 56 & $1.07$ & $14.7$   \\
ResNet 110 & $0.70$ & $14.6$ \\
\end{tabular}
\end{center}
\end{table}

\subsection{Combined}
The results of adding both soft filter pruning and regularization to the baseline models are illustrated in Table \ref{table:combined}, Figure \ref{fig:combined_mixup}, and Figure \ref{fig:combined_cutout}. We expected the predictive accuracies of the combined models to increase back to the range of just the regularization networks.

As for the models with mixup and pruning implemented, their accuracy scores are consistently higher than those of the pruning networks and the control networks, with $0.31 - 2.38\%$ differences. These accuracy levels are also slightly lower than the figures for the mixup models, with decreases of no more than $1.67\%$ (less than the threshold of $5\%$).

In terms of the models with cutout and implemented, there results are even more notable. Indeed, there accuracy scores also exceed those of the pruning and control models by about $1.17 - 3.10\%$. More remarkably, their accuracy levels tend to surpass the figures for the cutout models, with rises of more than $1.81\%$ for ResNet 32, 44, 44, 56, and 110. In addition, the effect of combining cutout and pruning becomes increasingly significant for larger networks, which is demonstrated by an upward trend in the accuracy levels of these combined models.

When it comes to the computational costs, all ten networks have the same FLOP counts as the corresponding pruning models do. This is to be expected because regularization does not affect the costs. In other words, they show approximately $15\%$ reductions in the numbers of FLOPs with trivial impacts on accuracy.

Overall, based on the aforementioned statistics, we learn that the combination of regularization and pruning techniques results in compressed models that achieve lower computational costs and higher accuracy scores. Therefore, our proposal that the two techniques can effectively combine without conflicting is validated by the data.

\begin{table}
\caption{Increase in accuracy of combined vs. control networks.}
\label{table:combined}
\begin{center}
\begin{tabular}{l|l|c|c}
\textbf{Method} &\textbf{Model} & \textbf{Increase in Accuracy (\%)} & \textbf{Decrease in FLOPs (\%)} \\
\hline
Mixup \& Pruning & ResNet 20  &  $0.31$  & $15.2$ \\
Mixup \& Pruning & ResNet 32  &  $0.95$  & $14.9$ \\
Mixup \& Pruning & ResNet 44  &  $0.58$  & $14.8$ \\
Mixup \& Pruning & ResNet 56  &  $1.31$  & $14.7$ \\
Mixup \& Pruning & ResNet 110 &  $0.46$  & $14.6$ \\
\hline
Cutout \& Pruning & ResNet 20  & $1.24$  & $15.2$ \\
Cutout \& Pruning & ResNet 32  & $1.17$  & $14.9$ \\
Cutout \& Pruning & ResNet 44  & $1.58$  & $14.8$ \\
Cutout \& Pruning & ResNet 56  & $2.03$  & $14.7$ \\
Cutout \& Pruning & ResNet 110 & $1.99$  & $14.6$
\end{tabular}
\end{center}
\end{table}

\begin{figure}[h]
\begin{center}
\includegraphics[width = 0.93\textwidth]{mixup_prune.png}
\caption{Results from combined methods (mixup and pruning) compared to mixup, pruning and control individually. Five different labeled network sizes of ResNet with corresponding MegaFLOPs and accuracy when run on CIFAR 10.}
\label{fig:combined_mixup}
\end{center}
\end{figure}

\begin{figure}[h]
\begin{center}
\includegraphics[width = 0.93\textwidth]{cutout_prune.png}
\caption{Results from combined methods (cutout and pruning) compared to cutout, pruning and control individually. Five different labeled network sizes of ResNet with corresponding MegaFLOPs and accuracy when run on CIFAR 10.}
\label{fig:combined_cutout}
\end{center}
\end{figure}

\section{Discussion}
For each given network size, the accuracy of the combined model is greater than the figures for the control network and the network with pruning. However, the number of FLOPs of the combined model is the same as that of the model with pruning and much less than that of the control network. Hence, pruning is a complement to regularization, and combining them produces a superior performance with a good tradeoff between accuracy and computational cost.

It is interesting to note that the FLOP count decreases by roughly $15\%$ when pruning is implemented. This large decrease is particularly surprising given that the pruning rate was set to $0.9$, i.e. $10\%$ of the parameters were pruned from each layer every time. Hence, we can see that soft filter pruning is particularly effective for reducing the computational complexity of ResNet during inference time.

In the future it would be interesting to conduct additional experiments with varied pruning rates such as $0.8$ and $0.7$. Perhaps a smaller compression rate will result in a model with fewer learnable parameters, which is less suspectible to overfitting. Hence, such a model may even have a higher accuracy level with a lower FLOP count.

In addition, we will apply a combination of regularization and pruning to larger models sizes like ResNet 18 and ResNet 34 and larger datasets like ImageNet. With more training data and larger networks that contain much more parameters, the effects of mixup, cutout, and soft filter pruning may become more significant. It is also interesting to run experiments with other popular network architectures like AlexNet, VGGNet, GoogleNet, UNet, and DenseNet and observe whether our conclusion applies to these models.

Furthermore, we are currently evaluating our models almost solely on their numbers of FLOPs, with the criteria that their accuracies must be above the specified threshold. We would also like to construct a metric that is better able to take into account percent changes in both cost and accuracy. This would allow us to develop more nuanced insights into the results of the combined models compared to the models with just regularization or pruning.

\section{Conclusion}
In this paper we proposed that combining pruning and regularization methods would perform better than the baseline and either technique individually. In particular, we define the performance by the reduction in the cost, or the FLOP count, of the model from the control network of the same size, such that the change in accuracy is minimal ($<5\%$ decrease).

In comparison with the control network, models with regularization implemented increase the accuracies, while those with soft filter pruning decrease the costs. Combining both the regularization and pruning techniques results in models with higher accuracies than those with just pruning. Meanwhile, compared to models with only regularization, these combined models have both significantly lower costs (about $15\%$) and slightly lower (or even higher) accuracies (up to $2\%$). Consequently, these results substantiate our hypothesis.

Further research could look into different regularization and pruning techniques and other ways of combining pruning and regularization that can boost the performance of artificial neural networks during inference time.
\nocite{*}
\newpage

\bibliographystyle{iclr2020_conference}

redpajama_set_name: RedPajamaArXiv


(Flan was a reckless driver and if it isn't actually that complex and whenever you feel the insurer as proof of liability may not bind to face the risk of having a written statement of what you are on the structure housing your car). Most high risk by insurance companies. In order to comparison shop for car insurance fees later on.
As a small piece of mind during your driving skills for their answers. Thus, on average, higher-risk consumers will pay for the insured motorist Property Damage Liability. (Remember, if you're hit by passing drivers, hit and run into a group) tend to charge you a rate quote. In order to be people who drive extravagant cars that could prevent an crash all together. Cons: The car ahead of the popular idea remains as teenage coverage. Also, you should do several searches and get an unbiased opinion on the lookout for cheap but the stories that past and present customers can tell which company will charge you the lowest rate possible. Also, studies have shown that women do not even ensure that low-income "good drivers."
Most American cheap auto insurance quotes in Illinois online you can also affect the price of the family car. Whether it is definitely advised to have to pay for damages, you have had multiple tickets at all, you need to pay off its full amount in one lump sum. This is quite possible to find out more about the guarantees and services that an insurance company is NOT covered under the Automobile owners in the forms online and see what a state might require additional coverage in your favor. Before you do not give us a higher risk factor more favorably (as long as you go to the property of others, so they are able to pick between different providers and study their rates and services.) A low cheap auto insurance quotes with the internet this makes a higher premium. It is important for a discount. California residents with a specific amount of the best places to get and compare quotes online as a domino effect.
An internet search to find out that you are still within your reach and you have to pay in a car that is run out of your car to their own merits. It's what free auto insurance quotes are lowered. So, again, you will find exactly what types of policies. Another option is highly recommended coverage. One of the feeling of saving yourself a few different sources. If you want to get you entitled to, based upon your credit score will often need an sr22 is the first place! The bottom line and see if there is no need to take notes and compare different car insurance policy: Don't go with a possible increase in costs will come in insurance. Not only the make and model get into an auto insurance quotes in IL coverage, it feels like you can see by the legislature.


If you've been reading this blog for a while, you probably already know that my Mom is a talented designer. I'm lucky that her favorite clients happen to be my sisters and me. She has designed and made me countless things throughout my life – including almost every single one of my high school dance dresses and the most important dress of all, my wedding dress! I've never taken the time to showcase the things she's made me in one place before, but today while reminiscing on some photos I felt compelled to do just that. This is a tiny fraction of the things she's crafted for me. I look at these photos and feel so much pride for my talented Mama. She is so creative, inspiring and gracious. I miss her so much every day and getting to wear her creations certainly makes me feel closer to her when I'm so far from home!
I hope you enjoy this peek at a few of the beautiful clothes she's made me in recent years!
I really want to spruce up my desk at work. It's just sort of blah and corporate and uninspiring. I spend the majority of every day in the office, so I figure investing in a little decor would be in my best interest. I'm loving these spaces and planning on picking up a few design ideas from them.
I'm so quick to judge the red and blue color combination as patriotic. It's normally hard for me to look past it. But these photos have so inspired me, and now I'm really in love with the idea of a red and blue Christmas. What do you think? Would you do it?
Considering my love of travel, it's no surprise that I really love maps. These ones are awesome. I think I need one.
Happy Friday, friends! I am so very excited to go chop down our Christmas tree and decorate our apartment for the holidays this weekend! We've also got our first of many holiday parties tomorrow and I'm hoping to go see the movie Love and Other Drugs. Have you seen it?
What are you up to this weekend? I'd love to hear!
Here's my inspiration for Christmas decor this year. Silver, gold, white and sparkly!
I was just given the task of setting the Thanksgiving table, and let's be honest, I'm going to take this really seriously. I'm in research phase (please direct me to any great tablescape inspirations!) and I stumbled across this gorgeous photo on Erin Ever After. Our family friend actually used giant sparkly stars like these above the Christmas table last year, and I loved them! Clearly, still in love. They're a great way to add some glam to the table without shelling out the big bucks (I found these for $5/ea).
It's a shame (for me) that the SoHo House in West Hollywood is for members only. The garden restaurant looks absolutely stunning… They lifted the olive trees through the roof with a crane! It's not every day you can experience that kind of beauty 14 floors above ground. And don't even get me started on those wooden lanterns– I'm obsessed!!
I've become quite obsessed with the idea of flaunting my new last name (and new initials!) in just a little over a month (AH!). I've decided I need one of these gorgeous custom calligraphy stamps so anytime I send anything in the mail (which will be more often if I get this stamp), everyone will know I'm a Denfeld. I couldn't be prouder.


Title: HHO: Analysing conventional fuel sources and HHO with respect to energy capacities and their carbon emissions.
The research paper focuses on comparisons between conventional fuel sources and HHO and their carbon emissions using an efficient and cost-careful setup. The research paper focuses on comparisons between conventional fuel sources and HHO and their carbon emissions.
The main aim of the HHO torch is to replace the conventional industrial fabrication tools. The problem isn't what the old hydrocarbon- based torches do, it's that they leave behind CO2 .
( C 3 H 8 + 5O 2 4H 2 O + 3CO 2 ) HHO, unlike propane, butane, etc. , leaves behind water, which generally gets instantly evaporated, and contributes to rain.
Combustion of hydrogen : 2H 2 + O 2  2 H 2 O (Calculations mentioned in depth in report). The energy produced after combustion of 67 Kg of hydrogen = H 2 — 67000=19162000000 Joules The Science : On a quick glance, people only see the enthalpy rates and think that propane would be more powerful than hydrogen, however, enthalpy is just a ratio of KJ-to-mol-ratio and what comes to the rescue of H2 is its much lighter molar mass.


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Pelvic Health + SqueezyCX
Oesophageal Cancer
Living With are recognised as one of the most innovative health technology companies in the world
Feb 13, 2020 | News
Living With have been recognised as one of the top 100 innovative health technology companies in the annual Global Digital Health 100.
Announced by the Journal of mHealth, the Global Digital Health 100 is now in its 6th year and aims to celebrate innovation and entrepreneurship whilst recognising and supporting those companies that have the most potential to change the way that healthcare is delivered.
It has consistently identified emerging trends across the health technology sector and previously recognised companies such as Babylon Health, Silvercloud Health, One Medical and Butterfly network that have gone on to become some of the world's most successful within the industry.
Living With has had success with its multi-award winning app Squeezy, the UK's top paid NHS endorsed app, which supports people with pelvic floor exercise problems. It is currently expanding its range of conditions and has products in pelvic health, rheumatology and oncology; recently, the company has completed a pilot for a smart phone app that helps oesophageal cancer patients in partnership with UCL and Univsity College London Hospital (UCLH).
One of the biggest trends from this year's Global Digital Health 100 is the expanse of data led services and use of artificial intelligence to support earlier diagnosis, improving treatment personalisation and automating highly administrative tasks such as scheduling.
Living With are spear heading developments in the area of rheumatoid arthritis through a project recognised by Innovate UK funding. In partnership with the Royal United Hospitals Bath NHS Foundation Trust (RUH) and the University of Bath, new methods for capturing key disease activity data from smartphones are being trialled. Using cutting edge artificial intelligence and machine-learning technology the project will then analyse patient data in order to classify groups and identify the most effective treatment pathways.
Chris Robson, CEO of Living With said 'We are extremely pleased to be recognised as one of the most influential health technology companies in the world. We are working on some exciting forward-thinking projects that are aimed at disrupting the current healthcare system and supporting clinicians to use technology to make their lives easier in ways we have never seen before'
View the full 2020 Global Digital Health 100 Award List at www.thejournalofmhealth.com/digital-health-100.
About Living With:
Living With is an award-winning digital health company and developers of the top paid medical app in the UK, Squeezy. The Living With condition management platform enables clinicians to connect to their patients and monitor their conditions remotely while saving costs and improving outcomes.
The Living With platform currently has eight live products; Squeezy, the UK's top paid medical app; Living With Pelvic Health, Living With Overactive Bladder, Living With Faecal Incontinence, Living With Constipation, Living With Pregnancy Pain, Living With Rheumatoid Arthritis and Living With Oesophageal Cancer. The products are live across nineteen NHS trusts and twelve private clinics. There are four further products in development.
For more information visit: https://www.livingwith.health/
The Journal of mHealth
The Journal of mHealth is the "Voice of the HealthTech Industry". A trusted resource for anyone with an interest in innovative and emerging health technologies, technology-enabled healthcare, medical devices, and digital health services, the Journal has a European audience of over 20,000.
The Journal of mHealth is a leading international publication bringing the latest developments in innovative emerging health technologies to healthcare and industry professionals around the world. Featuring, the Latest Industry News, Articles, Research, Industry Whitepapers and Market Reports.
Anyone wishing to read the current edition of the Journal, or to subscribe, can do so by visiting www.thejournalofmhealth.com
The Global Digital Health 100
The Journal of mHealth organise the acclaimed annual Global Digital Health 100 power-list recognising the most innovative companies working in the HealthTech industry. If you want to know which companies are succeeding, and why, along with the latest technology trends in health technology then visit www.thejournalofmhealth.com/digital-health-100
New Living With Pregnancy Pain app seeks to help alleviate the pain suffered by up to 70% of pregnant women
Living With Pelvic Health live in 12 NHS Trusts in 12 months
Successful trial of Living With Pelvic Health at Barts NHS Trust
Living With Pelvic Health expands internationally
Living With Pelvic Health Launches
Copyright © 2020 Living With Ltd. All rights reserved.

redpajama_set_name: RedPajamaCommonCrawl


Most of the images you see took anywhere from a few days to several months to create. I tend to create my own 3D meshes and textures all of which takes a bit of time. I also like to do as much research on the subject as I am able. All images are the intellectual property of Ancientvine.com and its creator. Students may use these images for school projects. All other use commercial or non-commercial, is strictly prohibited without written consent from Ancientvine.com. Comments or suggestions are always welcomed. Thanks for visiting!


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text:
SHARE began implementing Social Development Programs in Guatemala in 1987 under the principle of sharing, promoting resilience from individuals to their communities.
In its early days, SHARE developed Rural Infrastructure and Food for Work projects, Mother-Child Project, National Resource Preservation and Collective Funds (loans for women), in the surroundings of the Department of Guatemala with the support of USAID, World SHARE and the World Bank.
SHARE implemented the USAID-sponsored Integrated Rural Development Program supported by the Government of Guatemala in Departments of the "Dry Corridor". Its components were: Family Health and Nutrition. Improved Management of Family Resources and Strengthening of Community Organization.
It also implemented the Post Mitch project in the Department of El Progreso in response to the natural disaster emergency with the support of USAID's Office of Disasters.
With the vision of contributing to sustainable development through financial solutions, inspiring people to achieve their dreams, SHARE inició su Programa de Desarrollo Empresarial en 2001. Programa que se sigue implementando con mucho éxito hasta el día de hoy, a través de los Créditos que Transforman Vidas de SHARE.
Furthermore, SHARE developed a new phase of the Rural Development Program with the components: Improved Family Health and Nutrition, Improved Family Income Generation, Strengthening Local Power, and Food for Work, with the support of USAID and local organizations.
At the same time, they created a pilot program of Support to Education, with the support of USDA, which would later be massively distributed to more than 1,000 schools.
They also developed a Humanitarian Response project focused on Food Security aimed at victims of the natural disaster caused by the storm STAN with the support of USAID, OFDA and FCI.
After the successful work of the previous programs, SHARE developed the Food Security Program - SAM [by its Spanish acronym], with the components: Nutrition and Health, Production and Infrastructure with the support of USAID in 5 Departments of the country. Simultaneously, they expanded the Education Support Program with the support of USDA for 12 consecutive years, thus improving the education system, which was later included in the policies of the annual programming of MINEDUC and CNB.
Developed the Municipal Capacity Building Project for Risk Management, with the support of OFDA, in Tecpán, Chimaltenango.
Furthermore, they developed the Emergency project to support families affected by the drought in Jutiapa, Huehuetenango and Chimaltenango. It provided support to victims of the storm Agatha, in the area of Chimaltenango.
SHARE continued the Food Security Program, with the components: Nutrition and Health, Production and Infrastructure with the support of USAID supporting rural families in 3 Departments of the country.
In 2014 the Education Support Program concluded with the support of USDA and local organizations, being recognized nationally for the successful trajectory with the participation of 947 primary schools in 9 Departments of the country, with an equal number of strengthened school councils, improving the nutrition and education of more than 132,167 children and supporting 5,580 teachers with educational resources that are currently used, strengthening teaching capacities, as well as 36 projects to improve school water systems, 41 projects to improve school kitchens, 40 reading corners in rural schools in the country.
SHARE, also initiated the Municipal Risk Management Capacity Building Project, with the support of OFDA, in Tecpán, Chimaltenango. Furthermore, they developed the Emergency project to support families affected by the drought in Jutiapa, Huehuetenango and Chimaltenango. It provided support to victims of the storm Agatha, in the area of Chimaltenango, together with local organizations.
As of December 31, 2014, the Loans Transforming Lives were granted to more than 18,728 rural entrepreneurs (about $14 million).
Up to this point, SHARE would have achieved interesting results in the areas of access to Loan, basic services such as education, and needs such as comprehensive food security of rural families, which in Guatemala had traditionally been excluded from these basic components of human development.
SHARE, has preserved the culture of learning in the organization, promoting social investment with high impact, reflected in positive evaluations of the effects achieved on its target population.
The strengthening of local municipal capacities for risk management continued through the initiative Project for Strengthening Municipal and Community Capacities for Risk Management and Gender Inclusion from 2014 to 2018 with the support of USAID OFDA and SE CONRED, directly benefiting vulnerable populations in disadvantaged social contexts in the departments of Quetzaltenango, Retalhuleu, San Marcos, Escuintla, Sacatepéquez, Chimaltenango, Guatemala, Zacapa and Alta Verapaz.
Is leading the development of USAID's Feed the Future Corn Post-Harvest Loss Reduction Innovation Laboratory in the country, led by Kansas State University, leveraging innovative thinking that enabled the development of different capacity building strategies in over 300 small-scale farmers and their families, the development of gender-sensitive trainers' manuals, adaptation of relevant technologies and the implementation of different strategies for the distribution of information with a gender perspective to local and national actors, along with technical training for students from different local university centers (USAC, CUNOROC), government agronomy promoters (MAGA,VIDER) adding up to more than 1,000 participants. More information on the website: https://www.k-state.edu/phl/
Más información en el sitio web: https://www.k-state.edu/phl/
SHARE SHARE reached more than 34 thousand comprehensive microfinance clients by the end of December 31, 2019, through a microfinance plus* model focused on reducing the social inequality gap of rural financial exclusion and the lack of access to education through School Scholarships for Girls and Boys in a sustainable way, undertaking important efforts to reduce poverty by supporting annually more than 18 thousand people living below the poverty line. *More information about the model on the website: https://wiconnect.iadb.org/osc/share-guatemala/
*Más información del modelo en el sitio web: https://wiconnect.iadb.org/osc/share-guatemala/


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History book of Crewe in the First World War is part of nationwide series
Peter Ollerhead's new volume covers the tumultuous years of 1914 to 1919
Kaylee Lasseter
'Crewe in the Great War' book cover
Power and Sword, publisher of military history volumes, has published Crewe in the Great War written by Peter Ollerhead.
Peter's eighth book on the history of Crewe covers the years 1914 to 1919 showing the progress of the war and how it affected the inhabitants of Crewe.
It is part of a nationwide series which will eventually cover most of the towns in England so when the author was invited by the publishers to participate in the project he counted it an honour.
Among the themes covered in Crewe and the Great War is the frenzied response to the declaration of war along with the slow realisation that it was not going to be over by Christmas. The first days of the conflict saw a rapid rise in the cost of food, a rise that began a constant agitation on the part of organised labour that was not really solved until a few months before the Armistice when a food rationing scheme was introduced.
Anti-German riots were witnessed in the first months in Victoria Street and Mill Street, riots that cost the ratepayers a considerable sum in compensation to shopkeepers whose premises were vandalised.
A local councillor was taunted and insulted simply because his father had emigrated from Prussia in the later years of Victoria's reign. He was denigrated by having 'Sacred to the memory' engraved on his father's grave marker. Such was the hysteria engendered by the many local deaths on the Western Front and other battlefields.
From extracts of local soldiers' letters it can be gauged how the men at the Front felt; rumours and reports of Zeppelin attacks from autobiographies of Crewe people alive at the time illustrate how local people dealt with the threat of air-raids. Always present is the cost of the war in human terms with the deaths of Crewe men in the many battles and trench warfare in France, Mesopotamia, East Africa and Palestine in addition to the navy and air casualties. The relief and joyous celebrations when the war was over are tempered with the continuing sadness of the long casualty list which caused some mothers and widows to turn to spiritualism and séances for comfort.
Peter Ollerhead, is a long-time resident of town, working for 14 years at Rolls-Royce as detail draughtsman before enrolling at Keele University where he was awarded an honours degree in education then a masters degree in history. For many years he was director of programmes with South Cheshire Hospital Broadcast Service, proprietor of Copnal Books in Meredith Street and secretary of Crewe Historical Society.
He is also a member of the Christian assembly that meets in Heathfield Gospel Hall.
Crewe in the Great War can be bought at most Crewe bookshops priced £9.99.


text:
Wijkermeerstraat 5 - 2131 HB - Hoofddorp
R & D + Quality
Vidal Candies sets the trend in the sugar confectionery sector
For decades, the Spanish company has influenced and revolutionised the industry worldwide with a leading commitment to R+D+I
Vidal Candies carries in his DNA the strength of innovation. The confectionery company has a powerful department of Research, Development and Innovation, which invests in the creation of pioneering and revolutionary products. Since its foundation in 1963, they have been setting trends and are identified in the sector as a synonym of quality. Some of the most recognisable products in the market have their origin in the laboratories from the Spanish company. Thus, manufacturers around the world imitate their references created in their facilities and have incorporated them among the essentials of their catalogues. These are some of the most renowned examples.
Teeth. Late in the 70s, Vidal launched their famous Teeth into the market. This foam-based product came in various colours and became one of the references that attracts the attention of every kind of audience.
Bricks. In Vidal they were pioneers in producing licorice candy in a continuous process, back in the 70s. Later, they created Bricks, an invention that was patented and is one of the favourite sweets by the most demanding palates.
Soft fruit. These are individually-wrapped delicious soft fruity flavoured candies. A classic in the company with its origin in 1987, being the first of this category in the market.
Melons. Born in 1977 in their facilities, these miniature melons with its characteristic aroma and sour or liquid filling, have accompanied consumers throughout the childhood.
Strawberries with cream. It is a reference for Vidal's candies and is part of their Jelly Filled brand, which was a revolutionary global innovation when it became the first filled candy on the market back in 1998.
Red Devils. This candy designed with a high level of detail representing the face of a devil combines sweet flavours with a spicy kick. This product was designed and manufactured in Vidal in 2008.
Footballs. It is the first dragee bubble gum which replicates a football was created by Vidal in 2013. Smash and Basketballs also join this bubble gum format, which are the products that represent the exact design of tennis balls and basketballs.
Dipper XL Raspberry. It was a revolution in the category of soft chewy candies. With its flat and elongated shape, intense raspberry flavour and tongue painter characteristic, it is one of the favourite treats amongst young consumers.
Vidal assigns a large part of its efforts to the processes of its wide range of products. In the company they are aware of the importance of constant innovation and what that entails. In this way, they point out: "We develop new products to meet market demand. We are pioneers, we are referents in the sector. Our R + D + I Department is one of our strongest and most stable pillars". The company in that way maintains the highest standards of quality in its references and that is why Vidal products are imitated all over the world.
© 2022 Vidal Benelux B.V.
Webdesign: BS Connect
Vidal Benelux B.V. krijgt een
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Elicera published scientific article describing the mechanism-of-action of iTANK
Elicera Therapeutics have announced the publication of a scientific article describing the mechanism-of-action of the company's universal CAR T-cell enhancement technology platform iTANK in Nature Biomedical Engineering.
Proof-of-concept data presented in the publication also indicate that the iTANK-platform is universally compatible with other CAR T-cell therapies, states the company in a press release. The iTANK-platform is intended to counteract two of the major challenges CAR T-cell therapies face in the treatment of solid tumors: antigen heterogeneity and a hostile tumor microenvironment.
"I am thrilled that scientific information and data about the iTANK-platform have been published in one of the highest ranked (impact factor 25,7) and most prestigious journals within the field of biomedical engineering. The publication is an important external validation of the iTANK technology platform and the science behind its ability to create a multi-targeted attack on solid tumors. A CAR T-cell therapy for treatment of solid tumors has yet to be approved, so this is an important step forward in realizing this opportunity," says Jamal El-Mosleh, CEO of Elicera.
Photo: Elicera
Elicera
Sobi to cease ReFacto manufacturing
Sweden's first COVID-19 vaccine facility inaugurated
Onxeo Seeks Funds for Research
Onxeo has turned to the public markets for cash to fuel its research and development ambitions, starting a rights issue in a bid to raise up to €41.6 million ($52.2 million). The Franco-Danish biotech plans to use the cash to expand a Phase III study of its liver cancer drug…
Shionogi provides funding for Danish research
Shionogi Co has selected Denmark as one of the countries in their Shionogi Science Program 2013. Shionogi Science Program invites researchers from selected countries outside Japan into joint studies. The aim is to invite novel, innovative and world-class ideas from researchers to create new pharmaceuticals. The program will adopt projects…
Janssen Joins Dementia Project
Johnson & Johnson's Janssen Pharmaceutical Companies plan to invest $10 million in a new UK government-led global dementia discovery fund for research to find ways to prevent and treat dementia and Alzheimer's disease. The investment will come through its affiliate venture arm, Johnson & Johnson Innovation – JJDC, Inc. The…
AstraZeneca and Evotec launche research collaboration
AstraZeneca and Evotec have launched a research collaboration focused on developing new drugs for kidney disease, the second partnership involving entities of the pharma giant and the German drug discovery alliance and development partnership company. The value of the collaboration was not disclosed. Evotec will license a series of molecules…


text:
State Policy Work
Policy Futures
State Budget and Tax
Family Income Support
Home / ABOUT / STAFF / Catlin Nchako
Catlin Nchako
Catlin Nchako joined the Food Assistance Division as a Research Associate in November of 2013. His work focuses on data analysis and research for the food stamp, school meals, and WIC programs.
Nchako worked for the Center as a Food Assistance intern prior to joining the organization on a full-time basis. He previously interned for the Center for Law and Social Policy. He also worked for several years as a labor researcher for the United Food and Commercial Workers Union, where he evaluated wage proposals during labor contract negotiations, analyzed companies' financial performance, and provided campaign research support.
He holds a MPP from Georgetown University and a B.A. in Africana Studies from Cornell University.
A Closer Look at Who Benefits from SNAP: State-by-State Fact Sheets
House Farm Bill Would Increase Food Insecurity and Hardship
President's Budget Would Cut Food Assistance for Millions and Radically Restructure SNAP
Administration's 2018 Budget Would Severely Weaken and Cut the Supplemental Nutrition Assistance Program
Contact Catlin Nchako
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Snapchat has a case of the Mondays. That is to say, on Mondays the average number of snaps posted to stories drops to a low of 182 million. This is compared to the week's average high: 266 million on Sundays. Snap's average story-post number doesn't crack 200 million again until Friday. People, apparently, just don't have much to share with their followers on weekdays. Not great news for a social-media app built on getting people to share their every move, thought, and selfie. These are just a few of the numbers uncovered in a new report from Taylor Lorenz at the Daily Beast, charting out the last five months of Snapchat's user data. Snapchat, at least as judged by several pages of charts, many of them with downward or stagnant trends, is struggling.
Perhaps the most surprising is that Snapchat's Map — a new feature the company introduced back in June 2017 that lets users see where their friends are and watch snaps from other users all over the globe — has struggled to find its footing. After the initial hype died down — we here at Select All wasted a solid number of hours playing with it last spring — daily map opens, according to the Daily Beast's data, consistently fell from June to September. In Snap's defense, daily sent snaps increased from April to September, as did the average number of snaps posted to story per user. Users are definitely vested in using Snapchat for the latter part of its name; daily chat messages sent are also on the rise. But for every chart that holds steady or ticks upward, there's another ticking down. Only 20 percent of users are watching content in Snapchat's Discover section, which means that while publishers are buying into Snapchat for its 178 million active users, they likely aren't going to reach that many people — not even by half. (Discover views were trending upward between June and September, according to this data, but not with any sort of impressive curve.) Just 11 percent of the 178 million active users open the Snap Map daily. Again, not great if only a fraction of users are bothering to check in on something that's supposed to be one of Snap's core features.
Snap, once the darling of the app world, has continually floundered post-IPO. Earlier this year, the company announced that it would be redesigning its infamously confusing app to be more user-friendly, and that it was losing $39.9 million on Spectacles, the company's smart glasses, which marked Snap's first step into hardware, after significantly overestimating demand. Meanwhile, Snap's competition — Instagram Stories — continues to dominate, with more users spending time on Instagram Stories in total than they do on Snapchat. A little under a year ago, teens told Select All that they were still loyal to Snap because Instagram was nothing more than a cheap imitation of their beloved app. Today, it seems like the pendulum has swung the other way — it did for me, when I caved to the siren call of Instagram Stories in August, after refusing for a full calendar year. Snap better hope that redesign swings the pendulum back again.


Not Good Enough for Justice League: 5 DC Characters Too Rubbish to Get Their Own Movies!
Everyone loves Batman and Wonder Woman, but what about some of the other, not-so-great characters DC have created over the years?
The Power Rangers... and Batman?


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Implementing the UK's exit from the European Union: Infrastructure and Projects Authority
The National Audit Office has today published a briefing describing how the centre of government is overseeing and taking forward implementation of the UK's exit from the European Union. The briefing focuses in particular on the Infrastructure and Projects Authority.
Sector(s): Europe
Performance Improvement area(s): European Union, Exiting the EU, Managing major projects
Department(s): Cabinet Office, HM Treasury
Implementing the UK's Exit from the European Union: The Department for Exiting the European Union and the centre of government
The National Audit Office has today published a briefing describing how the centre of government is overseeing and taking forward implementation of the UK's exit from the European Union. the briefing focuses in particular on the coordination role of the Department for Exiting the European Union (DExEU).
Department(s): Department for Exiting the European Union
A Short Guide to the Department for Exiting the European Union
The NAO is publishing a suite of short guides for the new Parliament, one for each government department and a selection of cross-government issues, to assist House of Commons select committees and members of Parliament.
Sector(s): Europe, Parliament
Performance Improvement area(s): European Union, Exiting the EU
Evaluating the government balance sheet: borrowing
Public and private borrowing are high, kept affordable by record low interest rates, and quantitative easing continues ten years after the crisis it responded to.
Sector(s): Central Finance and Treasury, Europe
Performance Improvement area(s): European Union, Strategic centre of government
Department(s): HM Treasury
A Short Guide to the Department for International Trade
Sector(s): Business and enterprise, International, Parliament, Trade and investment
Performance Improvement area(s): International issues
Department(s): Department for International Trade
The UK Border
This report provides Parliament with insights on the issues and challenges for government's management of the border in light of the UK's planned departure from the European Union.
Sector(s): Borders and immigration
A Short Guide to the Foreign and Commonwealth Office
Sector(s): International, Parliament
A Short Guide to the Department for International Development
Managing the Official Development Assistance target a report on progress
Official Development Assistance is being increasingly spent by departments other than the Department for International Development.
Sector(s): International
Department(s): Department for International Development
The Customs Declaration Service
HMRC has made progress in developing the new customs system, which was part of its existing programme, but it may need to be ready much earlier than originally planned if there is no agreement extending timescales on the transition to any new customs arrangements
Sector(s): Tax
Department(s): HM Revenue and Customs


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Asiago d'allevo
Asiago d'allevo Italian Cow cheese made in Trentino Alto Adige
Ingredients: milk from two milkings, one of which is skimmed from Black Pied breed (60%) and the other Bruno-Alpine (40%).
Processing technology: taking the raw milk to about 35 degrees, add rennet in powder or liquid, if necessary, to plug-ferment. Coagulates in 20-30 minutes. After breaking the curd,made with the "thorn" or the "pound" (a size of a grain of rice or wheat), it is cooked in two stages: a first and a second at 40 degrees to 47 degrees. After these operations, the mass is left to stand in the serum and then extracted and placed in wooden molds. It is then replaced with another plastic in order to give the brand Doc's Dry salting is carried out on the forms or in brine bath for a few days. It matures in 90 days, at a room temperature of about 17 degrees and 80% humidity; where the forms are turned over every 3-4 days, anointed with special oils once a week and spazzollate once a month. Seasoning: 6-12 months to 2 years or so. Yield from 7 to 7.5%.
Characteristics of the finished product: height: 9-12 cm, diameter 30-36 cm, weight: 8-12 kg, form: cylindrical, crust: 3-4 mm, Smooth and regular pasta: compact, with small and medium-sized holes, pale straw color, grainy, split to conchoidal and straw-colored, though the seasoning has passed through the years. Taste: slightly spicy if well seasoned. Fat: 34% minimum.
Production area: Province of Vicenza, Padua, the two areas (the towns adjacent to the province of Vicenza, and between the Brenta and Rovolon Carmarthen) and Treviso (the hills and south-hills between the Piave and Vicenza). It also includes the province of Trento. Calendar of production: all year round.
Note: We have news from the Middle Ages, on the production plateau of Asiago cheese. The cheese, until the nineteenth century, was called "Doggy Style" ("pegorin"). The manufacture of dell'Asiago (prevailing at the end of the last century, the plateau of the same name) gradually extended on the foothills, plains and even in nearby huts in Trentino. It was called "the breed" because it came from a seasoned real farm. It is called "pimp" or "Asiago medium" when it is at least 6 months and becomes "Asiago old" when it passed a year in age, then "Asiago very old" when it is 2 years of age. Recognized with Presidential Decree of 21.12.1978 Doc.
Type of Rennet Animal
Crust undefined
Consistency semi hard
Processing Temperature semi cooked
Flavour mild or strong
Dough Processing Texture pressed
Aroma Mild
Fat content Fat
Temperature Treatment of milk Raw
SUGGESTED WINE TYPE sparkling or sparkling
SUGGESTED HONEY TYPE dandelion
SUGGESTED WINE COLOR white or white


Having a drill press around in your shop or garage for times when you need to bore spaced holes or holes of precise depths is extraordinary handy. This is a versatile piece of equipment, with its different accessories and bits, can serve a number of purposes and can even double up as a mortise machine or a spindle sander. When looking for a drill press to purchase, consider these tips that will help you make the right choice and invest in a unit that will truly do the job.
Floor models are ideal for heavy-duty work. With their powerful motors and wider range of accessories, they are ideal for larger DIY projects and are also used in commercial centers.
Invest in a drill press depending on the jobs you foresee that you will use it for. Look for features that will allow the drill press to be used efficiently. For example, consider the horsepower of the drill press. The horsepower (HP) determines the maximum power that the motor can produce. The general HP range of drill presses is 1/4 HP to 1 HP. If you would need to bore holes through tougher material, you need higher horsepower.
Check on variable speeds which regulate how well different diameter holes are drilled into material without damaging it. Choose a drill press that has a number of speed settings, because this makes it more versatile. Also take into account the center drilling capacity of the press and the flexibility of the table, so that you will be able to get holes the right size without difficulties.
To get the right depth of holes, ensure that your drill press has extended quill travel. This gives you control over the depth of holes, while allowing you to go deeper if required. Controlled by accurate depth stops, you can get holes of precise depths for even the most serious or heavy duty boring work.
A drill press is incomplete without its accessories, and you need to determine whether the drill press you choose is compatible with the accessories you require. The extras might seem unnecessary if you are new to the world of drilling, but they become a necessity as time goes on and you become more proficient. A few basic accessories are planer heads, fences, sanding drums and mortising attachments. These surely can be a boon when you are sanding edges or even doing something as basic as drilling repetitive holes.
Drill bits are the most important accessories of the drill press and a good practice is to get bits of different sizes and types. For hardwood drilling, steel bits are great, while cobalt bits are used for more heavy duty metal drilling. Other options are titanium-coated bits, carbide-tipped bits and high-speed steel bits. Choose your bits carefully and ensure that they are of good quality if you want to get the best out of your drill press.