text:
\section*{Acknowledgments}
We thank Joost Bastings for sharing his multitasking Nematus model, Wilker Aziz for discussions about formulating the problem, Stella Frank for finding and explaining the qualitative examples to us, and Afra Alishahi, Grzegorz Chrupała,
and Philip Schulz for feedback on earlier drafts of the paper. DE acknowledges the support of NWO
Vici grant nr. 277-89-002 awarded to K. Sima'an, an Amazon Academic Research Award, and a hardware grant from the NVIDIA Corporation.
\section{Conclusion}

We decompose multimodal translation into two sub-problems: learning to translate and learning visually grounded representations. In a multitask learning framework, we show how these sub-problems can be addressed by sharing an encoder between a translation model and an image prediction model. Our approach achieves state-of-the-art results on the Multi30K dataset without using images for translation. We show that training on separate parallel text and described image datasets does not hurt performance, encouraging future research on multitasking with diverse sources of data.
Furthermore, we still find improvements from image prediction when we improve our text-only baseline with the out-of-domain parallel text. Future work includes adapting our decomposition to other NLP tasks that may benefit from out-of-domain resources, such as semantic role labelling, dependency parsing, and question-answering; exploring methods for inputting the (predicted) image into the translation model; experimenting with different image prediction architectures; multitasking different translation languages into a single shared encoder; and multitasking in both the encoder and decoder(s).
\section{Data}\label{sec:data}

\begin{table}
\renewcommand{\arraystretch}{1.3}
\centering
\begin{tabular}{ccccc}
\toprule
& Size & Tokens & Types & Images\\
\midrule
\multicolumn{5}{l}{Multi30K: parallel text with images}\\
En & \multirow{2}{*}{31K} & 377K & 10K & \multirow{2}{*}{31K}\\
De & & 368K & 16K & \\
\midrule
\multicolumn{5}{l}{MS COCO: external described images}\\
En    & 414K & 4.3M & 24K & 83K \\
\midrule
\multicolumn{5}{l}{News Commentary: external parallel text}\\
En   & \multirow{2}{*}{240K} & 8.31M & \multirow{2}{*}{17K} &  --\\
De   & & 8.95M & & --\\
\bottomrule
\end{tabular}
\caption{The datasets used in our experiments.}\label{tab:datasets}
\end{table}

We evaluate our model using the benchmark Multi30K dataset \cite{ElliottFrankSimaanSpecia2016}, which is the largest collection of images paired with sentences in multiple languages. This dataset contains 31,014 images paired with an English language sentence and a German language translation: 29,000 instances are reserved for training, 1,014 for development, and 1,000 for evaluation.\footnote{Multi30K also contains 155K independently collected descriptions for German and English. We do not use this data.}

The English and German sentences are preprocessed by normalising the punctuation, lowercasing and tokenizing the text using the Moses toolkit. We additionally decompound the German text  using Zmorge \cite{Sennrich2014}. This results in vocabulary sizes of 10,214 types for English and 16,022 for German.

We also use two external datasets to evaluate our model: the MS COCO dataset of English described images \cite{Chen2015}, and the English-German News Commentary parallel corpus \cite{Tiedemann2012}. When we perform experiments with the News Commentary corpus, we first calculate a 17,597 sub-word vocabulary using SentencePiece \cite{Schuster2012} over the concatentation of the Multi30K and News Commentary datasets. This gives us a shared vocabulary for the external data that reduces the number of out-of-vocabulary tokens.

Images are represented by 2048D vectors extracted from the `pool5/7x7\_s1' layer of the GoogLeNet v3 CNN \cite{Szegedy2015}.
\section{Discussion}

\subsection{Does the model learn grounded representations?}

A natural question to ask if whether the multitask model is actually learning representations that are relevant for the images. We answer this question by evaluating the Imaginet decoder in an image--sentence ranking task. Here the input is a source language sentence, from which we predict its image vector $\mathbf{\hat{v}}$. The predicted vector $\mathbf{\hat{v}}$ can be compared against the true image vectors $\mathbf{v}$ in the evaluation data using the cosine distance to produce a ranked order of the images. Our model returns a median rank of 11.0 for the true image compared to the predicted image vector.
Figure \ref{fig:discussion:examples} shows examples of the nearest neighbours of the images predicted by our multitask model. We can see that the combination of the multitask source language representations and {\sc imaginet} decoder
leads to the prediction of relevant images. This confirms that the shared encoder is indeed learning visually grounded representations.

\subsection{The effect of visual feature vectors}

\begin{figure*}
 \begin{subfigure}[b]{1\textwidth}
 \centering
\includegraphics[width=0.75\textwidth]{images/dev_example_notext.png}
\caption{Nearest neighbours for ``a native woman is working on a craft project .''}
\vspace{2ex}
\end{subfigure}
 \begin{subfigure}[b]{1\textwidth}
 \centering
\includegraphics[width=0.75\textwidth]{images/dev_example2_notext.png}
\caption{Nearest neighbours for ``there is a cafe on the street corner with an oval painting on the side of the building .''}
\end{subfigure}
\caption{We can interpret the {\sc imaginet} Decoder by visualising the predictions made by our model.}\label{fig:discussion:examples}
\end{figure*}

We now study the effect of varying the Convolutional Neural Network used to extract the visual features used in the Imaginet decoder.
It has previously been shown that the choice of visual features can affect the performance of vision and language models \cite{Jabri2016,Kiela2016}.
We compare the effect of training the {\sc imaginet} decoder to predict different types of image features, namely: 4096D features extracted from the `fc7`' layer of the VGG-19 model \cite{Simonyan2015}, 2048D features extracted from the `pool5/7x7\_s1' layer of InceptionNet V3 \cite{Szegedy2015}, and 2048D features extracted from `avg\_pool` layer of ResNet-50 \cite{He2016}.
Table \ref{tab:results:features} shows the results of this experiment.
There is a clear difference between predicting the 2048D vectors (Inception-V3 and ResNet-50) compared to the 4096D vector from VGG-19).
This difference is reflected in both the translation Meteor score and the Median rank of the images in the validation dataset. This is likely because it is easier to learn the parameters of the image prediction model that has fewer parameters (8.192 million for VGG-19 vs. 4.096 million for Inception-V3 and ResNet-50).
However, it is not clear why there is such a pronounced difference between the Inception-V3 and ResNet-50 models\footnote{We used pre-trained CNNs (\url{https://github.com/fchollet/deep-learning-models}), which claim equal ILSVRC object recognition performance for both models: 7.8\% top-5 error with a single-model and single-crop.}.
Further work is needed to understand the difference in these results.

\begin{table}
\centering
\renewcommand{\arraystretch}{1.3}
\begin{tabular}{lcc}
\toprule
& Meteor & Median Rank \\
\midrule
Inception-V3 & 56.0 $\pm$ 0.1 & 11.0 $\pm$ 0.0\\
Resnet-50 & 54.7 $\pm$ 0.4 & 11.7 $\pm$ 0.5 \\
VGG-19 & 53.6 $\pm$ 1.8 & 13.0 $\pm$ 0.0 \\
\bottomrule
\end{tabular}
\caption{The type of visual features predicted by the {\sc imaginet} Decoder has a strong impact on the Multitask model performance.}\label{tab:results:features}
\end{table}

\section{Experiments}

We evaluate our multitasking approach with in- and out-of-domain resources. We start by reporting results of models trained using only the Multi30K dataset. We also report the results of training the {\sc imaginet} decoder with the COCO dataset. Finally, we report results on incorporating the external News Commentary parallel text into our model. Throughout, we report performance of the  En$\rightarrow$De translation using Meteor \cite{denkowski:lavie:meteor-wmt:2014} and BLEU \cite{Papineni:2002:BMA:1073083.1073135} against lowercased tokenized references.

\subsection{Hyperparameters}

The encoder is a 1000D Gated Recurrent Unit bidirectional recurrent neural network \cite[GRU]{Cho2014} with 620D embeddings. We share all of the encoder parameters between the primary and auxiliary task. The translation decoder is a 1000D GRU recurrent neural network, with a 2000D context vector over the encoder states, and 620D word embeddings \cite{Sennrich2017}. The Imaginet decoder is a single-layer feed-forward network, where we learn the parameters $\mathbf{W_{vis}} \in \mathbb{R}^{2048\text{x}2000}$ to predict the true image vector with $\alpha = 0.1$ for the Imaginet objective (Equation \ref{eqn:imaginet}). The models are trained using the Adam optimiser with the default hyperparameters \cite{Kingma2015} in minibatches of 80 instances. The translation task is defined as the primary task and convergence is reached when BLEU has not increased for five epochs on the validation data. Gradients are clipped when their norm exceeds 1.0. Dropout is set to 0.2 for the embeddings and the recurrent connections in both tasks \cite{Gal2016}. Translations are decoded using beam search with 12 hypotheses.

\subsection{In-domain experiments}\label{sec:in_domain_all}

\begin{table}
\centering
\renewcommand{\arraystretch}{1.3}
\begin{tabular}{lccc}
\toprule
& Meteor & BLEU \\
\midrule
NMT & 54.0 $\pm$ 0.6 & 35.5 $\pm$ 0.8\\
\newcite{Calixto2017c} & 55.0 & 36.5 \\
\newcite{Calixto2017b} & 55.1 & 37.3 \\
Imagination & 55.8 $\pm$ 0.4 & 36.8 $\pm$ 0.8\\
\newcite{toyama2016neural} & 56.0  & 36.5 \\
\newcite{Hitschler2016} & 56.1 & 34.3 \\
Moses & 56.9 & 36.9\\
\bottomrule
\end{tabular}
\caption{En$\rightarrow$De translation results on the Multi30K dataset. Our Imagination model is competitive with the state of the art when it is trained on in-domain data. We report the mean and standard deviation of three random initialisations.}
\label{tab:results:in-domain}
\end{table}

We start by presenting the results of our multitask
model trained using only the Multi30K dataset. We compare against state-of-the-art approaches and text-only baselines. Moses is the phrase-based machine translation model \cite{Koehn2007} reported in \cite{Specia2016}. NMT is a text-only neural machine translation model.
\newcite{Calixto2017c} is a double-attention model over the source language and the image.
\newcite{Calixto2017b} is a multimodal translation model that conditions the decoder on semantic image vector extracted from the VGG-19 CNN.
\newcite{Hitschler2016} uses visual features in a target-side retrieval model for translation. \newcite{toyama2016neural} is most comparable to our approach: it is a multimodal variational NMT model that infers latent variables to represent the source language semantics from the image and linguistic data.

Table \ref{tab:results:in-domain} shows the results of this experiment.
We can see that the combination of the attention-based translation model and the image
prediction model is a 1.8 Meteor point improvement over the NMT
baseline, but it is 1.1 Meteor points worse than the strong Moses baseline. Our approach is competitive with previous approaches that use visual features as inputs to the decoder and the target-side reranking model. It also competitive with \newcite{toyama2016neural}, which also only uses images for training.  These results confirm that our multitasking approach uses the image prediction task to improve the encoder of the translation model.

\begin{table}[t]
\centering
\renewcommand{\arraystretch}{1.3}
\begin{tabular}{lcc}
\toprule
& Meteor & BLEU \\
\midrule
Imagination & 55.8 $\pm$ 0.4 & 36.8 $\pm$ 0.8 \\
Imagination (COCO) & 55.6 $\pm$ 0.5 & 36.4 $\pm$ 1.2 \\
\bottomrule
\end{tabular}
\caption{Translation results when using out-of-domain described images. Our approach is still effective when the image prediction model is trained over the COCO dataset.}\label{tab:results:ood-coco}
\end{table}

\subsection{External described image data}\label{sec:experiments:ood-images}

\begin{table}
\centering
\renewcommand{\arraystretch}{1.3}
\begin{tabular}{@{}lcc}
\cmidrule[.08em](r{1ex}){1-3}
 & Meteor & BLEU \\
\cmidrule[.05em](r{1ex}){1-3}
NMT & 52.8 $\pm$ 0.6 & 33.4 $\pm$ 0.6\\
+ NC & 56.7 $\pm$ 0.3 & 37.2 $\pm$ 0.7 \\
+ Imagination & 56.7 $\pm$ 0.1 & 37.4 $\pm$ 0.3 \\
+ Imagination (COCO) & 57.1 $\pm$ 0.2 & 37.8 $\pm$ 0.7 \\
\newcite{Calixto2017c} & 56.8 & 39.0 \\
\cmidrule[.08em](r{1ex}){1-3}
\end{tabular}
	\caption{Translation results with out-of-domain parallel text and described images. We find further improvements when we multitask with the News Commentary (NC) and COCO datasets.}\label{tab:results:ood-both}
\end{table}

Recall from Section \ref{sec:problem} that we are interested in scenarios where $x$, $y$, and $v$ are drawn from different sources. We now experiment with separating the translation data from the described image data using $\mathcal{D}_{image}$: MS COCO dataset of 83K described images\footnote{Due to differences in the vocabularies of the repsective datasets, we do not train on examples where more than 10\% of the tokens are out-of-vocabulary in the Multi30K dataset.} and $\mathcal{D}_{text}$: Multi30K parallel text.

Table \ref{tab:results:ood-coco} shows the results of this experiment.
We find that there is no significant difference between training the {\sc imaginet} decoder on in-domain (Multi30K) or out-of-domain data (COCO). This result confirms that we can separate the parallel text from the described images.

\subsection{External parallel text data}

\begin{table*}[t]
\centering
	\begin{tabular}{ccccccc}
    \toprule
    & \multicolumn{2}{c}{Parallel text} & \multicolumn{2}{c}{Described images} \\
    & Multi30K & News Commentary & Multi30K & COCO & Meteor & BLEU \\
    \midrule
    \parbox[t]{2mm}{\multirow{3}{*}{\rotatebox[origin=c]{90}{Zmorge}}} & \checkmark   &   &   &       & 56.2   & 37.8 \\[2.5ex]
    & \checkmark   &   &  \checkmark  &           & 57.6   & 39.0 \\
    \midrule
    \parbox[t]{2mm}{\multirow{4}{*}{\rotatebox[origin=c]{90}{Sub-word}}} & \checkmark   &   &   &       & 54.4   & 35.0 \\
	& \checkmark   &   \checkmark    &   &               & 58.6   & 39.4 \\
    & \checkmark   & \checkmark  & \checkmark   &   & 59.0   & 39.5 \\
    & \checkmark   &  \checkmark &   &   \checkmark      & \textbf{59.3}   & \textbf{40.2} \\
\bottomrule
\end{tabular}
    \caption{Ensemble decoding results. Zmorge denotes models trained with decompounded German words; Sub-word denotes joint SentencePiece word splitting (see Section \ref{sec:data} for more details).}\label{tab:results:ensemble}
\end{table*}

We now experiment with training our model on a combination of the Multi30K and the News Commentary English-German data. In these experiments, we concatenate the Multi30K and News Commentary datasets into a single $\mathcal{D}_{text}$ training dataset, similar to \newcite{Freitag2016}. We compare our model against \newcite{Calixto2017c}, who pre-train their model on the WMT'15 English-German parallel text and back-translate \cite{Sennrich2016b} additional sentences from the bilingual independent descriptions in the Multi30K dataset (Footnote 2).

Table \ref{tab:results:ood-both} presents the results. The text-only NMT model using sub-words is 1.2 Meteor points lower than decompounding the German text. Nevertheless, the model trained over a concatentation of the parallel texts is a 2.7 Meteor point improvement over this baseline (+ NC) and matches the performance of our Multitasking model that uses only in-domain data (Section \ref{sec:in_domain_all}). We do not see an additive improvement for the multitasking model with the concatenated parallel text and the in-domain data (+ Imagination) using a training objective interpolation of $w = 0.89$ (the ratio of the training dataset sizes). This may be because we are essentially learning a translation model and the updates from the {\sc imaginet} decoder are forgotten. Therefore, we experiment with multitasking the concatenated parallel text and the COCO dataset ($w=0.5$). We find that balancing the datasets improves over the concatenated text model by 0.4 Meteor (+ Imagination (COCO)). Our multitasking approach improves upon Calixto et al. by 0.3 Meteor points. Our model can be trained in 48 hours using 240K parallel sentences and 414K described images from out-of-domain datasets. Furthermore, recall that our model does not use images as an input for translating unseen data, which results in 6.2\% fewer parameters compared to using the 2048D Inception-V3 visual features to initialise the hidden state of the decoder.

\input{examples}

\subsection{Ensemble results}

Table \ref{tab:results:ensemble} presents the results of ensembling different randomly initialised models. We achieve a start-of-the-art result of 57.6 Meteor for a model trained on only in-domain data. The improvements are more pronounced for the models trained using sub-words and out-of-domain data. An ensemble of baselines trained on sub-words is initially worse than an ensemble trained on Zmorge decompounded words. However, we always see an improvement from ensembling models trained on in- and out-of-domain data. Our best ensemble is trained on Multi30K parallel text, the News Commentary parallel text, and the COCO descriptions to set a new state-of-the-art result of 59.3 Meteor.

\subsection{Qualitative Examples}

Table \ref{tab:results:examples} shows examples of where the multitasking model improves or worsens translation performance compared to the baseline model\footnote{We used MT-ComparEval \cite{Klejch2015}}. The first example shows that the baseline model makes a significant error in translating the pose of the children, translating ``on their stomachs'' as ``on their faces''). The middle example demonstrates that the baseline model translates the dog as walking (``läuft'') and then makes grammatical and sense errors after the clause marker. Both models neglect to translate the word ``dangling'', which is a low-frequency word in the training data. There are instances where the baseline produces better translations than the multitask model: In the bottom example, our model translates a bird flying through the water (``durch'') instead of ``over'' the water.
\section{Introduction}

Multimodal machine translation is the task of translating sentences in context, such as images paired with a parallel text \cite{Specia2016}. This is an emerging task in the area of multilingual multimodal natural language processing. Progress on this task may prove useful for translating the captions of the images illustrating online news articles, and for multilingual closed captioning in international television and cinema.

Initial efforts have not convincingly demonstrated that visual context can improve translation quality.
In the results of the First Multimodal Translation Shared Task, only three systems outperformed an off-the-shelf text-only phrase-based machine translation model, and the best performing system was equally effective with or without the visual features \cite{Specia2016}. There remains an open question about how translation models should take advantage of visual context.

\input{model_diagram}

We present a multitask learning model that decomposes multimodal translation into learning a translation model and learning visually grounded representations. This decomposition means that our model can be trained over external datasets of parallel text or described images, making it possible to take advantage of existing resources. Figure \ref{fig:model} presents an overview of our model, Imagination, in which source language representations are shared between tasks
through the Shared Encoder. The translation decoder is an
attention-based neural machine translation model \cite{Bahdanau2015}, and the
image prediction decoder is trained to predict a global feature vector of an
image that is associated with a sentence \cite[{\sc imaginet}]{Chrupala2015}. This decomposition encourages grounded learning in the shared encoder because the {\sc imaginet} decoder is trained to imagine the image associated with a sentence.
It has been shown that grounded representations are qualitatively different from their text-only counterparts \cite{Kadar2016} and correlate better with human similarity judgements \cite{Chrupala2015}.
We assess the success of the grounded learning
 by evaluating the image prediction model on an image--sentence ranking task to determine if the shared representations are useful for image retrieval \cite{Hodosh2013}. In contrast with most previous work, our model does not take images as input at translation time, rather it learns grounded representations in the shared encoder.

We evaluate Imagination on the Multi30K dataset \cite{ElliottFrankSimaanSpecia2016} using a combination of in-domain and out-of-domain data. In the in-domain experiments, we find that multitasking translation with image prediction is competitive with the state of the art. Our model achieves 55.8 Meteor as a single model trained on multimodal in-domain data, and 57.6 Meteor as an ensemble.

In the experiments with out-of-domain resources, we find that the improvement in translation quality holds when training the {\sc imaginet} decoder on the MS COCO dataset of described images \cite{Chen2015}.
Furthermore, if we significantly improve our text-only baseline using out-of-domain parallel text from the News Commentary corpus \cite{Tiedemann2012}, we still find improvements in translation quality from the auxiliary image prediction task. Finally, we report a state-of-the-art result of 59.3 Meteor on the Multi30K corpus when ensembling models trained on in- and out-of-domain resources.\footnote{The implementation will be available upon publication.}

The main contributions of this paper are:
\begin{itemize}
\item We show how to apply multitask learning to multimodal translation. This makes it possible to train models for this task using external resources alongside the expensive triple-aligned source-target-image data.

\item We decompose multimodal translation into two tasks: learning to translate and learning grounded representations. We show that each task can be trained on large-scale external resources, e.g. parallel news text or images described in a single language.

\item We present a model that achieves state of the art results without using images as an input. Instead, our model learns visually grounded source language representations using an auxiliary image prediction objective. Our model does not need any additional parameters to translate unseen sentences.

\end{itemize}

\section{Imagination Model}

\subsection{Shared Encoder}\label{sec:model:encoder}

The encoder network of our model learns a representation of a
sequence of $N$ tokens $x_{1 \ldots n}$ in the source language with a
bidirectional recurrent neural network \cite{Schuster1997}. This representation is shared between the different tasks.
Each token is represented by a one-hot vector $\mathbf{x_i}$, which is mapped into an embedding $\mathbf{e_i}$ through a learned matrix
$\mathbf{E}$:
\begin{align}
	\mathbf{e_i} = \mathbf{x_i} \cdot \mathbf{E}
\end{align}
A sentence is processed by a pair of recurrent neural networks, where one captures the sequence left-to-right (forward), and the other captures the sequence right-to-left (backward). The initial state of the encoder $\mathbf{h_{-1}}$ is a learned parameter:
\begin{align}
	\overrightarrow{\mathbf{h_i}} = \overrightarrow{\text{RNN}}(\overrightarrow{\mathbf{h_{i-1}}}, \mathbf{e_i}) \\
    \overleftarrow{\mathbf{h_i}} = \overleftarrow{\text{RNN}}(\overleftarrow{\mathbf{h_{i-1}}}, \mathbf{e_i})
\end{align}
Each token in the source language input sequence is represented by a concatenation of the forward and backward hidden state vectors:
\begin{align}
	\mathbf{h_i} = [\overrightarrow{\mathbf{h_i}}; \overleftarrow{\mathbf{h_i}}]
\end{align}
\subsection{Neural Machine Translation Decoder}

The translation model decoder is an attention-based recurrent neural network \cite{Bahdanau2015}. Tokens in the decoder are
represented by a one-hot vector $\mathbf{y_j}$, which is mapped into an embedding  $\mathbf{e_j}$ through a learned matrix $\mathbf{E_y}$:
\begin{align}
	\mathbf{e_j} = \mathbf{y_j} \cdot \mathbf{E_y}
\end{align}
The inputs to the decoder are the previously predicted token $\mathbf{y_{j-1}}$, the previous decoder state $\mathbf{d_{j-1}}$,
and a timestep-dependent context vector $\mathbf{c_j}$ calculated over the encoder hidden states:
\begin{align}
    \mathbf{d_j} = \text{RNN}(\mathbf{d_{j-1}}, \mathbf{y_{j-1}},
    \mathbf{e_j})
\end{align}
The initial state of the decoder $\mathbf{d_{\text{-1}}}$ is a nonlinear transform of the mean of the encoder states, where $\mathbf{W}_{init}$ is a learned parameter:
\begin{align}
	\mathbf{d_{\text{-1}}} = \text{tanh}(\mathbf{W}_{init} \cdot \frac{1}{N} \sum_{i}^{N}
    \mathbf{h_i})\label{eqn:decoder_init}
\end{align}
The context vector $c_j$ is a weighted sum over the encoder hidden states, where $N$ denotes the length of the source sentence:
\begin{align}
	\mathbf{c_j} = \sum_{i=1}^{N} \alpha_{ji}\mathbf{h_i}
\end{align}
The $\alpha_{ji}$ values are the proportion of which the encoder hidden state vectors $\mathbf{ h_{1 \ldots n}}$ contribute to the decoder hidden state
when producing the $j$th token in the translation. They are computed by a feed-forward neural network, where $\mathbf{v_a}$,
$\mathbf{W_a}$ and $\mathbf{U_a}$ are learned parameters:
\begin{align}
	\alpha_{ji} &= \frac{\text{exp}(e_{ji})}{\sum_{l=1}^{N} \text{exp}(e_{li})}\\[1ex]
	e_{ji} &= \mathbf{v_a} \cdot \text{tanh}( \mathbf{W_{a}} \cdot \mathbf{d_{j-1}}  + \mathbf{U_{a}}  \cdot \mathbf{h_i} )
\end{align}
From the hidden state $\mathbf{d_{j}}$ the network predicts the conditional distribution of the next token $y_{j}$, given a target
language embedding $\mathbf{e_{j-1}}$ of the previous token, the current hidden state $\mathbf{d_j}$, and the calculated context vector
$\mathbf{c_j}$ . Note that at training time, $y_{j-1}$ is the
true observed token; whereas for unseen data we use the inferred token $\hat{y}_{j-1}$ sampled from the output of the softmax:
\begin{align}
p(y_{j}|y_{<j}, c) = \text{softmax}(\text{tanh}( \mathbf{e_{j-1}}  + \mathbf{d_j}  + \mathbf{c_j} ))
\end{align}

The translation model is trained to minimise the negative log likelihood of predicting the target language output:
\begin{align}
  J_{NLL}(\theta, \phi^t) = - \sum_{j} \text{log p}(y_j|y_{<j}, x)
\end{align}

\subsection{Imaginet Decoder}

The image prediction decoder is trained to predict the visual feature vector of the image associated with a sentence \cite{Chrupala2015}.
It encourages the shared encoder to learn grounded representations for the source language.

We represent a sentence by first producing a sequence of the concatenated hidden state vectors in the Encoder (Section \ref{sec:model:encoder}). Then, the same way we initialise the hidden state of the decoder (Eqn. \ref{eqn:decoder_init}), we represent a source language sentence as the mean of the encoder states. This is the input to a feed-forward neural network that predicts the visual feature vector $\mathbf{\hat{v}}$ associated with a sentence with parameters $\mathbf{W_{vis}}$:
\begin{align}
	\mathbf{\hat{v}}  = \text{tanh}( \mathbf{W_{vis}} \cdot \frac{1}{N} \sum_{i}^{N}
\mathbf{h_i})
\end{align}

This decoder is trained to predict the true image vector $\mathbf{v}$ with a margin-based objective, parameterised by the minimum margin
$\alpha$, and the cosine distance $d(\cdot, \cdot)$. The contrastive examples $\mathbf{v'}$ are drawn from the other instances in a minibatch:
\begin{align}
	\begin{split}
	J_{MAR}(\theta, \phi^t) = \sum_{ \mathbf{v'} \neq \mathbf{v}}
    \text{max}\{0, \alpha & - d( \mathbf{\hat{v}}, \mathbf{v}) \\[-2ex]
    & + d( \mathbf{\hat{v}} , \mathbf{v'})\}\label{eqn:imaginet}
    \end{split}
\end{align}
\section{Problem Formulation}\label{sec:problem}

Multimodal translation is the task of producing target language translation $y$, given the source language sentence $x$ and additional context, such as an image $v$ \cite{Specia2016}. Let $x$ be a source language sentence consisting $N$ tokens: x$_1$, x$_2$, $\ldots$, x$_n$ and let $y$ be a target language sentence consisting $M$ tokens: y$_1$, y$_2$, $\ldots$, y$_m$. The training data consists of tuples $\mathcal{D} \in (x, y, v)$, where $x$ is a description of image $v$, and $y$ is a translation of $x$.

Multimodal translation has previously been framed as minimising the negative log-likelihood of a translation model that is additionally conditioned on the image, i.e. $J(\theta) = - \sum_{j} \text{log p}(y_j|y_{<j}, x, v)$. Here, we decompose the problem into learning to translate and learning visually grounded representations. The decomposition is based on sharing parameters $\theta$ between these two tasks, and learning task-specific parameters $\phi$. We learn the parameters in a multitask model with shared parameters in the source language encoder. The translation model has task-specific parameters $\phi^t$ in the attention-based decoder, which are optimized through the translation loss $J_{T}(\theta, \phi^t)$.
Grounded representations are learned through an image prediction model with task-specific parameters $\phi^g$ in the image-prediction
decoder by minimizing $J_{G}(\theta, \phi^g)$. The joint objective is given by mixing the translation and image prediction tasks with the parameter $w$:
\begin{align}
	J(\theta, \phi) = w J_{T}(\theta, \phi^{t}) +  (1 - w) J_G(\theta, \phi^{g})
\end{align}
Our decomposition of the problem makes it straightforward to optimise this objective without paired tuples, e.g. where we have an external dataset of described images $\mathcal{D}_{image} \in (x, v)$ or an external parallel corpus $\mathcal{D}_{text} \in (x, y)$.

We train our multitask model following the approach of \newcite{Luong2016}. We define a primary task and an auxiliary task, and a set of parameters $\theta$ to be shared between the tasks. A minibatch of updates is performed for the primary task with probability $w$, and for the auxiliary task with $1-w$. The primary task is trained until convergence and weight $w$
determines the frequency of parameter updates for the auxiliary task.
\section{Related work}

Initial work on multimodal translation has focused on approaches that
use either semantic or spatially-preserving image features as inputs to a translation model. Semantic image features are typically extracted from the final pooling
layer of a pre-trained object recognition CNN, e.g.
`pool5/7x7\_s1' in GoogLeNet \cite{Szegedy2015}. This type of feature vector has been used
conditioning input to the encoder \cite{ElliottFrankHasler2015,Huang2016}, in
the decoder \cite{Libovicky2016}, or as additional features in a phrase-based
translation model \cite{Shah2016,Hitschler2016}.
Spatially-preserving image features are extracted from deeper inside a CNN, where the position of a feature in the tensor is related to its position in the image. These features have
been used in ``double-attention models'', which calculate independent context vectors of the source language hidden states and a
convolutional image feature map
\cite{Calixto2016,Caglayan2016b,Calixto2017c}.
Similar to most of these approaches, we use an attention-based translation model, but our multitask model does not use images for translation.

More related to our work are the recent papers by \newcite{toyama2016neural}, \newcite{Saha2016}, and \newcite{Nakayama2016}. \newcite{toyama2016neural} extend the Variational Neural Machine Translation model \cite{zhang2016variational} by inferring latent variables to \emph{explicitly} model the semantics of source sentences from both image and linguistic information. Their model does not condition  on images for translation. They report improvements on the Multi30K data set when using multimodal information, however, their model adds additional parameters in the form of ``neural inferrer'' modules. In our multitask model, the grounded semantics are represented \emph{implicitly} in the hidden states of the shared encoder. Furthermore, they assume Source-Target-Image aligned training data; whereas our approach achieves equally good results if we train on separate Source-Image and Source-Target datasets.

\newcite{Saha2016} study cross-lingual image description where the task is to generate a sentence in language $L_{1}$ given the image, given only Image-$L_{2}$ and $L_{1}$-$L_{2}$ parallel corpora.
They propose a Correlational Encoder-Decoder to model the Image-$L_{2}$ and $L_{1}$-$L_{2}$ data. Their model learns
correlated representations for paired Image-$L_{2}$ data and decodes
$L_{1}$ from this joint representation. Similarly to work,
the encoder is trained by minimizing two loss functions: the Image-$L_{2}$ correlation loss, and the $L_{1}$ decoding cross-entropy loss.

\newcite{Nakayama2016} consider a zero-resource problem
where the task is to translate from $L_{1}$ to $L_{2}$ but only
Image-$L_{1}$ and Image-$L_{2}$ corpora are available.
Their model embeds the image, $L_{1}$, and $L_{2}$ in a joint multimodal space learned through minimizing a multi-task ranking loss
between both pairs of examples. The main difference between this approach and our model is that we focus on
\emph{enriching} source language representations with visual
information, rather than addressing the zero-resource issue.

Multitask Learning improves the
generalisability of a model by requiring it to be useful for more than one task \cite{Caruana1997}.
This approach has recently been used to improve
the performance of sentence compression using eye gaze as an
auxiliary task \cite{Klerke2016}, and to improve shallow parsing
accuracy through the auxiliary task of predicting keystrokes in an
out-of-domain corpus \cite{Plank2016}. These works hypothesise a relationship between
specific biometric measurements and specific NLP tasks motivated by cognitive-linguistic theories.
More recently, \newcite{Bingel2017} analysed the beneficial relationships between primary and auxiliary sequential prediction tasks.
In the translation literature, multitask learning has been used to learn a one-to-many
languages translation model \cite{Dong2015}, a multi-lingual translation model with a single attention mechanism shared across multiple languages \cite{Firat2016}, and in multitask sequence-to-sequence learning without an attention-based decoder \cite{Luong2016}. In our multitask framework we explore the benefits of grounded learning in the specific case of multimodal translation. Our model combines sequence prediction with continuous (image) vector prediction, compared to previous work on multitask learning for sequence prediction tasks.

Visual representation prediction has been tackled as an unsupervised and supervised problem. In an unsupervised setting, \newcite{Srivastava2015} propose an LSTM Autoencoder to predict video frames as a reconstruction task or as a future prediction task. In a supervised setting, \newcite{Lin2015} use a conditional random field to imagine the composition of a clip-art scene for visual paraphrasing and fill-in-the-blank tasks. \newcite{Chrupala2015} predict the image vector associated with a sentence using an L2 loss; they found this approach improves multi-modal word similarity compared to text-only baselines. \newcite{Gelderloos2016} predict the visual feature vector associated with a sequence of phonemes using a max-margin loss, similar to our image prediction objective. \newcite{Collell2017} learn to predict the visual feature vector associated with a word for word similarity and relatedness tasks; their best approach combines a word representation with the predicted image representation, which allows zero-shot learning for unseen words. \newcite{Pasunuru2017} propose a multi-task learning model for video description that combines unsupervised video frame reconstruction, lexical entailment, and video description in a single framework. They find improvements from using out-of-domain resources for the entailment and video frame prediction tasks, similar to the improvements we find from using out-of-domain parallel text and described images.

redpajama_set_name: RedPajamaArXiv


text:
Dozens Feared Dead in Nigeria Church Attack
Dozens of worshippers were feared dead in an attack on a Catholic church in Nigeria on Sunday, state lawmakers said.
Legislator Ogunmolasuyi Oluwole said the attackers targeted the St. Francis Catholic Church in Ondo state just as the worshippers gathered on Pentecost Sunday, with gunmen opening fire and detonating explosives. He said that numerous children were among the dead.
The Vatican said that Pope Francis "prays for the victims and for the country, painfully attacked at a time of celebration, and he entrusts everyone to the Lord, that God might send His Spirit to console them."
"It is a black Sunday in Owo. Our hearts are heavy," Ondo Governor Rotimi Akeredolu tweeted Sunday. "Our peace and tranquility have been attacked by the enemies of the people."
The governor was away in Abuja, taking part in his party's primary elections ahead of next year's polls but suspended his activities and returned to Ondo hours later with state security officials.
Akeredolu said that the attackers would be hunted and prosecuted. He also urged residents to remain calm and not resort to retaliation. There has been no claim of responsibility for the attack.
There was no immediate death toll, but Adelegbe Timileyin, who represents Nigeria's lower legislative chamber, said at least 50 people had been killed, while others said the figure would turn out to be higher. Video footage of the scene showed worshippers lying in pools of blood as people around them wailed.
It was not immediately clear who was behind the attack. While much of Nigeria has struggled with security issues, Ondo is widely known as one of Nigeria's most peaceful states. The state, though, has been caught up in a rising violent conflict between farmers and herders.
"In the history of Owo, we have never experienced such an ugly incident," said lawmaker Oluwole. "This is too much."
The Christian Association of Nigeria condemned the attack, with spokesman Bayo Oladeji saying, "What happened in Owo today is an unprovoked attack on innocent people worshipping God and to [the] Christian Association of Nigeria, it is condemnable; it is unacceptable. We're tired of people going to church and being killed."
Church officials said many of the wounded were fighting for their lives in hospitals. Some residents launched a blood donation drive to help them.
Nigeria is currently facing a wave of violence by armed gangs. A week ago, the prelate of the Methodist Church of Nigeria, Samuel Kanu Uche, was kidnapped on his way to the airport in southeastern Abia state.
He was released two days later after the church raised about $240,000 and paid the kidnappers.
Source: Voice of America
Posted in Medical
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General Assembly Confirms Inger Andersen of Denmark Executive Director, United Nations Environment Programme, Following Recommendation by Secretary-General
Following the recommendation of the Secretary-General, after consultation with Member States, the General Assembly confirmed Inger Andersen of Denmark as Executive Director of the United Nations Environment Programme (UNEP) for a further four-year term…

redpajama_set_name: RedPajamaCommonCrawl


text:
Former DePaul student's happy ending after $20k GI Bill debacle
Her father served in the military with the promise of free tuition. Until DePaul hit them with a hefty bill.
Mackenzie Murtaugh, News Editor|January 12, 2020
Courtesy of Paige Dotson
Former DePaul student Paige Dotson frequently visits Chicago.
In 2008, the government passed the Post-9/11 GI Bill which offers educational benefits to service members who served 90 or more days on active duty since September 10, 2001. The tiered system benefits both those who served the country and their dependents, with much of their college tuition covered by the government.
Paige Dotson, a former DePaul student, knows the system all too well. Dotson's father, Russell, served 22 years with the U.S. Navy, doing both active duty and reserve. The Chicago Sun-Times reports that he spent much time overseas — six stints, each time lasting for over a year. He even served an extra four years just to qualify for the bill, knowing his two kids back at home would need it. He sacrificed his role in his kids' lives and his marriage for the bill and peace of mind for his kids after they graduate. Ideally, they could comfortably attend college without their debt looming in the back of their minds. The Navy told Dotson's father with confidence that each of his kids would receive two years of college free of charge, allowing them to be the first in their family with a college degree. Unfortunately, a mathematical error forced him and his daughter to battle for the funding he risked his life for.
Dotson, who is originally from Michigan, began attending DePaul in the fall of 2016 as a pre-med student. She lived on campus and developed friendships she still cherishes to this day. At the beginning of her spring term, her college life took a huge turn when the government failed to make the proper payments to the university. Dotson was then notified that she owed the university about $20,000 in tuition, plus what was already paid for by the government because her father did not actually qualify for the payment program.
The Defense Department, which calculated his service time to ensure he qualified, had used the date of his benefit-transfer application and not his re-enlistment date, leaving him 89 days short of service. To make matters worse, he would have only had to serve six weekend days of service because he was a reservist serving one weekend a month. He even had unused "authorized absence" days that could have covered the six days missing. But because the Navy officials would not change his retirement date, this couldn't happen.
"It's bullshit," Dotson said when she was informed of her debt. "I thought to myself: I'm not going to pay that."
She immediately went to the university's Veteran Affairs office for advice, but without much success. The office had not dealt with an issue like this before. Because the Navy specifically said that her father did not serve long enough to receive the funding, not much could be done by the office. Dotson says that DePaul and their services did help, but the issue needed to be first addressed and then fixed by the government, not the university.
Dotson began working three different part-time jobs on top of her education in order to keep herself afloat during this time. Eventually, she had to withdraw from classes at DePaul because the tuition rate was too high. After about a year off from school, she transferred to the University of Michigan where she could receive in-state tuition.
Without many options and back in her home state, Dotson wanted to get her story out there so other families dealing with her issue knew they weren't alone. She reached out to her former DePaul journalism professor Sandra Guy for advice. Guy, who worked for the Sun-Times for 20 years, reached out to journalist Stephanie Zimmermann, who typically reports on consumer issues which includes debt, making her a perfect fit to tackle the Dotson story.
"[Dotson] was an excellent student, astute, mature, asked probing questions," Guy said. "I would hope that any of my students would feel comfortable reaching out to me with problems like this."
Dotson made the point that without DePaul's journalism classes and Guy as an instructor, she doubts her story would have gotten the attention of Senator Dick Durbin and other political figures around the Midwest to send a letter to the Navy that called out their "bureaucratic failures" that caused the problem seen by so many military families.
Chicago still has an important place in Dotson's heart. She frequently visits the city and campus to see the friends she made in her short time living here.
"I really love DePaul and miss it so much," Dotson said. "I would still be there if this didn't happen."
Dotson is on track to graduate from Michigan in the winter of 2021. She would have been graduating this year if this situation hadn't occurred , but she doesn't feel defeated. It had to happen, she said. Without Sandra Guy and the resources she contacted at DePaul, she might be $20,000 in debt and not in school right now.
One Response to "Former DePaul student's happy ending after $20k GI Bill debacle"
David Yarnall on January 15th, 2020 11:31 am
am I supposed to feel pity here. Trying being a parent of a student with over 125k in debt. We'll be chasing this debt for the next 30 years
DePaul adds new Board of Trustees members
DePaul senior, elected official Bushra Amiwala talks burnout, image and pushing her limits
Blue Demon Challenge donations double initial goal of $1 million
DePaul event encourages attendees to rethink perceptions of drug use
Programming error registers hundreds of non-citizens
Illinois to continue voter registration program after error
Chicago's low-income communities receiving a $20M investment
1-year-old Chicago toddler shot in parents' gun struggle
Martin Sandoval charged with red-light camera bribery
DePaul mental health services don't impress
DePaul Faculty Council discusses diversity, renames art school
Declining enrollment will factor into DePaul's 2020-21 budget
In Brief: DePaul President's Report highlights university successes, goals
'Know what you owe': Student loan advice from a professional fitness adviser


Tucson, Ariz. - An illegal immigrant working for a drug-trafficking organization was apprehended Wednesday in the West Desert. While being interviewed by Ajo Station Border Patrol agents, the subject provided information leading to yesterday's arrest of fellow drug smugglers, as well as the location of a stash-house.
Border Patrol agents and U.S. Immigration and Customs Enforcement's (ICE) Homeland Security Investigations (HSI) conducted surveillance of the designated location. When they observed a vehicle leave the house, agents followed the vehicle. Eventually, the driver of the vehicle pulled into a gas station and walked inside. Agents walked by the vehicle and saw bundles of marijuana in plain view. Agents apprehended the driver and seized the vehicle containing about 330 pounds of marijuana.
Based on intelligence gathered from the driver of the vehicle, agents executed search warrants for the house - still under surveillance. HSI agents and detectives from the Pinal County Sheriff's Office apprehended one subject at the residence and seized 20 additional bundles of marijuana weighing 440 pounds. They also retrieved a shotgun, a revolver, roughly 200 rounds of ammunition, scales, ledgers, U.S. currency and cellular phones.
ICE HSI took custody of the two subjects, now being held on various federal charges.
As Arizona law enforcement efforts continue to disrupt transnational criminal activities, Customs and Border Protection and other federal, state, local and tribal agencies are committed to keeping communities safe. Partnerships between law enforcement agencies serve as a formidable force against smugglers and other criminals.
Customs and Border Protection welcomes assistance from the community. Report suspicious activity by calling the Border Patrol toll free 1 (877) 872-7435. All calls will be answered and remain anonymous.


text:
Theatre Review: Green Day's American Idiot provides raucous, wild fun
Lisa Much
Green Day's punk opera, American Idiot landed at the Palace Theater last night. The raucous, wild musical provides a roller coaster of a journey that may feel more like a rock concert than "musical theater." However, American Idiot serves as a gem of theatrical design that must be seen for the experience, despite the fact that at least half of Tuesday night's audience felt highly uncomfortable the entire time.
Green Day began as part of the punk scene in the late eighties in the Bay Area and reached break-out success with their 1994 album Dookie. They found international acclaim throughout the nineties, with many punk fans accusing them of "selling out" particularly with the 1997 release of "Good Riddance (Time of Your Life)." After a decline in popularity in the late nineties, the band took a sabbatical to write and record their self-described best work. Finding inspiration from The Who's Tommy, Green Day released American Idiot, a politically-charged rock opera album, in September 2004. In 2010, American Idiot appeared on stage, first in the Bay Area then New York.
The show follows the story of three young men who grew up in generic Suburbia: Johnny (the Jesus of Suburbia), Will, and Tunny. Fed up with life in their town, the three plan to escape to the city, but right before leaving Will finds out his girlfriend, Heather, is pregnant. He stays home with her. Despite this, Johnny and Tunny hop a bus to join the revolution and excitement of city life. Unable to cope with the adjustment, Tunny joins the military and is deployed. Alone and frustrated, Johnny searches for friends, girls, and ultimately purpose.
Rather than make a standard announcement about silencing cell phones, the show kicks off with a news announcement about North Korea. As the curtain rises, we see the cast staring towards at least thirty television screens flashing news, trash TV, and pop culture before the cacophony erupts into "American Idiot" complete with powerful dancing and pulsing strobes. The energy of the young cast flows off the stage. Alex Nee captures the angst and vulnerability of Johnny with his voice that possesses an uncanny resemblance to Green Day frontman Billie Joe Armstrong. Casey O'Farrell's (Will) sweet voice provides a welcome change of pace from the rock-overload during "Give Me Novacaine." Alyssa Di Palma stands out as the famed Whatsername balancing the dream of the girl with the reality of the woman.
American Idiot won two Tonys: best scenic design and best lighting. Thankfully, the touring version maintains these two talented designers as well as the original costume and projection designer. All merge into a beautiful blend of rock concert, punk-centric chaos. Uniquely enough, the show uses a substantial amount of live video feed and flying actors. Innovative and refreshing use of scaffolding and other miscellaneous parts of the set aid in the dare-devil nature of the show, particularly in "Holiday."
American Idiot, the album, came out at the right point in my life. The political pointedness and permeating nihilism conjures up nostalgia of those "F– everything" days. This brings up another point: please see this show if possible, but prepare for it. American Idiot steers away from traditional CAPA offerings. The show contains copious cussing, sex, near nudity, intravenous drug use, other drug use, anti-American themes, loud music, and bright lights/strobes. It is a Green Day album thrown on stage—not for every "theater-goer," but certainly worth a watch purely for the spectacle.
Photos by John Daughtry.
American Idiot is in town through March 24 at the Palace Theatre, 34 W. Broad Street and plays Wed.-Sat. at 8 pm; Sat. at 2 pm; Sun. at 1 pm & 6:30 pm. Ticket prices range. More information can be found online at www.capa.com.
Lisa Much is a recent transplant to Columbus as of June. She comes from Chicago having freelanced as a stage manager and prop designer. She holds a BA in Sustainable Theater from Green Mountain College in Vermont.
Connect with Lisa
Broadway in Columbus , Lisa Much , reviews , Theatre Reviews


text:
Key School Custom Program
High-Impact Projects
& Community Building
Key School's interest in a Custom Program with VISIONS indicates a commitment to the profound learning that happens when groups of people undertake meaningful projects in another culture.
VISIONS has worked in Urubamba, Peru, for 24 years. We have seen that these long-term relationships are the foundation for creating mutual understanding and real change.
We invite you to be an extension of the relationships that we have forged, to have fun and make a difference!
About VISIONS Custom Programs
We believe that stepping outside of ourselves and encountering new perspectives fosters profound and positive change. We know that young people who engage in meaningful service in new environments gain exceptional insight—it's been our program model for more than three decades.
VISIONS Custom Programs are exciting opportunities to explore another culture and develop new ways of thinking while giving back and digging deep.
"I gained a wider perspective of the world and gratitude for my life. By going on this trip, I have learned so much more about the world, others, and myself, and I feel that it has made me a better person."
Dylan Ragas
Custom Programs contain elements of a VISIONS summer program along with ingredients tailored to meet the goals of your group
When the objectives of the program are met, participants will:
Increase their knowledge of the culture, history and society of the host community
Gain the community living skills required to thrive as a group
Develop the hard skills required on a worksite, including tool use
Develop a respect for diversity, in the broadest sense
Increase confidence as contributing members of society
Increase positive attitudes towards interacting, learning and exploring across cultures
Meaningful Service Work: Choose from multiple projects each day
Cultural Exchange: Live and work as part of the community
Adventurous Exploration: See some of the area's best sights
Reflection & Community Building: Group meetings to reflect on the experience
SERVICE & IMMERSION
The work involves ambitious projects that may be a combination of construction work and social service. Projects are done with community members and participants get to choose from two or three different worksites each day. In one to two weeks in Peru, we are often able to complete the majority of a new school classroom or additional water canal system for an agricultural community. If there are enough participants, we can also help instruct English and other activities in school.
ADVENTUROUS EXPLORATION
After work ends around 2 pm, the rest of the day includes cultural exchanges, recreation and other activities anchored in the community. We typically set aside one or two full-day activities or excursions for every five work days. Your group will experience the "must-see" sights along with activities that come from long-standing relationships in the community.
In addition to the spontaneous and ongoing community building we do with our local hosts, we meet as a group a few evenings a week in order to reflect on the volunteer and cultural experiences, and to listen to what others have to share.
"My VISIONS program was absolutely amazing. I was surrounded by the most incredible people 24/7, and that really makes a trip like this."
Sierra O'Brien
The picturesque Urubamba Valley was first settled by ancient Peruvians, who were drawn by the mild climate and fertile river basin near Machu Picchu. Today, the town of Urubamba bustles with the activities of farmers and producers, artisans and craftspeople, and a vibrant array of Quechua traditions.
Your Home in Peru
You'll be amazed by how quickly the cobblestone byways of Urubamba become as familiar as the streets in your hometown. VISIONS has been working with this community for nearly two decades, which means our relationships with locals run deep and strong. On this foundation of mutual trust and respect, you'll make your own connections by hanging out with locals daily, while shopping at the farmers market, working alongside maestros on service projects, making traditional foods with our house cook, Neyda, learning about Incan history from project manager Nico, and playing soccer with "Urubambino" kids. All this conversation means ample opportunities to practice speaking Spanish and, more importantly, listen to the stories of people from another culture.
Urubamba,
"I wanted you to know that things could not have been better with the trip. VISIONS leaders were attentive, energetic, intuitive, and kindly firm, just as I would have hoped. We worked well as a team and the students rose to the challenges facing them."
Anne Weston
Heathwood Hall; Custom Program
Our time will be service-focused, and in the cross-cultural context of working and spending time with our Peruvian friends. Our after-work activities will be a blend of cultural immersion and adventure. The following gives an overview of a typical 9-day program.
Arrival (Day One)
Flight arrival to Cuzco met by VISIONS staff
Approx. 1-hour drive to our homebase
Settle in / unpack / orientation / town tour
Bienvenida Welcome Dinner!
Work / Activity Days (5 or 6 total)
Breakfasts at homebase
Rotating worksites and homebase crews
End work ~ 2 pm
Afternoon activities*
Dinners at homebase
Evening activities*
Full Excursion Day (1 or 2)
Machu Picchu (early morning train from Ollantaytambo, partial day at ruins, return on evening train)
Additional option: Take a full day for Pisac or Chinchero ruins & artisan market
Travel to departure airport & hasta lluego!
Accommodations: We live together in a rented house in a neighborhood of Urubamba. Accommodations are basic, but clean and comfortable. There are several bunk rooms, bathrooms and showers, running water, and an indoor patio for gathering. A full packing list is provided before the program.
Food: Meals and snacks are varied and healthy. Members of the group take turns preparing breakfasts and lunches, and a local cook prepares our dinners. Vegetarian options are available and participants with special food requirements provide that information before the program begins.
*AFTERNOON & EVENING ACTIVITIES
Learn local crafts including pottery, chocolate-making, weaving and jewelry making
Visit the Moray and Chinchero ruins in the Sacred Valley
Tour the Salineras salt flats, used by ancient and contemporary Peruvians
Shop at the Pisac artisan market—one of the largest in South America
Take dance classes, play soccer with locals, and use "Bamba time" to explore town
Spend a whole day with a local family in a neighboring town
Go stargazing, listen to live music and share stories by a bonfire
Explore the magnificent ruins of Machu Picchu during a one- or two-day excursion (if time allows)
NICO JARA
When VISIONS began working in Peru in 1999, one of our first connections was with Nico Jara. He began as our professional driver that first year, but he quickly proved himself so talented and capable on many fronts, he became our project coordinator. He's also a close friend, whose knowledge about the Sacred Valley runs deep.
Nico is with our program every day the kids are there, and digs in alongside us at worksites. He's eager to laugh, joke around, and as a great soccer player, he spearheads plenty of games during the summer. Of Quechua descent (the indigenous people of the Andes), Nico speaks fluent Quechua and Spanish, but he knows only a few words of English, so there are ample opportunities to practice a lot of Spanish when they become his friends.
A professional driver during the rest of the year, Nico is going on 60 but could pass for half that. His wife Berta is a licensed nurse at the local clinic in Cuzco. They have two daughters; their youngest, Patty, worked with VISIONS the summer of 2014.
Nico's favorite part of the VISIONS summer is helping the rural campo communities in Yanahuara, where we do many irrigation projects. He is particularly passionate about our work with irrigation and water conservation, which helps farmers keep their crops and livelihoods.
Nico loves to say, "Everything is possible." His tireless physical contributions are matched only by his relentless optimism and positivism. It's infectious. So much so, there are now countless VISIONS students and leaders who during their time in Urubamba came to share his belief, and have carried the idea that everything is possible out into the world.
Peru Blog Posts
Language Immersion & Community Service Abroad
Roll up your sleeves and join us for an immersion program in the Dominican Republic or Peru! During projects and excursions, while enjoying meals with community members, and in day-to-day life while you live as part of a local neighborhood…you can't resist practicing your Spanish.
While service work is at the root of each teen service program, it's in equal measure with cultural immersion that transcends the experience of an average tourist. In all of our homes away from home, we live very much as local people do, and we are part of the fabric of daily life.
VISIONS Featured In The New York Times!


Here's a link to the extra credit! We'll do a practice one tonight.
First 30 mins or so…. Mid-Term test… take as a group and help each other.
Then, for next 60-90 mins, play around in the studio. Experiment with portraiture and lighting.
Last, edit. I'm planning on at least one Photoshop demo: selective color.
and add your favorite shots of the semester!!


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PBI Press Publishes Schmidt Kramer Injury Lawyer Scott Cooper's Book On Civil Depositions
Posted Scott B. Cooper on Feb 05, 2013 in News
PBI Press Publishes Schmidt Kramer Injury Lawyer Scott Cooper's Book On Civil Depositions: A to Z of Civil Depositions
To order the book go to the link below.
http://www.pbi.org/
Mr. Cooper is a partner in the firm of Schmidt Kramer in Harrisburg, where he specializes in personal injury law with an emphasis on motor vehicle accident and insurance cases. An active member of the Pennsylvania Association for Justice (PAJ), Mr. Cooper serves as its 2012–2013 president. He is also chair of its Legislative Policy Committee and serves on both the Executive Committee and Board of Governors. In both 2011 and 2012, Mr. Cooper was named by Pennsylvania Super Lawyers as one of the top 100 lawyers in the state. In 2008, Mr. Cooper was honored by PAJ and was awarded its prestigious Milton D. Rosenberg Award for leadership and efforts for victims' rights. In 2005, Mr. Cooper received the association's prestigious George F. Douglas Amicus Curiae Award for outstanding appellate advocacy. In addition, Mr. Cooper was selected as a Pennsylvania Super Lawyer in 2006 and 2011 and a Pennsylvania Super Lawyer Rising Star in 2005.
He was also voted "Reader's Choice Attorney" by Harrisburg magazine. Mr. Cooper was a contributing editor for the second and third editions of the leading treatise on the Pennsylvania Motor Vehicle Financial Responsibility Law, Pennsylvania Motor Vehicle Insurance: An Analysis of the Financial Responsibility Law.
He has been involved in and argued many significant appellate cases, including Rupert v. Liberty Mutual, Prudential v. Colbert, Mullin v. Commonwealth, Richmond v. Prudential, L.S. v. Eschbach, Ricks v. Nationwide, Wirth v. Aetna, Nott v. Aetna, Blood v. Old Guard, Sackett v. Nationwide, Everhart v. PMA, State Farm v. Rosenthal, Craley v. State Farm, Erie v. Baker, Orsag v. Farmers, Generette v. Donegal, GEICO v. Ayers, Heller v. League of Cities, Smith v. Rohrbaugh.
Mr. Cooper is a member of the American Association for Justice (AAJ) and the Pennsylvania and Dauphin County Bar Associations. He received his J.D. from Widener University School of Law (1993) and his B.A. from the University at Albany (1990).


Aerial photographs courtesy of Georgia State University Library Blog.
(APN) ATLANTA — In the 1960s, the City of Atlanta, through legal and political maneuvers, displaced thousands of Black families in the former neighborhood Buttermilk Bottoms to create the Civic Center, which, only fifty years later, is now itself the subject of possible demolition.
Also in the 1960s, the City of Atlanta decimated parts of majority Black and mixed-race neighborhoods in Mechanicsville, Peoplestown, and Summerville, for the creation of the original Braves stadium, followed by Turner Field.
Last year, the Atlanta Braves announced they will be leaving Turner Field, and Atlanta altogether; and moving to Cobb County, meaning the likely end to the stadium complex itself.
What this pattern reveals is one of Atlanta's most negative traits: the idea that nothing is sacred – not neighborhoods, not history, not the housing needs of low-income residents, and sometimes, not even the development projects that are used to justify the destruction of neighborhoods in the first place.
Where the Boisfeuillet Jones Civic Center now stands, there was once a neighborhood called Buttermilk Bottoms, housing thousands of Black families who were uprooted to make way for this monolithic, under-utilized venue.
"Buttermilk Bottoms was the most distressed neighborhood in Atlanta back then [in the 1950's]. Some homes had no electricity and most did not have indoor plumbing," Joyce Dorsey, Executive Director of the Fulton Atlanta Community Action Authority, told Atlanta Progressive News.
These families had little to no assistance with relocating, according to the book, Race, Class, and Urban Expansion by Prof. Larry Keating of Georgia Tech.
Then Mayor Ivan Allen ignored federal mandates and city ordinances demanding that redevelopment and rebuilding of public housing take place, once the construction of the Civic Center was completed in 1965, Keating's book states.
Announced this year, Atlanta Mayor Kasim Reed and other powers that be are looking to demolish the Civic Center in order to make way for the construction of a Hollywood-type studio.
Mayor Reed touts that it will bring urban renewal and jobs: the same thing that Mayor Allen claimed almost fifty years ago.
This begs the question: how many more Black communities will be displaced in Atlanta to make way for large venues that only have a shelf life of about forty or fifty years?
Some cities treasure historic buildings. In Atlanta, anything older than thirty years is considered by developers and their local government puppets to be decrepit, dilapidated, and antiquated.
Atlanta has a long history of displacing black families, from Buckhead to Candler Park to South Atlanta. Large event venues, majority White subdivisions, novelty shopping centers, and poor excuses for parks all exist today because of this trend.
The Civic Center construction displaced over one thousand Black households.
One group that fought the Civic Center was called U.-RESCUE, which stood for "Urban Renewal Emergency: Stop, Consider, Understand, Evaluate." Efforts by neighborhood groups to acquire decent affordable housing only delayed, but did not stop, the mass evictions.
In concession to activists, the powers that be promised a public housing community to be called U Rescue Villa, a senior high rise Cosby Spears, and some subsidized housing, all of which is in a neighborhood now known as Bedford Pines.
Bedford Pines provided only 218 of its 1,400 rental units as subsidized housing for low-income families. This was less than 16 percent of the houses that were destroyed in the razing of Buttermilk Bottoms, according to Keating's book.
Fast forward forty years: U Rescue Villa was quietly destroyed in 2008, as part of the Atlanta Housing Authority's demolition of ten public housing communities. Only Cosby Spears remains of the neighborhood's public housing inventory.
And Bedford Pines itself is endangered, with efforts by people like Peggy Denby of Midtown Ponce Security Alliance; and Councilman Kwanza Hall (District 2), to "revitalize" the area.
So much for the promises made to U.-RESCUE back in the 1960s. Those promises were all quietly forgotten and then revoked upon.
"We didn't put up a fight because we knew this move had to happen. Churches got involved to help families buy new homes. Even Mayor Allen would visit the churches, preaching his assistance. I remember families being offered a stipend of 600 dollars. Back then, that was a lot of money. You put that down on a home and your monthly payments were less than one hundred dollars a month. Depending on where you relocated determined what class you ended up in," Dorsey told APN.
There were many families that ended up moving into the areas that are now East Atlanta and Kirkwood, while others struggled to adapt or even find public housing. Those who did not find or were not offered public housing were left to their own devices.
"Anytime the government tells you taxes won't go up with development, they're not telling the truth. Somebody got paid. Streets, parking, it's going to change the whole environment. The Civic Center changed the whole thing," Charlie Christian, a former resident of Summerhill, told APN.
Christian recalled the conditions of the neighborhoods and those who lived there pre- and post- Civic Center construction. "The more people that are moved into one place, the more problems you're gonna have. Once those high rises went in, it changed everything," he said.
Atlanta built the Atlanta-Fulton County Stadium in 1965 to attract a major sports team; and in 1966, the Milwaukee Braves relocated to Atlanta from Wisconsin.
Eventually, that stadium was demolished in 1997, and another stadium was built across the street to accommodate the Olympics. That stadium is now known as Turner Field, and it too is about to be demolished, so that the Braves can continue their ongoing migration – this time, to Cobb County, Georgia.
When the Atlanta/Fulton Stadium was built in 1965, the neighborhoods surrounding it were promised urban renewal made up of mixed used developments, according to Keating's book.
"Those people in Summerhill were fooled," Christian said.
The feasibility study of the new stadium was aimed at supporting its construction and encouraging a baseball team to move to Atlanta; urban renewal of the area did not enter into the conversation.
The residents of those surrounding neighborhoods–Mechanicsville, Peopletown, and Summerhill–were showered with promises that were never kept.
A neighborhood fund, funded by a portion of parking revenue at Turner Field, has been used to fund the three respective neighborhood associations, who have, in turn, used the funds to cover community events, services, and projects. However, the funding has not been enough to make a dent in the community's infrastructure needs.
In Peoplestown, the neighborhood was 49 percent Black and 51 percent White pre-construction, according to Keating's book.
By 1970, five years after the stadium was built, the majority of the Jewish merchant population had vacated Peoplestown, leaving a population of approximately 5,100, 89 percent of whom were Black.
Those 5,100 in Peoplestown and many thousands more in Summerville and Mechanicsville, were now at the mercy of the massive influx of cars trying to park in too few spaces needed to accommodate a sold-out stadium.
Property owners in the surrounding neighborhoods demolished or burned down their houses to make way for makeshift parking lots, charging drivers a fee for use, Keating's book states.
This demolishing of houses ran two to three blocks deep in all directions, leaving a desolate reminder of the houses that once stood in those vibrant communities, the book states.
For those who stayed, their streets were turned into one-way thoroughfares to allow the flow of traffic to and from the stadium. Homeowners were unable to leave their homes for a simple grocery store errand, as it was nearly impossible to get back without driving against traffic.
Also, as previously reported by APN, residents' car insurance rates went up from being in close proximity to the stadium, where many car break-ins occur.
There were talks of a MARTA stop in the original agreements with the city and the Braves; that never happened.
The neighborhoods surrounding the stadium were left to suffer. Some of the worst crime rates in the city occur around the stadium. Housing values are upside down and the roads look like the surface of the moon, tore up and full of potholes the size of craters.
"The old neighborhood will never be the way it used to be. The old neighborhood will never be the same. The community and places of business," Christian said.
Christian's family was one of the many families that moved around from pocket to pocket as the City developed through low-income neighborhoods.
And now, in Atlanta's Vine City neighborhood, two historic Black churches are being displaced so that the Atlanta Falcons football team can be accommodated with a new, taxpayer-subsidized stadium, just down the street from the current stadium. All this to prevent the Falcons from moving somewhere else.
With the advent of Civic Center, Turner Field and its predecessor, the Falcons Stadium, and even the freeway through Old Fourth Ward, neighborhoods including Buttermilk Bottoms and portions of Mechanicsville, Peoplestown, and Summerville have altogether suffered a loss of over 68,000 people, according to rough estimates by local planners, Keating's book states.
Perhaps as many at 17,000 households did not find adequate housing after all the construction had ceased, the book states.


text:
2020 Media Supplement
NDLXP.com
Unofficial Visit Guide
playing for peace
2018-19 Men's Lacrosse Roster
#50 Ryder Garnsey
Class Junior
Hometown Wolfeboro, NH
High School Phillips Academy
Bio / Statistics
CAREER AWARDS & HONORS
2018 USILA Honorable Mention All-American
2018 ACC All-Tournament Team
2017 Tewaaraton Award Watch List
2017 Inside Lacrosse Midseason First Team All-American
2017 Inside Lacrosse Preseason Third Team All-American
2017 Preseason All-ACC Team
2016 ACC Freshman of the Year
AS A SENIOR (2019)
Appeared in two games for Notre Dame in his senior season.
Recorded 10 points off seven goals and three assists, all coming in the two NCAA Championship games.
Posted a hat trick with three goals and an assist in the win over Johns Hopkins (5/12) in the first round of the NCAA tournament.
Scored a team-high four goals and added two assists against Duke (5/18) in the NCAA Championship quarterfinals.
Appeared in all 15 games for the Irish, starting nine.
Led the team in points, recording 43 points off 29 goals and 14 assists.
Fired 52 shots on goal, including a season-high six SOG on four occasions.
His 29 goals ranked second on the team.
Twice scored five goals, recording six points total both times (5-1-6) – against Michigan (3/21/18) and at Duke in the ACC Tournament (4/27/18).
Recovered 21 ground balls, including a season-best six at North Carolina (4/21/18).
Forced a turnover against both Duke (4/27/18) and Denver (5/13/18).
Started 14 games as an attackman for the Irish in his second season
Led the team with 43 points and assists with 23
Ranked fourth on the team in goals with 20
Totaled 27 ground balls, ranking fifth on the squad
Scored a career-high six goals and added two assists in the season-opening win over Georgetown (2/18) to finish with a career-best eight points
Recorded a career-high four assists at Denver (3/12) while adding two goals for a six-point performance
Posted at least one goal and one assist in 11 outings
Scooped up a season-high six ground balls in the regular season win over Marquette (4/11)
AS A FRESHMAN
Named the ACC Freshman of the Year
Started five of the 15 games he played in during his rookie season, finishing with 27 goals, 12 assists, 13 ground balls and two caused turnovers
Ranked second on the team in goals (27), third in assists (12) and third in points (39)
Scored a career-high five goals in a 17-7 win at Syracuse (4/2)
Posted eight multiple goal games
Scored four goals and dished out two assists in the regular season finale against Army (5/8)
Found the back of the net three times in the first round of the NCAA Championships against Air Force (5/14)
Recorded at least one point in 14-of-15 games, including each of the last 10 outings
PERSONAL AND PREP
Ranked number one by Inside Lacrosse on its 2015 Top 40 Attackmen list and number two on the Inside Lacrosse Power 100 Freshmen list
Played a post-grad year at Phillips Academy Andover in Andover, Massachusetts
Posted 47 goals and 46 assists under the direction of head coach Steve Moreland at Phillips Academy
Previously suited up for Brewster Academy (Wolfeboro, New Hampshire), where he is the all-time leading scorer
Played club lacrosse for 3d Lacrosse
Earned Under Armour All-America honors
A U.S. Lacrosse High School All-America pick
New England Player of the Year as senior
Also played hockey all four years at Brewster Academy
Son of Fred and Meighan Garnsey
Has two siblings–Charlie and Erin
Sister Erin played lacrosse at New Hampshire
Enrolled in the College of Arts and Letters as a film, television and theatre major.


Looking for the cheapest car insurance in Emmet, Nebraska? We can help you compare the best deals. Simply enter your Zip Code in the form above and you will be on your way to finding cheap Emmet, NE auto insurance quotes from the best providers in Nebraska.
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Here is a couple of shots of my CoCo 3.
The dual 3.5" floppy is something I cobbled together myself. I had detailed its making on the old CoCo site which inexplicably disappeared one night. Like I expect this site to do any day now .
The bigger board in the background does the conversion form the CoCo hi-res video(CGA?) to VGA. There must be a better solution than this(a GBS 8200 V4) these days and if so I would appreciate hearing about it. The little dongle board is an RS2323 to USB adapter for drivewire.
I wish I could use my PC Monitor with my Coco 3. And I wish I had a Floppy Controller so I use a 3.5 Floppy Drive. And you have a nice setup.


text:
Baseline Characteristics and Patient Reported Outcomes from a Juvenile Dermatomyositis Registry Inception Cohort
Jessica Neely1, Kaveh Ardalan2, Adam Huber3 and Susan Kim4, 1University of California San Francisco, San Francisco, CA, 2Duke University Medical Center, Durham, NC, 3Dalhousie University, Halifax, NS, Canada, 4UCSF Benioff Children's Hospital, San Francisco, CA
Meeting: ACR Convergence 2021
Keywords: dermatomyositis, observational research, Patient reported outcomes, Pediatric rheumatology, registry
Date: Sunday, November 7, 2021
Session Title: Pediatric Rheumatology – Clinical Poster II: SLE, JDM, & Juvenile Scleroderma (0764–0785)
Session Type: Poster Session B
Background/Purpose: Juvenile dermatomyositis (JDM), the most common inflammatory myopathy of children, is rare, with an estimated incidence of 2-4 in 1 million children. Given the challenges of studying JDM, large observational cohort studies are invaluable resources for disease characterization, long-term monitoring, and comparative effectiveness studies, to ultimately improve the care and outcomes of children with JDM.
The Childhood Arthritis and Rheumatology Research Alliance (CARRA) Registry, designed a longitudinal inception cohort that includes the systematic data collection, including patient reported outcomes, planned over 10 years.
Here, we describe the baseline patient demographics, disease characteristics, initial assessments, patient/parent-reported outcomes and treatments for an inception cohort of children with JDM enrolled to the registry in the first year.
Methods: The CARRA Registry began enrollment of JDM subjects in January 2019. Eligible subjects were < 18 years of age at disease onset and diagnosed with JDM based on clinical judgement of the treating rheumatologist. Enrolled subjects had disease for < 6 months and treatment naïve or treated with systemic therapy for < 12weeks. This Registry is approved by Duke University Institutional Review Board (IRB) and each participating site. Consent/assent was obtained from eligible participants by the local investigators.
Results: 119 JDM subjects were enrolled from 41 sites. Most were female (63.4%), and white (72.3%) with a median diagnosis age 8.0 years (IQR 4.0-11.5), and median age of disease onset 7.0 years (IQR 3.5-7.5) (Table 1). They had characteristic rashes (92.4%), elevated muscle enzymes (83.2%), physician global score 4.0 (IQR 2.5-5.0) and manual muscle testing score 63.5 (IQR 51.0-75.0). Calcinosis (3%) and interstitial lung disease (< 1%) were uncommon.
CHAQ results (median 0.75, IQR 0.03-1.875), and patient/parent global assessments of disease activity (median 3, patient IQR: 1.75-5.25; parent IQR: 1-7) showed mild-moderate disability. PROMIS® Pediatric Global Health 7 scores, PROMIS measures for pain interference, physical function scores for mobility, and upper extremity function were commonly worse than 95% of the general pediatric population based on published normative data (Table 2).
Approximately half of subjects, 54.6% (n=64), were treated according to a Consensus Treatment Plans (CTP) (Figure 1).
Conclusion: In its first year 119 JDM patients were successfully enrolled into the CARRA Registry. They were similar to historic cohorts, with a trend toward earlier time to diagnosis and milder disease. PROs were also collected successfully, and our findings reveal moderate to severe patient and parent-reported disease impact. Published CTPs were also used to treat over half of the enrolled patients, suggesting that pragmatic trials may be feasible in the CARRA Registry.
This growing registry provides infrastructure to advance future clinical research and paired specimens in the CARRA biorepository will enable future translational research. Together, these efforts enhance trial readiness for future therapeutic studies, including comparative effectiveness trials which will help to advance care and outcomes for JDM.
Table 1: Demographics, Diagnostic and Clinical Features
Table 2: Patient/Parent-Reported Outcome Measures
Published Consensus Treatment Plans for JDM patients with Moderate, Skin predominant disease
Disclosures: J. Neely, None; K. Ardalan, None; A. Huber, None; S. Kim, None.
Neely J, Ardalan K, Huber A, Kim S. Baseline Characteristics and Patient Reported Outcomes from a Juvenile Dermatomyositis Registry Inception Cohort [abstract]. Arthritis Rheumatol. 2021; 73 (suppl 9). https://acrabstracts.org/abstract/baseline-characteristics-and-patient-reported-outcomes-from-a-juvenile-dermatomyositis-registry-inception-cohort/. Accessed January 27, 2023.
« Back to ACR Convergence 2021
ACR Meeting Abstracts - https://acrabstracts.org/abstract/baseline-characteristics-and-patient-reported-outcomes-from-a-juvenile-dermatomyositis-registry-inception-cohort/


text:
British Scientists Clone Dinosaur
Rumor: Scientists at a British or Korean university have successfully cloned a dinosaur from preserved fossils.
David Mikkelson
Claim: Scientists at a British university have successfully cloned a dinosaur from preserved fossils.
Example: [Collected via e-mail, March 2014 and Twitter, January 2016]
I just read an article stating that British scientists had cloned a dinosaur. is this true??
Korean Scientists Clone Dinosaur https://t.co/q9fA90WEmS
— therealyang2da (@Yang2da) January 21, 2016
Origins: In March 2014, the NewsHound web site published an article positing that scientists had, in a procedure similar to the one employed by characters in the fictional Jurassic Park novel and film, extracted viable DNA from a fossil and used it to clone a baby dinosaur:
Scientists at Liverpool's John Moore University have successfully cloned a dinosaur, a spokesman from the university said yesterday.
The dinosaur, a baby Apatosaurus nicknamed "Spot," is currently being incubated at the University's College of Veterinary Medicine.
The scientists extracted DNA from preserved Apatosaurus fossils, which were on display at the university's museum of natural science. Once the DNA was harvested, scientists injected it into a fertile ostrich womb.
"Ostriches share a lot of genetic traits with dinosaurs," said Dr. Gerrard Jones, a biology professor at LJMU and the project's leading scientist. "Their eggshell microstructures are almost identical to those of the Apatosaurus. That�???�??�?�¢??s why the cloning worked so perfectly."
Those in the scientific community say the dinosaur cloning — the first ever of its kind — is a milestone for genetic engineering.
The story was easily spotted as nothing more than a hoax, however, for a variety of reasons:
First of all, the NewsHound web site was not a news site at all, and it had, in place of reporting on actual events, reproduced a number of other hoaxes and spoofs as if they were real news, such as long-debunked stories about a Chinese man suing his wife over giving birth to an ugly baby, Apple paying Microsoft [sic] a $1 billion debt all in nickels, a planetary alignment causing gravity on Earth to be negated for five minutes, and Google Earth helping to locate a woman who had been stranded on a desert island for seven years.
Second, the Newshound article's photograph of the baby dinosaur was not an image of a dinosaur at all; it was a repurposed picture of a baby kangaroo which was originally published by an Australian community group to accompany an article about rescuing injured macropods (kangaroos and wallabies) and is actually an illustration of an unfurred joey (newborn kangaroo):
Finally, no other reports of this story or any other corroborating information related to it has been reported by the mainstream press or any other credible news outlet.
Variations: On 24 August 2015, the fake news web site LinkBeef published a nearly identical version of the above-reproduced claim; it featured the same photograph as prior iterations and held:
Scientists at Sooam Biotech Research Facility have successfully cloned a dinosaur, a spokesman from the university said yesterday.
The dinosaur, a baby Allosaurus nicknamed "Spot" is currently being incubated at the Sooam Biotech Research Facility.
The scientists extracted DNA from preserved Allosaurus fossils, which were on display at the facility's museum of natural science. Once the DNA was harvested, scientists injected it into a fertile ostrich womb.
"Ostriches share a lot of genetic traits with dinosaurs," said Dr. Woo Su, a biology professor at LJMU and the project's leading scientist. "Their eggshell microstructures are almost identical to those of the Allosaurus. That's why the cloning worked so perfectly."
By David Mikkelson
David Mikkelson founded the site now known as snopes.com back in 1994.
hoaxes ASP Article
Did Pope Francis Say 'God Has Instructed Me to Revise the Ten Commandments'?
What We Know About Elon Musk and the Emerald Mine Rumor
Is Amazon Giving Away Pallets of Lost Packages on Facebook?


text:
Radlett Office
Fine & Country Radlett
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Is there a way in Salesforce to configure that user cannot use his/her password same as his/her username or first name or last name? So for example, if my name is Jangay Varalaxmi and username is jangayvaralaxmi@gmail.com, then I want to setup/configure something that prevents me from using jangay21, jangay or varajangay as my password and it should not include special character "!". I have looked at various blogs and website but did not find any solution.
May I suggest you please refer the below link to reference.
Please mark it as best answer if the information is informative.


The NHS is once again hitting the headlines, with many hospitals on 'black alert' and a large number of elective operations being postponed to reduce the extreme pressure on services over this winter period - referred to in the media as a 'winter crisis'. Here we bring together a range of our content examining the reasons for this.
The NHS relies on the reoccurrence of supposedly non-recurrent winter funding, but is still kept on its toes each year wondering how much money will be available and when it will be provided. Siva Anandaciva looks at how it works.
Matthew Kershaw highlights the need for a fundamental change to the way NHS emergency care is provided.
The media and groups of NHS staff are describing an A&E service in crisis, while the government says the NHS is more prepared for winter than ever before. What does the latest national data say about how A&E departments are coping compared with previous winters?
Black alerts: for emergencies only?
Matthew Kershaw considers whether the frequency of black alerts this winter is putting staff and patients under unsustainable pressure.
Chris Ham considers what needs to be done to stabilise the NHS and ensure that this winter's crisis does not recur.
Delayed transfers of care: a target that misses the mark?
David Maguire considers what progress has been made in reducing delayed transfers of care.
What is happening to waiting times in the NHS?
We examine the big issues and recent NHS performance on waiting times.
What's going on with A&E waiting times?
Waiting times in accident and emergency (A&E) departments are a key measure of how the NHS is performing. In recent years, patients have been waiting longer in A&E; here we look at why that is.
The QMR features survey results from NHS trust finance directors and CCG finance leads, and interactive charts on selected NHS performance data.


text:
William (Hong-Chun) Youn
piano | Korea, °1982
PIANO 2007 : Laureate
Born in Seoul, South Korea, William (Hong-Chun) Youn began playing the piano at the age of six. Since his debut with the Seoul Philharmonic Orchestra at the age of eleven, William Youn has appeared regularly as a soloist with Orchestras such as The Cleveland Orchestra, the Staatsorchester Halle, the Südwestdeutsche Philharmonie Konstanz, the Belgian National Orchestra, and the Toronto Philharmonia among others, and has performed in the world's most prestigious venues including the Lincoln Center (New York City), the Disney Hall (LA), the Musikverein (Vienna), the Palacio de Bellas Artes (Mexiko City), the Palau de la Musica (Barcelona), and the Auditorio Nacional (Madrid). His performances have been broadcasted by the Bavarian Radio Munich, the National Public Radio Boston, and the Czech Radio.
After his early studies in Korea, William Youn was accepted at New England Conservatory's Preparatory School and at the Walnut Hill School in Boston at the age of thirteen. After five years of his studies in Boston, he went on to study in Hannover, Germany, where he continued his musical education with Bernd Goetzke. From 2006 to 2009, he was awarded a scholarship to study at the renowned International Piano Academy at Lake Como where he worked with musicians such as William Grant Nabore, the director of the Academy, Dmitri Bashkirov, Fou Ts'ong, Andreas Staier, Charles Rosen and Menahem Pressler. He has also performed in masterclasses by Andras Schiff, Christopf Eschenbach and Yo Yo Ma.
William Youn is a prizewinner at international competitions such as the Cleveland International Competition, the Concorso Alessandro Casagrande, the Shanghai Piano Competition, and was a finalist at the Busoni Competition. Sony BMG Korea has released several of his recordings, including the Schumann Piano Concerto with the Prague Radio Symphony Orchestra.
His Recording "Chopin, Schumann, Wolf", which was released worldwide in 2010 by the label Ars Produktion in celebration of three composers' Anniversary, has been selected as the recording of the Month by the Pizzicato Magazine of Luxembourg and has received the prestigious Exellentia Award.
Final 2007: Youn Hong-Chun - Gálvez-Taroncher
Your browser doesn't support HTML5 audio tag.
Centre for Fine Arts/Salle Henry Le Boeuf
Miguel Gálvez-Taroncher La Luna y la Muerte
William (Hong-Chun) Youn, Soloist
Gilbert Varga, Conductor
National Orchestra of Belgium, Orchestra
Final 2007: Youn Hong-Chun - Rachmaninov
Sergey Rachmaninov Concerto n. 3 in D minor op. 30 (Allegro non tanto | Intermezzo | Alla breve)
Final 2007: Youn Hong-Chun - Mozart
Wolfgang Amadeus Mozart Sonata n. 10 in C major KV 330 (Allegro maestoso | Andante cantabile | Allegretto)
Semi-final 2007: Hong-Chun Youn - concerto - Mozart
Royal Conservatoire Brussels/Big hall
Wolfgang Amadeus Mozart Concerto n. 20 in D minor KV 466 (Allegro | Romance | Rondo)
Paul Goodwin, Conductor
Orchestre Royal de Chambre de Wallonie, Orchestra
Semi-final 2007: Hong-Chun Youn - recital - Chopin
Fryderyk Chopin Ballade n. 4 in F minor op. 52
Semi-final 2007: Hong-Chun Youn - recital - Jongen
Joseph Jongen Etude de concert op. 65/2
Semi-final 2007: Hong-Chun Youn - recital - Defoort
Kris Defoort Dedicatio VI
Semi-final 2007: Hong-Chun Youn - recital - Schumann
Robert Schumann Davidsbündlertänze op. 6 (Davidsbündlertänze op. 6 I | Davidsbündlertänze op. 6 II)
Final (29/05/2007)
Wolfgang Amadeus Mozart Sonata n. 10 in C major KV 330
Sergey Rachmaninov Concerto n. 3 in D minor op. 30
William (Hong-Chun) Youn, piano
National Orchestra of Belgium, dir. Gilbert Varga
Semi-final
Récital (15/05/2007)
Robert Schumann Davidsbündlertänze op. 6
Concerto (18/05/2007)
Wolfgang Amadeus Mozart Concerto n. 20 in D minor KV 466
Orchestre Royal de Chambre de Wallonie, dir. Paul Goodwin
First round (08/05/2007)
Johann Sebastian Bach Prelude and Fugue n. 4 in C sharp minor BWV 849
Fryderyk Chopin Etude in B minor op. 25/10
Béla Bartók Etude op. 18/1
Franz Liszt Paraphrase sur un thème de Rigoletto de Verdi