text:
\section{Introduction}
Entity alignment is the task of finding entities from different knowledge graphs (KGs) that refer to the same real-world identity. Recently, increasing attention has been paid to the utilization of KG representation learning rather than symbolic formalism for tackling this task. Representation learning models encode KGs into vector spaces, where relation semantics of entities can be assessed by the learned embedding operations, such as the relation-specific translation \cite{TransE} or rotation \cite{RotatE}. For embedding-based entity alignment, the similarity of entities is measured by the distance of entity embeddings. It has shown great potentials in dealing with the symbolic heterogeneity problem and benefits the entity alignment task in both~monolingual and cross-lingual scenarios~\cite{IPTransE,MTransE}.

Most recently, graph neural networks (GNNs) \cite{GCN,GAT,MixHop} have emerged as a powerful model to learn vector representations for graph-structured data. In GNNs, the representation of a node is learned by recursively aggregating the representations of its neighboring nodes. A recent work \cite{K-GNN} has proved that GNNs have the same expressiveness as the Weisfeiler-Leman (WL) test \cite{WL} in terms of identifying isomorphic subgraphs. It provides the theory basis of using GNNs for entity alignment between different KGs as similar entities usually have similar neighborhood. Recently, several studies \cite{GCN_Align,KGMatching,MuGNN,RDGCN,AVR-GCN} have exploited GNNs for embedding-based entity alignment, and have achieved promising results.

\begin{figure}[!t]
	\center
	\includegraphics[width=0.9999\linewidth]{neighborhood_example.pdf}
	\caption{Non-isomorphic relational neighborhood of \textit{Kobe Bryant} in DBpedia (left) and Wikidata (right), respectively.}
	\label{fig:example}
\end{figure}

However, existing GNN-based entity alignment models still face a critical problem. As different KGs usually have heterogeneous schemas and data incompleteness \cite{KGIdentification}, the counterpart entities usually have dissimilar neighborhood structures. Figure~\ref{fig:example} gives an example. The neighborhood of the two entities referring to \textit{Kobe Bryant} is inconsistent to each other, especially containing different sets of neighboring entities. The statistics on DBpedia-based benchmark datasets for entity alignment~\cite{JAPE} also show that the majority of aligned entity pairs have different neighboring entities. Particularly, the percentages of such entity pairs reach $89.97\%$ between Chinese-English, $86.19\%$ between Japanese-English and $90.71\%$ between French-English, respectively. Different neighborhood structures would easily cause a GNN to yield different representations for counterpart entities.

The challenge of resolving this issue lies in the difficulty of fully mitigating the non-isomorphism in the neighborhood structures of counterpart entities from different KGs. Even though we assume that the two KGs are complete (the goal of MuGNN \cite{MuGNN}), due to the schema heterogeneity, the counterpart entities still inevitably have dissimilar neighborhood structures. For example, in Figure~\ref{fig:example}, \textit{United States of America} is among the one-hop (direct) neighbors of \textit{Kobe Bryant} in Wikidata. However in DBpedia, it is a two-hop neighbor. Motivated by the fact that the semantically-related information can appear in both direct and distant neighbors of counterpart entities, we propose the KG alignment network AliNet which aggregates both direct and distant neighborhood information. Specifically, each AliNet layer has multiple functions to aggregate the neighborhood information within multiple hops. To reduce noise information, we further employ an attention mechanism for the distant neighborhood aggregation to find out important neighbors in an end-to-end manner. Finally, we use the gating mechanism to combine the output representations of the multiple aggregation functions, obtaining the hidden representations in the current layer. We also design a relation loss to refine entity representations and enable AliNet to capture some special structures such as the triangular relational structure. We perform thorough experiments with detailed ablation studies and analyses on five entity alignment datasets, demonstrating the effectiveness of AliNet and each of its technical contributions.

\begin{figure*}[!t]
	\center
	\includegraphics[width=0.95\linewidth]{graph.pdf}
	\caption{Illustration of GNNs for entity alignment. In (\romannumeral1), (\romannumeral2) and (\romannumeral3), $(a, a')$, $(b, b')$ and $(d, d')$ denote three pairs of pre-aligned entities while others are entities to be aligned. The dotted lines in (\romannumeral4) means the alignment relationship.}
	\label{fig:graph}
\end{figure*}

\section{Preliminaries}
\label{sect:preli}
\subsection{GNNs}
In GNNs, the representation of a node is learned by recursively aggregating the feature vectors of its neighbors. Different aggregation strategies lead to different variants of GNNs.
\subsubsection{GCN} A very popular variant of GNNs is the vanilla GCN \cite{GCN}. The hidden representation of node $i$ at the $l$-th layer ($l\geq 1$), denoted as $\mathbf{h}_i^{(l)}$, is computed by:
\begin{equation}
	\label{eq:gcn}
	\mathbf{h}_i^{(l)} = \sigma \Big (\sum_{j\in \mathcal{N}_1(i)\cup \{i\} } \frac{1}{c_i} \mathbf{W}^{(l)} \mathbf{h}_j^{(l-1)} \Big ),
\end{equation}
where $\mathcal{N}_1(\cdot)$ represents the set of one-hop neighbors of the given entity, $\mathbf{W}^{(l)}$ is the weight matrix of the $l$-th layer and $c_i$ is the normalization constant. $\sigma(\cdot)$ is an activation function. The vanilla GCN encodes a node as the \textit{mean pooling} of the representations of its neighbors and itself from the last~layer. The input vector fed to the first layer is denoted as $\mathbf{h}_i^{(0)}$.

\subsubsection{R-GCN} Conventional GNNs only consider the node-wise connectivity in a graph and ignore edge labels such as the relations in KGs. R-GCN \cite{R-GCN} addresses this issue by distinguishing different neighbors with relation-specific weight matrices. It computes $\mathbf{h}_i^{(l)}$ as follows:
\begin{equation}
\label{eq:rrgcn}
\mathbf{h}_i^{(l)} = \sigma \Big (\mathbf{W}_0^{(l)}\mathbf{h}_i^{(l-1)} + \sum_{r\in \mathcal{R}}\sum_{j\in \mathcal{N}_r(i)} \frac{1}{c_{i,r}} \mathbf{W}_r^{(l)} \mathbf{h}_j^{(l-1)} \Big ),
\end{equation}
where $\mathbf{W}_0^{(l)}$ is the weight matrix for the node itself and $\mathbf{W}_r^{(l)} $ is used specifically for the neighbors having relation $r$, i.e., $\mathcal{N}_r(i)$. $\mathcal{R}$ is the relation set and $c_{i,r}$ is for normalization.

\subsection{Entity Alignment of KGs}
We formally represent a KG as $\mathcal{G}=(\mathcal{E},\mathcal{R},\mathcal{T})$, where $\mathcal{E}$ is the set of entities, $\mathcal{R}$ is the set of relations, and $\mathcal{T}=\mathcal{E}\times\mathcal{R}\\\times\mathcal{E}$ is the set of triples. Without loss of generality, we consider the entity alignment task between two KGs, i.e., $\mathcal{G}_1=\\(\mathcal{E}_1,\mathcal{R}_1,\mathcal{T}_1)$ and $\mathcal{G}_2=(\mathcal{E}_2,\mathcal{R}_2,\mathcal{T}_2)$. Given partial pre-aligned entity pairs $\mathcal{A}^{+}=\{(i,j)\in\mathcal{E}_1 \times \mathcal{E}_2|i \equiv j\}$ where $\equiv$ means the alignment relationship, the goal of the task is to find alignment of remaining entities via entity embeddings.

\subsection{GNNs for Entity Alignment}
Recent GNN-based entity alignment models include GCN-Align \cite{GCN_Align}, GMNN \cite{KGMatching}, MuGNN \cite{MuGNN}, RDGCN \cite{RDGCN} and AVR-GCN \cite{AVR-GCN}. GCN-Align and GMNN are built based on the vanilla GCN. RDGCN introduces dual relation graphs to enhance the vanilla GCN. AVR-GCN extends R-GCN using a TransE-like relation-specific translation operation \cite{TransE}. Before aggregation, each entity representation is translated from its tail entity representations using relation vectors. We argue that such relation-specific translation and R-GCN introduce a high complexity with the overhead of trainable parameters. More importantly, the aforementioned models do not take the non-isomorphism in KG structures into consideration. While MuGNN \cite{MuGNN} notices the structure incompleteness of KGs and proposes a two-step method of rule-based KG completion and multi-channel GNNs for entity alignment. However, the learned rules rely on relation alignment to resolve schema heterogeneity.

\subsubsection{Isomorphic structures are beneficial}
GNNs would learn the same representation for the entities that have isomorphic neighborhood structures with identical feature vectors representing corresponding neighbors \cite{PowerGCN}. We show that, in some cases, if two entities have isomorphic neighborhood structures and only partially pre-aligned neighbor representations, GNNs can also capture the similarity of other neighbors to be aligned. Figure \ref{fig:graph} (\romannumeral1) gives an example. For simplicity, here we consider a single-layer GCN. We can let pre-aligned entities have the same representation by minimizing their Euclidean distance, i.e., $\mathbf{h}_a^{(0)}=\mathbf{h}_{a'}^{(0)}$, $\mathbf{h}_b^{(0)}=\mathbf{h}_{b'}^{(0)}$ and $\mathbf{h}_d^{(0)}=\mathbf{h}_{d'}^{(0)}$ as well as $\mathbf{h}_a^{(1)}=\mathbf{h}_{a'}^{(1)}$, $\mathbf{h}_b^{(1)}=\mathbf{h}_{b'}^{(1)}$ and $\mathbf{h}_d^{(1)}=\mathbf{h}_{d'}^{(1)}$ in the ideal condition. By the mean-pooling based aggregation, we have $\mathbf{h}_b^{(1)}= \sigma(\mathbf{W}^{(1)}(\mathbf{h}_b^{(0)}+\mathbf{h}_a^{(0)}+\mathbf{h}_c^{(0)})/3) $ and $\mathbf{h}_{b'}^{(1)}= \sigma(\mathbf{W}^{(1)}(\mathbf{h}_{b'}^{(0)}+\mathbf{h}_{a'}^{(0)}+\mathbf{h}_{c'}^{(0)})/3) $, yielding $\mathbf{h}_c^{(0)}=\mathbf{h}_{c'}^{(0)}$. Finally, the counterpart entities would have the same representation. This indicates that the alignment information between entities can be propagated across the different GNN layers and different isomorphic graphs given partially pre-aligned neighborhood. However, for entity alignment between different KGs, it is impossible to require the two KGs to have isomorphic structures due to the schema heterogeneity. Figure \ref{fig:graph} (\romannumeral2) gives an example of non-isomorphic graph structures, where $c$ and $c'$ would have different representations due to their different neighborhood structures.

\subsubsection{Only structures are not enough} Conventional GNNs fall short of characterizing  some special subgraph structures such as triangular graphs. Figure \ref{fig:graph} (\romannumeral3) shows a simple example. In this case, if we use mean-pooling aggregation, we would get that $\mathbf{h}_a^{(1)}=\mathbf{h}_{a'}^{(1)}=\mathbf{h}_b^{(1)}=\mathbf{h}_{b'}^{(1)}$ because the four entities have isomorphic neighborhood structures. While in fact, $a$ and $b$ are different entities with a specific relation. We should take relations into consideration. Although R-GCN \cite{R-GCN} considers relations in the aggregation function, it relies on relation alignment for identifying similar entities. Let us review Eq. (\ref{eq:rrgcn}). R-GCN needs to learn a weight matrix $\mathbf{W}_r$ for each relation $r$. If the relations of two KGs are not pre-aligned (e.g., $r_1 \equiv r'_1$ and $r_2 \equiv r'_2$), the relation-specific aggregation functions in R-GCN would fall short of propagating the alignment information of entities.

\begin{figure*}[!t]
	\center
	\includegraphics[width=0.88\linewidth]{framework.pdf}
	\caption{Overview of the KG alignment network (AliNet) with gated two-hop neighborhood aggregation.}
	\label{fig:alinet}
\end{figure*}

\subsubsection{Compensation with distant neighborhood and relations} The schema heterogeneity of different KGs usually brings about the mixture of direct and distant neighbors of counterpart entities. To reduce the effects of the non-isomorphism in neighborhood structures, we propose introducing distant neighborhood information. We show a toy example in Figure \ref{fig:graph} (\romannumeral4). The one-hop neighbors of two counterpart entities $a$ and $a'$ are different and only contain two pairs of counterpart entities ($b,b'$) and ($c,c'$). The one-hop neighbor $d$ of $a$ is in fact the distant neighbor $d'$ of $a'$. The distant neighbors $e$ and $f$ of $a$ are aligned with the one-hop neighbors $e'$ and $f'$ of $a'$, respectively. It is intuitive that if we can include the distant neighbors $e$ and $f$ in the neighborhood aggregation for $a$, and also take $d'$ into consideration for $a'$, the GNN would learn more similar representations for $a$ and $a'$. However, as can be seen, not all the distant neighbors are helpful. Therefore, the aggregation of distant neighbors should be attentive and selective. This is the key motivation of AliNet. To further enhance the expressiveness of AliNet, we also take relation semantics into consideration without introducing relation vectors.

\section{Knowledge Graph Alignment Network}

In AliNet, the entity representations are learned by a controlled aggregation of their neighborhood information within $k$ hops by the gating mechanism. Without loss of generality, in the following, we show the case of aggregating both the one-hop and two-hop neighborhood information ($k=2$). The network architecture is illustrated in Figure~\ref{fig:alinet}. Note that AliNet can also be extended to more hops.

\subsection{Gated Multi-hop Neighborhood Aggregation}
The one-hop neighbors of an entity are the most important neighborhood for GNNs to characterize the entity. We aggregate these neighbor representations using the vanilla GCN layers. Specifically, at the $i$-th layer, the hidden representation of entity $i$ by aggregating its one-hop neighbors, denoted as $\mathbf{h}_{i,1}^{(l)}$, can be computed using Eq. (\ref{eq:gcn}).

As discussed before, it is not enough to only aggregate one-hop neighbors. Although a GCN with $L$ layers can capture the structural information within the entity's $L$-hop neighbors, such layer-by-layer propagation is not efficient. For two-hop neighborhood aggregation, we introduce the attention mechanism because directly employing the original aggregation of GCN would cause noise information to propagate through layers. Specifically, let $\mathcal{N}_2(\cdot)$ be the set of two-hop neighbors of the given entity. The hidden representation of entity $i$ by aggregating its two-hop neighborhood information at the $l$-th layer, denoted as $\mathbf{h}_{i,2}^{(l)}$, is computed as follows:
\begin{equation}
\label{eq:alinet2}
\mathbf{h}_{i,2}^{(l)} = \sigma \Big (\sum_{j\in \mathcal{N}_2(i)\cup \{i\}} \alpha^{(l)}_{ij} \mathbf{W}_2^{(l)} \mathbf{h}_j^{(l-1)} \Big ),
\end{equation}
where $\alpha^{(l)}_{ij}$ is a learnable attention weight for entity $i$ and its neighbor $j$. $\mathbf{W}_2^{(l)}$ is the weight matrix. The computation of attention weights is introduced in the next subsection.

Inspired by the skipping connections in neural networks \cite{HighwayNet,ResNet,RSN}. We propose to use the gating mechanism to combine the information from one-hop and two-hop neighbors directly. Specifically, the hidden representation $\mathbf{h}_{i}^{(l)}$of entity $i$ at the $l$-th layer is computed as follows:
\begin{equation}
\label{eq:gating}
\mathbf{h}_{i}^{(l)} = g(\mathbf{h}_{i,2}^{(l)}) \cdot \mathbf{h}_{i,1}^{(l)} + (1-g(\mathbf{h}_{i,2}^{(l)})) \cdot \mathbf{h}_{i,2}^{(l)},
\end{equation}
where $g(\mathbf{h}_{i,2}^{(l)}) = \sigma(\mathbf{M} \mathbf{h}_{i,2}^{(l)} + \mathbf{b})$ serves as the gate to control the combination of both one-hop and two-hop neighborhood. $\mathbf{M}$ and $\mathbf{b}$ are the weight matrix and bias vector, respectively.
\subsection{Attention for Distant Neighborhood}
The number of the more distant neighbors of an entity can grow exponentially than the number of its one-hop neighbors. It is intuitive that not all the distant neighbors contribute to the characterization of the central entity. Hence, for two-hop neighborhood aggregation, we compute the attention weights between entities for highlighting useful neighbors. The graph attention network GAT \cite{GAT} applies a shared linear transformation to entities in each attention function. However, as the central entity and its neighbors in KGs can be quite different, such shared transformation would cause a deleterious effect to correctly distinguishing between them. Instead, we use two matrices $\mathbf{M}_1^{(l)}$ and $\mathbf{M}_2^{(l)}$ for the linear transformations of the central entity and its neighbors, respectively. Formally, the attention weight $c_{ij}^{(l)}\in \mathbb{R}$ between $i$ and $j$ at the $l$-th layer is computed as follows:
\begin{equation}
\label{eq:my_atten}
c_{ij}^{(l)} = \text{LeaklyReLU}[(\mathbf{M}_1^{(l)} \mathbf{h}_{i}^{(l)})^{\top}(\mathbf{M}_2^{(l)} \mathbf{h}_{j}^{(l)})],
\end{equation}
Finally, we normalize attention weights using the softmax function to make them comparable across different entities:
\begin{equation}
\label{eq:atten_soft}
\alpha^{(l)}_{ij} = \text{softmax}_j(c^{(l)}_{ij})=\frac{\exp(c^{(l)}_{ij})}{\sum_{n\in\mathcal{N}_2(i)\cup \{i\}}\exp(c^{(l)}_{in})}.
\end{equation}

\subsection{Contrastive Alignment Loss}
We minimize the contrastive alignment loss to let the representations of aligned entities have a very small distance while those of unaligned entities have a large distance:
\begin{equation}\label{eq:ali_loss}\small
	\mathcal{L}_1 = \sum_{(i,j) \in \mathcal{A}^{+}}||\mathbf{h}_i - \mathbf{h}_j|| + \sum_{(i',j') \in \mathcal{A}^{-}}\alpha_1[\lambda - || \mathbf{h}_{i'} - \mathbf{h}_{j'}||]_+,
\end{equation}
where $\mathcal{A}^{-}$ is the set of negative samples generated by randomly substituting one of the two pre-aligned entities. $||\cdot||$ denotes the $L_2$ vector norm. $[\cdot]_+=\max(0,\cdot)$ The distance of negative samples is expected to be larger than a margin $\lambda$, i.e., $|| \mathbf{h}_{i'} - \mathbf{h}_{j'}||>\lambda$. $\alpha_1$ is a hyper-parameter for balance.

Previous work usually uses the hidden outputs at the last layer as the final representations of entities, i.e. $\mathbf{h}_i=\mathbf{h}^{(L)}_i$ where $L$ denotes the number of layers. However, as discussed in Section \ref{sect:preli}, the representations of each layer all contribute to propagating alignment information. Therefore, we use the hidden representations of all layers. Formally, we have
\begin{equation}\label{eq:concat}
\mathbf{h}_i = \mathop{\oplus}\limits_{l=1}^L\text{norm}(\mathbf{h}_i^{(l)}),
\end{equation}
where $\oplus$ represents concatenation and $\text{norm}(\cdot)$ is the $L_2$ normalization for reducing the trivial optimization procedure of artificially increasing vector norm \cite{TransE}.

\subsection{Relation Semantics Modeling}
As KGs provide semantic relations between entities, it is natural to incorporate the semantics of the relational facts into entity modeling. As discussed in Section \ref{sect:preli}, R-GCN needs the structures of two KGs to be highly similar or relation alignment for entity alignment. Here, we borrow the translational assumption from TransE \cite{TransE}. To avoid overhead of parameters, we do not introduce additional relation-specific embeddings. The representation for $r$, denoted as $\mathbf{r}$, can be retrieved via its related entity embeddings:
\begin{equation}
	\label{eq:relation}
	\mathbf{r} =\frac{1}{|\mathcal{T}_r|} \sum_{(s,o) \in \mathcal{T}_r}(\mathbf{h}_s-\mathbf{h}_o),
\end{equation}
where $\mathcal{T}_r$ is the subject-object entity pairs of relation $r$. Then we minimize the following relation loss for refinement:
\begin{equation}
	\label{eq:trans_loss}
	\mathcal{L}_2 = \sum_{r\in \mathcal{R}}\frac{1}{|\mathcal{T}_r|} \sum_{(s,o) \in \mathcal{T}_r}||\mathbf{h}_s-\mathbf{h}_o-\mathbf{r}||,
\end{equation}
where $\mathcal{R}$ is the set of the total relations in the two KGs.

\subsection{Implementation}
Next, we introduce implementation details of AliNet.
\subsubsection{Objective}
The final objective of AliNet is the combination of the contrastive alignment loss and relation loss, aiming at injecting relation semantics to the preserved graph structures:
\begin{equation}
	\label{eq:loss}
	\mathcal{L} = \mathcal{L}_1 + \alpha_2 \, \mathcal{L}_2,
\end{equation}
where $\alpha_2$ is a hyper-parameter to weight the two losses. The objective is optimized using the Adam optimizer. All the learnable parameters including the input feature vectors of entities are initialized by the Xavier initialization \cite{Xavier}. The adjacency information is a sparse matrix obtained from the relational triples $\mathcal{T}_1$ and $\mathcal{T}_2$. The neighborhood aggregation can be done by the sparse matrix multiplication between the adjacency matrix and the entity representation matrix, making the storage complexity linear to the number of entities and triples.

\subsubsection{Generalization to $k$-hop neighborhood}
Here we consider aggregating the neighborhood information within $k$ hops. Let $\rho_1(\mathbf{h}_{i,1}^{(l)},\mathbf{h}_{i,2}^{(l)})$ be the gating combination for the one-hop and two-hop neighborhood aggregation in Eq. (\ref{eq:gating}). We use $k-1$ gating functions to combine the information recursively:
\begin{equation}
\label{eq:gating_k}
\mathbf{h}_{i}^{(l)} = \rho_{k-1}(\cdots\rho_2(\rho_1(\mathbf{h}_{i,1}^{(l)},\mathbf{h}_{i,2}^{(l)}),\mathbf{h}_{i,3}^{(l)})\cdots).
\end{equation}

\subsubsection{Neighborhood augmentation}
The proposed gated multi-hop neighborhood aggregation expands the direct neighbors of an entity in an end-to-end manner. To further implement this idea, we propose a heuristic method to add edges among pre-aligned entities. Specifically, if two entities $i$ and $j$ of KG$_1$ have an edge while their counterparts $i'$ and $j'$ in KG$_2$ do not, we add an edge linking $i'$ and $j'$. The goal is to mitigate the non-isomorphism by adding such balanced edges.

\subsubsection{Alignment prediction} Once trained AliNet, we can predict entity alignment based on the nearest neighbor search among entity representations in the cross-KG scope. Given a source entity $i$ to be aligned in KG$_1$, its counterpart in KG$_2$ is: $i'=\arg \min_{j \in \mathcal{E}_2} \pi (\mathbf{h}_i, \mathbf{h}_{j}),$
where $\pi()$ is a distance measure such as Euclidean distance. Here we still use the combined representations to measure the distance of entity embeddings.

\section{Experiments}
In this section, we evaluate AliNet on the entity alignment task. The source code of AliNet is accessible online\footnote{\url{https://github.com/nju-websoft/AliNet}}.

\begin{table*}[!t]
	\centering
	\resizebox{\textwidth}{!}{
		\begin{tabular}{lcclcclcclcclccl}
				\toprule
				\multirow{2}{*}{Methods} & \multicolumn{3}{c}{DBP\textsubscript{ZH-EN}} & \multicolumn{3}{c}{DBP\textsubscript{JA-EN}} & \multicolumn{3}{c}{DBP\textsubscript{FR-EN}} & \multicolumn{3}{c}{DBP-WD} & \multicolumn{3}{c}{DBP-YG} \\
				\cmidrule(lr){2-4} \cmidrule(lr){5-7} \cmidrule(lr){8-10} \cmidrule(lr){11-13}  \cmidrule(lr){14-16}
				& H@1 & H@10 & MRR & H@1 & H@10 & MRR & H@1 & H@10 & MRR & H@1 & H@10 & MRR & H@1 & H@10 & MRR  \\ \midrule
				MTransE \cite{MTransE} & 0.308 & 0.614 & 0.364 & 0.279 & 0.575 & 0.349 & 0.244 & 0.556 & 0.335 & 0.281 & 0.520 & 0.363 & 0.252 & 0.493 & 0.334 \\
				 IPTransE \cite{IPTransE} & 0.406 & 0.735 & 0.516 & 0.367 & 0.693 & 0.474 & 0.333 & 0.685 & 0.451 & 0.349 & 0.638 & 0.447 & 0.297 & 0.558 & 0.386 \\
				JAPE \cite{JAPE} & 0.412 & 0.745 & 0.490 & 0.363 & 0.685 & 0.476 & 0.324 & 0.667& 0.430 & 0.318 & 0.589 & 0.411 & 0.236 & 0.484 & 0.320 \\
				AlignE \cite{BootEA} & 0.472 & 0.792 & 0.581 & 0.448  & 0.789 & 0.563 & 0.481 & 0.824 & 0.599 & 0.566 & 0.827 & 0.655  & 0.633 & 0.848 & 0.707 \\
				GCN-Align \cite{GCN_Align} & 0.413& 0.744 & 0.549 & 0.399 & 0.745 & 0.546 & 0.373 & 0.745 & 0.532 & 0.506 & 0.772 & 0.600 & 0.597 & 0.838 & 0.682 \\
				SEA \cite{SEA}& 0.424 & 0.796 & 0.548 & 0.385 & 0.783 & 0.518 & 0.400 & 0.797 & 0.533 & 0.518 & 0.802 & 0.616 & 0.516 & 0.736 & 0.592\\
				RSN \cite{RSN}& 0.508 & 0.745 & 0.591 & 0.507 & 0.737 & 0.590 & 0.516 & 0.768 & 0.605 & 0.607 & 0.793 & 0.673 & 0.689 & 0.878 & 0.756\\
				MuGCN \cite{MuGNN} & 0.494 & \textbf{0.844} & 0.611 & 0.501 & \textbf{0.857} & 0.621 & 0.495 & \textbf{0.870} & 0.621 & 0.616 & 0.897 & 0.714 & 0.741 & 0.937 & 0.810\\
				\midrule
				TransH \cite{TransH}& 0.377 & 0.711 & 0.490 & 0.339 & 0.681 & 0.462 & 0.313 & 0.668 & 0.433 & 0.351 & 0.641 & 0.450 & 0.314 & 0.574 & 0.402\\
				ConvE \cite{ConvE}& 0.169 & 0.329 & 0.224 & 0.192 & 0.343 & 0.246 & 0.240 & 0.459 & 0.316 & 0.403 & 0.628 & 0.483 & 0.503 & 0.736 & 0.582\\
				RotatE \cite{RotatE} & 0.485 & 0.788 & 0.589 & 0.442 & 0.761 & 0.550 & 0.345 & 0.738 & 0.476 & 0.479 & 0.776 & 0.579 & 0.599 & 0.835 & 0.680\\
				\midrule
				GCN \cite{GCN} & 0.487 & 0.790 & 0.559 & 0.507 & 0.805 & 0.618 & 0.508 & 0.808 & 0.628 & 0.613 & 0.850 & 0.698 & 0.733 & 0.909 & 0.796\\
				GAT \cite{GAT} & 0.418 & 0.667 & 0.508 & 0.446 & 0.695 & 0.537 & 0.442 & 0.731 & 0.546 & 0.540 & 0.781 & 0.625 & 0.563 & 0.806 & 0.648 \\
				R-GCN \cite{R-GCN} & 0.463 & 0.734 & 0.564 & 0.471 & 0.754 & 0.571 & 0.469 & 0.758 & 0.570 & 0.574 & 0.791 & 0.651 & 0.617 & 0.829 & 0.692\\
				\midrule
				AliNet (w/o rel. loss \& augment) & 0.511 & 0.798 & 0.611 & 0.527 & 0.794 & 0.622 & 0.520 & 0.848 & 0.635 & 0.642 & 0.877 & 0.726 & 0.745 & 0.918 & 0.806\\
				AliNet (w/o rel. loss) & 0.525 & 0.790 & 0.619 & 0.539 & 0.796 & 0.638 & 0.535 & 0.839 & 0.645 & 0.679 & 0.887 & 0.750 & 0.773 & 0.935 & 0.832\\
								AliNet & \textbf{0.539} & 0.826 & \textbf{0.628} & \textbf{0.549} & 0.831 & \textbf{0.645} & \textbf{0.552} & 0.852 & \textbf{0.657} & \textbf{0.690} & \textbf{0.908} & \textbf{0.766} & \textbf{0.786} & \textbf{0.943} & \textbf{0.841}\\
				\bottomrule
			\end{tabular}}
			\caption{Result comparison on entity alignment}
	\label{tab:ent_alignment}
\end{table*}

\subsection{Datasets}
Following the latest progress \cite{BootEA,MuGNN}, we use the following datasets and training-test splits.
\begin{itemize}
	\item DBP15K \cite{JAPE} has three datasets built from multi-lingual DBpedia, namely DBP\textsubscript{ZH-EN} (Chinese-English), DBP\textsubscript{JA-EN} (Japanese-English) and DBP\textsubscript{FR-EN} (French-English). Each dataset has $15,000$ reference entity alignment and about four hundred thousand triples.
	\item DWY100K \cite{BootEA} are extracted from DBpedia, Wikidata and YAGO3. It has two datasets, namely DBP-WD (DBpedia-Wikidata) and DBP-YG (DBpedia-YAGO3). Each dataset has $100,000$ reference entity alignment and more than nine hundred thousand triples.
\end{itemize}

\subsection{Comparative Models}
We compare with recent embedding-based entity alignment models: MTransE \cite{MTransE}, IPTransE \cite{IPTransE}, JAPE \cite{JAPE}, AlignE \cite{BootEA}, GCN-Align \cite{GCN_Align}, SEA \cite{SEA}, RSN \cite{RSN} and MuGNN \cite{MuGNN}. Note that some recent GNN-based models like GMNN \cite{KGMatching} and RDGCN \cite{RDGCN} incorporate the surface information of entities into their representations. As our model solely relies on structure information, we do not take these models into comparison. For ablation study, we develop three variants of AliNet, i.e., AliNet (w/o rel. loss) that does not optimize the relation loss, AliNet (w/o rel. loss \& augment.) that does not employ the relation loss and neighborhood augmentation, and the full model AliNet.

For comprehensive comparison, we also choose some KG embedding models and GNN variants as baselines. Conventional KG embedding models are usually evaluated on the task of link prediction. However, as studied in \cite{MultiKE}, some of them can also be used for entity alignment. We select TransH \cite{TransH}, ConvE \cite{ConvE} and RotatE \cite{RotatE}. TransE \cite{TransE} has already been exploited for entity alignment by MTransE and IPTransE. For GNNs, we choose GCN \cite{GCN}, GAT \cite{GAT} and R-CGN \cite{R-GCN} as baselines. The re-tuned versions of GCN, GAT and R-GCN are implemented by ourselves following the same pipeline as AliNet for fair comparison.

\subsection{Implementation Details}
We search among the following values for hyper-parameters, i.e., the learning rate in $\{0.0001,0.0005,0.001,0.005,0.01\}$, $\alpha_1$ in $\{0.1,0.2,\dots,0.5\}$, $\alpha_2$ in $\{0.01,0.05,0.1,0.2\}$, $\lambda$ in $\{1.0,1.1,\dots,2.0\}$, the hidden representation dimension of each layer in $\{100,200,300,400,500\}$, the number of layers $L$ in $\{1,2,3,4\}$, and the number of negative alignment pairs in $\{5,10,15,20\}$. The selected setting is that $\lambda=1.5$, $\alpha_1=0.1$, $\alpha_2=0.01$. The learning rate is $0.001$. The batch size for DBP15K is $4,500$, and for DWY100K is $10,000$. We stack two AliNet layers ($L=2$) and each layer combines the one-hop and two-hop information ($k=2$). The dimensions of three layers (including the input layer) are $500$, $400$ and $300$, respectively. The activation function for neighborhood aggregation is $\text{tanh}()$, and the one for the gating mechanism is $\text{ReLU}()$. We sample 10 negative samples for each pre-aligned entity pair. We use early stopping to terminate training based on the Hits@1 performance with a patience of 5 epochs. We use CSLS \cite{Word_Translation} for nearest neighbor search.

Following convention, we report the Hits@1, Hits@10~and MRR (mean reciprocal rank) results to assess entity alignment performance. Higher Hits@$1$, Hits@$10$ and MRR scores indicate better performance. Note that, the Hits@1 is equivalent to precision. As the nearest neighbor search can always find a counterpart for each entity to be aligned, the recall and F1-measure also have the same value as Hits@1.

\subsection{Main Results}
We present the entity alignment results in Table \ref{tab:ent_alignment}. We can see that AliNet outperforms the state-of-the-art structure-based embedding models for entity alignment by Hits@1 and MRR. For example, on DBP\textsubscript{FR-EN}, AliNet achieves a gain of $0.036$ by Hits@1 compared with RSN, and $0.057$ against MuGNN. We think that these results have demonstrated the superiority of AliNet. As the DBP15K datasets are extracted from the multi-lingual DBpedia, the schema heterogeneity of them is much weaker than that of DWY100K which are extracted from different KGs. AliNet also achieves the best Hits@10 results on DWY100K, demonstrating its practicability. We find that the neighborhood augmentation method leads AliNet a gain of $0.012-0.037$ by Hits@1. This is because it can reduce the non-isomorphism in the neighborhood structures of pre-aligned entities. The results further support our motivation of mitigating the non-isomorphism in KG structures for entity alignment. AliNet shows better performance than AliNet (w/o rel. loss), showing the effect of the relation loss.

In comparison to the re-tuned GNN variants GCN, GAT and R-GCN, AliNet also achieves better performance. As the GCN baseline has the same training process as AliNet and the difference only lies in the choice of GNN layers (i.e., GCN layers for one-hop neighborhood aggregation or AliNet layers for both one-hop and two-hop neighborhood aggregation), these results can demonstrate the effectiveness of integrating multi-hop information. Both GAT and R-GCN fail to outperform GCN. We attribute such observation to that the direct neighbors of an entity are less dissimilar than distant neighbors, and hence may not require an attention-based neighborhood aggregation to select relevant neighboring entities. This is also the reason for choosing GCN layers rather than GAT layers in the one-hop neighborhood aggregation of AliNet. For R-GCN, as discussed in Section \ref{sect:preli}, it cannot well capture the similarity of neighborhood structures of counterpart entities.  We also observe that the GCN baseline outperforms many other embedding-based entity alignment models including another GCN variant GCN-Align. This again validates the effectiveness of our framework.

\subsection{Analyses}
\subsubsection{Aggregation strategies of multi-hop neighborhood}
The underlying idea of AliNet is to extend the neighborhood of entities by attentively aggregating multi-hop neighborhood with the gating mechanism. To gain a deep insight into this point, we further design three variants of AliNet using different strategies to aggregate multi-hop neighborhood. The first one, denoted as AliNet (mix), borrows the idea from MixHop \cite{MixHop}, which has a similar motivation for node classification in general graphs. It takes the two-hop neighbors as one-hop and uses GCN layers to directly aggregate such mixed neighborhood information. The second one, denoted as AliNet (add), replaces the gating mechanism with addition operator. In the last variant AliNet (gat), we replace the proposed attention mechanism with GAT \cite{GAT}. Due to space limitation, we only show the results on DBP15K in Table \ref{tab:multi_hop_results}. We find that AliNet (mix) fails to achieve promising performance, which indicates that using GCN layers for two-hop neighborhood aggregation is not effective because it would introduce much noise information. AliNet (add) does not show very satisfactory results because addition cannot selectively combine important representations across dimensions like the gating mechanism. AliNet (gat) also achieves slightly lower performance than AliNet, showing the effect of our attention mechanism. Based on these results and the performance of the GCN baseline shown in Table \ref{tab:ent_alignment}, we can come to the conclusion that the multi-hop neighborhood information indeed contributes to entity alignment while the gating and attention mechanisms are crucial to capture important information in distant neighbors.

\begin{table}[!t]
	\centering
		\resizebox{\linewidth}{!}{
		\begin{tabular}{lcclcclccl}
				\toprule
				\multirow{2}{*}{Methods} & \multicolumn{3}{c}{DBP\textsubscript{ZH-EN}} & \multicolumn{3}{c}{DBP\textsubscript{JA-EN}} & \multicolumn{3}{c}{DBP\textsubscript{FR-EN}}\\
				\cmidrule(lr){2-4} \cmidrule(lr){5-7} \cmidrule(lr){8-10}
				& H@1 & H@10 & MRR & H@1 & H@10 & MRR & H@1 & H@10 & MRR \\ \midrule
				AliNet (mix) & 0.227 & 0.611 & 0.350 & 0.294 & 0.696 & 0.426 & 0.258 & 0.674 & 0.391 \\
				AliNet (add) & 0.498 & 0.801 & 0.602 & 0.515 & 0.813 & 0.618 & 0.501 & 0.839 & 0.585 \\
				AliNet (gat) & 0.517 & 0.803 & 0.618 & 0.531 & 0.810 & 0.632 & 0.523 & 0.845 & 0.636\\
				AliNet & \textbf{0.539} & \textbf{0.826} & \textbf{0.628} & \textbf{0.549} & \textbf{0.831} & \textbf{0.645} & \textbf{0.552} & \textbf{0.852} & \textbf{0.657} \\
				\bottomrule
			\end{tabular}}
	\caption{Results on DBP15K w.r.t. aggregation strategies}
	\label{tab:multi_hop_results}
\end{table}

\begin{figure}[!t]
	\center
	\includegraphics[width=0.99\linewidth]{layer_num.pdf}
	\caption{Results on DBP15K w.r.t. the number of layers.}
	\label{fig:layer_num}
\end{figure}

\subsubsection{Impact of the number of layers and choice of $k$}
We first report the results of AliNet with 1 to 4 layers on DBP15K in Figure \ref{fig:layer_num}. AliNet with 2 layers achieves the best performance over all the three metrics. We observe that when AliNet has more layers, its performance declines as well. Although more layers allow AliNet to indirectly capture more distant neighborhood information by layer-to-layer propagation, such distant neighbors would introduce much noise and lead to more non-isomorphic neighborhood structures. Besides, we further show the results of the two-layer AliNet that considers different hops of neighborhood information in each layer in Table \ref{tab:k_hop_results}. We can see that considering two-hop neighborhood leads to the best results. This is similarly attributed to the aforementioned reasons regarding aggregation of multi-hop neighbors. This is further verified by an analysis about DBP15K. For example, in DBP\textsubscript{ZH-EN}, each Chinese entity has 6.6 one-hop neighbors on average and this number for each English entity is 8.6. However, between their one-hop neighbors, there are only 4.5 pairs of counterpart entities, leaving 2.1 Chinese one-hop neighbors and 4.1 English ones unaligned. If considering two-hop neighbors, the numbers of unaligned one-hop neighbors are reduced to 0.5 for Chinese and 0.9 for English, respectively. The numbers have less room to be reduced by introducing more distant neighbors. This suggests us that aggregating two-hop neighborhood information is enough.

\begin{table}[!t]
	\centering
	\resizebox{\linewidth}{!}{
		\begin{tabular}{lcclcclccl}
				\toprule
				\multirow{2}{*}{Methods} & \multicolumn{3}{c}{DBP\textsubscript{ZH-EN}} & \multicolumn{3}{c}{DBP\textsubscript{JA-EN}} & \multicolumn{3}{c}{DBP\textsubscript{FR-EN}}\\
				\cmidrule(lr){2-4} \cmidrule(lr){5-7} \cmidrule(lr){8-10}
				& H@1 & H@10 & MRR & H@1 & H@10 & MRR & H@1 & H@10 & MRR \\ \midrule
				GCN & 0.487 & 0.790 & 0.559 & 0.507 & 0.805 & 0.618 & 0.508 & 0.808 & 0.628 \\
				AliNet & \textbf{0.539} & \textbf{0.826} & \textbf{0.628} & \textbf{0.549} & \textbf{0.831} & \textbf{0.645} & \textbf{0.552} & \textbf{0.852} & \textbf{0.657} \\
				AliNet ($k=3$) & 0.461 & 0.786 & 0.571 & 0.484 & 0.802 & 0.590 & 0.450 & 0.813 & 0.575  \\
				AliNet ($k=4$) & 0.386 & 0.721 & 0.501 & 0.407 & 0.706 & 0.516 & 0.373 & 0.745 & 0.499\\
				\bottomrule
			\end{tabular}}
	\caption{Results on DBP15K w.r.t. $k$ values}
	\label{tab:k_hop_results}
\end{table}

\begin{figure}[!t]
	\center
	\includegraphics[width=0.9999\linewidth]{overlap.pdf}
	\caption{Average OC of one-hop neighbor sets of correct alignment during the first 200 training epochs on DBP15K.}
	\label{fig:overlap}
\end{figure}

\subsubsection{Analysis of neighborhood overlap} Furthermore, we make an empirical statistics on the overlap coefficient (OC) of the one-hop neighbors for each pair of counterpart entities in the correctly-found alignment. A~high OC value for two entities means that they have a large overlap between their one-hop neighbors. We predict entity alignment and compute the average OC values of the correctly-predicted alignment every epoch. Figure \ref{fig:overlap} shows the value changes during the first 200 training epochs of AliNet and GCN. We find that the values display a decreasing trend. This indicates that it is relatively easy for GNN-based models to find the counterpart entities having a large proportion of common one-hop neighbors. The OC values of AliNet are smaller than those of GCN. This indicates that AliNet can effectively align the entities with smaller overlap in their one-hop neighbors.

\subsubsection{Performance based on different layers} In AliNet, we propose to use the combined representations of all layers as the final entity representations for predicting entity alignment. Here, we further examine the performance based on layer-specific entity representations. We report the entity alignment results on DBP15K in Figure \ref{fig:layer} due to space limitation. The input layer is the randomly initialized feature vectors for entities to be tuned in AliNet. On top of this, we stack two AliNet layers (i.e., Layer 1 and Layer 2). ``Combination'' means the combined representations computed by Eq. (\ref{eq:concat}). We can see that the representations of different layers show different performance of entity alignment. Layer 1 shows the best results among the three layers. As expected, the combined representations finally outperform the layer-specific representations.

\section{Related Work}
We hereby discuss related work to this paper. Particularly, as we have covered the majority of GNN-based entity alignment models in Section \ref{sect:preli}, we focus on discussing other families of models here. Most other models, such as MTransE \cite{MTransE}, IPTransE \cite{IPTransE}, JAPE \cite{JAPE}, AlignE and BootEA \cite{BootEA}, NAEA \cite{NAEA} as well as OTEA \cite{OTEA}, use TransE \cite{TransE} to learn entity embeddings. Meanwhile they learn a linear mapping or minimize the distance between the embeddings of pre-aligned entities. On top of KG structures, some work like KDCoE \cite{KDCoE}, AttrE \cite{AttrE} and MultiKE \cite{MultiKE}, incorporates additional profile information of entities such as textual descriptions and literal names for KG embedding. Differently, AliNet exploits the basic graph structures without using additional information. To further improve entity alignment performance, IPTransE, BootEA, KDCoE and NAEA use semi-supervised learning.

We also notice the recent work \cite{GMN} that uses GNNs for comparing the similarity of two graphs. Differently, we focus on the node-level rather than graph-level similarity comparison. \cite{KampffmeyerCLWZ19} captures the hierarchical structures of entities by introducing hypernym-hyponym links between nodes and their ancestors/descendants. Our work is also related to KG embedding that aims at learning vector representations for KG completion. There are translational models such as TransE \cite{TransE}, TransH \cite{TransH} and TransR \cite{TransR}, bilinear models such as ComplEx \cite{ComplEx}, SimplE \cite{SimplE} and RotatE \cite{RotatE}, and deep models such as ConvE \cite{ConvE}, R-GCN \cite{R-GCN} and RSN \cite{RSN}. We refer interested readers to the recent survey \cite{KRL} for more details on KG embedding.

\begin{figure}[!t]
	\center
	\includegraphics[width=0.9999\linewidth]{layer_results.pdf}
	\caption{Hits@1 results w.r.t. different layers on DBP15K.}
	\label{fig:layer}
\end{figure}

\section{Conclusion and Future Work}
In this paper, we propose AliNet for entity alignment, aiming at mitigating the non-isomorphism among the neighborhood structures of counterpart entities in an end-to-end manner. AliNet captures the neighborhood information within multiple hops by a gating mechanism in each layer. It employs an attention mechanism for multi-hop neighborhood aggregation to reduce noises. We further propose a relation loss to enhance the expressiveness of AliNet. Our experiments on five datasets demonstrate the effectiveness of AliNet. For future work, we plan to incorporate side information of entities in other modalities into the preserved graph structures.

\subsubsection*{Acknowledgments}
This work is supported by the National Key R\&D Program of China (No. 2018YFB1004300), the National Natural Science Foundation of China (No. 61872172), and the Key R\&D Program of Jiangsu Science and Technology Department (No. BE2018131).

\bibliographystyle{aaai}

redpajama_set_name: RedPajamaArXiv


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A screamingly powerful Mitsubishi Lancer Evolution blows past you on the freeway. Some young man is having fun with one of the most powerful cars on the road-for its price.
However, the Lancer compact sedan on which it's built tends to be forgotten, while Japanese brand competitors sell in the hundreds of thousands. But the Lancer is a good-looking, fun-to-drive little sedan and is Mitsubishi's best-selling vehicle. I recently spent a week with an Apex Silver GTS model that proved its mettle.
The car blends in, with familiar proportions. However, the face is especially aggressive, with a huge mouth that's like a shark coming in for the kill. The "eyes" stare intently, and the sharp edges and squinting taillamps create some visual tension-as if the car is just waiting to jump onto the road and strike.
Inside, the dash has a twin-pod shape over the gauges-evoking an Alfa Romeo, while the main dash panel flares towards you like that in a BMW. The steering wheel is finely detailed and, like the rest of the interior, blends silvery accents with matte black finished surfaces. It feels sporty. Mitsubishi's tag line, "Different for a Reason," may be their way of putting it.
For 2011, the Lancer comes in four models (below the separately-badged Evolution). These start with the entry-level DE and progress through the ES, GTS, and finally, the Ralliart. The first three are front-wheel drive, but the Ralliart powers all four wheels with an automatic all-wheel-drive system.
The DE and ES provide motivation with a 2.0-liter, 152-horsepower inline four through a five-speed manual transmission or a continuously-variable automatic. The Ralliart turbocharges that engine for a whopping 237 horsepower and runs it through what it calls an "automated manual" with the requisite magnesium steering column-mounted shifter paddles. Good thing it has four tires putting that extra power to the ground.
The GTS, like my tester, has a larger 2.4-liter four that adds 20 horsepower above the 2.0-liter version. Choose from the manual or CVT transmissions. The CVT is perfectly well behaved. If you want to use the paddle shifters, you're selecting "ratios" from the otherwise continuously variable "gearbox." I, as usual, let it do its thing, and it delivered a respectable 24.8 miles per gallon (EPA numbers are 23 City/30 Highway). EPA Green Guide numbers are a hybrid-like 9 for Air Pollution and a 7 for Greenhouse Gas-in California.
The DE has the basic things you want, such as a 140-watt, 4-speaker AM/FM/CD audio system; power windows, locks and mirrors; remote keyless entry and anti-lock brakes. The ES sweetens the deal with air conditioning, a split folding rear seat, steering wheel with audio and cruise control switches and a few other niceties.
The GTS's interior extras include leather on the steering wheel, shift knob and brake handle; automatic climate control; sport bucket seats and the FUSE HandsFree Link System, which allows wireless access to Bluetooth-enabled cell phones, an iPod or a USB drive with the use of simple voice commands.
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The Ralliart also features a sporty aluminum hood with heat extractor vent, chrome side lower door moldings and inside, premium "sport fabric" on the seats.
Pricewise, the numbers are encouraging. My GTS tester, with all the aforementioned, came to $24,355, including shipping. The DE with nothing extra can be had for as little as $15,955.


Abstract: 本論文以實驗的方式,探討固態衍生燃料稻桿加入添加物米糠及米殼的成型性質,並利用田口品質工程的方法找尋最佳化的參數條件。實驗主要分為兩部分,分別為成型機台半自動化改良及稻桿加入添加物成型性質之研究。而在成型機台改良部分,乃將成型模具產生的成品數量由1個增加到5個,並設計新扣環夾具與脫模底膜機構使成型機台達到半自動化。 在成型性質探討部份使用田口法找尋最佳製程參數條件,在第一次田口法中所找出的添加物米糠及米殼的最佳製程條件分別為添加物含量0.05 g;稻桿含水率8 %;添加物含水率分別為8 %與4 %而成型壓力為50 kg/cm2。根據上述所得實驗結果進行驗證實驗,得知米糠成型壓力在30 kg/cm2、35 kg/cm2與50 kg/cm2;而米殼在40 kg/cm2、45 kg/cm2與50 kg/cm2;添加物含水率4 %、6 %及8 %;添加物米糠含量0.410 g(20 %)、0.512 g(25 %)與0.615 g(30 %),而米殼含量0.05 g (2.5%)、0.102 g (5 %)與0.205 g (10%)時有最佳的成型製程參數條件。因此藉由驗證實驗所得結果必須再進行第二次田口法分析。最終結果發現添加物米糠含量0.615 g;稻桿與添加物的含水率為6 %;成型壓力50 kg/cm2,而添加物米殼含量0.205 g;稻桿與添加物含水率8 %;成型壓力35 kg/cm2的條件下有最佳製程條件,並且得知添加米糠對成品密度有增加的趨勢,而米殼則沒有。 而在成品硬度與燃燒熱值分析部份,在添加米糠條件下對硬度與燃燒熱值皆有上升的趨勢,而米殼部份則無明顯上升。 This thesis used way of experiment to studied pelleting property of rice straw with additive of rice bran and rice husk. Furthermore, the Taguchi method was used in order to found optimal parameters of pelleting experiments. The experiment was divided into two parts, semi-automation design of pelleting machine and research of pelleting properties for rice straw with additive respectively. In design of pelleting machine, the RDF pellet can be produced from one to 5 for new design of pelleting mold. The jig of fastener ring and mechanism of bottom mold were also designed. The first Taguchi method was used in this thesis that found optimal parameters in pelleting experiments of rice straw with additive of rice bran and rice shell. The optimal parameters were amount of additive 0.05 g, moisture content of rice straw 8 %, moisture content of rice bran 8 %, moisture content of rice shell 4 % and pelleting pressure 50 kg/cm2.The verifying experiments were undertaken according to results of first Taguchi method. The optimal parameters of additive of rice bran and rice shell were found in verifying experiments. In part of rice bran, the pelleting pressure 30 kg/cm2, 35 kg/cm2 and 50 kg/cm2, amount of additive 0.410 g (20 %), 0.512 g (25 %) and 0.615 g (30 %). In part of rice husk, the pelleting pressure 40 kg/cm2, 45 kg/cm2 and 50 kg/cm2, amount of additive 0.05 g (2.5 %), 0.102 g (5 %) and 0.205 g (10 %), both moisture content of additive 4 %, 6 % and 8 % have optimal density for RDF pellets of rice straw with additive. The second Taguchi method was used from results of verifying experiment. The optimal parameters of rice bran included amount of rice bran 0.615 g, moisture content of rice straw and rice bran 6 %, pelleting pressure 50 kg/cm2. In part of rice husk, the conditions of amount 0.205 g, moisture content of rice husk and rice bran 8 %, pelleting pressure 35 kg/cm2 have optimal pelleting properties and density of RDF pellets increased in additive of rice bran, there is not the additive of rice husk .All have the tendency to raised to hardness and heat value under adding the additive of rice bran, but the rice husk has not obviously risen.


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Showing posts from April 19, 2020
Lauran Hibberd - Pantayo - The Modern Society - Hostile Tribes - Beach Scvm
Lauran Hibberd is back for a tenth time on Beehive Candy with the song and video for 'Old Nudes', which sees the slacker pop artist in (as to be expected) splendid form. === Toronto by-way-of Philippines quintet Pantayo share 'V V V (They Lie)', where the driving beats are matched with some fabulous vocals and a haunting musical backdrop. === Our second helping of The Modern Society this year comes in the form of 'Liquid Love' and this annoyingly young band demonstrate plenty of musical maturity and the good humoured video is a bonus. === Hostile Tribes (Paul Baird) has just shared 'Locals' a rhythmic piece where his vocals sway in and out of the track and the video plays into the trippy vibe. === Beach Scvm have shared a new video for 'Forever Sunday' an upbeat dream pop song with a hint or two of shoegaze for good measure. ------------------------------------------------------------------------------------------ Lauran Hibberd - Old Nu
Eliot Bronson (feat Lori McKenna) - Hazel English - Sophia Boro - Ed Rome - TTRRUUCES
Eliot Bronson shares 'Even This Is Going To Pass' featuring Lori McKenna and it's a gentle beautiful ballad with exquisite harmonies. === We have two tracks from Hazel English debut album the title track 'Wake Up' plus 'Born Like' both giving a good feel for this superb pop and rock collection. === Sophia Boro has released 'Nobody Knows' ahead of her debut E.P, the song is both creative and striking, this is promising. === Reggae fans are in for a treat with Ed Rome new Feel No Pain E.P, where he mixes Ska, roots reggae and dub. === TTRRUUCES have just shared 'Evil Elephant' along with a video and this is one fabulous song. Whether you agree with the sentiments or not this is a wonderful track. ------------------------------------------------------------------------------------------ Eliot Bronson - Even This Is Going To Pass (feat. Lori McKenna). Eliot Bronson whose talents with "bluesy harmonies and a dark sense of humor c
Empathy Test - Roly Witherow - The Texas Gentlemen
Empathy Test forthcoming single 'Monsters' is a powerful anthem that exudes indie and synth musical traits and passionate vocals. === We are always on the lookout for different but good and Roly Witherow meets all of those hopes with 'Row Bullies Row' where punk meets sea shanty and folk (wonderful). === The Texas Gentlemen have just shared 'Ain't Nothin' New' it's a little psychedelic, a tiny bit country rock and a whole lot addictive, a fine taster for their July album release. ------------------------------------------------------------------------------------------ Empathy Test - Monsters. Monsters is the forthcoming single from synth pop magicians, Empathy Test, the lead track from their forthcoming album which looks to cement them as one of the most inventive, emotionally affecting and addictive musical experiences around. Written collectively by all four band members, Monsters is described by the band themselves as, "an ode to anxie
Mirabelle - Abigail Lapell - Oracle Sisters - Mind Shrine
Mirabelle has a new single out entitled 'Betty' taken from her upcoming album 'Late Bloomer', the new song is a slow burning and atmospheric rocker. === From Abigail Lapell we have the gorgeous and timeless natural folk piece 'Down By The Water'. === Released today Oracle Sisters have shared 'Most Of All' the second single from upcoming EP 'Paris I', the song is a dreamy mixture of melodic rock and catchy pop moments. === A brand new video from Mind Shrine was also shared today for '5 Long Days' a song that has a fresh indie vibe and hooks galore. ------------------------------------------------------------------------------------------ Mirabelle - Betty. Montreal singer-songwriter Mirabelle is sharing her new single "Betty", from her upcoming album Late Bloomer, out on Simone Records on May 29th. "Betty" just might be the backbone of Laurence Hélie's upcoming album. It's the first new song she wrote, the spark in the void that
Cuddle Magic - Baron Minker - Castle Black - Andria Piperni - Freya Beer
Cuddle Magic have given us a feel for their forthcoming album with 'What If I' and its an exquisite chamber pop song, we've received two other tracks and I have to say this is a very promising album. === Baron Minker shares the addictive new song 'Anthem of the Rich and the Bored' his genre mixing music, is impressive and some. === Brooklyn's Castle Black have a new video for 'Dead In A Dream' where the bands post punk influences combine with some powerful rock vibes. === Montreal singer song-writer Andria Piperni just released 'Coming Home To You' and it's one of those songs that stands out in the seemingly crowded Neo Soul/R&B world. === We are a little bit proud to have been ahead of the crowd with Freya Beer and her last single and are equally content to just share her latest song 'Arms Open Wide' which is equally hook filled and wonderful. ---------------------------------------------------------------------------------
Carmanah - Eleri Angharad - Reliant Tom - NIIKA
Carmanah new song 'Best Interests' is a soulful and expansive rocker that slowly lets it's musical hooks dig in deep. === From Swansea, Wales Eleri Angharad has shared 'Blank Walls' where her beautiful vocals are notable on this singer-songwriter piece. === Our second feature this year for Reliant Tom comes in the form of 'The Sky Is Falling' which has some post rock vibes as it builds in intensity and refinement. === from NIIKA we have her brand new song 'The Cage' along with a very fine animated video, the track itself is centered around some mesmerizing vocals and a sophisticated musical backdrop. ------------------------------------------------------------------------------------------ Carmanah - Best Interests. Like the rainforests from which their name derives, the steady beat of the west coast can be found within the sound of Carmanah. From muddy blues-funk to light soaring melodies, Carmanah creates music that curves and tumbles throu

redpajama_set_name: RedPajamaCommonCrawl


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Minions: Puppy
A Minion, seeing many owners walk their dogs, wants a puppy of his own. He tries to leash a ladybug but fails. Luckily, a UFO that sweeps away the ladybug somehow agrees to become a Puppy.
Movie Star Rating : 7.2 Read More
JLA Adventures: Trapped in Time
Get ready for a battle of the ages when the Justice League faces off against its archenemies, the Legion of Doom, in an all-new movie from DC Comics. A mysterious being known as the Time Trapper arises, and a sinister plan led by Lex Luthor sends the Legion of Doom back in time to eliminate Superman, Wonder Woman and Batman before they become super heroes. For Aquaman, Flash and Cyborg, along with teen super heroes Karate Kid and Dawnstar, the stakes have never been higher, the rescue mission never deadlier. So join the fight for the future as the Justice League confronts its ultimate challenge… the threat of having never existed!
The Longest Ride
The lives of a young couple intertwine with a much older man as he reflects back on a lost love while he's trapped in an automobile crash.
A recently cheated on married woman falls for a younger man who has moved in next door, but their torrid affair soon takes a dangerous turn.
Alice Through the Looking Glass
Alice Kingsleigh returns to Underland and faces a new adventure in saving the Mad Hatter.
A vacationing family encounters an alien threat in this pulse-pounding thriller based on the real-life Brown Mountain Lights phenomenon in North Carolina.
Robot Overlords
Earth has been conquered by robots from another galaxy and the human survivors must stay in their homes, or risk incineration.
An artist falls for a married young woman while he's commissioned to paint her portrait. The two invest in the risky tulip market in hopes to build a future together.
The November Man
An ex-CIA operative is brought back in on a very personal mission and finds himself pitted against his former pupil in a deadly game involving high level CIA officials and the Russian president-elect.


The sixth largest police organization in North America has recognized its IT machine needs a facelift, and will be ready to unveil its new look by next spring.
on an implementation of an employee relationship management (ERM) system built on the J2EE standard from Atlanta-based Workbrain. The project will help solve issues that arose four years ago and may rescue a human resources department that has been mired in paper, according to Bill Gibson, director of HR.
Each interaction with TPS's 10,000 employees was documented on paper, whether it was a vacation request, notification of name change or an upgrade to a benefits plan. The process was time-consuming, inefficient and costly.
"We are a large complex enterprise with a complicated workforce," said Gibson, who shared the details of TPS's HR upgrade at an International Association for Human Resource Information Management meeting last Friday in Toronto.
One of the most significant factors complicating the HR equation at Toronto Police Services is the scheduling and payment of overtime and court time for officers. The police force spends more than half of its $500 million operating budget on salaries and overtime. The ability to better manage those resources, said Gibson, was attractive to TPS.
The technology landscape is antiquated: a dated mainframe and a number of legacy systems that don't talk to each other. A 20-year-old time and attendance system, for example, used to track officers' shifts, which made the extraction and analysis of real-time data virtually impossible, Gibson said. Information was housed in silos, across departments, and there was no easy way for management to view employee-related information.
This year, Gibson said, things are beginning to change.
"We wanted to upgrade in 1999, but along came Y2K and we had to postpone our plans," said Gibson.
Workbrain's Web-based application, ERM 3.0, will integrate with TPS's existing Peoplesoft payroll and HR applications. The system, which will go live in March of 2003, is expected to relieve a lot of the headaches traditionally associated with human resources.
For employees, the upgrade means self-serve access to most HR functions; for management, it means better analytical capabilities, Gibson said. The tool will give TPS greater visibility into its workforce costs and allow them drill down to understand the factors that affect those costs.
"Employees will no longer have to fill out paper forms to request time off," said Bill Gibson, TPS's director of human resources. "And we'll be able to reduce the administrative costs associated with processing overtime and court time (for the officers)." Toronto Police Services spends $32 million a year on overtime and court time costs for its 7,000 officers.
In an economy where technology investments are tough to justify, automating human resources is an easy sell, said Daniel Debow, Workbrain's marketing director. Pinpointing the exact return on investment for the software is complicated, Debow said, but typically customers have found the ROI to take between 12 and 18 months.
There's also a huge cut in the time it takes to process a transaction.
"Typically, an employee makes a request to HR and it takes about 30 days to process," said Debow. "After implementing ERM, the time is cut to about one week.
The cost of implementing Workbrain's ERM system depends on the number of users and the amount of functionality required, and could range anywhere from a few thousand dollars to a few million.
Companies opt for massive technology upgrades for a couple of reasons. Sometimes, the investment is necessary for competitive reasons. Other times, an overhaul is undertaken to wipe out inefficiencies that are needlessly draining cash from the company.


It is very agonising to notice events that are taking place within the late icon musician Oliver Mtukudzi's family. He must be turning in his grave by now.
What is really pushing Selmor and Sandra to appear so much concerned about their late father's legacy, while at the same time attacking their stepmother Daisy in public?
My main issue here is about Selmor and Sandra. We may all be aware that at one time they were not in good books with their father, raising suspicion why the two daughters now seem to care so much, a feat they failed when their father was still alive.
To make matters worse, Selmor left her bedridden father for dead, while she went to London for a show. Very sad, indeed.
Furthermore, where did their mother Melody Murape get the audacity, who had long back divorced and was snubbed at Mtukudzi's funeral, to tell those who cared to listen that Selmor is the annointed one of her father.
It's now clear that the three are being driven by jealous, greediness, hatred and selfishness towards Daisy. It's now clear the three are maximising every opportunity they get in the media to say whatever, especially when it is directed at Daisy.
After all, we thought the Tuku brand was bigger than those greedy people whom we demand to have cool heads and plan life as a family and to not cause divisions within the family.
Selmor, Sandra and their mother must leave everything to the Mtukudzi family elders to solve the matter rather than exposing their level of ignorance to the public.
He will never turn in his grave because the dead know nothing according to Ecclesiastes .You seem to side with only 1 party who maybe paid you a few dollars to write this sick piece.You need to understand that Melody and her kids were/are bitter for losing their father to Daisy and what they are doing is very natural.And Daisy herself had a hand in everything that is happening now coz she wanted Melody "to be forgotten "as if she never existed judging by what happened at Tuku funeral.Even paMemorial she never consulted the girls despite them being the eldest.Ini my father remarried when my mom died and when he died paspeech we gave equal chances to the families of both wives-ndochivanhu chaicho ichocho.
And who are you to write such nonsense as well. Why dont you also leave it to the Tuku family to sort out their mess.
saka iwe munyori wakwana papi ipapa ,Nemiwo Newsday munazvo,kusvibisirwa brandi nam renyu nekamunhu kapiwa Teni husa aka.
this is poor journalism and its apparent the writer was paid to write against the Selmor side of the family.But that was Tuku first family and he never spoke against that side of the family.Its apparent that Daisy want to monopolise the Mtukudzi legacy but then its the greediness on her part.Its never easy in families with such a scenario as tuku had and the family at moments like this should be left to work things out.since selmor show was well attended,it shows the fans are with her.
Tarisai stop your nonsense.Verify and qualify your soUrces.If anything DAISY CLOSED THE DOOR TO EVERYONE WHEN TUKU GOT SERIOUSLY ILL.YOURSELF DID YOU EVER TRIED TO GO TO PAKARE PAYE OR NORTON TO VISIT HIM,if you had you would write a better story.ARE YOU PAID TO WRITE THIS BY DAISY.DAISY BLOCKED ANYTHING AND ANYONE.DO YOU THINK TUKU WOULD DIE IN ZIMBABWE IF EVERYBODY KNEW.HE COULD HAVE BEEN FLOWN OUT ANYTIME ANY DAY.HE WOULD HAVE DIED IN BETTER CARE.
I am betting my last bond coin that this article was written by Daisy herself using a nom de plume!
This type of journalism leaves a lot to be desired . Firstly it is not balanced , secondly the write knows nothing about the general background of the social issues affecting this family . Please journalists dont claim to know everything .
For starters there was nothing wrong for Serlmor to go to London for a show because she gets feeds her family with music only . She has been a singer all her life , saka dai akaenda saizvozvi ndokudzoka baba vake vasina kufa were going to make noise and accuse her of negligency . Again by that time mudhara Tuku was always in and out of hospital so you cant say Serlmor acted wrongfully . Kusawirirana kwaSerlmor nababa vake ndezvavo veropa rimwe its not for outsiders to judge . Its normal mwana kurwa zhanje dzaamai vake.
On the other hand Daisy stood by her late husband through and through and she must be respected for that . What Daisy should do now is to embrace the family kutoda vana vemurume waka sevake nehama dzemurume wake. Daisy should live with her decisions because akadanana nemurume aive nemhuri yake and any negative reactions from these people is normal . Daisy should go to Tuku's relatives ndivo vanokwanisa kupedza nekudzora ana Selmor not anyone else. As per our tradition and culture its normal for the society to sympathize nevana vemufi and that is why people are backing Selmor and Sandra . Vana ava havasi mapenzi and vane hunhu sababa vavo the late Hero Mutukudzi .
@ Chalubva… You the only men with senses here and my advise is Newsday should engage you to be the editor of their newsroom ifected by this crap. a journalist must give a balanced story the dos and donts of the characters involved in the story. This writer must leave office anemakuhwa kuteerera one mother of the family without investigating both ends… its like the writer was never a newsday but DAISY kkkkkk. ofcourse with due respect Mother Mutukudzi stood with her husbund to the last breath and she must be saluted on tht. On family estate she is out of touch with reality as all children are equal to their father inheritance, she must be closer to these children than before otherwise anozviruza akafarisa. Tuku's children CHILDREN vese kumberi but vanoda a good guidance since magirls vanorwira shanje dzaanaMai vavo wich is health unlike boys are partial pabarika.
mmmm, Newsday manyadzisa apa!!!! musadaro plz, this article is a very low point for this reputable newspaper. we all see who is the main cause in the Tuku family.
Not aware of the family intricacies hangu but this is an utterly useless article devoid of any facts and littered with speculation. Unless the writer can explicitly qualify her statements which i doubt, If i was Selmor or Sandra i would surely sue for defamation.
Majournalist should not be invoolved in the Mutukudzi family politics. Its normal in our African culture that families fight for zvinhu zvomufi mostly where there are children from different mothers. The best people to handle the politics are the elders of the family and they are also the best to unite the family. People should respect Daisy as the surviving and loved spouse for Tuku. People should respect life yaTuku from the onset, i.e kusiyana kwaakaitana nefirst wife and kuzoroora kwaakaita esecond wife, all that was Tuku's life and people should respect his decision as that. There should also be respect for all Tukus children, and Daisy should understand that, and Daisy should also fight to unite Tuku's children as one family/blood. Ana Selmor and Sandra should also respect Daisy as their fathers wife akachengeta baba wavo kusvikira Tuku afa. Shanje hadzivake musha, asi dzinoputsa musha.
When it comes to Tuku's legacy, whoever in his family is capable of taking the legacy ahead should do so, be it mwana wake, muzukuru or mukadzi wake, its now about capability and that person should be supported right through. I think Tuku wasn't so dull that he died without a will that deals with the distribution of his wealth. The will should be there and that should be respected by all family members concerned.
So readers are now being given space to pen articles nhai. I thought kuti gutter journalism inowanikwa ku Herald chete. Mr Editor why would you stoop so low kusvika paku publisher rubbish yakadaro. I respect Newsday Brand but apa marasika big time. You owe Mtukudzi daughters an apology. This is rubbish. The article lacks research and it is unbalanced. I think in your tomorrow's newspaper you are also going to give us their side of the story.
Newsday, this kind of paid reporting is really low of you. Your writer was obviously paid to write this one sided and judgemental piece meant to deceive the readers and sway public opinion towards Daisy. No sources are mentioned, the piece is devoid of neutrality or balance. I will no longer read anything on this website again. Goodbye.
The article in my humble opinion is rather not objective and the language used is skewed towards a personal vendeta.


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Новости буддизмаНовостиРитуал устранения препятвий в Москве 23 октября
Его Святейшество Далай-лама – лауреат Темплтоновской премии 2012
Опубликовал: Валерий Павлов 04 Май 2012. Опубликовано в Новости
Источник: сайт фонда Темплтона
The Templeton Prize
The Templeton Prize each year honors a living person who has made an exceptional contribution to affirming life's spiritual dimension, whether through insight, discovery, or practical works.
Established in 1972 by the late global investor and philanthropist Sir John Templeton, the Prize is a cornerstone of the John Templeton Foundation's international efforts to serve as a philanthropic catalyst for discoveries relating to the Big Questions of human purpose and ultimate reality.
The monetary value of the prize is set always to exceed the Nobel Prizes to underscore Templeton's belief that benefits from discoveries that illuminate spiritual questions can be quantifiably more vast than those from other worthy human endeavors.
WEST CONSHOHOCKEN, Pa., 4 May, 2012 – The Dalai Lama, the Tibetan Buddhist spiritual leader whose long-standing engagement with multiple dimensions of science and with people far beyond his own religious traditions has made him an incomparable global voice for universal ethics, nonviolence, and harmony among world religions, has won the 2012 Templeton Prize.
For decades, Tenzin Gyatso, 76, the 14th Dalai Lama — a lineage believed by followers to be the reincarnation of an ancient Buddhist leader who epitomized compassion – has vigorously focused on the connections between the investigative traditions of science and Buddhism as a way to better understand and advance what both disciplines might offer the world.
Specifically, he encourages serious scientific investigative reviews of the power of compassion and its broad potential to address the world's fundamental problems — a theme at the core of his teachings and a cornerstone of his immense popularity.
Within that search, the «big questions» he raises — such as «Can compassion be trained or taught?» – reflect the deep interest of the founder of the Templeton Prize, the late Sir John Templeton, in seeking to bring scientific methods to the study of spiritual claims and thus foster the spiritual progress that the Prize has recognized for the past 40 years.
The announcement was made this morning online at www.templetonprize.org, via email to journalists, and on Twitter via @TempletonPrize by the Templeton Prize office of the John Templeton Foundation in West Conshohocken, Pennsylvania.
The Prize will be presented to the Dalai Lama at a ceremony at St. Paul's Cathedral in London on the afternoon of Monday, May 14. A news conference with the 2012 Prize Laureate will precede the ceremony. Both events will be webcast live at www.templetonprize.org and to global media on a pool basis. Photography from the events will also be pooled.
Valued at £1.1 million (about $1.7 million or €1.3 million), the prize is the world's largest annual monetary award given to an individual and honors a living person who has made exceptional contributions to affirming life's spiritual dimension.
The announcement praised the Dalai Lama for his life's work in building bridges of trust in accord with the yearnings of countless millions of people around the globe who have been drawn by the charismatic icon's appeal to compassion and understanding for all.
«With an increasing reliance on technological advances to solve the world's problems, humanity also seeks the reassurance that only a spiritual quest can answer,» said Dr. John M. Templeton, Jr., president and chairman of the John Templeton Foundation and son of the late Prize founder. «The Dalai Lama offers a universal voice of compassion underpinned by a love and respect for spiritually relevant scientific research that centers on every single human being.»
He also noted that the Dalai Lama's remarkable record of intellectual, moral and spiritual innovations is clearly recognized by the nine Prize judges, who represent a wide range of disciplines, cultures and religious traditions. The Prize judges evaluate — independently of each other — typically 15 to 20 nominated candidates each year and then individually submit separate ballots — from which a tally then determines the selection of each year's Laureate.
The Dalai Lama responded to the prize in the humble style that has become his signature. «When I heard today your decision to give me this quite famous award, I really felt this is another sign of recognition about my little service to humanity, mainly nonviolence and unity around different religious traditions,» he said in a video available at www.templetonprize.org.
In other brief videos on the Prize website, the Dalai Lama elaborates on key issues including his call for humanity to embrace compassion as a path to peace, both personally and on a global scale. "You can develop genuine sense of concern of well-being of others, including your enemy," he states in one video. «That kind of compassion — unbiased, unlimited — needs training, awareness.»
The Right Reverend Michael Colclough, Canon Pastor at St. Paul's Cathedral, welcomed this event: «A non-violent voice of peace and reason in a calamitous world, the Dalai Lama represents core values cherished by many different faiths. The award of the Templeton Prize to the Dalai Lama under the dome of St. Paul's Cathedral will be a reminder that working towards peace and harmony is a practical and spiritual challenge to all faith communities.»
The Dalai Lama is no stranger to honors and accolades, with scores to his name. In 1989, he was awarded the Nobel Peace Prize for his advocacy of nonviolence as the path to liberation for Tibet. He becomes the second Templeton Prize Laureate to have also received the Nobel Peace Prize; Mother Teresa received the first Templeton Prize in 1973, six years before her Nobel.
He often notes that the rigorous commitment of Buddhists to meditative investment and reflection similarly follows the strict rules of investigation, proof and evidence required of science.
Among his most successful efforts is the Mind & Life Institute, co-founded in 1987 to create collaborative research between science and Buddhism. The Institute hosts conferences on subjects such as contemplative science, destructive and healing emotions, and consciousness and death. While initially beginning as quiet academic affairs, they have evolved into enormously popular public events.
In 2005, after a series of dialogues at Stanford University among the Dalai Lama, scientists in the fields of neuroscience, psychology, and medicine, and contemplative scholars, the university became the home of the Center for Compassion and Altruism Research and Education. The interdisciplinary discourse recognized that engagement between cognitive sciences and Buddhist contemplative traditions could contribute to understanding of the human mind and emotion. The center now supports and conducts rigorous scientific studies of compassion and altruistic behavior.
Many of these conferences have led to popular best sellers written or co-written by the Dalai Lama, including The Art of Happiness (1998), The Universe in a Single Atom (2005), and The Dalai Lama at MIT (2006). All told, he has authored or co-authored more than 70 books.
The Dalai Lama's love of science is also evidenced in the Science for Monks program, created in 2001 to teach science in Buddhist monastic centers of higher learning in India. The program engages Indian and Western scientists to explore connections between Tibetan Buddhist traditions and science, and teach methods of scientific inquiry in physics, quantum mechanics, cosmology, biology, neuroscience, and mathematics.
This openness to new ideas and cutting edge findings has set him in the rare pantheon of internationally respected religious leaders and also has given him a stature among secular audiences unlike any other religious leader.
Indeed, in his recommendation to the Prize committee, Richard Davidson, founder of the Center for Investigating Healthy Minds at the University of Wisconsin-Madison, wrote, "More than any other living human being, His Holiness the Dalai Lama has served humanity to catalyze the advancement of 'spiritual progress' and to help us all to cultivate a better understanding of the spiritual dimensions of human experience."
ЕСДЛ о получении этой премии
Премия Темплтона признана академическими и религиозными элитами, так, лауреата премии 1997 Й. Барбура в Кремле приветствовал как президент РАН Ю. Осипов, так и один из иерархов РПЦ. Фонд Темплтона финансирует проекты Mind and Life Institute
Источник: http://board.buddhist.ru/showthread.php?t=19811&goto=newpost
Течения буддизма
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Michael Wilder (born August 17, 1962) is an American chess grandmaster and a J.D graduate of the University of Michigan. FIDE awarded him the grandmaster title in 1988. He won the 1988 U.S. Chess Championship. He also tied for third in the 1987 U.S. Championship and tied for first at the 1987 London Open. Mr. Wilder retired from chess in the late 1980s and is now a practicing attorney. As of March 2007, his FIDE Elo rating was 2540. His current USCF rating, which has not changed since December 1994, is 2601.

Michael Wilder lives in the U.S. with his wife and two daughters. He is a practicing tax attorney at McDermott Will & Emery in Washington, D.C.

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1962 births
Living people
20th-century American Jews
American chess players
Jewish chess players
Chess grandmasters
University of Michigan Law School alumni
21st-century American Jews

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The Kardashians, Wiz Khalifa, Ty Dolla $ign at Epic 2016 Billboard...
The "2016 Billboard Music Awards" official after party was a star-studded, epic proportions at Drai's Nightclub as chart-topping celebrities took over the dazzling rooftop...
Legendary Music Superstar Celine Dion to Receive Icon Award at "2016...
Renowned musical performer Celine Dion will be honored with the esteemed ICON Award on the "2016 Billboard Music Awards" live from T-Mobile Arena on Sunday,...
Britney Spears to Receive Millennium Award and Perform Medley of Greatest...
Britney Spears will receive this year's Millennium Award at the "2016 Billboard Music Awards." The Billboard Millennium Award recognizes outstanding career achievements and influence in...


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Rediscovering the outdoors
By Cathy Cook
Published 7:00 am Saturday, March 28, 2020
With spring flowers blooming and weather warming, the outdoors offers an opportunity to connect with nature or relieve stress through physical activity.
Cross country and tennis coach Chris Wise encourages his team to rediscover the outdoors by running in a place that's pleasant, whether it's the open road or a trail in the wilderness.
"I would just encourage people, whether they're active or not active, to become active in this time," said Wise. "It's good for your ability to cope with situations where the world seems to be spinning out of control."
Physical activities like running, cycling or kayaking can give people an opportunity to relive stress, to immerse themselves in the world around them and have time alone to think through problems, said Wise.
"I go in all types of weather, because I appreciate that sense of actually experiencing the change of the seasons and watching the spring flowers and the fall flowers," said Wise.
One place to walk through nature is the Crosby Arboretum, which is offering free admission to its 64 acres of trails and native plants during this time.
Visitors can see the native azaleas or iris along the pond or visit the savanna exhibit to watch pitcher plant flowers unfurl, said Director Pat Drackett.
"The neat thing about the Arboretum is that many years ago when they designed it, they designed it for perpetual change. It's not like a typical garden where everything's irrigated and kept in one way," said Drackett. "People will see things they didn't see last week. They'll see blooms that are coming. They'll see surprises around every bend of the trail."
Every year the humidity, water and weather conditions affect which plants bloom and when, to create a constantly evolving tapestry of plant life, Drackett said.
The site has experienced an increase in visitors, with over 200 people last weekend. With that increase has been an increase in littering. Visitors using trashcans and taking all of their belongings home with them, along with following CDC recommendations to practice social distancing, will help ensure both the Arboretum wildlife and the visitors can stay healthy.
People can also connect with nature from their living room or backyard by visiting southeasternflora.com, a site that helps users identify flowers. Children could also create an at home plant press or use an iron and wax paper to press flowers, said Drackett. The Mississippi State Extension Service has directions on projects like pressing flowers, along with gardening resources on its website.
Along with physical activity, Wise likes to connect with the natural world through his vegetable garden.
"To me, it's a spiritual thing. I can get out there and plant seed, but I didn't make that happen. It's a connection with God to me. It's a sense of helping Him and helping achieve a goal of allowing the plant to be able to bear fruit," said Wise.
Wise has a half-acre of sweet corn, along with butterbeans, red beans, squash, zucchini, yellow squash, tomatoes, bell peppers, lettuce and broccoli. Growing vegetables gives him the chance to play in the dirt, but also gives a sense of self-reliance.
"You can help provide for yourself and maybe in doing that, take some of the burden off of what's needed for people that can't," said Wise.
Gardening can also give people a sense of control in an uncertain time, Wise said.
Arboretum blooms: Iris, buttercups and flame azaleas!
By Patricia R. Drackett Director, The Crosby Arboretum, Mississippi State University Assistant Extension Professor of Landscape Architecture Admission to the... read more