text:
Phospho-ALK (Tyr1604) Antibody
Phospho-ALK (Tyr1604) Antibody #3341
Western blot analysis of extracts from Sup-M2 cells, using Phospho-ALK (Tyr1604) Antibody (A,B) or ALK Antibody (C,D). The phospho-specificity of the antibody was characterized by treating the membrane with calf intestinal alkaline phosphatase (CIP) (B,D) after Western transfer. (Sup-M2 cells provided by Dr. Stephan W. Morris, St. Jude Children's Research Hospital, Tennessee.)
Phospho-ALK (Tyr1604) Antibody 3341
This product has been approved for use in this application by CST. No data images are currently available.
3341L 300 µl
MW (kDa) 80 (NPM-ALK) 220 (ALK)
ALK (D5F3®) XP® Rabbit mAb#3633
ALK (31F12) Mouse mAb#3791
Phospho-ALK (Tyr1586) (3B4) Rabbit mAb#3348
Phospho-ALK (Tyr1282/1283) (D39B2) Rabbit mAb#9687
Phospho-ROS1 (Tyr2274) Antibody#3078
Phospho-ALK (Tyr1078) (D28B4) Rabbit mAb#12127
Phospho-Ret (Tyr905) Antibody#3221
Phospho-ALK (Tyr1278/1282/1283) Antibody#3983
Phospho-ALK (Tyr1096) (D96H9) Rabbit mAb#6962
Phospho-EGF Receptor (Tyr1068) (D7A5) XP® Rabbit mAb#3777
Phospho-ALK (Tyr1604) Antibody detects ALK only when phosphorylated at Tyr1604 (equivalent to Tyr664 of NPM-ALK). This antibody may cross-react with other activated protein tyrosine kinases including EGFR.
Polyclonal antibodies are produced by immunizing animals with a synthetic phosphopeptide corresponding to residues surrounding Tyr1604 of human ALK. Antibodies are purified by protein A and peptide affinity chromatography
Anaplastic lymphoma kinase (ALK) is a tyrosine kinase receptor for pleiotrophin (PTN), a growth factor involved in embryonic brain development (1-3). In ALK-expressing cells, PTN induces phosphorylation of both ALK and the downstream effectors IRS-1, Shc, PLCγ, and PI3 kinase (1). ALK was originally discovered as a nucleophosmin (NPM)-ALK fusion protein produced by a translocation (4). Investigators have found that the NPM-ALK fusion protein is a constitutively active, oncogenic tyrosine kinase associated with anaplastic lymphoma (4). Research literature suggests that activation of PLCγ by NPM-ALK may be a crucial step for its mitogenic activity and involved in the pathogenesis of anaplastic lymphomas (5).
A distinct ALK oncogenic fusion protein involving ALK and echinoderm microtubule-associated protein like 4 (EML4) has been described in the research literature from a non-small cell lung cancer (NSCLC) cell line, with corresponding fusion transcripts present in some cases of lung adenocarcinoma. The short, amino-terminal region of the microtubule-associated protein EML4 is fused to the kinase domain of ALK (6-8).
Phosphorylated Tyr664 of NPM-ALK (equivalent to Tyr1604 of full length ALK) is required for the interaction with PLCgamma (5). Site-directed mutagenesis of this tyrosine residue results in the loss of oncogenic activity of NPM-ALK (5).
Stoica, G.E. et al. (2001) J Biol Chem 276, 16772-9.
Iwahara, T. et al. (1997) Oncogene 14, 439-49.
Morris, S.W. et al. (1997) Oncogene 14, 2175-88.
Morris, S.W. et al. (1994) Science 263, 1281-4.
Bai, R.Y. et al. (1998) Mol Cell Biol 18, 6951-61.
Rikova, K. et al. (2007) Cell 131, 1190-203.
Takeuchi, K. et al. (2008) Clin Cancer Res 14, 6618-24.
Soda, M. et al. (2007) Nature 448, 561-6.
Select Your Pathway/Protein Tyrosine Kinases & Substrates Protein: Q9UM73
UniProt ID: Q9UM73


It's hilarious because it's not even a parody.
Yay, someone who agrees with me.
And this is a series. So great.
Omg was that video serious, lol, I hope not.
Of course it's not, the video is obviously satire.
BUMP FOR AN IMPORTANT WARNING.
IF YOU SEE THIS MAN, SMACK HIM ACROSS THE FACE.
I think I would have even without the warning.


text:
\section{Introduction}
Text generation is a subarea of natural language processing (NLP), appearing in the 1970s \cite{goldman1974computer}. Its main purpose is the automatic generation of natural language texts, which can satisfy particular communicative requirements \cite{liu2009encyclopedia}. Text generation can be a constituent of AI-based tools related to machine translation, dialogue systems, etc. Computational generation of stories is specifically challenging task, as it refers to the problem of selecting a sequence of events or actions that meet a set of criteria and can be told as a story \cite{alhussain2021automatic}. Many studies focus on automatic story generation \cite{cheong2014suspenser}, however, a limited number of them emphasize drama generation \cite{theaitre2020}.

Dramatic texts differ from other genres by having dialogues of acting characters, authorial notes, scenes, and other specific elements, usually written for the purpose of being performed on stage \cite{lethbridge2004basics}. Therefore, the methods described in research devoted to generation of narratives or poetry is not always applicable for the drama generation. The approaches considered in our study are mentioned in Section \ref{relatedwork}.

Nowadays, some of the most advanced methods for text generation comprise transformer decoder or encoder-decoder architecture pre-trained on large-scale unsupervised texts. In previous study referring to drama generation, GPT-2 is applied \cite{theaitre2021}. In the current study, we propose an approach to the generation of drama texts in German, based on the production of outlines \cite{fan-etal-2018-hierarchical, yaoetal}, and compare it with two baseline GPT-2 models. The detailed information about these models and their comparison can be found in Section \ref{approach}. The datasets, used as training materials for the system, are described in Section \ref{preprocessing}.

In order to analyze the performance of story generation models, various evaluation metrics can be involved \cite{alabdulkarim2021automatic}. For the models represented in the current study, we propose automatic quantitative evaluation along with manual qualitative analysis, described in Section \ref{results}. The main challenges and limitation referring to the proposed approach and ideas for further improvement of drama generation are discussed in Section \ref{discussion}.

\section{Related Work}
\label{relatedwork}

Automatic text generation has long been a task of research interests, and various approaches have been proposed to improve the quality of generated outputs. Among all genres, story generation sees the most innovation and progress. Before the era of deep learning, some structural and planning-based models have been applied to perform story generation. The prevalence of RNN \cite{rumelhart1986learning} and LSTM \cite{hochreiter1997long} motivated researches to introduce deep learning to the field of text generation, which results in higher model capacity and better performance. Leveraging language models with more complex architecture and pre-trained on large scale datasets further improved the generation quality by a considerable margin. \cite{alabdulkarim2021automatic}

In addition to the increasing complexity of model architecture, researchers are also committed to proposing innovative generation schemes. \citeauthor{peng2018towards} attempted to steer generation by adding control factors. They extracted control factors from existing corpora and trained a model conditioned on them, so that users can control the generation process by selecting different control factors. \citet{fan-etal-2018-hierarchical, fan2019strategies} explored the possibility of a hierarchical story generation process, where an intermediate stage expands the given prompt and simplifies the following generation process by conditioning it on expanded prompts. Similarly, \citet{consistency-enhanced-story-generation} also applied a two-stage story generation scheme, where the system additional generates a story outline as a guideline for the second stage. It is shown that the hierarchical generation scheme effectively enhances the consistency and coherency of outputs.

Despite the similarities with story generation, drama generation faces some extra challenges. Firstly, a drama play is usually longer than the upper limit of pre-trained language models, thus an iterative generative process is necessary. Secondly, the lack of prompt-output data makes it impossible to adopt the same approaches as in story generation, and the model must learn to generate plays from nothing. The inherent difficulty of drama generation task discourages researches in this field. To our best knowledge, the only drama generation model is THEaiTRE project \cite{theaitre2020, theaitre2021}. The system leverages a GPT-2 model to generate each scene step by step conditioned on both local and remote contexts. However, the generative model is not fine-tuned on any drama texts, and the generation process requires intensive human interference, which compromise usability of the model and is not suitable for amateur users.

\section{Drama Preprocessing}
\label{preprocessing}

 \subsection{Corpora}
\begin{table*}[hpt]
\small
\centering

    \begin{tabularx}{\textwidth}{l X X}
    \hline
    \multicolumn{1}{l}{\textbf{Method}}
    & \multicolumn{1}{l}{\textbf{Parameters}}
    & \multicolumn{1}{l}{\textbf{Functionality}}\\

    \hline

    \_\_init\_\_

    & {\textbf{output}}: the empty dictionary that is filled with the data from processed XML file
    & - initializes instant variables used for the XML tags and processed text\\
    &
    & - assigns the empty "output" dictionary to the instance variable\\

    \hline

    startElement

    & {\textbf{xml\_tag}}: the start xml-tag (of the <tag> form) which is passed to the method from the file
    & - stores xml-tag and its attributes in instance variables\\
    & {\textbf{attrs}}: attributes of the tag\\

    \hline

    endElement

    & {\textbf{xml\_tag}}: the end xml-tag (of the </tag> form) which is passed to the method from the file
    & - stores the text processed between start and end tags into a specific instance variable\\

    \hline

    characters

    & {\textbf{content}}: the text between start and end xml-tags
    & - processes the text by skipping empty lines, tokenizing text into words at spaces\\

    &
    & - normalizes words spelling if needed (in GerDraCor only)\\

    &
    & - stores processed words by adding them into a list\\

    \hline
    \end{tabularx}

\caption{XMLHandler Class Structure}
\label{tab:xmlhandler}
\end{table*}

The input for the neural model were dramas from the German Drama Corpus (GerDraCor) developed by the Drama Corpora Project (DraCor) \cite{fischer_frank_programmable_2019} and German Text Archive (Deutsches Textarchiv or DTA) \cite{DTA_2022}.

GerDraCor consists of 591 German dramas, with the earliest written in the 1640s and the latest in the 1940s. 46 dramas appeared to be the same with the ones in DTA and were removed, resulting in 545 dramas used from GerDraCor. In the corpus, speakers, stages and sets\footnote{Stages and sets are texts describing the setting (decorations, position of characters) or commenting on characters' actions and manner of speech.}, scenes and acts are annotated. There is also metadata available for the whole corpus and containing information about number of speakers and their sex, number of acts and words, etc.

DTA, hosted by the CLARIN service center at the Berlin-Brandenburg Academy of Sciences and Humanities, is the largest single corpus of historical New High German that contains around 1500 cross-genre texts from the early 16th to the early 20th century. 92 drama texts with an orthographic normalization of historical spelling were extracted from the corpus. One of them was excluded, as it was a poem. All historical spellings are adopted true to the original, i.e., they are not implicitly modernized. However, modern or otherwise normalized equivalents of historical writings may be noted with the tags <orig> (historical spelling) and <reg> (modernized/normalized spelling) \cite{DTA_2022}.

The standard GerDraCor format and DTA basic format (DTABf), which were used in this work, follow the P5 guidelines of the Text Encoding Initiative (TEI), which are specified for the annotation of historical printed works in a corpus \cite{DTA_2022, fischer_frank_programmable_2019}. The TEI Guidelines for Electronic Text Encoding and Interchange determines and document markup languages for the representation of the structural and conceptual text features. They refer to a modular, extensible XML schema, consisting of a set of markers (or tags) and accompanied by detailed documentation, and they are published under an open-source license\footnote{https://tei-c.org/}.

The following sections describe how dramas from aforementioned sources were parsed and preprocessed in Python.

\subsection{Drama Parsing}
Parsing of dramas in XML format was performed with XMLHandler class inheriting from ContentHandler class from "xml.sax" module. This class reads xml-tags and operates with their parameters and/or content between starting and closing tags. The class contains methods that were overwritten in order to suit the task of parsing dramas from both GerDraCor and DTA (Table \ref{tab:xmlhandler}).

The tag passed to "startElement" and "endElement" defined how the content between tags should be stored. For example, if "startElement" read <TEI> tag, then the value of the "xml:id" tag was stored from that as drama id; if a tag "</text> was passed to the "endElement", then it signaled of the end of the drama, and stored all the previously parsed text in a dictionary under the drama id as a key. The text itself was the content read and written in "characters" method and could be the speech of a particular character between specific opening and closing "speech tags", or, similarly, a description of a stage or a set. Additionally, inside "characters", text was orthographically normalized: historical spelling of words was replaced with modern spelling, which was looked up in a file containing obsolete-modern spelling pairs and was produced earlier with a File Comparator (described in detail in Section \ref{sec:comparator}). That was done for GerDraCor exclusively, as DTA already contained normalized versions of dramas.
In general, XMLHandler was designed to go through each drama, and extract all the drama text, excluding the front page and the cast list. Further, parsed dramas were consequently written into a single text file. In order to separate dramas and their parts from each other, specific tags were introduced: "\$" as opening tag and "@" as a closing tag, which were followed by the attribute name or value without a blank space. For example, at the start/end of each drama a line with an opening/closing tag and drama id was written (e.g., "\$id\_ ger000569" at the beginning and "@id\_ger000569" at the end) (Table \ref{tab:parsetags}).

\begin{table*}[hpt]
\small
\centering
\resizebox{\linewidth}{!}{
    \begin{tabular}{llll}
    \hline
    \multicolumn{1}{l}{\textbf{Attribute name}}
    & \multicolumn{1}{l}{\textbf{Text following the "\$" or "@" tag}}
    & \multicolumn{1}{l}{\textbf{Text enclosed between tags}}
    & \multicolumn{1}{l}{\textbf{Example of opening/closing tag}} \\

    \hline

    Drama id

    & id\_dramaid
    & Parsed drama
    & \$id\_ger000569 / @id\_ger000569\\

    \hline

    Set / stage$a$

    &
    & A set / a stage
    & \$/@\\

    \hline

    Scene/act$b$

    & scene
    & A scene / an act
    & \$scene / @scene\\

    \hline

    Speaker id

    & sp\_\#speakername
    & A speech of a particular character
    & \$sp\_\#detlev / @sp\_\#detlev\\

    \hline
    \end{tabular}
}
\footnotesize{$^a$ There was no text following "\$" and "@" signs for sets and stage, and the text was enclosed just between those signs.\\ $^b$ 126 dramas in GerDraCor and 15 dramas in DTA did not contain scenes and were separated by acts or equivalent text delimiters, which were marked with a "scene" tag.}\\
\caption{Tags Used in Parsed Dramas with Examples}
\label{tab:parsetags}
\end{table*}

\begin{table*}[hpt]
\small
\centering

    \begin{tabularx}{\linewidth}{l| X| X| X| X| X| X| X| X}
    \hline

    {\textbf{transliterated}}	& Hinweg &	sie &	nah'n &	Dort &	sind &	wir &	sicher & \\
    {\textbf{normalized}} &	Hinweg &	sie	& nah &	'n &	Dort &	sind &	wir &	sicher  \\

    \hline
    \end{tabularx}
\caption{Example of Erroneously Added Blank Space After Normalization}
\label{tab:blank_space}
\end{table*}

The function for writing parsed drama allowed to produce two different outputs: dramas with the whole text parsed or only characters' speeches (separated by scenes as well) without sets or stages. Eventually, the latter version was used for the further model training. Figure \ref{fig:parsed_ex} shows an example of a drama parsed from GerDraCor with characters' speeches alone.

\begin{figure}[!htp]

\noindent\fbox{\begin{minipage}{18em}

\$id\_ger000066

…$^a$

\$scene

$^b$

\$sp\_\#dalton

Ein abscheuliches Unglück – ich kann es nicht erzählen – dieser Tag ist der letzte
dieses Hauses.

@sp\_\#dalton
\\

\$sp\_\#frau\_von\_wichmann

Dalton – ist es –

@sp\_\#frau\_von\_wichmann
\\

\$sp\_\#dalton

Belmont –

@sp\_\#dalton
\\

\$sp\_\#frau\_von\_wichmann

Ach – lebt meine arme Julie noch?

@sp\_\#frau\_von\_wichmann

…

@scene

…

@id\_ger000066
\end{minipage}
}

\footnotesize{$^a$ "…" replaces the text skipped in this example.\\ $^b$ Blank lines are added for the convenience of reading the example.}
\caption{A Shortened Example of a Drama Parsed Only with Speeches}
    \label{fig:parsed_ex}

\end{figure}

\subsection{File Comparator}
\label{sec:comparator}
Since it was undesirable for generated dramas to contain antiquated spellings and characters, the version of DTA texts used for training the model was the normalized version offered by the resource. GerDraCor did not offer normalized versions of their drama texts, though. To mitigate the influence of historical spelling on the training of the model, an effort was made to normalize GerDraCor texts by using DTA texts.

The DTA offers different versions of each of their drama texts, two of which were important for the File Comparator.

\font\vse=cmr10 at 5pt
\setbox0=\hbox{a}
\begin{enumerate}
    \item \textbf{transliterated}: A character-normalized version with transliterated orthography. Given the age of many of the dramas, the original texts included characters outside the Latin-1 encoding, as for example the 'langes s' (U+017F) or the elevated 'e'(U+0364) for marking umlauts.
    \item \textbf{normalized}: A version standardized with regard to spelling, as well as transliterated orthography. Historical spellings such as "Erk\raise\ht0\rlap{\hbox to \wd0{\hfil\vse e\hfil}}\box0ndtnuß." and "weißheyt" are transferred to their modern equivalents "Erkenntnis" and "Weisheit".
\end{enumerate}

Therefore, a collection of word pairs was created, by comparing the transliterated and the normalized versions of the DTA drama texts (Table \ref{tab:transnorm}). Punctuation and other unwanted characters (e.g., "\%", "(", "/") were cleaned from the strings before comparison. Each word pair consists of the old spelling of a word, as well as its modern equivalent. Using this list of word pairs, words in GerDraCor with the old spelling could be changed into their new form.

Since the normalized version resolved hyphenation at the page and line break and sometimes replaced one word with two words, or connected two words into one, the word pairs could not be collected by simply comparing each line word by word in both version. Sometimes, it was indicated in the DTA normalized version, if words were previously merged (e.g., "wie\_es" in the normalized version corresponded to "wie's" in the original text). However, such indication was not done consistently: "thu's" for example was normalized into "tu es" without an underscore, and therefore, could be treated by the algorithm as two words rather than a single unit.

Issues like these could be easily solved by checking for a specific pattern. The algorithm detects words ending with "'s" in the transliterated version and tests whether the corresponding word in the transliterated version is followed by an "es", and if this is the case, then the normalized version likely contains two words (e.g., transliterated "thu's" is correctly paired with the normalized "tu es").  But sometimes the normalized version added spaces between words, which could not be predicted and caused wrong indexing, meaning two different words in the line to be compared to each other, as shown in the example in Table \ref{tab:blank_space}. Added space in the normalized version ("nah 'n") causes the algorithm to combine wrong words in pairs, e.g., "Dort – 'n", meaning that "'n" is considered a normalized version of "Dort".

In order to exclude wrong pairs, where two different words were treated as normalized and transliterated versions of the same word, an algorithm to compare the similarity of words was implemented. If the normalized version was too different from the transliterated version, the word pair was considered faulty (consisting of two different words).
Firstly,  Levenshtein Distance was used to find possibly faulty word pairs. With using similarity threshold of 3, which appeared to be the most optimal threshold, this method excluded 576 word pairs, but many of them seemed to be correct edits of old spellings (Table \ref{tab:levenshtein}).

For that reason, it was decided to try another method and estimate word similarity in each pair with the SequenceMatcher class from the "difflib" module. SequenceMatcher uses "Gestalt Pattern Matching" algorithm for string matching. In case, similarity ratio between words in a pair was less than 0.5\footnote{Ratio varies from 0 to 1, where 0 means no commonalities and 1 means identical strings.}, this word pair was deleted from a list of transliterated-normalized pairs. As getting rid of wrong pairs was the priority, the 0.5 threshold allowed us to exclude as many as possible faulty pairs at the cost of losing a few correct ones. Although, this method excluded 712 pairs (more than Levenshtein distance), more of them looked like real faulty pairs (Table \ref{tab:seqmatcher}).

Thus, the final version of File Comparator normalizes words by using word pairs left after excluding faulty word pairs with SequenceMatcher.

While parsing GerDraCor, if the word from drama was found in the dictionary of word pairs, it was lowered, changed to its normalized version and restored with regards to its original capitalization.

\begin{table}[hb]
\small
\centering

    \begin{tabularx}{\linewidth}{X X}
    \hline
    \multicolumn{1}{l}{\textbf{Transliterated}}
    & \multicolumn{1}{l}{\textbf{Normalized}}\\

    \hline

    Ueberraschungen	& Überraschungen\\
    Medicinerei	& Medizinerei\\
    practicieren &	praktizieren\\
    Caffeegeschirr &	Kaffeegeschirr\\
    Cigarettentasche &	Zigarettentasche\\
    Hausflurthür &	Hausflurtür\\
    Nachtheil &	Nachteil\\
    Legirung &	Legierung\\
    legirt &	legiert\\
    Gratulire &	Gratuliere\\
    nothwendigerweise &	notwendigerweise\\
    adressirt &	adressiert\\
    cuvertiert &	kuvertiert\\
    todtgeboren &	totgeboren\\

    \hline
    \end{tabularx}

\caption{Examples of Pairs Collected from Transliterated and Normalized Versions of DTA Drama Texts}
\label{tab:transnorm}
\end{table}

\begin{table}[ht]
\small
\centering

    \begin{tabularx}{\linewidth}{X X}
    \hline
    \multicolumn{1}{l}{\textbf{Transliterated}}
    & \multicolumn{1}{l}{\textbf{Normalized}}\\

    \hline

    Wohlhäbige	& Wohlhabende\\
    Verlaubst &	Laubest\\
    Thu's &	tue es\\
    daß's &	dass es\\
    hoamgangen &	heimgegangen\\
    Zen &	Zähne\\
    veracht' &	Acht\\

    \hline
    \end{tabularx}

\caption{Examples of Word Pairs Excluded After Checking for Faulty Word Pairs with the Levenshtein Distance Algorithm}
\label{tab:levenshtein}
\end{table}

\begin{table}[ht]
\small
\centering

    \begin{tabularx}{\linewidth}{X X}
    \hline
    \multicolumn{1}{l}{\textbf{Transliterated}}
    & \multicolumn{1}{l}{\textbf{Normalized}}\\

    \hline

    Hizt &	Jetzt\\
    nachi &	nage\\
    itz	& Jets\\
    Creyß &	Kreis\\
    Flick &	Flügge\\
    Vehd &	Fett\\
    dy	& die\\

    \hline
    \end{tabularx}

\caption{Examples of Word Pairs Excluded After Checking for Faulty Word Pairs with the SequenceMatcher Algorithm}
\label{tab:seqmatcher}
\end{table}

\section{The Proposed Approach}
\label{approach}

Inspired by the two-stage story generation approaches employed by \cite{fan-etal-2018-hierarchical, yaoetal}, we also decided to divide the drama scene generation process into two stages. However, while \citeauthor{fan-etal-2018-hierarchical} first generate a storyline, which is subsequently used as input to the model that generates the story, we train a model to produce outlines, which become part of the input prompt in the second stage. Likewise, our approach is different from \citeauthor{yaoetal}'s in that it uses just 10 keywords instead of one keyword per sentence in the story. With this approach, we aim to guide the generation process of the model by providing it with the keywords summarizing the most important parts of each scene. Our second goal is to reduce the workload of the user by allowing them to provide only 10 keywords and let the hierarchical model do the rest of the work.

First, we fine-tune a GPT-2 model (the \textit{outline model}) to generate outlines of scenes based on an input of keywords extracted from the text. In the second step, we fine-tune a second model (\textit{the generation} model) to generate scenes based on input which consists of the outline of the scene, a summary of the remote context as well as that of the local context.

\subsection{GPT-2}
GPT-2 \cite{Radford2019Gpt2} has been demonstrated to achieve state-of-the-art results in a range of NLP tasks such as natural language inference, semantic similarity, text classification as well as question answering. Moreover, GPT-2 has successfully been used for story generation \cite{consistency-enhanced-story-generation,seeetal}.

GPT-2, introduced by \citeauthor{Radford2019Gpt2}, is an auto-regressive transformer consisting of 12, 24, 36 or 48 decoder blocks, depending on the size of the model. In contrast to BERT \cite{bert}, which consists of encoder blocks only, GPT-2 stacks decoder blocks. Furthermore, an important property of GPT-2 is its autoregressivity, i.e. the model conditions the next token on the previous token thus allowing text generation.

According to \citeauthor{Radford2019Gpt2}, an additional key feature of GPT-2 is its ability to learn a downstream task in a zero-shot manner, i.e. without any need for parameter tweaking or modifications to the architecture of the model.

GPT-2 was trained with a slightly modified language modeling objective: instead of estimating the conditional distribution $P(output|input)$, GPT-2 estimates $P(output|input,task)$. But, instead of separately modeling this at the architectural level, the task can be prepended to the input sequence.

As there is no official GPT2 model for German, we use the German GPT2 model\footnote{https://huggingface.co/dbmdz/german-gpt2} uploaded to Huggingface. It uses the 12-block setting, resulting in a 117M parameter model.
The model was trained on a 16GB and 2,350,234,427 tokens data set consisting of data from the Wikipedia dump, EU Bookshop corpus, Open Subtitles, CommonCrawl, ParaCrawl and News Crawl.

\subsection{Pre-processing \& Train/Dev/Test Split}
First, we pre-process the Dracor dataset, generating training instances needed for training both models. As both the outline and the generation models use scenes and gold standard outlines as input, we generate those first.

For both \textit{scenes} and \textit{outlines} we create two versions: one with speakers left in the text and one without speakers. The first version serves as input to both models, while the latter is only used once during the keyword extraction process. In the first version, each utterance starts with a <speaker name:> and is followed by a newline character, so that the actual utterance is on a separate line. For the version without speakers, we simply make sure each utterance is on a separate line. For sentence boundary detection, we employ the NLTK tokenizer for sentences from the NLTK Tokenizer package \footnote{https://www.nltk.org/api/nltk.tokenize.html}.

In addition, as there are no real outlines available for the plays, we experiment with two summarization algorithms to get the \textit{gold standard outlines}. First, following \citeauthor{consistency-enhanced-story-generation}'s approach we employ TextRank, an extractive text summarization algorithm, to extract the outlines from scenes. We also try abstractive summarization with a BERT2BERT model\footnote{https://huggingface.co/mrm8488/bert2bert\_shared-german-finetuned-summarization} trained on MLSUM, a dataset of 1.5M online news articles. Upon inspection, we found that the BERT2BERT model's output was unsatisfactory: most of the time the summary consisted of 2-3 sentences and was often truncated. Furthermore, as the format of a play presupposes some form of a dialogue, it is quite different from that of a normal text written in prose.  We hypothesize that the strange output of the model is due to it having been trained on news articles. Thus, we proceed with utilizing the TextRank algorithm for outline generation. Prior to performing summarization with TextRank, we remove the speakers, and add them back in to each sentence in the outline.

\subsection{The Outline Generation Model}

As the data set we use does not have gold standard outlines, we decided to follow \citeauthor{consistency-enhanced-story-generation}'s approach, in which they use Textrank (\textit{add citation} to extract the outline of the story (or the scene in our case). We then utilize these outlines as the ground truth output for our model. As input to the \textit{outline model}, we use keywords extracted from the scenes and their outlines.

\subsubsection{Keyword extraction}
In the search for a keyword extraction algorithm which could yield a good set of keywords for each scene/outline, we have experimented with 6 different algorithms: \textit{Yake} \cite{yake}, \textit{Rake} \cite{rake}, \textit{MultiRake} \footnote{https://pypi.org/project/multi-rake/}, \textit{KeyBert}  \cite{keybert}, \textit{TextRank}\cite{textrank} and \textit{tf-idf}.

\paragraph{Keyword Extraction Algorithms}
\textit{RAKE} first generates a set of candidate keywords for the document subsequently creating a co-occurrence matrix from those. In the next step, for each candidate a score, defined as the sum of its member word scores, is calculated. The word scores are calculated using word frequency (freq(w)), word degree (deg(w)),and (3) ratio of degree to frequency (deg(w)/freq(w)).

\textit{MultiRake} is simply the multilingual version of the \textit{RAKE} algorithm which has some additional parameters such the addition of one's own stopwords or the possibility to vary the length and number of keywords.

\textit{KeyBert} is based on creating BERT embeddings for both the individual tokens in a document as well as the document itself. Then, the cosine similarity of the embedding of each word and the document in which the word appears is calculated. Those words that have the highest cosine similarity with the document embedding are identified as the keywords of the document.

\textit{TextRank} is a graph-based ranking model which takes into account the co-occurrence of words in a window of \textit{N} words, adding edges between those nodes and then applying applying a ranking algorithm until convergence.

In contrast to the algorithms mentioned above, \textit{tf-idf} not only quantifies the importance of a term to a specific document in a collection of documents but also off-setts it by the number of occurrences of this term in other documents in the set. This allows to mitigate the effect of highly frequent terms occurring in a large number of documents on the final score.

\begin{equation}
    tf(t,d)= \frac{f(t,d)}{\sum\limits_{t^\prime \in d}{f(t^\prime,d)}}
\end{equation}

\textit{YAKE} differs from the other algorithms in that it relies on a set of features which are supposed to characterize each term. These include \textit{casing}, the \textit{position} of the word in the document, \textit{word frequency}, \textit{word relatedness to context} and \textit{frequency of word in  different sentences}. Finally, these features are combined into a single score which represents the word (\textit{S\textsuperscript{w}}).

\begin{equation}
    S(kw) = \frac{\prod_{w\in{kw}}S(w)}{TF(kw)*(1+\sum_{w\in kw}S(w))}
\end{equation}

\paragraph{Algorithm Comparision}
In order to select the most suitable algorithm for this task, we performed a qualitative evaluation of the keyword extraction results. We used a small set of 5 randomly chosen scenes. Upon inspection of the extracted keywords, we observed that only the keywords obtained using \textit{tf-idf} and \textit{TextRank} actually yielded acceptable results. For example, \textit{Rake}, \textit{MultiRake} and \textit{YAKE} return quite a few repeating keyword or keywords or keywords that differ only in the grammatical case. Furthermore, some keywords were simply a concatenation of neighboring tokens which do not make much sense when put together, especially if the preceding tokens are missing. In addition, \textit{RAKE} and \textit{MultiRAKE} return lowercased version of the keywords, which can be problematic for German text, as casing signals the POS of a word and thus serves an important function, distinguishing nouns from
other parts of speech. As GPT-2 uses byte-pair-encoding, the starting vocabulary, i.e. the set of all individual characters, consists of both lower and upper case characters. This means that when the BPE algorithm learns to merge adjacent characters, it treats \textit{AB} and \textit{ab} as different tokens.

In light of our observations, we decided to extract keywords using \textit{tf-idf} and \textit{TextRank} and train two outline models.

\paragraph{Keyword extraction}
As a large number of scenes are quite long and the keyword extraction algorithms often return phrases that are only uttered once by the speaker, we decided to try out keyword extraction from both \textit{whole scenes} and \textit{outlines of scenes}. We have noticed that keywords extracted from outlines are often more relevant to the outline. As a result, our models are trained on \textit{keywords extracted from outlines}, where the outline version is that without speakers.

Another important parameter for our keywords input is the number of keywords (\textit{k}) to be extracted from the scenes. Our experiments have shown that when \textit{k} > 10, many of the terms in the lower half of the keyword list are extremely random and unrelated to the outline. As a result, we chose \textit{k}=10 for both \textit{tf-idf} and \textit{TextRank}.

\paragraph{Tf-idf}
Despite using existing implementations of \textit{tf-idf}\footnote{https://scikit-learn.org/stable/modules/generated/sklearn.
feature\_extraction.text.TfidfVectorizer.html} and \textit{TextRank} \footnote{https://pypi.org/project/pytextrank/}, we had to apply some pre-processing steps.
In the case of \textit{tf-idf}, we first apply a \textit{SpaCy}\footnote{https://spacy.io/} POS tagger with the \textit{de\_core\_news\_sm} German model in order to exclude auxiliary verbs, particles, adpositions and adverbs. In addition, any tokens appearing in NLTK's stopword list for German are dropped.

\paragraph{TextRank}
Similarly, we only keep keywords that are not part of the list of German stopwords. In addition, as \textit{TextRank} extracts sequences of tokens, not individual tokens, repetitions containing tokens that only differ by grammatical case are inevitable. In this case, we discard repeated keywords. For instance, in the case of \textit{die gute Oma} and \textit{der guten Oma} we only keep the lemmatized version of the first occurrence of the keyword.

\subsubsection{Model training}
To fine-tune the German GPT-2 model to produce outlines given keywords as input, we concatenate the keywords \textit{K} and the corresponding outline \textit{O} extracted using TextRank and separate them with the \textit{<SEP>} token. In addition, the concatenated sequence \textit{C} is prepended with a \textit{<BOS>} token and a \textit{<EOS>} token is attached to the end of the concatenated input.

The model is trained for 3 epochs with a training batch size of 4 and a test batch size of 8. The default optimizer AdamW is used and the number of warm up steps for the learning rate scheduler is set to 500. The model is evaluated every 400 steps. During training, we compute the \textit{cross-entropy} of the tokens in \textit{C}.

At test time, the model is fed the sequence \textit{<BOS>} + \textit{K} + \textit{<SEP>} and is expected to generate the outline tokens. Generation stops once the \textit{<EOS>} token is generated. We use \textit{top p sampling}, wherein the next token to be generated is selected from the vocabulary items that make up 70\% (top\_p=0.9) of the probability mass. In addition, \textit{repetition\_penalty} is set to 2.0.

As has been mentioned before, we have trained two versions of the outline model (using the same settings): one in which the keywords are extracted using \textit{tf-idf} and the other using \textit{TextRank}. The two models are evaluated with respect to their performance on the downstream task of scene generation, discussed in Section \ref{sec:quanresults}.

\subsection{The Generation Model}
In the second part of our system, another fine-tuned GPT-2 model is leveraged to generate a drama scene from given start and outline. The generation model can be characterized by the following three aspects:
\begin{enumerate}
   \item Iterative generation: As many drama scenes are longer than the upper length limit of GPT model (1024 tokens), it is not possible to generate a whole scene at once. Therefore, we adopt an iterative generation strategy: in each iteration, the model only generates 100 tokens, and all generated tokens are then fed into a summarizer to produce the prompt for next iteration.
   \item Dynamic prompt: In our system, the prompt is split into three individual parts: outline, summary of remote context and local context. The outline is either drawn from the original play or generated by the first part of the system, and remains unchanged in all generative iteration. When the outline and generated outputs are longer than 924 tokens, only the nearest 250 tokens are preserved, and the remote context is summarized by a TextRank model. The three parts are concatenated with a <SEP> token to form the prompt of each generation step. In this way, our model can maintain local coherency as well as memorizing important information even if it is mentioned far ahead of the current position. The introduction of outlines provides a guideline for the plot and guarantees global consistency. Thus, the model is provided with dynamic prompt with different information in each iteration. A figure describing the model structure can be found below. (Figure \ref{fig:model_architecture})
   \begin{figure}[htp]
    \centering
    \includegraphics[width=7.5cm]{generation_model_architecture.jpg}
    \caption{Model architecture }
    \label{fig:positional_bias}
\end{figure}

   \item Automatic post-editing: Despite the improved performance of our fine-tuned GPT-2 model, it still fails to produce drama as human experts. This can be attributed to the inherent difficulty of drama generation and the diverse writing styles and formats of our collected training corpora. To address some recurring format problems, we apply a few automatic post-editing methods. In particular, we have resolved the following issues:
   \begin{itemize}[leftmargin=0pt]
       \item Repetitiveness: As the input information is relatively little at the beginning of generation, the model tends to repeat sentences from prompt or generated lines. To counter repetitiveness, we set the repetition penalty to 1.01 and forbid repeated 4-grams during generation. We also discard any new lines (excluding character names) that have already been generated. Since we have a strict penalty for repetition, it can occur that the model cannot generate a valid line in an iteration. To prevent these cases, the model returns 10 sequences each time for the post-editing module to select, and it is backed off to generation without outline when none of the 10 returns include any valid lines.
       \item Bad character names: In most cases the model is able to identify characters in the play and continues the dialogue with their names. However, it sometimes misspells names or adds new characters abruptly, which harms the plot consistency. In our system we identify misspelling by its edit distance from any given character name. If the edit distance is small (less than or equal to 2), it is considered as misspelling and the wrong name is corrected to a given character name. Otherwise, the new name is seen as an invalid character and will be removed along with its speech.
       \item Empty speeches: The model may output character names at the start of a new line but does not assign any speech to them. We manage to resolve this problem by identifying character names followed immediately by another name and discarding the lines.
   \end{itemize}
\end{enumerate}

\section{Experiments and Results}
\label{results}
To study the effectiveness of our proposed approach, we compare our models with baseline GPT-2 models. In particular, we have two baseline models: a not fine-tuned GPT-2 model and a GPT-2 model fine-tuned on the same training set but with no outline or summarization (-dynamic prompt). All models are based on a German-language GPT-2 model named german-gpt2 from HuggingFace.\footnote{https://huggingface.co/dbmdz/german-gpt2}

For generation model training, we first extract prompt-generated output pairs from collected corpora and fine-tune our model on them. In particular, the outline part in the prompt is extracted from the original scene using TextRank. We run the training for 3 epochs with a batch size of 8, evaluating on the dev set every 400 steps. A default optimizer is used with 500 warm-up steps and the checkpoint with the lowest perplexity on dev set is chosen, with an early stopping patience of 10. The fine-tuned baseline model is trained on the raw drama scripts directly with the same set of hyperparameters as generation model training, except that it is trained for 10 epochs, as there are fewer optimization steps in each epoch compared to generation model fine-tuning.

\subsection{Evaluation Metrics}

To evaluate the performance of our approach, we adopt several automatic quantitative evaluation metrics as well as a manual qualitative analysis. 100 scenes from test set are generated by each model given start of the scene (approximately 100 tokens) as well as an outline (approximately 200 tokens, set to NULL for baseline models) and their performance is measured and compared by the following metrics.

\begin{itemize}
    \item \textbf{Average number of sentences per speech}: In general, drama is comprised of conversations, which means each character is supposed to take turns to give their speeches. Thus, it is important that the model should not generate a text where only one or two characters give very long speeches. Average number of sentences per speech is a metric reflecting how well the generated plays resemble a human written play in format. Abnormally high value in this metric indicate that model fails to capture the format features of drama.
    \item \textbf{Average sentence length}: Average sentence length is a simple yet effective measurement of performance of generative models\cite{kincaid1975derivation, roemmele2017evaluating}. While too long sentences might harm readability, too short sentences are more likely to be incorrect or illogical in the context. In our experiment, we compare the average sentence length of generated texts to that of human written scripts, to evaluate and compare how each model performs in generating fluent and readable sentences.
    \item \textbf{Perplexity}: We also measure the perplexity score of the generated scenes from each model (including human written plays) using german-gpt2. Perplexity is usually assigned by a to-be-evaluated language model on a real text \cite{jelinek1977perplexity}, while in our case we reverse the process and leverage a pre-trained language model to evaluate the fluency and coherency of generated texts. In particular, to balance the evaluation efficiency and accuracy, we use a stride of 100. Lower perplexity score indicates better coherency.
    \item \textbf{N-gram overlap}: For n=1,2,3, we measure the F1 score of n-gram overlap between the start of scene and generated text. Low value means lower similarity between the generated text and start of the scene.
    \item \textbf{Topic drift}: In addition to n-gram overlap, we also calculate the overlap for the first half and second half of generated texts separately, and measure the proportion of decrease in F1 as a metric for topic drift. We assume that, if a story is globally consistent, the topic drift should be relatively small, while a story lacking plot consistency tends to have larger topic drift.
    \item \textbf{Distinct-n}: To examine the model's ability of producing diverse words and phrases, we also compute the average proportion of unique n-gram in the whole generated text \cite{li2015diversity}. Higher proportion of unique n-grams reflects that the model is highly capable of generating unseen words or phrases, either by rephrasing existing expressions or introducing new contents.

\end{itemize}

\begin{table*}[htp]
\small
\centering
\resizebox{\linewidth}{!}{
    \begin{tabular}{lcccccc}
    \hline
    \multicolumn{1}{l}{\textbf{Models}}
    & \multicolumn{1}{c}{\textbf{w/o fine-tuning}}
    & \multicolumn{1}{c}{\textbf{w/o outline}}
    & \multicolumn{1}{c}{\textbf{w extracted outline}}
    & \multicolumn{1}{c}{\textbf{w TextRank outline}}
    & \multicolumn{1}{c}{\textbf{w TF-IDF outline}}
    & \multicolumn{1}{c}{\textbf{Human}}\\

    \hline
    Sentences per speech

    & 64.74
    & 5.40
    & 4.47
    & 5.97
    & 6.04
    & \textbf{3.21}\\

    Sentence length

    & 4.60
    & 6.89
    & 6.16
    & 6.82
    & 6.60
    & \textbf{8.82}\\

    Perplexity

    & 20.58
    & 18.90
    & 19.18
    & 18.82
    & 19.46
    & \textbf{17.13}\\

    \hline
    1-gram overlap

    & 0.11
    & 0.19
    & \textbf{0.20}
    & 0.17
    & 0.18
    & 0.10\\

    2-gram overlap

    & 0.012
    & 0.028
    & \textbf{0.040}
    & 0.020
    & 0.022
    & 0.009\\

    3-gram overlap

    & 0.0007
    & 0.0053
    & \textbf{0.0109}
    & 0.0024
    & 0.0032
    & 0.0013\\
    \hline
    Topic drift (2-gram)

    & -8.33\%
    & 34.3\%
    & \textbf{18.2\%}
    & 27.3\%
    & 42.3\%
    & 20.0\%\\

    Topic drift (3-gram)

    & 28.6\%
    & 66.3\%
    & \textbf{23.0\%}
    & 34.6\%
    & 79.2\%
    & 57.1\% \\

    \hline
    Distinct-1

    & 0.503
    & 0.433
    & 0.463
    & 0.438
    & 0.443
    & \textbf{0.576}\\

    Distinct-2

    & 0.880
    & 0.842
    & 0.860
    & 0.846
    & 0.846
    & \textbf{0.921} \\

    Distinct-3

    & 0.969
    & 0.963
    & 0.966
    & 0.962
    & 0.960
    & \textbf{0.982}\\
    \hline
    \end{tabular}
}
\caption{Automatic evaluation results on 100 test set.}
\label{tab:results}
\end{table*}

\subsection{Quantitative Results}
\label{sec:quanresults}
Table \ref{tab:results} shows the results of our automatic evaluation. It is obvious that the model without fine-tuning fails to produce texts that are formally similar to drama: each speech consists of on average 64.74 sentences and each sentence is composed of only 4.6 words, indicating it just start a speech randomly and many sentences are only one phrase or even one word. For this reason, it will not be analyzed later in this section. All other models perform reasonably well in average number of sentences per speech. The human-written scripts have the lowest value.

Similar patterns can be observed in average sentence length and perplexity. Human-written scripts demonstrate the best readability and coherency. Among the machine generation approaches, despite the gap being trivial, the model with no outline and the model with outline generated by keywords (TextRank) display superiority to the model with extracted outlines in terms of fluency.

When it comes to n-gram overlap, the model with extracted outlines has by far the highest overlap with the given start of the scene, followed by the model with no outlines. The models with generated outlines do not reach a decent result, probably because of the poorer quality of outlines. It is worth mentioning that the real drama scripts have the lowest overlap score. We attribute this to the outstanding ability of human experts of rephrasing and controlling the flow of plot, thus it is not directly comparable to machine generation approaches.

Besides, we notice that introducing extra outline information indeed contributes to a better global consistency: models using both extracted outlines and outlines generated by TextRank keywords show competitive or even better performance than the human-written plays in topic drift, while model that do not leverage such information suffer severely from topic drift.

Finally, no significant difference is observed in the ability of using diverse vocabulary among machine generation models. Human playwrights, as we have expected, show their irreplaceable advantage in diction.

\subsection{Qualitative Analysis}

\subsubsection{Qualitative Evaluation of Outlines}
Firstly, in most of the cases, only the first line of the outline contains a speaker. Naturally, this makes it impossible for the subsequent generation model not to come up with random characters that do not appear in the outline. Furthermore, after the first couple of sentences, the generated outline
quite often consists of direct speech followed by a reporting clause (i.e. "sagte der Mann" - "a man said", "fragte er" - "he said"), as can be seen in both generated outlines in Table \ref{table:gen_outl_append} in the Appendix. This is quite surprising, considering that the gold standard outlines do not contain any such text, as all of the drama pieces are in dialogue format. A possible explanation for this could be that the amount of drama texts used for training is insignificant compared to the large amounts of news data the model was pre-trained on.

\subsubsection{Qualitative Evaluation of drama texts}
Manual evaluation reveals that none of the models were able to produce coherent and meaningful texts. On average the texts created by the model with no outline are shorter compared to the texts from other models, which mostly end after a maximum iteration number is reached. Though all of the models produced texts that ended with the repetition of mildly changed words or phrases, the model using an extracted outline did so more frequently. This can be seen quite well in the generated example text found in Table \ref{table:generated_ex_1_1} Part 3/3 given in the appendix and might explain the extremely low topic drift values for this model.
The two models using generated outlines did not introduce as many new characters and did not switch between speakers as often as the other two models, creating mostly long monologues instead of dialogues.
All of the models overused '>>' and '<<' in normal dialog and started a lot of sentences with a hyphen. Since this is a problem occurring in all of the models, it can be assumed that the varying formalization across different dramas used in the training process caused this issue.
In multiple drama texts two or more following hyphen were used to mark pauses in speech. One example of an excessive use of hyphen in the original drama texts can be found in the excerpt from 'Die Pietisterey im Fischbein-Rocke' given in Figure \ref{fig:excerpt} in the appendix. The models tend to overuse hyphens in a way, that hinders meaningful text generation instead.

\section{Discussion and Outlook}
\label{discussion}
Our proposed method described above is able to handle some known issues like lack of global information. However, drama generation/completion is a challenging task even for human experts, and in our work, there are still some problems remaining unresolved:
\begin{enumerate}
    \item Abrupt ending: Although a special token <endoftext> is added to the end of each scene and used for training, we notice that in most cases the generation only stops when a maximum iteration number is reached. This will lead to an abrupt ending problem. A better method should be explored to provide more control over the story ending without dramatically harming the conciseness of drama.
    \item Non-uniform format: Despite an extra post-editing process during generation, some inconsistency in format is still not completely avoided. Some bad names are not detected as well and are thus kept in the text and compromise readability.
    \item Instability: While some previous works \cite{theaitre2020, theaitre2021} rely on manual intervention to detect and prevent unsatisfactory generation results, we decided to adopt a more convenient fully automatic approach, which inevitably induces accumulated errors and results in instability in generation.
    \item Incompetence of generating a whole play: The proposed model can only generate one scene at a time and cannot produce a whole play. Future work can focus on this more challenging task, for example by introducing an additional layer to the hierarchy, aiming to generate outlines for each scene based on a outline of the whole play and summary of previous scenes.
\end{enumerate}

\section{Conclusion}
\label{conclusion}
This paper compares the quantitative results of different models attempting the generation of German drama texts. Furthermore it explores the opportunity of generating German drama texts with extracted outlines. While the quantitative results of the models suggested sensible outcomes, qualitative analysis of the generated texts found them to be lacking in regards of coherency, meaning and form. A lot of issues can be hypothesized to stem from the varying formalization in the drama texts used in the training of the models and the poor quality of the generated outlines. A bigger and cleaner dataset of German drama texts would be desirable for further testing of generational models.

redpajama_set_name: RedPajamaArXiv


BUENOS AIRES (Reuters) - Argentina's peso ended the week stronger while interest rates on short-term central bank notes crept higher, sopping up pesos in the foreign exchange market that might otherwise have scrambled toward safe-haven U.S. dollars.
Argentina's central bank sold 104.831 billion pesos ($2.77 billion) in high-interest, seven-day notes known as 'Leliq' at average interest rates of 73.314 percent.
The peso ARS=RASL closed 1.32 percent higher on Friday at 37.9 per dollar, ending the week up 8.97 percent, traders said.
While the high Leliq rates have calmed the volatile currency, economists have voiced concerns over the long-term sustainability of the strategy.
Argentina's central bank has said it would keep interest rates above 60 percent until December in a bid to combat rising inflation, which is expected to surpass 44 percent in 2018, according to the most recent central bank poll.
This week's gains come after a revised standby financing agreement with the International Monetary Fund announced last week upped the country's credit line to $57 billion from $50 billion.
Turning to the IMF for support was a politically sensitive move for market-friendly President Mauricio Macri, who is up for re-election next year. Many voters blame the Fund for the country's 2002 economic crisis, which pushed millions of middle-class Argentines into poverty. With high interest rates choking off credit, the economy is expected to keep shrinking next year.
In the United States, Federal Reserve policymakers have signaled support for gradual interest rate hikes that would likely reroute investment from Argentina and other fragile emerging market economies. The peso has lost around half its value against the dollar in 2018.


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MP3 Keefe Marzell - Drawn Windows
A jazz/r&b artist with hip-hop & funk elements. An innovative, downright intoxicating blend of musicianship, exuberance and substance.
11 MP3 Songs
JAZZ: Smooth Jazz, URBAN/R&B: Funk
KEEFE MARZELL AND VINTAGE
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"The group is called Vintage,� explains Marzell, "because underneath the smooth jazz melodies, all of our music has an old-school funk feel. Everything we do is groove-based – not just rhythmic, but groovin'. In addition to that, most of the musicians on the album have played with contemporary Christian acts and gospel artists as well as in church, so there is a lot of spirituality in this music. But no matter what our backgrounds are, we all know that to really connect with listeners we have to play with emotion. That's what makes all the difference.�
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DRAWN WINDOWS is Marzell's vision. He played drums and keyboards, produced, arranged and engineered the album along with co-writing most of the material. Marzell's background includes playing drums on tours with such top smooth jazz artists as Bob James, Kirk Whalum and Marion Meadows; R&B act New Birth; and Christian groups such as Edwin Hawkins, Commissioned, Donald Vale, and The Clark Sisters.
On DRAWN WINDOWS, Marzell surrounded himself with top musicians. The other half of the Vintage rhythm section is bassist Terome "T-Bone� Hannon (Jewel, The Judds, Amy Grant, The Boys Choir of Harlem), who played with Marzell regularly for the past two decades (including their own wild'n'funky R&B group, Raw), but who died of a brain aneurysm the day after they finished recording DRAWN WINDOWS. The album ends with a tribute to him, "Goodbye Terome,� a studio jam featuring his bass playing. The other core member of Vintage is keyboardist Marcus Abernathy (The Winans, Tim Bowman, Virtue). In 1996, when Marzell was hired as the musical director to put together a touring band for the group Commissioned, he brought in Hannon and Abernathy. During that two-year tour, the three musicians began writing their own music and developing the sound that became Vintage.
Also on DRAWN WINDOWS are some of Detroit's finest smooth jazz musicians. Guitarist Tim Bowman (The Winans, Ben Tankard) also played with Commissioned and is a successful recording artist in his own right, as are saxophonists Randy Scott (Grover Washington Jr., Ronnie Laws, Hiroshima) and Norma Jean Bell (Parliament, Narada Michael Walden, Frank Zappa). Also making appearances on the recording are keyboardist Kevin Crosby (New Birth, Alicia Myers, KC & The Sunshine Band, The Dramatics), bassist Jimmy Ali (George Clinton, Parliament, Funkadelic, Earl Klugh) and bassist Prince Edward Tate (he and Marzell grew up together and have performed in their church for many years).
Perhaps the best example of the band's sound is the tune appropriately titled "Vintage.� "We were definitely going with an old-school rhythm feel that mixes P-Funk with a little reggae and then in the middle lays some Chick Corea-type soloing over the top,� explains Marzell. The CD also contains an extended second version of the tune subtitled "My Prospective� that features a bass solo. "This tune was inspired by a friend of mine, Wayman Tisdale, the former NBA star and now a major jazz artist, who often critiqued my music and encouraged me.�
DRAWN WINDOWS begins with "More Is Pleasure,� a take-off on co-writer Morris Pleasure's name. Pleasure (Janet Jackson, Earth Wind & Fire, Boney James, Michael McDonald) handed Marzell the basic structure of the tune which Keefe crafted into a solid smooth jazz production. Randy Scott penned "Now & Forever� and Keefe brought in his cousin, Tim Slaughter, to sing on it. Marzell first learned the old Christian song "Love Thinking of U� when he was with Commissioned, but here he arranges it as a jazz instrumental. The title tune, according to Marzell, "was meant to be an eclectic but melodic grooving tune with a John Scofield-type of crazy harmony at the ending.� On "New Jack Swing,� Marzell laid down a melodic drum solo and then brought in two rappers to match his cross-rhythms with funny lyrics for "a new take on jazz.� With "Through The Night,� Keefe took an R&B vocal tune by Norma Jean Bell, who is queen of the house sound in Europe, and rearranged it into smooth jazz. The strongly melodic "Enter Slowly� is an attempt to create smooth jazz with an inherent erotic feeling.
Marzell, who also owns and runs Ultra Sounds Studio in Detroit, has written and recorded music for numerous national radio and television advertising campaigns including companies such as Jeep Cherokee, Dominos Pizza, Plymouth Neon Voyager and Red Lobster restaurants. He also engineered recordings for 112, Virtue, Commissioned, Rance Allen Group, Shelby Brown, Marooned and Dennis Coffey (Jackson Five, The Temptations, Rare Earth). Working with Coffey completed a circle for Marzell begun when Keefe started playing drums at age ten and his father insisted he memorize the entire rhythm performance of Rare Earth's epic 20-plus-minutes-hit "Get Ready.� The next year Keefe made his first public appearance (at a school talent show) and performed Coffey's "Enter The Dragon� along with Kool and the Gang's "Jungle Boogie.�
Marzell was born in Chicago and lived there until he was seven-years-old when his family moved to Detroit. His father also was a drummer. Keefe grew up listening to church music first, jazz second (The Jazz Crusaders, Jimmy Smith) and then R&B/rock (Stevie Wonder, Isaac Hayes, Santana, Buddy Miles). As Marzell got a little older, he was influenced by George Clinton's Parliament/Funkadelic, Jean-Luc Ponty ("his drummer Rayford Griffin was one of my early idols�), John McLaughlin and the Mahavishnu Orchestra with Narada Michael Walden, and Mint Condition.
Several noteworthy occurrences left their mark on the teenaged Marzell. When he was 16, he joined a group of older musicians playing all summer in clubs. "They taught me to be sensitive to the music and to play dynamically with the music and not just mindlessly keep the beat.� But Keefe broke his neck in gymnastics class, had to learn to walk again and was unable to play music for a year while he recovered. Eventually he studied piano at the local college before getting back into drumming. He became a fulltime professional musician when he toured the United States for two years with Christian act Donald Vale which led to other touring gigs.
After his group Raw was unable to secure a record deal ("they said we were too wild, too undisciplined, too funky�), Marzell felt he hit bottom when he was driving a cab for 12-hour shifts. But he returned to the church, developed his faith and spirituality, "and my life turned around in dramatic fashion.� He was hired by jazz act Marion Meadows (Joe Henderson, Eddie Daniels, Norman Connors) and toured with him for three years ("a very enjoyable and inspirational time�). This led to tours with Bob James, Kirk Whalum (Whitney Houston, Barbra Streisand, Quincy Jones), New Birth (who had a big hit in the Seventies with "Wildflower�) and Commissioned (whose album was produced by Boys 2 Men). Marzell regularly plays in front of 4,000 worshipers at the Greater Grace Temple pentecostal church in Detroit (founded by his late uncle and now headed by his cousin), and performed (with Marcus Abernathy) on the Greater Grace Temple Choir's GRACE-N-PRAISE album.
"Vintage is a loose grouping of great musicians that came together for this recording and can perform live too,� Marzell says. "Despite the fact that we all have played many types of music during our careers, none of us are specifically singers, so we picked the smooth jazz genre because it is primarily instrumental music and there is a history of R&B and funk incorporated into it. We believe we bring our own particular twist to the whole thing. We start with a deep groove, and then the songs develop through spirituality and emotion.
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redpajama_set_name: RedPajamaCommonCrawl


text:
Q: C# .NET Windows Forms Block/Supress Upload Files in Webbrowser Component Is there any way to block uploading files (any file) from a website accessed by a component webbrowser (C#)? I've been trying several ways but nothing works...

A: I don't know any easy or difficult way of acheiving this as the openFileDialog is called automatically by the MSHTML.dll upon clicking on the html input file field. There isn't any interface, method or property that can programmatically override or prevent the dialog from opening.
I've found an approach I think would work for this case. What you need to do is to identify every HTTP header that includes "Content-Type: multipart/form-data" via the "WebBrowser.Navigating" event, as this is used every time a file is about to stream from the local computer to the server. Then you could cancel the data stream immediately.

A: I finally solved this problem!
the solution was: hide the dialog box! I used the object id (window id) to find the dialog box. It's the only way to "block" any dialog box!
see the code below:
    // P/Invoke declarations
    [DllImport("user32.dll", SetLastError = true)]
    static extern IntPtr FindWindow(string id, string caption);

    [DllImport("user32.dll")]
    private static extern IntPtr SendMessage(IntPtr hWnd, int msg, IntPtr wp, IntPtr lp);

    public void Execute()
    {
        IntPtr hWnd = FindWindow("#32770", null);

        if (!hWnd.Equals(IntPtr.Zero))
        {
            SendMessage(hWnd, 0x0010, IntPtr.Zero, IntPtr.Zero);
        }
    }

I hope this solution helps other users!

redpajama_set_name: RedPajamaStackExchange


text:
Q: MVC 4 Razor Checkboxlistfor formatting issue I was trying to implement a 'CheckboxListFor' method but am having some issues with formating in which the text for the checkbox does not align on the side of the checkbox but instead is below the checkbox.

Code in View:
     @{
         var htmlListInfo = new HtmlListInfo(HtmlTag.table, 4, null,
                                             TextLayout.Default, TemplateIsUsed.No);
     }

      @Html.CheckBoxListFor(model => model.KeywordIDs,
                                     model => model.Keywords,
                                     model => model.KeywordId,
                                     model => model.Name,
                                     model => model.SelectedKeywords,
                                     htmlListInfo
       );

Any insight would be greatly appreciated.

A: Ok gr8.. Minor change.
Try this code.
have to apply css to fix it.
<style type="text/css">
    label
    {
        display: inline-block !important;
        padding: 5px !important;
    }
</style>
@{

    var htmlListInfo = new HtmlListInfo(HtmlTag.table, 4, null, TextLayout.Default, TemplateIsUsed.No);

    @Html.CheckBoxListFor(model => model.PostedCities.CityIDs,
                      model => model.AvailableCities,
                      city => city.Id,
                      city => city.Name,
                      model => model.SelectedCities,
                      htmlListInfo)
}

let me know your comments.


AMT provides the very high quality "communication connection" products for a wide variety of business computer networking systems. Our main focus is physical layer data communication installation and service. AMT is dedicated to providing an unparalleled level of customer satisfaction through quality efficient and pleasant service to our clients.
n the year1995, four enterprising individuals, who were genius, dynamic and visionary, confident of their capabilities and inherent talent, joined together and formed the company, ADVANCED MICRO TECHNOLOGY (PVT) LTD, (AMT), to provide Information and Communication Technology (ICT) infra structure with end-to-end solutions to fulfill the demands in line with the future growth of Information Technology needs.
563/17, Sarasavi Mw, Nawala Road, Rajagiriya. Sri-Lanka.


Here R More (Good Weather, 2016) is a book that showcases the work of visual artist Hartmut Austen, an assistant professor in the Art, Art History, and Film Department. The book collaborators are Haynes Riley (design) and Ruben Nusz (text). Austen, who was born in Germany, is an abstract painter who has been influenced by German Neo-Expressionism and American minimalism and conceptual art. Austen said the book was a way to bring his art out into the world, beyond the walls of a gallery where this work was first displayed in an exhibition of the same name. He talks about the book with the BC Libraries.
This entry was posted in Boston College Authors and tagged abstract art, Art Art History & Film Department, BC Libraries. Bookmark the permalink.


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Ali Sadr
Ali sadr cave
Ali sadr cave of hamedan and iran
Ali-Sadr One of the World largest canopy caves in the world as Natural Tourist Attraction is in Hamadan. Ali-sadr cave rocks are related to the Jurassic period from the second period of geology. So this journey took about 190 million years ago. From the entrance you pass, you will encounter a cool atmosphere and this will make you forget about the heat. The lake is clear here, so that you can see the floor. You should wear a rescue vest and ride a boat and cross the stone walls. You should continue to take a boat out of the boat. But do not worry, because you'll be riding a boat again, passing a narrow path, from where there are no more boats.
Google play applications and Google Sites for caves in Iran
About Alisadr Cave in Hamadan
Ali-sadr, the largest water cave and One of the largest canopy caves in the world. Ali-sadr cave rocks are related to the Jurassic period from the second period of geology.
So this journey took about 190 million years ago. From the entrance you pass, you will encounter a cool atmosphere and this will make you forget about the heat. The lake is clear here, so that you can see the floor. You should wear a rescue vest and ride a boat and cross the stone walls. You should continue to take a boat out of the boat. But do not worry, because you'll be riding a boat again, passing a narrow path, from where there are no more boats.
In the summer of 2001, a German/British expedition surveyed the cave, finding to be 11 kilometers long. The main chamber of the cave is 100 meters by 50 meters and 40 meters high.
Ali-sadr Cave Detail
The Ali-sadr cave walls can extend up to 40 meters high, and it contains several large, deep lakes. The Ali-sadr cave has a river flowing through it and most travel through the cave system is done by boat. More than 11 kilometers of the cave's water canals have been discovered so far. Some routes are 10 to 11 kilometers long and all lead to "The Island", a centrally located large atrium.
Due to the difference in temperature outside and inside the cave and the coolness of the air within it, it is one of the attractive attractions for the summer season's hot season.
In the Ali-sadr cave you will encounter fantastic geological landscapes and you can see the miracle of nature closely.
Some caves pass 10-11 km, but still not all canals and communication networks of the cave have been discovered. Now, after a creeping cave, the pedestrian cave begins in the cave, and tourists after a relatively long distance Once again, they have been traversed by boat, but they are now discovered 14 kilometers from the caves, but only 4 kilometers from the streets, which are covered with a light-emitting diode, are visited. In general, a visit to the vast area of ??the Great Qandil islands of the third and fourth islands of the Cave Riding and Passage From the bend of the blue passageways and the corridors with the crystalline ceiling, Effect is truly one of the masterpieces of the creation world, and nothing can describe all this splendor and beauty.
Hours and Visits and Tickets of Ali-sadr
In the first six months of the year, from 8:00 to 19:00, you can go here in the first six months of the year from 8:00 to 16:00
Ali-sadr Cave Address
Ali Sadr, Hamadan Province, Iran
Other top and best caves in Iran
moghan
katale khor
qeshm salt cave
Hamedan Travel Guide
Cave Reference in Iran
Iran Caves
Updated Time: 2022-10-12 14:32:00


The Witching Hour, after midnight under a full moon a palomino bucks feeling the spirits of the night. This lovely Spirit Horse is really shines in the brightness of the moon. The texture of the muted turquoise blues have a batik quality contrasting nicely with the gold and sienna of the palomino horse.
The Witching Hour is a lovely print on archival somerset velvet paper.


Explore posts in the same categories: bad ideas, Climate follies, Clinton, Economic follies, Journalism and its discontents, McCain, media, Obama, Policy, political follies, Politics, Republican knavery, Stupidity, Who thought that was a good idea?
This entry was posted on April 17, 2008 at 10:04 pm and is filed under bad ideas, Climate follies, Clinton, Economic follies, Journalism and its discontents, McCain, media, Obama, Policy, political follies, Politics, Republican knavery, Stupidity, Who thought that was a good idea?. You can subscribe via RSS 2.0 feed to this post's comments.
You goldurned elitist. Twenty-eight bucks may not matter to you, but for some people it's the difference between movie night and TV night.
It's a great political move, though, and no one's going to call him on it (significantly), with the democrats tearing each other apart. We already give the oil companies enough money in subsides.. why not just make it a government, no-profit company? Oh, shoot, that's socialism.


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News, Reviews and Features 'Like nothing else currently in the West End': BAT OUT OF HELL – West End ★★★★★
'Like nothing else currently in the West End': BAT OUT OF HELL – West End ★★★★★
In London theatre, Musicals, Opinion, Reviews by Olivia Mitchell 22nd April 2018 Leave a Comment
Buy tickets for Bat Out of Hell
Dominion Theatre, London – until 27 October 2018
Bat is well and truly back and at home in its new space: the Dominion Theatre. When I first saw this show back on its press night at the Coliseum I wasn't the biggest fan. The performances were outstanding but the book itself really let it down. This time, however, I knew what to expect so was ready for a carefree night of relaxed, rocking fun.
Obviously, the story is still the same (although this time around it did feel less cheesy) but if you can overlook its short-comings you are destined to have an electric night. The show has matured a lot and it's evident that work is continually being done to make it the best show it can be.
Get all social media for Bat Out of Hell & its original cast on www.stagefaves.com
Jim Steinman's rock musical is an electrifying rollercoaster of drama, lights, confetti, and so much more. Everything is thrown together to create a show that's refreshing and really like nothing else currently in the West End. This show is a Romeo and Juliet-esque retelling of Peter Pan. With Strat, the leader of The Lost who's frozen at aged 18, falling in love with Raven, the girl whose dad is the tyrannical ruler of Obsidian and doesn't want her leaving the house, let alone mixing with The Lost. Bat Out of Hell is a wild ride from the opening moment to the final bows.
It's not a stretch to say that the cast of this show is potentially the most talented currently on the West End. There is not a weak link throughout, with every member not only committing themselves fully to the show and looking as though they're truly loving it, but showcasing their outstanding vocals and performing with enough vigour to make us feel as though we're at a rock concert.
A search call might need to be put out for the roof of the Dominion because Andrew Polec and Christina Bennington's voices completely blew it off last night. The two bounce off each other as if they're electrical currents and their ridiculously powerful voices compliment each other beautifully. The pair are receiving high praise for Bat Out of Hell and I doubt it's the only show they'll be applauded for in the future- they're definitely ones to watch!
Also vocally outstanding is Danielle Steers who once again received an audible gasp from the audience when she sung her first notes of Two Out of Three Ain't Bad. Her voice is sexy, strong and oh-so-powerful; when she joins forces with Wayne Robinson (Jagwire) they create vocal bliss as well as working with each other wonderfully.
As Raven's parents who are fighting to regain their spark, Sharon Sexton (Sloane) and Rob Fowler (Falco) are fantastic. Again they have voices to die for and are suitably wacky in their roles. The duets are stellar and they work fantastically as a pair to complement each other rather than over powering one another. Each of the leads truly command the stage but the ensemble also get a chance to shine, especially during Objects in the Rearview Mirror May Appear Closer Than They Are. Mention must go to Giovanni Spano, Patrick Sullivan and Vicki Manser who really caught my eye throughout.
If you were to search for the definition of Energetic, I wouldn't be surprised if the cast of Bat Out of Hell popped up. Every ensemble moment is full-out and it's amazing how many intricate details are woven in. This is one of those shows that you need to see several times just to pick up every detail; there's action on every inch of space. The innovative use of live-camerawork throughout the show helps pick up these details very cleverly. The screens on either side of the stage and the use of vast projections allows the large space of the Dominion theatre to feel intimate whilst also adding to the futuristic, always being watched, vibe of the show. Jon Bausor has done a wonderful job creating this set and all the surprises included in it.
Patrick Woodroffee's lighting is blinding and is just what you'd expect and need in a show like this, whilst Emma Portner's choreography is suitably strange and futuristic, with the sharp movements adding to the frenetic energy on stage.
Everything about Bat Out of Hell is extra and excessive but that's the charm and appeal of it. The story isn't the focus; instead, we get to hear Jim Steinman's rousing music and see it performed with a vigour and drive that seems unattainable for 8 shows a week. What's special is that the cast do attain it and give 110% every show. If you like energetic, fast paced shows then For Crying Out Loud go and see Bat Out of Hell. Below all the lights and drama this is a show about love. From the audience reaction and instantaneous standing ovation when the last note was played, it's clear that a lot of people love this show. I am definitely a Bat convert and thoroughly recommend you see and EXPERIENCE this show for yourself.
Always surrounded by music from a young age, Olivia Mitchell is currently completing a degree in Classical Singing. To honour her passion for musical theatre in particular, in 2015, she started her blog Rewrite This Story, where she reviews shows, interviews performers and writes about all things theatrical. She tweets via @RewriteThisWeb.
Read the full article from Olivia Mitchell, published on 22 April 2018
Tags: Andrew Polec, Bat Out of Hell, Christina Bennington, Danielle Steers, Dominion Theatre, Giovanni Spanno, Jim Steinman, Jon Bausor, Musical theatre, Olivia Mitchell, Rewrite This Story, Sharon Sexton, StageFaves, Wayne Robinson


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Home » The Expanse Cancelled By Syfy – No Season 4; Series Shopped Elsewhere
The Expanse Cancelled By Syfy – No Season 4; Series Shopped Elsewhere
May 10, 2018 by RenewCancelTV 10 Comments
Syfy has cancelled The Expanse after three seasons. The space drama will not be renewed for Season 4, Deadline reports.
RenewCancelTV has further confirmed the cancellation.
Chris McCumber, President, Entertainment Networks for NBCUniversal Cable Entertainment, said of the cancellation:
"The Expanse transported us across the solar system for three brilliant seasons of television. Everyone at Syfy is a massive fan of the series, and this was an incredibly difficult decision. We want to sincerely thank The Expanse's amazing cast, crew and all the dedicated creatives who helped bring James S.A. Corey's story to life. And to the series' loyal fans, we thank you most of all."
Added Alcon Entertainment co-founders and co-CEOs Andrew Kosove and Broderick Johnson:
"We are very disappointed the show will not be returning to Syfy. We respect Syfy's decision to end this partnership but given the commercial and critical success of the show, we fully plan to pursue other opportunities for this terrific and original IP."
The Expanse Season 4 is dead at Syfy, however the series will be shopped to other outlets. Stay tuned to find out if the space drama finds new life elsewhere.
Filed Under: Cancelled, Headlines, The Expanse
About RenewCancelTV
RenewCancelTV.com editor - obsessed with the fate of all your favorite TV shows. Follow @Twitter | Facebook | Pinterest | Instagram
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midas777 says
wow the one truly great show on SYFY and they cancel – first Dark Matter now this really don't get what they are thinking here – they should hold on to these gems
Momster says
It's like they want to croak it. We just removed SyFy from our favorites menu. …like they care. ;o)
You're right! I loved Dark Matter and now The Expanse. I would like to cancel SyFy!!!
Huh…SyFy cancelling anothe Sci-Fi show, this time for no reason….my guess is they are creating space for more reality TV or maybe wrestling…this network should really be renamed as they have no interest in anything remotely Sci-Fi any more. One of the best shows on TV cancelled just about sums up the networks these days and how they are run by morons
They went downhill back when they changed the name. Nothing there we watch now. Not about to start anything new, that's for certain. If the SyFy execs want to kill off the network, they're doing a brilliant job!!
I am so freaking upset with you SyFy !! I mean come on people wth is going on with these networks anymore I just do not understand them. All they want to put on TV anymore is Reality Shows, or Dating Shows or Variety Shows. That's why I have always Loved SyFy Network because of the Amazing Science Fiction or just out right Different TV Shows you all bring to us. But just as was said above first y'all cancel Dark Matter which was an AWESOME SHOW with an Amazing Cast & now you want to Cancel The Expanse which is another AWESOME SHOW with an Amazing Cast. I also Love Wynonna Earp & Killjoys 2 more AWESOME SHOWS with Amazing Cast's, so PLEASE SYFY leave them here for us to Enjoy & Please leave em alone, Pretty Please ..
Hope they listen, but… doubt it. When they changed their name, it should have been a clue: They didn't "get" the sci-fi scene then, and they still don't. Time to let the network rest in peace.
Wait. Do you hear that? It's the sound of us removing SyFy from our favorite button on our remote. They officially have NOTHING we watch now. First Dark Matter, now Expanse. They never give shows a chance to begin with, so no use starting fresh with anything new there. Ta-ta, SyFy.
I'm shocked. I can't believe they are canceling this show. It was starting to get interesting. It was one of my favorites. Hopefully someone else picks up this show. It too good to let drop. There so much more to the story.
Michael Zand says
I'd heard "The Expanse" was one of the most expensive shows on SyFy. So much so, that it was nicknamed "The Expense." It was also one of THE BEST shows on SyFy. Very disappointing.


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San Diego's laser lightsmiths sculpt life from darkness
Artistic Laser Productions - Facebook
Paula and Marc of San Diego's Artistic Laser Productions are conductors of light through music.
If you have spent any time enjoying the San Diego club scene you have probably experienced the fun and fantastical world of synchronized laser light shows. These brilliantly choreographed visual shows use cutting edge technology, music, and artistic flair to heighten the joy of a buzzing party atmosphere.
San Diego's Artistic Laser Productions (ALP) taps into that energy and have been doing so in some form or another for over two decades. It all started when owner Paula Steger and her husband Marc decided they weren't happy with their careers as jewelers, they discussed what else they could do that would make going to work every day a joyful experience instead of a daunting one.
It got to a point where the ideas they had weren't quite firming up until they revisited a very important date in their relationship. And like light through a clear-cut diamond, an idea was captured.
"Backup to 1976, when we were dating," recalls Paula who says they just celebrated a 41-year wedding anniversary. "Our favorite date was Laserium (an awesome planetarium laser show). Fast forward to 1998 and a night sitting in the dark listening to jazz music. Marc started to play with a laser pointer, making patterns on the wall in time to the music….we suddenly looked at each other and said (as though well-rehearsed) 'That's it!' We bought our first laser projector that November and ALP was launched."
Their new venture would have to officially wait to launch until the couple was approved for a federal license. "That happened April 12, 1999," she says. "Before that, we had to run shows with a 'babysitter' (an employee of our licensed mentor) – which was fine with us, as we had a lot to learn. We've been intensely happy ever since, and can't imagine doing anything else."
Believe it or not, laser shows are a lot more than just setting up a projector and dazzling the audience with jaw-dropping special effects. Paula says they must follow strict rules and regulations.
"Laser shows have a lot of safety issues and only a company licensed by the FDA/NCDRH can set them up and must run them, or train a venue LD to run them under our license."
Set up time depends on the length of the event and how detailed you want it to be. Paula says she has taken up to three nights to set up production, and that's with a crew of six. Shows can take less time, but that depends on the client and how much detail they provide. She recalls a time when a busy customer made a timing error.
"The shortest setup time was 20 minutes when our client had two events going on in Bakersfield (a three-hour drive for us) and had given us the wrong start time for the one we were doing. We thought we had arrived about an hour and a half before show time but soon found out that wasn't the case. Thank goodness, this was a simple show to set. But, boy, did we have to hustle to make it happen! On average, though, we usually need about two hours to set our show."
There might be a common misconception that DJ's are in control of the light show from a switch or control somewhere on their deck. Actually, the laser production team does all that and they have it down to a science.
"If there is a specific piece or set of music that needs precise timing, we get it well ahead of the event and build a script of visuals to go with it. We then create our visual show matched to the microsecond with the music, to be autoplayed at the event," says Paula adding that most of the time theey run the lasers in real time to the music. "We are both musicians, so playing along with the music is fun for us."
"We also do a lot of work with cover bands and tribute bands. What we like to do with them is get their set lists ahead of time, then match our visuals (aerials and/or graphics and animations) to each song. We create a set of them to be played live with the performers (as a live show is never exactly the same timing as prerecorded music, true?)."
Recently Paula and her team ran all the laser lighting for publisher Johnathan Hale's charity event called the "Glow Pool Party." The party was to help raise money for The Trevor Project and relied heavily on laser showmanship and soul-stirring music.
"It means a lot that SDPIX did this event solely to raise money for the Trevor Project," Paula says. "The fact that even in today's sociological climate it is still needed is something Marc and I can't quite grasp. But, having an organization that is always there advocating for and supporting LGBTQ youth is vital. And, of course, it's an organization that doesn't get nearly the recognition and publicity the "big" charities get. We're delighted we could help. Here's a "small world" story for you. I noticed that Trevor Project just became a grantee of the Google AI Impact Challenge (congrats!). Wednesday, we were doing lasers for a Google AI conference in Santa Barbara!"
As far as the event itself, she says it couldn't have gone more smoothly, "Johnathan and his team were as gracious to us as they were to their guests--who were all absolute delights," she says. "And they chose all the best lighting to complement our lasers (they said we were to be the star of the show), which created the perfect ambiance for a magical night."
In some ways even though Paula and Marc left their careers as jewelers, they continued to orchestrate light and control it. But whereas gemstones rely on facets to distribute points of light, lasers can actually be controlled to evoke emotions.
You might call Paula and Marc laser lightsmiths, they are artists using elements as old as time. They create a world through darkness and just like those first rays, bring life to everything that falls within its spell.
If you would like to hire Artistic Laser Productions for your next event you can do it the modern way with a PM to any of their social media accounts, but Paula prefers another way.
"In spite of having the latest in high tech laser show equipment, we're really old school," Paula confesses, "the best thing to do is pick up the phone and call us to have a conversation about how we can help them create the most memorable event ever."
You can call Paula at (760) 802-2752 from 10 am-10 pm 7 days a week. she says you will only have to leave a message if they are at a gig.
Get more information by visiting their YouTube, Facebook or Instagram accounts.
You can also visit their website HERE.
Tags: Artistic Laser Productions, SDPIX, Johnathan Hale


Lead capture from LinkedIn, Facebook, Twitter, Google+, Gmail, Yelp and many more. Free CRM included.
ConvergeHub Lead Generator captures leads from social sites, email and more.
Sales and marketing professionals often spend hours each day gathering prospects from social sites and manually saving them for future follow up.
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Simple and powerful – One click captures all details of your prospect in an instant!
ConvergeHub is a cloud-based all-in-one CRM software for small and medium businesses, and provides Sales, Marketing, Customer Service and Billing in one easy-to-use platform. Whether it is running marketing campaigns across multiple channels, defining a sales process, tracking sales activity, sharing documents with team and partners or responding to customer service cases - ConvergeHub enables businesses to do all this and much more.
Using ConvergeHub, small businesses get the power to engage, communicate and collaborate with prospects and customers at every stage and from any device. They find it easy to attract new customers and build lasting relationships with existing ones.


Milwaukee Shockwave Impact Duty Driver Guides are engineered for extreme use and durability, providing up to ten times the life of other bits. Made from proprietary steel and heat treated to control hardness, the Shockwave Impact Duty Driver Bits feature an innovatively designed geometric shock absorption zone to extend life. The forged tip allows for a secure grip and maximum contact to the fastener, eliminating stripping or slipping. Broadly targeting cordless users, the Shockwave Impact Duty accessory line is not only ideal for heavy duty impact applications, but delivers a full system solution for all the user's drilling and fastening needs. Bulk (250-Pack)age.
Magnetic guide helps with different driving angles. Longer guide allows access to hard to get to areas.
This magnetic drive guide can be used in a power screwdriver, but I also use it as a manual screwdriver with Phillips or slotted bits. On the end of the drive with the 1/4" hex base, I attach a 1/4" magnetic finger-grip socket with a 1" diameter circular grip. This allows me to turn the drive manually very easily, or I can attach a ratchet with a 1/4" drive for increased torque. This works so well that I think Milwaukee should make a finger-grip socket attachment as an available option for their drive guide.


Ireland's World Player of the Year Johnny Sexton went off injured midway through the first-half of their Six Nations clash with Scotland at Murrayfield.
The 33-year-old fly-half's enforced substitution — after taking several hefty tackles early on — dealt a huge blow to the defending champions hopes of bouncing back following their 32-20 defeat by England last Saturday in Dublin.
Sexton, who was replaced by Joey Carbery, had been pivotal in setting up Ireland's second try by Jacob Stockdale, but as an indication all was not well, Conor Murray took the conversion.
Unlike the Irish, the Scots opened their campaign with a victory, beating Italy at Murrayfield last weekend.
One consolation for the teams is that Sexton and Hogg have two weeks to recover as the tournament takes a break next weekend.


text:
Entextualising Ideologies about English and Multilingualism in a University Language Policy
Hult, Francis M.
Lund University.
Källkvist, Marie
Lund University.ORCID iD: 0000-0002-8686-9959
2013 (English)In: The English Language in Teaching in European Higher Education, 19 April - 21 April 2013, Copenhagen: Programme and abstracts, 2013, p. 20-21Conference paper, Oral presentation with published abstract (Refereed)
Contemporary multilingualism in Sweden led to the creation of a language law in 2009 (SFS 2009:600), making Swedish the official language and passing on the responsibility for protecting the continued use of Swedish in all domains of society to public sector institutions. Given the growing need for English in today's globalized tertiary-level education and scientific research, Swedish universities now need to develop their own procedures and policies that attend both to the language law and to the need to be globalized.
This paper, then, which reports on part of a larger ethnographic/discourse analytic project, examines how ideologies about English and multilingualism are entextualized in a language policy that was developed by a committee at a major Swedish university. Using nexus analysis (Scollon & Scollon, 2004), we map the discourses in place reflected in the policy in order to lift forward how core language ideologies are intertwined with institutional language planning. Analysis brings to light intertextual connections to language ideologies reflected in the national language law, in particular (i) 'clear language' in all the university's communication regardless of language used and (ii) Swedish as the main language to be used in all official documents that have legal force. Moreover, the need to be globalized and accessible to non- Swedish-speaking individuals is to be met by Swedish-English bilingualism, resemiotized (Scollon & Scollon 2004) in the policy as 'parallel language use', in most of the university's communication. Further, Swedish is stipulated as the main medium of instruction in first-cycle undergraduate courses, with growing use of English at the second- and third-cycle levels, and in the university's research activities. Finally, reflecting Sweden's linguistic hierarchy (Hult 2012), multilingualism was backgrounded in the policy text but still framed as important asset to the university.
2013. p. 20-21
General Language Studies and Linguistics Educational Sciences
Humanities; Pedagogics and Educational Sciences
EiE The English Language in Teaching in European Higher Education, 19 April - 21 April 2013, Copenhagen
Conference Program & Abstracts
General Language Studies and LinguisticsEducational Sciences


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Cambodian team with our two friends from Mexico in Glasgow.
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Cambodian team playing in George Square in Glasgow.