text:
\section{Introduction}
\label{sec:intro}
Based on an editorial report of the Natural Methods \cite{st_nm}, Spatial Transcriptomics (ST) is the future of studying disease because of its capabilities in measuring gene expression of fine-grained areas (i.e., different windows) of tissue slides. However, ST is in low throughput due to limitations in concurrent analysis for the candidate windows \cite{st_time}. To accurately predict gene expression from each window of a  tissue slide image (\cref{fig:intro}), this paper proposes a solution named Exemplar Guided Network (EGN), while allowing efficient and concurrent analysis.
\begin{figure}[!t]
    \centering
    \includegraphics[width=0.75\linewidth]{fig/intro.pdf}
    \caption{Overview of fields. Each fine-grained area (i.e., window) of a tissue slide image is with distinct gene expression. Here is an example, we have a tissue slide image with three windows, and each of the windows corresponds with expression of four different gene types. Our goal is to predict the gene expression of each window.
    }
    \label{fig:intro}
\end{figure}

In literature, previous works adopt end-to-end neural networks, namely STNet \cite{stnet} and NSL \cite{nsl}, to establish a mapping between gene expression and the slide image window. STNet is a transfer learning-based approach that finetunes a pretrained DenseNet for this task.
On the contrary, NSL maps the color intensity of the slide image window to gene expression by a single convolution operation. Though amenable to high throughput
because of using neural networks, their prediction performance is inferior.

We investigate two important limitations of the existing approaches \cite{stnet,nsl}.
\textit{1)} Local feature aggregation: gene expression prediction can be considered as individually grouping the identified feature of each gene type from the slide image window. The long-range dependency among identified features is needed to reason about complex scenarios \cite{c1,c2}, as those features are generally non-uniformly distributed across the slide image window (see \cref{sec:mo} for details). STNet (i.e., a pure convolution approach) emphasizes local context during feature aggregation. It fails to bring interaction among features that are apart from each other. By experimenting with extensive SOTA architectures, we show that models with local feature aggregation achieve low performance when comparing to models with long-range dependencies.
\textit{2)} Vulnerable assumption: identifying gene expression by directly detecting the color intensity of the slide image window is vulnerable. By experimenting on standard benchmark datasets, we show this approach only works in extreme cases (for example, in the STNet dataset \cite{stnet}, tumour areas of slide image windows are usually purple, which benefits predicting tumour-related gene expression). This method shows a negative Pearson correlation coefficient (PCC) when evaluating the model with the least reliable gene expression prediction, i.e., PCC@F in \cref{tab:eva}.

In this paper, we propose an EGN~framework to address the above limitations. EGN~uses ViT \cite{ViT} as a backbone and incorporates exemplar learning concepts for the gene expression prediction task. To enable unsupervised exemplar retrieval of a given slide image window, we use an extractor for defining a representation space to amount similarity between slide image windows. For brevity, we name the extractor output as a global view, which avoids confusion with the ViT representations. Note that, the representation ability of the global view is evidenced in the experiment section. Then, we have a ViT backbone to progressively construct presentations of the given slide image window under long-range dependency. Meanwhile, we have an EB block to revise intermediate ViT representations by the global views of the given slide image window, the global views of the exemplars, and the gene expression of exemplars. The global view of the given slide image window additionally serves as a prior to facilitating the long-range dependency, apart from the ViT backbone. To allow dynamic information propagation, we iteratively update the global views of the given slide image window and exemplars based on the status of the intermediate ViT representations. Semantically, the former update corresponds with `what gene expression is known ?',  and the latter corresponds with `what is gene expression the model wants to know ?'. Finally, we have an attention-based prediction block to aggregate the exemplar-revised ViT representations and the global view of the given slide image window to predict gene expression.

Our contributions are summarised below:
\begin{itemize}
    \setlength\itemsep{-0.2em}
    \item We propose an EGN~framework, a ViT-based exemplar learning approach, to accurately predict gene expression from the slide image window.
    \item We propose an EB block to revise the intermediate ViT representation by using the nearest exemplars of the given slide image window.
    \item Experiments on two standard benchmark datasets demonstrate our superiority when comparing with SOTA approaches.
\end{itemize}

\section{Related work}
This section first review the study of gene expression prediction. Then, we summarise recent achievements of exemplar learning in both natural language processing and computer vision domains.

\noindent \textbf{Gene Expression Prediction.}
Measuring gene expression is a basic process in developing novel treatments and monitoring human diseases \cite{enformer}. To increase process accessibility, deep learning methods have been introduced to this task. Existing methods predict the gene expression either from DNA sequences \cite{enformer} or slide images \cite{stnet,nsl,he2rna}. This paper explores the latter approach. Meanwhile, these image-based approaches are divided into two streams. First, Schmauch \textit{et al.} \cite{he2rna} employs a multi-stage method including pretrained ResNet feature extractions \cite{resnet} and a K-Means algorithm to model the Bulk RNA-Seq technique \cite{bulk}. It measures the gene expression across cells within a large predefined area that is up to $10^{5} \times 10^{5}$ pixels in a corresponding slide image \cite{he2rna}. However, this approach is ineffective for studies that require fine-grained gene expression information, such as tumour heterogeneity \cite{tumor}. Second, on the contrary, He \textit{et al.} \cite{he2rna,densenet} and Dawood \textit{et al.} \cite{nsl} design a STNet and an NSL framework to predict the gene expression for each window (i.e., fine-grained area) of a slide image. This corresponds with the ST technique \cite{st}. We model ST to predict gene expression, as this potentially solves the bulk RNA-Seq prediction task simultaneously \cite{stnet}. For example,  aggregation of gene expression predictions for each window across a slide image results in a bulk RNA-Seq prediction.

\begin{figure*}[!t]
    \centering
    \includegraphics[width=0.9\linewidth]{fig/model.pdf}
    \caption{EGN~framework. Our networks are trained in a two-stage manner. In the stage of unsupervised exemplar retrieval (\cref{sec:extractor}), we learn an extractor $\mathbf{E}(\cdot)$ and a decoder $\mathbf{G}(\cdot)$ with image reconstruction objectives. After convergence, we use the extractor $\mathbf{E}(\cdot)$ with a distance metric for unsupervised exemplar retrieval.  For example, given $\mathbf{X}_{i}$, we obtain the global view of $\mathbf{X}_{i}$, i.e., $\mathbf{e}_{i}$ and $\mathbf{e}_{i} = \mathbf{E}(\mathbf{X}_{i})$, and construct the nearest exemplar set $\mathcal{K}_{\mathbf{X}_{i}} = \{\mathbf{e}_{j},\mathbf{y}_{j}\}$, where $\mathbf{e}_{j}$ is  the exemplar global view,  and $\mathbf{y}_{j}$ is  the exemplar gene expression.
    In the stage of exemplar learning (\cref{sec:predict}), we train a network $\mathcal{C}(\cdot,\cdot,\cdot)$ to predict gene expression $\mathbf{y}_{i}$ from $\mathbf{X}_{i}$, $\mathbf{e}_{i}$, and $\mathcal{K}_{\mathbf{X}_{i}}$. We use a vision transformer (ViT) as our backbone. The proposed EB block is interleaved with the vision transformer blocks. With a projector, we refine  $\mathbf{e}_{i}$ and $\mathcal{K}_{\mathbf{X}_{i}} = \{\mathbf{e}_{j},\mathbf{y}_{j}\}$ to $\mathbf{h}^{0}$, $\{\mathbf{r}^{0}\}$, and $\{\mathbf{s}^{0}\}$. Then, they are used by the EB block to revise the ViT patch representation and are updated to $\mathbf{h}^{t}$, $\{\mathbf{r}^{t}\}$, and $\{\mathbf{s}^{t}\}$, where $1 \leq t \leq \mathsf{T}$, and $\mathsf{T}$ is the number of layers. Finally, we have a prediction block that concatenates the refined global view $\mathbf{h}^{\mathsf{T}}$ of $\mathbf{X}_{i}$ and the attention-pooled ViT patch representation, to achieve the gene expression prediction task.
    }
    \label{fig:model}
\end{figure*}

\noindent \textbf{Exemplar Learning.} The K-nearest neighborhood classifier is the most straightforward case of exemplar learning. It classifies the input by considering the class labels of the nearest neighbors.  Exemplar learning is a composition of retrieval tasks and learning tasks \cite{clipcnn}. It has been widely employed to increase the model capability by bringing in extra knowledge of similar exemplars \cite{vqa1,vqa2,vqa3,vqa4,retro,mtr,gtm,rea,vtr,fac,fae,faf,dia}.  Patro \textit{et al.} \cite{vqa1}, Chen \textit{et al.} \cite{vqa2},  Teney \textit{et al.} \cite{vqa3}, and Farazi \textit{et al.} \cite{vqa4} refine the representation of given input with the closest match of text embedding and/or other visual embeddings to help the Visual Question Answering task. Borgeaud \textit{et al.} \cite{retro}, Wu \textit{et al.} \cite{mtr}, Urvashi \textit{et al.} \cite{vtr}, and Kelvin \textit{et al.} \cite{rea} combine the examplar learning with transformers for language generation tasks. Their key idea is to enable a more knowledgeable attention mechanism, by picking exemplars as key and value representation. Similarly, Philippe \textit{et al.} \cite{vtr} incorporates the attention mechanism with exemplar learning to bring a real-time visual object tracking framework. Other applications of exemplar learning, including fact checking, fact completion, and dialogue, could be found in \cite{fac,fae,faf,dia}. However, most of the above approach does not apply to our task because of the domain shift. This paper investigates an application of exemplar learning in gene expression prediction from slide image windows. As a result, we devise an EB block to adapt exemplar learning into our task.

\section{EGN Framework}
\label{sec:mo}

\noindent\textbf{Problem Formulation.}
We have a dataset collection of tissue slide images, where each image contains multiple windows, and each window is annotated with gene expression. For brevity, we denote the dataset collection as pairs of a slide image window $\mathbf{X}_{i} \in \mathbb{R}^{\mathsf{3} \times \mathsf{H} \times \mathsf{W}}$and gene expression $\mathbf{y}_{i} \in \mathbb{R}^{\mathsf{M}}$, i.e., $\{(\mathbf{X}_{i}, \mathbf{y}_{i})\}_{i=1}^{\mathsf{N}}$, where $\mathsf{N}$ is the collection size, $\mathsf{H}$ and $\mathsf{W}$ are the height and width of $\mathbf{X}_{i}$, and $\mathsf{M}$ is the number of gene types. We aim to train a deep neural network model to predict $\mathbf{y}_{i}$ from $\mathbf{X}_{i}$.

From the ST study, there exist two main challenges.
\textit{1)} Long-range dependency: gene expression-related features are non-uniformly distributed over the slide image window $\mathbf{X}_{i}$. Refer to Fig.~3 of \cite{stnet} for evidence. The interactions among these features are needed to group expression of the same gene type.
\textit{2)} Skewed gene expression distribution: the expression of some gene types has a skewed distribution, similar to the imbalance class distribution problem. This skewed distribution (see \cref{fig:dis} for an example) poses challenges in predicting expression of these gene types. In this paper, we attempt to mitigate them by learning from similar exemplars.

\noindent\textbf{Model Overview.} With these motivations, we have designed the EGN~framework (\cref{fig:model}) containing an extractor network for exemplar retrieval and a prediction network that learns from retrieved exemplars. Networks are trained on two stages.
\textit{1)} Unsupervised exemplar retrieval (\cref{sec:extractor}): we train an extractor $\mathbf{E}(\cdot)$ to construct the global view of the slide image window $\mathbf{X}_{i}$, i.e., $\mathbf{e}_{i} = \mathbf{E}(\mathbf{X}_{i})$ and $ \mathbf{e}_{i} \in \mathbf{R}^{\mathsf{D}}$,
where $\mathsf{D}$ is the representation dimension. This global view $\mathbf{e}_{i}$ is used for retrieving the nearest exemplar of $\mathbf{X}_{i}$ to form a set $\mathcal{K}_{\mathbf{X}_{i}} = \{[\mathbf{e}_{j}, \mathbf{y}_{j}] \mid j \in \Upsilon_{i} \}$, where $\Upsilon_{i}$ contains indexes of nearest exemplars,  $\mathbf{e}_{j}$ is a global view of a nearest exemplar, and $\mathbf{y}_{j}$ is the paired gene expression of the exemplar.
\textit{2)} Exemplar learning (\cref{sec:predict}): we train a model $\mathbf{C}(\cdot,\cdot,\cdot)$ that maps $\mathbf{X}_{i}$, $\mathbf{e}_{i}$, and $\mathcal{K}_{\mathbf{X}_{i}}$, to $\mathbf{y}_{i}$. Our $\mathbf{C}(\cdot,\cdot,\cdot)$ is based on a ViT backbone. We bring interactions between $\mathbf{e}_{i}$ and $\mathbf{e}_{j}$ to leverage $\mathbf{y}_{j}$ with a proposed EB block. Then, for accurately predicting $\mathbf{y}_{i}$, the updated $\mathbf{e}_{i}$ is used to revise intermediate ViT representations of $\mathbf{X}_{i}$. Note that, $\mathbf{e}_{i}$ also serves as a prior to facilitating the long-range interactions. With the introduction of exemplars, our framework dynamically benefits when predicting gene expression.

\begin{figure}[!t]
    \centering
    \includegraphics[width=0.83\linewidth]{fig/dis.pdf}
    \vspace{-0.3em}
    \caption{Gene expression distributions of STNet dataset \cite{stnet}. Each gene expression is log-transformed. (a) is  well distributed expression of gene RPS18. (b) and (c) are long-tail distributed expression of gene S100A8 and gene RPS10.}

    \label{fig:dis}
\end{figure}

\subsection{Unsupervised Exemplar Retrieval}
\label{sec:extractor}
To retrieve the exemplar of a given slide image $\mathbf{X}_{i}$ in an unsupervised manner, we propose to train an extractor (i.e., an encoder) with a decoder for image reconstructions. After convergence, we discard the decoder, and couple the extractor with a distance metric to amount the similarity between each pair of slide image windows for the exemplar retrieval.

\noindent \textbf{Method.}
The StyleGAN \cite{stylegan} applies a style code (i.e., a low dimension vector) to modulate convolution operations during image generations. As indicated by \cite{stylespace}, the style code captures both fine-grained and high-level image attributes of a training dataset in an unsupervised manner. Meanwhile, in the style code,  each scalar
is highly disentangled, i.e., each of them tends to independently control an image attribute. This attribute independency is desired when amounting similarly between images.
Thus, we borrow the style code for our unsupervised exemplar retrieval, where the style code is a global view of the given image input. Conventionally, to obtain the style code of a given slide image window, one needs to train a styleGAN and perform a GAN inversion \cite{stylespace} separately. Instead, we jointly train an encoder/extractor $\mathbf{E}(\cdot)$ (which replaces the GAN inversion) and a StyleGAN generator $\mathbf{G}(\cdot)$, with an image reconstruction objective. Afterwards, we can directly obtain the style code by a simple forward pass in the extractor $\mathbf{E}(\cdot)$.

\noindent \textbf{Objective.} We employ a least absolute deviation loss $\mathcal{L}_{1}$, a LPIPS loss $\mathcal{L}_{\text{LPIPS}}$, and a discriminator loss $\mathcal{L}_{\mathbf{F}}$ to optimize the image reconstruction, where $\mathbf{F}(\cdot)$ is a discriminator. Note that, the reconstruction indirectly impacts the representation ability of the style code. $\mathcal{L}_{1}$ constrains pixel-wise correspondence, while $\mathcal{L}_{\text{LPIPS}}$ and $\mathcal{L}_{\mathbf{F}}$ promotes a better reconstruction fidelity. We have
\begin{align*}
    &\mathcal{L}_{1} = \lvert \mathbf{X}_{i} - \mathbf{G}(\mathbf{E}(\mathbf{X}_{i})) \rvert, \\
    &\mathcal{L}_{\text{LPIPS}} = \lVert \phi(\mathbf{X}_{i}) - \phi(\mathbf{G}(\mathbf{E}(\mathbf{X}_{i}))) \rVert_{2}, \\
    &\mathbf{L}_{\mathbf{F}} = u\Big(\mathbf{F}\big(\mathbf{X}_{i}\big)\Big) + u\Big(-\mathbf{F}\big(\mathbf{G}(\mathbf{E}(\mathbf{X}_{i}))\big)\Big),
\end{align*}
where $\phi(\cdot)$ is a pretrained LPIPS network \cite{lpips}, and $u(\cdot)$ is a Softplus function \cite{stylegan}. Let $\mathbf{X}$ be a distribution of slide image windows. The overall objective is
\begin{equation*}
    \mathcal{L}_{\mathbf{E}} = \min_{\mathbf{G},\mathbf{E}} \max_{\mathbf{F}} ~ \mathbb{E}_{\mathbf{X}_{i} \sim \mathbf{X}} \Big[\mathcal{L}_{1} + \mathcal{L}_{\text{LPIPS}} + \mathcal{L}_{\mathbf{F}} \big ].
\end{equation*}
In a later section, we show $\mathbf{E}(\cdot)$ effectively encodes the gene expression-related features, by combining a pretrained $\mathbf{E}(\cdot)$ with a linear layer for gene expression prediction.

\begin{figure}[!t]
    \centering
    \includegraphics[width=0.83\linewidth]{fig/exp.pdf}
    \caption{Overview of the exemplar retrieval. For example, blue cycles are pre encoded global views.  Given $\mathbf{X}_{i}$, we extract $\mathbf{e}_{i}$ with $\mathbf{E}(\cdot)$ and retrieve the nearest exemplars.
    }
    \label{fig:exp}
\end{figure}

\noindent\textbf{Exemplar Retrieval.} After convergence, we use the representation space of the extractor $\mathbf{E}(\cdot)$ for the exemplar retrieval. Note that, we call the extractor output, the style code, as a global view. The slide image window $\mathbf{X}_{i}$ is encoded into a global view, by $\mathbf{e}_{i} = \mathbf{E}(\mathbf{X}_{i})$. Then, we measure the similarity between each given pair of the slide image, for example, $\mathbf{X}_{i}$ and $\mathbf{X}_{j}$, with the Euclidean distance $\mathcal{L}_{2}$, for constructing the nearest exemplar set $\mathcal{K}_{\mathbf{X}_{i}}$. We empirically verify the optimal size of $\mathcal{K}_{\mathbf{X}_{i}}$ in \cref{sec:ab}. To generalize the model performance, we restrict that the candidate image pairs are from different patients. The overall process is presented in \cref{fig:exp}. In the experiment section, we compare the proposed exemplar retrieval with alternative retrieval approaches.

\subsection{Exemplar Learning}
\label{sec:predict}
Our model $\mathbf{C}(\cdot,\cdot,\cdot)$ is composed of a projector, a backbone, an EB block, and a prediction block. It maps a slide image window $\mathbf{X}_{i}$, a corresponding global view $\mathbf{e}_{i}$, and a retrieved nearest exemplar set $\mathcal{K}_{\mathbf{X}_{i}}$ to gene expression $\mathbf{y}_{i}$, i.e., $\mathbf{y}_{i} = \mathbf{C}(\mathbf{X}_{i}, \mathbf{e}_{i}, \mathcal{K}_{\mathbf{X}_{i}})$. Our model brings interactions between $\mathcal{K}_{\mathbf{X}_{i}}$ and $\mathbf{e}_{i}$ to progressively revise the intermediate representations of $\mathbf{X}_{i}$ in the ViT backbone.

\noindent\textbf{Projector.} The global views $\mathbf{e}_{i}$ and $\mathbf{e}_{j} \in \mathcal{K}_{\mathbf{X}_{i}}$ summarise a wide range of dataset-dependent attributes. We refine the global view to concentrate on the gene expression of interest by several multi-layer perceptrons (MLPs). Firstly, the global view $\mathbf{e}_{i}$ of the given slide image window $\mathbf{X}_{i}$ is projected by $\text{MLP}^{0}_{h}(\cdot)$. Secondly, for $\mathbf{e}_{j} \in \mathcal{K}_{\mathbf{X}_{i}}$, as the associated gene expression $\mathbf{y}_{i} \in \mathcal{K}_{\mathbf{X}_{i}}$ is available, we empower the refinement of $\mathbf{e}_{j}$ by $\mathbf{y}_{j}$. We concatenate $\mathbf{e}_{j}$ and $\mathbf{y}_{j}$ before feeding to $\text{MLP}^{0}_{r}(\cdot)$. Thirdly, with $\text{MLP}^{0}_{s}(\cdot)$, $\mathbf{y}_{j}$ is projected to the model dimension.  We have
\begin{align*}
    \mathbf{h}^{0}_{i} = \text{MLP}^{0}_{h}(\mathbf{e}_{i}), \;\; \mathbf{r}^{0}_{j} = \text{MLP}^{0}_{r}([\mathbf{e}^{j}, \mathbf{Y}{j}]), \;\;
    \mathbf{s}^{0}_{j} = \text{MLP}^{0}_{s}(\mathbf{y}_{j}),
\end{align*}
where the superscript of $\mathbf{h}^{0}_{i}$, $\mathbf{r}^{0}_{j}$, and $\mathbf{s}^{0}_{j}$ denotes that they are initial refined global view, and $[\cdot,\cdot]$ is a concatenation operator. $\text{MLP}^{0}_{h}(\cdot)$ and $\text{MLP}^{0}_{s}(\cdot)$ are two layer perceptrons with a ReLU activation function. $\text{MLP}^{0}_{r}(\cdot)$ shares the same parameters with $\text{MLP}^{0}_{h}(\cdot)$ but has an extra linear layer on top of it.

\noindent \textbf{Backbone.} We use the ViT as our backbone \cite{ViT}. It has a patch embedding layer and vision transformer blocks.  The patch embedding layer tiles the slide image window $\mathbf{X}_{i}$ into non-overlapping patches before flattening and feeding to a linear layer. The outputs are $\mathbf{Z}^{0}=[\mathbf{z}^{0}_{l} \mid  l \in 1,\cdots, \mathsf{L}]$, where $\mathsf{L}$ is the total number of patches, and $\mathbf{Z}^{0}$ is a matrix representation of the projected patches. Each vision transformer block composes of a self-attention layer and a feedforward layer, which performs global interaction among $\mathbf{Z}^{0}$.  Assuming there are $\mathsf{T}$ layers, and let $t \in [1,\cdots, \mathsf{T}]$. We denote the $l^{th}$ patch representation at $t^{th}$ layer as $\mathbf{z}^{t}_{l}$.
\begin{figure}[!t]
    \centering
    \includegraphics[width=\linewidth]{fig/block.pdf}
    \caption{EB block architecture. The inputs are $\mathbf{h}_{i}$, $\{\mathbf{r}^{t}_{j} \mid j \in \Upsilon_{i} \}$, and $\{\mathbf{s}^{t}_{j} \mid j \in \Upsilon_{i} \}$, and $\mathbf{Z}^{t}$.
    Firstly, we project $\mathbf{s}_{j}$ to $\mathbf{s}^{t+1}_{i}$ and have interactions between $\mathbf{h}_{i}$ and $\mathbf{r}_{j}$.
    This interaction is assisted by $\mathbf{s}^{t+1}_{i}$ and obtains $\hat{\mathbf{h}^{t+1}}_{i}$ and  $\mathbf{r}^{t+1}_{i}$. Secondly, $\hat{\mathbf{h}}^{t}_{i}$ is projected to the number of patches in the ViT backbone, to scale the magnitude of the ViT patch representation $\mathbf{Z}^{t}$. By their interactions,  $\hat{\mathbf{h}}^{t+1}_{i}$ and $\mathbf{Z}^{t}$ are updated to $\mathbf{h}^{t+1}_{i}$ and $\mathbf{Z}^{t+1}$.
    }
    \label{fig:tbc}
\end{figure}
\begin{table*}[!t]
    \centering
    \caption{Quantitative gene expression prediction comparisons with SOTA methods in STNet dataset and 10xProteomic dataset. We bold the best results. We use `-' to denote unavailable results. Models are evaluated by four-fold cross-validation and three-fold cross-validation in the above datasets. Our proposed EGN~framework consistently outperforms the SOTA methods in  MAE$_{\times10^{1}}$, PCC@F$_{\times10^{1}}$, PCC@S$_{\times10^{1}}$ and PCC@M$_{\times10^{1}}$ for both datasets. The CycleMLP finds the best MSE$_{\times10^{2}}$ in the 10xProteomic dataset.
    }
    \vspace{-0.7em}
    \resizebox{\linewidth}{!}{
    \begin{tabular}{c|l|cccccccc}
         \toprule
         & Methods &  STNet \cite{stnet} & NSL \cite{nsl} & ViT \cite{ViT} & CycleMLP \cite{cyclemlp} & MPViT \cite{mpViT} & Retro \cite{retro} & ViTExp & Ours \\
         \midrule
         \multirow{5}{*}{\rotatebox[origin=c]{90}{\makecell[c]{STNet\\Dataset}}} &MSE$_{\times10^{2}}$&4.52 & - &4.28 & 4.41 &4.49& 4.53 & 4.46 & \textbf{4.10}\\
         &MAE$_{\times10^{1}}$ &1.70 & - &1.67 &1.68 &1.70& 1.71& 1.69 &\textbf{1.61}\\
         &PCC@F$_{\times10^{1}}$ &0.05 & -0.71  & 0.97 &1.11 &0.91& 0.99 &0.87 &\textbf{1.51}\\
         &PCC@S$_{\times10^{1}}$&0.92&0.25  &1.86 &1.95 &1.54& 1.74 &1.72&\textbf{2.25}\\
         &PCC@M$_{\times10^{1}}$&0.93&0.11  &1.82 &1.91 &1.69&1.79 &1.74& \textbf{2.02}\\
         \midrule
         \multirow{5}{*}{\rotatebox[origin=c]{90}{\makecell[c]{10xProteomic\\Dataset}}}  &MSE$_{\times10^{2}}$& 12.40 & - & 7.54 & \textbf{4.69}&5.45 & 5.25 & 5.04 & 5.49\\
         &MAE$_{\times10^{1}}$& 2.64 & - & 2.27 & \textbf{1.55}&1.56 & 1.65 & 1.66 & \textbf{1.55}\\
         &PCC@F$_{\times10^{1}}$&1.25 &-3.73 & 5.11 & 5.88 & 6.40 & 5.46 & 5.59 & \textbf{6.78}\\
         &PCC@S$_{\times10^{1}}$&2.26 & 1.84 & 4.64 & 6.60 & 7.15 & 6.35 & 6.36 & \textbf{7.21}\\
         &PCC@M$_{\times10^{1}}$&2.15 & 0.25 & 4.90 & 6.32 & 6.84 & 6.04 & 6.00 & \textbf{7.07}\\
         \bottomrule
    \end{tabular}}
    \label{tab:eva}
\end{table*}

\noindent\textbf{EB Block.} This block is interleaved with the vision transformer blocks, which brings $\mathbf{s}^{t}_{j}$, i.e., knowledge about gene expression of the nearest exemplar, to each ViT patch representation $\mathbf{z}^{t}_{l}$. Firstly, we project $\mathbf{s}^{t}_{j}$ (i.e., a projection of gene expression of the exemplars) to $\mathbf{s}^{t+1}_{j}$. With $\mathbf{s}^{t+1}_{j}$, we have interactions between $\mathbf{h}^{t}_{i}$ and $\mathbf{r}^{t}_{j}$ (i.e., refined global views) to obtain $\hat{\mathbf{h}}^{t+1}_{i}$ and  $\mathbf{r}^{t+1}_{j}$. As $\hat{\mathbf{h}}^{t+1}_{i}$ and $\mathbf{r}^{t+1}_{j}$ are initialized from the same extractor $\mathbf{E}(\cdot)$, their interactions could serve as a bridge for the revision of the ViT patch representation. Secondly,  considering $\hat{\mathbf{h}}^{t+1}_{i}$ is initially extracted by the reconstruction based-network $\mathbf{E}(\cdot)$, $\hat{\mathbf{h}}^{t+1}_{i}$ is expected to contain spatial information about the input image distribution. With $\hat{\mathbf{h}}^{t+1}_{i}$, we revise the ViT patch representation $\mathbf{z}^{t}_{l}$, obtaining $\mathbf{h}^{t+1}_{i}$ and $\mathbf{z}^{t+1}_{l}$. This revision process brings interactions between $\hat{\mathbf{h}}^{t+1}_{i}$and $\mathbf{z}^{t}_{l}$ to propagate information about possible gene expression (which receives from the exemplars) and patch representation statuses. The overall process is shown in \cref{fig:tbc}.

To do so, firstly, we concatenate $\mathbf{h}^{t}_{i}$ and the difference $\mathbf{h}^{t}_{i} - \mathbf{r}^{t}_{j}$ to bring interactions between $\mathbf{h}^{t}_{i}$ and $\mathbf{r}^{t}_{j}$, with $\text{MLP}^{t}_{m}(\cdot)$. We chunk the outputs to $\mathbf{m}_{h,j}$ and $\mathbf{m}_{r,j}$.  Then, they are fused with $\hat{\mathbf{s}}_{j}$ to retrieve gene expression from $\mathbf{s}^{t+1}_{j}$ (a projection of $\mathbf{s}^{t}_{j}$).
Semantically, $\mathbf{m}_{h,j}$ summarises `the existing knowledge of gene expression', and
$\mathbf{m}_{r,j}$ tells `the desired knowledge'. Mathematically, we have
\begin{align*}
    &\hat{\mathbf{h}}^{t+1}_{i} = \mathbf{h}^{t}_{i} + \text{Avg}([\mathbf{m}_{h,j} \cdot \mathbf{s}^{t+1}_{j} \mid \forall j \in \Upsilon_{i}]), \\
    &\mathbf{r}^{t+1}_{i} = \mathbf{r}^{t}_{i} + \mathbf{m}_{r,j} \cdot \mathbf{s}^{t+1}_{j}, \\
    &\mathbf{m}_{h,j}, \mathbf{m}_{r,j} = \text{Chunk}(\sigma(\text{MLP}^{t}_{m} (\mathbf{h}^{t}_{i},\mathbf{h}^{t}_{i} - \mathbf{r}^{t}_{j}))),\\
    &\mathbf{s}^{t+1}_{j} = \text{MLP}^{t}_{s}(\mathbf{s}^{t}_{j})
\end{align*}
where $\text{MLP}^{t}_{m}(\cdot)$ is a Multi-layer perceptron, the $\text{Chunk}(\cdot)$ operator equally splits the input into two outputs, and $\sigma(\cdot)$ is a Sigmoid function. To avoid notation overloading, we remain to use $\text{MLP}^{t}_{s}(\cdot)$ as a single-layer perceptron. Secondly, $\hat{\mathbf{h}}^{t+1}_{i}$ and $\mathbf{Z}^{t}$ reciprocate each other by scaling the magnitude of each patch representation $\mathbf{z}^{t}_{l} \in \mathbf{Z}^{t}$:
\begin{align*}
    &\mathbf{Z}^{t+1} = \mathbf{Z}^{t} + \text{MLP}^{t}_{z}(\mathbf{O}^{t}_{z}), \\
    &\mathbf{h}^{t+1}_{i} = \hat{\mathbf{h}}^{t+1}_{i} + \text{MLP}^{t}_{h}(\text{Avg}(\mathbf{O}^{t}_{h})), \\
    &\mathbf{O}^{t}_{h},\mathbf{O}^{t}_{z}= \text{Chunk}(\text{MLP}^{t}_{o}(\mathbf{Z}^{t}) \cdot \sigma(\text{MLP}^{t}_{\hat{h}}(\hat{\mathbf{h}}^{t+1}_{i}))),
\end{align*}
where $\text{MLP}^{t}_{z}(\cdot)$, $\text{MLP}^{t}_{h}(\cdot)$, $\text{MLP}^{t}_{o}(\cdot)$, and $\text{MLP}^{t}_{\hat{h}}(\cdot)$ are single-layer perceptrons. $\mathbf{O}^{t}_{h}$ and $\mathbf{O}^{t}_{z}$ are the fusion of the ViT patch representations $\mathbf{Z}^{t}$ and $\hat{\mathbf{h}}^{t+1}_{i}$. Note that, the output dimension of $\text{MLP}^{t}_{o}(\cdot)$ is the number of patches in the ViT, and each scalar of the output is used to scale a corresponding patch from $\text{MLP}^{t}_{o}(\mathbf{Z}^{t})$. By scaling the magnitudes of each ViT patch representation, we indirectly inject the knowledge about gene expression of the exemplars, while updating the refined global view $\hat{\mathbf{h}}^{t+1}_{i}$ to facilitate both interactions with the exemplars and the ViT representation in following layers. One may note that the current operation is single-head based, which can be extended to a multi-head operation by expanding the output dimension of $\text{MLP}^{t}_{o}(\cdot)$ in practice.

\noindent\textbf{Prediction Block.} Following \cite{crs}, each ViT patch representation shows different priorities toward the gene expression prediction task. We apply an attention pooling layer $\text{AttPool}(\cdot)$ to aggregate $\mathbf{z}^{\mathsf{T}}_{l} \in \mathbf{Z}^{\mathsf{T}}$. Moreover, $\mathbf{h}^{\mathsf{T}}_{i}$ is a progressively refined global view of the slide image window $\mathbf{X}_{i}$, which captures high-level attributes of $\mathbf{X}_{i}$. We concatenate $\mathbf{h}^{\mathsf{T}}_{i}$ and $\text{AttPool}(\mathbf{Z}^{\mathsf{T}})$ for the  prediction. We have
\begin{align*}
    \mathbf{y}_{i} = \text{MLP}_{g}([ \mathbf{h}^{\mathsf{T}}_{i},\text{AttPool}(\mathbf{Z}^{\mathsf{T}})]),
\end{align*}
where $\text{MLP}_{g}$ is a single-layer perception.

\noindent \textbf{Objective.} We optimize $\mathbf{C}(\cdot,\cdot,\cdot)$ with mean squared loss (i.e., the Euclidean distance) $\mathcal{L}_{2}$ and batch-wise PCC $\mathcal{L}_{ppc}$. We have
\begin{align*}
\mathcal{L}_{total} = \mathcal{L}_{2} + \mathcal{L}_{ppc}
\end{align*}

\begin{figure*}[!t]
    \centering
    \includegraphics[width=\linewidth]{fig/emb.pdf}
    \caption{Quantitative evaluation of the top performed models from \cref{tab:eva} and \cref{tab:net}. We employ t-SNE \cite{tsne} for dimension reduction of model latent space. We use the extra labels (i.e., tumour and normal) from the STNet dataset for annotations.
    }
    \label{fig:vis}
\end{figure*}

\section{Experiments}
\noindent\textbf{Datasets.} We perform experiments in the publicly available STNet dataset \cite{stnet} and 10xProteomic datasets\footnote{\url{https://www.10xgenomics.com/resources/datasets}}. The STNet dataset contains roughly 30,612 pairs of the slide image window and the gene expression. This dataset covers 68 slide images from 23 patients. Following \cite{stnet}, we target predicting expression of 250 gene types that have the largest mean across the dataset. The 10xProteomic dataset has 32,032 slide image windows and gene expression pairs from 5 slide images. We select target gene types in the same way as the STNet dataset. We apply log transformation and min-max normalization to the target gene expression. Note that, our normalization method is different from \cite{stnet} (they use log transformation and a custom normalization, i.e., dividing the expression of each gene type by the sum of expression from all gene types, for each slide image window). Our normalization method allows independent analysis of gene expression prediction.

\subsection{Experimental set-up}

\noindent\textbf{Baseline methods}. We compare with extensive SOTA methods from gene expression prediction, ImageNet classification benchmarks, and exemplar learning.
\begin{itemize}
    \setlength\itemsep{-0.2em}
    \item STNet \cite{stnet} and NSL \cite{nsl}. They are the SOTA methods in gene expression prediction.
    \item ViT \cite{ViT}, MPViT \cite{mpViT} and CycleMLP \cite{cyclemlp}. We use the SOTA ImageNet classification methods in our task. They are strong baselines in our task.  Specifically, we use ViT-B, MPViT-Base, and CycleMLP-B2. Please refer to \cite{ViT,mpViT,cyclemlp} for details.
    \item Retro \cite{retro} and ViTExp. We explore the SOTA exemplar learning methods. However, Retro is originally developed for natural language processing. We adapt it by providing the extractor output $\mathbf{e}$ as the exemplar representations. ViTExp directly concatenates the exemplar representations to the ViT patch representation. The exemplar representations are added with an embedding to be differentiated from the patch representation of the slide image window. Both Retro and ViTExp are based on the ViT-B architecture \cite{ViT}.
\end{itemize}

\noindent\textbf{Evaluation metrics.} We evaluate the proposed methods and alternative baselines with PCC, mean squared error (MSE), and mean absolute error (MAE).
We use PCC@F, PCC@S, and PCC@M to denote the first quantile, median, and mean of the PCC.
The PCC@F verifies the least performed model predictions. The PCC@S and PCC@M measure the median and mean of correlations for each gene type, given predictions and GTs for all of the slide image windows. Meanwhile, the MSE and MAE measure the sample-wise deviation between predictions and GTs of each slide image window for each gene type. Note that, for MSE and MAE, the lower value means better performance. In contrast, for PCC@F, PCC@S, and PCC@M, the higher value indicates better performance.

\noindent\textbf{Implementation details\footnote{Codes are available at: \url{https://github.com/Yan98/EGN}}.} The architectures and optimization settings for our extractor follow \cite{s2f}. For the exemplar learning, we implement EGN~by using the \textit{Pytorch} framework \cite{pytorch}. EGN~is trained from scratch for 50 epochs with batch size 32. We set the learning rate to $5 \times 10^{-4}$ with a cosine annealing scheduler. Our weight decay is $1 \times 10^{-4}$.  We use a ViT backbone with the following settings: patch size 32, embedding dimension 1024, feedforward dimension 4096, attention heads 16,  and depth 8. We interleave the proposed EB block with the ViT backbone at frequency 2, where each block has 16 heads and dimension 64, namely the block has 16 unique operations with dimension 64. Our EB block uses 9 nearest exemplars. All the experiments are conducted in 2 NVIDIA Tesla P100 GPUs.

\begin{table*}[!t]
    \begin{minipage}{0.55\linewidth}
    \centering
    \includegraphics[width=\linewidth]{fig/nmk.pdf}
    \captionof{figure}{Ablation study on number of exemplar used in EB block. This number is varied from one to fifteen, and we present PCC@M, MSE, and MAE in sub-figure (a), (b), and (c).
    }
    \label{fig:nmk}
    \end{minipage}
    \hspace{1em}
    \begin{minipage}{0.4\linewidth}
    \centering
    \caption{Ablation study on exemplar retrieval. We compare with representation from AlexNet and ResNet50 for exemplar retrieval.}
    \vspace{-0.7em}
    \resizebox{0.9\linewidth}{!}{
    \begin{tabular}{llccc}
       \toprule
       \multirow{2}{*}{\makecell[l]{Feature\\Space}} &  \multirow{2}{*}{\makecell[c]{Distance\\Metrics}} &  \multirow{2}{*}{MSE$_{\times10^{2}}$} &
       \multirow{2}{*}{MAE$_{\times10^{1}}$} & \multirow{2}{*}{PCC@M$_{\times10^{1}}$} \\
       & \\
       \midrule
       $\mathbf{E}(\cdot)$  & $\mathcal{L}_{2}$ & \textbf{4.10} & \textbf{1.61} & \textbf{2.02} \\
       $\mathbf{E}(\cdot)$  & $\mathcal{L}_{1}$  & 4.13 & 1.64 & 1.80 \\
       $\mathbf{E}(\cdot)$  & $cosine$ .  & 4.24 & 1.63 & 1.83 \\
       AlexNet  & LPIPS & 4.46 & 1.70 & 1.74\\
       ResNet50 & $\mathcal{L}_{2}$ & 4.12 & 1.62 & 1.89\\
       ResNet50 & $\mathcal{L}_{1}$ & 4.13 & 1.62 & 1.83\\
       ResNet50 & $cosine$ &  4.16 & 1.64 & 1.94 \\
       \bottomrule
    \end{tabular}}
    \label{tab:ret}
    \end{minipage}
\end{table*}
\subsection{Experimental results}
We compare our EGN~framework with the baselines on the STNet dataset and the 10xProteomic dataset (\cref{tab:eva}). As the gene expression prediction task emphasizes capturing the relativity variation,  we bias on the PCC-related evaluation metrics, i.e., PCC@F, PCC@S, and PCC@M. Our EGN~consistently achieves the SOTA performance in MAE, PCC@F, PCC@S, and PCC@M.
Our findings are as follows:
\textit{1)} It's worth noting that Retro and ViTExp use the ViT as the backbone. However, their performance is lower than the ViT. Both Retro and ViTExp use attention-based operations to directly have interactions between the ViT representations and the global views of the exemplars, when comparing with our EB block. This validates the necessity of using the global view of the input slide image window as a bridge to introduce the knowledge of exemplars to the ViT representations;
\textit{2)} CycleMLP and MPViT, the SOTA methods in the ImageNet classification task, lead to the second-best performance in the STNet dataset and 10xProteomic dataset in PCC-related evaluations. Meanwhile, CycleMLP finds the best MSE in 10xProteomic. Note that, PCC-related evaluation metrics are most important in our task. By using the exemplars, our model that uses the vanilla ViT backbone outperforms them with a reason marginal in PCC-related evaluation metrics, while overall achieving similar MSE and MAE with them;
\textit{3)} The PCC@F of our model significantly outperforms the baseline methods. This metric evaluates the worst model capability, by calculating the first quantile of PCC across all gene types. Note that, the majority of gene types covered by the first quantile have skewed expression distributions, which are the most challenging part of the prediction task. Our method has 0.038 - 0.040 higher than the second-best method in PCC@F;
and \textit{4)} STNet and NSL fail to achieve good performance. Again, the gene expression-related feature is usually non-uniformly distributed across the slide image window input. They do not have long-range interactions to capture the expression of the same gene type from these features. Moreover, NSL even shows a negative correlation with PCC@F. This validates our claims that predicting gene expression directly with the color intensity is vulnerable, and it is only feasible in extreme cases (recall the example of tumour-related gene expression in \cref{sec:intro}).

\noindent\textbf{Quantitative Evaluation.}
We present the latent space visualization (\cref{fig:vis}), by considering the top performed models from \cref{tab:eva} and \cref{tab:net} (see details of `Backbone only' and `w/o Projector' settings in \cref{sec:ab}).
Note that, the `Backbone only' setting has a smaller number of parameters than a regular ViT-B. The extra labels (i.e., tumour and normal) from the STNet dataset are used for annotations.
To enable a clean visualization, we randomly sample 256 representations of the slide image window for each label, i.e., tumour and normal. By gradually using the proposed components (from \cref{fig:vis} (c) to \cref{fig:vis} (e)), i.e., introduction exemplars to our model, our method sufficiently separates the tumour representations from the normal representations, by having an improved gene expression prediction than alternative approaches.

\subsection{Ablation study}
\label{sec:ab}
We study the capability of each model component by conducting a detailed ablation study in the STNet dataset.

\noindent\textbf{Extractor Capability.} We explore alternative approaches for retrieving exemplars (\cref{tab:ret}). We compare with representations from AlexNet \cite{alex} and ResNet50 \cite{resnet}. Note that, they are pretrained on ImageNet, and our $\mathbf{E}(\cdot)$ is learned in an unsupervised manner. We explore diverse distance matrices including LPIPS \cite{lpips}, $\mathcal{L}_{2}$, $\mathcal{L}_{1}$, and $cosine$ (i.e., cosine similarity). We have the following findings:
\textit{1)} LPIPS distance retrieved exemplars lead to bad model performance. This distance is trained based on the human perception of regular images, where these images are different from the slide image windows. Thus, it retrieves bad exemplars and damages the model performance;
\textit{2)} Retrieving exemplars with ResNet50 lead to decent performance, though the pretrained ResNet50 lacks gene expression-related knowledge. This retrieval method complements the knowledge about image textures learned from the ImageNet for our task, which enhances the versatility of our model representation and verifies our model robustness;
and \textit{3)} Using the extractor $\mathbf{E}(\cdot)$ with $\mathcal{L}_{2}$ distance achieves the best performance.

\noindent\textbf{EB Block Settings.} Firstly, we present PCC@M (\cref{fig:nmk}(a)), MSE (\cref{fig:nmk}(b)), and MAE (\cref{fig:nmk}(c)), by varying the number of exemplars used in the EB block from one to fifteen. Having nine exemplars find the best PCC@M and MAE, and we have the best MSE by using ten exemplars. Again, our task emphasizes capturing relative changes. Thus, our final recommendation is to use nine exemplars. Secondly, we ablate the EB block architectures and the frequency of interleaving with the vision transformer blocks (\cref{tab:blc}). This balances the model's capability in structuring the ViT representations and receiving knowledge from the exemplars. We have the best PCC@M, by setting heads, head dimension, and frequency to 8, 64, and 2.

\begin{table}[!t]
    \centering
    \vspace{-1em}
    \caption{Ablation study on the EB block.}
    \vspace{-0.7em}
    \resizebox{0.85\linewidth}{!}{
    \begin{tabular}{ccccc}
        \toprule
        {Heads} & {\makecell[l]{Head Dim}} & {Frequency} & {PCC@M$_{\times10^{1}}$}\\
        \midrule
         4  & 32 & 2 & 1.84\\
         4  & 64 & 2 & 1.76\\
         4  & 64 & 3 & 1.78\\
         8  & 32 & 2 & 1.85\\
         8  & 64 & 2 & \textbf{2.02}\\
         8  & 64 & 3 & 1.88\\
         16 & 32 & 2 & 1.86\\
         16 & 64 & 2 & 1.82\\
         16 & 64 & 3 & 1.80\\
        \bottomrule
    \end{tabular}}
    \label{tab:blc}
\end{table}

\noindent\textbf{Model Architectures.} We present the performance of `Pretrained $\mathbf{E}(\cdot)$', `Backbone only', `w/o EB block', and `w/o projector' in \cref{tab:net}.  `Pretrained $\mathbf{E}(\cdot)$' adds one trainable linear layer to $\mathbf{E}(\cdot)$, while freezing weights of $\mathbf{E}(\cdot)$ for gene expression prediction. This is also known as linear probing in the literature. `Backbone only' uses the ViT backbone architecture. Note that, we use a non-regular ViT architecture which is determined by a binary search. `w/o EB block' is equivalent to concatenating the pooled ViT representations with the global view for predicting gene expression. `w/o projector' replaces the projector with a linear layer to unify the dimension.
Our findings are as follows:
\textit{1)} The `Pretrained $\mathbf{E}(\cdot)$' setting is a strong baseline. Note that, this setting has better performance than the STNet and NSL (\cref{tab:eva}). Our $\mathbf{E}(\cdot)$ captures gene expression-related features in an unsupervised manner, showing the potential of our unsupervised exemplar retrieval;
\textit{2)} The `w/o EB block' setting achieves worse performance than the `Backbone only' setting. Concatenating the global view for gene expression prediction with a single linear layer disables interactions between the global view and the ViT representations. This potentially causes inconsistent behaviors and results in poor performance;
and \textit{3)} With all proposed components, we have the best performance.

\begin{table}[!t]
    \centering
    \vspace{-1em}
    \caption{Ablation study on model architectures.
    }
    \vspace{-0.7em}
    \resizebox{0.85\linewidth}{!}{
    \begin{tabular}{lccc}
        \toprule
        Settings & MSE$_{\times10^{2}}$ & MAE$_{\times10^{1}}$ & PCC@M$_{\times10^{1}}$  \\
        \midrule
        Pretrained $\mathbf{E}(\cdot)$ & 4.85 & 1.76 & 1.56 \\
        Backbone only & 4.31 & 1.67 & 1.80\\
        w/o EB Block &  4.65 & 1.72 & 1.70\\
        w/o projector & 4.22 & 1.63 & 1.85\\
        \midrule
        Ours & \textbf{4.10} & \textbf{1.55} & \textbf{2.02} \\
        \bottomrule
    \end{tabular}}
    \label{tab:net}
\end{table}

\section{Conclusion}
This paper proposes an EGN~framework to accurately predict gene expression from each fine-grained area of tissue slide image, i.e., different windows. EGN~uses the ViT as a backbone while integrating with exemplar learning concepts. We first have an extractor to retrieve the exemplars of the given tissue slide image window in an unsupervised manner. Then, we propose an EB block to progressively revise the ViT representation by reciprocating with the nearest exemplars. With extensive experiments, we demonstrate the superiority of the EGN~framework over the SOTA methods. EGN~is promising to facilitate studies on diseases and novel treatments with accurate gene expression prediction.

{\small
\bibliographystyle{ieee_fullname}

redpajama_set_name: RedPajamaArXiv


Im not a parent. I dont know what its like to have a teen who hates school. I cant imagine how frustrating that must be.
But, Ive tutored enough students to see how stressful and frustrating it is for parents.
I get how badly you want your teen to do their best at school. I understand how crucial it is for your teen to get the best start possible to their young adult life. Its excruciating to see a teen with truckloads of potential who doesnt give toss about school.
If this sounds like a fairly accurate description of your teen, then today Im here to try and offer you some help with getting them on the right track.
The very first thing to do is to put yourself under the microscope. Are you helping or inadvertently hindering your teens progress? If youre in the habit of nagging your teen to study and to do their homework, youve got to stop. Long term, nagging isnt going to get you or your teen anywhere. Your job is to be encouraging and supportive. You want your teen to know youre their friend, not their enemy.
The next thing is to make sure that the focus of your encouragement is on your teens effort, and NOT their grades.
Of course grades are important, but if your teen is completely unmotivated about school, then weve got to get a few things sorted first before getting straight As becomes the goal.
If your teen hates school, encouraging them to get good grades is pointless. They may not even believe its even possible at this stage.
Making an effort is something your teen CAN do today, getting straight As probably isnt.
Getting your teen to do just half an hour of homework/study each day after school is realistic. Its something that they can complete successfully, and its something that you can reward your teen for doing.
Thinking about this may help your teen find something about school they can really enjoy.
If your teen hates reading and would rather do household chores than write an English essay, please dont force them to like it. Of course its important that they try their best in every subject, but sometimes youve got to call a spade a spade, and accept that your teen is never going to like every subject.
Whats far more likely to see your teen succeed down the track, is if you encourage them to pursue the subjects that they do enjoy. There are successful people in every industry. Your teen doesnt need to be a doctor or a lawyer to have a good career. If they have a subject that they like and enjoy, thats the thing they should really go for.
One of the biggest issues with getting teens to care about school, is that they often dont give a toss about the future. The part of their brain that deals with things like consequences and repercussions is not fully developed. Literally.
So the cruel irony is that the time your teen is at high school is one of the most important times for them to be thinking about the future, but many arent able to. This means its up to YOU to help your teen think about their future. Not many teens do this without encouragement.
A huge motivator for me at school was my parents pointing out how un fun it would be to spend my days working behind the check out counter at the supermarket because thats what would be waiting for me if I didnt do well at school. Whether thats what would have happened or not is up for debate, but the point is it was a great motivator. Maybe something like this would motivate your teen too?
If I had to some up this post with one word, it would be positivity. Thats what has the best chance of getting your teen to see the importance of putting in the effort at high school.
Good grades are won and lost in the way students approach the actual sitting of the exam.
Obviously its the hard work put in before exams that counts the most, but it would be a huge mistake for your teen to not appreciate how important having an in exam strategy is.
Today Im talking about the 3 most important things to do in an exam to ensure your teen gets the most out of their exam time.
Most exams allow for about 15 minutes of reading time before the exam actually starts. This time is not 15 minutes of leisure time, its 15 minutes that can make or break your teens exam results.
This time is invaluable. It allows your teen to read over the exam, get an idea of its length and format, and sometimes they might even be able to start jotting points down on scrap paper. The most important thing for your teen to do FIRST, is to skim read the whole exam. Dont let them underestimate how important this is.
Its crucial that your teen does this so they know how long the exam is before they start writing.
Some exams will allow your teen to write on scrap paper before writing time starts. If theyre only allowed to read, then just make sure they read as much of the exam as possible, and that they start to think about their answers.
Students who get good grades dont only know their stuff, they time manage their exams too. One of the biggest mistakes your teen can make is to spend more than the allocated time on one question and consequently run out of time on another. At the end of the day, what counts is the overall grade your teen gets for the subject. There arent any prizes for answering one question incredibly well when you fail to answer five other questions worth just as many marks. Your teen MUST plan their exam time, and stick to this schedule.
Being in an exam room is a high stress environment. All of the work your teen has put in leading up to it depends on the next few hours, and theres usually a huge time pressure.
This pressure cooker environment can play funny tricks on your mind. About an hour after my last exam a couple of weeks ago, I realised that I forgot to include something really, really obvious in an essay. It was a point that I knew really well. There was no reason for me to forget it in the exam.


Virtual Dynamics is an advanced simulation software for dynamic analysis of powertrain, driveline and their components. It is a suite of products composed of Virtual Engine and Virtual Gearbox together providing all building blocks needed to create dynamic models of engine, transmission, conventional and hybrid-electric drivelines. Virtual Dynamics uses the core technology of the world leading Multi-Body-Simulation Software MSC Adams® as numerical integrator, pre- and post-processing features. The template based architecture perfectly combines the advantages of single purpose software – ease of use and multi-purpose software - no limitations in extendibility.
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The unique communicator technology and the template-based architecture support modular modeling.
Virtual Dynamics provides a modular modeling approach. One can build and analyze individual subsystems, which may consist of only one component or more. Virtual Dyamics is a so-called template-based product: every subsystem is derived from a template, which acts as a blueprint for the subsystem and defines its topology. Subsystems can be adjusted and refined to different fidelities as appropriate for the desired analysis. The unique communicator technology and the exceptional template-based architecture enable both modular and scalable modeling that correspond to both the user's experience and the functional simulation demands. Using the communicator technology, a complete powertrain can be built with the subsystems and run as a fully coupled model.
Virtual Dynamics and Adams Car share the template-based architecture that perfectly combines the advantages of single-purpose software – ease of use and multi-purpose software – no limitations in extendibility. Given the same template-based architecture and communicator technology, Virtual Dyamics`s powertrain model can be directly coupled to Adams Car's vehicle model and this complete powertrain and vehicle system-level model can be simulated dynamically.
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The continuously increasing mechanical and thermal load of modern engines calls for design optimizations over a wide range of aspects. The optimization of design variables such as the firing order is still challenging. FEV Virtual Engine can play a central role in the realization of this comprehensive task.


Visual Voices, a professional lecture series sponsored by the School of Art, invites eight nationally recognized visiting artists and designers to speak about their work and the world of art and design. In addition to providing a look inside the studios of these top professionals, the series offers students the opportunity to interact with them during daytime seminars and studio visits.
The lectures are open to the public and take place in Harris Theater on select Thursday evenings from 7:30 to 9 p.m. For more information, please contact, Harold Linton.
Past Speakers have included: Anne Harris, Dean Kessmann, Kevin Wyllie, Alyce Myatt, Barry Nemett, R.G. Brown, Karen Kunc, Lynell Burmark, Lucy Lippard, Krzysztof Wodiczko, Steve Heller, Rusty Smith, W.C. Richardson, William Dunlap, Lori Nix, Robert Whitman, Steve Kurtz, James Elkins, Ellen Lupton, Pyllis Rosenzweig, Terry Barret, Kuiyi Shen, Renee Stout, Sam Gilliam, John Moore, Bill Christenberry, Carrie and Kevin Burke.


text:
Will Massachusetts's Stand Against Child Marriage Spur the Rest of the Nation to Act Against this Child Abuse?
Amanda Parker (left), Rep. Kay Khan (center), and Sen. Harriette Chandler at a May 2017 press conference in support of a bill to end child marriage in Massachusetts. Both Rep. Khan and Sen. Chandler were still sponsors when the current bill passed.
Published 8/16/2022
On July 28, Massachusetts finally banned child marriage after years of activism from AHA Foundation and many others. Below, our Senior Director Amanda Parker reflects on this milestone achievement.
Victory at last! I cannot begin to express how much joy I feel right now. On July 28, Governor Charlie Baker signed the Massachusetts budget bill, which includes a provision totally banning child marriage in the state.
Amanda with Unchained at Last founder Fraidy Reiss at a Chain In protest against child marriage in Massachusetts, May 2017.
This victory is the culmination of SIX years of advocacy and activism from AHA Foundation and its allies in this fight, such as Unchained at Last and countless other survivors, legislators, and activists. As AHA's Senior Director, I personally have visited Massachusetts several times over the past six years to push for legislation to end the horrific practice of child marriage, and I feel a sense of great achievement now.
After years of legislation failing to make way, the hard work of AHA and our allies has finally paid off. Above all, our supporters made this victory possible, so they can take as much pride in this triumph as I do. Thanks to them, the gruelling effort in Massachusetts is now over, and girls' childhoods are protected.
But the fight is not yet over. Yes, it is great news that Massachusetts has become the 7th state to ban child marriage—but this means that tens of thousands of girls in 43 states are still NOT protected from this child abuse!
We have won a battle, yet the war is far from over. I hope the victory in Massachusetts will lead to more momentum to end child marriage across the nation. It is a stain on America's soul that we must scrub out. That this appalling practice is legal in a majority of states is a betrayal of the highest American ideals: this is not what we stand for! So, even as we celebrate this victory, we must go forward to protect every girl in our nation, and I trust and hope that you will continue to stand with us as we do so.
Representative Kay Khan Responds to the momentous victory in Massachusetts
One of our key allies in Massachusetts was Representative Kay Khan, who has tirelessly fought to end child marriage in her state. We reached out to her for her thoughts on this momentous victory.
AHA Foundation: How did you feel when you heard that Governor Baker signed the bill to end child marriage in Massachusetts?
Rep. Khan: After many years, I was pleased and excited to learn that we were finally able to get this bill across the finish line.
AHA Foundation: How did the Massachusetts Coalition to End Child Marriage support you in getting this legislation passed?
Rep. Khan: The Massachusetts Coalition to End Child Marriage has been an endless source of energy and determination in bringing this piece of legislation across the finish line at every possible opportunity.
AHA Foundation: What does the passing of this bill mean to you personally? What does it mean for the girls at risk of this abuse in the Commonwealth?
Rep. Khan: As a mother of 3 and grandmother of 7, it is so important to me that they see the Commonwealth of Massachusetts take a stand for girls.
I believe [the Massachusetts victory] will send a strong message to other states that progress is being made on this issue—do not be left behind!
AHA Foundation: What message do you think or hope the passing of this bill in Massachusetts will send to other states that have not yet banned child marriage?
Rep. Khan: I believe it will send a strong message to other states that progress is being made on this issue—do not be left behind!
AHA Foundation: Massachusetts is only the 7th state to ban child marriage. When we posted this fact on social media, many people commented about their surprise that child marriage was still legal in other states and that it took years to pass the Massachusetts legislation. What do you think needs to be done to ensure the remaining 43 states without laws banning child marriage follow Massachusetts' example and finally ban child marriage?
Rep. Khan: The response to this legislation has been overwhelmingly positive with strong bipartisan support and a total of 122 co-sponsors (out of 200 legislators total) in the House and the Senate. Many co-sponsors didn't even realize children could be legally married in Massachusetts and were as shocked as I was to learn this practice still existed. No one in the Legislature came out in opposition to this bill. It has really been a matter of so many supporters across the Commonwealth coming together and working tirelessly to educate the members of the Legislature and making sure that the issue stayed at the forefront of their minds until it was finally passed. The other states must commit to this kind of steadfastness.
Read More: Jenn's Story of Surviving Child Marriage and Advocating to Protect Girls in Massachusetts
Read More: Representative Kay Khan on Her Fight to End Child Marriage in Massachusetts
Find Out More About Child Marriage in the U.S.—And How You Can Help End It

redpajama_set_name: RedPajamaCommonCrawl


There are many inspiring reasons to stay in the UK when the holidays come around, or when you have the urge to just get away from it all for a few days. Even so, there are two that really stand out for readers of the Good Access Guide. The first is that there are plenty of options to hand when it comes to disabled friendly holiday accommodation and the second is that UK is literally brimming over with national parks to visit, some more famous than others.
Britain has earned international reputation for the beauty of its national parks. They are, without a doubt, considered to be some of the country's greatest national treasures. With a total of 15 national parks in the UK, 10 in England, three in Wales and two in Scotland, they are the nation's pride and joy. The official designation of Britain's first national parks, Dartmoor, The Lake District, The Peak District and Snowdonia, occurred in 1951 and with 16.4 million visitors per year, The Lake District is by far the most popular. But what about the parks that seem to get less exposure on travel websites and in travel magazines?
The Yorkshire Dales offers the most quintessential reflection of British countryside. Every which way you look the landscapes of this park appear to have been stolen from those found on UK postcards. Heather moorlands, stone-built villages and traditional farming landscapes are paired alongside the UK's finest limestone scenery, flower-rich hay meadows and even an underground labyrinth of caves.
The great thing about the park, for those with mobility concerns, is that there are 17 routes to explore that have been dubbed the "Miles without Stiles" routes. All options are easy to download or can be bought directly from the park's various centres. If you love the water, there's a really pretty circular walk that is 100% accessible to all around Grimwith Reservoir. Wildlife lovers might want to visit the nature reserve at the Water Houses end. This route travels along a rough grass gradient of 1:10 for the first 500m and then converts into a well-surfaced track, at least 300cm wide from start to finish.
There are an impressive 22 lochs located in this national park, together with 50 rivers, three sea lochs, two forest parks and 21 small mountains that climb to a little over 3,000 ft. Loch Katrine is one of the lochs that can be easily accessed and enjoyed, whatever mobility issues you may have. The path begins at the Trossachs Pier. It's smooth and tarmacked, with the occasional gradient.
If you have a passion for flora, however, then you really must visit the Benmore Botanic Gardens. Fully accessible for anyone in a wheelchair or on a scooter, these gardens are located on the Cowal Peninsula within an incredible mountainside setting. The views are dramatic, to say the least. For anyone more interested in urban settings, there are lots of villages and small towns to explore, including Aberfoyle Village, Balloch Village and Callander Town.
South Downs, the UK's youngest national park, was officially added to the country's national treasure list in 2010. Similar to The Yorkshire Dales, South Downs is home to a series of routes that cater to the needs of those with limited mobility. There's actually quite a good video of wheelchair user, Mel, who travels regularly to The South Downs National Park with her dog, Jasper, to take in the fresh air, enjoy the variety of smells of the countryside and watch her furry friend play in the water.
So, whatever your mobility and whatever you really enjoy when travelling, whether that be urban locations, water, countryside, mountains, or gardens, the UK's national parks will have a range of options for you to explore. While the Lake District and the Peak District might benefit from the greatest amount of media attention, the remaining 13 are just as enchanting.


Survey on employees' use of mobile chat tools got an attention-getting treatment.
Content marketers know that sponsored surveys are a great way to get attention and generate leads. These were among the objectives of a survey launched by Spider Oak, which operates in the data privacy and encryption space. The effort's success has won Spider Oak and its communications agency, INK, first place in the "Use of Visual Design" category in PR Daily's 2017 Digital PR & Social Media Awards.
In 2016, Spider Oak introduced a new enterprise chat tool designed to compete with the likes of Slack, Hipchat and Skype. Strong security is one of the product's strengths, but security wasn't high on organizations' list of priorities when it came to employee-facing chat. A survey to demonstrate just how pervasive the problem is made perfect sense.
Ensuring the results grabbed attention was another matter. Rather than the typical press releases and media pitches, Spider Oak's communications agency, INK, shared the results in a format that would span the wide screens with which most people would view the document.
INK employed data visualization to show the results, accompanied by short bursts of text. The visually arresting document was distributed primarily to an email list of leads and targets. (Spider Oak does not collect customer information, consistent with its commitment to privacy.) The document opened doors to top-tier tech media and found an audience that shared it via social media.
Congratulations to Caitlin New, Emily Grossman, Ryan Riggins and Madison Scullin for demonstrating the creative potential in a document designed for PDF.


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> Topics > Health & Safety - working conditions > News list > REACH, exposure limits and nanotechnologies on the agenda...
REACH, exposure limits and nanotechnologies on the agenda of a seminar on protecting workers from chemicals
A shake-up in chemicals marketing through implementation of the REACH regulation, revision of the directive protecting workers against carcinogens, setting occupational exposure limit values for a new series of chemical substances: the EU is back to business in 2008 with a densely packed agenda on chemical hazards control.
The potential impact on workers' health makes these big challenges in the European trade union book. Their enormous technical complexity requires input from external experts with ties to academia, and a forthright dialogue with the representatives of the European institutions. Tony Musu, the ETUI-REHS' chemical hazards expert, had drawn up a more than ample agenda for this third "Worker protection and chemicals" seminar.
The labelling and classification of chemicals was the first item of business. In June 2007, the Commission put forward a proposal to bring EU law into line with the Globally Harmonized System (GHS) - a United Nations scheme to harmonize the different hazardous chemical classification and labelling (C&L) systems worldwide. The European proposal for a GHS Regulation still has to be adopted by Parliament and Council, which ought to happen by next July at the latest, according to Alick Morris of DG Social Affairs. That will usher in a seven year transitional period during which the EU legislation (Directive 67/548/EEC for classification and labelling (C&L) of dangerous chemical substances and Directive 1999/45/EEC for C&L of dangerous preparations) will live alongside the GHS regulation.
During this time, therefore, workers will have to cope with different labels and risk phrases for the same type of product depending on which C&L system the producer has chosen. Trade unionists fear that having these two systems running in tandem for a relatively long period is likely to make it even harder for workers to see their way through the already complex system that is meant to inform them about the hazardous substances they have to handle. That might explain the Commission representative's playing up of the need for workers to be given training.
Trade unions are also concerned about what impact GHS implementation might have on the EU law that protects workers against dangerous chemicals. Mr. Morris sought to offer reassurance: "The GHS criteria for classifying carcinogenic and mutagen substances are comparable to those in the Carcinogens and Mutagens Directive."
Hildo Krop, a researcher with IVAM, a Dutch sustainable development institute, was distinctly less sanguine about the impact of GHS on worker protection. He argued that GHS will make it even harder for workers to get hold of toxicological data, especially for mixtures. "GHS will downgrade the quality of labelling and classification, as well as safety data sheets", he argued.
The second debate on day one of the seminar - on occupational exposure limit values - will have done little to allay union fears. Tony Musu set out the ETUC's stall on the current revision of the Carcinogens and Mutagens Directive. Getting reprotoxins included in the Directive is top of the European trade union organization's agenda. The ETUC also wants tighter exposure limits that take better account of recent scientific knowledge to be adopted for benzene, hardwood dust and vinyl chloride monomer, the only three carcinogens for which binding exposure limits have been set in the directive.
The European trade unions also want substances that are recognized as carcinogens by the WHO and some European States (crystalline silica and softwood dust, in particular) to be included in the revised Directive.
New systems for setting exposure limits recently adopted in the Netherlands and Germany were presented and sparked an instructive debate between trade union representatives on their organizations' involvement in this kind of supremely scientific process. The consensus was that the top priority was to replace carcinogens by less toxic substances, and that setting exposure limits for carcinogens must remain a secondary aim.
There was no way a seminar on protecting workers from chemical hazards could ignore the REACH regulation, in force since June 2007. Emilio Gatti of the Milanese section of Italy's CISL trade union confederation gave a presentation on his organization's activities to support REACH implementation in Lombardy workplaces. Dominique Defriese, of Belgium's FGTB trade union, presented a very detailed guide to how REACH works that will be supplied to workers' reps in Belgian firms. The ETUI-REHS is adapting it to be put out soon in several European languages.
REACH provides a system of authorization before chemicals that are most dangerous to human health can be put on the market. A list of priority substances that will require authorization is to be drawn up in the next few months. Third parties (NGOs, civil society, business concerns, etc.) will be allowed to send certain information in to the competent European authorities. Tatiana Santos of ISTAS, a research institute with links to Spain's Workers' Committees, argued that European trade unions must grasp this opportunity to call attention to the substances of highest concern found in work places.
Active trade union involvement also looks to be needed where nanotechnologies are concerned. Growing industrial use of nanomaterials (for manufacturing sports equipment, sunscreens, paints, etc.) is causing widespread concern not just among consumers but also among material producers. The European Commission clearly has little reassurance to offer them at this stage. "The environmental and health risks related to nanomaterials are in theory covered by the EU regulatory framework", said DG Environment's Henrik Laursen. He nevertheless acknowledged that, "implementation of that regulatory framework remains difficult, due to the lack of scientific data and rapid pace of change in the market". The Commission representative believes that the requirements of REACH apply to nanomaterials but admits that the regulation contains no specific provisions on nanomaterials. The trade union attendees argued that civil society and the institutions had to exercise tighter control over these infinitely small technologies. The German and British codes of conduct presented at the seminar might be worthwhile avenues of exploration.


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David Lee Roth Posts New Song, "One Piece Thermo-Molded Country Plastic Chair"
By Josh Hart 2012-11-05T18:22:53Z
In a surprise move, Van Halen frontman David Lee Roth has just posted a brand new song online. Stream "One Piece Thermo-Molded Country Plastic Chair" below.
The track was uploaded as part of Roth's ongoing web series, the aptly named Roth Show. The song, which was co-written with Scotty Emerick, is dedicated "to all who are affected by and recovering from Hurricane Sandy."
Van Halen were forced to postpone all touring activities after Eddie Van Halen was forced to undergo emergency surgery following an acute attack of diverticulitis back in August. Eddie is expected to make a full recovery, and the band look to be back on the road sometime in 2013.


Rakesh Singh (Emraan Hashmi), a scamster, is a kingpin of a group executing the cheats happening in the top exams throughout the country. He hunts down a scholar student in Satyendra Dubey (Snighadeep Chatterjee) convinces him to write the exams for rich spoilt brats. This inaugurates the easy and grey way for Satyendra aka Sattu to earn money.
He continues to work for Rakesh and finally, after a point of time, he breaks down and surrenders to the pressure. Rakesh, in a shock, moves ahead from that incident and that's when he starts getting into multiple legal issues. The rest of the story revolves around Rakesh explaining to the world why he's doing nothing wrong.


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Q: Unable to install NET Framework 4.7.1 on Server Core installation Windows Server 2016 or "Update is not applicable to your computer" for NET Framework 4.7.1 on Server Core installation Windows Server 2016
Trying to update net framework to 4.7.1 on a Server Core installation but no dice. The installation starts to extract the files for the setup, runs the setup.exe /x86 /x64 /redis flashes the .NET logo and shows the license terms windows to continue. Accepts and clicks install and the installation just disappears without further explanation.
The problem exists on another server - identical source and installation. But it works on a identical source and installation but with Desktop Experience.
Looking at the log file...
[2/25/2018, 20:5:3] === Logging started: 2018/02/25 20:05:03 ===
[2/25/2018, 20:5:3] Executable: C:\NDP471-KB4033342-x86-x64-AllOS-ENU.exe v4.7.2558.0
[2/25/2018, 20:5:3] --- logging level: standard ---
[2/25/2018, 20:5:3] Successfully bound to the ClusApi.dll
[2/25/2018, 20:5:3] Error 0x800706d9: Failed to open the current cluster
[2/25/2018, 20:5:3] Cluster drive map: ''
[2/25/2018, 20:5:3] Considering drive: 'C:\'...
[2/25/2018, 20:5:3] Drive 'C:\' has been selected as the largest fixed drive
[2/25/2018, 20:5:3] Directory 'C:\b2f695ed043f576c934668\' has been selected for file extraction
[2/25/2018, 20:5:3] Extracting files to: C:\b2f695ed043f576c934668\
[2/25/2018, 20:13:3] Extraction took 33.562 seconds
[2/25/2018, 20:13:3] Executing command line: 'C:\b2f695ed043f576c934668\\Setup.exe   /x86 /x64 /redist'
[2/25/2018, 20:13:36] Exiting with result code: 0x0
[2/25/2018, 20:13:36] === Logging stopped: 2018/02/25 20:13:36 ===

Looking in the CBS.log file. But nothing indicating why the installation doesn't go through.
Copying the extracted files to a new path while the installation package has not stopped - running: wusa.exe Windows6.1-KB4019990-x64.msu
Finally something to work with, it shows: The update is not applicable to your computer
Looking at all requirements from Microsoft doesn't explicit state the Framework 4.7.1 is not supported on Server Core.
So would appreciate all theories or ideas on how to troubleshoot this for a solution ?

Windows OS...
PS C:\> systeminfo.exe
Host Name:                 SERVERNAME
OS Name:                   Microsoft Windows Server 2016 Standard
OS Version:                10.0.14393 N/A Build 14393

Windows features...
PS C:\> get-windowsfeature -name *framework* | where-object {$_.Installed -match "True"}
Display Name                                            Name                       Install State
------------                                            ----                       -------------
[X] .NET Framework 4.6 Features                         NET-Framework-45-Fea...        Installed
    [X] .NET Framework 4.6                              NET-Framework-45-Core          Installed
    [X] ASP.NET 4.6                                     NET-Framework-45-ASPNET        Installed

Windows Updates...
PS C:\> Get-Hotfix
Source        Description      HotFixID      InstalledBy          InstalledOn
------        -----------      --------      -----------          -----------
SECDC9SRV101  Update           KB3192137     NT AUTHORITY\SYSTEM  2016-09-12 00:00:00
SECDC9SRV101  Update           KB4049065     SECDC9SRV101\Admi... 2018-02-25 00:00:00
SECDC9SRV101  Update           KB4077525     NT AUTHORITY\SYSTEM  2018-02-25 00:00:00

A: Try to install Windows update KB4033393

On Windows 10 Anniversary Update, Windows 10 Creators Update, and
  Windows Server 2016 you can find this as Update for Microsoft Windows
  (KB4033393) under Installed Updates in Control Panel.

which includes .NET Framework 4.7.1 on board.
Another source link - https://docs.microsoft.com/en-us/dotnet/framework/deployment/deployment-guide-for-developers where you can download offline bundle and install it as it described here - https://support.microsoft.com/en-us/help/4033342/the-net-framework-4-7-1-offline-installer-for-windows

A: For Core, make sure you are using /q or else the install dialog will fail.  Also, get-windowsfeature won't show .Net 4.7 installed.  Not sure what is going on there.
You can run this reg command to see the release of .net
reg query "HKLM\Software\Microsoft\NET Framework Setup\NDP\v4\Full" /v Release

If it's 70805 (hex) or higher, you are good.

redpajama_set_name: RedPajamaStackExchange


I just got a few more of these Sleek Sticks from Essie, which are rolling into stores about now – several readers in the comment section on the last Sleek Stick post are saying where they found them if you want to go on the hunt.
I'm slightly obsessed with the Love to Love You wraps.
I didn't have any luck getting the new Repstyle to work – it's supposed to give a reptilian finish using a magnet, but maybe it's too cold, I don't know, but I got nothing! Let me know if you used this and how you got along with it.
The font wraps look really cool! Definitely want to try these!!
The Love To Love You wraps are my favourite of the Essie designs!
Crocodile nails? Have they seriously not run out of effects yet?!
Ooh, I really wanted to try the Repstyle – what a shame it didn't work!
Jaaaane, have you seen these? Dior/Claridges collection previewed in Harrods Mag for March 2013. So beautiful!
Thanks for flagging up.. they're amazing!
Rep style doesn't work? Oh bugger, I liked the look of those! Grrrr. Back to the drawing board Essie!!
Repstyle does work for me but not after using two coats as Essie suggest. Trick I've found is to use a base coat, then use a fairly generous layer of repstyle polish and immediately use the magnet.


It's real simple. The only time we ever remove bandages is if you're assigned wound management, if you're changing a dressing; does that make sense? If we have to look at a wound, then we would be assigned wound management.
It goes back to me saying Simon says; Simon didn't say take off the wound dressing and do a management. Simon said, "Check the abdomen out." All we're going to do is check the abdomen where we can. We're going to look around the dressing. We're going to look at the surrounding tissue. We're going to look at the bandage itself to see if it's draining but by no means are we taking it off if Simon doesn't tell us to do it.
Then when it comes time to listening to the abdomen, well if there's a big bulky dressing, you're not going to listen on top of the dressing. You don't know what's hidden under there. You might put pressure accidentally and hurt the patient because you don't have that superman x-ray vision where you know exactly where to place that stethoscope when you can't see what's under that bandage.
The other thing is too, we always use a stethoscope directly on skin, so if you go put it on a bandage, it makes a scratchy sound, a staticky sound and you can't hear adequately to have a proper assessment so you listen outside of the bandage; wherever you can get closest to the bandage, that's where you're going to listen, but never do you put a stethoscope on the bandage and never do you take the bandage off at all unless Simon tells you do to so.
There's your tip for a big bulky dressing on the abdomen. For more information, check out our website www.ALTClinicalWorshop.com.


First Lohri of a newly born babies holds significant importance for people in the states of Punjab, Haryana, Himachal Pradesh and Delhi. Here first Lohri is considered to be an important gift-giving occasion. Parents along with relatives and friends invited to participate in the auspicious occasion shower gifts and blessings on the new born baby.
If you need help picking a design we are always available! Email us at sk9669@hotmail.com for all requests and inquiries and we are happy to serve you the best.


Bugger me, it's been quiet on here for a bit eh?
Well, I've got a few things to stick up here soon but I need to upload some photos onto Flickr first. I've been away again with my lovely girlfriend and I'm sure that there are one or two nice photos of our trip that I'd like to share.
In the meantime there's plenty of other things on the internet to be reading. Carry on.


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With the real estate market on the down-swing, it is more crucial than ever to do whatever it takes to make houses that are going on the market more eye-catching and appealing. That's where professional home staging comes in. It only makes sense to spend the extra time and money necessary to give a residence that sharper edge because not only will it sell quicker, but it could mean a higher selling price.
Even if you already have your own business, check out Decorate-Redecorate and visit the wonderful assortment of professional home staging online courses that are available. You will also find a number of tools that will help make the process easier.
As a professional, you are clearly aware of the importance of keeping current with the latest information and techniques, regardless of what business you happen to be involved in. As a home stager and redecorator, you also know that increasing your professional home staging education can often help you clear out any of those little problem areas that have been plaguing you.
If you are new to the business or just starting out, you may have done some checking on how costly and time consuming a degree in interior design can be. But at Decorate-Redecorate, you can take the professional home staging education course of your choice and it is not going to cost you an arm and a leg to do it.
These courses are easy to understand, thorough, professional courses that will take you to the top of your game. So if you already have a professional business in operation but it has not reached the level you hoped for when you first started, then you owe it to yourself to take a look at what we offer and take the necessary steps to put your business over-the-top.
Remember, décor is more important than ever for people who want or need to sell their home quickly. With so many people getting caught by their interest only loans, a lot of people need a quick sale to avoid having their home repossessed. As a home stager, you can be the person who can get them out of this predicament quickly when you restage their home.
Don't cheat yourself out of the opportunity to start or improve your professional home staging business. Trek on over to www.decorate-redecorate.com right now and check out all of the ways in which you can benefit from the smart, top-notch courses, books, and tools that are offered. The prices are so reasonable that you won't regret your decision.


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Giving Tree returns to Morinville to help local seniors
The Town of Morinville's Family and Community Support Services Department are once again hoping Morinville and area residents will make a difference for a senior or an adult within the community. […]
Town may get new Community Services Advisory Board
In a unanimous decision (excluding the absent Councillor Rob Ladouceur) Council has passed Councillor Stephen Dafoe's motion to create a Terms of Reference for a new Community Services Advisory Board. […]
Town wins Health Education Award
The Town of Morinville is the recipient of the Providing Health Education Award.
Melonie Dziwenka, FCSS/Community Program Coordinator said she received an email Aug. 25 from Patrick MacQuarrie, Program Officer – Communities Choose Well, Alberta Recreation and Parks Association (ARPA). […]
GIRLS program returns to help young women understand themselves
By Stephen Dafoe
Morinville –Girls between the ages of 11 and 18 will have the opportunity to learn about themselves in a fun way starting next week. The Girls in Real Life Situations (GIRLS) program starts Apr. 9 and will run Tuesday evenings for the next seven weeks.
Rebecca Holland, who provides community resources for the Sturgeon School Division… […]
Families being asked to connect by disconnecting
Morinville – Residents are being asked to use this Family Day to disconnect from technology and connect with themselves, their families and their communities. Family and Community Support Services (FCSS) departments throughout the province are promoting the Family Day Unplugged challenge, and Morinville is offering a number of prizes to those… […]
Morinville has a new face at FCSS desk
Morinville – There is a new face in the Town's Family and Community Support Services (FCSS) Department. Karin Debenham took on the role of FCSS Coordinator Dec. 3, filling a position that had been vacant for a few months. Debenham comes to Morinville with 15 years experience, having previously worked in the field in Spruce Grove… […]
Morinville's new youth worker ready to make his mark
Morinville – Youth programming in the community will continue its efforts to engage Morinville's teens and tweens. Nicholas Valcourt stepped into the position left vacant Aug. 8 by A.D. Richardson, who has left community services work to pursue her goals to become a professional artist… […]
Council approves FCSS Grant funding
Morinville – Three local initiatives will receive $6,170 in Family and Community Support Services (FCSS) Grant funding after Morinville Town Council approved recommendations from the FCSS Advisory Board during Council's Jan. 10 meeting… […]
Town of Legal releases its town manager
Legal – The Town of Legal will be restructuring its administration after the dismissal of Town Manager Cory Brown over the weekend.
Mayor Albert St. Jean said an emergency council meeting was called Saturday to deal with some internal issues.
"What came out of that is that council has resolved that we terminate Cory's position as CAO," St. Jean said Monday afternoon, adding… […]
Legal moves closer to recreation subsidy
Legal – Families who find their budgets a little short to cover recreational activities for their children may soon have some assistance from the Town of Legal. The idea of a recreational subsidy was put forward by the Town's Community Services Board (CSB) and this week came before both Legal Town Council and the CSB for further tweaking. The proposal will return to council may 16 for final approval… […]
Grocery store helps town with program funding
Morinville – A Town program that helps low income families gain access to Community Services programs got a healthy injection of funding, thanks to the owners, staff and customers of the local No Frills grocery store.
Owners Chris and Tracey Mansbridge presented Morinville's Family and Community Support Services (FCSS) Coordinator, Amy Dribnenky, with a cheque for $1,586 Aug. 6 … […]
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Some Singapore secondary schools and junior colleges take in students who show an aptitude for sports. This yearly exercise is known as the Direct School Admission (DSA).
We have put together these lists of schools (see below) based on information available on the Ministry of Education website to help you start your search. Please contact the schools to confirm the sport.
If there is an error or change in information, please feel free to leave us a comment so that we can update.
This DSA exercise has given students with athletic ability educational opportunities they otherwise may not have gotten. You can read about the experience of DSA student-athletes below.
DSA Student-Athlete, 19: "I DSA-ed into secondary school and JC. Both times, my national examination marks were well off the cut-off point for my respective institutes.
"It was difficult for me to cope with my studies due to my sports commitments. Furthermore, as I was a National Team member, I had to train for various international Games and competitions. Most of the time, I barely managed to escape being retained.
"I believe that my teachers have been integral in helping me through my school life. They were very supportive and gave me extra lessons whenever I requested them. In the end, I managed to get into the university and course of my choice without much hassle and I really have to thank my schools for that.
School Sports Coach, 40: "I have true concerns about DSA. To me, DSA has led to three outcomes.
"Firstly, primary schools have become a place where 12-year-olds are pushed in sports to achieve results to get that elusive DSA place in that top school. It is very competitive and kids burn out, they dislike sports and they hate the pressure on them. Coaches are employed to produce results instead of developing fundamental skills for future development.
"Secondly, top schools offer students DSA places to help them win national schools competitions. These students come with low aggregates and can barely cope with the academic studies. When the DSA student wants to focus more on studies, they are reminded of their DSA obligations. How do you cope in a top academic school when you are 30 points below the PSLE cut-off and are forced to train 5 times a week in sports. Think of the child and not just national schools titles.
"Thirdly, burnout. Sports becomes a vehicle to get into a top school. It is no longer played for enjoyment and health. It does not make sense. In the end, you have frustrated students who can't keep up academically and are forced to give up their sport.
This is a list of Singapore secondary schools that take in students with sporting talent. Click on the school name to go to its website.
This is a list of Singapore junior colleges and schools with an Integrated Programme (IP) that take in students with sporting talent. Click on the school name to go to its website.


text:
DHAHIR AL-RIWAYA PDF
Abu 'Abdullah Muḥammad ibnu-l-Ḥasan Ibn Farqad ash-Shaybānī the father of Muslim His works, known collectively as zahir al-riwaya, were considered authoritative by later Hanafis; they are al-Mabsut, al-Jami al-Kabir, al-Jami al- Saghir. Wajdi al-Ahdal and the Broken Yemeni Nation. Authors al-Muqalih, 'abd al-' aziz () Dirasat fi al-Riwaya wa-l-Qissa al-Qasira ft al-Yaman. vol. 1, 1st ed. Dahir, Rahab () "Muqabala Ma'a Al-katib Al-yamaniyy Wajdi al-Ahdal. Hammer ): Sirat al-Malik 'Ag'ib wa Carib (Égypte, /) — Ms arabe n° Sirat al-malik al-Dahir Baybars [tasa/J al-riwaya al-Samiyya (Récit du roi .
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Organized in the conventional order of legal chapters and concise in its presentation, the Kafi The Sufficing truly lived up to its name, allowing students of law to easily identify the Zahir al-Riwayah rulings of the Dhahkr school without having to pour over all six books and their relatively less-organized contents.
Maria May 28, Though he was born to a soldier, he was much more interested in pursuing an intellectual career, as opposed to a military one.
Abd Allah ibn Abbas taught. Very few later jurists differentiated between the three categories when compiling their legal compendiums. Al-Shaybani wrote Introduction to the Law of Nations at the end of the 8th century, a book which provided detailed guidelines for the conduct of jihad against unbelievers, as well as guidelines on the treatment of non-Muslim subjects under Muslim rule.
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Ali fourth caliph taught. Abdurrahman and Clarke, p.
Abu Hanifa's Books? – IslamQA
Ibn Shihab al-Zuhri died taught. Umar ibn Abdul Aziz raised and taught by Abdullah ibn Umar.
International Review of the Red Cross. Hammad bin ibi Sulman taught. Abu Yusuf wrote Usul al-fiqh.
Leave djahir Reply Cancel reply Your email address will not be published. Umar second caliph taught. Looking forward to some more articles in this subject. Malik ibn Anas — wrote Muwattajurisprudence from early Medina period now mostly followed by Sunni in Africa and taught.
Sorry, your blog cannot share posts by email. Thus, al-Rashid remarked that he "buried law and grammar side by side.
His fields of specialization include Islamic law, hadith, and education. Ali ibn Husayn Zayn al-Abidin — taught. Muslim ibn al-Hajjaj wrote Sahih Muslim hadith books. Abu Hurairah — taught. Anasfounder of the Maliki school of Fiqh.
A few works, however, relate the rulings of the madhhab according to their category, amongst them is the still rare but oft-quoted Muhit of Radi al-Din al-Sarakhsi. Said dahhir al-Musayyib taught. By using this site, you agree to the Terms of Use and Privacy Policy.
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Qasim ibn Muhammad ibn Abu Bakr taught and raised by Aisha. Farwah bint al-Qasim Abu Bakr's great grand daughter Jafar's mother. It was during this period he exerted his widest influence. Muslim scholars of the Hanafi School. Abd Allah ibn al-Zubayr taught by Aisha, he then taught. When al-Shaybani duahir 18 inhowever, Abu Hanifa died after having taught him for only two years. I need your help. Urwah ibn Zubayr died dhahirr by Aisha, he then taught.
Abu Hanifa's Books?
Hisham ibn Urwah taught. These books are called Nawadir because they are transmitted through singular rather than mass-transmitted or well-known chains.
Bilal Ali is a teacher of Islamic studies by profession and a researc her in the Islamic sciences.
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