text:
'Gossip Girl' Cast: Where Is Monet and Will Savannah Smith Return?
By Molli Mitchell On 8/12/21 at 12:35 PM EDT
Culture Gossip Girl TV HBO Max
Gossip Girl on HBO Max introduces a new generation to the world of Gossip Girl. The once mysterious blogger is no longer in hiding (and is no longer Dan Humphrey) and the gossip is now being dished by a Constance Billard teacher, Kate Keller (played by Tavi Gevinson). The reboot also features new characters, with many serving as a nod to the original cast.
Savannah Smith plays Monet de Haan, one of Julien Calloway's "minions" who loyally served her throughout the first five episodes. Episode 6 dropped on Thursday, August 12—and she was noticeably missing from it.
In the week leading up to the episode's release, Smith could not be seen in any of the trailers or artwork for the series. Where is Monet and will Savannah Smith return for Gossip Girl Part 2?
Newsweek has everything you need to know.
Where is Monet de Haan in 'Gossip Girl' Episode 6?
Monet de Haan decided to turn her back on Julien and Luna (Zión Moreno) in Episode 5 of Gossip Girl.
As Julien's PR and content creator, Monet had become frustrated with Julien's decision to embrace authenticity and strike up a friendship with her half-sister Zoya. Worse, Julian had begun to ignore Monet's advice and expertise.
'Gossip Girl' Reboot Return Date
In the end, Monet carried out the ultimate friendship betrayal and sabotaged Zoya and Julien's look for Halloween.
She sided with Julien's rivals, Pippa and Bianca, and encouraged them to recreate Blair (Leighton Meester) and Serena's (Blake Lively) looks at their senior prom as their costume, overpowering Zoya and Julien's Solange and Beyoncé look.
Monet's decision to side with their rivals may explain her absence in the midseason finale which shifts its focus away from the everyday drama at Constance Billard, and instead dives deep into the relationships between Aki (Evan Mock) and his father Rodger (Malcolm McDowell), and Obie (Eli Brown) and his mother Helena (Lyne Renée).
OG Gossip Girl fans are also aware that Blair's minions did not get as much air time as the main cast, but they were present in pretty much every episode so Monet's absence came as a bit of surprise.
Perhaps Monet is off conspiring against Julien or planning an epic comeback of her own for Gossip Girl Part 2. After all, Kate Keller did describe Monet as the "school villain" in Episode 1 of the reboot, so there is a lot more to her.
Savannah Smith and Zión Moreno as Monet and Luna in "Gossip Girl" Karolina Wojtasik/HBO Max
Many fans agree, with some taking to social media to express their sadness at Monet's absence from Episode 6.
One Gossip Girl fan tweeted: "Wait I knew something was missing from this episode no MONET??? the whole next episode better be about her or I'm suing #gossipgirl."
A second fan wrote: "either way I miss queen Monet #GossipGirl."
Another said: "Gossip Girl just not the same without Monet I fear."
'Gossip Girl' Episode 6 Full Recap
'Modern Love' Season 2 Release Date, Cast, Trailer, Plot
A Full Recap of 'Gossip Girl' Episode 5
Will Savannah Smith Return to 'Gossip Girl'?
There is good news for fans of Monet and Savannah Smith.
A representative for Smith confirmed to Newsweek that she will be returning in "future" episodes of Gossip Girl as Monet.
In an interview with Refinery29, Gossip Girl showrunner Joshua Safran also said there would be plenty more Monet and Luna in the second half of Gossip Girl, as well as more about the back stories and lives beyond Julien's world.
Smith has already shared some background details about her character. After Monet was spotted kissing a girl in Episode 3 of Gossip Girl, Smith posted about her sexuality on Instagram and Twitter.
She tweeted, "so yea, she's into girls," and followed up with, "And only girls."
so yea, she's into girls ;)
— Savannah Smith (@ssavannahsmith) July 21, 2021
Speaking to Essence, Smith said: "She's evil! She's cutthroat. She's the maker of all drama, and she makes people cry. I definitely thought about the angry Black girl trope, but it's just not there.
"Monet is a very complex character," she continued. "In the first few episodes, you'll see her on the surface level, but you can tell that there's a reason she acts the way she does."
Hopefully, by the end of Season 1, we will all know Monet a bit better.
The first six episodes of Gossip Girl are streaming on HBO Max now.
Update 8/13/21, 5:50 a.m. ET: This article was updated to add a comment from Savannah Smith's representative.


Hey people, Dani and I have relocated to our new place and although it's painfully overcrowded with boxes and a magazine collection from a TV special called something like "The sad reality of people who never throw anything out"… we still have room to move and are already happy with our choice of location.
We're living in Yarraville now, amongst a whole crew of other late-20s-nicely-dressed-with-kids-on-the-way-or-at-least-planned-to-be people. There's good coffee to be had, second hand furniture and 2 book shops, goodbye Sundays with nothing to do!
Cornershop – good food, good drinks, warm heaters.
I will post up some shots of the new setup soon… until then, be excellent to each other.
* thanks to Tom for putting this on a mix tape in 1996 as a laugh, little did we know how "metal" my tastes would become. I listened to this album last night, whilst wearing a black beanie with "Kings Cross Sydney" on it, in my Toyota Camry, which is unfortunately one of the least brutal Metal cars around.


Need opinion. I am including a scan of these 2 wacky items. Got them way back in the early 1970's when regular series were being issued. Problems is I have no idea how I got them. My brother was also collecting then. Dont know if they came through him. I eventually ended up with everything. They are made of a cloth material. Scan of back of Quacker Oats is included. Not patch.
The only other time I tried to find out what they were, was I sent a letter and photos of them to Roxanne Toser of the Non-Sport Update after I got hold of her book THE BEAT GOES ON from 1997(her Wackys reference book). She told me she did not know what they were.
Of note, have a total of 8 of them. 2 are Series 1 and the other 6 are Series 2.
Just a guess, but it looks like they were cut from a bolt of wacky fabric, which is known to exist. Don't know off the top of my head if the titles on the fabric are known. Gallery might have more info?
I agree. These are from the cloth bolts (of which there are a number of different ones).
I have several pieces of swatches from some of the Wacky cloth bolts. Although these two titles are not on the samples I have, the pics look exactly like how it would appear if a title was cut out of the cloth. On the samples I have, the titles are often printed overlapped leaving few entire titles.
Scoot, any chance you might show a scan of one of your cloth samples.
Years ago, when I started to see info on Wacky Packages, I saw photos of pieces of Wacky cloth. I figured the ones I had may have come from cloth. However, since I didn't have any cloth, was not sure. Makes alot of sense, somebody just cut it up.
Thanks to everyone for their input on this.
Don't think I've ever seen such clear photos of that fabric. Darned cool.


text:
@implementation SDMapAnnotation

-(id)initWithCoordinate:(CLLocationCoordinate2D)coordinate andTitle:(NSString *)title {

    _coordinate = coordinate;
    _title = title;

    return self;

}

@end

redpajama_set_name: RedPajamaGithub


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text:
\section{Introduction}
Graph data can represent various networks in the real world, such as citation networks of papers and link relations of Web pages.
Node classification on graph data is one of the significant tasks in machine learning, and various models \cite{node2vec, lp, minami} have been proposed to solve this.
Among these models, GCN (graph convolutional network) \cite{gcn} has attract a lot of attention by outperforming traditional methods.
Furthermore, various GNNs (graph neural networks) \cite{gnn, gat, gin, jknet} based on GCN have been proposed, and achieve state-of-the-art performances.\\

\noindent
There are concepts called homophily and heterophily which indicate the distribution of labels on graph data.
Most GNNs assume graph data with homophily that nodes with the same label exist nearby.
So that they may not perform well on graph data with heterophily that nodes with different labels are nearby.
To address this problem, H2GCN \cite{h2} aggregates and combines the features of neighboring nodes by depths.
As a knowledge distillation framework using GNN,  CPF (Combination of Parameterized label propagation and Feature transformation \cite{cpf}) trains student model based on teacher GNN model.
In CPF, the student model better performs than teacher model by fully leveraging information of graph data.
However, the high expressive power of H2GCN and CPF may hurt learning on some data, and it is difficult for a particular GNN to outperform other GNNs on all graph data.\\

\noindent
Quantitative indicators are required to analyze the properties of graph data.
Average degree is an indicator of the structural properties of graph data which represents the average number of edges a node has.
In addition, edge homophily ratio is an indicator about homophily and heterophily on graph data which indicates the ratio of edges connecting nodes with the same label.\\

\noindent
In this paper, we propose a hierarchical model selection framework (HMSF) which selects an appropriate GNN model by analyzing the indicators of graph data.
First, we analyze how the average degree affects the training of GCN and H2GCN.
Based on this analysis, we demonstrate a method to select a GNN model trained as a teacher model.
Then, edge homophily ratio is estimated by predicting the labels of all nodes using the output of this teacher model.
Based on this edge homophily ratio, we analyze the effectiveness of the student model in CPF and demonstrate a method to decide whether HMSF selects CPF.\\
\\\noindent
The contributions of this paper are summarized as follows:
\begin{itemize}
\item \textbf{Weaknesses of Previous Works}: We point out the weaknesses of previous works that performs poorly on several datasets. We demonstrate that H2GCN has a feature thinning on graph data with high average degree, and CPF gives rise to a problem about label-propagation suitability.
\item \textbf{Model Selection by Average Degree and Edge Homophily Ratio}: We propose a hierarchical model selection framework (HMSF) which can select an appropriate GNN model for each dataset. HMSF uses average degree and edge homophily ratio to determine the usefulness of previous GNN models.
\item \textbf{Extensive Experiments}: Our evaluation experiments show that HMSF can select model which achieves high performance for nine benchmark datasets. Furthermore, we demonstrate some specific examples that H2GCN and CPF does not perform well based on our analyses: (i) the aggregated features at nodes with high degree are thinned in H2GCN, and (ii) CPF using LP is not suitable for graph data with heterophily.
\end{itemize}
\noindent
The rest of this paper is structured follows: In Section 2, we explain various concepts of graph neural networks. In Section 3, we analyze the limitations of previous studies arising from the properties of graph data. In Section 4, we present indicators about graph and formulate the HMSF using these indicators. In Section 5, we discuss the related works. In Section 6, we conduct experiments and analyze about HMSF across multiple datasets. In Section 7, we conclude the paper and discuss future work.
\section{Notation and Preliminaries}

\subsection{Semi-supervised Learning for Node Classification}
We describe semi-supervised learning for the node classification in graph data. Let $G = (V, E)$ be a graph with a node set $V$ and an edge set $E$. Each node $v \in V_L$ contained in a subset of the node set $V_L \subset V$ has a label $y_v \in Y$ where $Y$ is a label set. The object of node classification is to predict the label of an unlabeled node $v \in V_U = V \backslash V_L$. We can use an $F$-dimensional feature vector $X_v\in \mathbb{R}^F$ for each node $v\in V$ for learning.

\subsection{GCN (Graph Convolutional Network)}
GCN \cite{gcn} is a basic GNN model that aggregates the feature vectors of neighboring nodes and average them. Let $N_{i}(v)$ be the set of nodes that exist exactly $i$ hops away from node $v$. The output $h_{v}^{(k)}$ of node $v$ at the $k$-th layer in GCN is expressed as:

\begin{eqnarray}
{h'}_{v}^{(k)} & = & \sum_{u \in N_{1}(v) \cup \{v\}} h_{u}^{(k-1)} (d_{v,1}+1)^{-1/2}(d_{u,1}+1)^{-1/2};  \nonumber \\
h_{v}^{(k)} &= & \sigma \Bigl( {h'}_{v}^{(k)} W_k \Bigr), \nonumber
\end{eqnarray}
where $h_{v}^{(0)} = X_{v}$, $d_{v,i} = |N_{i}(v)|$, $W_k$ is the learnable weight matrix of the $k$-th layer, and $\sigma$ is the activation function. The output layer of GCN (the $K$-th layer) outputs the class of node $v$ as follows:

\begin{equation}
f_{\mbox{\it gcn}}(v ; W_0,...,W_K) = \mathrm{softmax}(h_v^{(K)}). \nonumber
\end{equation}

\subsection{H2GCN}
H2GCN \cite{h2} is an expressive variant of GNNs that distinguishes the aggregated feature vectors by depth. The aggregated feature vector of nodes at depth $i$ from node $v$ in the $k$-th layer is represented as follows:

\begin{equation}
r_{v,i}^{(k)} = \sum_{u \in N_{i}(v)} r_{u}^{(k-1)} d_{v,i}^{-1/2}d_{u,i}^{-1/2}. \nonumber
\end{equation}
The representations of the 0-th layer $r_v^{(0)}$, the middle layer $r_v^{(k)}\ (1 \leq k \leq K)$, and the terminal layer $r_v^{(\mathrm{final})}$ in H2GCN are given by the following equations, respectively:

\begin{eqnarray}
r_{v}^{(0)} & = & \sigma \bigl( X_{v} W_e \bigr); \nonumber\\
r_{v}^{(k)} & = & \mathrm{CONCAT} \bigl( r_{v,1}^{(k)}, r_{v,2}^{(k)} \bigr); \nonumber\\
r_{v}^{(\mathrm{final})} & = &  \mathrm{CONCAT} \bigl( r_{v}^{(0)}, r_{v}^{(1)}, ..., r_{v}^{(K)} \bigr), \nonumber
\end{eqnarray}
where $W_e\in\mathbb{R}^{F \times p}$ and $W_c \in\mathbb{R}^{(2^{K+1} - 1)p \times |Y|}$ are the learnable weight matrix, and $\rm{CONCAT}$ is the operation to concatenate the vectors. The output layer of H2GCN outputs the class of node $v$ as follows:

\begin{equation}
f_{\mbox{\it h2gcn}} ( v ; W_e,W_c) = \mathrm{softmax} \bigl( r_{v}^{(\mathrm{final})} W_c \bigr). \nonumber
\end{equation}

\subsection{CPF}
CPF (Combination of Parameterized label propagation and Feature transformation) \cite{cpf} is a knowledge distillation framework that uses a trained GNN as a teacher model and improves accuracy by additional training in the student model. CPF learns feature-vector and graph-structure information, which GNNs do not fully utilize, using MLP (multi-layer perceptron) \cite{mlp} and LP (label propagation) \cite{lp}, respectively. The FT (feature transformation) module for the feature vector $X_v$ of node $v$, and the PLP (parameterized label propagation) module for node $v$ and its neighboring nodes $N_1(v)$ are given as follows:

\begin{eqnarray}
f_{\mbox{\it ft}}(v) & = & \mathrm{softmax(MLP}(X_v)); \nonumber\\
f_{\mbox{\it plp}}^{(k)}(v) & = & \sum_{u \in N_{1}(v) \cup \{v\}} w_{uv} f_{lp}^{(k-1)}(u). \nonumber
\end{eqnarray}
Here, the PLP module is initialized as follows:

\begin{equation}
f_{\mbox{\it plp}}^{(0)}(v) =
    \begin{cases}
(0,...,1,...,0) \in \mathbb{R}^{|Y|} & \forall v \in V_{L};\\
(\frac{1}{|Y|},...,\frac{1}{|Y|},...,\frac{1}{|Y|}) \in \mathbb{R}^{|Y|} &  \forall v \in V_{U}.   \nonumber
    \end{cases}
\end{equation}
$w_{uv}$ represents the weight for the edge between nodes $u$ and $v$, which is expressed by the following equation using the confidence score $c_{v}\in \mathbb{R}$ assigned to each node $v$.

\begin{equation}
w_{uv} = \frac{exp(c_{u})}{\sum_{u' \in N_{1}(v) \cup \{v\}} exp(c_{u'})} \nonumber
\end{equation}
The output of the CPF combining the two modules is represented by the following equation using the learnable balance parameters $\alpha\in [0, 1]$ for PLP and FT:

\begin{equation}
f_{\mbox{\it cpf}}^{(k)}\ (v ; \hcircle{}) = \alpha_v \sum_{u \in N_{1}(v) \cup \{v\}} w_{uv} f_{\mbox{\it cpf}}^{(k-1)}\ (u) + (1 - \alpha_v) f_{\mbox{\it ft}}(v), \nonumber
\end{equation}
where $\hcircle{}$ is the parameter set used by CPF, and $f_{\mbox{\it cpf}}^{(0)}\ (v)$ is initialized in the same way as $f_{\mbox{\it plp}}^{(0)}(v)$. The final CPF objective function is expressed in the following equation using the output $f_{\mbox{\it gnn}}(v)$ of the teacher model and the L2 norm $||\cdot||_{2}$:

\begin{equation}
\underset{\hcircle{}}{\mathrm{min}} \sum_{u \in V_U} || f_{\mbox{\it gnn}}(v) - f_{\mbox{\it cpf}}^{(K)}\ (v ; \hcircle{}) ||_{2}. \nonumber
\end{equation}

\subsection{Edge Homophily Ratio}
The edge homophily ratio $h$ is an indicator which expresses the ratio of edges in a graph that connect nodes with the same label. Graph data with homophily have a high $h$, whereas graph data with heterophily have a low $h$. The edge homophily ratio is calculated as follows:

\begin{equation}
h = \frac{|\{(u, v) : (u, v) \in E \land y_u = y_v \}|}{|E|}. \nonumber
\end{equation}

\section{Problems in Previous Works}
\subsection{Feature Thinning in H2GCN}
We demonstrate that H2GCN does not perform well on graph data with a high average degree. For simplicity, we consider the zeroth layer $r_v^{(0)}$ in H2GCN as a vector with 0 and 1 (e.g., [0.0,... ,1.0,... ,1.0]). The final layer $r_v^{(\mathrm{final})} = \mathrm{CONCAT} \bigl( r_{v}^{(0)}, r_{v}^{(1)}, ..., r_{v}^{(K)} \bigr)$ is the combined vector representation of each layer, $r_v^{(0)}$, $r_{v}^{(1)} = \mathrm{CONCAT} (r_{v,1}^{(1)}, r_{v,2}^{(1)})$, etc. Here, $r_{v,1}^{(1)} = \sum_{u \in N_{1}(v)} r_{u}^{(0)} d_{v,1}^{-1/2}d_{u,1}^{-1/2}$ is a vector that aggregates the features of the neighbor nodes of $v$, and the feature amount aggregated from each neighbor node $u \in N_{1}(v)$ takes the value $d_{v,1}^{-1/2}d_{u,1}^{-1/2}$ normalized by the degree $d_{v,1} = |N_{1}(v)|$ of node $v$ and the degree $d_{u,1} = |N_{1}(u)|$ of neighbor node $u$.
Therefore, when the degree of $u$ and $v$ is very high, the feature amount aggregated from $u$ takes very small value.
Furthermore, when neighboring nodes have different features, $r_{v,1}^{(1)}$ is flattened to a small value owing to the aggregation of various smaller features (e.g., [0.2,... ,0.18,... ,0.21]).
In this case, the features of the self-node can be distinguished because they are expressed as a vector with 0 and 1 in $r_v^{(0)}$, but the features of the vectors aggregated from neighboring nodes are difficult to distinguish because they take close values.
This phenomenon also occurs in each $r_{v,i}^{(k)}$ aggregated at other layers and depths.
Figure \ref{fig:aggr} illustrates the aggregated vectors when the features of neighboring nodes are aggregated at nodes with low/high degree.

\begin{figure}[H]
\begin{center}
  \includegraphics[scale=0.6]{aggr_en2.eps}
  \caption{An example of aggregation at nodes with low/high degree}\label{fig:aggr}
\end{center}
\end{figure}
\noindent
The normalization of aggregated features is applied in GCN as well.
However, in GCN, the feature vectors of node and its neighbors are aggregated into one vector and not separated like H2GCN.
So that, even if the aggregated features take very similar values, GCN can learn small differences of features about neighbors.
On the other hand, the large differences of features in zeroth layer constructed 0 and 1 are emphasized in H2GCN.
This leads to H2GCN ignoring small differences of features in aggregated vectors.
In other words, the high expressive power of H2GCN may not perform well when the average degree is high.
For the reason above, GCN is more effective compared to H2GCN for graph data with high average degree.

\subsection{Label Propagation Suitability}
LP is an algorithm that propagates the label of a node to its neighbors based on the assumption that nodes with same label exist nearby.
It means that LP is useful for graph data with homophily.
On the other hand, graph data with heterophily does not satisfy the assumption of LP since most of the edges connect nodes with different labels.
Figure \ref{fig:lp} illustrates an example of applying LP to a graph data with heterophily.

\begin{figure}[H]
\begin{center}
  \includegraphics[scale=0.6]{lp_en2.eps}
  \caption{An example of applying LP to a graph data with heterophily (edge homophily ratio = 0.29). The labels of labeled nodes $v_i$ are propagated to unlabeled nodes $u_i$.}\label{fig:lp}
\end{center}
\end{figure}
\noindent
In the case of Figure \ref{fig:lp}, the label (red) of labeled nodes $v_2$ and $v_3$ are different from the true label (yellow) of neighboring node $u_2$ and $u_3$.
Therefore, LP predicts incorrect label (red) to $u_2$ and $u_3$ by propagation of label.
In CPF, the student model uses LP to get structural information of graph data that is not captured in teacher model.
However, if CPF is applied to graph data with heterophily, CPF are not always helpful owing to the propagation of different labels by LP.
In Section \ref{experiment}, we prove that the student model of CPF actually performs poorly compared to the teacher GNN model on graph data with heterophily.

\section{Hierarchical Model Selection Framework}
We propose HMSF based on the analysis in the previous section.  The overall process flow in HMSF is presented in Figure \ref{fig:sequence}. We assume that weights are learned in GCN by minimizing the following cross-entropy loss:
\begin{equation}
\hat{W}_0,...,\hat{W}_{K_1} = \underset{W_0,...,W_{K_1}}{\mathrm{argmin}} \Bigl(-\sum_{v \in V_L}\!f_{\mbox{\it gcn}}(v ; W_0,...,W_{K_1}) \mathrm{ln} (y_v) \Bigr). \nonumber
\end{equation}
In H2GCN, the weights are also learned by minimizing the following cross-entropy loss:
\begin{equation}
\hat{W}_e,\hat{W}_c = \underset{W_e,W_c}{\mathrm{argmin}} \Bigl( - \sum_{v \in V_L} f_{\mbox{\it h2gcn}}(v ; W_e,W_c) \mathrm{ln} (y_v) \Bigr). \nonumber
\end{equation}

\begin{figure}[H]
\centering
  \includegraphics[width=15.0cm]{sequence_step.eps}
  \caption{Process Flow in the Hierarchical Model Selection Framework}\label{fig:sequence}
\end{figure}

\subsection{(Step 1) : Model Selection by Average Degree}

In HMSF, we first evaluate the average degree $\bar{d}$ via the following equation:
\begin{equation}
\bar{d} = \frac{\sum_{v \in V} |N_{1}(v)|}{|V|}. \nonumber
\end{equation}
As in the left-hand side of Figure \ref{fig:sequence}, depending on the average degree, we then determine the trained GNN model as follows:

\begin{equation}
f_{\mbox{\it gnn}}(v) =
    \begin{cases}
f_{\mbox{\it gcn}}(v ; \hat{W}_0,...,\hat{W}_{K_1}) & \mathrm{if}\quad \beta \leq \bar{d}; \\
f_{\mbox{\it h2gcn}}(v ; \hat{W}_e, \hat{W}_c) & \mathrm{otherwise},   \nonumber
    \end{cases}
\end{equation}
where the parameter $\beta$ is a boundary to determine whether the average degree of the graph data is high or not. In this way, we can select GCN to be used for graph data with high average degree; H2GCN is selected otherwise.

\subsection{(Step 2) : Model Selection by Edge Homophily Ratio}
Edge homophily ratio is calculated using the labels of all nodes in the inputted graph data. However, since we can only use the labels of nodes in the training data, the exact edge homophily ratio cannot be calculated. In this framework, we use the output $f_{\mbox{\it gnn}}(v)$ of the trained GNN model to give all nodes the predicted label $\ y'_v = \mathrm{argmax}(f_{\mbox{\it gnn}}(v))$. This allows us to estimate the edge homophily ratio using the predicted labels for all nodes by computing the following equation:

\begin{equation}
\frac{|\{(u, v)\!:\!(u, v)\!\in\!E\!\land\!\mathrm{{\scriptstyle argmax}}(f_{\mbox{\it gnn}}(u))\!=\!\mathrm{{\scriptstyle argmax}}(f_{\mbox{\it gnn}}(v)) \}|}{|E|}. \nonumber
\end{equation}
The CPF parameter $\hcircle {}$ is learned by training the student model $f_{\mbox{\it cpf}}^{K_2}\ (v ; \hcircle{})$ to minimize the error in the output of the teacher model via the following equation:

\begin{equation}
\hat{\hcircle{}} = \underset{\hcircle{}}{\mathrm{argmin}} \Bigl( \sum_{u \in V_U} || f_{\mbox{\it gnn}}(v) - f_{\mbox{\it cpf}}^{K_2}\ (v ; \hcircle{}) ||_{2} \Bigr). \nonumber
\end{equation}
As in the right-hand side of Figure \ref{fig:sequence}, we determine the final output of the framework as follows:

\begin{equation}
f_{\mbox{\it hmsf}}\ (v) =
    \begin{cases}
f_{\mbox{\it cpf}}^{K_2}\ (v ; \hat{\hcircle{}}) & \mathrm{if}\quad \gamma \leq h'; \\
f_{\mbox{\it gnn}}(v) & \mathrm{otherwise},  \nonumber
    \end{cases}
\end{equation}
where h' is the estimated edge homophily ratio and the parameter $\gamma$ is a boundary to determine whether the data has a high edge homophily ratio. Thus, we can select $f_{\mbox{\it gnn}}(v)$ for graph data with low edge homophily ratio; CPF, which takes $f_{\mbox{\it gnn}}(v)$ as the teacher model, is selected otherwise.

\section{Related Work}
We discuss related work on model selection and GNN in this section.
AutoHEnsGNN\cite{autohensgnn} is one of the model selections for GNN which automatically builds a hierarchical ensemble model.
AutoHEnsGNN uses proxy evaluation to evaluate many GNNs to generate a high performance GNN pool, then explore a hierarchical ensemble.
However, since there is no exploration in graph data with heterophily on this method, its effectiveness for such graph data is unknown.\\

\noindent
Various GNNs based on GCN have been proposed to improve the accuracy of node classification in graph data.
GAT\cite{gat} uses the attention mechanism to learn different weights for each neighborhood.
GCN-LPA\cite{gcnlpa} uses Label Propagation for regularization.
SGC\cite{sgc} reduces model complexity by removing non-linear transformations in GCN.
Geom-GCN\cite{geom} uses the bi-level aggregation and extracts structural information of graph data using geometric relationships in latent space.
GNN has a problem called over-smoothing that adversely affects accuracy when the number of layers of GNN is increased.
To solve this problem, JKNet\cite{jknet} captures local information by combining representations of hidden layer.
DropEdge\cite{dropedge} employs a renormalized graph convolution matrix with randomly removed edges.
APPNP\cite{appnp} uses Personalized PageRank to capture information on a wide range of nodes without increasing the number of layers.
GCNII\cite{gcnii} constructs a deep model with initial residual and identity mapping.
As a GNN considering homophily and heterophily of graph data, CPGNN\cite{cpgnn} propagates pre-computed label predictions by a matrix that holds the probability that each label is connected.
FAGCN\cite{fagcn} flexibly extracts information by combining high-pass filters and low-pass filters.\\

\noindent
Since there is little exploration in the problem about graph data with heterogeneity, GNN proposed in the early days may perform poorly on graph data with heterophily.
Besides, the traditional GNN may not be able to fully leverage the structural information and feature vector information that LP and MLP can capture.
In HMSF, it can flexibly learn information of graph data by combining models that can address these problems.
Specifically, it basically selects H2GCN which can perform well on both graph data with homophily and heterophily and selects GCN only for graph data with high average degree that H2GCN performs poorly.
Furthermore, it selects CPF to leverage the structural information and feature vector information more fully than teacher GNN model, and do not select CPF only for graph data with heterophily that LP in CPF does not perform well.

\section{Experiments}\label{experiment}
In this section, we conduct experiments to prove the effectiveness of HMSF across multiple datasets.
Specifically, we compare HMSF with traditional GNN by the accuracy of node classification in two dataset settings.

\subsection{Dataset}
We use the nine datasets summarized in Table \ref{tab:dataset}.
Each dataset is frequently used as a benchmark in GNNs, and their details are as follows.

\begin{itemize}
\item \textbf{Cora, Citeseer, Pubmed (Citation Data)} \cite{pubmed, coracite} is a citation network of papers, where nodes represent papers, edges represent citation relations, and labels represent fields of papers. Bag-of-words is used as the feature vector.

\item \textbf{Texas, Wisconsin, Cornell} \cite{geom} is a network representing the link relationships of university web pages, where nodes represent web pages, edges represent link relations, and labels represent page categories. Bag-of-words is used as the feature vector.

\item \textbf{Squirrel, Chameleon} \cite{geom} is a network of Wikipedia link relations related to a specific topic, where web pages correspond to nodes, link relations to edges, and labels are classified from page traffic.

\item \textbf{Actor} \cite{geom} is a network of the co-occurrence of actors in Wikipedia, where actors correspond to nodes, page co-occurrences to edges, and labels are classified from words in Wikipedia pages.
\end{itemize}

\begin{table}[H]
    \begin{center}
\caption{Dataset Overview} \label{tab:dataset}
  \begin{tabular}{ c | c c c c } \hline
&Nodes&Edges&Classes&Number of Features\\ \hline
Cora&2708&5278&7&1433\\
Citeseer&3327&4552&6&3703\\
Pubmed&19717&44327&3&500\\
Texas&183&279&5&1703\\
Wisconsin&251&450&5&1703\\
Actor&4600&26659&5&932\\
Squirrel&5201&198353&5&2089\\
Chameleon&2277&31371&5&2325\\
Cornell&183&277&5&1703\\
\hline
\end{tabular}
\end{center}
\end{table}

\subsection{Experimental Setup}
We use two types of data splits: (i) 10 random splits dividing the training/test/validation data into 48\%/20\%/\\32\%, respectively, for each label based on H2GCN\cite{h2}, and (ii) the training data based on GCN\cite{gcn}, with 20 nodes for each label for training, 1000 for testing, and 500 for validation.
For Texas, Wisconsin and Cornell which have few nodes, we use up to 5 nodes for each label for training, 100 for testing, and 50 for validation.
For Cora, Citeseer and Pubmed, we use the same fixed data split as GCN.
For other data, we create our own 10 different splits since GCN did not conduct experiments on these datasets.
We run experiments on each dataset with 10 seeds and calculate the mean accuracy.
We employ Adam\cite{adam} as an optimizer.
We implement H2GCN  based on the model available on \url{https://github.com/GemsLab/H2GCN} and implement GCN and CPF from scratch. \\

\noindent
We use GCN with $K_{\mbox{\it gcn}} = $2 layers and H2GCN with $K_{\mbox{\it h2gcn}}\in\{1, 2\}$ layers, and set the dimension of the middle layer to $d_{\mbox{\it gnn}} = 64$ for both GNNs.
We use the number of dropout rate $dr \in \{0.0, 0.5\}$, the weight decay $wd \in \{0.0005, 0.00001\}$, and the activation function of H2GCN $\sigma \in \{ $ReLU, None$\}$, and select the best combination of parameters based on the performance on validation dataset.
We train the GNN for $e = 1000$ epochs and stop training if the validation loss does not decrease while 200 epochs.
We adopt the accuracy on the test data at the epoch when the validation accuracy is maximized.
We use the CPF with a two-layer MLP, and set the dimension of the middle layer of the MLP to $d_{\mbox{\it mlp}} = 64$.
We use the number of dropout rate of the MLP $dr_{\mbox{\it mlp}} \in \{0.5, 0.8\}$, the number of layers of the PLP $K_2 = 8$, the dropout rate of the PLP $dr_{\mbox{\it plp}} = 0.8$, the learning rate $lr \in \{ 0.01, 0.001\}$, and the weight decay $wd \in \{0.01, 0.001\}$, and select the best combination of parameters based on the performance on validation dataset.
We train the CPF for $e = 2000$ epochs and stop training if the validation loss does not decrease while 200 epochs.
We adopt the accuracy on the test data at the epoch when the CPF stops learning.
We also use $\beta \in \{2, 10, 50, 100\}$ and $\gamma \in \{0.2, 0.4, 0.6, 0.8\}$, and select the best combination as well.

\subsection{Experimental Results on the Splits Provided by H2GCN \cite{h2}} \label{sec:ef}

Table \ref{tab:result1} shows the values of indicators and the accuracies of GNN models incorporated in HMSF.
We use the result of HMSF when $\beta = 10$ and $\gamma = 0.6$.
The best results are highlighted in bold for each dataset.
It shows that Squirrel and Chameleon have a high average degree, and GCN is the best model on these datasets.
On the other hand, H2GCN performs well compared to GCN on other data which have low average degree.
CPF shows a lower accuracy than its teacher model on datasets where $h$ is less than 0.5, and CPF and its teacher model take almost close accuracy on datasets with $h$ greater than 0.5.
This is because this data split has many training data, and the teacher model can perform well.
Overall, we can observe that HMSF selects models that achieve high performance for each dataset.

\begin{table}[H]
   \caption{Experimental results of HMSF on the splits provided by H2GCN \cite{h2}. The parentheses in CPF indicate the GNN model used as the teacher model and H2 in Selected Model means H2GCN.
}
\label{tab:result1}
  \hspace{-1.2cm}
  \begin{tabular}{l | c c c c c c c c c } \hline
&texas&wisconsin&actor&squirrel&chameleon&cornell&Cora&citeseer&Pubmed\\ \hlin
Average Degree&3.14&3.65&7.02&76.30&27.58&3.04&3.90&2.77&4.50\\%&-\\
Actual Value $h$&0.06&0.18&0.22&0.22&0.23&0.30&0.81&0.74&0.80\\%&-\\
Estimated Value $h'$&0.08&0.16&0.23&0.32&0.39&0.37&0.86&0.87&0.85\\ \hline\hline
HMSF& \textbf{84.86}& \textbf{85.29}& \textbf{35.20}& \textbf{49.26}& \textbf{66.38}& \textbf{81.62}&87.63&\textbf{76.76}&88.92\\%&\textbf{72.95}\\
Selected Model&H2&H2&H2&GCN&GCN&H2&CPF(H2)&CPF(H2)&CPF(H2)\\ \hline
GCN&59.19&58.04&29.86&\textbf{49.26}&\textbf{66.38}&58.11& 87.14 &75.24 &87.84\\%&63.45\\
CPF(GCN)&56.49&59.61&29.68&47.66&61.95&55.41&86.76&74.73&88.13\\%&62.27\\
H2GCN&\textbf{84.86} &\textbf{85.29} &\textbf{35.20} &35.24 &57.21 &\textbf{81.62} &\textbf{87.89} &76.65 &\textbf{89.39}\\%&70.37\\
CPF(H2GCN)&81.35&82.16&34.63&34.44&52.26&79.46&87.63&\textbf{76.76}&88.92\\%&68.62\\
\hline
 \end{tabular}
\end{table}
\noindent
Table \ref{tab:result1comp} shows the comparison of accuracy between HMSF and other baseline methods for each dataset and mean accuracy.
Accuracy for models marked with `*' are the results from \cite{h2, gcnii, geom}.
HMSF has the 2.13\% higher mean accuracy than H2GCN-2 which has the highest mean accuracy among each method.
We can observe that the model which performs well for each dataset is different, and GCNII and Geom-GCN not incorporated in HMSF achieve the best performance on Cora, Citeseer and Pubmed.
However, the mean accuracy of HMSF is about 10\% higher than Geom-GCN.
Furthermore, the mean accuracy excluding Actor and Squirrel of HMSF increases 7.19\% compared to GCNII.
This result proves that HMSF can achieve stable performance by selecting the useful model for each dataset.

\begin{table}[H]
   \caption{Comparison of accuracy between HMSF and other baseline methods. * denotes the experimental results from \cite{h2, gcnii, geom}.
}
\label{tab:result1comp}
  \hspace{-0.5cm}
  \begin{tabular}{ l | c  c  c  c  c  c  c  c  c | c} \hline
&Texa.&Wisc.&Acto.&Squi.&Cham.&Corn.&Cora&Cite.&Pubm.&Mean Acc\\ \hline
HMSF& \textbf{84.86}&85.29& 35.20& \textbf{49.26}& \textbf{66.38}& 81.62&  87.89 &76.65& 89.39&\textbf{72.95} \\
GCN &59.19& 58.04& 29.86& \textbf{49.26}& \textbf{66.38}& 58.11& 87.14& 75.24& 87.84& 63.45\\
CPF(GCN)&56.49 &59.61& 29.68 &47.66 &61.95 &55.41& 86.76& 74.73& 88.13 &62.27\\
H2GCN &\textbf{84.86}& \textbf{85.29}& 35.20& 35.24& 57.21& \textbf{81.62}& 87.89 &76.65 &\textbf{89.39}& 70.37\\
CPF(H2GCN)&81.35& 82.16& 34.63& 34.44 &52.26& 79.46 &87.63 &\textbf{76.76} &88.92 &68.62\\ \hline
GAT*&58.38& 55.29& 26.28& 30.62& 54.69& 58.92&82.68& 75.46& 84.68&58.56\\
H2GCN-1*&\textbf{84.86}&\textbf{86.67}&\textbf{35.86}&36.42&57.11&\textbf{82.16}&86.92&77.07&89.40&70.72\\
H2GCN-2*&82.16&85.88&35.62&37.90&59.39&\textbf{82.16}&87.81&76.88&89.59&70.82\\
GCNII*&69.46&74.12&-&-&60.61&62.48&\textbf{88.49}&77.08&89.57&-\\
Geom-GCN-I*&57.58&58.24&29.09&33.32&60.31&56.76&85.19&\textbf{77.99}&\textbf{90.05}&60.95\\
Geom-GCN-P*&67.57&64.12&31.63&38.14&60.90&60.81&84.93&75.14&88.09&63.48\\ \hline

 \end{tabular}
\end{table}

\subsection{Experimental Results on the Splits Provided by GCN \cite{gcn}} \label{sec:few}

Table \ref{tab:result2} shows the values of indicators and the comparison of accuracy between HMSF and other baseline methods for each dataset and mean accuracy.
We use the result of HMSF when $\beta = 10$ and $\gamma = 0.6$.
The best results are highlighted in bold for each dataset.
We summarize below what we observe from these results:

\begin{itemize}
\item The estimated value of the edge homophily ratio $h'$ is very close to the actual value $h$ for all datasets, and the error is at most 0.24 in Chameleon.
This is sufficient as a predicted value to determine whether to select CPF.

\item On Cora, Citeseer, and Pubmed which have a value of $h'$ greater than 0.6, CPF has the highest accuracy.
On the other hand, for other data which have a value of $h'$ less than 0.6 except Actor, Squirrel, and Cornell, we can observe that the accuracy of CPF is lower than its teacher model.
Besides, the accuracy of CPF is higher than its teacher model on Actor, Squirrel, and Cornell.
This is because the teacher model cannot learn sufficiently since there is few training data on this data split, and the additional learning by MLP of CPF is more effective than the adverse effects of LP.

\item On Texas, Wisconsin, Actor, and Cornell, which have low $h'$ and low average degree, H2GCN shows higher accuracy compared to GCN.
On the other hand, GCN has a higher accuracy compared to H2GCN on Chameleon which has high average degree.
Squirrel also has a high average degree, but the accuracies of GCN and H2GCN take close value.
This is because the training data on this data split setting is very few and the learning is not stable, so that the effective model on Squirrel changes depending on the data split.

\item The above observation proves that HMSF has the highest mean accuracy and can achieve stable performance by selecting the optimal model for each dataset.

\end{itemize}

\begin{table}[H]
    \caption{Experimental results of HMSF and other baseline methods on the splits provided by GCN \cite{gcn}. The parentheses in CPF indicate the GNN model used as the teacher model and H2 in Selected Model means H2GCN.
}
\label{tab:result2}
  \hspace{-1.5cm}
  \begin{tabular}{ l | c  c  c  c  c  c c  c  c | c } \hline
&Texa.&Wisc.&Acto.&Squi.&Cham.&Corn.&Cora&Cite.&Pubm.& Mean Acc\\ \hline
Average Degree&3.14&3.65&7.02&76.30&27.58&3.04&3.90&2.77&4.50&-\\
Actual Value $h$&0.06&0.18&0.22&0.22&0.23&0.30&0.81&0.74&0.80&-\\
Estimated Value $h'$&0.12&0.23&0.34&0.39&0.47&0.27&0.83&0.82&0.87&-\\
\hline \hline
HMSF&\textbf{78.10}&\textbf{74.00}&25.03&28.19&\textbf{50.71}&67.70&\textbf{83.51}&72.17&\textbf{80.57}&\textbf{62.22}\\
Selected Model&H2&H2&H2&GCN&GCN&H2&CPF(H2)&CPF(H2)&CPF(H2)&-\\\hline
GCN&53.50&43.80&23.94&28.19&\textbf{50.71}&55.20& 81.82& 71.80& 79.19&54.24\\
CPF(GCN)&54.40&43.70&24.58&27.01&46.43&57.20&82.68& \textbf{72.59}& 80.23 &54.31\\
H2GCN&\textbf{78.10}&\textbf{74.00}&25.03&28.65&44.55&67.70&80.87& 70.76& 79.84 &61.06\\
CPF(H2GCN)&77.10&70.30&\textbf{25.15}&\textbf{30.60}&41.84&\textbf{72.50}&\textbf{83.51}& 72.17& \textbf{80.57}&61.53\\
\hline
 \end{tabular}
\end{table}

\subsection{Analysis of Feature Thinning at Nodes with High Average Degree}
We investigate whether the features at nodes with high degree are thinned when aggregating the features of neighboring nodes.
We illustrate the relationship between degree and variance in Figure \ref{fig:bunsan} by calculating the variance of the feature vector which aggregates the features of the 0 hop (self-loop), 1 hop, and 2 hop neighboring nodes for all nodes on the graph.
We used Cora (in Figure \ref{fig:bunsan}(a)) and Squirrel (in Figure \ref{fig:bunsan}(b)) based on the data splits provided by H2GCN \cite{h2}, and set the horizontal axis as the degree of each node and the vertical axis as the variance of the feature vector.
Note that points of 0 hop and 1 hop  use the degree of 1 hop away, whereas points of 2 hop use the degree of 2 hop away.

\begin{figure}[H]
\begin{center}
\includegraphics[width=10.0cm]{corasqui_tate_title4_en.eps}
  \caption{Relationship between degree and variance}\label{fig:bunsan}
\end{center}
\end{figure}
\noindent
On both datasets, the variances of 0 hop are high, and the variances of 1 hop tend to have low value compared to 0hop.
Furthermore, in the case of 2 hop, the degree is much larger than 1 hop, and the variance is lower than 1 hop.
Comparing (a) and (b), there are many 1 hop nodes which have variance exceeding 0.01 in (a), whereas most nodes have variance below 0.01 in (b).
In addition, regarding 2 hop, most nodes in (a) have degree between 0 and 200 and variance between 0.0 and 0.005; in (b), on the contrary, there are many nodes with degree exceeding 200 and variance close to 0.0.\\

\noindent
As observed from a result of above, the variances of the feature vector become low if nodes have a high degree.
The feature vector with low variance means that the amount of features of vector is flattened, and H2GCN becomes difficult to distinguish the difference between features of aggregated vectors compared to 0 hop vectors.
Therefore, we prove that H2GCN is far from the optimal model for graph data with high average degree.

\subsection{Analysis of Label Propagation Suitability}
We investigate the reason why CPF perform poorly compared to its teacher model on graph data with a low edge homophily ratio.
Figure \ref{fig:cpf} illustrates the relationship between the edge homophily ratio per node and $\alpha$ which is a learnable balance parameter of trained CPF for each node.
We use Cora (in Figure \ref{fig:cpf}(a)) as a dataset with a high edge homophily ratio, and Texas, Wisconsin, Cornell (in Figure \ref{fig:cpf}(b)) as a dataset with a low edge homophily ratio on the data splits provided by H2GCN \cite{h2}.
The edge homophily ratio per node for node $x$ indicates the ratio of edges connecting to a node which has the same label among edges connected to a node $x$.
The parameter $\alpha\in [0, 1]$ in CPF is a balance parameter that adjusts the output ratio of MLP and LP for each node.
Specifically, $\alpha$ close to 1.0 means that LP is more effective than MLP, and $\alpha$ close to 0.0 means that MLP is more effective than LP on CPF.

\begin{figure}[H]
\begin{center}
\includegraphics[width = 10.0cm]{Ronbunzu_tate3_en.eps}
  \caption{Relationship between homophily ratio at node level and alpha value}\label{fig:cpf}
\end{center}
\end{figure}
\noindent
Comparing (a) and (b) in Figure \ref{fig:cpf}, many nodes assign large weights on LP in (a), many nodes assign large weights on MLP in (b).
This result indicates that LP is not  effective for graph data with a low edge homophily ratio compared to MLP.
In the case such as (b), CPF uses almost MLP to train the student model.
However, MLP is less expressive than teacher GNN model, so it is difficult for CPF to achieve better performance than its teacher model.
Furthermore, especially in H2GCN which perform well on graph data like (b), it already has a mechanism like MLP, there is almost no information from feature vectors that can be captured only by MLP.
These results prove that selecting CPF for graph data with a low edge homophily ratio does not improve the result, but rather likely to decreases compared to its teacher model.

\section{Conclusion}
In this paper, we have proposed a hierarchical model selection framework (HMSF) by analyzing the effectiveness of GNN models based on average degree and edge homophily ratio.
We have demonstrated that HMSF can select an appropriate model and achieve the highest mean accuracy in evaluation experiments across multiple benchmark datasets.
We have also proved that the variance of the feature vector which aggregates the features of neighboring nodes takes small value on nodes with high degree, and LP in CPF is not effective on graph data with low edge homophily ratio.\\

\noindent
In the experiment on the setting of data splits with low training data, GCN or H2GCN is selected for Actor, Squirrel, and Cornell in our HMSF, but CPF(H2GCN) outperforms its teacher model H2GCN even on graph data with heterophily.
As a future work, we consider another indicator to select the appropriate CPF model for low training data.
In addition, we need to improve the effectiveness of HMSF by incorporating new GNN models which achieve state-of-the-art performances.

\small
\bibliographystyle{junsrt}

redpajama_set_name: RedPajamaArXiv


text:
France vs. Portugal betting tips & specials, EURO 2016 Final
Betting Planet > Sports Betting Tips > France vs. Portugal betting tips & specials, EURO 2016 Final
EURO 2016 Final – 9pm local time on Sunday, July 10, 2016 (5am AEST Monday) at Stade de France, Saint-Denis
Watch it live and free on SBS
France Euro 2016 form
GS – Defeated Romania 2-1
GS – Defeated Albania 2-0
GS – Drew with Switzerland 0-0
16 – Defeated Republic of Ireland 2-1
QF – Defeated Iceland 5-2
SF – Defeated Germany 2-0
Portugal Euro 2016 form
GS – Drew with Iceland 1-1
GS – Drew with Austria 0-0
GS – Drew with Hungary 3-3
16 – Defeated Croatia 1-0 (AET)
QF – Defeated Poland 1-1 (5-3 P)
SF – Defeated Wales 2-0
Win-draw-win odds
France – $2
Draw – $3.10
Portugal – $4.50
Odds by Sportsbet.com.au
After four fascinating weeks of highlights, lowlights, upsets and triumphs, it's time to decide the 2016 European Championship once and for all.
France, despite a few nervy moments in the group stage, have lived up to every expectation.
Since coming from a goal down to dispatch a plucky Ireland side in the round of 16, les Bleus have looked like the titleists elect.
Their big guns have fired in a big way, and none more so than Antoine Griezmann.
The Atletico Madrid forward netted twice in the semifinal win over Germany to take his tournament tally to six goals – three more than his closest rivals for the Golden Boot.
It was a depleted German outfit that trotted out in Marseille, missing mainstays such as Mario Gomez, Mats Hummels and Sami Khedira through a mix of injuries and suspension.
But the French could argue they've gone through the entire tourney without their best XI, given Lassana Diarra, Jeremy Mathieu, Mamadou Sakho and Raphael Varane withdrew in the weeks prior.
They are also without Karim Benzema and Mathieu Valbuena, whose off-field issues saw them excluded from consideration, while Hatem Ben Arfa was controversially overlooked.
That hasn't stopped France scoring 13 goals in only six games. The only other team to reach double figures so far is Wales, with 10.
Now on the verge of steering his nation to a record-equalling third European Championship title, Didier Deschamps reckons his charges have already made history – no matter what happens in Paris on Sunday.
"It was a long time since we'd beaten Germany but that didn't mean anything today. We're into the final and there's a trophy up for grabs," the France coach said after the semifinal win on Thursday night.
"I'm delighted for the players, it was a very tough game against a team who made us suffer but we suffered together.
"When you see the passion and the fervour in the stands around the ground… This team has everything it takes to be loved. It's a great emotion – we had to suffer but never gave up and that's great for me.
"This is a great story – the players wrote history by knocking out Germany. We don't have the power to solve people's problems but we can generate emotions so they forget their worries. We generate passion and fervour, we can see that."
France will look to harness that home-crowd energy on Sunday when they encounter one of European football's great enigmas.
In any other year, with any other qualification format, Portugal would not have made it this far.
Before their semifinal clash on Wednesday, the Seleccao had not won a single game in regular time at the Euro 2016 finals.
They failed to beat any of Austria, Hungary and Iceland in the group stage, and yet were rewarded with a spot in the soft side of the knockout draw.
It took a clumsy extra-time winner to get past Croatia in the round of 16 and a penalty shootout to eliminate Poland in the quarters.
Many expected more of the same in the last four when they faced a free-scoring Wales side led by Gareth Bale.
But it was another Real Madrid star who grabbed the headlines as Portugal, at long last, decided it was time to get a result in 90 minutes.
Cristiano Ronaldo turned the game on its head early in the second half, opening the scoring with a powerful header before setting up Nani (albeit fortuitously) to poke home three minute later.
CR7 now has three goals for Euro 2016 – his best haul at a major tournament – and is rated a $6.50 chance at Sportsbet to bag the first in the final.
But not everybody is dancing to Ronaldo's tune.
The win over Wales did nothing to assuage the notion that Madrid's greatest ever goalscorer is a one-man band whose self-serving play is ultimately detrimental to Portugal's football.
As former England defender Paul Parker put it in his Eurosport column: "I can't find a good reason why Portugal winning the tournament would make me feel good about life or my Sunday dinner.
"All we are hearing about Portugal is Cristiano Ronaldo when you have 10 other players on the pitch. He has always made it all about him when it is a team sport.
"They are a very poor team who lack a striker."
Euro 2016 final betting tips
France to win – $2 at Sportsbet
Correct score 2-0 France – $9 at CrownBet
France should do what they did at the 1998 World Cup and lift the trophy on home soil. Their attack is too good, their midfield too strong and their goalkeeper, Hugo Lloris, a very hard man to beat. Portugal have talented attacking players, but is there any coherent system there besides 'give it to Ronaldo'? France to get it done in regulation time.
Antoine Griezmann goalscorer odds
First goalscorer – $4.60 at CrownBet
To score a hat-trick – $34 at CrownBet
Griezmann's form at France 2016 has drawn comparisons to the great Michel Platini, who scored nine goals at the 1984 Euro finals. The Atleti man needs a treble on Sunday to match that record. He might not quite manage that much, but he's a red-hot prospect to get on the scoresheet.
Cristiano Ronaldo specials online
Portugal's first goal – $4 at Sportsbet
First goalscorer + Portugal win – $7.50 at Sportsbet
Like him or not, Ronaldo is a wizard in front of goal. He's the most complete finisher in football and perhaps the best header of a ball going around. If Portugal pull off something special, the Madrid man will be involved in one way or another.
Possible France & Portugal team lineups
France: Lloris, Sagna, Umtiti, Koscielny, Evra, Matuidi, Pogba, Sissoko, Payet, Griezmann, Giroud.
Portugal: Rui Patricio, Soares, Pepe, Fonte, Guerreiro, Pereira, Mario, Sanchez, Adrien Silva, Nani, Ronaldo.
European Championship, FR, PT, soccer
Previous post Punters fancy France & Ronaldo in EURO 2016 final betting
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redpajama_set_name: RedPajamaCommonCrawl


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text:
Wind and solar resources are abundant throughout the world, but their dependence on uncontrollable factors such as weather has been one of the biggest impediments to their development.
However in recent years, countries such as Denmark and Germany have pioneered and adopted strategies, planning tools, and technologies to cost-effectively and reliably manage these integration challenges.
Sere Wind Farm in South Africa | Photo by International Rivers
By leveraging these strategies, practices, and technologies early in the evolution of their electricity grid systems…
African and other developing economies could leapfrog to clean, reliable, low-impact, and cost-efficient energy systems and avoid increasingly riskier investments in coal and large hydroelectric dam projects.
In this paper, we examine several recent studies to assess the prospects for renewable energy development in developing countries, with a particular emphasis on African countries.
We first demonstrate that the changing economics of wind and solar technologies are making them increasingly cost-effective, while coal and large hydroelectric projects are becoming riskier investments due to significant cost overruns, uncertainty in future fuel costs, and climate change.
We then present the evidence of large potential for wind and solar resources and discuss opportunities for their development with low social and environmental impacts.
Finally, we highlight different emerging strategies that are being deployed around the world to efficiently manage the variability and uncertainty of large-scale grid-connected wind and solar energy generation.
Comparison of annual average capacity factors (A) and adjusted capacity factors for the daily top three peak hours (i.e., capacity value) (B) for wind zones in the Eastern and Southern African Power Pools.
Some wind zones with high annual average capacity factors for wind generation (yellow to red) have low capacity value (blue to yellow), while some areas with relatively low annual average capacity factors (blue to yellow) have high capacity value (yellow to red). This figure is reproduced from Wu et al, 2015.
Report | Rights of Rivers
Powering Conflict: An Analysis of Business and Human Rights Responsibilities in the Salween Basin
Rivers for Recovery: Protecting Rivers and Rights Essential for a Just and Green Recovery
Consultation Protocols: Instrument for the Defense of Territories and Rights
Protocolos de Consulta: Instrumento para a Defesa de Territórios e Direitos


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A public ceremony to honor the most recent three Washington state Medal of Honor recipients from Operation Enduring Freedom will be held at the Capitol Rotunda on April 2, 2014 at 11:00 am.
SFC Leroy A. Petry, SSG Ty M. Carter and CPT William D. Swenson all reside in this State and will be joined by at least two of the four Medal of Honor recipients also calling Washington home: COL Joe Jackson, Vietnam; MAJ Bruce Crandall, Vietnam; PFC Thomas Kinsman, Vietnam and PFC Wilburn Ross, World War II.
Governor Jay Inslee will be joined by MG Terry Ferrell, the Commanding General of the 7th Infantry Division at JBLM as keynote speakers.
The 56th I Corps Army Band brass quintet and the I Corps Color Guard will be participating, and the event's Master of Ceremonies will be the director of the Washington State Department of Veterans Affairs, Lourdes E. Alvarado-Ramos.
The names of Petry, Carter and Swenson will be unveiled at the Washington state Medal of Honor Monument on the Capitol campus following the indoor ceremony, bringing the total of MOH names on the monument to 91 and the total of living recipients in Washington State to seven.
See links below to read their Medal of Honor citations.
The last name added to the State's Medal of Honor Monument was that of Major Bruce P. Crandall on April 16, 2007.
As of this release there are only six living Medal of Honor recipients from Operation Enduring Freedom in the United States.


Wonderful spacious 3Bd/2.5Bth townhome with great canyon view. Kitchen offers granite counter tops in and lots of storage. Fireplace in the living room, upstairs full size laundry, Dual pane window throughout the house. Large master bedroom with dual sinks in bathroom. Enjoy the canyon view, with your morning coffee, from your private patio. Walking distance to Elementary school and Calavera Hills Park. Complex features large pool and Spa.
Provided By: Cornerstone Estates & Homes.


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North America Gets Dawn, Heroes Of Mana This Summer
News / by Chris Chester /
Square Enix has confirmed plans to ship Dawn of Mana for the PlayStation 2 on May 22 in North America. This game marks the next chapter in the company's World of Mana collection of games. The RPG will mark the series' first foray into 3D, and will include "new characters and environments" as well as "familiar monsters and elemental spirits from previous Mana titles."
In addition, the game will, for a limited time, be offered alongside a Breath of Mana sampler disc featuring musical selections from the soundtrack originally released in Japan. While specific selections have not been disclosed, it's probably safe to assume that the disc will include Dawn of Mana's title track by Ryuichi Sakamoto. This gift will be given to those who pick up the game from Amazon, Game Crazy, GameStop and EB Games.
In addition, Heroes of Mana, a new real-time strategy game developed exclusively for the Nintendo DS, will ship in North America during late summer 2007. Apart from this different take on the evolving Mana tree, the game will also feature a ranking system tracked by the Nintendo Wi-Fi Connection.


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US announces sale of 21m acres of oil and gas, offshore Texas
The US Department of the Interior has announced it will offer more than 21 million acres at a lease sale for oil and gas exploration and development, offshore of Texas.
The proposed Lease Sale 233, which is expected to take place in New Orleans in August 2013, will be the third offshore auction under the Obama administration's Outer Continental Shelf Oil and Gas Leasing Program for 2012-2017.
The Interior said the latest sale will include all available unleased areas in the Western Gulf of Mexico Planning Area.
It will include 3,953 blocks, spread across more than 21.1 million acres, located from nine to 250 miles offshore, in water depths ranging from 16 to more than 10,975 feet (5 to 3,346 metres).
According to the Bureau of Ocean Energy Management (BOEM), the proposed sale is expected to produce about 116 to 200 million barrels of oil and 538 to 938 billion cubic feet of natural gas.
The sale follows the first two auctions in the current Five Year Program, which includes a 39m acre sale held in March, attracting more than $1.2bn in high bids, and a 20m acre sale held in November 2012, attracting nearly $134m.
Commenting on the lease sale, Interior Secretary Sally Jewell said that the Gulf of Mexico is a cornerstone of America's energy portfolio.
"This proposed lease sale reflects President Obama's continued commitment to safely and responsibly develop our domestic energy resources to help create jobs, foster economic opportunities and reduce America's dependence on foreign oil," Jewell added.
"Interior Secretary Sally Jewell said that the Gulf of Mexico is a cornerstone of America's energy portfolio."
Land and Minerals Management Acting Assistant Secretary and BOEM Director Tommy Beaudreau said: "This proposed sale is another important step to promote responsible domestic energy production through the safe, environmentally sound exploration and development of the nation's Outer Continental Shelf energy resources."
"We are advancing the administration's goal of continuing to safely increase vital oil and gas production, while encouraging diligent development and a fair return to taxpayers for these valuable public resources that belong to all Americans," Beaudreau added.
The Interior department proceeded with the auction after extensive environmental analysis, public comment and consideration of the best scientific information available.
Image: Interior Secretary Sally Jewell. Photo courtesy of United States Department Of The Interior.
Lucas Energy to Sell Oil and Gas Properties in Eaglebine and Eagle Ford Trend in Texas
Caerus Oil and Gas to Acquire Natural Gas Assets from PDC Energy for US$200MM to Increase its Asset Base in Northeast Colorado and Kansas
Freeport - McMoRan to Acquire Plains Exploration & Production for US$6.9 Billion and 64% Stake in McMoRan Exploration for US$2.1 Billion to Add Oil and Gas to its Natural Resources Portfolio
United States Department Of The Interior
Equinor looking to divest stake in Nigerian offshore field


Kept'cha waiting, huh? Well strap your butts in, because I have an I D E A.
I actually have an entire document for this concept that's been fermenting for a while, but it's through the lense of a Sonic fangame, and considering the current situation it'd be best to redress it for the sake of survival. And frankly, I'm too lazy right now!
In any case, if you couldn't tell by the title, it's YET ANOTHER RPG, this one probably starring furries. It would follow a gang of athletic heroes as they are unknowingly sucked into another dimension, filled with weirdness, zaniness, and all SORTS of expandy goodness! The King and Queen of this land want to fatten them up for dinner, so they must fight their way out and hopefully stop this abduction scheme once and for all!
As mentioned before, this will play more like Earthbound/Mother 2 than anything else. You run all over an overworld and run into enemies on the map, with the opportunity to back-attack or BE back-attacked. Battles, on the other hand, are more akin to final fantasy, with both sides of the conflict visible on the battlefield.
Fitness: The health stat, plain and simple. It's how much health a character has.
Brains: The mana bar by another name. It is the amount of Red Star Power (mana by another name) a character has be default.
Muscle: The physical attack stat, dictating how much damage physical attacks deals to the target.
Bones: The physical defense stat, dictating how much damage physical attacks deals to themselves.
Wits: The stat that determines how much power special moves have.
Smarts: The stat that calculates how much resistance one has to special moves.
Speed: Speed! Imagine that! It determines in what order characters will perform during a round.
The classes I have in mind are a mage, a shaman, a fighter, a tinker and a monk. Feel free to guess which stats go to which!
Of course, the main selling point and stand-out mechanic of this game would be The Expansions. Various items, attacks and hazards in the game will cause the victim to expand in one of four ways. Each form of expansion will decrease two stats and in crease two others, while cutting into one's speed, requiring one to think differently about how that character will operate during the battle.
There is also a limit to how much one can expand. Both party members and enemies can bloat up to three stages, with the stat effects growing stronger with each stage, before they reach their limit and explode, knocking them out instantly.
Stage one: Bloated; looks a bit pudgier than normal. Boosts two stats by ¼, decreases others by ¼. Reduces speed to 3/4.
Stage two: Ballooned; quite big, round and full. Boosts and de-boosts increase to 1/3. Speed is reduced to 2/3.
Stage three: Burstable; little more than a massive struggling globe. Boosts and de-boosts are increased to ½ of a character's normal stats. Speed is reduced to ½.
Force Feeding: The subject is fed massive amounts of food in short amounts of time, making them quite fat in the process. This boosts muscle and bones, but weakens wits and smarts.
Gas Inflation: The subject is pumped up with massive amounts of gas, turning them into a living balloon. This boosts one's offenses, but cripples one's defenses.
Berry Bloating: The subject becomes bloated with fruit juice by any number of means, slowly blowing them up into a massive fruit. This increases wits and smarts, but weakens muscle and bones.
Water Logging: The subject is filled with excessive amounts of liquid, rendering them a water balloon. This powers up both defenses, but lowers one's offenses.
These ailments are all mutually exclusive. Someone who's gas-inflated cannot be force-fed, for example. Party members aren't the only ones who can be afflicted; many enemies can be expanded as well.
Some moves can only be accessed when expanded in a certain way, and will be greyed out if such requirements aren't met.
One can gain stages of expansion both in battle and on the overworld, putting items that add stages into consideration. Being expanded to any stage doesn't affect overworld interactions, beyond the danger of being an easier target for on-field enemies.
I'll update this post as my funk dies down. Until then, feel free to comment, critique, and suggest!
Indeed very ambitious, but I hope this sees completion!
Here's a suggestion. Perhaps through having a character be affected by multiple expansions over time, they can unlock even higher stages of expansion, giving them further growth and changing how previous stages look to show how their limits have increased?
I have to agree, the status effects are a good idea, but progression of a character would probably be desirable rather than only having temporary effects. I've played other things that have similar systems, and while they are fun, adding that character progression would add a bit more to the experience. it could even be part of the leveling up, and characters could be based around certain aspects, like a fat-based character and/or an inflation-based character, etc.
that could make for classes in their "element", giving bonuses for effects familiar to the class, and even a rock-paper-scissors system! for example, the fat character having bonuses for being fat and ways to get fatter, but if, say, they're inflated, they might suffer a bit more of a debuff, and have a tough time getting fat again, or need to deliberately fatten themselves to reverse the effect.
This sounds like a fun game. I would actually look into playing this if and when it gets made.
Hey folks! I'm sorry to say that isn't a game I'm developing, per say, just shooting out ideas to germinate and grow.
But nevertheless, the feedback you've all given is fantastic. My entire goal was to encourage being expanded, and what you've brought up would do exactly that.
I imagine something more akin to the jobs system in Final Fantasy III (or dress spheres from X-2, if you're more familiar with those). Anyone in the party can take on a desired class at any time, with their own dedicated type of expansion to them. As sanity.exe suggested, this could have a rock-paper-scissors style to things, with each type having advantages and disadvantages over the other (The druid, a berry-expansion class, being strong against inflation but weak to fat).
And just how much they can expand can be a stat in its own right. There'd still have to be a limit on HOW many stages would be allowed, but a few more wouldn't hurt anybody.
With the types of expansion now basically being Pokemon types, this could open up things to expand or limit the number (water inflation's a stretch in my eyes). Rock-paper-scissors or rock-paper-scissors-lizard-Spock? Feel free to say!
I already have ideas for class types (warrior=fat, mage=inflation, rogue=inflation, druid=berry, monk=fat, shaman=berry, tinker=inflation, paladin=fat, etc).
Thank you ALL once again for the feedback! I'll update as soon as possible!
I like the varied class systems many games have adapted, but what if instead of classes having an element, you could have a destiny-esque setup, with a few archetypes like warrior, mage, rogue, etc. BUT with each broken up into subtypes, like of fat, inflation, etc.
each could have an element they are most comfortable with, but a warrior could utilize inflation skills, or a fat mage, or a berried rogue. that could essentially encompass the multitude of different classes and elements, but keep them sectioned into easier trees and subtrees.
Oh, you're gonna GET IT. Right now, I'm just thinking of the holy Fighter/Mage/Thief triad, each able to go on their own, but also with a specification/sub-class for each of the expansion types.
The warrior, for instance, could specialize as a berserker (fat), paladin (berry), cavalier (inflation), or as nothing at all. By choosing no one path, they are safe, but are limited. By specializing, they can do serious business, but open themselves to a key weakness.
Of course, if the ambition sensors spike again, I can throw in other base classes (tinker, man. I wanna DO IT).
The only feedback I have at this point is that, while I like the stat names, I am sort of bothered that three of them (Brains, Wits, Smarts) are basically synonyms. Granted, having put some amount of time into designing my own stat blocks (that will likely never be used, of course), I can appreciate how annoying it is to pick out names for things that are both descriptive and unique.
I think the aim with Wits and Smarts was to be a sort of "Wisdom" and "Intelligence" stats respectively without over using those naming conventions. Brain to me at least sounds different to be distinct from Wits and Smarts, a sort of difference like how Fitness and Muscle have. They seem synonymous but just like life, just because someone has great muscles doesn't inherently mean their fit.
But yeah, naming things is hard. If a better name could be found that be great, but if not no big worries from my end Though it is a trap to subdivide traits as it can look messy on paper (Lookin' at you D&D 3-3.5e). But the current stat pool is small enough to not have that much of a concern imo.
I like this idea. Just wish there were other things like the ability to select a species (humans included).
Hello again, folks! All I have to say right now is that I've already renamed the Wits and Smarts stats to better separate them from Brains. In regards to the traits-for-each species idea, I'm afraid that I have an established cast of characters instead of custom avatars in mind, so I didn't really have species-traits in mind.
But I'm still open to suggestions! If previous critiques are any indication, this is gonna be a long stream of good ideas!
Finally translated that document to make it perfectly copyrighted material free I promise.
Took most of the feedback in mind during this rewrite. Feel free to critique this sucker!
Hmm interesting looks good this is a fetish game so an amazing plot isn't really needed the characters are fine of course you'll add more than 5 playable characters.


The villa was perfect for our family get together. The trip had been planned forever; finally we were all gathered in one heavenly spot. Friends and family together equal a really good time. It does not matter that some of us are definite Wrinklies while others are somewhat wrinkle free. We enjoy one another's company – as it should be. The Wrinklies are known to go to bed later and rise earlier than some of the younger set on a number of occasions – party-animals we are proud to say.
Leaving Palma and heading into the hinterland was exciting. The weather was very warm and windfree. Our phones gave us directions on Googlemaps. We never had this when we were younger, nor did we have the benefit of a satnav in a rented car – gone are the days of the navigator trying to make a paper map small enough to read without knocking out the driver or enduring the added non-benefit of feeling decidedly car-sick.
The road took us down the center of a vast plain dotted with mud coloured houses with terracotta tiled rooves. Grove upon grove of almond trees stretched for miles ahead.
Eventually we took a route through a small village and turned down a very narrow road lined with stone walls. The sharp bends, often with tall cypress trees and creepers abundantly covering everything made it quite a hair-raising journey. Finally we came to the villa's impressive entrance gates and all too soon we were off-loading and settling into our dream place.
After a quick recce, of the house and garden we would call home for the next week, into the pool we went. G&T's, snacks and total immersion into life in a villa in Mallorca – Bliss!
Parties in the evening and exploring during the day – nothing could be more heavenly. Each day our "party planner" had designated a theme for us. We celebrated a Wrinklie's 70th birthday with an all-white party; it happened to be full moon.
The villa is set in a large garden with rolling lawns, huge date palms and lovely flowering plants – I would not be a Wrinklie if I did not post some flower pictures.
Naturally when a family of foodies gets together there is a lot going on in the kitchen, a good sized kitchen is a must. This family prefers to cook over an open fire. Breakfast, lunch or supper – we all enjoy a good "braai." (BBQ) Cooking over an open fire means wherever we stay the first criteria to check is – is there a good and big enough fireplace! This villa offered perfect facilities for us cooks.
The second criteria is – is the dining area big enough for the family and friends? In the case of this villa the dining area is fantastic. A large table is set under a sprawling grape vine right off the kitchen and right next to the braai area. We were in 7th Heaven.
Fun and adventures over the week kept us floating; Oh and plenty of a very good white wine!


Please support the MFFS mission! On April 27, 2019 FYI Media will be partnering with MFFS to host a fundraising telethon. Moving Forward Family Services is counting on your support. Please give whatever you can!
During National Volunteer Week, we take time to recognize Canadians who give their time and skills to help build up their communities and make a difference in the lives of others.
Ramma Kher's new book on struggles, eventual triumphs.
Group exhibition will feature artists from India whose lens-based practices explore gender, religion and sexual identity.
Reaffirming our commitment to India's culinary roots, ITC Maratha brings you the 'Quintessentially Maharashtrian' feasts. Enjoy the local flavours of Maharashtra, with an exhaustive range of recipes that showcase the culinary influence of the region, crafted into an extraordinary compilation.
What Inspires Your Home Decor Style?
(NC) It's no surprise that designers are very visual people, which is why they often look to the world around them for design inspiration. From old photographs to runway trends, inspiration is everywhere we turn.


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Q: Unable to run Airflow 1.10 on Mac OS The latest version of Airflow 1.10 requires some setup for the logger.
I carefully followed the instructions and created a config folder with airflow/config_templates/airflow_local_settings.py and added it to PYTHONPATH.
Unfortunately when running airflow I get this error:
ValueError: Unable to configure handler 'processor':
  Cannot resolve 'airflow.utils.log.file_processor_handler.FileProcessorHandler':
    dlopen(/Users/arikliber/miniconda3/envs/py36-6/lib/python3.6/site-packages/psutil/_psutil_osx.cpython-36m-darwin.so, 2):
      Symbol not found: ___CFConstantStringClassReference
  Referenced from: /Users/arikliber/miniconda3/envs/py36-6/lib/python3.6/site-packages/psutil/_psutil_osx.cpython-36m-darwin.so
  Expected in: flat namespace
    in /Users/arikliber/miniconda3/envs/py36-6/lib/python3.6/site-packages/psutil/_psutil_osx.cpython-36m-darwin.so

Everything was installed on a fresh miniconda environment.
Anyone got Airflow 1.10 on a Mac OS (I have 10.13.6) and mind sharing the workflow?

A: I used the brew installation of python 3.6, a venv from -m venv and installed airflow v 1.10 by checking out the source at branch v1-10-stable and installing with pip -e <path/to/apache-airflow-src-dir>/ After installing I used pip check to find that click 7.0 had been installed by flask but that flask-appbuilder didn't like that, so I had to unstall that and install click 6.7. You could try and modify the setup.py to put flask-appbuilder before flask in the ordered requirements.

redpajama_set_name: RedPajamaStackExchange


When the history of the evolution of our liberal constitutional order is written, we must recognise not only those who participated in the drafting of the Constitution but also those who systematically and conscientiously prepared the legal infrastructure for the interpretation and application of the Constitution.
Dr Seth Bimpong-Buta indeed belonged to both categories of constitution makers.
His sterling contributions to Ghana's constitutional and legal order traversed four critical areas.
First, he was an eminent legal educator.
He served as Director of legal education and Head of the Ghana School of Law (Makola) for many years with distinction.
In this capacity, he superintended the transition of law graduates into admission to the Ghana Bar through a rigorous practical training programme.
Second, the legal profession of Ghana has benefited immensely from Dr Bimpong-Buta's outstanding legal scholarship.
His illuminating writings on the Constitution and interpretation and other subjects have been widely cited by the Bench, Bar and law students.
His legal scholarship earned him an election to the fellowship of the Ghana Academy of Arts and Sciences (FGA) and a Noma literary award for distinguished writers.
Third, he made a major contribution to the evolution and application of the 1992 Constitution by participating in the deliberations of the Consultative Assembly, writing an erudite treatise on the Constitution and facilitating the interpretation of the Constitution by publishing reliable and timely reports of Supreme Court decisions on the Constitution.
I had the honour of presiding over launching of Dr Bimpong-Buta's magnum opus on the 1992 Constitution.
Perhaps the most enduring legacy of Dr Bimpong-Buta to Ghana was his editorship of Ghana Law Reports and, after retirement, of the Supreme Court Law Reports.
It can be safely asserted that the interpretation of the 1992 Constitution could not have been meaningfully undertaken by the Supreme Court without his dedicated and single handed production of a steady stream of Supreme Court Law Reports.
This critical service made a unique contribution to the evolution of the jurisprudence of the Courts in the interpretation, clarification and elaboration of the basic concepts of the 1992 Constitution.
Bimpong-Buta therefore eminently deserves to be recognised as one of the principal founding fathers of the Constitution and an architect of the legal infrastructure of the Constitution.
As a person, Bimpong-Buta struck me as a quiet and modest but extremely capable colleague in the Ghana Academy of Arts and Sciences and the Council of the Ghana Arbitration Centre.
He was kind enough to present me with complimentary copies of his Supreme Court Reports.
We shall sorely miss his deep commitment to the law and our constitutional order and his stoic sense of duty.
My deepest sympathies go to his family, both nuclear and extended.
The popular conception of legal practice usually depicts high drama in which two adversaries, peculiarly attired in wig and gown, engage in a battle of words and wit before a judge.
Many aspiring lawyers are attracted by the thrill and glamour of this drama.
The uninitiated is therefore unaware of vast areas of non-contentious legal practice in which dedicated lawyers make critical contributions to law and society without the theatrics of litigation in the court room.
One such critical area is legislative drafting, and Nii Tettey Chinery Hesse was one outstanding practitioner in this field.
He devoted his entire professional life to legislative drafting.
He could have become a judge, an Attorney-General or a high profile public officer.
But he studiously eschewed the glare of publicity, preferring to work unobtrusively and conscientiously in his "chambers" to produce critical legislative and constitutional documents not only in Ghana, but in Sierra Leone, Zambia and Uganda.
I was privileged to be Nii Tettey's colleague in three phases of my professional life.
I recall that when I was a young Assistant State Attorney in 1960-61, the then Attorney-General, Geoffrey Bing QC, proudly introduced Nii Tettey to me as a bright up and coming law officer who had drafted the Interpretation Act of Ghana.
A decade later, as Solicitor General and later Deputy Attorney-General (1969-77), I witnessed the prodigious legislative output of Nii Tettey, then Chief Parliamentary Draftsman.
He supervised a small band of dedicated parliamentary draftsmen and worked for long hours to produce major enactments crucial to the administration of the country.
His industry, meticulous attention to detail and his appreciation of the larger implications of the legislative process were exemplary.
My third professional collaboration with him took place in 1991 when we both served as members of the Committee of Experts charged with the formulation of proposals for the 1992 Constitution.
As Chairman of the Committee, I observed Nii Tettey at his professional best, patiently drawing our attention to lessons from his international experience, carefully studying the constitutional formulations and meticulously translating them into sophisticated legal drafting.
Nii Tettey's role in the making of the 1992 Constitution continued during the deliberations of the Consultative Assembly in 1991-1992.
When the Consultative Assembly rashly dispensed with the services of the Committee of Experts as technical advisers, it was ultimately compelled to rely on the expertise of the two legislative draftsmen in our Committee, namely, Nii Tettey and Sabina Ofori-Boateng, in drafting the final constitutional document.
The contribution of these two outstanding public servants to the drafting of the 1992 Constitution is yet to be properly recognized by this country.
Nii Tettey's professional services were not confined to Ghana.
He served with distinction as consultant in legislative drafting in Sierra Leone, Zambia and finally in Uganda.
In Uganda, he played a major role in the drafting of that country's Constitution.
A grateful Government of Uganda has already held a memorial service in Nii Tettey's honour.
On the personal side, Nii Tettey literally loved to surround himself with people.
He was extremely affable, kind and compassionate.
A social encounter with Nii Tettey, enlivened by his irrepressible sense of humour, was an unforgettable experience. When my international assignments took me to Uganda in 1995, Nii Tettey graciously hosted dinners on two occasions to facilitate my interaction with key personalities in that country.
Back home in Ghana, his birthdays were always celebrated with grand and convivial reunions of family and friends.
At his last birthday party, he promised another such party if, by God's grace, he attained 88. Alas, that promise did not materialise. However, we are taught by our common faith that a more joyous and eternal reunion in the company of our Lord and Maker awaits us.
And so, dear Nii Tettey, Fare Thee Well. Rest in peace till we meet again.
The writer is also known as Dr. S.K.B. Asante. He is the Omanhene of Asante Asokore.


With the queer classic "La Cage aux Folles" as its centerpiece, the Castro Theatre spotlights the films of actor, writer, director Ugo Tognazzi (1922-90) on Sat., April 27.
It was another exciting week of culture-hopping over in Out There-land! Let us walk you through it.
Singaporean director Eric Khoo creates a unique family drama in "Ramen Shop." The film celebrates his world-class city's extraordinary food culture against the backdrop of a beautiful young chef's passionate search for his roots.
The 2019 edition of the San Francisco International Film Festival features a significant number of artist tributes, several with special appeal to LGBTQ filmgoers.
April showers find the Castro Theatre movie palace fulfilling several roles: presenting classic repertory in style, and hosting screenings of the 2019 San Francisco International Film Festival.
"The Brink" is Alison Klayman's scary but insidiously entertaining portrait of former Trump campaign architect Steve Bannon.
"The Gospel of Eureka," a new documentary by Michael Palmieri and Donal Mosher, is the perfect antidote to the divisiveness being inflamed by the current administration.
Springtime is in the air, and for Out There that means big-time film festivals are on the horizon.
"Hotel Mumbai" is a powerful if awkwardly timed tutorial about just how hard it is to be a courageous global citizen in an era when all decent values appear to have been swept aside.


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Scientist concedes his controversial MS therapy, once a source of great hope, is 'largely ineffective'
By Helen Branswell Nov. 28, 2017
Italian physician Paolo Zamboni has publicly concluded that a therapy he developed and dubbed "the liberation treatment" does not cure or mitigate the symptoms of MS. Nathan Denette/The Canadian Press/AP
What many hope will be the final chapter in an unfortunate saga in multiple sclerosis research appears to have been written by the scientist who started the affair in the first place.
Italian physician Paolo Zamboni has publicly acknowledged that a therapy he developed and dubbed "the liberation treatment" does not cure or mitigate the symptoms of MS. A randomized controlled trial — the gold standard of medical research — he and other Italian researchers conducted concluded the procedure is a "largely ineffective technique" that should not be recommended for MS patients.
The trial's result comes as no surprise to neurologists, most of whom felt Zamboni's theory lacked plausibility from the moment news of it exploded through the MS community in 2009.
Many of those same neurologists, though, saw their relationships with their patients fractured as belief in the liberation therapy took hold in the community of patients and their families in Canada, parts of the United States, and farther afield. Doctors advising caution against a procedure that hadn't been proved to work or even to be safe were derided as standing in the way of innovation to protect their own practices.
Dr. Jock Murray, an MS expert and retired professor from Dalhousie University in Halifax, Nova Scotia, said the history of MS is laden with incidences like the Zamboni episode — though he said this one lasted longer than most.
"Belief always trumps evidence," Murray told STAT in an interview Monday.
Bold claim of multiple sclerosis gene discovery comes under withering attack
Zamboni, who works at University of Ferrara Hospital, did not respond to STAT's email inquiry about the trial.
The vascular surgeon was moved to look for the cause of MS because his wife suffers from the disease. In 2009, Zamboni published a paper suggesting the neurological damage the condition causes was triggered by pooling of blood in the brain that was the result of inadequate drainage.
He called the condition — which he said was common among MS patients — "chronic cerebrospinal venous insufficiency," or CCSVI.
Zamboni further suggested inserting stents — which are designed to hold open arteries — into veins in the neck would increase blood drainage and improve symptoms.
His 2009 study appeared to show it worked, though skeptics were quick to note major flaws. There were no controls — a group that didn't get the treatment to use as comparators — and both the patients and the doctors knew everyone was treated. A study of this kind is open to the placebo effect and to researcher bias — both of which can result in individuals seeing something they hoped to see.
Zamboni's recently published study is the type his critics would have liked him to have done the first time — a double-blind randomized controlled trial in which neither patients nor the researchers assessing whether there were improvements post-treatment knew which patients got the actual procedure and which received a sham therapy.
The Italian researcher's 2009 claim received some prominent and unchallenging coverage from a couple of major Canadian media outlets, and the clamoring for the procedure began.
"These patients looked like they were miraculous. And that got played over and over and over. Within 72 hours, everybody in Canada — all the patients and their families — knew about this new procedure," said Murray. "So there was a great demand then for everyone to seek it out."
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Patients weren't willing to wait for science to conclude if Zamboni was correct or if his treatment was safe. Clinics sprang up in a number of countries — Poland, Costa Rica, and India among them — offering the controversial procedure. Many came home rapturously claiming benefit, only to see what others suggested was a placebo effect wane over time. A few people who underwent the procedure died.
Patients pressured the MS societies in the U.S. and Canada to advocate for access to the treatment, and in Canada, there were strident calls for the national health care system to pay for the procedure.
Meanwhile, the scientific community scrambled to try to test Zamboni's theory and the procedure. Study after study concluded his idea was wrong. In fact, a key study in 2014 concluded that the neck veins of MS patients were no different from those of people who don't have the condition. But disproving a theory takes time. And hope dies reluctantly.
Millions of dollars were spent trying to see if Zamboni was correct. Untold research hours were diverted from other lines of MS inquiry. Previously collaborative relationships between doctors and patients, as well as patients and the foundations set up to advocate for their needs, were damaged, Murray said. "It caused a lot of trouble."
Earlier this year, Canadian researchers reported on a large, randomized double-blinded study — the kind designed to protect against researcher bias and the placebo effect — they had conducted on the liberation treatment. The results, presented to the Society of Interventional Radiology's annual scientific meeting in March, showed the technique didn't work. The study is currently under review at a top-tier medical journal, lead author Dr. Anthony Traboulsee, an associate professor of neurology at the University of British Columbia, said Tuesday.
Gut germs play role in multiple sclerosis, studies show. Are probiotics for MS next?
"We hope these findings, coming from a carefully controlled, 'gold standard' study, will persuade people with MS not to pursue liberation therapy, an invasive procedure that carries the risk of complications, as well as significant financial cost," Traboulsee said at the time of presentation.
And now, Zamboni's paper showing the same has been published in JAMA Neurology.
The liberation treatment is one in a long list of therapies that have been embraced by the MS community but were ultimately found to be ineffective, said Murray. Hyperbaric oxygen chambers, snake venom, anti-coagulants, histamine desensitization therapy — all have had their turn.
The particular nature of MS — for a lot of patients, symptoms worsen, then ease before worsening again — makes it easy to appear as if a treatment has had a benefit, Murray noted.
He worried about the impact these episodes have on the MS community. "To have one after another fail, of this kind of thing, is very disheartening. And then they don't know who to believe."
Senior Writer, Infectious Disease
Helen covers issues broadly related to infectious diseases, including outbreaks, preparedness, research, and vaccine development.
@HelenBranswell
U.S. would seek global approach to updating Covid-19 vaccines,…
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Liquitex-Super Heavy Gesso. An innovative, impasto, titanium white gesso having the ability to hold sculptural shape. Provides all the attributes of traditional acrylic gesso. Ideal for thick and sculptural application with a knife or brush. This package contains one 32oz container of super heavy gesso.


McKinleyville High's Assistant Principal Danielle Carmesin and Arcata Police Department's Juvenile Diversion Counselor Crystal Perez will be facilitating an upcoming Parent Project class.
The nationally-recognized Parent Project classes will be hosted on Wednesday evenings from 6 to 9 p.m. beginning October 18 at the McKinleyville High School Library. This 11-week class series is geared to help parents improve adolescent children's school attendance and performance, reduce family conflict, curb drug and alcohol use, prevent runaways, stop violent behavior and develop strong, in-home prevention and intervention strategies for youth gang involvement.


I remember reading Bruce Tate's books on Java and EJB , which were all about anti-patterns, years ago and enjoying them. It was refreshing to read about how NOT to apply certain technologies. While there were plenty of books out there on how to do great things with Java and EJB (stop laughing), many of us using those technologies also found some not so great things we could do with them.
In much the same vain of Bruce Tate's books, I give you a Scrum anti-pattern. Most Scrum teams will use a daily task board, either real or virtual. The task board contains a Sprint's user stories and the associated user story tasks in the form of cards. It's normal to see a few columns across the board along the lines of, "To Do", "In Progress", "To Verify", "Done", etc. The idea is for a team member to put his name on a task card and then move the card into the appropriate column. This is done throughout the day and especially at the daily Scrum. Below is an example of a Scrum task board.
Pretty simple. Below is the task board gone wrong. The difference is subtle and deserves an explanation as to why this is wrong.
Notice the extra column, "Today's Work". Having additional columns isn't an issue, but this particular column presents a problem. The way the team uses this task board is to move tasks to "In Progress" when they've started work. But, since the team will sometimes start a task but then need to start on something else, the team decided they needed a column that showed what was in progress currently, thus the "Today's Work" column. For example, in the daily scrum yesterday, Joe said he's going to get Task A done. Joe moves this task to the "Today's Work" column. Today, Joe tells the team in the daily scrum that he needed to start work on Task B for another story to help it keep moving forward, so he moves Task A to the "In Progress" column and makes sure Task B is in "Today's Work", as he didn't finish that task yet. The problem is that we have impediments hiding under the "In Progress" column now. Rather than Joe saying that he was impeded from completing Task A, he moves it to a column ("In Progress") that makes it seem like all is fine. All might be fine, but how do we know? It would be better to mark Task A as impeded. Joe could then make it clear to the team why he made the decision to not complete Task A in order to start on Task B. Everyone would see that Task A is in progress and it's impeded (as opposed to simply "in progress"). The Scrum Master can help the team tackle this impediment.
Impediments aren't inherently bad, but hidden impediments are. The task board gone wrong makes it far too easy to hide impediments while also making it difficult for the team to tackle properly. We all struggle with identifying impediments, so it's important that we make sure our management tools are setup to help us make impediments visible and hard to miss.
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Reblogged this on Jrolofer's Blog…@elvasco1.
Hello Mike 🙂 I think you should add landscape category to your menu and tag your landscape photos as landscape too. This is beautiful!
Every colour in the rainbow is in this photo.
I just love the colors of the West!