text:
\section{Introduction}

Recent advances in deep learning have led an end-to-end neural approach to task-oriented dialog problems that can reduce a laborious labeling task on states and intents \citep{bordes2017}.
Many researchers have applied sequence-to-sequence models \citep{vinyals2015a} that are trained in a supervised learning (SL) and a reinforcement learning (RL) fashion to generate an appropriate sentence for the task. In SL approaches, given the dialog histories so far, the model predicts the distribution of the responses from the task-oriented system \citep{eric2017,de2017,zhao2018}.
However, the SL approach typically requires a lot of training data to deal with unseen scenarios and cover all trajectories of the vast action space of dialog systems \citep{wen2016}.
Furthermore, because the SL-based model does not consider the sequential characteristic of the dialog, the error may propagate over time that causes an inconsistent dialog \citep{li2017,zhao2016}. To address this issue, RL has been applied to the problem \citep{strub2017,das2017b}. By learning the intrinsic planning policy and the reward function, RL approach enables the models to generate a consistent dialog and generalize better on unseen scenarios. However, these methods struggle to find a competent RNN model that uses back-propagation, owing to the complexity of learning a series of sentences \citep{lee2018}.

As an alternative, \cite{lee2018} have recently proposed "Answerer in Questioner's Mind" (AQM) algorithm that does not depend on a limited capacity of RNN models to cover an entire dialog.
AQM treats the problems as twenty question games and selects the question that gives a maximum information gain. Unlike the other approaches, AQM benefits from explicitly calculating the posterior distribution and finding a solution analytically. The authors showed promising results in the task-oriented dialog problem, such as GuessWhat \citep{de2017}, where a questioner tries to find an object that is in answerer's mind via a series of Yes/No questions. The candidates are confined to the objects that are presented in the given image (less than ten on average). However, this simplified task may not be general enough to practical problems where the number of objects, questions and answers are typically unrestricted. For example, GuessWhich is a generalized version of GuessWhat that has a greater number of class candidates (9,628 images) and a dialog that consists of sentences beyond yes or no \citep{das2017b}.
Because the computational complexity vastly increases to explicitly calculate the information gain over the size of the entire search space, the original AQM algorithm is not scalable to a large scale problem. More specifically, the number of the unit calculation for information gain in GuessWhat is $10$ (number of objects) $\times$ 2 (Yes/No), while that of GuessWhich is $10,000$ (number of images) $\times$ $\infty$ (answer is a sentence) which makes the computation intractable.

One of the interesting ideas \cite{lee2018} suggested is to retrieve an appropriate question from the training set.
Retrieval-based models, which are basically discriminative models that select a response from a predefined candidate set of system responses, are often used in task-oriented dialog tasks \citep{bordes2017,seo2017a,liu2017}. It is critical not to generate sentences that are ill-structured or irrelevant to the task. However, such a discriminative approach does not fit well with complicated task-oriented visual dialog tasks, because asking an appropriate question considering the visual context is crucial to successfully tackle the problem. It is noticeable that AQM achieved high performance even with a retrieval-based approach in GuessWhat by making the candidate set of questions form the training set. However, \cite{han2017} pointed out that there exist dominant questions in GuessWhat which can be generally applied to all images (contexts), such as ``is it left?'' or ``is it human?''. Since GuessWhich is a more complicated task where questions dominant for the game are less likely to exist, it is another reason why the original AQM is difficult to be applied.

To address this, we propose a more generalized version of AQM, dubbed AQM+. Compared to the original AQM, the proposed AQM+ can easily handle the increased number of questions, answers, and candidate classes by employing an approximation based on subset sampling.
Particularly, unlike AQM, AQM+ generates candidate questions and answers at every turn, and then selects one of them to ask a question.
Because our algorithm considers the previous history of the dialog, AQM+ can generate a more contextual question. To understand the practicality and demonstrate the superior performance of our method, we conduct extensive experiments and quantitative analysis on GuessWhich. Experimental results show that our model could successfully deal with the answers in sentence and significantly decrease 61.5\% of the error while the SL and RL methods decrease less than 6\% of the error. The ablation study shows that our information gain approximation is reasonable. Increasing the number of sampling by eight times brought only a marginal improvement of percentile mean rank (PMR) from 94.63\% to 94.79\%, which indicates that our model can effectively approximate the distribution over the large search space with a small number of sampling.
Overall, our experimental results provide meaningful insights on how AQM framework can further provide an additional improvement on top of the SL and RL approaches.

Our main contributions are summarized as follows:
\begin{itemize}
    \item We propose AQM+ that extends the AQM framework toward the more general and complicated tasks. AQM+ can handle a more complicated problem where the number of candidate classes is extremely large.
    \item At every turn, AQM+ generates a question considering the context of the previous dialog, which is desirable in practice. In particular, AQM+ generates candidate questions and answers at every turn to ask an appropriate question in the context.
    \item AQM+ outperforms comparative deep learning models by a large margin in Guesswhich, a challenging task-oriented visual dialog task.
\end{itemize}

\section{Related Works}

A task-oriented visual dialog problem has recently been paid attention in the field of computer vision and natural language processing \citep{kim2017}. GuessWhat is one of the famous task-oriented dialog tasks, where the goal is to figure out a target object in the image through a dialog that the answerer has in mind \citep{de2017}.
However, GuessWhat is relatively an easy task because it only allows the answer form of yes or no.
The baseline visual question answering (VQA) model achieves 78.5\%.
In the object guessing task (i.e., GuessWhat task itself),
the state-of-the-art averaged accuracy of SL, RL \citep{zhang2018a}, and AQM \citep{lee2018} reached 44.6\% and 60.8\%, and 72.9\% at the 5th round, respectively. Random guessing baseline has an accuracy of 16.0\% \citep{han2017}, thus RL algorithms achieve 53.3\% error decrease, whereas AQM achieves 67.7\%.

GuessWhich is a cooperative two-player game that one player tries to figure out an image out of 9,628 that another has in mind \citep{das2017b}.
GuessWhich uses Visual Dialog dataset \citep{das2017a} which includes human dialogs on MSCOCO images \citep{lin2014} as well as the captions that are generated.
Although GuessWhich is similar to GuessWhat, it is more challenging in every task including asking a question, giving an answer, and guessing the target class.
For example, unlike GuessWhat that can be answered in yes or no, the answer can be an arbitrary sentence in GuessWhich.
Therefore, the VQA task in the Visual Dialog dataset is much studied than the GuessWhat dataset \citep{lu2017,seo2017}.

Similar to GuessWhat, SL and RL approaches have been applied to solve the GuessWhich task and they showed a moderate increase in performance \citep{das2017b,jain2018,zhang2018b}. However, based on the authors' recent Github implementation\footnote{https://github.com/batra-mlp-lab/visdial-rl} of the papers in ICCV \citep{das2017b}, SL and RL methods have shown that only 6\% of error is diminished through the dialog compared to the zeroth turn baselines which only use generated caption.

\section{Algorithm: AQM+}

\subsection{Problem Setting}

\begin{figure}[t]
\centering
\includegraphics[width=1.00\textwidth]{iclr19-task-c}
\caption{Illustration of AQM+ applied for GuessWhich task. The goal of GuessWhich is to figure out a correct answer out of 9,628 test images by asking a sequence of questions. }
\label{fig:iclr19_fig1-1}
\end{figure}

In our experiments, a questioner bot (Qbot) and an answerer bot (Abot) cooperatively communicate to achieve the goal via natural language. Under the AQM framework, at each turn $t$, Qbot generates an appropriate question $q_t$ and guesses the target class $c$ given a previous history of the dialog $h_{t-1}=(q_{1:t-1},a_{1:t-1},h_0)$. Here, $a_t$ is the $t$-th answer and $h_0$ is an initial context that can be obtained before the start of the dialog.
We refer to the random variables of target class and the $t$-th answer as $C$ and $A_t$, respectively. Note that the $t$-th question is not a random variable in our information gain calculation. To distinguish from the random variables, we use a bold face for a set notation of target class, question, and answers; i.e. $\mathbf{C}, \mathbf{Q}$, and $\mathbf{A}$.

Figure \ref{fig:iclr19_fig1-1} explains the AQM+ algorithm applied to GuessWhich game. In Figure \ref{fig:iclr19_fig1-1}, $c$ is the image with three elephants, $q_1$ is ``Are there many people?'', $a_1$ is ``Yes it is.'', $a_2$ is ``How many elephants?'', and $h_0$ is ``There are elephants walking in the zoo.''
In GuessWhich game, $\mathbf{C}$ is the set of test images whose size is 9,628. The size of $\mathbf{Q}$ and $\mathbf{A}$ is theoretically infinity as questions and answers can be more than one word.

\begin{figure}[t]
\centering
\includegraphics[width=1.00\textwidth]{iclr19-model-g}
\caption{\textcolor[rgb]{0,0,0}{Architecture of AQM+ and comparative models.} SL and RL have their main neural modules as Qgen $p^\dagger$ and Qscore $f^\ddagger$, while AQM has aprxAgen $\tilde{p}$ used for Qpost \textcolor[rgb]{0,0,0}{$\hat{p}$} and Qinfo \textcolor[rgb]{0,0,0}{$\tilde{I}$}. AQM+ contains all five modules and uses these to make subsets $\mathbf{Q}_t$, $\mathbf{A}_t$, and $\mathbf{C}_t$, thus achieving approximated estimation on information gain for large-scale inference, along with efficient contextual question generation.}
\label{fig:iclr19_fig1-2}
\end{figure}

\begin{table}[t]
\centering
 \caption{Notation of Qbot's Modules}
    \begin{tabular}{ l  l  l }
     \hline
     \multicolumn{1}{c}{Module} & \multicolumn{1}{c}{Function} & \multicolumn{1}{c}{Explanation} \\ \hline
     Qgen & $p^\dagger(q_t|h_{t-1})$ & a question generating RNN \\ \hline
     Qscore & $f^\ddagger(c|h_t)$ & a score measuring RNN \\ \hline
     aprxAgen & $\tilde{p}(a_t|c,q_t,h_{t-1})$ & an approximated answer generating RNN \\ \hline
     Qinfo & $\tilde{I}[C,A_t;q_t,h_{t-1}]$ & an information gain calculation function by  Equation \ref{eq:eq1} \\ \hline
     Qpost & $\hat{p}(c|h_t)$ & a posterior calculation function by Equation \ref{eq:eq2} \\ \hline \\
  \end{tabular}

 \caption{Notation of Learning Settings}
    \begin{tabular}{ l  l }
     \hline
     \multicolumn{1}{c}{Learning Setting} & \multicolumn{1}{c}{Explanation} \\ \hline
     indA & Like SL, aprxAgen is trained from training data \\ \hline
     depA & Like RL, aprxAgen is trained from the dialog with Abot \\ \hline
     trueA & aprxAgen shares the parameter with Abot \\ \hline
  \end{tabular}
\end{table}

\subsection{Preliminary: SL, RL, and AQM Approaches}
In SL and RL approaches \citep{das2017b,jain2018,zhang2018b}, Qbot consists of two RNN modules. One is ``Qgen", a question generator finding the solution that maximizes its distribution $p^{\dagger}$; i.e. $q^*_t = \argmaxA p^{\dagger}(q_t|h_{t-1})$.
The other is a ``Qscore'', a class guesser using score function for each class $f^{\ddagger}(c|h_t)$.
Two RNN modules can either be fully separated two RNNs \citep{strub2017}, or share some recurrent layers but have a different output layer for each \citep{das2017b}.

On the other hand, in the previous AQM approach \citep{lee2018}, these two RNN-based models are substituted to the calculation that explicitly finds an analytic solution. It finds a question that maximizes information gain or mutual information $\tilde{I}$, i.e.  $q^*_t = \argmaxA_{q_t \in \mathbf{Q}_{fix}} \tilde{I}[C,A_t;q_t,h_{t-1}]$, where
\begin{equation}
\begin{aligned}
& \tilde{I}[C,A_t;q_t,h_{t-1}] = \sum_{c \in \mathbf{C}} \sum_{a_t \in \mathbf{A}} \hat{p}(c|h_{t-1})  \tilde{p}(a_t|c,q_t,h_{t-1})
      \ln \frac{\tilde{p}(a_t|c,q_t,h_{t-1})}{\tilde{p}'(a_t|q_t,h_{t-1})}.
\end{aligned}
\label{eq:eq1}
\end{equation}

Here, a posterior function $\hat{p}$ can be calculated with a following equation in a sequential way, where $\hat{p}'$ is a prior function given $h_0$.
\begin{equation}
\hat{p}(c|h_t) \propto \hat{p}'(c|h_0) \prod^t_{j=1} \tilde{p}(a_j|c,q_j,h_{j-1}) = \hat{p}(c|h_{t-1}) \tilde{p}(a_t|c,q_t,h_{t-1})
\label{eq:eq2}
\end{equation}
In AQM, Equation \ref{eq:eq1} and Equation \ref{eq:eq2} can be explicitly calculated from the model. For ease of reference, let us name every component one by one. A module that calculates an information gain $\tilde{I}$ is referred to as ``Qinfo" and a module that finds an approximated answer distribution $\tilde{p}(a_t|c,q_t,h_{t-1})$ is referred to as ``aprxAgen". In AQM, aprxAgen is a model distribution that Qbot has in mind where the target is the true distribution of an answer generator $\bar{p}(a_t|c,q_t,h_{t-1})$, which is referred to as ``Agen''. Finally, ``Qpost" denotes a posterior $\hat{p}$ calculation module for guessing a target class.

As AQM uses full set of $\mathbf{C}$ and $\mathbf{A}$, the complexity depends on the size of $\mathbf{C}$ and $\mathbf{A}$. For the question selection, AQM uses a predefined set of candidate questions ($\mathbf{Q}_{fix}$), which is not changed for a different turn.

\subsection{AQM+ Algorithm}

In this paper, we propose AQM+ algorithm, which uses sampling-based approximation, for tackling the large-scale task-oriented dialog problem. The core differences of AQM+ from the previous AQM are summarized as follows:

\begin{itemize}
    \item The candidate question set $\mathbf{Q}_{t,gen}$ is sampled from $p^{\dagger}(q_t|h_{t-1})$ using a beam search at every turn. Previously, \cite{lee2018} used a predefined set of candidate questions $\mathbf{Q}_{fix}$.
    For example, one way to obtain $\mathbf{Q}_{fix}$ is to select questions from the training dataset randomly, called ``randQ".
    \item The answerer model (aprxAgen, $\tilde{p}$) that Qbot has in mind is not a binary classifier (yes/no) but an RNN generator. In addition, aprxAgen does not assume $\tilde{p}(a_t|c,q_t) = \tilde{p}(a_t|c,q_t,h_{t-1})$, which is not even an appropriate assumption when the previous and current questions are sequentially related. For example, $p(a_2=\text{``yes'' }|\text{ }c, q_2=\text{``is left?''}) \neq p(a_2=\text{``yes'' }|\text{ }c, q_2=\text{``is left?''},a_1=\text{``yes''},q_1=\text{``is right?''})$. Regardless of the left term, the probability of the right term is almost zero.
    \item To approximate the information gain of each question, the subsets of $\textbf{A}$ and $\textbf{C}$ are also sampled at every turn. The previous algorithm used full set of $\textbf{A}$ and $\textbf{C}$. We describe an additional explanation on our information gain approximation, infogain\_topk as below.

\end{itemize}

\textbf{Infogain\_topk} The equation for Infogain\_topk is as follows:
\begin{equation}
\begin{aligned}
& \tilde{I}_{topk}[C,A_t;q_t,h_{t-1}] \\
& = \sum_{a_t \in \mathbf{A}_{t,topk}(q_t)} \sum_{c \in \mathbf{C}_{t,topk}} \hat{p}_{reg}(c|h_{t-1})  \tilde{p}_{reg}(a_t|c,q_t,h_{t-1})
      \ln \frac{\tilde{p}_{reg}(a_t|c,q_t,h_{t-1})}{\tilde{p}'_{reg}(a_t|q_t,h_{t-1})},
\end{aligned}
\end{equation}

\noindent
where $\hat{p}_{reg}$ and $\tilde{p}_{reg}$ is a normalized version of $\hat{p}$ over $\mathbf{C}_{t,topk}$ and $\tilde{p}$ over $\mathbf{A}_{t,topk}(q_t)$, respectively. Here,
$\tilde{p}'_{reg}$ is obtained by using both $\hat{p}_{reg}$ and $\tilde{p}_{reg}$ as follows:

\begin{gather}
\hat{p}_{reg}(c|h_{t-1}) = \frac{\hat{p}(c|h_{t-1})}
{\sum_{c \in \mathbf{C}_{t,topk}} \hat{p}(c|h_{t-1})}\\
\tilde{p}_{reg}(a_t|c,q_t,h_{t-1}) = \frac{\tilde{p}(a_t|c,q_t,h_{t-1})}{\sum_{a_t \in \mathbf{A}_{t,topk}(q_t)} \tilde{p}(a_t|c,q_t,h_{t-1})}\\
\tilde{p}'_{reg}(a_t|q_t,h_{t-1}) = \sum_{c \in \mathbf{C}_{t,topk}} \hat{p}_{reg}(c|h_{t-1}) \cdot \tilde{p}_{reg}(a_t|c,q_t,h_{t-1})
\end{gather}

Each set is constructed by the following procedures.

\begin{itemize}
	\item $\mathbf{C}_{t,topk}$ $\leftarrow$ top-K posterior test images (from Qpost $\hat{p}(c|h_{t-1})$)
    \item $\mathbf{Q}_{t,gen}$ $\leftarrow$ top-K likelihood questions using the beam search (from Qgen $p^\dagger(q_t|h_{t-1})$)
    \item $\mathbf{A}_{t,topk}(q_t)$ $\leftarrow$ top-1 generated answers from aprxAgen for each question $q_t$ and each class in $\mathbf{C}_{t,topk}$ (from aprxAgen $\tilde{p}(a_t|c,q_t,h_{t-1})$)
\end{itemize}

Top-K samples may lead our approximation to be biased toward plausible (high-probability) candidate classes and plausible candidate answers. However, we chose to use top-K samples because our main goal is to reduce the entropy over plausible candidate classes and answers, not over the whole candidate classes and answers.

In general, the AQM+ algorithm can deal with various problems where $|\mathbf{C}_{t,topk}|$, $|\mathbf{Q}_{t,gen}|$, and $|\mathbf{A}_{t,topk}(q_t)|$ are all different. Here, $|\cdot|$ denotes the cardinality of a set. We can vary the size of each set and control the complexity of the AQM+ algorithm. In our experiments, however, we mainly considered the problem when  $|\mathbf{C}_{t,topk}| = |\mathbf{Q}_{t,gen}| = |\mathbf{A}_{t,topk}(q_t)|$.
More specifically, $|\mathbf{C}_{t,topk}|$ is equal to $|\mathbf{A}_{t,topk}(q_t)|$ because our model finds a single best answer $a_t$ given a pair $(q_t,c)$ that maximizes $\tilde{p}(a_t|c,q_t,h_{t-1})$.
Therefore, $|\mathbf{A}_{t,topk}|=|\mathbf{Q}_{t,gen}| \cdot |\mathbf{C}_{t,topk}|$ per information gain calculation where $\mathbf{A}_{t,topk}=\{\mathbf{A}_{t,topk}(q_t)|q_t\in \mathbf{Q}_{t,gen}\}$.
For the detailed explanation, see Algorithm \ref{alg:alg1} in Appendix A.

We also explain the extended sampling method on candidate answers for cases where $\mathbf{A} \neq \mathbf{C}$ is required. In the extended method, aprxAgen first generates top-m answers for each candidate question and each candidate class, where $m$ is the smallest integer satisfying $|A| \leq |C| \cdot m$. After that, the candidate answers are randomly removed, leaving only $|A|$ answers.

\subsection{Learning}

In all SL, RL, and AQM frameworks, Qbot needs to be trained to approximate the answer-generating probability \textcolor[rgb]{0,0,0}{distribution} of Abot.
In AQM approach, aprxAgen does not share the parameters with Agen, and therefore also needs to be trained to approximate Agen. AQM can train aprxAgen by the learning strategy of the SL or RL approach.
We explain two learning strategies of AQM framework below: indA and depA.
In SL approach, Qgen and Qscore are trained from the training data, which have the same or similar distribution to that of the training data used in training Abot.
Likewise, in indA setting of AQM approach, aprxAgen is trained from the training data.
In RL approach, Qbot uses dialogs made by the conversation of Qbot and Abot and the result of the game as the objective function (i.e. reward).
Likewise, in depA setting of AQM approach, aprxAgen is trained from the questions in the training data and following answers obtained in the conversation between Qbot and Abot.
We also use the term trueA, referring to the setting where aprxAgen is the same as Agen, i.e. they share the same parameters.
Both the previous AQM algorithm and the proposed AQM+ algorithm use these learning strategies.

\section{Experiments}

\subsection{Experimental Setting}

\textbf{GuessWhich Task}
GuessWhich is a two player game played by Qbot and Abot. The goal of GuessWhich is to figure out a correct answer out of 9,628 test images by asking a sequence of questions.
Abot can see the randomly assigned target image, which is unknown to Qbot.
Qbot only observes a caption of the image generated from Neuraltalk2 \citep{vinyals2015a}.
To achieve the goal, Qbot asks a series of questions, to which Abot responds with a sentence.

\textbf{Comparative Models}
We compare AQM+ with three comparative models, SL-Q, RL-Q, and RL-QA \citep{das2017b}.
In SL-Q, Qbot and Abot are trained separately from the training data.
In RL-Q, Qbot is initialized by the Qbot trained by SL-Q and then is fine-tuned by RL. Abot is the same as the Abot trained by SL-Q, and is not fine-tuned further. In the original paper \citep{das2017b}, it was referred to as Frozen-A.
By the way, in an RL-QA setting, not only Qbot but also Abot is concurrently trained with Qbot. In the original paper, it was referred to as RL-full-QAf.
We also compare our AQM+ with ``Guesser'' algorithm. Guesser asks a question generated from SL-Q algorithm and calculates posterior by Qpost of AQM+.

\textbf{Non-delta vs. Delta Hyperparameter Setting}
The important issue in our GuessWhich experiment is delta setting.
In the paper of \cite{das2017b}, SL-Q, RL-Q, and RL-QA algorithms achieve moderate increases of the performance. In SL-Q, 88.5\% of percentile mean rank (PMR) is improved to 90.9\%.
In RL-QA, 90.6\% of PMR is improved to 93.3\%.
Here, 93.3\% of PMR at the zeroth turn means that the model can predict the correct image to be more likely than the other 8,983 images out of 9,628 candidates after exploiting the caption information solely.
However, \cite{das2017b} found that another hyperparameter setting, delta, makes much progress on their algorithm. Delta setting refers to different weights on loss and learning decay rate. Based on the authors' recent report on Github, SL-Q and RL-QA methods have shown that less than 6\% of error is diminished through the dialog compared to the zeroth turn baseline which only uses generated caption.
The PMR of the target (class) image which only uses the caption is around 95.5, but the dialog does not improve the PMR to more than 95.8.
We use both non-delta setting (the setting in the original paper) and delta setting (the setting in Github) to test the performance of AQM+.

\textbf{Other Experimental Setting}
As shown in Figure \ref{fig:iclr19_fig1-2}, our model uses five modules, Qgen, Qscore, aprxAgen, Qinfo, and Qpost.
We use the same Qgen and Qscore modules as the comparative SL-Q model.
In Visual Dialog, Qgen and Qscore share one RNN structure and have different output layers for each.
The prior function is obtained from $\hat{p}'(c|h_0) \propto$ $exp(\lambda \cdot f^\ddagger(c|h_0))$ using Qscore, where $\lambda$ is a balancing hyperparameter between prior and likelihood.
We set $|\mathbf{C}_{t,topk}| = |\mathbf{Q}_{t,gen}| = |\mathbf{A}_{t,topk}(q_t)|$ = 20.
The epoch for SL-Q is 60. The epoch for RL-Q and RL-QA is 20 for non-delta, and 15 for delta, respectively. \textcolor[rgb]{0,0,0}{Our code is modified from the code of \cite{modhe2018visdialrlpytorch}, and we make our code publically available\footnote{https://github.com/naver/aqm-plus}.}
\textcolor[rgb]{0,0,0}{All experiments are implemented and fine-tuned with NAVER Smart Machine Learening (NSML) platform~\citep{sung2017nsml, kim2018nsml}.}

\begin{table}[t]
\centering
 \caption{Test percentile mean rank (PMR) in 10th round. Caption refers the 0th round PMR of SL-Q. The results of comparative deep models in the non-delta setting is from the paper of \cite{das2017b}.}
    \begin{tabular}{ l  c c c c c c}
     \hline
     & Caption & SL-Q & RL-QA & AQM+ w/ indA & AQM+ w/ depA & AQM+ w/ trueA \\ \hline
    non-delta & 88.5 & 90.9 & 93.3 & 94.64 & 97.45 & 99.87  \\ \hline
    delta & 95.45 & 95.72 & 95.69 & 97.17 & 98.25 & 99.22  \\ \hline
  \end{tabular}

  \label{table:table2}

\end{table}

\begin{figure}[t]
\centering
\begin{subfigure}[b]{0.48\textwidth}
\caption{Non-delta Hyperparameter Setting}
\includegraphics[width=\textwidth]{iclr19-resultplot-nondelta-h}
\end{subfigure}
\begin{subfigure}[b]{0.48\textwidth}
\caption{Delta Hyperparameter Setting}
\includegraphics[width=\textwidth]{iclr19-resultplot-delta-h}
\end{subfigure}
\caption{Test percentile mean ranks on GuessWhich experiments.}
\label{fig:iclr19_fig2}
\end{figure}

\subsection{Comparative Results}

Figure \ref{fig:iclr19_fig2} shows the PMR of the target image for our AQM+ and comparative models across the rounds.
Figure \ref{fig:iclr19_fig2}a corresponds to the non-delta setting in the original paper \citep{das2017b} and Figure \ref{fig:iclr19_fig2}b corresponds to the delta setting proposed in the Github code.

We see that SL-Q and RL-QA do not significantly improve the performance after a few rounds, especially for the delta setting.
In delta setting, SL-Q increases their performance from 95.45\% to 95.72\% at 10th round, and RL-QA increases their performance from 95.44\% to 95.69\%. It means that error drop of SL-Q and RL-QA algorithms is 5.74\% and 5.33\%, respectively.
On the other hand, AQM-indA increases its PMR from 95.45\% to 96.53\% at the fifth round and reaches 97.17\% at the 10th round.
Likewise, AQM-depA increases its PMR from 95.45\% to 97.48\% at the fifth round and reach 98.25\% at the 10th round, decreasing 61.5\% of error.
Note that Guesser w/ indA achieves 96.37\% at the 10th round, outperforming SL-Q by a significant margin. It shows that not only the question generation but also the guessing mechanism affects the performance degeneration of SL and RL algorithms.

\subsection{Ablation Study}

\begin{figure}[t]
\centering
\begin{subfigure}[b]{0.48\textwidth}
\caption{No Caption Experiment (indA, Non-delta)}
\includegraphics[width=\textwidth]{iclr19-nocaption-indA-h}
\end{subfigure}
\begin{subfigure}[b]{0.48\textwidth}
\caption{Random Candidate Answers (Non-delta)}
\includegraphics[width=\textwidth]{iclr19-randomA-nondelta-h.png}
\end{subfigure}
\caption{Left column shows the results of ablation studies on no caption experiments. Right column shows the result of ablation studies on random candidate answers experiments, where candidate answers are sampled from the training data.}
\label{fig:iclr19_fig3_1}
\end{figure}

\begin{figure}[t]
\centering
\begin{subfigure}[b]{0.48\textwidth}
\caption{Number of QAC Experiment (indA, Non-delta)}
\includegraphics[width=\textwidth]{iclr19-QAC-nondelta-indA-h}
\end{subfigure}
\begin{subfigure}[b]{0.48\textwidth}
\caption{Number of Q Experimnet (indA, Non-delta)}
\includegraphics[width=\textwidth]{iclr19-numQ-nondelta-indA-h.png}
\end{subfigure}
\begin{subfigure}[b]{0.48\textwidth}
\caption{Number of A Experiment (indA, Non-delta)}
\includegraphics[width=\textwidth]{iclr19-numA-nondelta-indA-h.png}
\end{subfigure}
\begin{subfigure}[b]{0.48\textwidth}
\caption{Number of C Experimnet (indA, Non-delta)}
\includegraphics[width=\textwidth]{iclr19-numC-nondelta-indA-h.png}
\end{subfigure}
\caption{The result of ablation studies on different sizes of the subsets of candidate questions, answers, and classes. In the subfigure (a), the size for three subsets are the same to $K$.}
\label{fig:iclr19_fig3_2}
\end{figure}

\textbf{No Caption Experiment}
We test our AQM+ algorithm where no caption information exists.
For the zeroth prediction, we simply replace the prior function from Qscore with a uniform function. Since Qgen in either SL-Q or RL-QA is trained also assuming the existence of the caption, we tried two alternative settings to approximate experiments without a caption. The first trial is the zero-caption experiment, where the caption vector is filled with zeros.
The second trial is the random-caption experiment, where the caption vector is replaced with a random caption vector, which is not related to the target image.
Figure \ref{fig:iclr19_fig3_1}a shows that AQM+ performs well for both zero-caption and random-caption setting.
By contrast, SL-Q and RL-QA do not work at all. It seems SL-Q and RL-QA are not trained on the situation where zero-caption vector or even totally wrong caption vector comes.
Though training SL-Q and RL-QA for these situations can increase their performance, it is evident that SL and RL algorithms are not robust to unexpected environments.
Likewise, we also run no caption experiments for depA setting. For more ablation studies, see Figure \ref{fig:iclr19_fig6_} in Appendix B.

\textbf{Random Candidate Answers Experiment }
One of our main arguments is that generating candidate questions from Qscore and candidate answers from aprxAgen at every turn makes AQM+ effectively deal with general and complicated task-oriented dialogs.
Supporting the argument, we conducted the experiments under the setting where the answer set is randomly selected from the training data and then fixed. Random selection of candidate answers decreases the performance from 94.64\% to 92.78\% at indA, non-delta, and the 10th round.
Appendix B also includes a discussion on the setting with a predefined candidate question set $\mathbf{Q}_{fix}$.

\textbf{Number of QAC Experiment}
We also changed the size of subset $K$ = $|\mathbf{Q}_{t,gen}|$=$|\mathbf{A}_{t,topk}(q_t)|$=$|\mathbf{C}_{t,topk}|$ to check our efficiency of information gain approximation, using non-delta setting.
Figure \ref{fig:iclr19_fig3_2}a shows the experimental results.
Note that AQM+ with the setting of $K=1$ corresponds to Guesser.
In the setting of non-delta and indA, 94.64\% of PMR is achieved when $K$ is 20, whereas 94.79\% is achieved when $K$ is 40. Note that 8 times (2 x 2 x 2) complexity increase just improves 0.15\% of PMR, showing the efficiency of the setting of $K$=20 in our experiments.
On the other hand, this result also implies that increasing $K$ would make further improvement on the performance.
Likewise, in depA setting, changing $K$ from 20 to 40 increases the PMR from 97.44\% to 97.77\%. For more ablation studies, see Figure \ref{fig:iclr19_fig7_} in Appendix B.
We also changed the size of each subset, $|\mathbf{Q}_{t,gen}|$, $|\mathbf{A}_{t,topk}(q_t)|$, and $|\mathbf{C}_{t,topk}|$. Figure \ref{fig:iclr19_fig3_2}b-d shows the results. $|\mathbf{Q}_{t,gen}|$ has the most effect, whereas $|\mathbf{A}_{t,topk}(q_t)|$ has the least effect.

\begin{figure}[t]
\centering
\includegraphics[width=1.00\textwidth]{iclr19-retrieval-b_}
\caption{Qualitative results on image retrieval of AQM+. Left column shows true images and their corresponding caption, and right column contains selected top-$k$ images.}
\label{fig:iclr19_fig4}
\end{figure}

\textbf{Generated Questions and Selected Images }
Figure \ref{fig:iclr19_fig4} shows the top-k images selected by AQM+'s posterior. Non-delta and indA setting is used.
The figure shows that relevant images to the caption remained after few dialog turns. The bottom number in the image denotes posterior of the image AQM+ thinks of.
We also compare selected examples of generated dialog of SL-Q, RL-QA, and AQM+ w/ indA for delta setting. See Figure \ref{fig:iclr19_fig8_} in Appendix C for the results.

\section{Discussion}

\subsection{Difficulty of GuessWhich }
According to our results, we infer that PMR degradation of comparative SL and RL models during the dialog is not caused by forgetting dialog context to ask an appropriate question.
Comparative results between AQM+ and Guesser show that the improvement from AQM+'s Qpost is significant, which implies that the major constraint of SL and RL is the limited capacity of RNN and its softmax score function.

Another reason for the poor performance lies in the current status of VQA models. According to \cite{das2017a},
they discovered a variety of models, one of which is used in both the study of \cite{das2017b} and our experiments, and
can already reach 41.2\% for answer retrieval accuracy from 100 candidate answers, solely using the question without exploiting image and history information. Fully exploiting these factors, however, increases the performance only slightly to 45.5\%.
As discrimination on different images relies on image and history information, Qbot suffers to gain meaningful information through the dialog.
Therefore, applying AQM+ to the GuessWhich problem means that we not only solve a very complicated problem, but also find that the AQM framework is applicable to the situation where the answer has high uncertainty.

\subsection{Notes on Comparative Analysis}

\textbf{Fine-tuning both Qbot and Abot through RL }
Though RL-QA is the main setting in the work of \cite{das2017b}, there are some reports indicating that fine-tuning both Qbot and Abot is unfair \citep{de2017,han2017}\textcolor[rgb]{0,0,0}{, as one of the ultimate goals in this field is to make a questioner be able to talk with human.}
If the distribution of Abot is not fixed during RL, Qbot and Abot can make their own language which is not compatible to natural language \citep{kottur2017}.
To prevent this problem, many studies added the objective function of language model during RL \citep{zhu2017,das2017b}.
However, even though the generated dialog is tuned to be like human dialog, the performance of RL-QA on the conversation with human would decrease compared to SL-Q, because the distribution of Abot become far from human's \citep{chattopadhyay2017,lee2018}.
Moreover, achieving a good performance by fine-tuning both Qbot and Abot is much easier than fine-tuning only Qbot \citep{zhu2017,han2017}.
Thus, it is reasonable to compare AQM+ w/ indA and AQM+ w/ depA with SL-Q and RL-Q, respectively.

\textbf{Compuational Cost}
AQM+ at $K$=20 uses 20$\times$20$\times$20 calculations for information gain. On the other hand, the previous AQM requires 20$\times \infty \times$9628 calculations for information gain, which makes the computation intractable. Even if we use only 100 candidate answers, which is in the Visual Dialog dataset \citep{das2017a}, the previous AQM requires 2500 times as many calculations (20M) as AQM+.
On the other hand, AQM+ requires more calculations and thus requires more inference time than SL or RL. AQM+ generates one question within around 3s when $K$=20, whereas SL generates one question within 0.1s.
We used Tesla P40 for our experiments. Though the complexity of our information gain is $O(K^3)$, $K$ does not increase the time required for the whole inference in proportion to the cube of K, when $K$=20.
It is because calculating the information gain is not the sole resource-intensive part in the whole inference process.

\subsection{Toward Practical Applications }
There are plenty of potential future works to improve the performance of AQM+ in real task-oriented dialog applications.
For example, robust task-oriented dialog systems are required for appropriately replying to user's questions \citep{li2017} and responding for chit-chat style conversation \citep{zhao2017}. The question quality can also be improved by diverse beam search approaches \citep{vijayakumar2016,li2016}, which prevent sampling similar questions for the candidate set. We highlight two issues described below; online learning and fast inference.

\textbf{Online Learning} For a novel answerer, fine-tuning on the dialog model is required \citep{Krause2018}. If the experiences of many users are available, model-agnostic meta learning (MAML) \citep{finn2017} can be applied for few-shot learning. Updating the hyperparameter $\lambda$ in an online manner, which balances the effect of the prior and the likelihood, can also be effective in practice. If the answer distribution of user is different from our aprxAgen, we can increase $\lambda$ to decrease the effect of the likelihood.

\textbf{Fast Inference} AQM+'s time complexity can be decreased further by changing the structure of aprxAgen. In specific, we can apply diverse methods such as skipping the update of hidden states in some steps \citep{seo2018}, using convolution networks or self-attention networks \citep{yu2018b,vaswani2017}, substituting matrix multiplication operation for hidden state update to weighted addition \citep{yu2018a}, and direct information gain inference from the neural networks \citep{belghazi2018}.

\section{Conclusion}
Asking appropriate questions in practical applications has recently been paid attention \citep{rao2018,buck2018}.
We proposed AQM+ algorithm that is a large-scale extension of AQM framework. AQM+ can ask an appropriate question considering the context of the dialog, handle the responses in a sentence form, and efficiently estimate information gain of the target class with a given question. This improvement makes our AQM framework to step forward toward practical task-oriented applications.
AQM+ not only outperforms the comparative SL and RL algorithms, but also enlarges the gap between AQM+ and the comparative algorithms comparing to the performance gaps reported in GuessWhat.
AQM+ acheives more than 60\% error decreases through the dialog, whereas the comparative algorithms only achieve 6\% error decreases.
Moreover, the performance of AQM+ can be boosted further by employing the models recently proposed in the visual dialog field such as other question generator models \citep{jain2018} and question answering models \citep{kottur2018}.

\subsubsection*{Acknowledgments}
\textcolor[rgb]{0,0,0}{The authors would like to thank Yu-Jung Heo, Hwiyeol Jo, and Kyunghyun Cho for helpful comments.
This work was supported by the Creative Industrial Technology Development Program (10053249) funded by the Ministry of Trade, Industry and Energy (MOTIE, Korea).}

\small
\setlength{\bibsep}{5.0pt}

redpajama_set_name: RedPajamaArXiv


Shin Splints – what is it?
'Shin Splints', or medial tibial stress syndrome, is a term that is used for pain felt anywhere along the shinbone from knee to ankle. It is a result of overworked muscles, tendons and bone tissue and often occur in athletes who've recently intensified or changed their training routines. People that play sports that involve a lot of running are particularly prone to this type of injury.
The pain associated with shin splints results from excessive amounts of force on the shinbone and the tissues attaching the shinbone to the muscles surrounding it. The excessive force causes the muscles to swell and increases the pressure against the bone, leading to pain and inflammation.
Shin splints also result from stress reactions to bone fractures. The constant pounding can cause minute cracks in the bones of the leg. If the body doesn't get time to rest then the tiny cracks can result in a complete fracture or a stress fracture.
Our chiropractors at Chiro & Sports Med are committed to providing chiropractic solutions to address your unique needs, whether you are experiencing a pinched nerve, bulging disc, back pain, neck pain, knee pain, headaches or even muscular tightness and tension. You may be searching for pain relief after an accident or experiencing injury. You may suffer from a specific condition like chronic back pain or a spinal condition. Our mission is to help reduce or eliminate pain and to prevent future problems and injury. We are here to improve your quality of life, your well-being and your ability to live and active healthy lifestyle.


text:
Q: How to launch actual browser instead of testing browser from geckodriver? (python, Selenium) How do I run my code and have Fire fox and chrome actually run from the web browsers to make the actual changes rather then running them in the testing browser? I thought changing the driver location from the one off of selenium to the actual exe location would do it, but it just gives me error about missing driver.
Can some one give me an example?
Also I thought you could launch firefox with the profile insted of the driver like so:
from selenium import webdriver
url = "<website name>"
fp = webdriver.FirefoxProfile('C:\\Users\\<admin>\\AppData\\Local\\Mozilla\\Firefox\\Profiles\\t18zcb43.<new profile>')
driver = webdriver.Firefox(fp)
driver.get(url)

redpajama_set_name: RedPajamaStackExchange


text:
Maureen McPhilmy Net Worth: Know More About Her Age and Husband
Shikshya Rimal
Home » Maureen McPhilmy Net Worth: Know More About Her Age and Husband
Maureen McPhilmy Family, Husband and Children
Maureen McPhilmy Age, Height and Body Measurements
Maureen McPhilmy Net Worth
Maureen Mcphilmy is a privileged person whose net worth is $4 million dollars. Maureen Mcphilmy is popular as an ex-wife of famous American Journalist, and TV host Bill O'Reilly. He is an American public relation executive who is always in the headlines because of her controversial marriage.
Quick Info:
Birthday: May 11, 1966 (age 51)
Ethnicity: White
Profession: Public Relation Executive
Maureen Mcphilmy married Jeffrey Gross and lives a healthy life with her Family. Maureen divorced her ex-husband Bill O'Reilly in the year 2011 and won the battle against him for the custody of their children.
She was born in Chittenango, New York City, USA. Born in a bourgeois family, her father worked in a market and her mother was a gardener. She was raised by her single mother as her parents were separated when she was just five.
Parents: N/A
Husband: Jeffrey Gross
Children: Madeline O'Reilly, Spencer O'Reilly
Maureen, born as Maureen Elizabeth Mcphilmy was a shy and quiet child. She as a kid was quite a loner and always stayed away from her friends and classmates. She attended and graduated from a local St. Peter's School.
Mcphilmy met her ex-husband O'Reilly in the year 1992. The couple were in a courtship for 4 years and got married in the year 1996.
After 15 years of marriage, she accused Bill of harassing and abusing her sexually and filed a case against him in the year 2010.
After fighting for almost 4 years, she finally won the case and the custody of her children against Bill. Maureen and Bill together had two children, Madeline, and Spencer O'Reilly.
Later, she fell in love with a police detective Jeffrey Gross and the couple got married. Jeffrey was a widower and had two kids with his late wife. The couple lives together happily with all of their kids in their luxury home.
Maureen Mcphilmy stands at an average height and her skin and hair are perfectly glossy and shiny. She has managed to look flawless and good even at the age of 51.
Her body measurement is average looking. Mcphilmy looks voluptuous and possesses a curvy body
The dark-eyed gorgeous lady has a curvaceous and plumpy figure. She is a well-dressed diva who likes to upgrade her personality and style according to the season.
Body Measurements: N/A
Plastic Surgery: N/A
How much is Maureen Mcphilmy worth? Maureen Mcphilmy net worth is estimated at $4 million dollars. Maureen as a PR officer earns a handsome amount of salary. She is living a happy and prosperous life with her husband Gross and family in her Long Island's home which worth $4 million dollars.
Salary: N/A
Net Worth: $4million
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redpajama_set_name: RedPajamaCommonCrawl


text:
Q: Update 2 containers using Pjax I'm working on a transport planner in Yii2.
The tasks are shown in a GridView.
Above the GridView I have a navigation to go through the dates.
like so:
<?php Pjax::begin(['enablePushState' => false]); ?>

                <?= Html::a('', ['planner/previous'], ['class' => 'btn btn-default glyphicon glyphicon-arrow-left pull-left']) ?>

                <?= Html::a(Yii::t('app', $session->get('date')), ['#'], ['class' => 'planner_date']); ?>

                <?= Html::a('', ['planner/next'], ['class' => 'btn btn-default glyphicon glyphicon-arrow-right pull-right']) ?>

<?php Pjax::end(); ?>

As you can see the current date is stored in a session.
The date is set using DateTime().
In my actionIndex in the Planner-model I pass the datesession to the PlannerSearch-model like:
$dataProvider = $dataModel->data(Yii::$app->request->queryParams, $session->get('datum'));

And then grab the session: PlannerSearch::search($params, $date), and use the $date variable in my query.
Maybe a terrible way of doing things, but I'm still a Yii (mvc) beginner..
What I actually want to achieve, is to go through the dates and update both the planner navigation date (.planner_date) and the GridView in the planner-view with the tasks for that day.
Anyone who can help?


While the pair hasn't commented on whether or not they're dating, fans might recall the two recently worked together on one of Braff's projects. The 44-year-old writer-director shot the the short film, In the Time It Takes To Get There, which came to life because of a movie poster contest. It starred Pugh, Alicia Silverstone and Andy King, from Netflix's Fyre Fest documentary, who made a small cameo.
Leading up to the short film's release, Zach shared several snaps of the "Fighting With My Family" actress on his social media accounts., Although, there was no hint the two were close.
The 44-year-old star hasn't been seriously tied to anyone romantically since dating model Taylor Bagley.


Have you been waiting all year to see Lovebox Weekender on tour? Great, they are going to be performing live near you! But wait, it looks like the concert is sold out, or, perhaps, the seats you are finding are not quite what you wanted.
Absolutely no problem, Lovebox Weekender tickets are available here for a more than reasonable (some even dare say cheap) price. So, whether you have to be standing mere feet from the stage or you are a working with a little tighter budget, find your Lovebox Weekender tickets at Coast to Coast Tickets and enjoy your favorite band in person.
The Lovebox Weekender is a three day extravaganza of "parties, performance and participation", mixing together music, hedonism, art, food and drink. The full 2010 lineup has yet to be announced, but Dizzee Rascal and Chase & Status have already been confirmed. It's sure to be just as amazing as always, and tickets will go fast, so don't wait, get your 2010 Lovebox Weekender tickets today!


If you're a fan of STRONG by Zumba® classes, then you already know that the non-dance, high-intensity class is designed to push you past your limits. You probably didn't know you were capable of doing so many burpees, squats, and punches in one single class. The fact that each of those moves is synchronized to a beat definitely helps to hit that mark and leave you feeling amazing (albeit breathless) at the end of 60 minutes.
So, are you ready to take on the latest #SBZChallenge? In Quadrant 1, the STRONG by Zumba exclusive song 'Invasion' pumps you up for the rest of class. But it also tests you and motivates you to complete 16 push-ups in one single song. Yes, 16! Easier said than done. Just ahead, these people show you that they're up for it. Are you game? Take the #SBZChallenge and tag @strongbyzumba on IG – you might just be featured on our feed.


Guiding you through all the steps to create a practical yet stylish Japanese apron, this is the perfect introduction to sewing clothes and mastering a sewing machine. Best of all you do not need to follow a pattern for this project making it less complicated and therefore ideal for beginners.
Small class sizes of 3 people ensure you'll get the most out of your time. Tea, coffee and biscuits provided!


text:
namespace root2hdf5
{
    namespace cint
    {
        // This method provides a convenient interface for executing code via
        // CINT which might be too long for the TROOT::ProcessLine method (which
        // has a limit of 2043 characters).  The method creates a temporary file
        // in which to write the code.  If the "compile" argument is true, then
        // this method will use the TSystem::CompileMacro method to generate and
        // load a shared library.  In this case, the provided code will not have
        // implicit access to the ROOT headers, so you need to include what you
        // need.  This is done instead of using TROOT::LoadMacro with a "+" sign
        // because the output directory of the compiled code can be controlled.
        // If the "compile" argument is false, this method will use the
        // TROOT::LoadMacro method, and will process the code without compiling
        // it, but cannot do things like generating dictionaries.  This method
        // returns true on success and false on failure.
        bool process_long_line(const std::string & long_line,
                               bool compile = false);
    }
}

redpajama_set_name: RedPajamaGithub


If you don't begin by unifying content marketing strategy, teams, and processes within your company, you face a tough task ahead. If you do start here, you can build an infrastructure that allows local content creators to collaborate with global leadership and tell stories that truly resonate with a new culture. The Skyword Global team can guide you as you set the tone at headquarters, align your organization, and solidify your core brand storytelling objectives. Deploy your story in multiple languages: English (US, UK, and AU), French, German, Italian, Japanese, Mandarin, Portuguese, Turkish, Spanish, Swedish, Russian, and counting.
dam. skyword dam. digital asset manager.


text:
title: CLI | KubeDB
menu:
  docs_0.11.0:
    identifier: es-cli-elasticsearch
    name: CLI
    parent: es-elasticsearch-guides
    weight: 65
menu_name: docs_0.11.0
---


Symmetry Massage is the clinical practice of Abi Chapman who has been practicing massage since 2008. Abi is a qualified Level 5 sports and remedial massage therapist, graduating from the Oxford School of Massage www.ossm.co.uk and an affiliated member of the Institute of Sports & Remedial Massage www.theisrm.com.
'My approach is compassionate and supportive. I listen, make a practical assessment, provide a treatment plan and facilitate change'.
Abi's main interests are netball, sailing, skiing, yoga and to maintain a healthy work/life balance.
Abi also treats at events such as the London Marathon, the Brighton Marathon and the Bleinham Palace Triathlon.


The interesting digital imagery below, is segment of The Cool Kitchen Cabinets Sacramento Collections post which is classified within Design, kitchen cabinet sacramento ca, discount kitchen cabinets sacramento ca, custom kitchen cabinets sacramento and published at September 18th, 2018 17:19:27 PM by admin.
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Take a tour around our shoppable inspiration board and find all the ideas you need to update your home interior. It's easy to find home design inspiration online, in magazines, or in homes we visit. But turning these Kitchen Cabinets Sacramento into reality is the real challenge.
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Everything you need to know about Kitchen Cabinets Sacramento, decoration ideas, advice and inspiration. Explore the best new residential interior design, ranging from tiny private homes to big apartment blocks. Dreaming of your perfect home but don't know where to start? Consider the team at Davin Interiors your creative partner to turn your design dreams into reality.
Home Interior Warehouse offers a wide selection of furniture and home decor items as well as design services in their Walled Lake and Plymouth showrooms. Last winter, we asked all of southwest Missouri's interior designers to show us their best projects for our annual Kitchen Cabinets Sacramento Design Awards contest.
Here is fundamental data on custom kitchen cabinets sacramento. We have the prime step for custom kitchen cabinets sacramento. Check it out for yourself! You can get The Inspired Kitchen Cabinets Sacramento You'll Love guide and look the latest The Cool Kitchen Cabinets Sacramento Collections in here.


text:
HSC Csíkszereda, Hoki Sport Club Csíkszereda, är en ishockeyklubb bildad 1929 från staden Miercurea Ciuc (vars namn på ungerska är Csíkszereda) i Rumänien. Klubben deltar parallellt i MOL Liga och den rumänska högstadivisionen för ishockey, Liga Națională de hochei. Klubben vann sitt första rumänska mästerskap 1949 och har sedan dess vunnit ytterligare 14 gånger per säsongen 2012/13 - där de vunnit det rumänska mästerskapet varje år från säsongen 2006/07. Klubben deltog tidigare i Panonska Liga och vann den säsongen 2003/04. Sedan 2008/09 deltar klubben i MOL Liga, som de också vunnit vid ett tillfälle, 2010/11. Sedan säsongen 2017-2018 MOL Liga har bytt namn till Erste liga. Csíkszereda spelar sina hemmamatcher i en ishall vid namn Vákár Lajos, som är uppkallad efter en före detta Csíkszereda-spelare med samma namn. Svensken Ulf Weinstock var lagets Huvudtränare säsongen 2009/2010.

Källor

Externa länkar
Officiell webbplats

Klubbar i MOL Liga
Ishockeyklubbar i Rumänien
Sportklubbar bildade 1929

redpajama_set_name: RedPajamaWikipedia


text:
Q: DDEV http://projectname.ddev.local doesn't work on Windows 10 while http://127.0.0.1:randomport does work Ddev started succesfully my project and tells me "project can be reached at http://example.ddev.local https://example.ddev.local, http://127.0.01:32771"
I read in config.yaml
# name: <projectname> # Name of the project, automatically provides
#   http://projectname.ddev.local and https://projectname.ddev.local

But my project can only be succesfully reached at http://127.0.01:32771 instead automatically provinding the http://projectname.ddev.local https://projectname.ddev.local.
how to solve this
Version and configuration information:

*

*windows 10 education

*ddev v1.7.1 installed by choco choco install ddev

*docker desktop v2.0.0.3
Edits:

*

*changed title

*add windows-10 tag

*typo

A: the problem is windows has 10 hosts max in etc/hosts and doesn't work any more after that.
The solution is ddev remove --all && ddev hostname --remove-inactive


The last time Jabari Parker visited the United Center, he played as if auditioning for the 2020 dunk contest.
The former Simeon star threw down six dunks in a lively, 20-point, six-assist performance as the Wizards handed the Bulls a franchise-record 10th straight home loss on Feb. 9. The performance, in which Bobby Portis added 10 points and 12 rebounds, came just three days after the Bulls traded the two to the Wizards for Otto Porter Jr.
In 17 games with the Wizards, Parker is averaging 14.8 points on 56 percent shooting, although just 31.5 percent from 3-point range. While his scoring average is up a half-point from his 39 games with the Bulls, his field-goal percentage is up from 47.4 percent and his rebounding average has climbed from 6.2 to 7.4 per game. His assists average also is up from 2.2 to 3.
Portis' scoring average also has climbed a half-point from 14.1 to 14.6 and he's shooting a robust 45.6 percent from 3-point range in his 17 games with the Wizards, including 12 starts.
The Wizards are 8-9 since the trade, which they made in light of John Wall's season-ending injury and to move Porter's contract of two years and $55.6 million remaining.
Porter's scoring average has jumped almost five points from 12.6 with the Wizards to 17.5 in his 15 starts for the Bulls. His shooting percentages have jumped from 45.7 to 48.3 percent overall and from 36.9 to 48.8 percent from 3-point range.
The Bulls are 7-8 in games Porter has played since the trade.
The Bulls listed their starting small forward as doubtful for Wednesday's game against the Wizards with a strained right rotator cuff. Porter banged his shoulder on Sunday in Sacramento and sat out Monday's victory in Phoenix. Zach LaVine is listed as questionable with a right thigh bruise.
Shaquille Harrison started for Porter against the Suns and posted four steals. He's the only player in the NBA this season to average at least 1.2 steals per game while playing fewer than 18 minutes.
Harrison told reporters after the game he had circled the game on his calendar after the Suns released him during training camp.
After finishing a back-to-back set of games Monday in Phoenix, the Bulls opted to stay overnight and fly cross-country on Tuesday. They didn't practice. ... The Bulls have scored 100 or more points 49 times this season. Once more over the century mark will tie for the most such games since the 1997-98 season.


text:
NGO Research Toolbox
Get evidence into NGO practice and policy – Get NGO practice into research
Where to find summaries of existing research findings?
Translating your own research findings
How to write a scientific research paper?
How to submit a paper to a scientific journal?
How to prepare an abstract for applying to present at a conference?
How to communicate your research findings?
Translating existing research findings
How to find existing research findings?
How to assess the power of evidence of existing research findings?
How to uptake research findings into NGO policy and practice?
How to share and exchange knowledge?
How to monitor and evaluate the uptake of research?
HEART – Health & Education Advice & Resource Team
"HEART helps time pressured decision-makers better understand, interpret and apply health, nutrition and education evidence."
a consortium of leading organizations in international development, health, nutrition and education (e.g. LSTM, IDS, FHI 360, funded by UK Aid
Year: started in November 2012
Purpose: Aim to support the use of evidence
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The traditional home of The Dark Knight tends to be seen as the dark streets of Gotham. However, Batman can also blend in perfectly with the streets of London which is what happens in this story. Following on the trail of a group of extremist terrorists who killed several veterans in Gotham, Batman journeys to Britain to track down their ring leader, the Ambassador for Syraqui and bring him to justice.
An American Batman in London deals with several key issues. The first of these is international terrorism. Set during the height of the Cold War and not long after the invasion of Afghanistan by the Soviet Union, the tale focuses on the impact of the terrorism. Batman's own horrified reaction to the terrorist is to hunt them down and bring them to justice. However, this raises the question- is Batman at all different from these terrorists? He strikes terror into his opponents, he uses methods outside the law and he is motivated by a belief that some may find bizarre. Of course, the key difference between Batman and the terrorists is that he doesn't kill and that he never attempts to force his own ideology onto others. This leads onto the second main point of the comic; are we responsible for the actions of the terrorists?
When confronting the Ambassador, Batman attempts to figure out why he would order such attacks and why his attitude to the deaths of innocents seems so blasé. The Ambassador merely points out that does not America "bomb other countries, do not Americans attack and destroy?" He then raises the point that because of the West's view that its way is best, they forced Eastern countries to fight back with terror because "fear is all we have left". Batman, after he manages to foil the terrorist plot, is left considering whether what the Ambassador said was true. It is a chilling thought and the writer of the story Alan Grant gives no easy answers to the questions posed by the Ambassador's remarks.
The artwork by Norm Breyfogle is brilliantly done. Breyfogle creates a murky and dark London backdrop for Batman to stride around so that he doesn't seem too out of place. Breyfogle also excels at reactions shots, particularly Batman, Commissioner Gordon and the Ambassador of Syraqui.
An American Batman in London is a fantastic story that manages to raise some important questions about Western Democracy's interference with the East as well as ideas about justice. A truly profound work I'd recommend that you give it a go.
Next Time: Lovers and Madmen!


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Richard Meade (1937–2015) was a British Olympic equestrian gold medalist.

Richard Meade may also refer to:
Richard Meade, 2nd Earl of Clanwilliam (1766–1805), Irish peer
Richard Meade, 3rd Earl of Clanwilliam (1795–1879), British ambassador
Richard Meade, 4th Earl of Clanwilliam (1832–1907), British Royal Navy officer and peer
Richard Kidder Meade (colonel) (1746–1805), George Washington aide-de-camp
Richard Kidder Meade (1803–1862), Virginia lawyer, plantation owner and politician
Richard Henry Meade (1814–1899), English surgeon and entomologist
Richard John Meade (1821–1894), British Indian Army general
Richard Worsam Meade I (1778–1828), American merchant and art collector
Richard Worsam Meade II (1807–1870), United States Navy officer
Richard Worsam Meade III (1837–1897), United States Navy officer
Sir Richard Meade (judge). British High Court judge

See also
Richard Mead (1673–1754), English physician
Richie Meade (born c. 1955), American lacrosse coach


Goutal's Encens Flamboyant is an intense and elaborate fragrance for men that blends rich spices and precious extracts for a warm and decadent scent.
Encens Flamboyant is an ode to frankincense, masterfully blended in three formats. Frankincense essence emerges first, revealing a colder facet to the precious resin with the ethereal and mineral freshness of cool stone. Frankincense resinoid follows with its warm and balmy aroma, spiced with cardamom and nutmeg, then finally, 'Vieille Eglise' frankincense arrives deliciously burnt, warmed by woody balsam fir and pure extract of lentisque.


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Home » Lecture » Text » Dua in Salah Made Easy - translation of prophetic dua for various points in Salah
Dua in Salah Made Easy - translation of prophetic dua for various points in Salah
Informal prayers (dua) can be made in any language. While reading the Quran and the minimum ritual requirements of formal prayers (Salah) must be in Arabic, there are many points in Salah where it is encouraged to make dua. This article presents a simple translation of prophetic dua for various points in Salah. Studying these prayers and incorporating some of them into the Salah will make the Salah more meaningful and engaging, particularly for those who convert, youth, and others for whom English is their primary language. They can also be used for inspiration to make dua following the example of the Prophet Muhammad, peace be upon him.
Opening Prayers:
The Salah is begun with raising the hands and recognizing the greatness of Allah saying, "Allah is greater than everything." After beginning the salah the Messenger of Allah would pause before starting to read Al-Fatiha. Abu Hurraira asked him about this pause. Heanswered, "I say: Oh Allah, put a distance between me and my mistakes as big as the distance between the East and the West. Oh Allah, clean me of my mistakes as white clothing is completely cleaned from dirt. Oh Allah, wash my mistakes away with pure cool water."
At other times the Messenger of Allah would say as he started his salah, "I have turnedmyself to face the One Who split apart the heavens from the earth. I do so standing for truth, and in a state of submission, and I am not one of those who worship and call upon others with Allah. My salah, my worship, my life, and my death are all for Allah, who has no partner, the Lord of Creation. This is what I was commanded to do, and I am one of those who submit to Allah as a Muslim. Oh Allah, You are the true King. There is no god deserving of worship except You. You are my Lord and I am Your property. I have been unjust to my soul, and I admit my sins, so forgive me.
No one can forgive my sins except You. Guide me to achieve the best character.
No one can guide me to it except You. Help me get rid of faults in my character. No one can help me get rid of my bad character traitsexcept You. I am committed to Your obedience and to following Your religion. All good is in Your hands, and doing good is the only way to get closer to you. I am with You, and Idepend on You. A person is blessed with worshiping You, and You are high above all imperfections. I seek Your forgiveness, and I turn back to You in repentance."
It is also related that he would sometimes say, "Oh Allah, I reject any imperfection attributed to You, and by praising You for Your greatness, I affirm this. Your name is blessed, Your greatness is superior to all, and there is no god deserving of worship except You."
Note: Omar Ibn Al-Khataab used to raise his voice with his opening dua to teach those praying behind him.
Prayers in the Ruku Position:
The Messenger of Allah, peace be upon him, forbade reading Quran in ruku and sujood.Both of these places are places for making dua (prayers). The minimum to complete the ruku is to say "subhana rubbi Al'Atheem" three times, (I reject all imperfections attributed to myLord the Most Great). However, the Messenger of Allah, peace be upon him, would often spend almost as long in ruku and sujood as he did standing and reading Quran.
He would also sometimes pray in ruku:
"Oh Allah, to You I bow in ruku, and in You I have put my faith and trust, and to You I submit completely. My hearing, sight, mind, and the core of my body are at peace in submitting to You."
Ibn Malik said, "I stood with the Messenger of Allah, peace be upon him, in prayer. Herecited surat Al-Baqarah. After reading every verse relating to Allah's mercy he would pauseand make a prayer asking for mercy. After reading every verse relating to Allah's punishment he would pause and make a prayer asking for protection from Allah's punishment. Then he made ruku for about as long as he had just been standing. He repeated in ruku, "I reject all imperfections attributed to Allah. Allah has absolute power, absolutecontrol, absolute authority, and total greatness."
In his ruku and sujood he would sometimes pray:
"I reject all imperfections attributed to You, Oh my God, and Lord, and by praising Yourgreatness, I affirm this. Oh Allah forgive me."In his ruku and sujood, he would say: "I worship You who are free from all imperfections, holy and pure, the Lord of the angels and the holy spirit (angel Gabriel)."
Standing up from Ruku:
When standing up from ruku we say "Sami Allahu Liman Hamida" which means "Allah hears and answers the prayers of those who praise Him." The Messenger of Allah, peace be upon him, would then say various dua at this point with the theme of praising God. For example:"Oh Allah, our Lord, all praise belongs to You, praise that fills the heavens and the earth andfills whatever You like. You are the most deserving of admiration and honor. The most truthful thing a slave said, and we are all your slaves (meaning we are all God's property), is: Oh Allah, no one can prevent someone from receiving something that you gave them, and no one can give something to someone that You have prevented them from having. A person with money, power, and greatness in this world has nothing to benefit them against Your absolute power."
Once the Messenger of Allah, peace be upon him, was leading the Muslims in congregation.When he raised himself from ruku he said, "Allah hears and answers the dua of those who praise him." A man in the congregation said, "Oh our Lord, all praise belongs to You, a lot of praise that is good, blessed, and permanent; Praise as our Lord loves and accepts to be praised." At the end of the salah, the Messenger of Allah said, "Who said that?" The man said, "I did." The Messenger of Allah, peace be upon him, said, "I saw about thirty threeangels racing to write down what you said." (Tirmithi)This story shows that prayers are to be customized so they are yours, and they come from your heart. Otherwise, the Messenger of Allah would have told the man to ask permission before saying a different prayer.
Note: Remember that praise is different from thanks. You thank a person for what they do for you, and you praise a person by recognizing their good qualities whether you get anything from them or not. When you stand up in prayer praising Allah, your focus is onappreciating the greatness of Allah's attributes whether or not they directly benefit you. For example, when you praise Allah for His great mercy, you can be thinking of a bird and how the bird finds food every day by Allah's mercy.
Prayers in Sujood Position:
The Messenger of Allah stayed in sujood about as long as his ruku repeating "subhana rabialalah." I reject all imperfections attributed to my Lord the Most High." The point is to humble yourself into recognizing how high above you your creator is. As a result, you are closest to Allah when you are in sujood so it is a good opportunity for making your personal prayers and requests to God.
He would also sometimes pray:
"Oh Allah, to You I have made sujood. In You I believe and have put my faith. To You I have submitted. I submit myself to the one who created me, gave me form, and gave me hearing and sight. Allah's name is a blessing in everything, and Allah is the best creator.""Oh Allah, forgive all my mistake, the small ones, the big ones, from the beginning to theend, the ones made in the open and the ones made in secret."
"Oh Allah, I seek protection with Your mercy from Your anger, and with Your forgivenessfrom Your punishment. I seek protection with You, Oh Allah, from You holding back Yourguidance from me. Forgive me my deficiency that I cannot praise You as You deserve to be praised, and as You praised Yourself."
Sitting between two Sujood:
It is related in Sahih Bukhari that the Messenger of Allah, peace be upon him, would spendabout the same amount of time in sujood, ruku and sitting between the two sujoods. While he was sitting he would say: "Oh Allah, forgive me, have mercy on me, guide me, and bless me."Sometimes he would just repeat "Oh Allah, forgive me", and sometimes he would add "Oh Allah, fix my deficiencies, give me good health, and raise me up to higher levels of faith."
Concluding the Salah:
There are 4 important elements to concluding the salah:
1- The tashahud
2- Prayer for the Messenger of Allah, peace be upon him
3- Dua before concluding
4- The Conclusion
The Tashahud (testimony):
There are several variations of the tashahud that are authentically attributed to the Messengerof Allah, peace be upon him. All of these variations are very similar in meaning:
"The attributes of permanence, perfection, and true greatness belong to Allah, along with allmy prayers and good deeds. Oh Messenger of Allah, may Allah give you peace and protection from all harm and may you receive Allah's mercy and blessings. May Allah'speace and protection be with me and with all good people who worship God. I am a witnessand I testify that there is no god deserving of worship except the one true god Allah, and Iam a witness and I testify that Muhammad is Allah's slave and Messenger."
Prayer for the Messenger of Allah, peace be upon him:
This prayer has many variations, some of which are longer than others. Here is one:"Oh Allah, give Your mercy to Muhammad and to the family of Muhammad as you were merciful to Ibraheem and the family of Ibraheem, and bless Muhammad and the family of Muhammad as you blessed Ibraheem and the family of Ibraheem. Oh Allah, out ofeverything that exists, You are deserving of all praise, and recognition of your greatness."
In this prayer we are asking Allah to give His mercy and blessing to Prophet Muhammad, peace be upon him. This is by having him honored in this world with the spread of his message and honored in the next by allowing him to lead his followers into Paradise. This prayer makes the connection back to Prophet Ibraheem, because he was blessed with the honor of having many prophets and generations of Muslims that descended from him.
Dua before concluding:
The Prophet Muhammad, peace be upon him, instructed that we can make any dua we want
before concluding our salah. In particular the Messenger of Allah, peace be upon him,encouraged us to make a dua asking Allah's protection from four things: from the punishment of Hell, from the punishment of the grave, from the tests of life and death, andfrom the harm of the anti-Christ.He would also sometimes prayer:
"O Allah, please give me Paradise and I seek your protection from Hell."
"O Allah protect me from making small sins and from debt."Note: A man commented to the Messenger of Allah, peace be upon him, that he was alwaysasking for protection from debt. The Messenger of Allah, peace be upon him, said, "When a man has a lot of debt, he starts to lie when he speaks –to make excuses- and he breakspromises." (Abu Dawood).
The Conclusion:
When we conclude our salah we say, "As-salaam al-aikum wa-rahmatu Allah" (May youhave peace and Allah's mercy) as we turn our heads to the right and to the left. The prayer is a visit with God. Now in concluding the visit, there is an acknowledgement of the angels tasked with writing our good deeds and bad deeds as we are about to get up and go about our regular activities. So we are not to forget our obligations to our creator as we go about the rest of our day.
May Allah accept our Salah and join us in Paradise.
Source: https://plainenglishquran.com/Dua_in_Salah_Made_Easy.html
Lecture, Text


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Home News After a summer of opening spots and festivals The Mitchells are excited...
After a summer of opening spots and festivals The Mitchells are excited to play a long set at The Southgate House Revival
The Mitchells
with Ben Knight & The Well Diggers, Umin
Southgate House Revival, 111 E 6th St, Newport, KY
$5 for 21+, $8 for 18-20, 8pm doors, 9pm show
Cincinnati-based indie rock group, The Mitchells, are playing at the Southgate House Revival with Ben Knight and the Well Diggers on Saturday, August 3rd. Also, featured will be local looping specialist and ukulele extraordinaire Umin. Tickets are available at the Southgate House Revival, Online at www.southgatehouse.com, and at Shake It Records in Northside.
We are very excited to play a longer show at The Southgate House Revival on August 3rd. It seems like it has been a long time since we have been able to do anything longer than a half hour set because we are usually opening up for bands or sharing a four band bill as different musical groups come through town. We are planning on playing our full show so we hope that everyone is ready for that. Its going to be a great night and we are really excited.
The Mitchells play music that draws from a number of different genres, including French New Wave pop, American folk, Baroque classical, and alternative rock. Live performance, particularly the Mitchells' love of gritty chamber music, as well as the recently classified indie rock scene, has led to the inclusion of many ideas inspired by enlightenment composers, modern rock masters, and the simple melodies birds make or that handheld radios produce whist spinning the dials. Fans and listeners have described the group's music in nostalgic terms, or more bluntly as "the soundtrack to your childhood," and while their style is constantly evolving, this aesthetic remains at their core. The band believes they are firmly rooted in the world of indie rock.
Ben Knight and The Well Diggers. After moving to Cincinnati, OH in 2011 Ben Knight made his rounds through the local open mic scene before joining forces with Elia Burkhart and Jeremy Smart. Ben grew up listening to Americana and outlaw country with his father and Motown and soul with his mother, which he cites as important musical influences.
UMIN. Umin is a solo show who creates delicate melodies woven together with his baritone ukulele and gigantic slew of looping pedals and affects. Umin dives into each creation building, balancing and crafting each song before the audiences eyes.
The Mitchells are:
Joseph Mitchell – Guitar, vocals
Matt Dolin – Guitar
Megan Scharff – viola, violin, keyboards
Carlos Mitchell – bass, vocals
Nicholas Mavridoglou – drums
You can also find The Mitchells on Facebook:
Http://www.facebook.com/TheMitchellsMusic
southgate house revival
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