text:
\section{Introduction}

The Encoder-Decoder model with the attention mechanism (EncDec\xspace)~\cite{DBLP:conf/nips/SutskeverVL14,cho-EtAl:2014:EMNLP2014,bahdanau:2015:ICLR,luong:2015:EMNLP} has been an epoch-making novel development that has led to great progress being made on many natural language generation tasks, such as machine translation~\citep{bahdanau:2015:ICLR}, dialog generation~\citep{shang:2015:ACL}, and headline generation~\citep{rush:2015:EMNLP}.
Today, EncDec\xspace and its variants are widely used as a strong baseline method in these tasks.

As often discussed in the community, EncDec\xspace sometimes generates sentences with repeating phrases or completely irrelevant phrases and the reason for their generation cannot be interpreted intuitively.
Moreover, EncDec\xspace also sometimes generates sentences that lack important phrases.
We refer to these observations as the problem of odd generation (\textit{odd-gen}) in EncDec\xspace.
The following table shows typical examples of \textit{odd-gen} actually generated by a typical EncDec\xspace.
\begin{figure}[ht]
 \centering
 \tabcolsep=1pt
 \scriptsize
\begin{tabular}{lp{66mm}}
 \toprule
  \multicolumn{2}{l}{\textbf{(1) Repeating Phrases}}  \\
 \midrule
  Gold: & {\tt duran duran group fashionable again}\\
  EncDec\xspace: & {\tt duran duran duran duran}\\
  \vspace{-5.0pt}\\
 \toprule
  \multicolumn{2}{l}{\textbf{(2) Lack of Important Phrases}}  \\
 \midrule
  Gold:& {\tt graf says goodbye to tennis due to injuries}\\
  EncDec\xspace:& {\tt graf retires}\\
  \vspace{-5.0pt}\\
 \toprule
  \multicolumn{2}{l}{\textbf{(3) Irrelevant Phrases}}  \\
 \midrule
  Gold:& {\tt u.s. troops take first position in serb-held bosnia}\\
  EncDec\xspace:& {\tt precede sarajevo}\\
 \end{tabular}
 \vspace{-10.0pt}
\end{figure}

This paper tackles for reducing the \textit{odd-gen} in the task of abstractive summarization.
In machine translation literature, coverage~\citep{tu:2016:ACL, mi:2016:EMNLP} and reconstruction~\citep{tu:2017:AAAI} are promising extensions of EncDec\xspace to address the problem of \textit{odd-gen}.
However, they cannot work appropriately on abstractive summarization.
This is because,
as discussed in previous studies, {\it e.g.},~\citet{nallapati:2016:CoNLL} and~\citet{suzuki:2017:EACL},
an abstractive summarization is a {\it lossy-compression} generation ({\it lossy-gen}) task
whereas a machine translation is a {\it loss-less} generation ({\it lossless-gen}) task.
Therefore, abstractive summarization does not hold the assumption of the equivalence of semantic information in source- and target-sides,
which is a fundamental assumption of the coverage and reconstruction methods.

Recently, \citet{zhou:2017:ACL} proposed incorporating an additional gate for selecting an appropriate set of words from given source sentence.
Moreover, \citet{suzuki:2017:EACL} introduced a module for estimating the upper-bound frequency of the target vocabulary given a source sentence.
These methods essentially address individual of the \textit{odd-gen} in \textit{lossy-gen} tasks.

In contrast to the previous studies, we propose a novel approach addressing all of the \textit{odd-gen} in \textit{lossy-gen} tasks.
The basic idea of our method is to incorporate an auxiliary module in addition to EncDec\xspace for modeling token-wise correspondence of the source and target,
which includes drops of source-side tokens.
We refer to our additional module as a Source-side Prediction Module (SPM).
We put the SPM on the decoder output layer to directly estimate the correspondence during the training process of EncDec\xspace.

We conduct experiments on a widely-used headline generation dataset~\citep{rush:2015:EMNLP} to evaluate the effectiveness of the proposed method.
We show that the proposed method outperforms the current state-of-the-art method on this dataset.
Additionally, we show that
our method has an ability to learn a reasonable token-wise correspondence without knowing any true alignments,
which may help reduce the \textit{odd-gen} of EncDec\xspace.

\section{Lossy-compression Generation}
\label{sec:headline}
We address the headline generation task introduced in~\citet{rush:2015:EMNLP}, which is a typical \textit{lossy-gen} task.
The source (input) is the first sentence of a news article, and the target (output) is the headline of the article.
Suppose $I$ and $J$ represent the numbers of tokens in the source and target.
An important assumption of the headline generation (\textit{lossy-gen}) task is that the relation $I > J$ always holds,
namely, the length of the target is shorter than that of the source.
This implies that we need to optimally select salient concepts included in given source sentence.
This selection indeed increases a difficulty of the headline generation for EncDec\xspace.

Note that it is an essentially hard problem for EncDec\xspace to learn an appropriate paraphrasing of each concept in the source, which can be a main reason for generating an irrelevant headline.
In addition to this difficulty, EncDec\xspace also needs to manage the selection of concepts in the source;
e.g, discarding the excessive amount of concepts from the source would cause a headline being too short,
and utilizing the same concept multiple times in the source may lead a redundant headline.

\section{Encoder-Decoder Model with Attention Mechanism (EncDec\xspace)}
\label{sec:base_model}

\begin{figure*}[t]
    \center
    \includegraphics[width=\hsize, scale=0.8]{04-crop.pdf}
    \caption{
    Overview of EncDec+SPM{}.
    The module inside the dashed rectangular box represents the SPM.
    The SPM predicts the probability distribution over the source vocabulary $\bm{q}_{j}$ at each time step $j$.
    After predicting all the time steps, the SPM compares the sum of the predictions $\tilde{\bm{q}}$ with the sum of the source-side tokens~$\tilde{\bm{x}}$ as an objective function $\ell_{\rm src}$.}
    \label{fig:overview}
\end{figure*}

This section briefly describes EncDec\xspace as the baseline model of our method\footnote{Our model configuration follows EncDec\xspace described in~\citet{luong:2015:EMNLP}.}.
To concisely explain EncDec\xspace, let us consider that the input of EncDec\xspace is a sequence of one-hot vectors $\bm{X}$ obtained from given source-side sentence.
Let $\bm{x}_i \in \{0,1\}^{V_{s}}$ represent the one-hot vector of $i$-th token in $\bm{X}$,
where $V_{s}$ represent a number of instances (tokens) in the source-side vocabulary $\mathcal{V}_{s}$.
We introduce $\bm{x}_{1:I}$ to represent $(\bm{x}_1,\dots,\bm{x}_{I})$ by a short notation, namely, $\bm{X} = \bm{x}_{1:I}$.
Similarly, let $\bm{y}_j \in \{0,1\}^{V_{t}}$ represent the one-hot vector of $j$-th token in the target-side sequence $\bm{Y}$,
where $V_{t}$ is a number of instances (tokens) in the target-side vocabulary $\mathcal{V}_{t}$.
Here, we define that $\bm{Y}$ always contains two additional one-hot vectors of special tokens $ \langle \textit{bos} \rangle ${} for $\bm{y}_0$ and $ \langle \textit{eos} \rangle ${} for $\bm{y}_{J+1}$, respectively.
Thus, $\bm{Y} = \bm{y}_{0:J+1}$, whose length is always $J+2$.
Then, EncDec\xspace models the following conditional probability:
\begin{align}
 p(\bm{Y} | \bm{X}) &=  \prod_{j=1}^{J+1} p( \bm{y}_{j} | \bm{y}_{0:j-1}, \bm{X})
 .
 \label{e:condpro}
\end{align}

EncDec\xspace encodes a source one-hot vector sequence $\bm{x}_{1:I}$,
and generates a hidden state sequence $\bm{h}_{1:I}$,
where $\bm{h}_{i} \in \mathbb{R}^{H}$ for all $i$, and $H$ is the size of the hidden state.
Then, the decoder with the attention mechanism computes the vector $\bm{z}_{j} \in \mathbb{R}^H$ at every decoding time step $j$ as:
\begin{align}
 \bm{z}_{j} &=  \mbox{AttnDec}(\bm{y}_{j-1}, \bm{h}_{1:I})
 .
 \label{e:atten_dec}
 \end{align}
We apply RNN cells to both encoder and the decoder.
Then, EncDec\xspace generates a target-side token based on the probability distribution $\bm{o}_j \in \mathbb{R}^{V_{t}}$ as:
\begin{equation}
  \bm{o}_j = \softmax(\bm{W}_{o}\bm{z}_{j} + \bm{b}_{o}),
  \label{e:predict}
\end{equation}
where $\bm{W}_{o} \in \mathbb{R}^{V_{t} \times H}$ is a parameter matrix and $\bm{b}_{o} \in \mathbb{R}^{V_{t}}$ is a bias term\footnote{For more detailed definitions of the encoder, decoder, and attention mechanism, see Appendices \ref{sec:append_encoder} and \ref{sec:append_decoder}, respectively.}.

To train EncDec\xspace, let $\mathcal{D}$ be a training data of headline generation that consists of source-headline sentence pairs.
Let $\theta$ represent all parameters in EncDec\xspace.
Then, we seek the optimal parameter set $\hat{\theta}$ that minimizes the following objective function $G_{1}(\theta)$ on the given training data $\mathcal{D}$:
\begin{align}
 G_{1}(\theta) &= \frac{1}{\vert \mathcal{D} \vert} \sum_{(\bm{X}, \bm{Y}) \in \mathcal{D}} \ell_{\rm trg} (\bm{Y}, \bm{X}, \theta), \nonumber\\
 \ell_{\rm trg}(\bm{Y}, \bm{X}, \theta) &= - \log \Big(
  p( \bm{Y} | \bm{X}, \theta) \Big)
 .
 \label{eq:loss1}
 \end{align}
Since $\bm{o}_{j}$ for each $j$ is a vector representation of the probabilities of $p( \hat{\bm{y}} | \bm{y}_{0:j-1}, \bm{X}, \theta)$ over the target vocabularies $\hat{\bm{y}} \in \mathcal{V}_{t}$,
we can calculate $\ell_{\rm trg}$ as:
\begin{align}
 \ell_{\rm trg}(\bm{Y}, \bm{X}, \theta)
 \ &= -\sum_{j=1}^{J+1}{\bm{y}_{j}^{\top} \cdot \log{ \big( \bm{o}_{j} \big)}}
 .
 \label{eq:loss1_1}
 \end{align}

In the inference step, we search for the best target sequence with the trained parameters.
We use a beam search to find the target sequence that maximizes the product of the conditional probabilities as described in Equation \ref{e:condpro}.
Among several stopping criteria for the beam search \citep{huang:2017:EMNLP}, we adopt the widely used ``shrinking beam'' implemented in RNNsearch~\citep{bahdanau:2015:ICLR} \href{https://github.com/lisa-groundhog/GroundHog}{https://github.com/lisa-groundhog/GroundHog}.

\section{Proposed Method: Source Prediction Module (SPM)}
\label{sec:proposed}
In Section~\ref{sec:headline}, we assumed that the selection of concepts in the source is an essential part for the \textit{odd-gen}.
Thus, our basic idea is to extend EncDec\xspace that can manage the status of utilization of the concepts during the generation.
More precisely, instead of directly managing concepts since they are not well-defined,
we consider to model token-wise correspondence of the source and target,
including the information of source-side tokens that cannot be aligned to any target-side tokens.

Figure \ref{fig:overview} shows the overview of the proposed method, SPM.
During the training process of EncDec\xspace, the decoder estimates the probability distribution over source-side vocabulary, which is $\bm{q}_{j} \in \mathbb{R}^{V_{s}}$, in addition to that of the target-side vocabulary, $\bm{o}_{j} \in \mathbb{R}^{V_{t}}$, for every time step $j$.
Note that the decoder continues to estimate the distributions up to the source sequence length $I$ regardless of the target sequence length $J$.
Here, we introduce a special token $ \langle \textit{pad} \rangle ${} in the target-side vocabulary, and assume that $ \langle \textit{pad} \rangle ${} is repeatedly generated after finishing the generation of all target-side tokens as correct target tokens.
This means that we always assume that
the numbers of tokens in the source and target is the same,
and thus, our method allows to put one-to-one correspondence into practice in the \textit{lossy-gen} task.
In this way, EncDec\xspace can directly model token-wise correspondence of source- and target-side tokens on the decoder output layer, which includes the information of {\it unaligned} source-side tokens by aligning to $ \langle \textit{pad} \rangle ${}.

Unfortunately, standard headline generation datasets have no information of true one-to-one alignments between source- and target-side tokens.
Thus, we develop a novel method for train a token-wise correspondence model indirectly by an unsupervised learning manner.
Specifically, we minimize a sentence-level loss instead of a token-wise alignment loss.
We describe the details in the following sections.

\subsection{Model Definition}
In Figure~\ref{fig:overview}, the module inside the dashed line represents the SPM.
First, the SPM calculates a probability distribution over the source vocabulary $\bm{q}_{j} \in \mathbb{R}^{V_{s}}$ at each time step $j$ in the decoding process by using the following equation:
\begin{equation}
  \bm{q}_{j}=\softmax(\bm{W}_{q}\bm{z}_{j} + \bm{b}_{q}),
\end{equation}
where $\bm{W}_{q} \in \mathbb{R}^{V_{s} \times H}$ is a parameter matrix like $\bm{W}_{o}$ in Equation~\ref{e:predict},
and $\bm{b}_{q} \in \mathbb{R}^{V_{s}}$ is a bias term.
As described in Section~\ref{sec:base_model}, EncDec\xspace calculates a probability distribution over the target vocabulary $\bm{o}_j$ from $\bm{z}_{j}$.
Therefore, EncDec\xspace with the SPM jointly estimates the probability distributions over the source and target vocabularies from the same vector $\bm{z}_{j}$.

Next, we define $\bm{Y}' = \bm{y}_{0:I}$
as a concatenated sequence of $\bm{Y}$ and a sequence of one-hot vectors of the special token $ \langle \textit{pad} \rangle ${} with the length $I-(J+1)$,
where $\bm{y}_{J+1}$ is a one-hot vector of $ \langle \textit{eos} \rangle ${}, and $\bm{y}_{j}$ for each $j \in \{J+2,\dots,I\}$ is a one-hot vector of $ \langle \textit{pad} \rangle ${}.
Then, we also define $\bm{Y}'=\bm{Y}$ if and only if $J+1=I$.
Note that the length of $\bm{Y}'$ is always equal to or longer than that of $\bm{Y}$, that is, $|\bm{Y}'| \geq |\bm{Y}|$ since the headline generation always assumes $I > J$ as described in Section~\ref{sec:headline}.
Figure~\ref{fig:overview} also shows an actual example of $\bm{Y}'$.

Let $\tilde{\bm{x}}$ and $\tilde{\bm{q}}$ be the sums of the all one-hot vectors in source sequence $\bm{x}_{1:I}$ and the all prediction of the SPM $\bm{q}_{1:I}$, respectively, that is,
\begin{equation}
 \tilde{\bm{x}} = \sum_{i=1}^{I}{\bm{x}_i}
  ,
  \quad \mbox{and} \quad

  \tilde{\bm{q}}   = \sum_{j=1}^{I}{\bm{q}}_{j}
  .
\end{equation}
Note that $\tilde{\bm{x}}$ is a vector representation of the occurrence (or bag-of-words representation) of each source-side vocabulary appeared in the given source sequence.

Then, EncDec\xspace with the SPM models the following conditional probability:
\begin{equation}
 p(\bm{Y}', \tilde{\bm{x}} | \bm{X}) = p(  \tilde{\bm{x}} | \bm{Y}', \bm{X})p(\bm{Y}'| \bm{X})
  .
 \label{e:condpro_joint}
\end{equation}
We define $p( \bm{Y}' | \bm{X})$ as follows:
\begin{align}
 p(\bm{Y}'| \bm{X}) = \prod_{j=1}^{I} p( \bm{y}_{j} | \bm{y}_{0:j-1}, \bm{X})
 ,
 \label{e:condpro_encdec_spm}
\end{align}
which is identical to $p( \bm{Y} | \bm{X})$ in Equation~\ref{e:condpro} except substituting $I$ for $J$ to model the probabilities of $ \langle \textit{pad} \rangle ${} that appear from $j=I-(J+1)$ to $j=I$.
Then, we define $p( \tilde{\bm{x}}| \bm{Y}', \bm{X})$ as follows:
\begin{equation}
 p( \tilde{\bm{x}}| \bm{Y}', \bm{X}) = \frac{1}{Z}\exp\left( \frac{-\| \tilde{\bm{q}} - \tilde{\bm{x}} \|_{2}^{2}}{C} \right)
 ,
 \label{e:condpro_spm}
\end{equation}
where $Z$ is a normalization term, and $C$ is a hyper-parameter that controls the sensitivity of the distribution.

\subsection{Training SPM}
\label{subsec:train_spm}

Let $\gamma$ represent the parameter set of SPM.
Then, we define the loss function for SPM as follows:
\begin{align}
 \ell_{\rm src}(\tilde{\bm{x}}, \bm{X}, \bm{Y}', \gamma, \theta)  &= -\log \Big( p(\tilde{\bm{x}} | \bm{Y}', \bm{X},  \gamma, \theta) \Big)
 \nonumber
 .
\end{align}
From Equation~\ref{e:condpro_spm}, we can derive $\ell_{\rm src}$ as
\begin{align}
  \ell_{\rm src}(\tilde{\bm{x}}, \bm{X}, \bm{Y}', \gamma, \theta)  = \frac{1}{C} \| \tilde{\bm{q}} - \tilde{\bm{x}} \|_{2}^{2} + \log (Z).
\end{align}
We can discard the second term of the RHS, that is $\log (Z)$, since this is independent from $\gamma$ and $\theta$.

We jointly train the SPM and EncDec\xspace.
Therefore, we regard the sum of SPM loss ($\ell_{\rm src}$) and EncDec\xspace loss ($\ell_{\rm trg}$) as an objective loss function.
Formally, we train the SPM with EncDec\xspace by minimizing the following objective function $G_2$:
\begin{align}
 G_{2}(\theta, \gamma) =& \frac{1}{\vert \mathcal{D} \vert} \sum_{(\bm{X}, \bm{Y}) \in \mathcal{D}}
 \Bigr(\ell_{\rm trg} (\bm{Y}', \bm{X}, \theta) \nonumber \\
 &+
 \ell_{\rm src}(\tilde{\bm{x}}, \bm{X}, \bm{Y}', \gamma, \theta)  \Bigr)
 \label{e:obj_spm}
\end{align}
Intuitively, our learning framework can be interpreted as the multi-task learning of two different tasks, $\ell_{\rm trg}$ and $\ell_{\rm src}$.

\subsection{Inference}
It is unnecessary to compute the SPM for the purpose of evaluating decoded target sequences.
Thus, we can utilize the identical procedure of beam search used in the base EncDec\xspace briefly introduced in Section \ref{sec:base_model}.
Similarly, it is also unnecessary to produce $ \langle \textit{pad} \rangle ${} after generating $ \langle \textit{eos} \rangle ${}.
Thus, the actual computational cost of our method for the standard evaluation phase is exactly the same as the base EncDec\xspace.

\section{Experiment}

\subsection{Dataset}
\label{subsec:dataset}
The origin of the headline generation dataset used in our experiments is identical to that used in~\citet{rush:2015:EMNLP},
namely, the dataset consists of pairs comprising the first sentence of each article and its headline from the annotated English Gigaword corpus~\citep{napoles:2012:AG}.

We slightly changed the data preparation procedure to achieve a more realistic and reasonable evaluation
since
the widely-used provided evaluation dataset already contains $ \langle \textit{unk} \rangle ${}, which is a replacement of all low frequency words.
This is because the data preprocessing script provided by the authors of \citet{rush:2015:EMNLP}%
\footnote{\href{https://github.com/facebookarchive/NAMAS}{https://github.com/facebookarchive/NAMAS}.}
automatically converts low frequency words into $ \langle \textit{unk} \rangle ${}.
As a result, generating $ \langle \textit{unk} \rangle ${} can be treated as correct in evaluatio
\footnote{In a personal communication with the first author of \citet{zhou:2017:ACL}, we found that their model decodes $ \langle \textit{unk} \rangle ${} in the same form as it appears in the test set, and $ \langle \textit{unk} \rangle ${} had a positive effect on the final performance of the model.}.
To penalize $ \langle \textit{unk} \rangle ${} in system outputs during the evaluation,
we removed $ \langle \textit{unk} \rangle ${} replacement procedure from the preprocessing script.
We believe this is a more realistic evaluation setting.

\citet{rush:2015:EMNLP} defined the training, validation and test split, which contain approximately 3.8M, 200K and 400K source-headline pairs, respectively.
We used the entire training split for training as in the previous studies.
We randomly sampled test data and validation data from the validation split since we found that the test split contains many noisy instances.
Finally, our validation and test data consist of 8,000 and 10,000 source-headline pairs, respectively.
Note that they are relatively large compared with the previously used datasets, and they do not contain $ \langle \textit{unk} \rangle ${}.

We also evaluated our experiments on the test data used in the previous studies.
To the best of our knowledge, two test sets from the Gigaword are publicly available by~\citet{rush:2015:EMNLP}%
\footnote{\href{https://github.com/harvardnlp/sent-summary}{https://github.com/harvardnlp/sent-summary}}
 and~\citet{zhou:2017:ACL}%
\footnote{\href{https://res.qyzhou.me}{https://res.qyzhou.me}}.
Note that both test sets contain $ \langle \textit{unk} \rangle ${}.

Table \ref{table:data_summary} summarizes the characteristics of each dataset used in our experiments.

\begin{table}[t!]
\centering
\small
 \tabcolsep 0.8mm
 \begin{tabular}{lcrrl}
\toprule
 \                & use $ \langle \textit{unk} \rangle ${}? &       size & \#.ref & source (split)\\
 \midrule
 Training         & No       & 3,778,230  &  1      & Giga (train) \\
 Validation       & No       &     8,000  &  1      & Giga (valid)  \\
 Test (ours)      & No       &    10,000  &  1      & Giga (valid)  \\
 \midrule
 Test (Rush)      & Yes      &     1,951  &  1      & Giga (test)  \\
 Test (Zhou)      & Yes      &     2,000  &  1      & Giga (valid)  \\
\bottomrule
\end{tabular}
\caption{Characteristics of each dataset used in our experiments}
\label{table:data_summary}
\end{table}

\subsection{Evaluation Metric}
We evaluated the performance in ROUGE-1 (RG-1), ROUGE-2 (RG-2) and ROUGE-L (RG-L)\footnote{We restored sub-words to the standard token split for the evaluation.}.
We report the F1 value as given in a previous study%
\footnote{
ROUGE script option is: ``\texttt{-n2 -m -w 1.2}''
}.
We computed the ROUGE scores by using the official ROUGE script (version 1.5.5).

\subsection{Comparative Methods}
To investigate the effectiveness of the SPM, we evaluate the performance of the EncDec\xspace with the SPM.
In addition, we investigate whether the SPM improves the performance of the state-of-the-art method: EncDec+sGate\xspace.
Thus, we compare the following methods on the same training setting.

\noindent\textbf{EncDec\xspace} This is the implementation of the base model explained in Section \ref{sec:base_model}.

\noindent\textbf{EncDec+sGate\xspace} To reproduce the state-of-the-art method proposed by~\citet{zhou:2017:ACL}, we combined our re-implemented selective gate (sGate) with the encoder of EncDec\xspace.

\noindent\textbf{EncDec+SPM} We combined the SPM with the EncDec\xspace as explained in Section \ref{sec:proposed}.

\noindent\textbf{EncDec+sGate+SPM} This is the combination of the SPM with the EncDec+sGate\xspace.

\subsection{Implementation Details}
\begin{table}[t!]
\centering
 \tabcolsep 0.1mm
 \small
\begin{tabularx}{\columnwidth}{lX}
\toprule
Source Vocab. Size $V_{s}$    & \multicolumn{1}{r}{5131}                              \\
Target Vocab. Size $V_{t}$    & \multicolumn{1}{r}{5131}                              \\
Word Embedding Size D    & \multicolumn{1}{r}{200}                               \\
Hidden State Size H & \multicolumn{1}{r}{400}                               \\
\midrule
RNN Cell               & Long Short-Term Memory (LSTM) \citep{hochreiter:1997:long}                                       \\
Encoder RNN Unit       & 2-layer bidirectional-LSTM                                       \\
Decoder RNN Unit       & 2-layer LSTM with attention \citep{luong:2015:EMNLP}                           \\
\midrule
Optimizer              & Adam \citep{kingma:2015:ICLR}                                                 \\
Initial Learning Rate  & 0.001                                                 \\
Learning Rate Decay    & 0.5 for each epoch (after epoch 9)                    \\
Weight $C$ of $\ell_{\rm src}$    & 10                                     \\
Mini-batch Size        & 256 (shuffled at each epoch)                          \\
Gradient Clipping      & 5                                                     \\
Stopping Criterion     & max 15 epochs with early stopping                        \\
Regularization         & Dropout (rate 0.3)                                 \\
Beam Search            & Beam size 20 with the length normalization           \\
\bottomrule
\end{tabularx}
\caption{Configurations used in our experiment}
\label{table:optim}
\end{table}

\begin{table*}[t]
\small
 \centering
 \tabcolsep 1mm
\begin{adjustbox}{width=\textwidth}
\begin{tabular}{lccclccclccc}
\toprule
               & \multicolumn{3}{c}{\textbf{Gigaword Test (Ours)}} &  & \multicolumn{3}{c}{\textbf{Gigaword Test (Rush)}} &  & \multicolumn{3}{c}{\textbf{Gigaword Test (Zhou)}} \\
               \cmidrule{2-4}\cmidrule{6-8}\cmidrule{10-12}
                                   & RG-1      & RG-2     & RG-L     &  & RG-1   & RG-2   & RG-L   &  & RG-1   & RG-2   & RG-L   \\
               \midrule
EncDec{}                         & 45.74     & 23.80    & 42.95    &  & 34.52  & 16.77  & 32.19  &  & 45.62  & 24.26  & 42.87   \\
EncDec+sGate{} (our impl. of SEASS) & 45.98     & 24.17    & 43.16    &  & 35.00  & 17.24  & 32.72  &  & 45.96  & 24.63  & 43.18   \\
EncDec+SPM{}$ {}^{\dag} ${}                 & 46.18     & 24.34    & 43.35    &  & 35.17  & 17.07  & 32.75  &  & 46.21  & 24.78  & 43.27   \\
EncDec+sGate+SPM{}$ {}^{\dag} ${}                & \textbf{46.41}     & \textbf{24.58}    & \textbf{43.59}    &  & \textbf{35.79}  & \textbf{17.84}  & \textbf{33.34}  &  & \textbf{46.34}  & \textbf{24.85}  & \textbf{43.49} \\
\midrule
ABS~\citep{rush:2015:EMNLP}        & -       & -      & -      &  & 29.55  & 11.32  & 26.42  &  & 37.41  & 15.87  & 34.70  \\
SEASS~\citep{zhou:2017:ACL}        & -       & -      & -      &  & 36.15  & 17.54  & 33.63  &  & \textbf{46.86}  & \textbf{24.58}  & \textbf{43.53} \\
DRGD~\citep{li:2017:EMNLP}         & -       & -      & -      &  & 36.27  & \textbf{17.57}  & 33.62  &  & -    & -    & -  \\
WFE~\citep{suzuki:2017:EACL}       & -       & -      & -      &  & \textbf{36.30}  & 17.31  & \textbf{33.88}  &  & -    & -    & -  \\
conv-s2s~\citep{gehring:2017:arxiv}& -       & -      & -      &  & 35.88  & 17.48  & 33.29  &  & -    & -    & -   \\
\bottomrule
\end{tabular}
\end{adjustbox}
\caption{Full length ROUGE F1 evaluation results. The top row shows the results on our evaluation setting. $\dag$ is the proposed model. The bottom row shows published scores reported in previous studies\protect\footnotemark. Note that (1) SEASS consists of essentially the same architecture as our implemented \protectEncDec+sGate\xspace, and (2) the top row is not directly comparable to the bottom row due to the difference of the preprocessing and the vocabulary settings (see discussions in Section~\protect\ref{subsec:results}).}
\label{table:result_summary}
\end{table*}
\footnotetext{
\citet{raffel:2017:ICML} also evaluates their model on Gigaword test set.
However, in a personal contact with the authors, we found that their evaluation setting, including the test data, critically differs from previous studies.
Thus, we do not present their published results on the table.
}

Table~\ref{table:optim} summarizes hyper-parameters and model configurations.
We selected the settings commonly-used in the previous studies, \textit{e.g.},~\cite{rush:2015:EMNLP,nallapati:2016:CoNLL,suzuki:2017:EACL}.

We constructed the vocabulary set using Byte-Pair-Encoding\footnote{\href{https://github.com/rsennrich/subword-nmt}{https://github.com/rsennrich/subword-nmt}} (BPE) \citep{sennrich:2016:ACL} to handle low frequency words, as it is now a common practice in neural machine translation.
The BPE merge operations are jointly learned from the source and the target.
We set the number of the BPE merge operations at $5,000$.
We used the same vocabulary set for both the source $\mathcal{V}_{s}$ and the target $\mathcal{V}_{t}$.
After applying the BPE, we found out that 0.1\% of the training split contained a longer target than source.
We removed such data before training.

\subsection{Results}
\label{subsec:results}
Table~\ref{table:result_summary} summarizes results on all test data.
We divide the table into two parts with a horizontal line.
The top row shows the results on our training procedure, and the bottom row shows the results reported in previous studies.
Note that the top row is not directly comparable to the bottom row due to the difference of the preprocessing and the vocabulary settings.

The top row of Table~\ref{table:result_summary} shows that EncDec+SPM{} outperformed both EncDec{} and EncDec+sGate{}.
This result indicates that the SPM can improve the performance of EncDec{}.
Moreover, it is noteworthy that EncDec+sGate+SPM{} achieved the best performance in all metrics even though EncDec+sGate{} consists of essentially the same architecture as the current state-of-the-art model, \textit{i.e.}, SEASS.

The bottom row of Table \ref{table:result_summary} shows the results of previous methods.
They often achieved higher ROUGE scores than our models especially in Gigaword Test (Rush) and Gigaword Test (Zhou).
However, this does not immediately imply that our method is inferior to the previous methods.
This observation is basically derived by the inconsistency of vocabulary.
In detail, our training data does not contain $ \langle \textit{unk} \rangle ${} because we adopted the BPE to construct vocabulary.
Thus, our models suffered from $ \langle \textit{unk} \rangle ${} in dataset when we conducted the evaluation on Gigaword Test (Rush) and Gigaword Test (Zhou).
Recall that, as described earlier, EncDec+sGate\xspace has the same model architecture as SEASS.
Then, the similar observation can also be found in EncDec+sGate{} and SEASS.

\begin{figure}[t]
  \begin{minipage}[b]{\hsize}
    \centering
    \includegraphics[keepaspectratio, width=\hsize]{repeat_02-crop.pdf}
    \subcaption{Repeating Phrases}\label{sfig:repeat_gen}
  \end{minipage}
  \begin{minipage}[b]{\hsize}
    \centering
    \includegraphics[keepaspectratio, width=\hsize]{comp02-crop.pdf}
    \subcaption{Lack of Important Phrases}\label{sfig:short_summary}
  \end{minipage}
  \caption{Comparison between EncDec{} and EncDec+SPM{} on the number of sentences that potentially contain the \textit{odd-gen}. The smaller examples mean reduction of the \textit{odd-gen}.}\label{fig:odd_gen_reduction}
\end{figure}

\begin{figure*}[t]
 \centering
 \tabcolsep=1pt
 \scriptsize

 \begin{tabularx}{\textwidth}{lXlX}
  \midrule
  \multicolumn{4}{l}{\textbf{(1) Repeating Phrases}}  \\
  \midrule
  Gold: & \texttt{duran duran group fashionable again} & Gold: & \texttt{community college considers building \$ \#\# million technology}\\
  EncDec{}: & \texttt{duran duran duran duran} & EncDec{}: & \texttt{college college colleges learn to get ideas for tech center}\\
  EncDec+SPM{}: & \texttt{duran duran fashionably cool once again} & EncDec+SPM{}: & \texttt{l.a. community college officials say they 'll get ideas}\\

  \midrule
  \multicolumn{4}{l}{\textbf{(2) Lack of Important Phrases}}  \\
  \midrule
  Gold:& \texttt{graf says goodbye to tennis due to injuries} & Gold: & \texttt{new york 's primary is most suspenseful of super tuesday races}\\
  EncDec{}:& \texttt{graf retires} & EncDec{}: & \texttt{n.y.}\\
  EncDec+SPM{}:& \texttt{german tennis legend steffi graf retires} & EncDec+SPM{}: & \texttt{new york primary enters most suspenseful of super tuesday contests}\\

  \midrule
  \multicolumn{4}{l}{\textbf{(3) Irrelevant Phrases}}  \\
  \midrule
  Gold:& \texttt{u.s. troops take first position in serb-held bosnia} & Gold: & \texttt{northridge hopes confidence does n't wane}\\
  EncDec{}:& \texttt{precede sarajevo} & EncDec{}: & \texttt{csun 's csun}\\
  EncDec+SPM{}:& \texttt{u.s. troops set up first post in bosnian countryside} & EncDec+SPM{}: & \texttt{northridge tries to win northridge men 's basketball team}\\
 \end{tabularx}
 \caption{Examples of generated summary. ``Gold'' indicates the reference headline. The proposed EncDec+SPM{} model successfully reduces \textit{odd-gen}.}
 \label{fig:raw_generation}
\end{figure*}

\section{Discussion}
\label{sec:discussion}
The motivation of the SPM is to prevent the \textit{odd-gen} with one-to-one correspondence between the source and the target.
Thus, in this section, we investigate whether the SPM reduces the \textit{odd-gen} in comparison to EncDec{}.

\subsection{Does SPM Reduce \textit{odd-gen}?}
\label{subsec:example}
For quantitative analysis, we hope to compute the statistics of generated sentences containing \textit{odd-gen}.
However, it is hard to detect the \textit{odd-gen} correctly.
Thus, we alternatively obtain a pseudo count of each type of \textit{odd-gen} as follows.

\noindent\textbf{Repeating phrases} We assume that a model causes repeating phrases if the model outputs the same token more than once.
Therefore, we compute the frequency of tokens that occur more than once in the generated headlines.
However, some phrases might occur more than once in the gold data.
To take care of this case, we subtract the frequency of tokens in the reference headline from the above calculation result.
Then, we regard the result of the subtraction as the number of repeating phrases in each generated headline.

\noindent\textbf{Lack of important phrases} We assume the generated headline which is shorter than the gold as containing the lack of important phrase.
Thus, we compute the difference of gold headline length and the generated headline length.

\noindent\textbf{Irrelevant phrases} We consider that the improvement of ROUGE scores implies the reduction of irrelevant phrases because we believe that the ROUGE penalizes irrelevant phrases.

Figure \ref{fig:odd_gen_reduction} shows the number of repeating phrases and lack of important phrases in Gigaword Test (Ours).
This figure indicates that EncDec+SPM{} reduces the \textit{odd-gen} in comparison to EncDec{}.
Thus, we consider the SPM accomplished the reduction of the \textit{odd-gen}.
Figure \ref{fig:raw_generation} shows sampled headlines actually generated by EncDec{} and EncDec+SPM{}.
We can clearly find that the outputs of EncDec{} contain the \textit{odd-gen} while those of the EncDec+SPM{} do not.
These examples also demonstrate that SPM successfully reduces \textit{odd-gen}.

\subsection{Visualizing SPM and Attention}
\label{subsec:visualize}
We visualize the prediction of the SPM and the attention distribution to see the acquired token-wise correspondence between the source and the target.
Specifically, we feed the source-target pair $(\bm{X}, \bm{Y})$ to EncDec{} and EncDec+SPM{}, and then collect the source-side prediction $(\bm{q}_{1},\dots,\bm{q}_{I})$ of EncDec+SPM{} and the attention distribution $(\bm{\alpha}_{1},\dots,\bm{\alpha}_{J})$ of EncDec{}.
We compute the attention distribution using the following equation:
\begin{equation}
  \bm{\alpha}_{j}[i] = \frac{\exp(\tp{\bm{h}_{i}} \bm{W}_{\alpha} \vec{\bm{z}}_{j})}{\sum_{i=1}^{I}\exp(\tp{\bm{h}_{i}} \bm{W}_{\alpha} \vec{\bm{z}}_{j})}
  \label{e:context_vec}
\end{equation}
where $\bm{W}_{\alpha} \in \mathbb{R}^{H \times H}$ is a parameter matrix, and $\bm{\alpha}_{j}[i]$ denotes the $i$-th element of $\bm{\alpha}_{j}$.
Here, $\vec{\bm{z}}_{j} \in \mathbb{R}^{H}$ is the decoder hidden state.
For source-side prediction, we extracted the probability of each token $\bm{x}_{i} \in \bm{X}$ from $\bm{q}_{j}, j \in \{1,\dots,I\}$.

\begin{figure}[t]
  \begin{minipage}[b]{\hsize}
    \centering
    \includegraphics[keepaspectratio, width=\hsize]{attn_map_small-crop.pdf}
    \subcaption{Attention distribution of EncDec{}}\label{sfig:attn}
  \end{minipage}
  \begin{minipage}[b]{\hsize}
    \centering
    \includegraphics[keepaspectratio, width=\hsize]{src_map_small-crop.pdf}
    \subcaption{Source-side prediction of EncDec+SPM{}}\label{sfig:src}
  \end{minipage}
  \caption{
    Visualization of EncDec{} and EncDec+SPM{}.
    The x-axis and y-axis of the figure correspond to the source sequence and the target sequence respectively.
    Token in the brackets represent the source-side token that is aligned with the target-side token of that time step.
  }\label{fig:heatmap}
\end{figure}

Figure \ref{fig:heatmap} shows an example of the heat map\footnote{For more visualizations, see Appendix \ref{sec:append_visualization}}.
We used Gigaword Test (Ours) as an input.
The brackets in the y-axis represents the source-side token that is aligned with target-side token.
We selected the aligned tokens in the following manner:
For the attention (Figure \ref{sfig:attn}), we select the token with the largest attention value.
For the SPM (Figure \ref{sfig:src}), we select the token with the largest probability over the whole vocabulary $\mathcal{V}_{s}$.

Figure \ref{sfig:attn} indicates that most of the attention distribution is concentrated at the end of the sentence.
As a result, attention provides poor token-wise correspondence between the source and the target.
For example, target-side tokens ``tokyo'' and ``end'' are both aligned with the source-side sentence period.
In contrast, Figure \ref{sfig:src} shows that the SPM provides the almost discrete correspondence between the source and the target.
The source sequence ``tokyo stocks closed higher'' is successfully aligned with the target ``tokyo stocks end higher''.
Moreover, the SPM aligned unimportant tokens for the headline such as ``straight'' and ``tuesday'' with $ \langle \textit{pad} \rangle ${} tokens.
Thus, this example suggests that the SPM achieved superior token-wise correspondence to the attention.
It is noteworthy that the SPM captured a one-to-one correspondence even though we trained the SPM without correct alignment information.

\section{Related Work}
In the field of neural machine translation, several methods have been proposed to solve the \textit{odd-gen}.
The coverage model~\citep{mi:2016:EMNLP,tu:2016:ACL} enforces the decoder to attend to every part of the source sequence to translate all semantic information in the source.
The reconstructor~\citep{tu:2017:AAAI} trains the translation model from the decoded target into the source.
Moreover, \citet{weng:2017:EMNLP} proposed the method to predict the untranslated words from the decoder at each time step.
These methods are designed to convert all contents in the source into a target language, since machine translation is a \textit{lossless-gen} task.
In contrast, we proposed SPM to model both paraphrasing and discarding to reduce the \textit{odd-gen} in \textit{lossy-gen} task.

We focused on the headline generation which is a well-known \textit{lossy-gen} task.
Recent studies have actively applied the EncDec\xspace to this task \citep{rush:2015:EMNLP, chopra:2016:NAACL, nallapati:2016:CoNLL}.
In the headline generation task, \citet{zhou:2017:ACL} and \citet{suzuki:2017:EACL} tackled a part of the \textit{odd-gen}.
\citet{zhou:2017:ACL} incorporated an additional gate (sGate) into the encoder to select appropriate words from the source.
\citet{suzuki:2017:EACL} proposed the frequency estimation module to reduce the repeating phrases.
Our motivation is similar to them, but we addressed solving all types of \textit{odd-gen}.
In addition, we can combine these approaches with the proposed method.
In fact, we reported in Section \ref{subsec:results} that the SPM can improve the performance of sGate with EncDec\xspace.

Apart from the \textit{odd-gen}, some studies proposed methods to improve the performance of the headline generation task.
\citet{takase:2016:EMNLP} incorporated AMR~\cite{banarescu-EtAl:2013:LAW7-ID} into the encoder to use the syntactic and semantic information of the source.
\citet{nallapati:2016:CoNLL} also encoded additional information of the source such as TF-IDF, part-of-speech tags and named entities.
\citet{li:2017:EMNLP} modeled the typical structure of a headline, such as ``Who Action What'' with a variational auto-encoder.
These approach improved the performance of the headline generation but it is unclear whether they can reduce the \textit{odd-gen}.

\section{Conclusion}
In this paper, we discussed an approach for reducing the \textit{odd-gen} in \textit{lossy-gen} tasks.
The proposed SPM learns to predict the one-to-one correspondence of tokens in the source and the target.
Experiments on the headline generation task show that the SPM improved the performance of typical EncDec{}, and outperformed the current state-of-the-art model.
Furthermore, we demonstrated that the SPM reduced the \textit{odd-gen}.
In addition, SPM obtained token-wise correspondence between the source and the target without any alignment data.

\section*{Acknowledgments}
We thank Qingyu Zhou, Colin Raffel and Peter J. Liu for helpful discussions.
We also thank Sosuke Kobayashi for helpful comments regarding the efficient implementation.

redpajama_set_name: RedPajamaArXiv


text:
@interface TyphoonRuntimeInjectionsTests : XCTestCase

@end

@implementation TyphoonRuntimeInjectionsTests
{
    MiddleAgesAssembly *factory;
}

- (void)setUp
{
    [super setUp];

    factory = [[MiddleAgesAssembly assembly] activate];
}

- (void)test_runtime_arguments
{
    Knight *knight = [factory knightWithRuntimeDamselsRescued:@6 runtimeFoobar:@"foobar"];
    XCTAssertEqual(knight.damselsRescued, (NSUInteger)6);
    XCTAssertEqual(knight.foobar, @"foobar");
}

- (void)test_runtime_block_arguments
{
    __block NSString *foobar = @"initial string";
    Knight *knight = [factory knightWithRuntimeDamselsRescued:@6 runtimeFoobar:(NSObject *) ^(NSString *blockArg){
        foobar = [NSString stringWithFormat:@"set from block %@",blockArg];
    }];
    XCTAssertEqual(knight.damselsRescued, (NSUInteger)6);
    ((void(^)(NSString *))knight.foobar)(@"arg");
    XCTAssertEqualObjects(foobar, @"set from block arg");
}

- (void)test_runtime_block_and_class
{

    Mock *mock = [factory mockWithRuntimeBlock:^NSString *{
       return @"Hello";
    } andRuntimeClass:[NSString class]];
    XCTAssertEqualObjects(((NSString *(^)(void))mock.block)(), @"Hello");
    XCTAssertEqual(mock.clazz, [NSString class]);

}

- (void)test_runtime_knight_quest
{
    Knight *knight = [factory knightWithRuntimeDamselsRescued:@3 runtimeQuestUrl:[NSURL URLWithString:@"http://google.com"]];
    XCTAssertEqualObjects([knight.quest imageUrl], [NSURL URLWithString:@"http://google.com"]);
    XCTAssertEqual(knight.damselsRescued, (NSUInteger)3);

    knight = [factory knightWithRuntimeDamselsRescued:@2 runtimeQuestUrl:[NSURL URLWithString:@"http://apple.com"]];
    XCTAssertEqualObjects([knight.quest imageUrl], [NSURL URLWithString:@"http://apple.com"]);
    XCTAssertEqual(knight.damselsRescued, (NSUInteger)2);
}

- (void)test_runtime_knight_with_nil
{
    Knight *knight = [factory knightWithRuntimeDamselsRescued:nil runtimeQuestUrl:[NSURL URLWithString:@"http://google.com"]];
    XCTAssertEqualObjects([knight.quest imageUrl], [NSURL URLWithString:@"http://google.com"]);
    XCTAssertEqual(knight.damselsRescued, (NSUInteger)0);
}

- (void)test_runtime_knight_with_method_arg_nil
{
    Knight *knight = [factory knightWithRuntimeDamselsRescued:@(12) runtimeQuestUrl:nil];
    XCTAssertNil([knight.quest imageUrl]);
    XCTAssertEqual(knight.damselsRescued, (NSUInteger)12);

    Knight *friend = (Knight *) knight.foobar;
    XCTAssertEqual(friend.damselsRescued, (NSUInteger)12);
}

- (void)test_runtime_knight_with_method_arg_and_two_nils
{
    Knight *knight = [factory knightWithRuntimeDamselsRescued:nil runtimeQuestUrl:nil];
    XCTAssertNil([knight.quest imageUrl]);
    XCTAssertEqual(knight.damselsRescued, (NSUInteger)0);
}

- (void)test_predefined_quest
{
    Knight *knight = [factory knightWithDefinedQuestUrl];
    XCTAssertEqualObjects([knight.quest imageUrl], [NSURL URLWithString:@"http://example.com"]);
}

- (void)test_circular_dependency_with_runtime_args
{
    Knight *knight = [factory knightWithCircularDependencyAndDamselsRescued:@32];

    Knight *anotherKnight = (Knight *) knight.foobar;

    XCTAssertEqual((int)knight.damselsRescued, 32, @"");
    XCTAssertTrue(anotherKnight.foobar == knight, @"");
    XCTAssertTrue(knight.foobar == anotherKnight, @"");
}

- (void)test_circular_dependency_with_runtime_args_as_nil
{
    Knight *knight = [factory knightWithCircularDependencyAndDamselsRescued:nil];

    Knight *anotherKnight = (Knight *) knight.foobar;

    XCTAssertEqual((int)knight.damselsRescued, (int)nil, @"");
    XCTAssertTrue(anotherKnight.foobar == knight, @"");
    XCTAssertTrue(knight.foobar == anotherKnight, @"");
}

- (void)test_circular_dependency_with_predefined_runtime_args
{
    Knight *knight = [factory knightWithPredefinedCircularDependency:@25];

    Knight *anotherKnight = (Knight *) knight.foobar;

    XCTAssertEqual((int)knight.damselsRescued, 25, @"");
    XCTAssertEqual(anotherKnight.foobar, knight);
    XCTAssertEqual(knight.foobar, anotherKnight);
}

- (void)test_dependency_with_predefined_runtime_args
{
    Knight *knight = [factory knightWithPredefinedCircularDependency:@27];

    Knight *anotherKnight = (Knight *) knight.foobar;

    Knight *thirdKnight = (Knight *) anotherKnight.foobar;

    XCTAssertEqual((int)knight.damselsRescued, 27, @"");
    XCTAssertEqual((int)thirdKnight.damselsRescued, 25, @"");

    XCTAssertTrue(thirdKnight != knight, @"");
    XCTAssertTrue(knight.foobar == anotherKnight, @"");
}

- (void)test_runtime_argument_with_reference_injection
{
    Knight *knight = [factory knightRuntimeArgumentsFromDefinition];
    XCTAssertEqualObjects(knight.foobar, [[NSURL alloc] initWithString:@"http://google.com"]);
}

- (void)test_runtime_argument_with_reference_injection_with_another_argument
{
    Knight *knight = [factory knightRuntimeArgumentsFromDefinitionWithRuntimeArg];
    XCTAssertEqualObjects(knight.foobar, [[NSURL alloc] initWithString:@"http://typhoonframework.org"]);
}

- (void)test_runtime_argument_with_reference_injection_with_another_runtime_argument
{
    Knight *knight = [factory knightRuntimeArgumentsFromDefinitionsSetWithRuntimeArg];
    XCTAssertEqualObjects(knight.foobar, [[NSURL alloc] initWithString:@"http://example.com"]);
}

- (void)test_runtime_argument_shortcut
{
    NSString *str = [factory stringValueShortcut];
    XCTAssertEqualObjects(str, @"Hello world!");
}

- (void)test_runtime_argument_shortcut_more_than_one_arg
{
    Knight *knight = [factory knightWithRuntimeQuestUrl:[NSURL URLWithString:@"http://appsquick.ly"]];
    XCTAssertEqualObjects(knight.quest.imageUrl, [NSURL URLWithString:@"http://appsquick.ly"]);
    XCTAssertTrue(knight.damselsRescued == 13);
}

- (void)test_runtime_argument_shortcut_block_and_class
{
    Mock *mock1 = [factory mockWithRuntimeClass:[NSString class]];

    XCTAssertTrue(mock1.clazz == [NSString class]);
    XCTAssertTrue([((NSString*(^)(void)) mock1.block)() isEqualToString:@"Hello"]);

    Mock *mock2 = [factory mockWithRuntimeBlock:^NSString * {
        return @"Hello2";
    }];

    XCTAssertTrue(mock2.clazz == [NSString class]);
    XCTAssertTrue([((NSString*(^)(void)) mock2.block)() isEqualToString:@"Hello2"]);
}

- (void)test_runtime_argument_shortcut_point_to_shortcut
{
    Knight *knight = [factory knightWithPredefinedRuntimeQuest];
    XCTAssertEqualObjects(knight.quest.imageUrl, [NSURL URLWithString:@"http://appsquick.ly"]);
    XCTAssertTrue(knight.damselsRescued == 13);
}

- (void)test_runtime_argument_with_error_writeback
{
    __autoreleasing NSError *error = nil;
    Sword *sword = [factory swordWithSpec:@"blue" error:[NSValue valueWithPointer:&error]];
    XCTAssertEqualObjects(sword.specification, @"blue sword");
    XCTAssertNil(error);

    __autoreleasing NSError *error2 = nil;
    Sword *yellow = [factory swordWithSpec:@"yellow" error:[NSValue valueWithPointer:&error2]];
    XCTAssertNotNil(yellow);
    XCTAssertNotNil(error2);
    XCTAssertEqual(error2.code, (NSInteger)404);
}

- (void)test_runtime_argument_with_proxy_object
{
    id <Quest> defaultQuest = [factory defaultQuest];
    XCTAssertNotNil(defaultQuest);

    DecoratedQuest *decoratedQuest = [DecoratedQuest decoratedQuestWith:defaultQuest];
    Knight *knight = [factory knightWithQuest:decoratedQuest];

    XCTAssertNotNil(decoratedQuest);
    XCTAssertNotNil(knight);
}

- (void)test_runtime_argument_with_ocmockito_mock
{
    id <Quest> mockQuest = mockProtocol(@protocol(Quest));
    Knight *knightWithMock = [factory knightWithQuest:mockQuest];

    XCTAssertNotNil(knightWithMock);
}

@end

redpajama_set_name: RedPajamaGithub


text:
5. Mistrzostwa Oceanii w Lekkoatletyce – zawody lekkoatletyczne, które odbyły się na Santos Stadium w Adelaide od 24 do 26 sierpnia 2000 roku. W imprezie wystartowali zawodnicy z państw skupionych w Oceania Athletics Association, a także gościnnie lekkoatleci z Kenii, Konga, Mali, Nigerii, Sierra Leone, Togo, Suazi, Ugandy, Wybrzeża Kości Słoniowej i Zimbabwe. Część zawodników z Australii i Nowej Zelandii startowała poza konkurencją.

Rezultaty

Mężczyźni

Kobiety

Tabela medalowa

Uwagi

Przypisy

Bibliografia

2000 w lekkoatletyce
Lekkoatletyka w Australii
2000
2000 w Australii
Sport w Adelaide

redpajama_set_name: RedPajamaWikipedia


text:
I ministri dei trasporti del Regno d'Italia si sono avvicendati dal 1916 al 1920 e dal 1944 al 1946.

Note

Voci correlate
Ministri dei trasporti della Repubblica Italiana

Altri progetti

Liste di ministri del Regno d'Italia


text:
Interview: Mika Tyyska
Roo Chapus
Guitarists and Bands , Interviews
Roo Chapus: One year has passed since the release of "In Motion". Can you sum up your highlights of 2012-2013. Beyond the music and artistic side, what were the goals with "In Motion", and did you reach them?
Mika Tyyska: Getting "In Motion" released was one of the highlights. I took two years to put it together. Around the release we did some exciting collaborations with the Hughes & Kettner amplifier company, Frankfurt Messe live demos and YouTube videos. Then I was invited to the amazing company Jam Track Central. The guys at Jam Track Central have been incredible supportive. I'm excited about the two packages I've done with them so far.
The first package was sort of remixed and reinvented versions of "In Motion" songs. More jamming and one lead guitar in front approach. The second package was based on mode themes, completely new compositions that I'm very proud of. I'll probably re-visit some of these song ideas for later music releases. Also, I was honored to do some fine jam videos for BendNote.com too, it's a great site for improvised jam videos. Good bunch of guys there as well! Also working with Roo and Mad Guitar Records has surely been good!
Then we did some fun live shows with the Mr. Fastfinger band - Lasse, Thomas and Kalle. I've gotten a great amount of new fans with all these projects, actually, I like to call them friends!
Roo Chapus: A mere twelve months later you are releasing a new EP, how productive! Why not wait, and perfect a longer third album?
Mika Tyyska: Putting together an album, the way I tend to do them, is a long and hard journey. It took three years between the first and second album. Didn't want to put two more years to produce an album again. It takes so much time, and it's damn expensive too. So I started thinking about putting out an EP, just keep it easy and maintain less stress for me.
I just wanted to focus on great compositions and put out good songs. Then maybe two months later or so I realized we're actually talking about a series of EPs. I started designing sets of EP releases. I thought it would be interesting to put out an EP twice a year or something.
After putting together one EP, I found it's much better for me to work in bunches of 3 or 4 songs at a time. When you have 10 or more songs waiting to be finished... ooh, it's too much!
Roo Chapus: It seems that the theme is very different from "In Motion", which was epic and dramatic. Here, the atmosphere is connected with the new video game you created and somehow reminds me of your previous album, "The Way of The Exploding Guitar."
Mika Tyyska: "In Motion" was supposed to be a more epic and dramatic album. That was the goal originally. It has a little more darker overall feel than the debut album. Maybe in the end, "In Motion" feels little more serious than it was supposed to. Anyways this time I wanted to create something more colorful and lighthearted - getting more into the roots of Mr. Fastfinger. "In Motion" more was about Mr. Fastfinger traveling in a lost world and other unknown territories. On the EP "Stringweaver", he's back on his home beach, practicing, playing and relaxing. I was actually more after the feel of the debut album, "The Way of the Exploding Guitar", rather than "In Motion" with this EP.
I feel that I really found a flow with these songs, they were much easier to produce than anything I'd done before. Both composition wise and guitar playing wise this was very relaxed. I think you can hear it. I guess the more you do it, the smoother it gets. I'll try not to have too many long breaks between making music again.
Oh, and the first song on "Stringweaver", "Awaki-Waki" was originally composed for the debut album. It was composed around the same time as songs "Epic" and "The Ninjas". It was left out probably because it wan't needed for the overall album I guess. Last December I accidentally ended up listening to old demos from 2007 or 2008, and I found the track. It had a nice, lighthearted, video game vibe.
Roo Chapus: This leads us to the video game you recently created with GuitarBots. How did all this come up? Are you happy with the final result? Should we expect more to come from you soon?
Mika Tyyska: After rediscovering the song "Awaki-Waki", the following week I was invited to visit the game company Ovelin in Helsinki. This company is filled with guitarists and it was funny when I went to their office, they had like eight guys all sitting in front of computers playing guitars!
Ovelin's mission with the game is to turn guitar practicing into exciting and motivated fun. That goes well with Mr. Fastfinger's philosophy. Collaboration with them felt very natural.
We started talking about the idea of creating a Mr. Fastfinger package for their game GuitarBots. I knew immediately that "Awaki-Waki" was going to be used here. Oh, and also I didn't create the online game GuitarBots, it's Ovelin's product. This Mr. Fastfinger thing was a collaboration. I basically just composed the musical material, made sure it transforms well into the game and supervised the look of the 3D Mr. Fastfinger on screen.
Roo Chapus: Video games are part of modern culture. Since you always keep very busy, it is hard to imagine you a hard gamer; however video games might have inspired you for the creation of Mr. Fastfinger's animations and all of his exotic world. Have you ever been a video game fan. What games impress you?
Mika Tyyska: Guitar playing is my favorite game. Cubase is a very nice game as well. I played a lot of video games as a kid, before I got obsessed with the guitar. It was the age of Commodore-64. I still have a love for those simple games, but rarely play anything. You can hear those Commodore-64 style bleeping sounds in Mr. Fastfinger music. Like in the song "Awaki-Waki" on the new EP. Also "Go-Flash-Go" and "The Ninjas" on "The Way of the Exploding Guitar" has some C-64 synth samples bleeping.
Roo Chapus: What are Mr Fastfinger's upcoming events and projects? Will you have holidays, and what are you doing then?
Mika Tyyska:Later this year there will be a very special dramatized "making of" movie or series coming up. Can't tell what's it all about yet. Secret. But anyways, this will mean a lot of cartooning work for me, as well as plenty of music production. The next EP will most likely be a soundtrack to this.
July will be the vacation month. Being together with family, seeing all the friends. Doing little things in the house and yard. Little trips and visits to places. Maybe I'll practice guitar playing just for the sake of practicing. The most important thing is to avoid all work related projects. Just to have a one month break from creativity is good. (mrfastfinger.net, madguitarrecords.com)
Mika Tyyska's guitar playing is more than just flashy riffs and exploding solos. It's full of strong melodies and rhythms, energy, joy, feelings and emotions. The end result is versatile instrumental music, a mixture of influences and styles. After two full length CDs with Mr. Fastfinger, "In Motion" and "The Way Of The Exploding Guitar", Tyyska has put out and EP-length release entitled "Stringweaver", and has a number of future EP projects in the works.
Mad Guitar Records' Roo Chapus caught up with Tyyska to discuss the recently released EP and to talk about his connections with the video gaming world, as well as other pursuits.

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Q: Ограничение движения мыши окном программы Как в Delphi 7 сделать, чтобы мышка не выходила за рамки нашей новой программы?
A: Можно, есть такая функция ClipCursor. В делфи она тоже есть (вроде в файле windows.pas). Параметр - TRect - прямоугольник, в котором нужно держать курсор.
Эту функцию настоятельно не рекомендуется использовать. Также не забывайте обновлять область по мере надобности и освобождать ее, когда программа завершится
A: Самое простое, и, пожалуй кривое, это определять положение мышки и если её координаты больше нужных, то кидать её в нужные =)

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I really like the fact that the curriculum extends beyond INTA and into STEM classes - I feel like being exposed to math, science, and computer science courses has really given me an edge over my peers at other institutions.
My advice to students is to make yourself different by developing a complementary skill to your primary education.
Senator Nunn, General Breedlove, and Admiral Winnifeld's visit to my Global Issues and Leadership classes is among the most memorable experiences that I am glad I had during my time here.


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The Stunning Clarity of its Sound
The Intricate Hardware and Electronics
The Well-Defined Body and Neck
Taylor 114ce 100 Series Acoustic Guitar
Final Opinion
Taylor 114ce Review 2021 – Is It Worth Its Price?
Editor's Note: Taylor guitars have embarked its brand as one of the best guitar makers in the world for years. And the Taylor 114ce 100 Series Acoustic Guitar is a grand testament to that claim. But, is the price worth it? Come on, it's Taylor. Why have second thoughts on buying this?
View now on Amazon.com $134/Month on Sweetwater.com
Why go for insanely expensive guitars when you can have the best for a price that hardly hurts the pocket? With the exquisiteness of its craftsmanship and the precision of its sound, you do not only get the value for your money but way more than what you pay for.
The Taylor 114ce certainly exudes the excellent quality that Taylor guitars have always been known for. For an acoustic guitar that costs under a thousand dollars, it is quite surprising how it even surpasses the more costly ones in several aspects.
There are a number of good things about this guitar that would naturally convince any musician (or aspiring ones) why the Taylor 114ce is the best deal.
Let's talk about them, shall we?
This work of art splendidly produces precise tones and superb high gain sustain that will surely give you the best musical gig experience. The intonation is as flawless as its bass levels blending perfectly producing that optimal sound quality that is given of high, if not the highest, importance when it comes picking the perfect acoustic guitar.
Finally, the tuners are also so carefully made and polished that it delivers impressive clarity when cranked up to their maximum which, by the way, is rarely done by a purely acoustic guitar making it suitable for any setting whether it be full band or even when playing alone or whether played acoustically or plugged in.
The 114ce provides a wide range of tonal possibilities that makes superior and creative musical arrangements fairly possible without spending much fortune. The quality of this guitar's hardware satisfies both aesthetic and musical utility.
It is gorgeously crafted with its perfect size and low position that is readily fit for a comfortable play made more comfortable by the strings which feel very gentle in the fingers making strumming and plucking easier even for beginners.
image from sweetwater.com
The six enclosed die cast tuners finely placed on top of a classy Taylor headstock and a Tusq bone saddle just beneath the ebony bridge system creates a magnificent intonation and stability as well as aesthetically pleasing structure.
These tuners are definitely not just for show as they create an incremental change in the tone which highly affects the performance of the guitarist.
What is more than its hardware are the beautifully designed electronics embedded inside the body of this guitar making it beautiful inside and out, literally. More particularly, the Taylor 114ce 100 series bears an ES-T internal pickup system which fully controls each string and their corresponding elements giving the guitarist full control of the instrument.
The onboard preamble is strategically placed in front and not sideways, unlike other typical acoustic-electric guitars available in the market, and the custom-made equalizer adds greatly to the richness of the sound it produces through the bass, volume, and treble switches.
A great acoustic guitar must not only sound great but should also look great even without its adornments. This guitar's well-structured build is already a winner when it comes to acoustic guitars.
This guitar is equipped with 20 frets, wide Tusq bone nut and pearloid dot inlays making it look fancy and elegant without going over board to make it look trashy.
Holding a scale of 25.5, its aesthetics are well emphasized by the splendid double varnish natural finish offering trustworthy protection to make it endure the longtime of use, fine looks and great feel to the touch.
image from global.rakuten.com
Its ebony fingerboard is supported by a bolt-on neck sapele that makes this guitar easy and pleasant to the hand giving the guitarist a comfortable play.
The body of the Taylor 114ce boasts of a sturdy spruce top and tortoise pickguard designed to withstand long hours of usage and prevent damage from excessive strumming. The durability of this guitar is brought about by the laminated sapele which composes its sides and back ensuring unquestionable resistance to inevitable environmental factors such as moisture and heat.
Also, the bracing pattern that is intelligently shifted forward preserves a stable and intact guitar body giving its well-built solid feels.
As perfect as this guitar may seem, one cannot simply have it all. Being in the middle ranged, when it comes to price, guitars, it is expected that there are flaws, albeit minor, to be found. Here are the list of the pros and cons that I found.
Great quality for value
Smooth neck for low friction
Requires little to no adjustment straight from the box
Sturdy and solid build
The finish is an effective built protection
No on board tuner
Heavy and bulky
The material is sensitive to the temperature change making it out of tune at times
Peg material are made of cheap injected plastic
Fret metal easily flats out when played with a lot of vibrato
After all that's been said, whether a beginner wading into the waters of music, a novice practicing to master and hone his skills, a professional who is still trying to perfect his craft, or even those who are just looking for a generous gift to their family or friends who are aspiring to be or are already musicians, the Taylor 114ce 100 Series Acoustic Guitar is a perfect choice for those who wants the best guitar at a price less than $1000.
The popularity of the Taylor 114ce may be due to the reliable craftsmanship of its brand. But there's plenty to love about it apart from its brand name. This guitar is a good choice for beginners because it's easy to play, durable, and has a smooth neck for low friction.
$134/Month on Sweetwater.com
Robert P. May 21, 2020 At 5:36 pm
I picked this guitar for my first big guitar purchase. After playing for a few years on a samick acoustic I paid a friend $30 for. Then a "martin lawsuit" 70's Takamine acoustic that i think I paid $100 for. Maybe less. It was time for upgrade.
So to prep I saved up maybe $200 of GC giftcards from Bdays and whatnot. Then I had a St. Paddys day coupon for spend 1k and get 200 off at GC. At the time for me…this was big money. The most expensive car I had bought up until then was 2,500. I had the day to spend at GC. I was in the acoustic room for hours. First guitar I picked up was the Taylor 114ce. It had so much playability compared to the Takamine I was used to. Much thinner neck with better action. Even though I have long fingers, I found I liked the feel of the thin neck and the shape was so comfortable. The highs were so clear and it projected very well. Fast forward 4 hours and I had played everything in that room that ranged from $500 to $1500. And of course a couple that were much more expensive. After all this I still kept going back to the Taylor I had first picked up. I also liked a Martin that was I believe a D15. It was a mahogony Martin with a nice warm sound. I ended up going with the Taylor even though I had in my mind that I was more interested in a Martin on my way to GC. Throw in a braided cord, set of strings, some odds and ends and the price was exactly 1k so I could make use of the coupon. Walked away with everything for around $600 out of pocket. I had come to the realization that the 114ce to me sounded better than everything in the whole acoustic room in its price range. Even better than a few that were quite a bit above its price. Although the GC acoustic room isnt always the best way to try a perfect example of a guitar..its certainty possible some of the higher cost guitars would have sounded better had they been properly set up and cared for. But for what they had in there, It was hard to justify the price increase when the Taylor sounded so good.
Fast forward around 10 years and life is very different. Where I've been able to afford an acoustic "upgrade" for quite some time now. But I've felt no need. It still sounds and plays great. Definitely a keeper. Have never gotten a bad review on the guitar itself or its sound. My playing is another story. But many "beautiful guitar", "wow, that sounds great", and "that taylor has such a sweet sound" comments. Over the years I've replaced the pegs, the strings and battery of course, and wiped it down. Still looks and sounds great. I would certainly recommend anyone looking at the $500 to $1500 range for an acoustic or acoustic electric seriously consider one of these. Welp, that was much longer of a comment than I intended, But I do feel its a contender for the best guitar of its type you could buy for the money. I definitely tested that theory.
Thanks for reading and best of luck.
Gretsch G5420T Review (2021)
"Electromatic" guitars can be traced back to the 1940s. And, in its current form, they are called "Streamliners." For almost a decade ago, specs...


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Pan Bazaar (also spelled as Panbazar) (অসমীয়া: পানবজাৰ) is a locality in Guwahati, India surrounded by localities of Paltan Bazaar, Ambari and Fancy Bazaar. Situated on the banks of the river Brahmaputra, it is part of the city centre.

Pan Bazaar means "betel-leaf mart" in English. The locality is known for its various administrative, cultural and religious buildings. It is also known for being a shopping district.

The District Court (Kamrup) and the Guwahati branch of the Reserve Bank of India are major administrative buildings located in Pan Nazaar.

Restaurants and bookshops in the area provide a unique environment. It is also a major wholesale market for drugs and pharmaceutical products and a hub for printing and publication. Close to Dighalipukhuri, there are many stores with traditional arts and crafts from Assam and other parts of the North East Region. Kachari Ghat, next to the district court is an important local river port.

Pan Bazaar is known as one of the cultural and educational hotspots of Gauhati. Area is house for many historic buildings like of Kamarupa Anusandhan Samiti, Assam State Museum and District Library. The Nehru Park and Sukreswar Temple are other major tourist magnets of this area.

Cotton University  also is situated here. Cotton College, Handique Girls College, Cotton Collegiate Government H.S. School and Don Bosco High School are prominent educational institutes of this area which has state level importance.

Pan Bazaar was one of the sites affected by the 2008 Assam bombings. In 2018, it was the site of a low intensity bomb blast which injured 4 people.

See also
 Beltola
 Chandmari
 Ganeshguri
 Maligaon
 Narengi

References

Bazaars in India
Neighbourhoods in Guwahati


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导出博客文章James Tedesco has welcomed the challenge of fending off multimillion dollar code-hopper Jarryd Hayne for the coveted NSW No. Cheap NFL Jerseys China .1 jumper next year.The Wests Tigers fullback fulfilled a childhood dream when he made his State of Origin debut for the Blues in game three last month.Tedesco toiled courageously, battling through a series of horror injuries to establish himself as the states best fullback and finally broke through on the back of sustained form.However the NRL return of Hayne, who was the incumbent NSW custodian before his departure to the NFL and Rugby Sevens, threatens to break Tedescos hold on the position.Rather than kick stones, Tedesco welcomed Haynes signing with Gold Coast on Wednesday as a coup for rugby league and an opportunity to test himself against one of the great fullbacks.Thats a challenge next year to keep that jersey off him, Tedesco said.He was fullback when NSW won the series and he had a great series.Thats the way it is - it could be Jarryd, it could be any fullback next year coming to take that jersey.Even though I was the last one to play in it, the jerseys not mine. Theres always players coming to take that spot.Haynes arrival back in Australia provides NSW coach Laurie Daley with a killer selection headache for the 2017 series.Hayne was the difference in the Blues breakthrough series win in 2014 while Tedesco was among the sides best in Origin III, running for 231m and going within inches of scoring in a standout performance.It also potentially sets up a mouth-watering clash between the two star fullbacks next Saturday when the Titans face the Tigers, should Hayne be cleared in time, as expected.The game will be played at Campbelltown Stadium, just 15km down the road from Minto where Hayne grew up, with a bumper crowd likely.I havent really played him since 2013, I think, Tedesco said.I think Ive improved a lot as a player since.He obviously set the standard as a fullback. He was one of the best fullbacks in the game at the time and its a chance for me to step up my game and get up on his level. Stitched NFL Jerseys . Jim Rutherford, President and General Manager of the Carolina Hurricanes, announced Wednesday that the team would assign Swedish forward Elias Lindholm to his nations team for the upcoming tournament. Cheap MLB Jerseys China .C. -- Chris Thorburn thinks one of the reasons the Winnipeg Jets have been successful under new coach Paul Maurice is that theyre playing together as a team. http://www.cheapnfljerseysoutlet.com/ . "We have always prided ourselves on the way we play defence. Having two big pieces back is going to be a key for us moving forward for years to come," said Knighthawks head coach Mike Hasen. BOSTON -- Harvards Tommy Amaker still feels the influence that Duke coach Mike Krzyzewski provided. Its helped lead him through a successful coaching career.Amaker became the winningest coach in Harvard history on Wednesday night when Chris Lewis scored a season-high 22 points and Seth Towns had 18 to lead the Crimson to a 74-66 road victory over local rival Boston College.It was Amakers 179th win in his 10th season, moving him ahead of his predecessor, Frank Sullivan (178-245), who was the coach from 1991-2007.Ill communicate with coach for sure, Amaker said. He has so many different guys that he likes to keep track of. I dont want to be a burden in any way, but obviously his influence has been paramount. Its been as big as it comes for me.Ive always thought of him as an amazing teacher, leader. Ive always tried emulate some of the things hes taught through the years.A star guard with the Blue Devils from 1983-87, the 52-year-old Amaker felt he could take Harvard to a successful level thats led to five Ivy League titles in the past six seasons.We always thought if we could build our basketball program to go along with the things that happen at Harvard, we would feel good about ourselves, and weve done that, he said.It was the third straight victory for Harvard (4-4).Jerome Robinson led Boston College (4-4) with 25 points. A.J. Turner scored 13.The Crimson looked dedicated to driving to the basket on most possessions from the start, collecting a number of easy looks when they shot near 60 percent in the opening minutes. It triggered a 13-2 spree that helped them open a 23-10 lead.The last couple of games I was encouraged of what we were doing defensively, but we took a step back, BC coach Jim Christian said. Wed played seven games. These guys have played a lot of minutes -- bad defense is bad defense.The Crimson pushed their advantage to 39-21 after Bryce Aikeens driving basket capped a 6-0 spurt. China Jerseys Outlet. The Eagles trailed by 19 points with just under 10 minutes to play, but made a late charge, closing the deficit to 69-60 on Robinsons 3-pointer from the left corner.Both teams then went nearly three minutes without a basket before Harvard closed it out.BIG PICTUREHarvard: The Crimson seemed to have figured out what type of team they have become after opening the season 1-3. They showed balance in a two-night span when they beat Northeastern on Tuesday and Boston College. On Tuesday, they scored only 18 points in the paint and they had 20 at halftime against the Eagles, finishing with 34.Were constantly trying to preach that we set the tone and be the aggressor early, Amaker said. I just thought they responded very well and made the necessary plays.Boston College: The Eagles need to find some more consistent scoring to go along with Robinson. The 6-foot-5 sophomore guard entered the game second in the Atlantic Coast Conference, averaging 20.1 per game.REFLECTIONIm very proud of that, Amaker said of the milestone. Im proud of our program and our team.PERFECT TESTThe Crimson looked at playing consecutive nights as a warm up to how things will be in conference play, when schools mostly compete on Fridays and Saturdays.We approached these two back-to-back games how wed see Ivy League play, said point guard Siyani Chambers, who had 11 assists. Were trying to figure out who we are.SERIESBC leads the all-time series 34-16 and had won the last two meetings after losing six straight.The two schools first met in the 1905-06 season when Harvard won 42-6.UP NEXTHarvard: At Houston of the American Athletic Conference on Friday.Boston College: Hosts Hartford from the America East Conference Friday. Cheap MLB Jerseys cheap jerseys Cheap Jerseys Free Shipping cheap nfl jerseys Cheap Jerseys USA China Jerseys Cheap NFL Authentic Jerseys Cheap Jerseys For Sale Cheap NFL Jerseys China Wholesale Jerseys Cheap NBA Jerseys ' ' '
August 23, 2018 4:06 AM MDT
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The impact of human mobility on disease spread
Due to continual improvements in transportation technology, people travel more extensively than ever before. Although this strengthened connection between faraway countries comes with many benefits, it also poses a serious threat to disease control and prevention. When infected humans travel to regions that are free of their particular contagions, they might inadvertently transmit their infections to local residents and cause disease outbreaks. This process has occurred repeatedly throughout history; some recent examples include the SARS outbreak in 2003, the H1N1 influenza pandemic in 2009, and--most notably--the ongoing COVID-19 pandemic.
Imported cases challenge the ability of nonendemic countries--countries where the disease in question does not occur regularly--to entirely eliminate the contagion. When combined with additional factors such as genetic mutation in pathogens, this issue makes the global eradication of many diseases exceedingly difficult, if not impossible. Therefore, reducing the number of infections is generally a more feasible goal. But to achieve control of a disease, health agencies must understand how travel between separate regions impacts its spread.
In a paper publishing on Tuesday in the SIAM Journal of Applied Mathematics, Daozhou Gao of Shanghai Normal University investigated the way in which human dispersal affects disease control and total extent of an infection's spread. Few previous studies have explored the impact of human movement on infection size or disease prevalence--defined as the proportion of individuals in a population that are infected with a specific pathogen--in different regions. This area of research is especially pertinent during severe disease outbreaks, when governing leaders may dramatically reduce human mobility by closing borders and restricting travel. During these times, it is essential to understand how limiting people's movements affects the spread of disease. To examine the spread of disease throughout a population, researchers often use mathematical models that sort individuals into multiple distinct groups, or "compartments." In his study, Gao utilized a particular type of compartmental model called the susceptible-infected-susceptible (SIS) patch model. He divided the population in each patch--a group of people such as a community, city, or country--into two compartments: infected people who currently have the designated illness, and people who are susceptible to catching it. Human migration then connects the patches. Gao assumed that the susceptible and infected subpopulations spread out at the same rate, which is generally true for diseases like the common cold that often only mildly affect mobility.
Each patch in Gao's SIS model has a certain infection risk that is represented by its basic reproduction number (R0) -- the quantity that predicts how many cases will be caused by the presence of a single contagious person within a susceptible population. "The larger the reproduction number, the higher the infection risk," Gao said. "So the patch reproduction number of a higher-risk patch is assumed to be higher than that of a lower-risk patch." However, this number only measures the initial transmission potential; it can rarely predict the true extent of infection. Gao first used his model to investigate the effect of human movement on disease control by comparing the total infection sizes that resulted when individuals dispersed quickly versus slowly. He found that if all patches recover at the same rate, large dispersal results in more infections than small dispersal. Surprisingly, an increase in the amount by which people spread can actually reduce R0 while still increasing the total amount of infections.
The SIS patch model can also help elucidate how dispersal impacts the distribution of infections and prevalence of the disease within each patch. Without diffusion between patches, a higher-risk patch will always have a higher prevalence of disease, but Gao wondered if the same was true when people can travel to and from that high-risk patch. The model revealed that diffusion can decrease infection size in the highest-risk patch since it exports more infections than it imports, but this consequently increases infections in the patch with the lowest risk. However, it is never possible for the highest-risk patch to have the lowest disease prevalence. Using a numerical simulation based on the common cold--the attributes of which are well-studied--Gao delved deeper into human migration's impact on the total size of an infection. When Gao incorporated just two patches, his model exhibited a wide variety of behaviors under different environmental conditions. For example, the dispersal of humans often led to a larger total infection size than no dispersal, but rapid human scattering in one scenario actually reduced the infection size. Under different conditions, small dispersal was detrimental but large dispersal ultimately proved beneficial to disease management. Gao completely classifies the combinations of mathematical parameters for which dispersal causes more infections when compared to a lack of dispersal in a two-patch environment. However, the situation becomes more complex if the model incorporates more than two patches.
Further investigation into Gao's SIS patch modeling approach could reveal more nuanced information about the complexities of travel restrictions' impact on disease spread, which is relevant to real-world situations -- such as border closures during the COVID-19 pandemic. "To my knowledge, this is possibly the first theoretical work on the influence of human movement on the total number of infections and their distribution," Gao said. "There are numerous directions to improve and extend the current work." For example, future work could explore the outcome of a ban on only some travel routes, such as when the U.S. banned travel from China to impede the spread of COVID-19 but failed to block incoming cases from Europe. Continuing research on these complicated effects may help health agencies and governments develop informed measures to control dangerous diseases. Source article: Gao, Daozhou. (2020). How does dispersal affect the infection size? SIAM J. Appl. Math. To be published.
Related Infection Articles from Brightsurf:
Halving the risk of infection following surgery
New analysis by the University of Leeds and the University of Bern of more than 14,000 operations has found that using alcoholic chlorhexidine gluconate (CHG) halves the risk of infection in certain types of surgery when compared to the more commonly used povidone-iodine (PVI).
How plants shut the door on infection
A new study by an international team including University of Maryland scientists has discovered the key calcium channel responsible for closing plant pores as an immune response to pathogen exposure.
Sensing infection, suppressing regeneration
UIC researchers describe an enzyme that blocks the ability of blood vessel cells to self-heal.
Boost to lung immunity following infection
The strength of the immune system in response to respiratory infections is constantly changing, depending on the history of previous, unrelated infections, according to new research from the Crick.
Is infection after surgery associated with increased long-term risk of infection, death?
Whether experiencing an infection within the first 30 days after surgery is associated with an increased risk of another infection and death within one year was the focus of this observational study that included about 660,000 veterans who underwent major surgery.
Revealed: How E. coli knows how to cause the worst possible infection
The discovery could one day let doctors prevent the infection by allowing E. coli to pass harmlessly through the body.
UK study shows most patients with suspected urinary tract infection and treated with antibiotics actually lack evidence of this infection
New research presented at this week's European Congress of Clinical Microbiology & Infectious Diseases (ECCMID) in Amsterdam, Netherlands (April 13-16, 2019) shows that only one third of patients that enter the emergency department with suspected urinary tract infection (UTI) actually have evidence of this infection, yet almost all are treated with antibiotics, unnecessarily driving the emergence of antimicrobial resistance.
Bacteria in urine doesn't always indicate infection
Doctors should think carefully before testing patients for a urinary tract infection (UTI) to avoid over-diagnosis and unnecessary antibiotic treatment, according to updated asymptomatic bacteriuria (ASB) guidelines released by the Infectious Diseases Society of America (IDSA) and published in Clinical Infectious Diseases.
Subsidies for infection control to healthcare institutions help reduce infection levels
Researchers compared three types of infection control subsidies and found that under a limited budget, a dollar-for-dollar matching subsidy, in which policymakers match hospital spending for infection control measures, was the most effective at reducing the number of hospital-acquired infections.
Dengue virus infection may cause severe outcomes following Zika virus infection during pregnancy
This study is the first to report a possible mechanism for the enhancement of Zika virus progression during pregnancy in an animal model.
Read More: Infection News and Infection Current Events


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The M. Selim Yalin Award was established by the IAHR Council in 2006 to honour the memory of Professor M. Selim Yalin (1925-2007), Honorary Member and Fluvial Hydraulics Section Chair (1986-1991). Professor Yalin is remembered for his prolific and pioneering research contributions in fluvial hydraulics and sediment transport, and for his inspirational mentoring of students and young researchers.The Award is made biennially by IAHR to one of its members whose experimental, theoretical or numerical research has resulted in significant and enduring contributions to the understanding of the physics of phenomena and/or processes in hydraulic science or engineering and who demonstrated outstanding skills in graduate teaching and supervision. The awards consisting of a certificate and cash prize are presented during the IAHR Biennial Congresses. The Award fund, which was established by the family and friends of Professor Yalin, is authorised to receive contributions from Association members and friends of Professor Yalin.
Arthur Thomas Ippen Award
The Ippen Award was established by the IAHR Council in 1977 to memorialise Professor Ippen, IAHR President (1959-1963), IAHR honorary member (1963-1974), and for many decades an inspirational leader in fluids research, hydraulic engineering and international co-operation and understanding. The Award is made biennially by IAHR to one of its members who has demonstrated conspicuously outstanding ability, originality, and accomplishment in basic hydraulic research and/or applied hydraulic engineering, and who holds great promise for continuation of a high level of productivity in this profession. The awards are made at the biennial congresses of IAHR, where the most recent recipient delivers the Arthur Thomas Ippen lecture. The Award fund, which was established by Professor Ippen's family is authorised to receive contributions from Association members and friends of Professor Ippen.
Harold Jan Schoemaker Award
The Schoemaker Award was established by the IAHR Council in 1980 to recognise the efforts made by Professor Schoemaker, Secretary (1960-1979), in guiding the Journal of Hydraulic Research in its formative years. The Award is made biennially by the IAHR to the author(s) of the paper judged the most outstanding paper published in the IAHR Journal of Hydraulic Research (JHR).
John F. Kennedy Student Paper Competition
The John F. Kennedy Student Paper Competition was established by the IAHR Council in 1992 to honor the memory of Professor John F. Kennedy, IAHR President (1979-1983) and Honorary Member (1989-1991). Professor Kennedy made significant and lasting contributions to the Association, and is remembered particularly for his/her efforts to foster younger-member membership and participation. The competition is associated with each IAHR world congress. A panel of judges selects the winning paper on the basis of written and oral presentations. The winner of the competition is awarded a cash prize from the Award fund, and a commemorative plaque, at the congress closing ceremony. The LOC may offer additional cash prizes and certificates to runners up from its own resources. The Award fund, established by Professor Kennedy's family, is maintained by the Association and is authorised to receive contributions from Association members and from friends of Professor Kennedy.
Willi H. Hager JHR Best Reviewer Award
In order to promote the value of peer review in advancing scientific discovery, Professor Willi Hager initiated a reviewer award for JHR. The JHR editor in consultation with the Associate Editors will select the best paper review conducted during the same period as papers are considered for the Schoemaker Award. A certificate will be presented in the General Members Assembly. Special consideration will be given to early career professionals. It is the responsibility of the Editor of JHR to notify the Secretariat of the name of the awardee 60 days 17 prior to the World Congress to ensure the certificate can be prepared and the awardee encouraged to attend the World Congress to receive the award in person.
JRBM Best Paper Award
This award, established in 2010, is supported by Taylor and Francis and awards anually the best contribution to JRBM with strong original research dealing with significant basin management problems. The award consists of an award certificate and monetary prize of 600 Euro. The Award recipient is selected by the JRBM Editor and the Editorial Board.
RIBAGUA Best Paper Award
This award is supported by IAHR and WCCE and awards biennially the best contribution to RIBAGUA in two categories: best technical paper and best scientific paper; both with strong original research dealing with significant water problems in Ibero-America. The award consists of an award certificate. The Award recipient is selected by the RIBAGUA Editor and the Editorial Board.
IAHR Hydro-Environment World Heritage Award
This Award established in 2012 is conferred by IAHR during the World Congress on a local infrastructure of lasting and international importance. It has the purpose of giving international professional recognition to the work of local hydro-environment engineers, and should demonstrably have stood the test of time (and be at least one hundred years old) and be accepted and valued nationally.
IAHR Industry Innovation Award
This Industry Innovation Award is conferred by IAHR during the World Congress on a recent local innovation in design of lasting and international importance. It has the purpose of giving international professional recognition to the work of local hydro-environment engineers.
Regional IAHR Hydro-Environment Heritage and Industry Innovation Awards
The IAHR Regional Hydro-Environment Heritage Award and IAHR Regional Industry Award are conferred by IAHR during one or more of the IAHR Regional Congresses on a local infrastructure of lasting and international importance. They have the purpose of giving international professional recognition to the work of local hydro-environment engineers, and should demonstrably have stood the test of time (and be at least one hundred years old) and be accepted and valued nationally:
IAHR Gerhard Jirka Award for Young Researchers
The award was introduced during the 5th IAHR Europe Congress in Trento in 2018 and was created in memory of Prof. Gerhard Jirka to award the best presentation given by a Young Professional during the biennial IAHR Europe Division Congress. It values the quality, originality and relevance of the presentation.
Stephen E. Coleman Award
The Stephen E. Coleman Best Paper Award is granted biennially for the best paper submitted to the River Flow Conference Series and authored or co-authored by a young researcher. This award was established by the IAHR technical committee on Fluvial Hydraulics in September 2014, in memory of Prof. Stephen E. Coleman (1966–2012).
Honorary Fellows of the IAHR Ecohydraulics Committee
Appointment as an Honorary Fellow of the Ecohydraulics Committee of the IAHR society is to honor exceptional achievement and/or service within our community and profession. This award may be given to a member or non-member of IAHR.Six months prior to the scheduled Ecohydraulics Symposium, the Chair will solicit from the members of the Ecohydraulics Committee confidential nominations to be sent to the Leadership Team. The nominations will be accepted for a three month period. Each nomination must include a one page justification of why the nominee should be considered for designation as an Honorary Fellow of the Ecohydraulics Committee. The Ecohydraulics Leadership Team (Chair, Secretary, and Past Chair) will review the nominations and make their selection. The selection must be unanimous. In the event that no formal nominations are received, the Leadership Team will at their discretion, select the awardee.The award will be given at each Ecohydraulics Symposium by the Ecohydraulics Chair at the symposium banquet and include a plague commemorating their designation as an Honorary Fellow of the Ecohydraulics Committee.More info
Donald R. F. Harleman Lectureship
The lectureship to be given during IAHR's next World Congress and subsequent congresses recognizes the early contributions to engineering hydraulics by Professor Harleman, and focuses on two areas of IAHR endeavor: artificial intelligence in the context of water science and engineering, and ecohydraulics.
APD Distinguished Membership Award
IAHR members of outstanding merit to the region may be awarded Distinguished IAHR APD Member of the Division by the Division executive committee. Not more than two Distinguished IAHR APD Members may be elected at each biennial business meeting of the committee.
IAHR-APD Best Paper Award
The IAHR-Asia Pacific Division award (hereinafter called the IAHR-APD award) was established by the IAHR Council in its meeting in Moscow of 3rd September 1983, to encourage presentation of results on hydraulic and water resources research at IAHR-APD biennial congresses. To this end biennially two awards can be credited by IAHR-APD to the authors of the two papers judged the most outstanding papers presented at the APD congress.One award should be for the author of the best paper from the host country and the other one to the author of the best paper from outside the host country. An award may not be made if the papers in one of the categories are judged inadequate.
Poul Harremoës Award
The Joint committee on Urban Drainage (JCUD) launched the Poul Harremoës award in 2005. Prof. Poul Harremoës was a globally renowned water engineer and Professor of Environmental Science and Engineering at the Technical University of Denmark (DTU). The award is given at each triannual International conference on Urban Drainage (ICUD) for the presentation of novel and ideally provocative ideas, relating to developments in urban drainage, by young authors (up to 35 years of age). The aim of the award is to inspire young researchers to push the field forward, to be bold and daring by challenging the status quo, and always asking the difficult questions. At each ICUD, three nominees are selected to present their submission in a plenary session of the ICUD. Each of these nominees is given the opportunity to publish their paper in Water Science & Technology. This virtual special issue of Water Science & Technology contains all published Poul Harremoës Award papers. This series not only shows a high quality of research, but also gives an interesting overview of the 'hot topics' in our field.
Phil H. Burgi Best Paper Award
Given the 20th anniversary of the IAHR Hydraulic Structures Technical Committee in 2018, the Leadership Team decided to create the Philip H. Burgi Best Paper Award, named after the first chair of the Technical Committee Philip H. Burgi. This award aims at rewarding the best technical paper presented during the International Symposium on Hydraulic Structures.The award has been delivered for the first time during the 7th International Hydraulic Structures Symposium in Aachen. The recipient receives a certificate and possibly a gift. Due to COVID-19 no award was presented in 2020.Given the 20th anniversary of the IAHR Hydraulic Structures Technical Committee in 2018, the Leadership Team decided to create the Philip H. Burgi Best Paper Award, named after the first chair of the Technical Committee. This award aims at rewarding the best technical paper presented during the International Symposium on Hydraulic Structures.
Best Paper Award and Best Reviewer Award
The Best Paper Award and the Best Reviewer Award are established in 2021 and started in 2022 by the IAHR and the China Institute of Water Resources and Hydropower Research(IWHR) to recognize the outstanding papers and reviewers which contributed to 'Journal of Ecohydrualics'(hereinafter referred to as the Journal) and to encourage scientists to contribute to the varied research in the field of ecohydraulics.The Awards are made biennially to 2 authors of the paper judged the most outstanding paper and 2 reviewers judged to provide most outstanding reviews of the paper submitted to the Journal.The Best Paper Award and the Best Reviewer Award will both consist of an award certificate and monetary prize of 1000 Euro.
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