text:
\section{Introduction}

\begin{figure}
    \centering
    \includegraphics[width=.48\textwidth]{supermaximal.png}
    \caption{Histograms of the lengths of super-maximal repeats occurring in each dataset. \texttt{textgen} refers to human generated text.}
    \label{fig:repeatstats}
\end{figure}

The introduction of powerful neural left-to-right language models in the last few years has revolutionzed the capacity to automatically generate human-like text.
Their power has grown proportionally to the worry that those models could be used to generate \textit{fake} documents,\footnote{referring here to machine-generated text as opposed to text containing objectively false statements} either news articles~\citep{grover}, comments~\citep{readattendcomment} or other type of text~\citep{buchanan2021truth} without revealing the non-human authorship of it.
Recently, a number of papers have investigated the capacity of humans and learning algorithms to determine if a given text is human or machine generated~\citep{ippolito2020,dugan2020,maronikolakis2020}.
However, so far all those methods are making two key assumptions: (i) the settings is framed in a supervised way for the machine learning methods, having access at training time to machine-generated samples from the same generator; (ii) the decision is done for each sample individually and independently.

\smallskip

In this paper, we argue that this is not realistic.

On one side any ill-intented adversary would take care of either retraining its own model, or modifying the generation in such a way as to drift significantly the distribution of generated text.
This can easily be done by using different sampling strategies~\citep{f2softmax,Welleck2020Neural,nucleus,massarelli-etal-2020-decoding}, an a-posterior re-weighting strategy or the use of one of a number of controlled generation methods currently available~\citep{plugplay,khalifa2020}.
More-over, the domain used to train such a classifier might be different from the one where it is deployed.
\citet{Bakhtin2021} studied this precise setting and showed that the quality of a human/machine discriminator degraded quickly when the prompts used to generate text where from a different domain.
On the other side, the problem of determining the origin of one fixed document is in most use cases not the real problem.
As an example, an article generated by an existing template-based data-to-text method~\citep{reiter2000} is arguably of less concern than an article diffusing false information written by a human author.
Indeed, one danger of the current language models lies in their capacity of generating a huge amount of human-like text but biased towards a desired opinion or topic~\citep{leroux2020,buchanan2021truth}.

We therefore frame the problem of detecting machine-generated texts as follows: given a  set of documents, and the suspicion that a large fraction of them is generated by machine; is it possible to detect those?
The departure of this setting from the current one is in defining it in a \textit{distributional} and \textit{unsupervised} way.

This setting is complicated by the fact that modern language models coupled with current decoding strategies follow very closely the statistics of generated tokens.
The key insight of our approach is that, while unigram frequency appears indistinguishable from human generated text, the same is not true for higher-level \textit{n}-grams.
In particular, the distribution of repeats present in the machine-generated text is quite different, as shown in Fig.~\ref{fig:repeatstats}.
While there is apparently nothing special in those repeats (see some examples in Fig.~\ref{fig:repeatsexample}) and they might pass absolutely undetectable when analyzing a single document, they become apparent when looking at a large sample.
Thus, we leverage this signal to pseudo-label the input documents and train an ensemble of classifiers on them. Our experiments show that a majority vote of these classifiers achieves over $90\%$ precision at \numprint{5000} when generating from GPT2-large using \texttt{top-k} sampling, and over $80\%$, for \texttt{nucleus} sampling.

\section{Related Work}
The capacity of large pretrained~\citep{word2vec,BERT} Transformer~\citep{Vaswani2017} models trained only with a cross-entropy loss~\citep{gpt3} are transforming what is possible with natural language processing techniques.
Out of the risks that those models pose~\citep{bender2021} here we are interested in their possibility of generating text which appears as human-generated but without revealing the true authorship.
While those models do commit errors -- ~\citet{dou2021scarecrow} enumerates and analyses 10 types -- they are surprisingly low and often hidden under otherwise very fluent language.
This has created a large line of work that tackles the problem of discriminating such documents, as surveyed in~\citet{jawahar2020}.
In their initial release strategy~\citep{solaiman2019release} OpenAI already mentioned approaches to use the same generation models to identify a text generated with that model, both through fine-tuning on annotated data as well as in a zero-shot setting, using the underlying probabilities.
\citet{ippolito2020} show that an automatic classifier can detect statistical patterns of the generated document and obtains an accuracy for short documents ($60$ tokens) between 70 and 90\% depending on the decoding strategy.
Surprisingly, their performance is different than that of humans, which have a harder time picking up those statistical signals but are unbeaten so far when recognizing incoherence or factually wrong statements.
~\citet{maronikolakis2020} obtains a similar conclusion that automatic models are better than humans for detecting fake headlines.
Studying the performance of automatic classifiers, \citet{Bakhtin2021} provide an exhaustive analysis varying generator and discriminator architecture as well as training data, concluding that performance of the discrimiantor is high if it is trained on data from the same domain.
However, any domain shift affects it in a severe way.
\citet{dugan2020} focuses specifically on the problem of detecting the boundary, from which moment the human generated prompt stops and the machine-written text starts.
The fact that pre-trained language models have statistical patterns which differ from human-generated text has been used in the past not only as feature for detection, but also as input for the interactive tool GLTR~\citep{gltr}.
This tool relies on the probability distribution as given by an inspection model: the case study detailed there shows that even when that inspection model is different than the generation model (although it is of the same family, different sizes of GPT2 to be precise) the highlighted patterns help humans detect the authorship.
It stops however at unigram features as well.
The concurrent work of \citet{uchendu2021turingbench} proposes a dataset of human and machine-generated samples, across a variety of models, and also frame the problem as authorship attribution.
The proposed baselines are all supervised however, although the authors perform a stylotmetric analysis, concluding the need ``to develop a model that unearth more subtle yet distinct patterns''.

All those studies focus on asking (crowd-sourced) human annotators to decide if a text was generated by a machine or a human.
\citet{clark2021human} points out that the high fluency of modern generation models, combined with a generally low expectation of what machines can accomplish, makes it hard to make this distinguished, even for lightly trained annotators.

\section{Background}

Modern language models are trained through maximum likelihood estimation.
Inferring new sequences is done in an auto-regressive manner, where each token's probability is estimated as a function of its previous context.
Greedy decoding -- this is, picking the most probable token -- is rarely used in open-ended generation.
Similarly, using the probability distribution directly to sample from it is also seldom used: while it mimicks the unigram distribution of tokens very accurately, it often generates incoherent text and makes it therefore easy for humans to detect~\citep{ippolito2020}.
The two most used decoding strategies reduce the universe of possible tokens: \texttt{top-k}~\citep{fan2018} which samples from the $k$ most probable words, and \texttt{nucleus} sampling~\citep{holtzman2019} which samples from the tokens making up the top $p$ probability mass.
Formally, let us assume $c$ is the prefix of fixed windows-size, and which is initially human-written (the prompt) but then gets extended with the generations so far.
$P(\cdot|c)$ is the probability distribution over the vocabulary of tokens $V$ of the language-model, given the context $c$.
These decoding strategies then sample from a re-normalized distribution which modifies the support: instead of being all of $V$, only the most probable tokens are considered.
This is, we sort all tokens $x^{(1)}, x^{(2)}, \dots, x^{(|V|)} $ according to their probability: $P(x^{(1)}|c) > P(x^{(2)}|c) > \dots > P(x^{(|V|)}|c)$, and define then:

$$ \displaystyle
P^k_\textit{\texttt{topk}}(x^{(i)} | c) =
\begin{cases}
\frac{P(x^{(i)} | c)} {Z} \text{ if }i \leq k \\
0 \text{ otherwise}\\
\end{cases}
$$

$$ \displaystyle
P^p_\textit{\texttt{nucl}}(x^{(i)} | c) =
\begin{cases}
\frac{P(x^{(i)} | c)} {Z} \text{ if } \displaystyle \sum_{j=1}^i P(x^{(j)}) \leq p \\
0 \text{ otherwise}\\
\end{cases}
$$

\noindent where $Z$ denotes the respective normalization constant, and $k$ and $p$ are respective constant which control the sparsity of the support.

\section{Method}
\label{sect:method}

It is well known that current language models trained on large-scale data allow for generation whose individual token probability mimics very closely that of human-generated text.
For example, a frequency-rank plot of text obtained through ancestral or nucleus sampling is almost indistinguishable from that of human text (the famous ``Zipf Law'')~\citep[Figure 7]{nucleus}.

However, the same does not happen for higher-order $n$-grams.
Fig.~\ref{fig:repeatstats} shows a histogram of the length of so-called super-maximal repeats\footnote{A repeated substring $r$ is super-maximal with respect to a collection of documents if there is no other repeat that contains $r$.}~\citep{gusfield1997} for a set of documents generated by each strategy.
Specifically, we consider equivalent sets of documents, where each set is generated using one decoding strategy over a language model, or sampled from human texts (details in Sect.~\ref{sect:experiments}).
The repeats are then computed on each set independently.
As can be seen from Fig.~\ref{fig:repeatstats}, ancestral sampling produces a much larger number of super-maximal repeats.
More interestingly, \texttt{nucleus} sampling not only follows a similar trend of producing more repeats, but that difference gets larger for longer repeats.
Any individual such repeat can hardly be considered a signal of being machine-generated: in Fig.~\ref{fig:repeatsexample} we depict some examples.
However, from this analysis it appears that they are much more likely to occur in machine-generated text than in a human one.

\begin{figure}
    \centering
    \begin{tabular}{l}
    I could hardly tell what \\
    \hline
    In the latter part of the \\
    \hline
    an important figure in the\\
    \hline
    that it is impossible for\\
    \hline
    they had nothing to do with\\
    \hline
    for long periods of time.\\
    \end{tabular}
    \caption{Examples of super-maximal repeats occurring at least 3 times in the set of documents generated with \texttt{nucleus} sampling.}
    \label{fig:repeatsexample}
\end{figure}

\smallskip

Starting from this analysis, we propose a method to detect machine-generated documents in a large collection, based on a self-training approach.
Pseudo-code of this algorithm is shown in Alg.~\ref{alg:detection}.

Given the full collection of documents, we first compute the full set of super-maximal repeats.
A repeated substring $r$ is super-maximal with respect to a collection of documents if $r$ does not occur in any other repeated substring of this collection.
Their number (and their number of occurrences) is linear in (and even bounded by) the combined length of all documents.
They can be computed efficiently in linear time with respect to the total length of the collection using a suffix-tree--like data structure~\citep{gusfield1997}, taking advantage of the fact that $r$ is \textit{not} super-maximal if it can be extended at either side by some symbol and still be a repeated substring: if there is a character $a$ such that $a.r$ or $r.a$ is also repeated, then $r$ is not super-maximal.
In our experiments we used a more memory efficient variation, based on a suffix array~\citep{manber1993suffix}, which in combination of the ``longest common prefix'' (LCP) array allows for fast computation of repeats~\citep{abouelhoda2004replacing}.
Their computation is very fast and only takes a few seconds even for the largest of the collections we consider.

Here we used super-maximal repeats that occur at least $3$ times (to reduce inclusions of spurious repetitions), and have at least 20 characters.
Note that they are computed on the non-tokenized, raw text and might therefore span several tokens.
Those values were fixed very early on in our experiments, and we did not modify or optimize them further.

A random subset of repeats are then used in order to pseudo-label documents containing those repeats as machine-generated.
The same amount of human-generated documents are then selected.
We explore two scenarios (which impact line~\ref{ln:unsupervised} of Alg.~\ref{alg:detection}): one fully unsupervised, where that selection is noisy, picking from the full collection (and therefore incurring in a $50\%$ mislabeling rate for the negative examples) and one where a separate collection of human text exists.
This pseudo-annotated dataset is then fed as input to a binary classifier.
The set of documents on which this classifier is trained is reduced (maximal $60$ documents, assuming that each occurrence of each of the $20$ repeats occurs in a different document), so we repeat this process $K$ times.
The final raking is obtained by simply summing the predictions of each individual classifier: each document in the dataset is ranked according to the amount of time it was predicted to be machine-generated.

\begin{algorithm}
\caption{Detection of machine-generated documents using repeats.}
\label{alg:detection}
\begin{algorithmic}[1]
\Function{Detection}{document set D, \# of models K}
\State repeats $\gets$ \textit{supermaximal(D, minLen, minOcc)}
\For{$k \in [1\dots K]$}
\State subset $\gets$ random.choice(\textit{repeats}, 20)
\State pos $\gets \{ d \in D : \exists r \in \textit{subset} \, :\,  r \in d \} $
\State neg $\gets \textit{random.choice}(D, |pos|) $ // we also experiment using true human text \label{ln:unsupervised}
\State $\textit{clf}_k \gets \textit{train}(pos,neg)$
\EndFor
\State $\displaystyle score_d \gets \sum_{k=1}^K \textit{clf}_k(\textit{machine} | d) >  \textit{clf}_k(\textit{human} | d)  \; \forall d \in D $
\Return $\textit{sort}(\textit{score})$
\EndFunction
\end{algorithmic}
\end{algorithm}

\section{Experiments}
\label{sect:experiments}

For the language generator, we decided to use a decoder-only architecture due to its faster inference speed (as opposed to a sequence-to-sequence model like for example T5~\citep{T52020}.
Because of the large number of generations we needed for these experiments (over \numprint{300000} sequences of of up to $512$ tokens) it was unfeasible to use GPT-3, which at the moment of writing was only accessible through a paid API.
We therefore settled on the largest decoder-only model that could be run on premises: GPT-2, accessed through HuggingFace's Transformers library~\citep{huggingface}.
In order to estimate the evolution of our metrics with increasing model size we used various model.
The main experiments were done with the following decoding algorithms: \texttt{topk} (k=10) and \texttt{nucleus} sampling (p=0.8), and other analysis used greedy decoding and ancestral sampling.

We downloaded the top 100 books from the Gutenberg Project, according to their Frequently Viewed page\footnote{\url{https://www.gutenberg.org/browse/scores/top}} on July 27, 2020, extracted their content and split the texts in paragraphs, keeping only the ones with at least 100 characters.
From there we extracted as prompt the two first sentences of each paragraph.
The continuation was used as human generation (\texttt{textgen}).

The final dataset was composed therefore of continuations of those discarded prompts.
To control for length, we filtered out generations with less than $300$ characters and we trimmed the generated text after the 300th character.
The resulting continuations (between $30k$ and $34k$, depending on the model version) were split evenly to obtain a dataset of machine generated and human continuations.
This is, for one given prompt its possible continuations (either human or machine-generated) appears only once.

We derive from this dataset two different settings:
One, \textbf{semi-supervised}, in which we keep $5\%$ of human-generated documents as negative examples, and another one, fully \textbf{unsupervised}, in which negative samples are chosen at random (and thus, have a 50\% error rate).

Super-maximal repeats were computed on the joint remaining documents, combining both human and machine-generated text.
To produce pseudo-labels of machine-generated text we used super-maximal repeats that are longer than $20$ characters and occur at least $3$ times, and trained classifiers using a distilled BERT model as provided by HuggingFace~\citep{huggingface}.\footnote{class \texttt{DistilBertForSequenceClassification}.}
We kept those values fixed across all experiments.

Here we report results in 6 settings, corresponding to 3 model sizes (corresponding to GTP-2 released model sizes of - respectively - 124M, 355M and 774M parameters) and 2 sampling strategies (\texttt{topk} and \texttt{nucleus}).

For evaluation, we assume a scenario where our proposed method would be deployed in a semi-automatic system where human annotators would receive a batch of suspicious documents to revise.
This is similar to how current detection of ambiguous and sensitive content is handled (such as fake news or hate speech).
In such a scenario, the accuracy of the final method is of less importance than its capacity to propose a set with high precision of machine generated text.
We therefore take metrics from information retrieval, in particular \textit{precision at $m$} which is the amount of documents that were correctly labeled as machine-generated in the first $m$ documents, when sorting them according to their final score.

\section{Results \& Analysis}
In Fig.~\ref{fig:acck} we report values for \textit{precision at $m$} for the \textbf{semi-supervised} scenario.
The documents are sorted according to the number of times individual experts voted that document to be machine-generated, and we show the results with increasing number of such experts (from 10 to 100).
As can been seen, \texttt{nucleus} sampled text is harder to detect than \texttt{top-k}, something that confirms the literature.

While there is a clear difference with different sampling techniques, larger models are not always harder to detect than smaller ones, and the final numbers are very close between GPT2-medium and GPT2-large.
Note that in almost all cases, the precision at \numprint{5000} remains higher than $80\%$ using 30 or more experts, and is close to $100\%$ for \texttt{topk}.

Finally, Fig.~\ref{fig:acckNoise} reports the same results when using noisy data not only for machine generated but also for human-generated documents.
This follows the \textbf{unsupervised} scenario where no other in-domain human generated text is available.
While the numbers are significantly lower, the precision at $m$ remains high for \texttt{topk}, and is around $80\%$ for \texttt{nucleus} sampling on GPT2-large for the first $100$ documents.

In Fig.~\ref{fig:examples} and~\ref{fig:examplesWrong} we show -- respectively -- correct and incorrect examples of the top-25 documents obtained through \texttt{nucleus} sampling on GPT2-Large, using noisy labels for the human-generated text (the case of Fig.~\ref{fig:acckNoise}).
Fig.~\ref{fig:examples} are indeed machine-generated, while Fig.~\ref{fig:examplesWrong} are human-generated and therefore wrongly retrieved.
Given an individual example, there does not seem to be nothing apparent which would allow to judge on their origin.
It is the distributional approach that obtains a signal by looking at a large collection of such documents.

\begin{figure}
    \centering
    \input{examples}
    \caption{Examples of sentences that were classified \textbf{\bgg{correctly}} as machine-generated, from the top-25 generations with \texttt{nucleus} sampling using GPT2-Large.}
    \label{fig:examples}
\end{figure}

\begin{figure}
    \centering
    \input{examples-wrong}
    \caption{Examples of sentences that were classified \textbf{\bgr{incorrectly}} as machine-generated, from the top-25 generations with \texttt{nucleus} sampling using GPT2-Large (this is, these are human-generated).}
    \label{fig:examplesWrong}
\end{figure}

\begin{figure*}
    \centering
    \includegraphics[width=\textwidth]{precAtK.png}
    \caption{Precision at $m$ for various models and two sampling strategies on the semi-supervised scenario.}
    \label{fig:acck}
\end{figure*}

\begin{figure*}
    \centering
    \includegraphics[width=\textwidth]{precAtK_noisy.png}
    \caption{Precision at $m$ for various models and two sampling strategies in the fully unsupervised scenario.}
    \label{fig:acckNoise}
\end{figure*}

\subsection{Ablating pseudo-relevance loop}
The use of repeats to capture a weak signal raises the question if the pseudo-relevance step is necessary, or if classifying all those documents that contain a supermaximal repeat is not a good enough baseline.
A first issue is that such a setting does not allow for fine-grained scoring: in all the datasets no document contained more than one supermaximal repeat.
Precision at $m$ is therefore not applicable, as the sorting only throws documents into two bins, depending if they contain or not such a repeat.

In Table~\ref{tb:ablation} we report the percentage of machine-generated documents out of all those that contain supermaximal repeats, for the various combination of decoding algorithm and model size.
In all cases those numbers are lower than what can be obtained after training a classifier on those documents (Fig.~\ref{fig:acck} and~\ref{fig:acckNoise}) pointing towards a better discrimination power by going through an additional classifier step.
For example, for nucleus sampling the percentage of documents containing a supermaximal repeat that are machine-generated is around $60\%$, while it is over $80\%$ with the additional classification step in the weakly supervised case (and around $70\%$ for comparable values of $m$ in the unsupervised one).
A similar absolute difference -- for higher values -- can be observed for top-$k$ sampling.

\begin{table}
\centering
\begin{tabular}{|cccc|}
\hline
decoding & model & prec@$m$ & $m$ \\
\hline
\multirow{3}{*}{nucleus} & small & $60.3$ & \numprint{5745} \\
 & medium & $58.0$ & \numprint{4705} \\
 & large & $57.6$ & \numprint{4856} \\
\hline
\multirow{3}{*}{top-$k$} & small & $75.0$ & \numprint{9095} \\
 & medium & $71.7$ & \numprint{7049} \\
 & large & $71.3$ & \numprint{7208} \\
\hline
\end{tabular}
\caption{Precision at $m$ values by classifying all documents containing super-maximal repeats as machine-generated.}
\label{tb:ablation}
\end{table}

\subsection{Full Classification}

\begin{figure}[bh!]
    \centering
    \includegraphics[width=.5\textwidth]{supervisedBaseline.png}
    \caption{Self-supervised (only using human-generated texts) approach for a classifier, compared to a fully supervised approach.
    The semi-supervised approach used $K=10$, and in all cases the text was generated by GPT2-Medium and the binary classifier is a fine-tuned distilled BERT model.
    }
    \label{fig:supervised}
\end{figure}

The approach described so far permits a semi-automatic human-in-the-loop system, where the classifier selects highly probable documents for further human inspection.
However, the self-training approach can be pushed even further, to train a classification model that aims to classify the whole data-set.
A standard approach in information retrieval to enrich the queries is \textit{pseudo-relevance feedback}: given a query, this approach assumes that the top-most documents are relevant.
A second query is then created, which takes the original one and enriches the query with terms from those top-ranked documents.
The intuition is that those alternative terms allow for richer information and might capture documents that would otherwise be missed.

Inspired by this, we aim to train a classifier that uses the top ranked documents of Alg.~\ref{alg:detection} and measure its accuracy on a fixed held-out data-set.
In Fig.~\ref{fig:supervised} we plot the resulting accuracies with increasing number of training data.
To create this pseudo training data we use again gold human-generated documents, while documents labelled as machine generated are the top ranked documents by the semi-supervised approach of Sect.~\ref{sect:method}.
The dataset here is obtained with GPT2-medium.
As comparison, we also report the results obtained with a fully supervised model on the same test data and the same number of training data.
The gap between the fully supervised and semi-supervised approaches is high for both decoding strategies.
However, the semi-supervised system only had gold labels for human-generated text and all the machine-generated labels were inferred from the data.

\section{Discussion}

Why does this work at all?
Existing zero-shot detection approaches, like the analysis done with 1.5B-GPT2 of \citet{solaiman2019release} or the GLTR interactive tool~\citep{gltr}, rely on the probabilities obtained by an inspection models, which in most cases have some familiarity with the generation model.
The close distribution of unigram frequencies of generated text might indicate that modern language models do not seem to have a clear statistical signature, a reason why the text generated by them is often hard to detect with classifiers.

However, the results in this work seem to indicate that those patterns are still present, but at the level of repetitions.
Assuming that the language models is one author, this work can be seen as detecting the single author who generated $50\%$ of the dataset.
The fact that this is (somehow) possible could be an indication that the prompt is not sufficient for the language-model to mimic the original author's style, and that some of its own style -- evidenced by the repetition of some phrases -- becomes apparent.
Further studies in the stylometry of the generated text and how this relates to the length and type of the prompt might provide more insights into the limitations and adaptability of large-scale language models.

\begin{figure*}[th!]
    \centering
    \includegraphics[width=300pt]{diversity.png}
    \caption{Histogram of diversity for different sampling strategies.}
    \label{fig:diversity}
\end{figure*}

Another related cause might be due to the \textit{diversity} of the generated text.
Fig.~\ref{fig:diversity}
plots the relative histogram of the diversity, measured as the number of different words divided by the number of tokens in the document.
As is well-known, \texttt{greedy} produces unvarying continuations and is prone to repetitions.
The other sampling techniques have a much closer distribution to human text (\texttt{textgen}).
Human text, however, is less peaked than the sampling strategies, including ancestral sampling (\texttt{sample}) which -- in average -- produces even more diverse text.
That diversity has a direct impact on the number of repetitions: a collection of documents with lower diversity is more probable to give rise to longer repetitions.
This might be another reason why \texttt{top-k} and \texttt{nucleus} have a larger set of such long repetitions.
Moreover, note that the detection accuracy is higher for \texttt{top-k} than for \texttt{nucleus}, something that correlates with their diversity.

\section{Conclusion \& Limitations}
We analyzed characteristics of machine-generated text, detecting that they tend to generate certain (well-formed) phrases more often than in human text.
Using that signal as an input to a series of classifier our experiments shows that this signal can be amplified to allow to rank documents in such a way that machine generated documents tend to appear in the beginning.
A limitation of this work is the balanced assumption of the training set, as well as the somehow rigid nature of exact repeats (which does allow fast computation however).
We believe that further stylometric analysis of generated text of such models could allow to provide additional warning signals that machine-generated content is flooding a certain corpus and even identify examples of such documents.


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CEREMONIAL START – Officials break ground for the 9-mile road to Katlian Bay project Tuesday afternoon. Pictured are, from left, D. Lance Mearig, DOT&PF Southcoast Region Director; Rachel Roy, Sitka Chamber of Commerce executive director; Perry Edwards, U.S. Forest Service Sitka district ranger; Garry White, executive director of Sitka Economic Development Association; Sitka Sen. Bert Stedman; Ken Cameron, Shee Atika Inc. CEO; John MacKinnon, Alaska DOT commissioner; Mayor Gary Paxton; and Kerry Kuenzi, president of K&E Alaska Inc. (Sentinel Photo)
Rebecca Aikey Mourned; Was With AmeriCorps
Created on Friday, 28 June 2019 13:06
Rebecca Aikey
Rebecca Aikey (Rebecca Winzer) went home to be with the Lord on June 17, 2019, while serving with AmeriCorps in Sitka. She was 26.
Rebecca was loved by so many and her sweet, loving, kind, and intelligent personality will be deeply missed.
Rebecca was born on May 27, 1993, to Judy and Carl Winzer in Simi Valley, California. The family moved to Coeur d'Alene, Idaho, when Rebecca was 8 years old. She loved the beauty of Northern Idaho and had many fond memories spending time outdoors with her family.
She also excelled academically and graduated from Coeur d'Alene Charter Academy in 2011.
After graduating from high school, Rebecca attended the University of Idaho and was awarded the Goldwater Scholarship in her sophomore year. She also was a member of the honor society Phi Eta Sigma and graduated Magna Cum Laude with a bachelor's of science with a major in physics and a minor in mathematics.
She also fulfilled all of the pre-med requirements and gained admission to the prestigious WWAMI medical program. She was enrolled in the program until she took a one-year break to work with the AmeriCorps.
Rebecca was loved by so many and will live on in the memories of her husband Mark Aikey and their son James; her father Carl and mother Sue; her mother Judy; and siblings Sara, Kira and Kate. She is currently reunited with her dear brother James, who passed away in 2012.


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Q: How can I edit the html file from a project in three.js editor and continue using it? I wanted to import the script to be able to control the camera as the link: Orbital Controls exemple, but I'm using the editor so I only have access to HTML when i publish the project.
there is any way to edit the HTML and continue using the editor?

redpajama_set_name: RedPajamaStackExchange


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Trainer Willie Mullins has re-routed the highly talented seven-year-old to Thursday's contest which is nearly six furlongs shorter than his previous target of the Cheltenham Gold Cup, partly due to stamina doubts but also because he and owner Rich Ricci also have Djakadam among the leading contenders for the blue riband event of the Cheltenham Festival.
As soon as it was confirmed Vautour was heading for the Ryanair Chase he became the hot favourite as he targets a Festival hat-trick having won the Supreme Novices' Hurdle and the JLT Novices' Chase in 2014 and 2015 respectively.
Since then, the French-bred gelding has maintained his record of never finishing out of the first two in his career and was beaten a head by Cue Card in the King George VI Chase at Kempton Park on Boxing Day having been caught close home by that rival.
But while Ricci also said "if he's only 90% right then we don't want to ruin the horse", the quality of the opposition needs to be put into context and jockey Walsh feels the right decision has been made.
The two biggest dangers to Vautour appear to be his stable companion Valseur Lido, ridden by David Mullins, and the other Gigginstown House Stud-owned runner Road To Riches, trained by Noel Meade, a horse of whom Walsh is clearly not afraid.
One opponent whose chances Walsh does rate is Smashing, trained by Henry De Bromhead and unbeaten in three starts this season, while other contenders are Dan Skelton-trained veteran Al Ferof and progressive Village Vic.


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Portable Text tooling in C#? Beautiful typography? A real-time app for Bachelor fans? This week's Community Digest has something for everyone.
There's a little bit of everything in this week's Digest. We've got beautiful typography, Portable Text tooling written in C#, TailwindCSS integrations, and even Sanity powering an app that lets you compete against your friends guessing who gets the next rose in The Bachelor.
New projects and tools like these are shared every day, so be sure to join the Slack community to keep up. If you want your project or tool featured in the Community Digests, post your creation in the #i-made-this channel. Can't wait to see what you come up with!
Don't get enough of the Sanity community? There's a Twitter list for that
Henrique Doro had the amazing idea that even though we spend plenty of time together in the Slack community, sometimes, we just want to keep spending time together in other places. He put together a Twitter list so that we can gather inspiration from each other in another corner of the internet.
An on-brand Studio for a wellness company with striking imagery
With a beautiful mix of typography, animation, and imagery, Ximena Pérez's second-ever Sanity project showcases a wellness consultancy's brand. The site itself features some peaceful interactions, in keeping with the Ubuntu branding.
The Ubuntu-branded Studio
Mixing on-brand typography and imagery for a striking section header.
Portable Text in C#?
We love rich text, but we also love it when rich text converts to JSON objects that can be run through a serializer and used in many applications and not just HTML. That's why Portable Text is an open-source specification. It's just the way rich text should be, and that's why it's exciting that Balázs Orbán and the team at Norwegian Health Informatics (nhi.no) have created an implementation in C# for use in .NET backends. The implementation takes the HTML that's generated from a backend and converts it over to Portable Text for use in a Sanity project or a front-end of your choice. That's a win for the portability of content on the web.
Source code and documentation are available on Github, and you can try a demo here.
Upgrading to Review Changes
Natalie Marleny is working on a new project and took the plunge into version 2.0.1 to give her client a great editing experience with Review Changes. See the original tweet with fully-gif'd Studio.
Tailwind colors shared between front-end and Studio
Thomas Brus has set up react-select to pull Tailwind configuration, shared between Studio and front-end, to allow for editors to choose from a list of colors in the Studio. A super handy tool when giving editors control over the way colors appear on their site.
A dropdown showing shades of green pulled from Tailwind's configuration
Powering an app for hardcore Bachelor fans
In the "not everything is a website" category, we've got Marcus Wood, who created an app for a Bachelor Fantasy League: Guess the Rose. It's powered with connections between Sanity and Firebase. According to Marcus, the entire scoring engine is inside the Studio:
This was my first project using Sanity and we managed to create an entire scoring engine within the CMS and marry it to Firebase to create a flexible solution to run the fantasy league.
A powerful page builder for a mezcal brand
Mike Wagz launched a new Shopify + Sanity store for a mezcal brand - Rosaluna. It's built with 11ty, Sanity, and Shopify. It features a robust page builder with meticulously chosen names for each component.
The Sanity Community Slack is a great place to meet up with new and experienced developers. Come be a part of the community, get help, and show us how you use Sanity.


The SHIVA project aims to create a tool that combines virtual reality, advanced geometric modelling, gesture analysis and digital fabrication in a framework for the modelling and physical fabrication of 3-dimensional shapes and objects. The system will be simple to use and disseminate, specifically enabling and improving the quality of life for individuals with impairments, by facilitating and promoting social inclusion and interaction. It will use, provided that patient pathologies allows for it, hands-free interaction, based on currently available hardware systems. Some of the most complex aspects of the system will be transparent to the user or patient. This will enable individuals with or without impairments who use the system to be able to interact with and model 3-dimensional objects that can then be physically manufactured. A set of specific interfaces will also be implemented for children with very low physical abilities (two-states interfaces for example).
Adults that need cognitive rehabilitation.


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Gang Injunction Approved for Sylmar/San Fernando in Southern California
As expected, and despite opposition from a number of community leaders, a gang injunction against the San Fer gang has been imposed by the courts on a nine-and-a-half square mile area of the Northeast San Fernando Valley, encompassing most of Sylmar, a northwest portion of Pacoima and all of San Fernando City (most of the rest of Pacoima has already been under a gang injunction for years against the Pacoima Flats, Projects Boys, and other Pacoima gangs).
This is reportedly the largest gang injunction area in Los Angeles
Already, young Latino men I know -- not in gangs -- have been stopped, arrested, and in one case almost photographed (to be part of a statewide gang data base). This last case was stopped when the young man's parents became involved and demanded their son not be photographed or placed on this data base. Finding that this young man had no gang ties, he was eventually released.
As I predicted, many youth not in San Fer, but also alleged San Fer members not involved in crimes, will be harassed and even arrested. Our juvenile facilities, jails and prisons are teeming with youth who shouldn't be there -- a gang injunction makes illegal what is otherwise legal activity: association, using a cell phone, or having tattoos. Now alleged gang members will find themselves going to jail for things that are not criminal.
If you make more laws, you make more lawless.
My work, and the work of many gang intervention workers, is to keep these young people out of the criminal justice system. Our work now has become twice as hard as these injunctions -- and other laws down the pike like the Runner Initiative slated for vote in November -- end up placing poor and often neglected youth behind bars faster and longer.
It's easy now to end up in jail -- it's harder to find treatment, help, jobs, schooling, viable alternatives to street life.
The sad thing was that at the Sylmar Neighborhood Council's Town Hall Meeting where I was on a panel opposing the proposed gang injunction, the majority of the people in attendance seemed to be older white residents. This community is mostly Latino with many recent immigrants. Sylmar High School is 95 percent Latino. Yet this community was hardly represented although it's their kids that will be most affected. There didn't seem to be any Spanish-language meetings or even translations considered to include them. Most of the whites in the audience appeared to be for the gang injunction (not all since I talked to some who opposed the injunction, and, of course, there were a number of Latinos for the injunction).
Still the racist nature of these laws is a thick as the smog in LA.
We need to keep our kids out of the juvenile lockups, jails and prisons. The way to do this is to provide jobs, training, meaningful education, creative/artistic opportunities, spiritual connections, and real caring community. Instead gang injunctions under the guise of "safety" fill our communities, even one like where I live, the so-called Huntington Estates, a nice, quiet, tree-lined section of San Fernando that is now under the San Fer gang injunction.
Once gang injunctions were allowed, they spread and even well-off mostly non-gang communities are included.
The city has promised prevention and intervention programs for youth. These are few and minimal (and the biggest chuck, about $30 million, must be decided by voters in November) while the police get billions of dollars. We've become a closed and scared society.
I know we can turn these youth around. I know we can get most of them out of the line of fire. I know we can find more meaningful and healthy alternatives to gangs. But these ideas, programs, and strategies are discarded for the ones that should be of last resort, like a gang injunction.
Just like we go to war and spend close to $10 billion a day in Iraq and Afghanistan to "stop terror" (creating more terror and better-organized "terrorists" in the process), we are going to war with our youth--usually the poorest, most pushed out, and in this case, mostly immigrant Latino youth.
I will be working with other community groups to track the effects of this injunction, which has no end date nor any exit plan for those caught under its net. Hopefully we can find a way to defeat this injunction, but also for other cities to learn not go to this way. In other communities where gang injunctions have been approved, gangs don't die or stop. They actually get squeezed out at so that we see them in other parts of the county, state, country, and even other countries (LA-based gangs have now become a big problem throughout the US, in Mexico, Central America, and even far-flung places like Cambodia and Armenia). Also these so-called gang members get better organized -- in juvenile lockup and prisons they go through "GANG 101" training. They return back to their communities, or even other communities, better organized, armed, and deadly.
You can't stop gang warfare with gang warfare.
We keep making the same mistakes and falling for the same traps. Even a local newspaper in San Fernando declared that a judge "puts halt to violent gang activity with injunction." This is simply a lie. Gang injunctions don't stop violent gang activity--they just spread it around.
I'll keep my readers posted from time to time on the outcomes from this gang injunction. One telling sign tonight--a police helicopter shining their lights a couple of streets from my house. Oh, yes, LA pioneered the "Ghetto Bird." But this part of San Fernando has had little or no crime for decades. Now, as part of the gang injunction "safety" zone, we're being treated like any other poor ghetto or barrio community (which shouldn't happen there neither).
Like I said, once you let this monster in, it keeps growing.


Helwig et al. PC1/3 and PC2 gene expression and post-translational endoproteolytic pro-opiomelanocortin processing is regulated by photoperiod in the seasonal Siberian hamster (Phodopus sungorus).
Mzhavia et al. Characterization of endothelin-converting enzyme-2. Implication for a role in the nonclassical processing of regulatory peptides.
J Biol Chem. 2003 Apr 25;278(17):14704-11.


This past weekend I spent the better part of the day at Science North. I have fond memories of Science North from family outings as a child and my recent visit rekindled a lot of my enthusiasm for hands on learning. I work in an archive where most visitors have very little hands on exposure to the archival material. Science North reminded me of the importance of interactive learning and making information accessible in creative ways.
One of my favourite parts of my visit included the floor dedicated to the landscape, animals, and ecosystems of Northern Ontario. This floor includes a 'forest lab' with trees, a nocturnal room complete with flying squirrels, and a number of other common Northern Ontario animals. The majority of the animals on this floor have spent their entire lives at Science North and are quite friendly — I actually saw a staff person petting a porcupine. This floor also includes an 'erosion table' that I remember loving as a kid. The erosion table is a giant sand and water table that allows children to see the impact of streams and running water on soil. Lots of messy fun. Overall, this floor allows visitors to see first hand distinct features of Northern Ontario's landscape and to touch and feel a variety of Northern animals and plants.
One of the special exhibits currently at Science Norther is Wildfire! A Firefighting Adventure in 4D. This exhibit was created in conjunction with the Ministry of Natural Resource (MNR) and the Ontario government and focuses on the MNR's forest fighting efforts. The 3D movie and accompanying motion seats provide insight into the workings of water bombers and forest fighting ground crews work. This was a really well done experience; though I do not recommend taking small children to to see Wildfire! as a couple of the smaller children in the audience found the experience on the frightening side. The Wildfire! trailer can be seen here.
Overall, I like Science North because it is truly a place for both kids and adults. I went sans children and had a great time, but there are tons of activities for families with children. Additionally, unlike the Ontario Science Centre in Toronto, Science north is rarely swarming with visitors. Adults can take their time enjoying the hands on stations without worrying about taking a child's place. Additionally, I found that I learned a surprising amount about Northern Ontario in a fun and interactive way. The visit to Science North was well worth the trip to Sudbury.


The cheerleaders competed at the UCA College Cheerleading Championship on January 14th. Kentucky finished 3rd, behind champion Central Florida and runner up Alabama. Additional details can be found on Varsity.com.


text:
<dataset>
    <authentication_system_conf
        prop="authenticationSystem"
        value="edu.northwestern.bioinformatics.psc.etc-system"/>
    <authentication_system_conf
        prop="unmapped"
        value="something"/>
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redpajama_set_name: RedPajamaGithub


text:
var _ = require('lodash');
var Store = require('store-prototype');
var request = require('superagent');

var RSVPStore = new Store();

var loaded = false;
var meetup = {};
var rsvp = {};
var attendees = [];

var REFRESH_INTERVAL = 5000; // 5 seconds

var refreshTimeout = null;
function cancelRefresh() {
	clearTimeout(refreshTimeout);
}

RSVPStore.extend({

	getMeetup: function() {
		return meetup;
	},

	getRSVP: function() {
		return rsvp;
	},

	getAttendees: function(callback) {
		return attendees;
	},

	rsvp: function(attending, callback) {
		cancelRefresh();
		request
			.post('/api/me/meetup')
			.send({ data: {
				meetup: SydJS.currentMeetupId,
				attending: attending
			}})
			.end(function(err, res) {
				if (err) {
					console.log('Error with the AJAX request: ', err)
					return;
				}
				RSVPStore.getMeetupData();
			});
	},

	isLoaded: function() {
		return loaded ? true : false;
	},

	getMeetupData: function(callback) {
		// ensure any scheduled refresh is stopped,
		// in case this was called directly
		cancelRefresh();
		// request the update from the API
		request
			.get('/api/meetup/' + SydJS.currentMeetupId)
			.end(function(err, res) {
				if (err) {
					console.log('Error with the AJAX request: ', err)
				}
				if (!err && res.body) {
					loaded = true;
					meetup = res.body.meetup;
					rsvp = res.body.rsvp;
					attendees = res.body.attendees;
					RSVPStore.notifyChange();
				}
				RSVPStore.queueMeetupRefresh();
				return callback && callback(err, res.body);
			});
	},

	queueMeetupRefresh: function() {
		refreshTimeout = setTimeout(RSVPStore.getMeetupData, REFRESH_INTERVAL);
	}

});

RSVPStore.getMeetupData();
module.exports = RSVPStore;


Prof Peter Talbot, and his team have developed a long life lithium battery.
Researchers at the Queensland University of Technology (QUT) have produced Australia's first lithium-ion battery after establishing the country's only facility capable of such manufacturing.
Professor Peter Talbot from QUT's Institute for Future Environments said the batteries were based on commercial battery formats comparable to those used to power Tesla vehicles.
"Importantly, as part of this project we identified the best lithium-based powders to use to create a battery of the highest energy-efficiency standards possible," he said.
"The powder is a combination of lithium and other compounds. We tested various compositions of chemicals until we were satisfied that we had achieved the best powder possible.
He said the research could be used to kick start a commercial lithium-ion battery manufacturing industry in Australia, with the batteries being one of the most popular types of rechargeable batteries used in portable electronics from mobile phones, to power tools and drones.
"This process could be automated to enable Australia to have a competitive advantage in a manufacturing space that is currently dominated by China.
"As the middle class in the ASEAN region grows, so too will the demand for lithium-ion battery operated goods.
Lithium is mined in several countries including Australia and Talbot said the facility could value-add to the mining industry as miners could have their materials validated at the plant.
Professor Talbot said the technology and processes developed at QUT as part of the project were suitable for use by any commercial battery manufacturing company.
He said now that QUT had developed the purpose-built facilities needed to produce the lithium-ion batteries the university could develop batteries for specific commercial applications.
Infrastructure built at QUT's Banyo facility to enable the project includes Australia's only electro-manufacturing room with zero humidity.
"This research wouldn't have been possible without the financial backing of the Australian and Malaysian governments and highlights the importance of international research partnerships in the efforts to solve global problems," Talbot said.
He said QUT's Banyo Pilot Plant Precinct offered wide scale scope to test a large variety of engineering products and processes while the university's Central Analytical Research Facility housed the instruments necessary to analyse the properties of materials.


text:
The Jew Who Wasn't There: Studies on Jews and Their Absence in Old Norse Literature
Cole, RKE; (2015) The Jew Who Wasn't There: Studies on Jews and Their Absence in Old Norse Literature. Doctoral thesis , Harvard University.
This dissertation explores certain attitudes towards Jews and Judaism in Old Norse literature. Regardless of an apparent lack of actual Jewish settlement in the Nordic region during the Middle Ages, medieval Icelanders and Norwegians frequently turned to the image of 'the Jew' in writing and in art, sometimes using him as an abstract theological model, or elsewhere constructing a similar kind of ethnic Other to the anti-Semitic tropes we find in medieval societies where gentiles really did live alongside Jews. The aim of this dissertation is to investigate the differing histories and functions projected onto the absent Jew in medieval Scandinavia.
UCL > Provost and Vice Provost Offices
UCL > Provost and Vice Provost Offices > UCL SLASH
UCL > Provost and Vice Provost Offices > UCL SLASH > Faculty of Arts and Humanities > SELCS
https://discovery.ucl.ac.uk/id/eprint/1574812
Export as JSONXMLCSV
Export as JSONCSVXML


text:
Ryan Giggs | Manchester United & Wales
Wales defender Ben Davies defends Ryan Giggs after Hungary defeat
Posted by Steven Tan | Source from Sky Sports
Ben Davies has branded criticism of Wales manager Ryan Giggs as unfair after their European Qualifier defeats to Croatia and Hungary.
Giggs' side now trail Group E leaders Hungary by six points, albeit with a game in hand, and are three adrift of Slovakia and Croatia in the race for second.
"I don't think that's fair," said Tottenham defender Davies. "He's brought a lot of young players in and we are all accountable for it.
"We have to stick together, it's not a time to be nitpicking and pointing fingers. I just didn't think it was a very good performance from us [against Hungary], we didn't play the way we can."
With five wins in his first 13 games in charge, Giggs has made a better start to life as Wales manager than his predecessor Chris Coleman who won just four in 13 - yet Coleman went on to become a national hero for guiding Wales to the semi-finals of Euro 2016.
"If there's one thing about this squad I know, it's that we won't just accept the position we're in," said Reading defender Chris Gunter, who won his 95th cap in Budapest.
"We need to get points quite quickly but we still have an opportunity to do so. In the last campaign (2018 World Cup qualifying) we had to put a run of results together and we almost did it."
Next up for Wales is Azerbaijan, who are bottom of the group, in September.
"You always want to be better off points-wise, but it's still early," added Gunter. "There was talk before Hungary of must-win and things like that. We haven't won, but as far as I'm concerned we're not out of the group."
"We've still got Hungary to play at home and got a game in hand on them, so we know that we've got some really big games ahead of us."
Related keywords: Wales, Ryan Giggs, Ben Davies
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Ryan Giggs has been inducted into the Welsh Sports Hall of Fame
Giggs, Cole and Neville welcome home United fans who cycled 1,200 miles from Barcelona to Manchester
Giggs makes a huge claim about United signing James
Hungary 1-0 Wales: Wales suffer qualification setback in Hungary
Daniel James must get used to getting kicked, says Ryan Giggs
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text:
STS-132 var en flygning i USA:s rymdfärjeprogram till Internationella rymdstationen ISS. Flygningen utfördes med rymdfärjan Atlantis. Man levererade och installerade bland annat den ryska modulen Mini-Research Module 1 (MRM1), samt hade med sig förnödenheter. Starten skedde den 14 maj 2010 kl. 20.20 svensk tid och landningen den 26 maj 2010 kl. 14.48 svensk tid. Uppdraget var Atlantis sista planerade rymdfärd, och hon gick därefter i pension efter 25 års service och 32 st flygningar.

Aktiviteter

Innan uppskjutning
Atlantis rullade över från sin hangar till Vehicle Assembly Building den 13 april 2010, överflyttningen tog längre tid än normalt då många fotografier togs längs vägen eftersom detta är Atlantis sista planerade överrullning. Atlantis rullade ut till startplatta 39A den 22 april 2010 efter några dagars försening på grund av dåligt väder.

Aktiviteter dag för dag

Dag 1: Atlantis startade som planerat kl. 20.20 svensk tid. Den 8 minuter långa färden till omloppsbana gick utan några problem. Besättningen öppnade därefter lastutrymmesdörrarna samt gjorde om Atlantis från en uppskjutningsfarkost till ett hem för de kommande 12 dagarna.

Dag 2: Besättningen undersöker Atlantis värmesköld, samt förbereder inför dockningen med ISS.

Dag 3: Atlantis dockade med ISS kl. 16.27 svensk tid. När färjan befann sig under ISS så genomförde befälhavare Ken Ham en 360 gradig flipp med Atlantis så besättningen på ISS kunde ta fotografier av färjans undersida. Man förberedde även inför EVA 1.

Dag 4: Den första rymdpromenaden utfördes med endast några mindre problem. Det som krånglade var en av stationens huvuddatorer som slutade fungera, och då stängs alla icke kritiska operationer och sätts i vänteläge, vilket även innefattade robotarmen som just då användes till rymdpromenaden. Datorn startade igång som vanligt efter ett tag och rymdpromenaden kunde fortsätta som vanligt.

Dag 5: MRM-1 dockades till ISS genom en förflyttning från Atlantis lastutrymme. Befälhavare Ken Ham styrde Atlantis robotarm och lämnade sedan över MRM-1 till stationens robotarm som styrdes av uppdragsspecialisten Garrett Reisman som i sin tur dockade MRM-1 till dess permanenta plats. Man fortsatte även med överföringsarbetena samt förberedelser inför EVA 2.

Dag 6: Den andra rymdpromenaden utfördes utan några problem. Överföringsarbetena fortsatte löpande under dagen.

Dag 7: Fortsatta överföringsarbeten samt förberedelser inför EVA 3. Besättningen hade även en ledig tid.

Dag 8: Den tredje och sista rymdpromenaden avklarades utan några större problem. Besättningen fortsatte även med de sista överföringsarbetena.

Dag 9: Besättningen hade ledig tid samt avklarade de sista överföringsarbetena. Förberedelser inför utdockningen ägde även rum.

Dag 10: Atlantis dockade ut från ISS kl. 17.22 svensk tid. Pilot Tony Antonelli flög sedan färjan ett varv runt stationen så bilder kunde tas. Han tände sedan Atlantis jetmotorer för att färjan skulle hamna på ett säkert avstånd från stationen.

Dag 11: Besättningen utförde de sista inspektionerna av färjans värmeskydd samt hade presskonferens med media.

Dag 12: Besättningen förberedde inför landning, genom att stuva undan material och montera upp flygstolarna som stuvades undan första dagen.

Dag 13: Atlantis landade på bana 33 på Kennedy Space Center kl. 14.48 svensk tid. Därmed avslutades uppdraget STS-132 och det sista uppdraget för Atlantis som därmed gick i pension efter 25 års service och 32 st flygningar.

Rymdpromenader
EVA 1: En KU-bandsantenn installerades, för att detta skulle fungera behövdes en hel del bultar lösgöras samt en del kablar kopplas om. Reisman och Bowen utförde rymdpromenaden.

EVA 2: Tre av sex batterier på P6s b-sida bytes ut. Batterierna på a-sidan byttes ut under STS-127. Bowen och Good utförde rymdpromenaden.

EVA 3: Resterande tre batterier på P6s b-sida byttes ut, alltså kom den andra och tredje rymdpromenaden att se nästan helt lika ut. Good och Reisman utförde rymdpromenaden.

Besättning
  Kenneth T. Ham befälhavare. Tidigare rymdfärder STS-124
  Dominic A. Antonelli pilot. Tidigare rymdfärder STS-119
  Stephen G. Bowen uppdrasspecialist. Tidigare rymdfärder STS-126
  Michael T. Good uppdragsspecialist. Tidigare rymdfärder STS-125
  Piers J. Sellers uppdragsspecialist. Tidigare rymdfärder STS-112, STS-121
  Garrett Reisman uppdrasspecialist. Tidigare rymdfärder Expedition 17

Väckningar
Under Geminiprogrammet började NASA spela musik för besättningar och sedan Apollo 15 har man varje "morgon" väckt besättningen med ett särskilt musikstycke, särskilt utvalt antingen för en enskild astronaut eller för de förhållanden som råder.

Se även
 Rymdforskning

Referenser

Externa länkar

 Svensk sida om STS-132

Rymdfarkoster
Rymdfärjeprogrammet
Internationella rymdstationen
2010 i USA
Rymdfartsåret 2010

redpajama_set_name: RedPajamaWikipedia


text:
Guillermo Del Toro Loved How Much Scary Stories To Tell In The Dark Freaked Out Test Audiences
By Nick Evans published 9 August 19
One of the great things about horror movies is the experience of sitting in a darkened theater and being scared out of your mind alongside a room full of strangers. That's the goal all horror movies are shooting for and one achieved by this weekend's Scary Stories to Tell in the Dark. In an exclusive interview with CinemaBlend's own Eric Eisenberg, producer Guillermo del Toro and director André Øvredal spoke about loving how much the film freaked out test audiences. They said:
André Øvredal: It's been fantastic to watch the test audiences react to like when he eats the... thing. It's like people are yelling and screaming at the theater. It was fantastic.Guillermo del Toro: It's been a very vocal movie. Some horror movies sort of a scare you into silence, and some horror movies, 'Come on! Don't go in there!' Don't get in there, don't go in there!' This is that kind of movie.
As a horror director, there's probably nothing more rewarding than seeing audiences viscerally react to the frightening images you've put onscreen, and André Øvredal got to have that validation in the test screenings for Scary Stories to Tell in the Dark. It sounds like he came close to spoiling a specific scene, but suffice it to say, someone in the film is going to eat something and it completely freaked everyone out.
That test audiences were yelling and screaming in the theater is a heck of a selling point for the movie. It's also the kind of reaction that you'll sometimes see in videos of the audience freaking out incorporated into horror trailers. According to Guillermo del Toro, Scary Stories to Tell in the Dark is that kind of horror movie, where the audience isn't quietly shrunk down in their seats, but is vocally trying to warn the characters about the fearful terrors that await them.
It's interesting to hear that the test audiences were so vocal and so freaked out because despite being a horror film, you might not think that Scary Stories to Tell in the Dark would be that scary. It is PG-13 after all and it is based on the collections of short horror stories written for children by Alvin Schwartz.
Yet despite the fact that the film is based on children's books and is aimed at a younger audience than most horror movies, there were no worries about making Scary Stories to Tell in the Dark too scary, as Guillermo del Toro and André Øvredal explained:
Guillermo del Toro: No [there wasn't any problem making it scary], because it's not gory, and it was not profane. The heart of the movie is such a good heart. The kids are so great and awesome as characters. Don't you think?André Øvredal: My goal was just to scare people, so to have a vast canvas like this with such a beautiful story and such great characters, and then be able to put them in peril and be able to just use all my tools as a filmmaker to make it really frightening, it was just a joy.
Scary Stories to Tell in the Dark was able to be scary and still be geared at younger audiences, and with a PG-13 rating because it wasn't gory or laced with profanity. Gore and profanity are common in horror films, but they (especially gore) are sometimes used as a crutch, with the gore being the scary thing and not just the icing on true narrative tension.
Therefore, without gore and profanity, André Øvredal was able to really try and make the film as scary as possible. The reason this seemingly worked so well, is that the film has heart and strong characters.
It's odd to compare it to Stranger Things since the books upon which Scary Stories is based predate that show by decades, but it is the modern touchstone. Like that show, it seems that the horror works in Scary Stories to Tell in the Dark because of the audience cares about the kids these things are happening to. You can see Guillermo del Toro and André Øvredal discussing the film with Eric in the video below.
Test audiences weren't the only ones who were freaked out and had a good time with Scary Stories to Tell in the Dark. The reviews for the film have been generally positive and it is Certified Fresh on Rotten Tomatoes.
Scary Stories to Tell in the Dark is now playing. Check out our 2019 Release Schedule to keep track of what's headed to theaters and stay tuned to CinemaBlend for the latest movie news.
Nick Evans
Nick grew up in Maryland has degrees in Film Studies and Communications. His life goal is to walk the earth, meet people and get into adventures. He's also still looking for The Adventures of Pete and Pete season 3 on DVD if anyone has a lead.
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Home » Wedding » Planning » Post Brexit Wedding plans?
A destination wedding is no longer a dream for some couples. After all what's not to love? For most it's the guaranteed sunshine or the setting of your dreams, often a lot cheaper than in the UK. Plus, you'll be surrounded by only your closest friends and family and won't have to worry about the guest list looking like Beyoncé is having a bash.
Still, there are tricky hurdles that can all but put the brakes on an overseas wedding, namely, the exchange rate.
Even if you find a great wedding organiser, or you decide to do everything else you could be facing language issues and deposit concerns all while trying to work out exactly how much it will cost you. Not to mention those expensive transfer fees when we're talking about large sums of money.
Wedding planner Illy Goodhall saw how her brides would look longingly on at the beach bridal wear, but then be put off by the additional fees and organising. And with the help of her sister and expert in money exchanges Zsofia Foster, she has set up her own online exchange company.
Their new exchange platform, Wedding Forex, allows couples not just to lock in an exchange rate for 12-months, but for suppliers to use their details on an invoice, cutting out the middleman on most transactions.
Plus, your guests can use it too! With the news that overseas guests can find themselves spending £700 each, those bad conversion rates can really add up. Not to mention suppliers like florists, photographers and venues, who often find themselves shelling out over a £1000 a year to change currencies.
How does the platform benefit a wedding party?
On average couples will spend £7,000 with local suppliers when planning a wedding abroad, which will be broken down into smaller payments to each supplier, incurring a number of transfer fees and variable exchange rates by banks or other third parties.
One of the reasons couples choose to get married abroad is to save on the cost of their wedding, but often some of this saving is lost in large transfer fees or unpredictable currency fluctuations – a la Brexit.
WeddingForex allows its clients to lock in an exchange rate and benefit from reduced transfer fees which could ultimately save them upwards of £1,000 on the overall cost of their wedding, not to mention a honeymoon or any additional travel requirements later in life.
They can also offer use of the service to their guests, for booking travel or accommodation, with guests spending an average of £700 to attend a destination wedding, savings could be significant for them too.
27-year old Illy has built up a successful wedding planning business over the past four years, she created the business with a team who have first-hand understanding of the wedding planning process, something a bank or other foreign exchange might not appreciate.
"I got the bug to plan weddings when I had my own, but before that I had been happy working in the Foreign Exchange industry. When I realised that overseas weddings were on the rise within my own clientele, I began to understand the pitfalls that they faced with regards to sending money abroad. They either worried whether it was safe or didn't realise they could lose out on transfer fees, often leading to more money being sent later down the line. It just seemed to click, and before long we launched a bespoke service just for the wedding industry.
So, what do you think? Would you use a dedicated wedding forex to make things easier? I'd love to find out.


The vinyl press of the cd out on SOA records. This is an official reissue of the Torino hc legends! Here you can find a selection of their discography that consisted of the legendary tape split with Negazione, a 7" and 12". This is 100% Italian hc from the 80ies finally on vinyl!


The Learning Cloud is an innovative learning platform that supports teachers, pupils and parents around the world.
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Jallianwala Bagh is an open garden in Amritsar.Popular for a standout amongst the most awful yet milestone occasions ever of. This is the place the Amritsar Massacre of 1919 occurred. The British Army fighters after accepting requests. General Dyer opened fire on a tremendous, unarmed assembling of men, ladies, and kids on April 13, 1919.
In April of 1919, Brigadier General R.E.H Dyer touched base in Amritsar and involved the city under his standard as the common organization under Miles Irving had stopped. Suspecting a revolt, General Dyer reported a restriction on gatherings and traditions by the general population, however, this data was not broadly engendered.
Jallianwala Bagh massacre on the thirteenth of April, 1919. A serene social occasion of individuals assembled in the Jallianwala Bagh to commend the celebration of Baisakhi. After getting the principal data about the get-together, General Dyer walked towards the Bagh with a force of ninety Sikh, Gurkha, Baluchi, Rajput troops from 2-ninth Gurkhas, the 54th Sikhs and the 59th Sind Rifles. History of the Jallianwala Bagh Dyer and the troops rose a brought stage up in the garden. Declared aimless and provocative terminating on a huge number of peaceful and unarmed Indians.
Dyer kept terminating until the point that the ammo was depleted, making guiltless and unarmed men, ladies and kids its unfortunate casualties. History of the Jallianwala Bagh Formally, the British Indian sources distinguished 379 individuals dead in the episode with 1,200 injured. In any case, the number of setbacks evaluated. Indian National Congress was an amazing 1,500 with 1,000 individuals surrendering to the projectile injuries.
Jallianwala Bagh massacre the injuries that were flung at Jallianwala Bagh . The British locate a substantial presence at two exceptionally specific structures in the garden. One of them is the divider towards the finish of the recreation center. Which is as yet loaded with the signs of up to 36 projectiles that were let go at the groups. Jallianwala Bagh massacre a well is arranged close to the passageway of the recreation center was the place numerous individuals going to the gathering had hopped with the end goal to evade the slugs. After the terminating has died down, many dead bodies were withdrawn from the well. These two structures are serious notices of our opportunity battle and the penances made by our antecedents to construct an autonomous India.
The bit of the divider with slug stamps alongside the well is protected as a dedication. Jallianwala Bagh Importance a light and sound show is facilitated here each night. Jallianwala Bagh Importance the demonstration described utilizing performing artist Amitabh Bachchan's voice, reproduces the occasions of 1919 that occurred. At Jallianwala Bagh Amritsar and is a significant intriguing and blending knowledge.
Jallianwala Bagh is situated on the Golden Temple Road and is effectively available. Brilliant Temple to Jallianwala Bagh separate is simply 1.3 kilometers. Along these lines, in the wake of visiting the Golden Temple on can stroll to this authentic stop or take a rickshaw. Individuals going from different parts of the city can settle on any method of nearby transport, for example, transport, or taxi. The closest airplane terminal to Jallianwala Bagh is Sri Guru Ram Dass Jee International Airport which is roughly 14 kilometers away. Different neighborhood transportation choices are accessible close to the air terminal premises which can be utilized to achieve Jallianwala Bagh with no issue.


For the average small business owner, scheduling appointments by phone takes up far too much time, time that could better be spent on other parts of your business. Even hiring an employee doesn't solve the inefficient nature of scheduling appointments by phone.
In this article, we are going to discuss the benefits of implementing an online scheduling portal. A scheduling portal is a public-facing web page where visitors can view your availability and schedule an appointment based on the types of appointments you've configured. The types of appointments you offer can be customized to fit the needs of how your business works.
A Scheduling portal offers a new layer of communication with your connections, prospects and website visitors. Consumers expect local businesses to have an online presence 1 and by adding a scheduling portal, you've introduced a visible, low-pressure way for someone to schedule an appointment with you.
You can easily manage the days and times you are willing to schedule appointments and events on connected calendars will be blocked off. Since a small business owner's schedule requires attention in many places, the ability to manage when an appointment can be scheduled helps avoid bookings during times dedicated to other important tasks.
While consumers expect to be able to call a business and get an answer right away2, they don't like receiving phone calls from businesses3. It's hard to get someone on the phone, especially younger consumers. A scheduling portal helps give those who don't answer but are interested an easy way to setup an appointment on their terms. By eliminating scheduling calls, you also reduce the confusion of going back and forth to find an agreeable date and time for your meeting.
A survey by Yodle shows that local businesses are viewed favorably among consumers. Local businesses generally have a reputation of more personalized service and expertise in their industry4. Consumers want more ways to communicate with those businesses online. Adding a scheduling portal gives your connections, prospects and website visitors a way to get in touch that fits in with their busy life.
Having a scheduling portal is an effective way to increase your appointments. As consumers continue to adopt more advanced technology, they expect businesses to follow. The benefit of implementing online scheduling is that it's a cost-effective way to improve your relationship with your customers.


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The spice grinder is fitted with the "best milling system (CRUSHGRIND®) in the world" and is composed of a pure ceramic material which possesses great hardness.
Rotation in either direction produces very good results.
The ceramic milling mechanism is resistant to corrosion and neutral to taste.
The abrasion-free ceramic milling mechanism guarantees constant quality over many years of use. Even the hardest of spices such as nutmeg and pepper can be ground effortlessly.
The aroma-tight lid protects the ground material from steam and dampness, and conserves the full aroma of the spices. Simultaneously, you can use the lid as an aid to dosage and grind directly into the lid.


Do you think of yourself as a super hero? You should. As a person with Attention Deficit Disorder, you keep company with some very impressive people.
Beethoven, Bill Cosby, Cher, Leonardo da Vinci and Walt Disney to name a few. Do a search online for famous people with ADHD and you'll see what I mean.
So if your mind works like da Vinci, or Einstein, isn't that a good thing?
And yet all this time, you've been thinking otherwise because you don't think like most people you know.
Hmmmm…..Joe the Plumber's brain, or Thomas Edison's? You choose.
Thanks for another insightful and SPOT ON post!
Only when I learned that my ADHD was a gift…did I begin to fully realize what I was capable of!
Our 9 y.o is inattentive type ADHD and we frequently remind him of his good qualities stemming from it and how he can harness those things viewed by some as 'pesky quirks' into something really great. He is highly intelligent, incredibly disorganized LOL, and is a great debater. Not sure how much I like that quality as his mother hehe.
We view it in negative light, mainly because the medical industry have felt inclined to make us believe its not normal. Of course this allows them be able to offer us drugs to supposedly 'fix' it.
Has anyone considered this might just be a side effect of evolution?
You make some good points, Mark. I think that another reason that ADHD is perceived negatively is because the majority of the population does not have it.
And yes, I have said for years that I believe ADHD can be seen as evolution in process. Think about the way the world is today, how fast we get information and the need for multi-tasking. I can't think of a more suitable environment for someone with Attention Deficit Disorder.
He's a lucky boy to have such positive and supportive parents. I think a positive self-image is important to all children, but especially so for those with ADHD. Viewing themselves in a positive light really allows them to soar and be all that they are capable of being.
Thank you! Yes – I agree – ADD is a gift and I wouldn't trade it for a minute!


text:
by GeoPolitic.org September 24, 20224:54 pm
THE ANTI-RUSSIAN PROPAGANDA CAMPAIGN KICKS INTO HIGH GEAR
If Vladimir Putin expected the western propaganda campaign to slacken in the wake of his speech this week announcing Russia is ready to accept four Ukrainian oblasts as new republics in Russia and ordering a partial military mobilization, he is very disappointed today. There has been a flood of reports claiming that the Russian stock market is collapsing and that Russian men of military age are fleeing the country in droves.
The reality is that reaction in Russia has been more positive than negative. Are there protests? Yes. Funny about that. I thought Putin's Russia was the reincarnation of the bad Soviet Union and anyone protesting would be carted off to the Gulag. There have been a few arrests, but the protests have not been quashed.
Western sources are circulating videos on social media of lines of cars trying to get out of Russia and lines of young men at the airports looking for a safehaven overseas. Is it true? Well, there are other videos of lines forming outside military recruitment offices in Russia.
So let us look at the economic data. Consider the claim that Russia's economy is collapsing:
Russia's stock market crashed on Wednesday following President Vladimir Putin's announcement of a partial mobilization to support his campaign in Ukraine. . . . After resisting for months to Western sanctions, the Russian economy is now starting to show signs of a significant decline, as decreased energy exports to Europe bring consequentially lower profits.
https://www.newsbreak.com/news/2755070907676/russia-s-stock-market-crashes-after-putin-s-partial-mobilization-message?noAds=1&_f=app_share&s=i3
Is that true? Not really. If we follow this logic then does the 20% decline in the U.S. stock market since November 2021 signal the end of America? Here are the facts about the Russian stock market:
The ruble-based MOEX Russia index closed 2.8% higher at 2,190 on Thursday, slightly rebounding after plunging 12.3% in the last two sessions as investors monitored corporate news amid further escalation of the war in Ukraine and fresh threats to the West. This week, President Putin ordered the country's first military mobilization since World War II and emphasized Russia's readiness to use its nuclear weapon arsenal after announcing plans to formally annex four Ukrainian territories through referendums set for this weekend. The annexation is seen by many as serious means of escalation, giving grounds for the Kremlin to consider Ukraine's recent counterattack an aggression on Russian soil with Western weapons. On the corporate front, Gazprom shares surged 8% as its board announced interim dividends of RUB 51 per share for its record-setting profits in the first half of 2022. Banks also booked gains after the central bank announced it extended reserve requirement relief measures.
https://tradingeconomics.com/russia/stock-market
What about the Ruble? If there really was an exodus of Russians fleeing to western and Asian nations those people would need foreign currency. Why? Because the Russian Ruble won't buy you a cup of coffee in Finland, Sweden or Germany. That means there should be a major increase in demand for foreign currency, which means the price of the Ruble should have plummeted. It did not. Check out this chart:
https://tradingeconomics.com/russia/currency
The price of the Ruble has remained steady since the end of July. There was no precipitous drop of any kind in the Ruble today.
The Russian ruble was at 60 per USD in September, remaining well above levels prior to Russia's invasion of Ukraine and cementing its rebound from the record low of 150 touched in March, supported by strict capital controls and trade imbalances. Higher prices for energy and commodities since the start of the war lifted export revenues and relative ruble demand amid a collapse in importing activity due to sanctions. On top of that, monetary authorities mandated hefty fees and negative interest rates on currencies from "unfriendly" states. The disparity is reflected in the widest current account surplus on record of USD 70.1 billion in Q2 from USD 17.3 billion in the corresponding period of the previous year. Strength in the ruble withstood the CBR cutting borrowing costs to below pre-invasion levels, amid continuous weekly deflation readings and calls for ruble devaluation by policymakers in the central bank and the State Duma. .
I think Putin's speech this week may dent his popularity, but opposition political parties are not likely to get a big boost. Putin is appealing to the Russian patriotism and that still has strong appeal in Russia. Also important to point out that the limited mobilization Putin announced only affects the men and women who serve in Russia's reserves. He is not going to raid the universities and dragoon students into the army. The use of reserve personnel means that the training cycle for reinforcements will be accomplished before the end of the year, if not sooner.
Let me conclude by giving you some of the hysterical headlights announcing the impending doom of Putin and Russia:
Putin's losses in Asia are bigger than in Ukraine
The end is near for Putin's war against Ukraine
Putin's call for Russian conscripts will require arms Moscow doesn't have, NATO chief says
Putin was so upset about these developments that he said this in his speech today marking the 1160th anniversary of Russian statehood (you read that right, more than 1000 years):
During more than a millennium, our statehood has lived through many eras, including cruel enemy invasions, disunity and the tragedies of feuds, but each of these difficult periods invariably ended with the revival of the Fatherland. The heroic generations of our people overcame difficulties and adversities, withstood the trials. They created and expanded the grandeur of our Fatherland and covered their names with glory.
We remember and cherish these truly outstanding people: Rurik and Prophetic Oleg, Princess Olga and Svyatoslav Igorevich, Prince Vladimir and Yaroslav the Wise, Vladimir Monomakh and Alexander Nevsky, Dmitry Donskoy and Sergius of Radonezh, Ivan III and Ivan the Terrible, Yermak, Minin and Pozharsky, Dezhnev and Bering, Peter the Great and Catherine the Great, Lomonosov and Pushkin, Suvorov and Ushakov, Alexander II the Liberator and Alexander III the Peacemaker, Brusilov and Denikin, Zhukov and Rokossovsky, Kurchatov, Korolev and Gagarin.
These and many other of our compatriots were larger-than-life, complex and occasionally controversial historical figures. Some of them saw Russia's future differently and were even on the opposite sides of the barricades. You know, when drafting this text, I scribbled in and crossed out names like Nicholas II, Lenin, Stalin. Apparently, not enough time has passed since then from a historical point of view for us to give comprehensive and objective assessments that are free from the pressure of ongoing political developments.
However, all of them, including statesmen, workers, warriors, pioneers, scholars, ascetics and saints and, most importantly, all our people made Russia a great global power and determined its future. . . .
Being part of a diverse Russian civilisation means happiness, but, let me repeat, it also means responsibility and duty. Our civilisation is distinct. It has its own path and there is not a bit of arrogance or a feeling of superiority in this. This civilisation of ours – this is what's important to us.
And we will fight for our Fatherland, our homeland of which there is only one, for our independence and sovereignty, for our culture and traditions. We will uphold and defend this in the name of our ancestors and our descendants, for the sake of Russia, its great history and its great future.
The Russians have seen a few more ups and downs than most countries, except for China. But this does not sound like a frightened leader who believes his back is to the wall and Russia is facing a new defeat.
By Larry Johnson Via https://sonar21.com/the-anti-russian-propaganda-campaign-kicks-into-high-gear/


text:
Q: Google script flextable span columns I am trying to make an input box which spans the 3 columns of the table. From looking online it seems like I need to use the
setStyleAttribute

but I am not sure and cannot decipher the documentation. I know from here that

A flexible table that creates cells on demand. It can be jagged (that is, each row can contain a different number of cells) and individual cells can be set to span multiple rows or columns.

A: Your hunch is correct, use setStyleAttribute to set the appropriate margin to a NEGATIVE value. if all the columns on your flextable are 40px wide, your control is in cell 0,0, and you want it to span across columns 1 & 2, you would use something like this:
app.getElementById('x').setStyleAttributes({'marginRight':'-80'});

see here for some variations on the property assignments, though the method syntax needs to follow the format given in the Google documentation


The item It's your first year teaching, but you don't have to act like it, Bob Kitchen represents a specific, individual, material embodiment of a distinct intellectual or artistic creation found in Bryn Athyn College, Swedenborg Library.
What if things aren't going as planned?
What's the best way to teach your subject?


BEFORE: My original plan for today is going to be altered slightly because my laptop is in for repairs, but it should be back by tomorrow and it'll be business as usual. Luckily, armed with my new library card, I picked up a whole bunch of new DVDs that I'll start getting through beginning with a fourth in a series, Pirates of the Caribbean: On Stranger Tides.
AFTER: Pirates of the Caribbean: The Curse of the Black Pearl was a great film and brought swashbuckling pirates to life on the screen. With this latest, On Stranger Tides, the magic that was present in the original is now all but gone. It's still funny and Captain Jack Sparrow (Johnny Depp) is his usual, fantastic, self, but the adventure and journey are sorely diminished and what is in the film is overly reliant on the supernatural.
The characters and their special kind of humor will always have a place in the realm of film. Seeing Sparrow and Barbossa (Geoffrey Rush) fight each other and watching Sparrow improvise another escape on the spot is a part of the franchise and will always have a certain level of appeal. With as many jokes about rum, mutiny, and parley that I've heard from these films, I still found myself laughing a bit. I couldn't resist but enjoying an elaborate escape in front of King George II (Richard Griffiths) involving a chandelier, plates full of food, and a doily carefully placed on the floor. But moments like these are few and far between in On Stranger Tides. Instead, it's filled with nonsense and lots of pointless set pieces.
Comedy has always been a part of this franchise, and as I said above, is still very much present, but I say it's too much. Many scenes were set up for a joke or a funny moment between Sparrow and another seaman. With a premise of trying to find the Fountain of Youth, opportunity is ripe, but On Stranger Tides squanders that. A good chunk of the beginning (around an hour or so) is spent in London, preparing everything before anyone even sets sail. The Fountain is like a pipe dream, something that feels like will never be reached. And then, all of a sudden there's a turning point. The ships are out at sea, on the way to their destination. It feels like a different film - the adventure and excitement picks up. But this too is soon lost. The focus shifts from pirates in search for a treasure to a supernatural quest with, not one, but two love stories thrown in for good measure.
I keep saying supernatural and giving it a negative connotation. The original The Curse of the Black Pearl had it's share of supernatural elements as well. Cursed gold that sustained life, only to be broken by the spilling of blood. And that was fine because it wasn't over done. It didn't feel like a fantasy, it felt like an out-of-this-world reality. Here, there are flying demon mermaids, a voodoo doll, a ship controlled by a sword, and to top it all off, zombies. With all of this going on, the Fountain of Youth isn't really the focus any longer; it's dealing with all of this nonsense. In addition to this, a few of the other story elements are similarly poorly thought out. What is the purpose of the religious man other than to produce the mermaid tear after falling in love with her? Why is there even a voodoo doll of Jack if it's hardly used and then just tossed off a cliff? What is the point of throwing Gibbs (Kevin McNally) in every couple of scenes besides the fact he memorized the map? Couldn't they have settled for a partially scorched map or made Gibbs' character more important if they really felt the need for him to stick around?
All in all, Pirates of the Caribbean: On Stranger Tides was a disappointment. It lacks the excitement and spirit that we've come to know and love from the franchise. Sure, there are still some laughs to be had but these are grossly outweighed by a terrible and out of control story. Don't bother watching this, even if you're like me and must watch everything in a franchise. It really isn't worth it.


text:
Q: What does inline-block mean for css? Can someone explain the difference between 'block' and 'inline-block' for the CSS display setting?

A: inline-block treats the element like other inline elements but allows the use of block properties.
Elements with display: block take up as much width as they are allowed and typically start on a new line.

A: Basically, it's a way to make elements inline, but preserving their block capabilities such as setting width and height, top and bottom margins and paddings etc. For example:

(Source: http://www.impressivewebs.com/inline-block/)

A: This is a wonderful detailed article about this topic: What's the Deal With Display: Inline-Block?