text:
\section{Introduction}

Visually-grounded conversational artificial intelligence (AI) is an important field that explores the extent intelligent systems are able to hold meaningful conversations regarding visual content. Visually-grounded conversational AI can be applied to a wide range of real-world tasks, including assisting blind people to navigate their surroundings, online recommendation systems, and analysing mass amounts of visual media through natural language. Current approaches to these tasks involve an end-to-end framework that maps the multi-modal context to a deep vector in order to decode a natural dialog response. This framework can be trained through supervised learning (SL) with the objective of maximizing the distribution of the response given a human-human dialog history. Given a large amount of conversational data, the neural end-to-end system can effectively learn to generate coherent and natural language.

While much success has been achieved by applying neural sequence to sequence models to open visual grounding conversation, the visual dialog system also needs to learn an optimal strategy to efficiently accomplish an external goal through natural conversations. To address this issue, various image guessing tasks such us GuessWhich \cite{GuessWhich} and GuessWhat \cite{GuessWhat} are proposed to evaluate a visual-grounded conversational agent on its ability to retrieve visual content via conversing in natural language. To obtain an optimal dialog policy, reinforcement learning (RL) is introduced to enable the neural end-to-end framework to model a more effective action distribution by exploring different dialog strategies. With the application of RL, the visual dialog system can generate more consistent responses and achieve a higher level of engagement in the conversation when compared to a dialog system trained via SL \cite{visdialrl,GuessWhat_RL}. A typical way to apply RL on a dialog system is to assign a task-related reward to influence the utterance generation process by treating each output word as the action step. A significant limitation of this approach is that it is difficult to achieve an optimal dialog policy that can both effectively complete the external goal and generate natural utterances \cite{rethinking_action_space,visdialrl}. As there is no ground truth reference during the RL training stage, the dialog system can only leverage the reward signal when generating the response. However, this approach often deviates from natural language as it is challenging to define a comprehensive reward that considers all aspects of the dialog quality, and in addition, assigns appropriate rewards to the large word vocabulary action space.

In this paper we propose a novel learning curriculum to address the challenge of joint learning between the dialog policy and language generation for task-oriented dialog systems. In our framework, we separate the training of the image retrieval policy from dialog generation by applying RL, with the goal of achieving an optimal policy for guessing the target image at every turn. In addition, we apply a language model objective function to optimize the utterance generator to mitigate language degeneration. We specifically study this framework in the image guessing task, GuessWhich, where a conversational agent attempts to guess a target image by asking a series of questions. When compared to state-of-the-art RL visual dialog systems, our method achieves superior performance in both task-accomplishment and dialog quality.

\section{Related Work}
\subsection{Visual Dialog System}
Visual dialog systems are an emerging area of interdisciplinary research that attracts both the vision and language communities due to the potential applications. \citet{visdial} proposed a visual dialog task in which a conversational agent attempts to answer questions regarding an assigned image based on a dialog history. To approach this task, they initially collected data by having two people chat about an image with one person acting as the questioner and the other as the answerer. GuessWhich \cite{GuessWhich} extends VisDial with the goal to build an agent that learns how to identify a target image through questions and answers. \cite{GuessWhat} additionally introduced a game in which a series of yes-or-no questions are asked by an agent in order to locate an object in an image. Many researchers approached these tasks via reinforcement learning (RL) with the goal of obtaining an optimal dialog policy. \citet{GuessWhat_RL}, for example, designed three rewards with respect to the goals of task achievement, efficiency, and question informativeness, in order to help the agent achieve an effective question generation policy for the GuessWhat game. \citet{visdialrl} applies reinforcement learning in the GuessWhich task and demonstrates a moderate improvement in accuracy compared to the supervised learning approach. Both methods apply RL on a neural end-to-end pipeline to jointly influence the language generation and dialog policy. Due the challenge of designing an appropriate reward for language generation, these methods generate responses that deviate from human natural language. \citet{JP}, proposed an approach involving hierarchical reinforcement learning and state-adaptation techniques that enable the agent to learn an optimal and efficient multi-modal policy. The bottleneck of \cite{JP}'s method, however, is that the system response is retrieved from a predefined human-written or system-generated utterance. The number of predefined responses are limited, therefore,  this method does not easily generalize to other tasks in real-world settings. We address these limitations by applying RL on a reduced, yet more relevant action space, while optimizing the dialog generator in a supervised fashion. We alternatively optimize policy learning to language generation to combine the two tasks together.

\begin{figure*}[h!]
\centering
\includegraphics[width=16cm]{Group.png}
\caption{The proposed end-to-end framework of the conversation agent for GuessWhich task-oriented visual dialog task}
\end{figure*}

\subsection{RL on Task-oriented Dialog System}
Various RL-based models have been proposed to train task-oriented dialog systems \cite{Williams_2007}.
In order to build a traditional modular-based dialog system, researchers first identify the semantic representation, such as the dialog acts and slots in user utterances. Then they accumulate these semantic representations over time to track the dialog state. Finally they apply RL to learn an optimized dialog policy given the dialog state \cite{dialogact_RL,sentimen_analysis_dialogact}.
Such modular-based dialog systems are effective in narrow task domains, such as searching a bus route schedule or reserving a restaurant through natural language, but they fail to generalize to complex settings where the size of the action space increases. Owing to the development of deep learning, RL on neural sequence-to-sequence models has been explored in more complex dialog domains such as open-domain conversation \cite{Deep_RL_Dialog} and negotiation \cite{dealNodeal}. However, due to the difficulty of assigning appropriate rewards when operating in a large action space, these frameworks cannot generate fluent dialog utterances. \citet{rethinking_action_space} proposed a novel latent action RL framework to marry the advantage of a module based approach and sequence-to-sequence approach. They learned the optimal dialog policy in a complex task-oriented dialog domain while achieving decent conversation quality. Here, we study a similar issue in a multi-modal task-oriented dialog scenario. We propose an iterative approach using RL to optimize the dialog policy and SL to optimize the generation of the system response.

\section{Method}
\subsection{Problem Setting}
In the GuessWhich problem, we aim to build an agent (Q-Bot) that attempts to guess an image $i_{tgt}$ that another agent (A-Bot) knows by asking it a series of questions. At the beginning of the conversation, the Q-Bot is primed with a short caption $c$ of the target image that is only known by A-Bot. At every round $t$, the Q-Bot generates a question $q_t$ to elicit as much information as possible about the target image and the A-Bot provides an appropriate answer $i_t$ with regard to $q_t$ and the target image. In the end, the agent guesses the target image among a set of images considering the entire conversation.

In addition, our dialog system also guesses a candidate image $i_t$ out of an image database $\mathcal{I} = \{i_k\}_{k=0}^{m} $ at every turn. This action models the process of sequentially updating the visual belief state on the target image based on the latest dialog history. Conditioned on the current guessed image and the prior dialog contexts, the system will generate an optimal question in order to get the maximum information from A-Bot that can strengthen the system's belief on the target image. At the end of the conversation, our Q-Bot will guess the target image based on the multimodal contexts $s_n = (q_{1:n}, a_{1:n}, i_{1:n}, c)$ consisting of the dialog history and the trajectory of guessed images.

\subsection{Model Architecture}
Our Q-Bot is constructed on top of a hierarchical encoder-decoder framework \cite{Hre_Enc_Dec}, which consists of three major components: The \textbf{Response Encoder}, the \textbf{Question Decoder}, and the \textbf{Image Guesser}. We introduce each component as follows:
\paragraph{Response Encoder}
The goal of the response encoder is to append the question $q_{t}$, the answer $a_{t}$, and the guessed image $i_{t}$ received at current round to the dialog history and obtain an updated vector representation of the multimodal context $s_{t}$. The image $i_t$ is encoded with a pre-trained convolutional neural network VGG-16 \cite{vgg} followed by a linear embedding layer and the image feature vector denoted as $z_{t}$. For the question and answer pair at the current round $(q_{t}, a_{t})$, we map them to a hidden state vector $f_{t}$ through the LSTM based \textit{QA Encoder}. We then apply a linear projection on the concatenation of $f_{t}$ and $z_{t}$ in order to obtain the multi-modal context vector $h_t$ for the current round. The context vector is then passed through another LSTM encoder: \textit{History Encoder} generates an updated dialog history representation $s_t = \text{HistoryEnc}(h_t, s_{t-1})$. We denote the trainable parameters for Response Encoder as $\theta_{e}$.
\paragraph{Question Decoder}
The question decoder is a two-layer LSTM network initialized with the most updated dialog history representation vector $s_t$ from the response encoder. It will sequentially sample the words to come up with the next question $q_t$. The learned parameters for question decoder are denoted as $\theta_{d}$.
\paragraph{Image Guesser}
 The Image Guesser attempts to identify the candidate image that best aligns with the dialog history. Given an image database $\mathcal{I} = \{i_k\}_{k=0}^{m}$ where we sample the candidate image, we first extract the image feature representations $\{z_k\}_{k=0}^{m}$ for all candidate images with the convolutional neural network and image embedding layer defined in response encoder. Then, we can sample a candidate image $i_k$ for the current turn based on the euclidean distance $d(z_k, s_t)$ between the image feature of the candidate image and the current dialog history vector. The image with the smallest euclidean distance is selected as the guess $i_t$ at the current round. The associated parameters for image guesser are defined as $\theta_{g}$.
 \subsection{Training Dialog System}
 We follow a two-stage training fashion as introduced in many previous end-to-end RL dialog systems \cite{visdialrl, GuessWhat_RL, rethinking_action_space}, where we first pre-train the dialog framework with a supervised objective then apply reinforcement learning to learn an optimal policy to retrieve the target image. The Supervised pre-training is a critical step that facilitates an effective policy exploration for RL training, as it is difficult to explore a complex action space with limited prior knowledge. During RL training, we introduce an alternative learning method between dialog policy exploration and natural utterance generation that addresses the issue of language degeneration in previous RL based visual dialog systems \cite{visdialrl}. We introduce each training method as follows.
 \subsubsection{Supervised Pre-training}
 During the supervised pre-training process, we jointly optimize the objective to generate questions and also predict target image features from dialog contexts. The task of question generation is optimized by maximizing the log conditional probability of the next question dependent on a ground truth dialog for every round of the conversation. For the image feature prediction, we minimize the mean square error (MSE) between the target image feature $z_{tgt}$ and the dialog context vector $s_t$ at each round. The joint loss function for supervised pre-training is:
 \begin{multline}
     \mathcal{L}_{SL}(\theta_r, \theta_d, \theta_g) = \alpha \sum_{t=0}^{n}\log{p(q_t|s_t)} \\
     + \beta \sum_{t=0}^{n} \text{MSE}(z_{tgt}, s_t)
     \label{eq:1}
 \end{multline}
 Where $\alpha$ and $\beta$ are weights assigned to the objective function of each task in the joint objective function. With SL pre-training process, the dialog system is facilitated with the ability to estimate a visual object and emit a natural language sentence given a dialog context.

 \subsubsection{Reinforcement Learning on Image Retrieval}
 In our framework, we treat the sequence of image guess through out the conversation as a partially observable markov decision process and train a policy network via RL to obtain an optimal strategy to retrieve the target image. We formally describe state, policy, action, rewards, and the training procedures in our pipeline.
 \paragraph{State}
 The dialog states in our framework consist of a combination of multimodal contexts, including the image caption $c$, the dialog history with A-Bot $[q_1, a_2, \dots, q_t, a_t]$, and the image guessing trajectories $[i_1, i_2, \dots, i_t]$.
 \paragraph{Policy}
 The dialog policy $\pi_{\theta_r, \theta_g}(i_t|S_t)$ is a stochastic policy that samples the candidate image to guess from an image set based on the previous dialog histories. The policy is learned from response encoder and image generator which is parameterized via $\theta_r$ and $\theta_g$.
 \paragraph{Action}
 The full action space is the number of images in the database that we can sample to guess an image. As the pre-trained process already enables the system to approximate a target image feature $z_{tgt}$ with the dialog history representation vector $s_t$,  we reduce the action space to the top-K nearest images, $s_t$, based upon the euclidean distance. The probability to sample an image $i_j$ is gained by applying a softmax function over the top-K candidates on their distance to $s_t$: $\pi(j) = \frac{e^{-d_j}}{\sum_{k=1}^{K} e^{-d_k}}$. $d_j$ represents the mean-square-distance between the $j$-th image and the dialog history state vector $s_t$.
 \paragraph{Rewards}
We use the ranking percentile of the target image with respect to the dialog history vector $s_t$ as the reward signal to credit the guess at each turn. The goal is to maximize the expectation value of the discounted return $\mathbb{E}[\sum_{t = 1}^{n} \gamma^t r_t]$ over the n-round conversation. $r_t$ is the ranking percentile of target image at round $t$ and $\gamma$ is the discounted factor between $(0,1)$.
 \paragraph{Training Procedure}
 Inspired from the RL training process on the iterative image retrieval framework \cite{dialog_interactive_image_retrieval}, we apply the policy improvement theory \cite{Intro_RL} to estimate an improved policy $\pi^{*}(s_t)$ from an existing policy $\pi(s_t)$ obtained from the pre-trained dialog system. Given a dialog state $s_t$ and the action $a_t$ derived from the existing policy, the value estimated by the current policy for taking the action $i_t$ is $Q_{\pi}(s_t, i_t) = \mathbb{E}[\sum_{t^{'} = t}^{n} \gamma^t r_t]$. To improve this, we explore a different action $i_t^{*} \neq i_t$ such that a larger policy value $Q_{\pi}(s_t, i_t^{*}) >  Q_{\pi}(s_t, i_t)$ estimated with the current policy is achieved. Then we can adjust the existing policy $\pi(s_t)$ to a new policy $\pi^{*}(s_t)$ that executes that optimal action $i_t^{*}$ given the current dialog state. The parameters of the policy can be effectively optimized via a cross entropy loss function conditioned on the derived optimal action $i_t^{*}$:
 \begin{equation}
     \mathcal{L}_{RL}(\theta_r, \theta_g) = \mathbb{E}[-\sum_{t = 1}^{n} \log(\pi_{\theta_r, \theta_g}(i_t^{*}|s_t))]
     \label{eq:2}
 \end{equation}
 Compared to the previous RL visual-grounded conversational agent, \cite{visdialrl}, there are several advantages of conducting policy learning on the action level of guessing the image. First, the action space of the top-k nearest neighbors are much smaller compared to the vocabulary size of the output words which reduces the difficulty to explore optimal strategies. Second, only the parameters of response encoder and image guesser will be optimized during the RL training stage. The question decoder stays intact so that it is less likely for the dialog system to suffer from language deviation.
 \subsubsection{Alternating Policy Learning and Language Generation}
 Although the parameters of the decoder won't be impacted during the RL training stage, the shared response encoder of the dialog context is still optimized with policy learning. The language distribution captured by both the response encoder and question decoder will gradually be differentiated from the original human dialog distribution. To prevent the potential language degeneration behavior, we alternatively optimize the dialog system with a policy learning objective in equation \ref{eq:2} and the language model objective function in equation \ref{eq:1} at every other epoch. It assures the dialog system maintains a good estimation of the human language distribution while also effectively exploring various dialog actions in order to achieve the task of guessing the right image.

\begin{table*}[]
\resizebox{\textwidth}{!}{%
\begin{tabular}{llll}
\hline Image & RL-Q-IG & SL-Q-IG & RL-Q \\ \hline \\ [-1.5ex]

\multirow{1}{*}{
\includegraphics[width=3cm]{banana.jpg}
}

& \color{bleudefrance} Q: are bananas in bunch? & \color{carrotorange} Q: what color are planes? & Q: is this outside? \\
& A: yes it's in bunch & A: no planes there & A: yes it is \\
& Q: are they on table? & \color{carrotorange} Q: are they in city? & Q: is there any people? \\
& A: yes they are & A: yes it is & A: yes ... \\
& \color{black} Q: is this in kitchen? & \color{carrotorange} Q: are they in baskets? & \color{carrotorange} Q: is this in zoo? \\
& A: I'm not sure & A: yes they are & A: no it is not \\
& Q: any people? & Q: any people? & Q: are there any people? \\
& A: Yes there are several people & A: yes there are & A: yes there are \\
& \color{bleudefrance} Q: any other fruits? & \color{carrotorange} Q: animals? & Q: is there any people? \\
& A: NO, only bananas & A: no animals & A: yes ... \\

\end{tabular}}
\caption{A dialog example with the ground truth caption: \textbf{bunches of bananas hang on a wall and arranged for sale.}  \color{bleudefrance} blue \color{black} indicates ideal relevant questions and \color{carrotorange} orange \color{black} indicates less relevant questions.}
\label{tab:my_label}
\end{table*}

\section{Experiments}
\subsection{AI-AI Image Guessing Game}
We evaluate the performance of our task-oriented dialog system by playing the image guessing game, GuessWhich, with an automatic answer bot. Our conversational agent's goal is to locate the target image out of the 9,628 test images by interacting with the other player in five conversation exchanges. We evaluate agent on both goal achievement and utterance generation quality using two automatic evaluation metrics Percentile Mean Rank (PMR) and perplexity respectively. PMR estimates how good the agent can rank the target image against other candidates in the test database based on its current dialog state. Perplexity estimates the closeness of the generated response to a reference utterance given a dialog context from the VisDial dataset.

\subsection{Human-AI Image Guessing Game}
To evaluate the ability of our task-oriented dialog system in a realistic conversational scenario, we also make our agent play the image guessing game with human users. The games are set up as 20-image guessing games where the agent attempts to guess a target image outside of a pool of 20 candidate images by asking a human player 5 rounds of questions. The objective of the human player is to play the role of answer bot and answer agent's question with respect to the target image.

In this setting, the performance of the agent on task accomplishment is evaluated by the game win rates. The quality of the dialogs are manually rated on four criteria: fluency, comprehension, diversity and relevance. Fluency defines the naturalness and readability of the generated question in English. Comprehension represents the consistency of the generated question with respect to the previous dialog context. Diversity evaluates the uniqueness of the questions generated within one game. Relevance presents how well the asked question is related to the target image and the given caption.

\subsection{Comparative Models}
We compare the performance of our model with state-of-the-art task-oriented visual dialog systems. Meanwhile we also perform an ablation study to evaluate the contribution of different designs in our framework. We introduce each model as follows:

\textbf{SL-Q}: The dialog agent from \cite{visdialrl}, which is trained with a joint supervised learning objective function for language generation and image prediction.

\textbf{RL-Q}: The dialog agent from \cite{visdialrl} which is fine-tuned on a trained SL-Q by applying RL to the action space of output word vocabulary.

\textbf{SL-Q-IG}: The dialog agent from this framework is build on top of the SL-Q. Compared to SL-Q, SL-Q-IG has an additional image guesser module that makes a guess on target image at every round. SL-Q-IG also has an image encoder which fuses the guessed candidate image into the dialog history tracker. We only train this model with the supervised learning objective introduced equation \ref{eq:1}.

\textbf{RL-Q-IG}: We use RL method to fine-tune SL-Q-IG. The RL method used is applied on action space of guessing candidate image. We alternate the model to optimize towards dialog policy learning and language generation.

\textbf{RL-Q-IG-NA}:  We fine-tune SL-Q-IG by applying RL to the action space of guessing candidate image and only optimized with policy learning objective function alone.

\textbf{RL-Q-IG-W}: The dialog agent from our framework, which is fine-tuned on a trained SL-Q-IG by applying reinforcement learning on output word vocabulary. It follows the same training procedures as RL-Q to conduct policy learning.

All the SL dialog agents are trained on the VisDial Dataset with the default setting from \cite{visdialrl} for 40 epochs. The RL dialog agents are then fine-tuned on their corresponding SL dialog agents for another 20 epochs. We evaluate every model on AI-AI image guessing games with the same answer bot, trained on the Visdial Dataset with the objective of visual question answering. We only evaluate RL-Q, SL-Q-IG and RL-Q-IG in human evaluation.

\subsection{Human-AI Evaluation Implementation}\label{human eval setting}
In order to evaluate the effectiveness of the model, we designed three human evaluation tasks. Six college students were recruited to conduct the evaluation. Each student evaluated 100 games using the ground truth captions and 30 games using human generated captions. An additional three evaluators each completed 30 rounds of the relevancy experiment.

\textbf{Ground Truth Captions} We generated 100 image guessing games that used the ground truth captions to ensure a consistent amount of information is supplied across all human evaluators. Each game consists of a randomly selected set of 20 images from the VisDial Dataset, with one image randomly chosen as the target. For each game, we test three different models, each twice, resulting in a total of 600 evaluated games from the 100 generated games. We keep the identity of the models anonymous to the evaluator.

During each game, the human evaluator is presented with a target image the agent is trying to guess. Five rounds of Q\&A take place in which the agent asks a question to elicit information and the human evaluator responds with a relevant truthful answer. At the end of each game, the evaluator is asked to rate the conversation on four criteria: fluency, relevance, comprehension and diversity.

\textbf{Human Generated Captions} In order to distinguish SL-Q-IG and RL-Q-IG in a more natural setting, we generate an additional 30 games, similar to the previous human evaluation task, except when beginning the game, the evaluator is asked to provide the caption for the target image instead of using the ground truth.

\textbf{Relevance Experiment} We noticed that the human evaluators found rating dialogues on the relevance criteria challenging and nuanced. In order to reduce the difficulty of rating dialogues using the relevance criteria, we designed a separate experiment in which, using the conversations obtained from the previous 600 evaluated ground truth games, a human evaluator is presented with three complete conversations side by side at each round. The evaluator then selects the most relevant conversation out of the three that corresponds to the image caption. Each of the three conversations have the same caption, however, correspond to a different model, thus allowing for an effective comparison between the relevancy of each model.

\section{Results}
\subsection{Results on AI-AI Image Guess Game}
\paragraph{Image Retrieval}
It is clear from Fig~\ref{fig:2} that our dialog system significantly outperforms the baseline models from \cite{visdialrl} in terms of PMR on every round of the dialog. PMR estimates how good the agent can rank the target image against other candidates in the test database. The biggest improvement gap is observed between SL-Q-IG and SL-Q. In comparison to SL-Q, SL-Q-IG tracks the additional context from the previously guessed images which leads to a better estimation of the target image. RL-Q-IG has better performance compared to SL-Q-IG in terms of PMR. This suggests that fine-tuning dialog systems with RL can further improve the success of guessing the correct image. The best image retrieval result is achieved by RL-Q-IG-NA, as the objective function of RL-Q-IG-NA is based solely on policy learning without consideration for the dialog generation quality.

\begin{figure}[h!]
\centering
\includegraphics[width=8cm]{PMR.png}
\caption{The percentile mean rank (PMR) over the 5-round dialog in the AI-AI image guessing game}
\label{fig:2}
\end{figure}

\begin{table}[h!]
\small
\centering
\begin{tabular}{|l|l|l|}
  \hline
  Model & PMR & Perplexity \\
  \hline
  SL-Q & $90.07\%$ & $79.49$ \\
  SL-Q-IG & $96.09\%$ & $61.42$ \\
  \hline
  RL-Q & $94.78\%$ & $544.97$\\
  RL-Q-IG & $96.81\%$ & $\boldsymbol{54.66}$ \\
  RL-Q-IG-NA & $\boldsymbol{96.88\%}$ & $363.88$\\
  RL-Q-IG-W & $96.65\%$& $227.35$\\
  \hline
\end{tabular}
\caption{RL-Q-IG-NA performs best in PMR and RL-Q-IG perform best in perplexity}
\label{table:table_2}
\end{table}
\begin{table*}[t]
  \centering
  \small
  \begin{tabular}{|c|c|c|c|c|c|}
    \hline
     Model & Win & Fluency & Relevance & Comprehension & Diversity \\
     \hline
     RL-Q & 59.6 & 4.19 & 3.22 & 2.60 & 2.50 \\
     \hline
     SL-Q-IG & 62.7 & 4.18 & 3.96 & 3.18 & 3.22 \\
     \hline
     \textbf{RL-Q-IG} & \textbf{67.5} & \textbf{4.40} & \textbf{4.02} & \textbf{3.50} & \textbf{3.25} \\
     \hline
  \end{tabular}
  \caption{Evaluation results on the human-AI image guessing game initialized with ground truth captions}
  \label{tab:1}
\end{table*}

\begin{table*}[t]
  \centering
  \small
  \begin{tabular}{|c|c|c|c|c|c|}
    \hline
     Model & Win & Fluency & Relevance & Comprehension & Diversity \\
     \hline
     RL-Q & 29.2 & 4.04 & 2.88 & 2.71 & 2.29 \\
     \hline
     SL-Q-IG & 40.6 & 4.16 & 3.19 & 2.75 & 2.69 \\
     \hline
     \textbf{RL-Q-IG} & \textbf{67.6} & \textbf{4.23} & \textbf{3.74} & \textbf{3.32} & \textbf{3.06} \\
     \hline
  \end{tabular}
  \caption{Evaluation results on the human-AI image guessing game initialized with human generated captions}
  \label{tab:2}
\end{table*}

Although our framework achieved an improved image retrieval accuracy, we observed, however, that there is little improvement gained in PMR after additional rounds of conversation. We suspect this is partially due to the fact that images from MSCOCO are composed of a diverse selection objects and background scenes, thus making images easily distinguishable with a detailed caption. In cases where candidate images are visually similar or the given caption is not informative, additional rounds of dialog are necessary to identify the target image.

\paragraph{Language Generation}
We observe a marginal increase of perplexity from SL-Q to RL-Q in Table~\ref{table:table_2}, thus demonstrating that there is a bottleneck when applying RL to improve the language generation. By decoupling the policy learning from the language generation and alternatively optimizing the dialog policy and language model, our RL-Q-IG avoids language deviation while still achieving an optimal dialog policy for the image retrieval task. To further evaluate the contribution from the RL and alternative training curriculum, we conduct two ablation studies. RL-Q-IG-NA is fine-tuned with a policy learning objective that excludes alternatively applying the language model loss. While RL-Q-IG-NA only achieves an incremental improvement over the full framework RL-Q-IG in terms of the PMR rate with less than $0.1\%$, it suffers from a dramatic increase of perplexity from 61.42 to 363.88, thus suggesting that alternatively applying the supervised learning objective can prevent the language model from deviating from the human language distribution. We additionally apply policy learning on the question decoder of SL-Q-IG and follow the RL fine-tuning process in \cite{visdialrl} to train the agent, RL-Q-IG-W. While applying word-level RL enables RL-Q to achieve a moderate improvement over SL-Q in terms of PMR, we did not observe, the same degree of advantage in RL-Q-IG-W over SL-Q-IG. Additionally, RL-Q-IG-W is affected by a marginal increase in perplexity in comparison to the SL pre-trained agent, which approves the drawbacks of applying RL on a large action space in language generation.

\subsection{Results on Human-AI Image Guess Game}
The performance of a dialog agent evaluated with a user simulator does not necessarily reflect its performance on real human \cite{GuessWhat}. We conduct human evaluation on different dialog agents. From the results summarized in Table~\ref{tab:1} and Table~\ref{tab:2}, we observe a consistent optimal performance of our method from conversations with AI agent to conversations with real human. Our RL-Q-IG significantly outperforms the baseline RL agent in all criteria for both settings. RL-Q-IG's advantage over SL-Q-IG is not significant in the game when agents are primed with ground truth image caption. This observation correlates with the result in the Human-AI game, as both RL-Q-IG and SL-Q-IG achieve superior PMR over $96\%$ when presented with the ground truth caption. However, if a human generated caption is given, the performance of the SL pre-trained agent suffers a big drop in all metrics except fluency while our RL agent maintains similar performance. Applying RL to fine-tune the dialog system  enables the agent to generate more consistent dialogs in unseen scenarios. We also notice a degradation of the baseline RL agent from its performance with the user simulator, which suggests deviation from natural language is due to the sub-optimal RL training on a large action space.

Besides a marginal improvement over the RL baseline model and SL pretrained agent in terms of decreased repetition and grammar mistakes, there is a distinct superiority in regards to the relevance to the image caption in the questions generated from our RL agent. For example, in Table~\ref{tab:my_label}, we demonstrate the three dialogs generated by RL-Q-IG, SL-Q-IG and RL-Q on one game. Given the image caption \textit{bunches of bananas hang on a wall and arranged for sale.}, RL-Q and SL-Q-IG ask very general questions that are not related to the caption such as \textit{``planes"}, \textit{``zoo"} and \textit{``animals"}. In comparison, our agent asks high-quality questions regarding the caption that covers \textit{``bananas"} and \textit{``fruits"}. These questions help our RL agent obtain useful information to guess the target image. This advantage is also evident from the results of comparative evaluation on the degree of relevance of the questions in Table \ref{tab:relevance}. We credit the positive result to the dialog policy, which explores multiple paths to conduct the conversation. The optimal path will involve a set of questions that obtains the maximum information of the target image such that it can construct the best estimation of the target image.

\begin{table}[]
    \small
    \centering
    \begin{tabular}{|c|c|}
        \hline
         Model & Prefered (\%) \\
         \hline
         RL-Q & 8.93\\
         \hline
         SL-Q-IG & 39.90 \\
         \hline
         \textbf{RL-Q-IG} & \textbf{51.20} \\
         \hline
    \end{tabular}
    \caption{Results on comparative evaluation of relevance on the human-AI image guessing dialogs}
    \label{tab:relevance}
\end{table}

\section{Conclusion and Future Work}
We present a novel framework for building a task-oriented visual dialog system. We model the agent to simultaneously optimize two actions: guessing the image and generating effective questions. We achieve this simultaneous optimization through alternatively applying reinforcement learning to obtain an effective image guessing policy, whilst also applying supervised learning to enhance the quality of generated questions. By decoupling the policy learning from language generation, we overcome language degeneration in the word-level reinforcement learning framework. Both analytical and human evaluation suggests our proposed framework leads to a higher task completion rate and an improved dialog quality.

In the future, we plan to collect a fashion retrieval visual dialog dataset which simulates a realistic application for multi-modal dialog systems. To address the limitation of a high image retrieval rate with just the use of captions from the VisDial dataset, we plan to format a challenging candidate image pool in which images are visually similar to each other. This will incentivize the dialog system to conduct multiple rounds of dialog in order to retrieve the target image successfully. Furthermore, we will explore additional task-oriented settings where we can decouple task accomplishment from language generation to evaluate the extent our framework can generalize to other conversational tasks.

\section*{Acknowledgments}
This work was supported in part by Cisco.

redpajama_set_name: RedPajamaArXiv


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Most people won't find the capital Chisinau or even Moldova on a map, but people have lived in the region for a very long time and the territory has seen a fair share of inhabitors and invaders. After the ever-present Romans came the Byzantine empire and later the Russians.
But Chisinau is an unexpected gem in the wilderness. Even though Moldova is one of the poorest countries in Europe, the capital has its fair share of impressive buildings and post-Soviet architecture.


Audio CDs / MP3 / M4A / OGG / WMA / WAV /MID / FLAC / APE / PLS Audio that is encoded in AAC is supported when saved as .M4A Supported Operating Systems: Windows XP, Vista, Windows 7, 8, 8.1, 10 (32-bit, 64-bit).
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Image Tuner is a free batch image resizing, renaming, conversion and watermarking software with a super-intuitive and straightforward interface. Based on an extremely fast image processing engine, this award-winning software has a bare minimum of controls and will help you with the most typical photo and image-related tasks.
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With Image Tuner you can apply a lot of effects to your images - flip, rotate, colorize, crop, round etc. Additionally, you can apply one of many filters to improve quality of your photos and pictures.
Using Image Tuner you can add watermark text or image to your photo, it could be your logo or just image name. Also you can remove image information or EXIF data stored in JPEG images.
- MS Notepad's .LOG functionality..
Los navegadores soportados todos los principales navegadores, incluyendo Opera, Firefox, Google Chrome, Safari e Internet Explorer.
► Corrige la vulnerabilidad de seguridad descrita en el siguiente artículo de Microsoft Knowledge Base:4013867 MS17-013:Esta actualización de seguridad resuelve una vulnerabilidad en Microsoft Silverlight. La vulnerabilidad podría permitir la ejecución remota de código si un usuario visita un sitio Web comprometido que contiene una aplicación de Silverlight especialmente diseñada. Un atacante no tendría ninguna manera de obligar a un usuario a visitar un sitio Web comprometido. En su lugar, el atacante tendría que convencer al usuario para que visite el sitio Web, normalmente por incitar al usuario a hacer clic en un vínculo en un mensaje de correo electrónico o mensaje instantáneo que lleva al usuario al sitio Web del atacante.
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If you have a question for a specific teacher, please include his or her name in the text (Dalton, Furst, Gonzalez, or Provinzino).
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text:
Q: Unable to login in open shift from eclipse I'm trying to login to open shift server from eclipse but getting this error "The server type, credentials, or auth scheme might be incorrect." But I'm able to login to my account from web with the same credentials.

A: OpenShift Online is not running OpenShift 3, so you need to make sure to select the correct Server Type in the drop down.

redpajama_set_name: RedPajamaStackExchange


I'm back from my mini holiday. which you may or may not have picked up on me going on, depending on how closely you read my blog. I left last Saturday, and I came back home yesterday (actually this morning at 2am).
Today has been weird. I usually feel weird the morning after coming home from visiting my boyfriend, but this was different, because it was our first holiday away together. We've stayed in hotels in London before, and he came to visit me last summer and we stayed in my parents' house while they were on vacation, but this was our first time going away together, properly.
Being in a long distance relationship isn't easy. You'd think it gets easier with time, and the being apart thing somewhat does, but the going our separate ways at an airport never does. It's the worst feeling in the world, going home but not with the one you love. We've been doing this for a year and a half now, and I'm getting really tired of it. I just wish we could live together so that at the end of a holiday like this we'd be going home together, not to separate homes. But with how our situations are now, neither of us are able to move so we'll just have to keep doing what we do and cope with the suckiness of it all.
I want to write a post about Alicante later, when I feel a little more normal than I do at this point. But it's coming!
I want to start blogging more regularly, and in doing so I want to do something my boyfriend suggested, which is this: For a week, I will not post a single thing. Everything I write will be scheduled, a post a day; that way I will create a backlog. It also helps that I have a list of things I want to blog about, that I've just written as the topics pop into my head when I don't have time to write the post itself. Then I will try to keep the schedule going so that I will be posting once a day, and we'll see how that goes!
Good afternoon, readers! It's been almost a week since I last posted, and I apologize for my absence. I just thought I'd take some time to explain what's going on in my usually not-ever-busy life.
Last Friday I was in uni – I had to come, someone brought cake! Then I had about a dozen errands to run due to being away for almost a week beforehand. I bought a new pillow! And let me tell you – a medium high hotel pillow, it's the width of my bed, it is like sleeping on a cloud! I had planned to relax and maybe do some thesis work over the weekend, but that didn't happen as I got a call from work asking if I could come on short notice, something to do with a mix-up of who was supposed to work and also someone being sick, so I had to fill in but yay money!
Tomorrow I have an appointment with the blood bank at 1.30. That means I need to get work done before 1pm – which in all likelihood means I'll not be going to uni tomorrow, but working from home for maybe two or three hours beforehand; I also need to make sure I eat two decent meals before going down there… And in the evening I'm supposed to be going to a social thing with JEF (Young European Federalists) where a woman is supposed to talk about EU for a little while and then we eat pizza and drink beer (or not, since I'll be donating blood earlier in the day)… but we'll see how I feel. On Friday there's girls' night at a friend's house at 6pm, which means I need to study earlier in the day and go home and eat and possibly get my wine if I decide I want to drink that. And on Saturday I'm going out of town at 2pm so I need to have packed and bought the things I still need to buy before Saturday which, with the rest of my week, means I probably need to do that today! Which makes me want to just leave uni right now to get that stuff done… but I feel like I need to work on my thesis since I'll be away from Saturday until next Thursday and I have so little time left to do things!
Anyway, that's my rant of the day! How's everyone doing this week?
I'm writing this from my new phone, the Huawei Honor 8, which arrived yesterday! I will write a reviews of it when I've used it a bit more, which was actually requested, and I'm keeping a list in my journal that I write as I go, but I want to use it for a week or two so I have a bit more experience with it, and maybe some negatives will pop up as well, but for now I'm loving it!
Today I wanted to write a little something about milestones, more specifically about birthdays, which popped into my head while I was on Twitter just now (follow me here). Three days ago, I was exactly 24.5 years old. But it hit me that I more often think of myself as 25 than 24, which is something I've never done before. I usually struggle to remember how old I am for weeks after my birthday, but I don't think that's be a problem this September. And I started thinking about why I feel this way.
24: not a big one, but to me this marks the beginning of your mid twenties (20-23 being early, 24-26 being mid, and 27-29 being late).
21: not a big one in my country, but to me this meant that I could now legally drink in the USA.
20: Finally not a teenager anymore! And for me and my fellow Norwegians, this also meant that we could buy all kinds of alcohol, not just below 22%.
18: this is probably the biggest. You can buy alcohol (up to 22% in my country), get your driver's licence, vote, and make all your decisions because you are legally an adult!
16: another not-so-big, but in Norway this is when you can start practice driving with an adult and also get your moped licence.
13: you're finally a teenager! You probably wanted for this one for a while, feeling like it took forever to get here, and having already gone through (at least some of) puberty.
And I guess that's it for the list. 25 is mostly considered a big day around the world though, I think. But I know a lot of people dread it because it makes them feel old. But it doesn't scare me – I already feel it.
What do you think? Got any thoughts to share on birthday milestones?
Good morning to you, wonderful reader!
It is Wednesday morning, nearly 11pm, and I'm sat on a couch in my parents' living room. I was supposed to go back to the city Monday morning, but on Sunday night we heard that the wind had grown stronger and found out that we were going to have a little storm, wind-wise, and although we didn't get any notifications about the ferries being cancelled I didn't particularly want to go out on the water in wind that strong, and especially not after my mom told be about one ferry that had a window taken out by a big wave not long ago. So I decided to stay here a little longer. Just as well, actually, because on Sunday I ordered my new phone, which I mentioned in this entry here. It said it would arrive in 1-3 working days, so we were hoping it would arrive yesterday, but when dad came home from a work trip he said the tracking said it's in the city and therefore he thought it'd be on the first ferry out to our town today. He's the one who'll get the message about it though so I just have to wait… I did get a few other things done the past couple of days as well, so my time here hasn't just been an extension of the weekend; yesterday I had a dentist appointment (and was told everything looks great), and I went with my mom to a meeting about noise reduction/isolation of houses, because our town is right next to an air force base which is getting new F35 fighter planes which will make a lot more noise than the F16s I grew up with.
On Monday, I felt that I did good work wise. I wrote three paragraphs on my methodology chapter, and sent it in to my supervisor (it was 6 pages long, not finished, but he's been asking for it). Then I wrote about 600-700 words more on my literature review, which still needs about 8 more pages before it's finished, but I'm getting there, and that one doesn't need to be finished before the end of April when my supervisor will get my entire thesis, all chapters put together, for one final reading. So I felt good! That was until my supervisor sent back the methodology chapter with his comments yesterday.
I read the email, and then all the comments on the chapter. Then I started to cry. My 16 pages long literature review needed to be restructured (which actually means rewritten, because most of the parts had to be changed as well, not just moved around), and rewriting makes it shorter, but it needs to be about 20 pages long, so I need to write more. That should've taken about 2 weeks, he said, and I'm sure I could have done it if each day consisted of about 40 hours instead of 24, but it doesn't, so I'm not finished. And now my methodology needs to be rewritten, clarified, a lot of stuff needs adding and explaining… a friend of mine told me to remember that I'd written it in a hurry so of course it wouldn't be great, and another told me to just go into robot mode and get stuff done, and my ever-supporting boyfriend told me I always react like this, I just need to remember that I've always been fine in the past and I will be fine this time too, it just feels like I won't because it's my master's thesis. My mom told me that it sounds like I just need to sit down and work.
And here I am, blogging. This entry has passed 600 words by now, so I know I am capable of writing lots of words fast, so how come everything on my thesis is going so slow? I think I know the answer to that. I don't want to write it. I don't want to do the work. I just want to get the degree so I can move on with my life. I've been in school for 19 years, that's 3/4 of my life, and the 6 years before that I can barely remember, apart from some glimpses here and there. They say that university, the years spent as a student, are supposed to be some of the best of your life. But they're also the most stressful. I constantly have the feeling that I'm not quite done for the day because there's always something that needs doing, because you're never done, but you can't spend all your awake hours working, either. But when I'm not working I feel guilty. And then I struggle to work because I feel so bad about myself. And then I feel like I'm stuck in this vicious circle that I can't get out of, I cannot see the light at the end of the tunnel, everything is just black, the air is black, and it's pushing down on me and I can't breathe and I just want it all to end.
That's how it feels, a lot of the time. That feels like what life is all about… And I know it's temporary. I know there will come a time in my life when it's not all about studying and getting my degree, or the next degree. But here's the thing. I have some certain destructive tendencies, which make it to the surface despite me working on burying them as far down as humanly possible, sometimes.
Next, eating. When I was younger, I didn't eat much. I went through a period a couple of years after I stopped eating meat where I noticed I lost a lot of weight and it became an obsession to lose weight and at a point I pretty much only ate dinner with my family because that was mandatory. Going to America for a year when I was 17 helped a little. But what helped the most was starting exercising when I was 19-20. Most of the time I eat good, and a friend of mine started calling me a hobbit, because I am short and I eat very often! But sometimes I slip up. Last week I had one of those. I'd even brought lunch with me when I went to a friend's house to work for the day, because the last time I did that I didn't eat enough throughout the day. But I didn't eat dinner that day. I was so hungry when I got home, but my roommate was cooking and I didn't want to intrude so I sat down. My two roommates and I talked for a couple hours but my headache, which arrived after the gym, was getting so bad, and because I didn't eat right away I got nauseated and couldn't eat. So I took a shower and went to bed. My actions were very noticeable the following day, with more headaches and extreme hunger, a so-called "hollow day".
And then I sleep. And tomorrow, I have to get up and do it all over again.
Anyone else experiencing anything like this? I'd love to hear from you!
Do you want to know a secret? I am a sucker for makeover programs on TV! And there are a lot of them. Usually they're broadcasted in the daytime, which is a time of day I never watch TV, and I also don't own a TV so everything I watch is on streaming providers, and then I usually watch series such as Friends, The Big Bang Theory, Criminal Minds, Blindspot, Grey's Anatomy… you know, shows and series with actors, not reality anything.
That's when I'm in the city. When I go home, I spend a lot of time with my mom in the living room and we watch daytime TV. Even if she's not around, I tend to spend time in the living room with the TV on if I'm working on my laptop or just relaxing. We watch a lot of shows, often British, some Norwegian, where people are selling or looking for new houses, or renovating theirs, or where people have tried and failed to renovate and then seek professional help… As well as garden/garden making show. Right now we're watching a show where a bunch of professionals fix up a house that a family inherited that was well suited for the grandmother who lived there before but not for this family of four, and now the host has sent the family away and they've started changing the house, and the host keeps sending the family small video clips of them tearing down walls and such!
I think what I enjoy so much about shows like this is seeing all the transformation that's possible to do, to know that even something so permanent as a wall in a house isn't necessarily permanent. And I would just love the opportunity to wear carpenter's pants and attack a wall with a sledgehammer sometime!
Anyone else secretly or not-so-secretly love makeover shows?
Two posts in one day – what a treat! Well. Depends on whether you like long rants and text that's not accompanied by any sort of pictures. Anyway. Like I said in my previous post, I've spent this weekend with my parents and brother in my childhood home. I also said there were four reasons why I went home, not counting the fact that I like spending time with my family and given the options of doing that or sitting in my bedroom in the city, there wouldn't even be a discussion on which one I'd choose. My last post was about one of the reasons I went home, this one is another (or, at least part of it).
I needed to get my swimsuits. Bikinis. Two-piece swim suits. Beach wear. Whatever you call it. The stuff you wear to the beach when you want to go swimming. I needed to pick up some of that, and I knew I had several here.
One of them, a very patterned bikini from a few years ago (read: 7 years ago), was completely ruined. The elastic in the waist on the bottoms and the part you tie around your body on the upper half was completely gone, probably due to bad washing after swimming pools and lakes and oceans. So that had to go in the trash.
Next up was a purple one with white polka dots, bought at the same time, but used less. That one had a safety pin put in on the side of the bottoms to hold it together. I removed it, and the bottoms fit better than they ever have (I should mention that I probably weigh more but I am a lot happier with my body now than I was a few years ago and I know I'm healthier than I was when I bought these). GREAT, I thought, I have bottoms that fit. The top however – can I just say OW! Much squeeze. So ow. Not very nice looking – or comfortable. This one used to be too loose, just like the bottoms, but the bottoms now fit and the top is too tight. Oh well. Next bikini.
I found a dark blue bikini I also bought a few years ago, although I don't remember when. This is the kind where you have to tie everything, and a naughty person pulling on one piece might make everything incredibly awkward for you. And what do you know – another bottom that both fit well and looked good! The same situation happened with the top here, which also had the same style as the previous one… However, I was in luck – my mom had given me a top that didn't fit her that was exactly the same color, and that one wasn't so bad! At least I can wear it without being squished. But I would worry about it falling off, going up or down… Either way, moving on.
I have a bikini top that I bought less than two years ago to wear at home with shorts if it got incredibly hot that summer – it didn't. The top was only ever tried on in a fitting room where I decided it looked good. Who knew that breasts could change size so quickly in your early 20s, when you haven't really changed much else in your life? Back then, I'd been working out quite a bit. I'd gone from a B cup to a C cup, and down in the size around the body. However, in the last year I've gone up yet another cup size, and I don't know what's caused this, about 10 years later than you'd expect! Either way, the beautiful top I got a year and a half ago is way too small and going onto my donation pile as it's not visibly used (as it should be, since I haven't used it)!
That left me with one more set to try on, a tankini I bought about three years ago. Back then I didn't think I was that unhappy with my body, just that I didn't like stuff too revealing, but in retrospect I can clearly see that I was not happy with myself at all. This tankini is kinda cool – it has a halterneck top, but from the breasts and down there is a crochet kind of material that covers your body down to your hips. The bottoms are high wasted and cover up all the things I usually cover with jeans, at least in the stomach area. And I'm not throwing this one away – however, it's not what I want to wear to the beach on vacation now, I want something a little less covered up.
So that leaves me with the dark blue bikini from three paragraphs ago. But my mom told me of a shop in the city where they sell pieces separately, in other words the more body friendly way of doing it, and the staff are comfortable and helpful as well. My mom told me to go there, as I haven't bought anything new in three years (that I've used) and I also haven't gone swimming since then. She told me to indulge myself, to treat myself to something new. So at some point this next week I'm going shopping, and trying to resolve this ever-lasting bikini problem.
Anyone else ever experience fashion crises like this? Anyone got any stories they want to share?
Good afternoon, readers! I hope you're all having a good weekend! I've not been posting anything because I've not been on social media a lot this weekend, as I decided to go home to my parents, for three, wait no, four, different reasons. One of these reasons was phone related.
As some of you might remember, in January I wrote this entry about using an iPhone for the first time due to my Samsung having been sent off to check if the things wrong with it were fixable. They weren't. After several weeks, I was told that I would be able to get my money back after February 1st. It's now been over a month since then. I've been to England in the meantime, and when I got home I was told to call someone but of course they were closed so I had to call the following day, and the woman I spoke to confirmed that it was a warranty issue and I could get my money back. That was over a week and a half ago, and my money is nowhere to be seen.
However, today I couldn't wait any longer, and decided to order a new phone after debating and comparing phones and operative systems for a couple of weeks. I knew that I did not want another Samsung, but I did land on a phone that runs on Android. Not iOS. After almost 6 weeks of using the iPhone, I've decided that I just don't want one. So I decided to write a post about why. Please note that this is not an attack on iPhone or its users, it's simply personal preference, and furthermore I am aware that some of the things on the following list may be changeable in the settings, but the fact that they're not already default is a minus in my book. Other issues I've been having might be due to the fact that I've been using an iPhone 4s, which is old, and newer iPhones may have fixes for these; I'm basing the following points strictly on my experience with this one iPhone 4s, so please do not consider this a review of anything else.
The first point on my list is something that I mentioned in the last post I wrote about Android/iOS, and that is the fact that if I write too long of a message in WhatsApp/Messenger (my two most used message apps) the message I'm replying to disappears, and I have to exit the message and go back on it again to see the rest. I can't close and reopen the keyboard in the same way that I could with my Samsung phone. I've been told that newer iPhones have an arrow next to the keyboard that allows for this though, so that might just be the 4s that's bad.
The fact that you cannot insert a memory card is a huge minus for me. You're stuck with whatever size memory you choose to get, minus approximately 2GB that are used for software (this one is 16gb, available space was 14.2 before I downloaded stuff, I believe).
The price of iPhones is ridiculous. I could get the 5s 16gb for a reasonable price, but that was about it. The SE with 64gb is where it seemed like I would be able to get the most for my money, but even that was pushing my budget, and I didn't like the width/length ratio on the newer iPhones, they looked too long and narrow for me, although I'm sure that's something you get used to.
Going back in apps have been an issue for me sometimes. An example is when I accidentally clicked on something in the Messenger app for Facebook where I could "react" to the last message sent, and I just wanted to go back, but I couldn't figure out how to and ended up having to give the message a thumbs up before I could get to see what the message actually said.
It seems to me that if I've been in a game and I've clicked the home button and exited the app, when I reopen the app it takes me to the main page of the app (think candy crush saga, where you have the map – that's where I'm taken, not back into the game). I know my Android phones do that too sometimes but that's usually when it's been a long time since I was in the app, and usually I got straight back into the game as I left it when I reopen the app.
I've missed SO many notifications on this phone due to the fact that I have to pull down the tab at the top to see the rest once I've swiped and opened one – you know, when you haven't used your phone in a while and you wake up the screen and have 5 notifications, and you swipe on one to see it, and then you forget that the rest were even there because when you turn the screen off they don't reappear. This, I've bee told, can be changed in the settings, but I don't want to have to do that, I think it should be default. Furthermore, on my Samsung phones, I could always see that I had unread notifications at the top of the screen where you see the clock and battery and wifi and signal – there were symbols there telling me what apps I had unread stuff in, which reminded me to pull down the tab and view them, which the iPhone doesn't.
This phone keeps telling me I have unread emails when I don't. First it was one – and I even clicked my way through all my emails in my inbox (which aren't many because I keep deleting and moving them to folders several times a day), but it still said I had one unread email. This past Friday, that changed to two unread emails. So now when it tells me I have 7 unread emails, I actually only have 5. I spoke to a roommate of mine who had experienced the same before and it lasted for months before it suddenly stopped. Really annoying!
I absolutely HATE that the iPhone turns itself off when it's cold. I live in Norway, and Norway is cold. I'll be walking home from the bus stop replying to a message, and it randomly shuts off. This is NOT something I can live with, and the fact that it's supposed to be a safety feature… to me it's just annoying and frustrating and I don't need this kind of stress in my life.
What do you mean I can't connect it to my windows computer and transfer my pictures over to an external harddrive?! Actually, this is an easy fix, I just have to upload them to a Google Drive folder, but that's not how I like to do things, and I like to keep all backups on this one harddrive of mine, together, so I need to then copy them out of the Google Drive and onto the harddrive which is just an annoying middle step for me.
It does not charge properly. And by that I mean that I have to blow on both ends of the charger cable, plus the box, plus the outlet, plus where you plug the charger into the phone, and then, MAYBE, it'll start charging. My friend told me that's most likely the cable, because it's a cheap cable, and she had the same issue with a cheap cable for her 5s, but a more expensive cable works fine. No. That just won't do. No one is better at breaking phone charger cables than I am, and I expect a cheap cable to charge just as well as a more expensive cable; it may be slower, but it should not do this, and I shouldn't have to do 7 different things and restart the phone twice before it'll charge – it should just start charging when I plug it in!
So there you have it – my list of 10 reasons I chose another Android phone instead of an iPhone when finally deciding to buy a new one. And let me tell you: no offense, but I am REALLY looking forward to giving this phone back to the friend I borrowed it from!
I've been a coffee drinker since I was 17. That'll be 8 years this summer/fall. And in this whole time, I have never ever seen the value in decaf coffee. What's the point in drinking coffee without caffeine, right? Decaf is for the weak!
When I was at my boyfriend's last month, he bought decaf coffee. And I had decaf coffee. And now, two weeks later, I'm sat here, it's 8pm, and I want coffee. But I don't want caffeine because it's 8pm and I don't drink coffee at this hour because I like to sleep… And for the first time I'm sat here wishing I had decaf. But I don't. So I made coffee with caffeine. I just really wanted the taste of coffee. I'll deal with the sleep consequences later.
How's everybody's evening going? Or, what time is it where you live?
Two toasted slices of rye bread. Sprinkle with olive oil. Half an avocado on each slice. Sprinkle salt and pepper on top. And of course: one cup of fresh, hot coffee on the side!
You should definitely try this if you haven't already. Super delicious!


text:
David Nolden (* 27. August 1995) ist ein deutscher Schauspieler.

Leben
Erste schauspielerische Erfahrungen sammelte David Nolden 2009 in der Schüler-Theatergruppe des Trierer Humboldt-Gymnasiums. Im Jahr darauf arbeitete er mit dem Konzeptkünstler Laas Koehler an der TUFA zusammen und machte experimentelles Straßentheater. Anlässlich eines Praktikums in der Statisterie des Theaters Trier, empfahl ihm ein Ensemblemitglied, sich bei einer Agentur zu bewerben, worauf hin Nolden erfolgreich für eine Rolle in dem Fernsehfilm Blutgeld gecastet wurde und damit 2013 sein Kameradebüt gab. Neben weiteren Fernsehrollen, u. a. in der ZDF-Serie SOKO Köln, spielt Nolden seit 2009 auch immer wieder am Theater Trier und war dort beispielsweise in Die Physiker von Friedrich Dürrenmatt oder Brechts Leben des Galilei zu sehen. Von 2015 bis 2018 spielte David Nolden in der ARD-Reihe Hotel Heidelberg in einer wiederkehrenden Rolle an der Seite von Ulrike C. Tscharre, Christoph Maria Herbst, Annette Frier, Hannelore Hoger, Maren Kroymann u. a.

2016 spielt er am Theater KOSMOS in Bregenz in der Uraufführung Jeunesse Dorée eine der Hauptrollen Aljosha.

Nolden spielte einen der Haupt-Charaktere in jungen Jahren in der finnisch-deutschen Co-Produktion Tom of Finland, die Finnlands Wettbewerbsbeitrag für die Academy Awards 2018 war.

Neben einem Improvisationskurs unter Leitung von Judith Kriebel, absolvierte David Nolden 2015 ein dreimonatiges Praktikum am Arcola Theatre in London. Außerdem nahm er im Jahre 2017 Schauspielunterricht bei Kathrin Ackermann. Seit 2018 ist er Mitglied der Actor's Guild of Great Britain.

2019 übernahm er eine durchgehende Rolle in der vierten und letzten Staffel Magda macht das schon.

David Nolden wohnt in Berlin.

Filmografie (Auswahl)
 2013: Blutgeld
 2013: Ein Wimpernschlag
 2014: Die Mütter-Mafia
 2014: SOKO Köln – Opa ist tot!
 2016: Hotel Heidelberg: Kramer gegen Kramer
 2016: Hotel Heidelberg: Kommen und Gehen
 2016: Wie ich berühmt werden wollte (Kurzfilm)
 2016: Verrückt nach Fixi
 2016: Hotel Heidelberg: Tag für Tag
 2017: Gute Arbeit Originals
 2017: Tom of Finland
 2018: Hotel Heidelberg: Kinder, Kinder!
 2018: Hotel Heidelberg: … Vater sein dagegen sehr
 2018: In aller Freundschaft – Liebeserklärungen
 2019: The Agreement
 2019: Zeit der Geheimnisse
 2019: Magda macht das schon!
 2020: Notruf Hafenkante
 2021: Rosamunde Pilcher: Der Stoff, aus dem Träume sind
 2021: In aller Freundschaft – Die jungen Ärzte – Irrwege
 2021: Die unheimliche Leichtigkeit der Revolution
 2023: Masters of the Air

Weblinks

 David Nolden bei agent brosmann

Einzelnachweise

Filmschauspieler
Theaterschauspieler
Deutscher
Geboren 1995
Mann

redpajama_set_name: RedPajamaWikipedia


With Mustard on the beat.
With Kid Ink's album campaign moving Full Speed ahead, the LA rapper has just unleashed his realest LP track yet in the form of "Be Real." Over a particularly hyperactive DJ Mustard beat, Kid Ink and Dej Loaf both serve pure IDGAF, the former throwing the first punch with lines like Been drinking like you're thirsty / How'd you get to my section in the first place? and the latter delivering the fatal blow with a teasing smile on her hook: You ain't got no cash, you ain't go no dollar...Let's just be honest, let's just be real. Look out for Full Speed on February 3.


GERD Linked to More Esophageal Cancers | Your Health. Your Lifestyle.
Posted in Cancer and tagged Barrett mucosa, cancerous tumors, esophageal cancer, gastroesophageal reflux disease by Sarah. Bookmark the permalink.


text:
Gorzów Wielkopolski Karnin – stacja kolejowa w Karninie; w Gorzowie Wielkopolskim, w województwie lubuskim.

W roku 2017 przystanek obsługiwał 0–9 pasażerów na dobę.

Połączenia
 Zbąszynek
 Gorzów Wielkopolski
 Zielona Góra

Przypisy

Linki zewnętrzne
 Opis stacji

Stacje i przystanki kolejowe w Gorzowie Wielkopolskim


It is an utmost strenuous and challenging aspect while running a web-page of a business. Greater number of traffic from seo services in Melbourne, would not merely lead towards an overwhelming situation because of low browsing speed of a website but also refrain potential customers to communicate with businesses effectively. On other hand, no one can deny that blissful professionals of web development always take account to manage flow of traffic. They always target germane and relevant audience so that a core purpose behind website development can be grasped without enduring any destructive or negative culmination. Hence, one should have to admire this admitted fact that website management should be implemented and executed by immensely technical and experienced experts.
Although corporate and business goals always settled in a same direction and they are directly related with each other, still such goals and objectives are different. Corporate goals deals with a broader picture of a business. On other hand, merchandise and routine trade activities which if executed in a particular manner, it would lead towards attainment of corporate objectives, Attention should be given here that a professional and effective website development always dispense ease for achievement of such goals and objectives. Businesses would remain in a position to target more audience, fetch more sales, compete effectively and ultimately can outperform in an immense and rigid competitive environment. Moreover, because of drastic achievement of media agencies competing in Australia, note that grabbing a money making deal would not be that much difficult. It means that one can grace its business with this most demanded and crucial intangible asset in comparatively low spending of money.
All media agencies and web developer Australia always possess an effective e-portal. Through this portal they not merely contact their customers and answer their queries but also furnish a detailed approach of their services. They first understand an actual demand of businesses by taking an eye as in which industry a particular business should have to compete. After that, they impart number of sample designs relevant to a business and in such designs any kind of alteration can be made depending upon the nature of a business. Moreover, they also bestow an opportunity to disclose relevant information about their charges, time needed and other critical things. So, e-hiring should be opted for engaging experts of website development.


text:
Comment: The Devil of Perversity
Will the new coalition government in Skopje be able to marginalise the extremists threatening to plunge the country into bloody conflict?
By Ferid Muhic
Last week, Macedonia's parliament rejected calls for a state of war to be declared. Instead, a new, broader coalition government of national unity came into being.
It is excellent news, especially as radicals - both Macedonian and Albanian - have been pushing for Skopje to formally go to war against Albanian fighters.
George Robertson, NATO secretary general, recently described the situation in the country in dramatic terms. "Macedonia is on the edge of an abyss!" he said.
The writer Edgar Allan Poe once recommended to his readers that they avoid approaching the edge of any abyss. The fear provoked by the looming depths becomes hypnotic, he wrote, and mesmerises the unwary until temptation overcomes sense and the unfortunate individual steps over the edge. Poe labelled this temptation "The devil of perversity".
Macedonian political leaders, in government and opposition, have long played fast and loose with this devil.
It all began with a clandestine "gentleman's agreement" to pursue private political and commercial interests through gradual and controlled manipulation of ethnic tensions between the two main ethnic groups - Macedonians and Albanians. The strategy paid great dividends.
It's no secret that some of the leading lights in the former ruling coalition - the Macedonian VMRO and the Albanian DPA - are among the richest individuals in the country.
But it was a dangerous strategy - as the recent fighting has illustrated. And now, after two months of clashes between security forces and Albanian armed groups in the mountains near the border with Kosovo, Macedonia has stumbled towards the edge of the abyss - or to give it its correct name, civil war.
All last week, parliamentary debates were dominated by calls from Macedonian politicians for a state of war to be declared. Albanian deputies strongly opposed the move. So too did the international community.
Those in favour believe a state of war would significantly enhance the efficiency of the military operation against the Albanian insurgency. Now that their demands have been rejected, they complain a crucial opportunity has been missed.
Hawks, the extremists on the Macedonian side believe the chance has gone to resolve the "Albanian issue" once and for all - by burning Albanian villages and forcing people to leave their homes.
Albanian extremists too are disappointed, believing a state of war would help them to secure their goal - an independent territory for Albanians.
But overwhelming military action would only increase the risk of terrible, uncontrollable violence. Further polarisation of the two communities would become inevitable.
The space for political dialogue would be dramatically narrowed for both sides. The possibility of a serious crisis, with key parties leaving the coalition, would increase.
Macedonia would also risk being discredited internationally should accusations of human rights violations surface, even if such claims proved to be untrue.
The pitfalls of declaring a state of war outweigh the benefits. Such a declaration would be akin to an amputation - a rapid and relatively easy surgical procedure, but one leaving the patient seriously debilitated.
The only solution still open to saving inter-ethnic cooperation rests with the new coalition government. It must demonstrate that it truly represents the republic's interests. Macedonian politicians need to unite, and be seen to be defending all the country's citizens, not just Macedonians.
Albanian politicians, meanwhile, need to distance themselves from any violence and explicitly condemn it no matter what the motive. Secondly, they must commit to the continued stability and integrity of the state, and to make clear that the only acceptable means for resolving their community's problems is through Macedonia's legal institutions.
Should the authorities remain divided, especially along ethnic lines, they will be incapable of guaranteeing security.
Extremists on both sides believe the decision not to declare a state of war is a setback. But perched as we are on the edge of an abyss, it is better and wiser to step backwards not forwards. Macedonia's political leaders have played too long with the devil of perversity, with civil war.
Many things have been destroyed in the process, many others seriously damaged. It remains to be seen whether the new coalition can marginalise the extremists. If it doesn't, it is only a matter of time before calls for a state of war will reverberate around parliament once again.
Ferid Muhic is a professor of philosophy at Skopje university
Macedonia, Kosovo
BCR Issue 246
http://tinyurl.com/y6jo9trv
Albanian Kids Skip School En Masse
Kosovo Atrocity Cover-up
Fading Hopes for Kosovo's Disappeared
Celebrating Gender Equality in Libya
IWPR's work with women-led NGOs highlighted at embassy event.
Young Iraqis Are Demanding Change
Nineveh Reborn
Kenya: Challenging Corruption
Investigative journalism is being used as a robust tool for advocacy.
Nigeria: Working Together for Change
Rwanda: How Trade in Banned Alcohol Wrecks Lives

redpajama_set_name: RedPajamaCommonCrawl


text:
The USSR Chess Championship was played from 1921 to 1991. Organized by the USSR Chess Federation, it was the strongest national chess championship ever held, with eight world chess champions and four world championship finalists among its winners. It was held as a round-robin tournament with the exception of the 35th and 58th championships, which were of the Swiss system.

Most wins
Six titles: Mikhail Botvinnik, Mikhail Tal
Four titles: Tigran Petrosian, Viktor Korchnoi, Alexander Beliavsky
Three titles: Paul Keres, Leonid Stein, Anatoly Karpov

List of winners

{|class="wikitable"
|-
! Edition !! Date !! Place !! Winner !! Score !! width=700| Notes
|-
|1 ||4–24 Oct 1920 || Moscow
| Alexander Alekhine || 12/15 (+9−0=6)
|Known as the All-Russian Chess Olympiad at the time, this tournament was later recognized as the first USSR championship.
|-
|2 ||8–24 Jul 1923 || Petrograd
| Peter Romanovsky ||10/12 (+9−1=2)
|
|-
|3 ||23 Aug–15 Sep 1924 || Moscow
|Efim Bogoljubov || 15/17 (+13−0=4)
|
|-
|4 ||11 Aug–6 Sep 1925 ||Leningrad
| Efim Bogoljubov ||14/19 (+11−2=6)
|
|-
|5||26 Sep–25 Oct 1927 || |Moscow
|Fedor BogatyrchukPeter Romanovsky
|14½/20 (+10−1=9)14½/20 (+12−3=5)
|All of Bogatyrchuk's tournament results were erased from Soviet records after he emigrated to Canada and was declared a nonperson.
|-
|6 || 2–20 Sep 1929 || Odessa
|Boris Verlinsky || 5½/8 (+4−1=3),4/5 (+4−1=0),and 3½/4 (+3−0=1)
|The tournament was conducted in three stages.
|-
|7 || 10 Oct–11 Nov 1931 || Moscow
|Mikhail Botvinnik || 13½/17 (+12−2=3)
|
|-
|8|| 16 Aug–9 Sep 1933 || Leningrad
| Mikhail Botvinnik || 14/19 (+11−2=6)
|
|-
|9 || 7 Dec 1934–2 Jan 1935 || Leningrad
|Grigory Levenfish Ilya Rabinovich
|12/19 (+8−3=8)12/19 (+9−4=6)
|
|-
|10 || 12 Apr–14 May 1937 || Tbilisi
| Grigory Levenfish || 12½/19 (+9−3=7)
|
|-
|11 || 15 Apr–16 May 1939 || Leningrad
| Mikhail Botvinnik || 12½/17 (+8−0=9)
|
|-
|12|| 5 Sep–3 Oct 1940 || Moscow
|Andor LilienthalIgor Bondarevsky
|13½/19 (+8−0=11)13½/19 (+10−2=7)
| Mikhail Botvinnik won the Absolute Championship,23 Mar–29 Apr 1941, Leningrad/Moscow, 13½/20 (+9−2=9)
|-
|13 || 21 May–17 Jun 1944 || Moscow
| Mikhail Botvinnik || 12½/16 (+11−2=3)
|
|-
|14 || 1 Jun–3 Jul 1945 || |Moscow
| Mikhail Botvinnik ||15/17 (+13−0=4)
|
|-
|15 ||2 Feb–8 Mar 1947 || Leningrad
|Paul Keres || 14/19 (+10−1=8)
|
|-
|16||10 Nov–13 Dec 1948 || Moscow
|David BronsteinAlexander Kotov
|12/18 (+7−1=10)12/18 (+10−4=4)
|
|-
|17 || 16 Oct–20 Nov 1949 || Moscow
|Vasily SmyslovDavid Bronstein
|13/19 (+9−2=8)13/19 (+8−1=10)
|
|-
|18 || 10 Nov–12 Dec 1950 || Moscow
|Paul Keres || 11½/17 (+8−2=7)
|
|-
|19 || 11 Nov–14 Dec 1951 || Moscow
|Paul Keres ||12/17 (+9−2=6)
|
|-
|20 || 29 Nov–29 Dec 1952 || Moscow
|Mikhail Botvinnik ||13½/19 (+9−1=9)
|Botvinnik defeated Mark Taimanov in a playoff +2−1=3.
|-
|21 || 7 Jan–7 Feb 1954 || Kyiv
|Yuri Averbakh || 14½/19 (+10−0=9)
|
|-
|22 || 11 Feb–15 Mar 1955 || Moscow
|Efim Geller || 12/19 (+10−5=4)
|Geller defeated Vasily Smyslov in a playoff +1=6.
|-
|23 || 10 Jan–15 Feb 1956 || Leningrad
|Mark Taimanov || 11½/17 (+8−2=7)
|Taimanov defeated Boris Spassky and Yuri Averbakh in a playoff.
|-
|24 || 20 Jan–22 Feb 1957 || Moscow
|Mikhail Tal ||14/21 (+9−2=10)
|
|-
|25 || 12 Jan–14 Feb 1958 ||Riga
|Mikhail Tal ||12½/18 (+10−3=5)
|
|-
|26 || 9 Jan–11 Feb 1959 || Tbilisi
|Tigran Petrosian ||13½/19 (+8−0=11)
|
|-
|27 || 26 Jan–26 Feb 1960 || Leningrad
|Viktor Korchnoi ||14/19 (+12−3=4)
|
|-
|28 || 11 Jan–11 Feb 1961 ||Moscow
|Tigran Petrosian ||13½/19 (+9−1=9)
|
|-
|29 ||16 Nov–12 Dec 1961 ||Baku
|Boris Spassky ||14½/20 (+10−1=9)
|
|-
|30 || 21 Nov–20 Dec 1962 ||Yerevan
|Viktor Korchnoi || 14/19 (+10−1=8)
|
|-
|31 || 23 Nov–27 Dec 1963 || Leningrad
|Leonid Stein || 12/19 (+6−1=12)
|Stein defeated Boris Spassky and Ratmir Kholmov in a playoff.
|-
|32 ||25 Dec 1964–27 Jan 1965 || Kyiv
|Viktor Korchnoi ||15/19 (+11−0=8)
|
|-
|33 || 21 Nov–24 Dec 1965 || Tallinn
|Leonid Stein ||14/19 (+10−1=8)
|
|-
|34 ||28 Dec 1966 – 2 Feb 1967 ||Tbilisi
|Leonid Stein ||13/20 (+8−2=10)
|
|-
|35 ||7–26 Dec 1967 ||Kharkiv
|Lev PolugaevskyMikhail Tal ||10/1310/13
|The tournament was a 126-player Swiss.
|-
|36 ||30 Dec 1968–1 Feb 1969 ||Alma-Ata
| Lev PolugaevskyAlexander Zaitsev
|12½/19 (+7−1=11)12½/19 (+6=13)
|Polugaevsky defeated Zaitsev in a playoff +2−1=3.
|-
|37 || 6 Sep–12 Oct 1969 ||Moscow
|Tigran Petrosian ||14/22 (+6−0=16)
|Petrosian defeated Polugaevsky in a playoff held in Feb 1970 by +2=3.
|-
|38 || 25 Nov–28 Dec 1970 || Riga
|Viktor Korchnoi ||16/21 (+12−1=8)
|
|-
|39 ||15 Sep–17 Oct 1971 || Leningrad
|Vladimir Savon || 15/21 (+9−0=12)
|
|-
|40 || 16 Nov–19 Dec 1972 || Baku
| Mikhail Tal || 15/21 (+9−0=12)
|
|-
|41 || 1–27 Oct 1973 || Moscow
| Boris Spassky ||11½/17 (+7−1=9)
|
|-
|42 || 30 Nov–23 Dec 1974 || Leningrad
|Alexander BeliavskyMikhail Tal ||9½/15 (+6−2=7)9½/15 (+6−2=7)
|
|-
|43 || 28 Nov–22 Dec 1975 || Yerevan
| Tigran Petrosian || 10/15 (+6−1=8)
|
|-
|44
|26 Nov–24 Dec 1976 || Moscow
|Anatoly Karpov || 12/17 (+8−1=8)
|
|-
|45 || 28 Nov–22 Dec 1977 || Leningrad
|Boris GulkoIosif Dorfman || 9½/15 (+4−0=11)9½/15 (+4−0=11)
|A playoff, held in 1978, was drawn +1−1=4.
|-
|46 || 1–28 Dec 1978 || Tbilisi
|Mikhail TalVitaly Tseshkovsky
|11/17 (+5−0=12)11/17 (+6−1=10)
|
|-
|47 || 29 Nov–27 Dec 1979 || Minsk
| Efim Geller || 11½/17 (+6−0=11)
|
|-
|48 || 25 Dec 1980–21 Jan 1981 || Vilnius
|Lev PsakhisAlexander Beliavsky
| 10½/17 (+8−4=5)10½/17 (+6−2=9)
|
|-
|49 || 27 Nov–22 Dec 1981 || Frunze
|Garry KasparovLev Psakhis
|12½/17 (+10−2=5)12½/17 (+9−1=7)
|
|-
|50 || 2–28 Apr 1983 || Moscow
| Anatoly Karpov || 9½/15 (+5−1=9)
|
|-
|51 || 2–28 Apr 1984 || Lviv
|Andrei Sokolov || 12½/17 (+8−0=9)
|
|-
|52 || 22 Jan–19 Feb 1985 || Riga
|Viktor GavrikovMikhail GurevichAlexander Chernin
|11/19 (+4−1=14)11/19 (+6−3=10)11/19 (+5−2=12)
|
|-
|53 || 4–28 Feb 1986 || Kyiv
| Vitaly Tseshkovsky || 11/17 (+6−1=10)
|
|-
|54 || 4–29 Mar 1987 || Minsk
| Alexander Beliavsky || 11/17 (+7−2=8)
|Beliavsky defeated Valery Salov in a playoff +2=2.
|-
|55 || 25 Jul–19 Aug 1988 || Moscow
| Anatoly KarpovGarry Kasparov
|11½/17 (+6−0=11)11½/17 (+6−0=11)
|
|-
|56 || 22 Sep–16 Oct 1989 || Odessa
|Rafael Vaganian || 9/15 (+5−2=8)
|
|-
|57 || 18 Oct–3 Nov 1990 || Leningrad
|Alexander BeliavskyLeonid YudasinEvgeny BareevAlexey Vyzmanavin
|8½/13 (+5−1=7)8½/13 (+4−0=9)8½/13 (+6−2=5)8½/13 (+5−1=7)
|
|-
|58 || 1–13 Nov 1991 || Moscow
|Artashes Minasian || 8½/11 (+7−1=3)
|Minasian won this Swiss-style tournament on tiebreak over Elmar Magerramov.
|}

See also
 Women's Soviet Chess Championship
 Russian Chess Championship

Publications
 Mark Taimanov, Bernard Cafferty, Soviet Championships, London, Everyman Chess, 1998 ()

References

Further reading

The Soviet Chess Championship 1920-1991
 RUSBASE (part V) 1919-1937,1991-1994
 RUSBASE (part IV) 1938-1960
 RUSBASE (part III), 1961-1969,1985-1990
 RUSBASE (part II) 1970-1984

Chess national championships
Championship
Chess
Recurring sporting events established in 1920
Recurring events disestablished in 1991


text:
Tag Archives: Wall Street
Jun. 7, 2012 Colin Powell Commonwealth Club
June 8, 2012 dj
On June 7, 2012 in Santa Clara at The Commonwealth Club, Dan Ashley of ABC KGO talked with General Colin Powell about his new book "It Worked for Me: In Life and Leadership." Powell wanted to talk about the principles that guided him from the immigrant neighborhoods of New York City to corridors of power and how a new generation can follow that path.
He talked about his career after retiring from the military as Chairman of Joint Chiefs of Staff. He turned down offers from Wall Street firms that simply wanted his name and reputation. He served as U.S. Secretary of State in the second Bush administration but left after the invasion of Iraq. He spoke about dealing with North Korea and Iran on the futility of developing nuclear weapons to stay in power. In his travels Powell discovered that leaders of all nations face the same problems of finding jobs for people. Perhaps when they realize they are all in same boat they will begin to row in the same direction.
Colin PowellCommonwealth ClubDan Ashley of ABC KGOinvasion of IraqIranJoint Chiefs of StaffNorth Koreanuclear weaponssecond Bush administrationU.S. Secretary of StateWall Street
Aug. 15, 2011 The New Yorker
August 15, 2011 dj
On Aug. 15, 2011 The New Yorker's James Surowiecki wrote "The Business Of Austerity" about how the Republican Congress caused the US Government to default. By harming the full faith and credit of the United States they caused the stock market to fall, hurting their friends on Wall Street. Austerity hurts recovery.
Dana Goodyear wrote "Grub" about eating bugs as a source of protein.
Elizabeth Kolbert wrote "Sleeping With The Enemy" about what happened to the Neanderthals. Apparently they were not as artistic or creative as humans.
Austerity hurts recoveryDana GoodyearElizabeth Kolbertfull faith and credit of the United StatesGrubJames SurowieckiNeanderthalsRepublican CongressSleeping With The EnemyThe Business Of AusterityThe New Yorkerthe stock market to fallUS Government to defaultWall Street
Aug. 2, 2010 The New Yorker
August 2, 2010 dj
Aug. 2, 2010 The New Yorker's James Surowiecki wrote "Blame Games" about Wall Street blaming the slow economic recovery from their collapse on Obama. They fear regulation that never happened. Fareed Zakaria noted that "…Fortune 500 companies are sitting on a cash hoard of 1.8 trillion." Do they fear a recovery even more? By pouting, if will they be missing out. They may be holding themselves back.
Elizabeth Kolbert wrote "The Scales Fall" about overfishing. There are no longer plenty of fish in the sea.
Copyright 2010 DJ Cline Al rights reserved.
Blame GamesElizabeth KolbertFareed ZakariaFortune 500 companies are sitting on a cash hoard of 1.8 trillionJames SurowieckiObamaoverfishingThe New YorkerThe Scales FallWall Street