text:
\section{Introduction}
\label{sec:intro}

In recent years, Deep Neural Networks (DNNs) have been successfully applied to Automatic Speech Recognition (ASR) for many well-resourced languages including Mandarin and English \cite{amodei2016deep, xiong2016achieving}. However, only a small portion of languages have clean speech labeled corpus. As a result, there is an increasing interest in building speech recognition systems for low-resource languages. To address this issue, researchers have successfully exploited multilingual speech recognition models by taking advantage of labeled corpora in other languages \cite{huang2013cross, heigold2013multilingual}. Multilingual speech recognition enables acoustic models to share parameters across multiple languages, therefore low-resource acoustic models can benefit from rich resources.

While low-resource multilingual works have proposed various acoustic models, those works tend to combine several low-resource corpora together without paying attention to the variety of corpora themselves. One common training approach here is to first pretrain a multilingual model by combining all training corpora, then the pretrained model is fine-tuned on the target corpus \cite{dalmia2018sequence}. During the training process, each corpus in the training set is treated equally and sampled uniformly. We argue, however, this approach does not take account of the characteristics of each corpus, therefore it fails to take advantage of the relations between them. For example, a conversation corpus might be more beneficial to another conversation corpus rather than an audio book corpus.

In this work, we propose an effective sampling strategy (Corpus Relatedness Sampling) to take advantage of relations among corpora. Firstly, we introduce the corpus-level embedding which can be used to compute the similarity between corpora. The embedding can be estimated by being jointly trained with the acoustic model. Next, we compute the similarity between each corpus and the target corpus, the similarity is then used to optimize the model with respect to the target corpus. During the training process, we start by uniformly sampling from each corpus, then the sampling distribution is gradually updated so that more related corpora would be sampled more frequently. Eventually, only the target corpus would be sampled from the training set as the target corpus is the most related corpus to itself. While our approach differs from the pretrained model and the fine-tuned model, we can prove that those models are special cases of our sampling strategy.

To evaluate our sampling strategy, we compare it with the pretrained model and fine-tuned model on 16 different corpora. The results show that our approach outperforms those baselines on all corpora: it achieves 1.6\% lower phone error rate on average. Additionally, we demonstrate that our corpus-level embeddings are able to capture the characteristics of each corpus, especially the language and domain information. The main contributions of this paper are as follows:
\begin{enumerate}
    \item We propose a corpus-level embedding which can capture the language and domain information of each corpus.
    \item We introduce the Corpus Relatedness Sampling strategy to train multilingual models. It outperforms the pretrained model and fine-tuned model on all of our test corpora.
\end{enumerate}

\section{Related Work}

Multilingual speech recognition has explored various models to share parameters across languages in different ways. For example, parameters can be shared by using posterior features from other languages \cite{stolcke2006cross}, applying the same GMM components across different HMM states \cite{burget2010multilingual}, training shared hidden layers in DNNs \cite{huang2013cross, heigold2013multilingual} or LSTM \cite{dalmia2018sequence}, using language independent bottleneck features \cite{vesely2012language, dalmia2018domain}. Some models only share their hidden layers, but use separate output layers to predict their phones \cite{huang2013cross, heigold2013multilingual}. Other models have only one shared output layer to predict the universal phone set shared by all languages \cite{schultz2001language, tong2017investigation, vu2013multilingual}. While those works proposed the multilingual models in different ways, few of them have explicitly exploited the relatedness across various languages and corpora. In contrast, our work computes the relatedness between different corpora using the embedding representations and exploits them efficiently.

The embedding representations have been heavily used in multiple fields. In particular, embeddings of multiple granularities have been explored in many NLP tasks. To name a few, character embedding \cite{kim2016character}, subword embedding \cite{bojanowski2017enriching}, sentence embedding \cite{kiros2015skip} and document embedding \cite{le2014distributed}. However, there are few works exploring the corpus level embeddings. The main reason is that the number of corpora involved in most experiments is usually limited and it is not useful to compute corpus embeddings. The only exception is the multitask learning where many tasks and corpora are combined together. For instance, the language level (corpus level) embedding can be generated along with the model in machine translation \cite{johnson2017google} and speech recognition \cite{li2018multi}. However, those embeddings are only used as an auxiliary feature to the model, few works continue to exploit those embeddings themselves.

Another important aspect of our work is that we focused on the sampling strategy for speech recognition. While most of the previous speech works mainly emphasized the acoustic modeling side, there are also some attempts focusing on the sampling strategies. For instance, curriculum learning would train the acoustic model by starting from easy training samples and increasingly adapt it to more difficult samples \cite{amodei2016deep, braun2017curriculum}. Active learning is an approach trying to minimize human costs to collect transcribed speech data \cite{riccardi2005active}. Furthermore, sampling strategies can also be helpful to speed up the training process \cite{cui2015multilingual}. However, the goals of most strategies are to improve the acoustic model by modifying the sampling distribution within a single speech corpus for a single language. On the contrary, our approach aims to optimize the multilingual acoustic model by modifying distributions across all the training corpora.

\section{Approach}
In this section, we describe our approach to compute the corpus embedding and our Corpus Relatedness Sampling strategy.

\begin{table*}[t]
\begin{center}
    \caption{The collection of training corpora used in the experiment. Both the baseline model and the proposed model are trained and tested with 16 corpora across 10 languages. We assign a corpus id to each corpus after its corpus name so that we can distinguish the corpora sharing the same language.} \label{tab:corpus}
    \begin{tabular}{c c c c | c c c c}
    \toprule
    {\bf Language} & {\bf Corpus Name} & {\bf Domain} & {\bf Utterance} & {\bf Language } & {\bf Corpus Name }  & {\bf Domain} & {\bf Utterance}\\
    \midrule
    English & TED (ted) \cite{rousseau2012ted} & broadcast & 100,000  & Mandarin & Hkust (hk) \cite{liu2006hkust} & telephone & 100,000 \\
    English & Switchboard (swbd)\cite{godfrey1992switchboard} & telephone & 100,000 & Mandarin & SLR18 (s18) \cite{THCHS30_2015} & read & 13,388 \\
    English & Librispeech (libri) \cite{panayotov2015librispeech} & read & 100,000  & Mandarin & LDC98S73 (hub) & broadcast & 35,999 \\
    English & Fisher (fisher) \cite{cieri2004fisher} & telephone & 100,000 & Mandarin & SLR47 (s47) \cite{primewords_201801} & read & 50,384 \\
    \midrule
    Amharic & LDC2014S06 (babel) & telephone & 41,403 & Bengali & LDC2016S08 (babel) & telephone & 60,663 \\
    Dutch & voxforge (vox) & read  & 8,492 & German & voxforge (vox) & read & 41,146 \\
    Spanish & LDC98S74 (hub) & broadcast & 31,615 & Swahili & LDC2017S05(babel) & telephone & 44,502 \\
    Turkish & LDC2012S06 (hub) \cite{saraclar2012turkish} & broadcast & 97,427 & Zulu & LDC2017S19 (babel) & telephone & 60,835 \\
    \bottomrule
    \end{tabular}
\end{center}
\end{table*}

\subsection{Corpus Embedding}
Suppose that $\mathcal{C}_t$ is the target low-resource corpus, we are interested in optimizing the acoustic model with a much larger training corpora set $\mathcal{S} = \{ \mathcal{C}_1, \mathcal{C}_2 ... \mathcal{C}_n \}$ where $n$ is the number of corpora and $\mathcal{C}_t \in \mathcal{S}$. Each corpus $\mathcal{C}_i$ is a collection of $(\mathbf{x}, \mathbf{y})$ pairs where $\mathbf{x}$ is the input features and $\mathbf{y}$ is its target.

\begin{figure}[h]
  \centering
  \includegraphics[width=0.7\linewidth]{arch.png}
  \caption{The acoustic model to optimize corpus embeddings.}
  \label{fig:arch}
\end{figure}

Our purpose here is to compute the embedding $\mathbf{e}_i$ for each corpus $\mathcal{C}_i$ where $\mathbf{e}_i$ is expected to encode information about its corpus $\mathcal{C}_i$. Those embeddings can be jointly trained with the standard multilingual model \cite{dalmia2018sequence}. First, the embedding matrix $E$ for all corpora is initialized, the $i$-th row of $E$ is corresponding to the embedding $\mathbf{e}_i$ of the corpus $\mathcal{C}_i$. Next, during the training phase, $\mathbf{e}_i$ can be used to bias the input feature $\mathbf{x}$ as follows.

\begin{align}
                \mathbf{h} = \mathrm{Encoder}(\mathbf{x}+\mathbf{e}_i; W, E)
\end{align}

where $(\mathbf{x}, \mathbf{y}) \in \mathcal{C}_i$ is an utterance sampled randomly from $\mathcal{S}$, $\mathbf{h}$ is its hidden features, $W$ is the parameter of the acoustic model and Encoder is the stacked bidirectional LSTM as shown in Figure.\ref{fig:arch}. Next, we apply the language specific softmax to compute logits $\mathbf{l}$ and optimize them with the CTC objective \cite{graves2006connectionist}. The embedding matrix $E$ can be optimized together with the model during the training process.

\subsection{Corpus Relatedness Sampling}
With the embedding $\mathbf{e}_i$ of each corpus $\mathcal{C}_i$, we can compute the similarity score between any two corpora using the cosine similarity.

\begin{align}
    score(\mathcal{C}_i, \mathcal{C}_j) = \frac{\mathbf{e}_i \cdot \mathbf{e}_j}{ | \mathbf{e}_i | |\mathbf{e}_j|}
\end{align}

As the similarity reflects the relatedness between corpora in the training set, we would like to sample the training set based on this similarity: those corpora which have a higher similarity with the target corpus $\mathcal{C}_t$ should be sampled more frequently. Therefore, we assume those similarity scores to be the sampling logits and they should be normalized with softmax.

\begin{align}
    Pr(\mathcal{C}_i) = \frac{\exp\big(T \cdot score(\mathcal{C}_i, \mathcal{C}_t)\big)}{\sum_j{\exp(T \cdot score(\mathcal{C}_j, \mathcal{C}_t))}}
\end{align}

where $Pr(\mathcal{C}_i)$ is the probability to sample $\mathcal{C}_i$ from $\mathcal{S}$, and $T$ is the temperature to normalize the distribution during the training phase. We argue that different temperatures could create different training conditions. The model with a lower temperature tends to sample each corpus equally like uniform sampling. In contrast, a higher temperature means that the sampling distribution should be biased toward the target corpus like the fine-tuning.

Next, we prove that both the pretrained model and the fine-tuned model can be realized with specific temperatures. In the case of the pretrained model, each corpus should be sampled equally. This can be implemented by setting $T$ to be $0$.
\begin{align}
    Pr(\mathcal{C}_i) = \lim_{T \to 0} \frac{\exp\big(T \cdot score(\mathcal{C}_i, \mathcal{C}_t)\big)}{\sum_j{\exp(T \cdot score(\mathcal{C}_j, \mathcal{C}_t))}} = \frac{1}{n}
\end{align}

On the other hand, the fine-tuned model should only consider samples from the target corpus $\mathcal{C}_t$ , while ignoring all other corpora. We argue that this condition can be approximated by setting $T$ to a very large number. As $score(\mathcal{C}_t, \mathcal{C}_t) = 1.0$ and $score(\mathcal{C}_i, \mathcal{C}_t) < 1.0$ if $i \neq t$, we can prove the statement as follows:

\begin{equation}
\begin{split}
Pr(\mathcal{C}_i) & = \lim_{T \to \infty}  \frac{\exp\big(T \cdot score(\mathcal{C}_i, \mathcal{C}_t)\big)}{\sum_j{\exp(T \cdot score(\mathcal{C}_j, \mathcal{C}_t))}} \\
& = \begin{cases}
1.0 & if\> \mathcal{C}_i = \mathcal{C}_t \\
0.0 & if\> \mathcal{C}_i \neq \mathcal{C}_t
\end{cases}
\end{split}
\end{equation}

While both the pretrained model and the fine-tuned model are special cases of our approach, we note that our approach is more flexible to sample from related corpora by interpolating between those two extreme temperatures. In practice, we would like to start with a low temperature to sample broadly in the early training phase. Then we gradually increase the temperature so that it can focus more on the related corpora. Eventually, the temperature would be high enough so that the model is automatically fine-tuned on the target corpus. Specifically, in our experiment, we start training with a very low temperature $T_0$, and increase its value every epoch $k$ as follows.

\begin{align}
T_{k+1} = a T_{k}
\end{align}
where $T_k$ is the temperature of epoch $k$ and $a$ is a hyperparameter to control the growth rate of the temperature.

\section{Experiments}
To demonstrate that our sampling approach could improve the multilingual model, we conduct experiments on 16 corpora to compare our approach with the pretrained model and fine-tuned model.

\subsection{Datasets}
We first describe our corpus collection. Table.\ref{tab:corpus} lists all corpora we used in the experiments. There are 16 corpora from 10 languages. To increase the variety of corpus, we selected 4 English corpora and 4 Mandarin corpora in addition to the low resource language corpora. As the target of this experiment is low resource speech recognition, we only randomly select 100,000 utterances even if there are more in each corpus. All corpora are available in LDC, voxforge, openSLR or other public websites. Each corpus is manually assigned one domain based on its speech style. Specifically, the domain candidates are \textit{telephone}, \textit{read} and \textit{broadcast}.

\subsection{Experiment Settings}
We use EESEN \cite{miao2015eesen} for the acoustic modeling and epitran \cite{mortensen2018epitran} as the g2p tool in this work. Every utterance in the corpora is firstly re-sampled into 8000Hz, and then we extract 40 dimension MFCCs features from each audio. We use a recent multilingual CTC model as our acoustic architecture \cite{dalmia2018sequence}: The architecture is a 6 layer bidirectional LSTM model with 320 cells in each layer. We use this architecture for both the baseline models and the proposed model.

Our baseline model is the fine-tuned model:  we first pretrained a model by uniformly sampling from all corpora. After the loss converges, we fine-tune the model on each of our target corpora. To compare it with our sampling approach, we first train an acoustic model to compute the embeddings of all corpora, then the embeddings are used to estimate the similarity as described in the previous section. The initial temperature $T_0$ is set to 0.01, and the growth rate is $1.5$. We evaluated all models using the phone error rate (PER) instead of the word error rate (WER). The reason is that we mainly focus on the acoustic model in this experiment. Additionally, some corpora (e.g.: Dutch voxforge) in this experiment have very few amounts of texts, therefore it is difficult to create a reasonable language model without augmenting texts using other corpora, which is beyond the scope of this work.

\subsection{Results}

\begin{table}[h]
\begin{center}
\caption{Phone error rate (\%PER) of the pretrained model, the baseline model (fine-tuned model) and our CRS(Corpus Relatedness Sampling) approach on all 16 corpora}
\begin{tabular}{l c c c}
\toprule
{\bf Corpus} & {\bf Pretrain PER}& {\bf Base PER} & {\bf CRS PER} \\
\midrule
English (ted) & 19.2 & 11.6 & 10.3 \\
English (swbd) &  24.9 & 15.3 & 14.1 \\
English (libri) & 12.1 & 6.5 & 5.4 \\
English (fisher) & 34.7 & 23.5 & 22.5 \\
Mandarin (hk) & 32.6 & 16.1 & 14.5 \\
Mandarin (s18) & 8.5 & 6.6 & 5.6 \\
Mandarin (hub) & 10.2 & 5.1 & 4.4 \\
Mandarin (s47) & 13.0 & 10.9 & 9.2 \\
\midrule
Amharic (babel) & 45.4 & 40.7 & 36.9 \\
Bengali (babel) &  47.0 & 41.9 & 40.0 \\
Dutch (vox) & 27.3 & 21.6 & 18.2 \\
German (vox) & 23.1 & 12.9 & 10.3 \\
Swahili (babel) & 17.7 & 16.1 & 14.5 \\
Spanish (hub) & 14.0 & 8.4 & 7.5 \\
Turkish (hub) & 49.2 & 45.6 & 44.9 \\
Zulu (babel) & 47.8 & 38.5 & 37.9 \\
\midrule
Average & 26.7 & 20.1 & \bf{18.5} \\
\bottomrule
\end{tabular}
\label{table:result}
\end{center}
\end{table}

Table.\ref{table:result} shows the results of our evaluation. We compare our approach with the baseline using all corpora. The left-most column of Table.\ref{table:result} shows the corpus we used for each experiment, the remaining columns are corresponding to the phone error rate of the pretrained model, the fine-tuned model and our proposed model. First, we can easily confirm that the fine-tuned model outperforms the pretrained model on all corpora. For instance, the fine-tuned model outperforms the pretrained model by 4.7\% on the Amharic corpus. The result is reasonable as the pretrained model is optimized with the entire training set, while the fine-tuned model is further adapted to the target corpus. Next, the table suggests our Corpus Relatedness Sampling approach achieves better results than the fine-tuned model on all test corpora. For instance, the phone error rate is improved from 40.7\% to 36.9\% on Amharic and is improved from 41.9\% to 40.0\% on Bengali. On average, our approach outperforms the fine-tuned model by 1.6\% phone error rate. The results demonstrate that our sampling approach is more effective at optimizing the acoustic model on the target corpus. We also train baseline models by appending corpus embeddings to input features, but the proposed model outperforms those baselines similarly.

One interesting trend we observed in the table is that the improvements differ across the target corpora. For instance, the improvement on the Dutch corpus is 3.4\%, on the other hand, its improvement of 0.6\% is relatively smaller on the Zulu dataset. We believe the difference in improvements can be explained by the size of each corpus. The size of Dutch corpus is very small as shown in Table.\ref{tab:corpus}, therefore the fine-tuned model is prone to overfit to the dataset very quickly. In contrast, it is less likely for a larger corpus to overfit. Compared with the fine-tuned model, our approach optimizes the model by gradually changing the temperature without quick overfitting. This mechanism could be interpreted as a built-in regularization. As a result, our model can achieve much better performance in small corpora by preventing the overfitting effect.

\begin{table}[h]
\begin{center}
\caption{The similarity between training corpora. For each target corpus, we show its most related corpus and second related corpus.}
\begin{tabular}{l | l | l}
\toprule
{\bf Target} & {\bf 1st Related Corpus} & {\bf 2nd Related Corpus}\\
\midrule
English (ted) & English (libri) & Turkish (hub) \\
English (swbd) & English (fisher) & Mandarin (hk) \\
English (libri) & English (ted)&  German (vox) \\
English (fisher) & English (swbd) &Mandarin (hk) \\
Mandarin (hk) & Bengali (babel) &English (swbd) \\
Mandarin (s18) & Mandarin (s47)& Mandarin (hub) \\
Mandarin (s47) & Mandarin (s18)& Dutch (vox) \\
Mandarin (hub) & Spanish (hub)& Tkish (hub) \\
\midrule
Amharic (babel) & Bengali (babel) & Swahili (babel) \\
Bengali (babel) & Mandarin (hk)& Amharic (babel) \\
Dutch (vox) & Mandarin (s47) & Mandarin(s18) \\
German (vox) & English (libri) & Mandarin (s47) \\
Swahili (babel) & Zulu (babel) & Amharic (babel) \\
Spanish (hub) & Mandarin (hub) & Turkish (hub) \\
Turkish (hub) & English (libri) & Mandarin (hub) \\
Zulu (babel) & Swahili (babel) & Amharic (babel) \\
\bottomrule
\end{tabular}
\label{table:relation}
\end{center}
\end{table}

To understand how our corpus embeddings contribute to our approach, we rank those embeddings and show the top-2 similar corpora for each corpus in Table.\ref{table:relation}. We note that the target corpus itself is removed from the rankings because it is the most related corpus to itself. The results of the top half show very clearly that our embeddings can capture the language level information: For most English and Mandarin corpora, the most related corpus is another English or Mandarin corpus. Additionally, the bottom half of the table indicates that our embeddings are able to capture domain level information as well. For instance, the top 2 related corpus for Amharic is Bengali and Swahili. According to Table.\ref{tab:corpus}, those three corpora belong to the \textit{telephone} domain. In addition, Dutch is a \textit{read} corpus, its top 2 related corpora are also from the same domain. This also explains why the 1st related corpus of Mandarin (hk) is Bengali: because both of them are from the same \textit{telephone} domain.

\begin{figure}
\centering
\begin{tikzpicture}
	\begin{axis}[%
	scatter/classes={%
		telephone={mark=square*,blue},%
		record={mark=triangle*,red},%
		broadcast={mark=o,draw=black}},	xmin=-100,xmax=125,ymin=-100,ymax=125]
	\addplot[scatter,only marks,%
		scatter src=explicit symbolic]%
	table[meta=label] {

x                  y                  label
-30.31571388244629 -32.897972106933594 telephone
2.3990633487701416 31.22985076904297 record
11.002802848815918 -73.944580078125 telephone
51.9562873840332 -47.479148864746094 record
-24.818572998046875 -9.482719421386719 telephone
-9.081963539123535 -54.546974182128906 telephone
-21.057504653930664 51.35031509399414 record
-55.622982025146484 -38.70508575439453 telephone
4.542168617248535 60.26102828979492 broadcast
77.91976165771484 7.000478744506836 record
68.43834686279297 -18.6563777923584 record
-49.72146224975586 -10.975892066955566 telephone
52.388553619384766 12.837336540222168 record
1.0258090496063232 3.9491748809814453 record
-3.7222859859466553 -27.329965591430664 record
39.575313568115234 -20.297710418701172 telephone
-22.801538467407227 14.41296100616455 telephone
-78.33529663085938 -25.326885223388672 telephone
14.079936027526855 -15.207659721374512 record
19.753564834594727 -44.717838287353516 telephone
42.81153869628906 -71.23609924316406 telephone
48.969696044921875 72.3545913696289 broadcast
83.62925720214844 38.655426025390625 record
-49.41233825683594 21.66193199157715 telephone
26.44234848022461 7.403919219970703 record
-73.8827896118164 6.017458438873291 telephone
-17.529891967773438 -77.6971664428711 telephone
-39.04698181152344 -57.32247543334961 telephone
29.086034774780273 52.09471893310547 record
32.02229309082031 29.5660457611084 record
57.19882583618164 47.49128723144531 broadcast
26.665611267089844 94.22811126708984 record
4.1577959060668945 85.670166015625 record
-22.751380920410156 83.13924407958984 broadcast
-53.254608154296875 55.57710647583008 telephone
		};
		\legend{telephone, read, broadcast}
	\end{axis}
\end{tikzpicture}
	\caption{Domain plot of 36 corpora, the corpus embeddings are reduced to 2 dimensions by t-SNE}
		\label{fig:domain}
\end{figure}

To further investigate the domain information contained in the corpus embeddings, we train the corpus embeddings with an even larger corpora collection (36 corpora) and plot all of them in Figure.\ref{fig:domain}. To create the plot, the dimension of each corpus embedding is reduced to 2 with t-SNE \cite{maaten2008visualizing}. The figure demonstrates clearly that our corpus embeddings are capable of capturing the domain information: all corpora with the same domain are clustered together. This result also means that our approach improves the model by sampling more frequently from the corpora of the same speech domain.

\section{Conclusion}
\label{sec:conclusion}
In this work, we propose an approach to compute corpus-level embeddings. We also introduce Corpus Relatedness Sampling approach to train multilingual speech recognition models based on those corpus embeddings. Our experiment shows that our approach outperforms the fine-tuned multilingual models in all 16 test corpora by 1.6 phone error rate on average. Additionally, we demonstrate that our corpus embeddings can capture both language and domain information of each corpus.

\section{Acknowledgements}
This project was sponsored by the Defense Advanced Research Projects Agency (DARPA) Information Innovation Office (I2O), program: Low Resource Languages for Emergent Incidents (LORELEI), issued by DARPA/I2O under Contract No. HR0011-15-C-0114.

\bibliographystyle{IEEEtran}

redpajama_set_name: RedPajamaArXiv


Enrique Aguilar Valls will be three years more in front of the Acequia Rascanya, after his re-election Sunday as Syndic of this irrigation channel.
Acequia Rascanya waters the huerta of Benimaclet (Valencia), Tavernes Blanques, Alboraya and Almàssera and brings together a total of 1,100 irrigators.
Enrique Aguilar is the current President of the Tribunal de las Aguas, for the biennium 2013-2015.


text:
ISO/CD 9472-10000
Health informatics — Personalized health navigation — Part 10000: Architecture
Standardization of a reference architecture that applies the core elements of navigational informatics technologies, (e.g., location status determination, destination objectives, and dynamic pathway guidance), to personalized health navigation including health and wellness assessment, health objectives identification and specification, and pathway determination including coordination with healthcare providers and services. The architecture will support real-time monitoring and status determination, and identification of emerging health navigation challenges as well as existing health conditions, including chronic, acute, and episodic. This document provides comprehensive model-based systems engineering specifications, allowing for implementation architectures that support a diverse set of infrastructure and operational approaches. Guidance is provided for conformant implementation architectures and system components.

redpajama_set_name: RedPajamaCommonCrawl


text:
Opening speech at the International Research Conference on the 15th Anniversary of the Constitution
Gianni Buquicchio
pp. 4-5[PDF]
It is with great pleasure that I am here today, to represent the Venice Commission of the Council of Europe, in this beautiful city of Tallinn and to open this conference on the occasion of the 15th anniversary of the adoption of the Constitution of Estonia. Your constitution has served you well in these 15 years, proving a firm basis for accession to the European Union. The Venice Commission is glad for having been of assistance in identifying in its opinion various issues related to this accession. In another opinion the Commission confirmed that the establishment of a constitutional review chamber is a perfectly valid model of constitutional control.
In order to celebrate this anniversary you chose a timely topic, as several constitutional courts have been criticised recently for being too 'activist'. A number of them have been under serious pressure with respect to decisions they have rendered. We have seen cases in the past where, for instance, state powers have 'punished' constitutional courts for delivering unwelcome decisions, by not appointing new judges, thereby trying to 'starve out' the court by pushing the number of remaining judges below that constituting a quorum. The Venice Commission has assisted courts in such situations through direct support and by giving opinions pointing out solutions for avoiding such problems in the future.
As you well know, constitutional courts are often unfairly accused of 'judicial activism', a term frequently used in a negative sense to describe the tendency of judges to follow a particular, sometimes political or personal, agenda. Constitutional courts repeatedly face such accusations and the oft-asked question of whether constitutional review by a constitutional court is really law or whether it should be considered politics.
It is true that, over time, constitutional courts have been entrusted with more and more tasks that go far beyond the role of the negative legislator — a role attributed to them by Kelsen , the inventor of 'specialised constitutional courts'. The negative legislator's tasks were envisioned as merely to set aside enacted laws that were not in line with the constitution.
However, the line between interpretation of the constitution and judicial activism is difficult to draw. This is where the accusations of judges meddling tend to come in. Politicians often expect courts, when interpreting the nation's constitution, to exercise judicial restraint — in other words, to refrain from striking down laws unless they are clearly not in line with the constitution. One technique is the interpretation of laws as in conformity with the constitution, which is sometimes less of an encroachment than striking them down.
But, sometimes, constitutional courts or equivalent bodies such as the Estonian Supreme Court cannot avoid filling in legal gaps through interpretation. This happens in cases where provisions might otherwise not be applicable or would not be applied in a constitutional manner (legal gaps are going to be the subject of the meeting of the Conference of European Constitutional Courts in Vilnius, Lithuania, in June 2008).
The constitutional court's role in this respect is legitimised directly by the constitution. Its active role in fulfilling its mandate is crucial. This should not be confused with judicial activism, which would involve the court making its own legislative judgments. Such action by the constitutional court would be a radical departure from its role as the guarantor of the constitution.
Constitutional courts have been introduced in many countries as a mechanism of constitutional review, and as such they adjudicate cases that challenge the constitutionality of laws and the actions of the executive. In this context, they are sometimes faced with questions to which the constitution does not give clear-cut answers or, in some cases, offers no answers at all. This often occurs in new areas, not covered by the constitution, due to the fact that these areas did not exist at the time of the constitution's adoption. An example can be found in the field of bioethics or areas where, over time, the perceptions and attitudes of society have changed — for instance, the attitude towards unions between same-sex partners.
It may also happen that the constitution is ambiguous on certain issues because its drafters agreed on a compromise formula that pleased all sides but left certain issues open. In such cases, it is up to the constitutional court to provide an answer. By providing one, it will have to develop the constitution without being a constituent power, which, of course, requires democratic legitimacy.
Taking into account the historical context and basing itself on the wording, the constitutional court develops the inherent values contained in the constitution through the systematic or teleological approach. In this way, the constitutional court ensures that the constitution remains a living, dynamic instrument that shapes the life of society, and vice versa, and not a static text that would be quickly outdated.
I believe that constitutional courts and equivalent bodies not only provide for the stability of the constitution and respect for the rule of law but have, beyond this classical approach, a distinctive role to play in furthering and strengthening the democratic process, in which the constitution serves as a main pillar.
The Venice Commission promotes the basic principles of the Council of Europe: democracy, the protection of human rights, and the rule of law. While each country is different and follows a different path at a different speed, these common goals apply to all of them. In pursuing these common goals, we can rely on a source that is an accumulated wealth of legal reasoning: the case-law and jurisprudence of constitutional courts and equivalent bodies. Their active role is therefore most dear to the Venice Commission.
I wish your country further success, building on the strength of your constitution as a cornerstone of democracy, the protection of human rights, and the rule of law.
pp. 4-5 [PDF]


text:
Q: Multiple selection for ListView in simple todo list android app not working I am new to Android development and am creating a simple todo list. I am trying to have my app change the background color of a particular item and keep it that background color (similar to highlighting) when I click on it. I've tried using setChoiceMode(ListView.CHOICE_MODE_MULTIPLE), but that gives me a checkbox, which is not really what I want. I don't want the checkbox, and instead, want to alter the background color.
I have read many threads on stackoverflow regarding this issue, but none of them solved my issue. Any suggestions would be greatly appreciated. Thank you!
My TodoActivity class is:
package com.example.simpletodo;

import java.util.ArrayList;

import android.app.Activity;
import android.os.Bundle;
import android.view.Menu;
import android.view.View;
import android.widget.AdapterView;
import android.widget.ArrayAdapter;
import android.widget.EditText;
import android.widget.ListView;

public class TodoActivity extends Activity {
    ArrayList<String> items;
    ArrayAdapter<String> itemsAdapter;
    ListView lvItems;

    @Override
protected void onCreate(Bundle savedInstanceState) {
    super.onCreate(savedInstanceState);
    setContentView(R.layout.activity_todo);
    lvItems = (ListView) findViewById(R.id.lvItems);
    items = new ArrayList<String>();
    itemsAdapter = new ArrayAdapter<String>(this, android.R.layout.simple_list_item_1, items);
    lvItems.setAdapter(itemsAdapter);
    items.add("First Item");
    items.add("Second Item");
    setupListViewListener();
    lvItems.setAdapter(new ArrayAdapter<String>(this, android.R.layout.simple_list_item_multiple_choice, items));
    lvItems.setChoiceMode(ListView.CHOICE_MODE_MULTIPLE);
}

private void setupListViewListener() {
    lvItems.setOnItemLongClickListener(new android.widget.AdapterView.OnItemLongClickListener() {
    @Override
        public boolean onItemLongClick(AdapterView<?> parent, View view, int position, long rowId) {
            items.remove(position);
            itemsAdapter.notifyDataSetChanged();
            return true;
        }
    });
}

public void addTodoItem(View v) {
    EditText etNewItem = (EditText) findViewById(R.id.etNewItem);
    itemsAdapter.add(etNewItem.getText().toString());
    etNewItem.setText("");
}

@Override
public boolean onCreateOptionsMenu(Menu menu) {
    getMenuInflater().inflate(R.menu.main, menu);
    return true;
    }
}

and my XML file is:
<RelativeLayout xmlns:android="http://schemas.android.com/apk/res/android"
    xmlns:tools="http://schemas.android.com/tools"
    android:layout_width="match_parent"
    android:layout_height="match_parent"
    tools:context="${relativePackage}.${activityClass}" >

    <ListView
        android:id="@+id/lvItems"
        android:layout_width="match_parent"
        android:layout_height="380dp"
        android:layout_above="@+id/button1"
        android:layout_alignParentLeft="true"
        android:layout_alignParentTop="true"
        android:layout_marginLeft="30dp"  />

    <Button
        android:id="@+id/button1"
        android:layout_width="wrap_content"
        android:layout_height="wrap_content"
        android:layout_alignParentBottom="true"
        android:layout_alignParentRight="true"
        android:layout_marginBottom="15dp"
        android:layout_marginRight="18dp"
        android:onClick="addTodoItem"
        android:text="@string/button_add" />

    <EditText
        android:id="@+id/etNewItem"
        android:layout_width="wrap_content"
        android:layout_height="wrap_content"
        android:layout_alignParentBottom="true"
        android:layout_alignParentLeft="true"
        android:layout_marginBottom="15dp"
        android:layout_marginLeft="10dp"
        android:layout_toLeftOf="@+id/button1"
        android:hint="@string/add_new_item" >

        <requestFocus />
    </EditText>

</RelativeLayout>

A: I apologize, changing my answer, as my first one was incorrect.  Here is how you would select/highlight multiple items using a default adapter:
    String[] strArray = {"row1", "row2", "row3"};

    ArrayAdapter<String> adapter = new ArrayAdapter<String>(this,
            android.R.layout.simple_list_item_activated_1, strArray);

    lv.setAdapter(adapter);
    lv.setChoiceMode(ListView.CHOICE_MODE_MULTIPLE);

The code above will give you highlighted selected items.  If you want checkboxes, replace with the layout below:
     android.R.layout.simple_list_item_multiple_choice

The functions below should let you  determine which items are currently selected.
    lv.getCheckedItemPositions();
    lv.getCheckedItemIds();

For anything more complicated (e.g. customizing background colors, appearance, etc), you will need to supply a custom layout and/or override the getView() functions of your adapters.  Here's an excellent tutorial on that:  http://www.vogella.com/tutorials/AndroidListView/article.html

redpajama_set_name: RedPajamaStackExchange


My misadventures in the world of comedy.
My writing partner and best buddy DJ Dangler and I will be launching a fun little podcast in the next few weeks. While writing, we end up having sometimes silly, sometimes introspective conversations. We decided to record them. In this first one, we talk about our favorite living wrestlers. We hope you like it.
The art is from my friend Joe Apel.
The intro music is from the Mutants.
My friend, screenwriter/improviser Sam Gasch and I have launched a brand new podcast. The trend in movies these days is that just about every big budget feature is a remake of something else. So every other week we will look at an old movie and figure out what's the ideal way for Hollywood to remake it.
Here's a link to the preview episode. Please subscribe in your podcasting catcher of choice and write us a review so we know how to improve the show.
Next week will be the first time in about two years that I'll be back in Michigan. I miss it a lot. I miss my friends. I miss my family.
I'm incredibly grateful Go Comedy is letting me have a show there on Thursday the 8th. Time is incredibly limited this trip because we're coming back to wedding plan. This show is a good opportunity to get to see a bunch of friends all at once! So, if you're going to the show, it will be over with by 9:00, we can all go down the street to a bar afterwards and hang out. What's that? You didn't know about this show? Well click HERE for more info. And if you still need tickets to the show, just click HERE.
I write best with a partner. I've tried a few different ones over the years before finding one who I really click with. And I found him completely by accident.
The hardest thing I found about writing with a partner is that one person is always going to be more motivated than the other. Some days you may be the motivated one, while other days it may be them. No relationship is truly 50/50 all the time.
Whenever I write something, I send it out to a bunch of people to get feedback. Very few people actually ever get back to me. DJ Dangler always does. And when he does, he's thorough. The first thing I remember critiquing was a spec script I wrote for Bob's Burgers. We went through it line by line picking apart dialogue that didn't feel true to characters and moments that didn't feel like the show. It was incredibly helpful.
I've been fortunate over the past year to shadow an established television writer as he works on various pitches for his own shows. He works predominately in procedurals and political dramas, while I'm mainly a comedy guy. But I knew the fundamentals I was learning were universal.
He always starts with a brief, but catchy biography about himself and why that makes him the best person to write what he's pitching. His origin story has become like folklore. He was a Washington DC speech writer, but some of the details change a little bit here and there. Most importantly, the drama amps up in a way that always hooks you right out of the gate.
From there he goes into the teaser for his show. If you don't know, that's basically the thing you see before the first commercial that, if done right, keeps you from changing the channel for the next thirty or sixty minutes. My mentor is great a teasers. Political drama and procedurals are pretty much at the bottom of the list of things I like to watch, but more often than not, his teasers grab me.
Then he talks about the pilot episode while giving pertinent information about all of the key characters. He gives you enough of their backstories to get you to really know who they are as people. Sometimes if the teaser doesn't grab me, his ability to create interesting characters does. His characters are always flawed. They feel real. Most importantly, they're interesting. We always know what their needs are and why they need it.
After he gives a pretty detailed beat by beat run down of the pilot episode, he goes over story beats for the entire first season of his show up to the cliffhanger ending. He wraps up by touching on the themes of further seasons so that whoever he's pitching to knows he's thought through this entire world.
Off the top of my head, I've sat through seven very detailed pitches in the past year from him. He's a work horse. In addition to learning how to pitch, I learned how to manage time and projects. I'll write more about that in the future.
So that brings me to our pitch.
Along with comedian DJ Dangler and artist Axel Ortiz, I created an animated show. My friend Joe Apel has been working in animation for the past ten years and directed me to someone at a network who might be looking for something maybe along the lines of what we created. I sent an email and a meeting was set up pretty quickly.
DJ spends most of his time on the road making his living off of stand up, while I'm currently choosing to take a little break from the road and focus my attention on California right now. When the meeting was set, DJ decided to fly back to not only be there for the meeting, but to make sure we were prepared.
Axel is just as much of a work horse as the aforementioned television writer. He's a veteran of the pitch meeting, but pointed out earlier that if we had a star attached to our show, that would give us an advantage. Over the past six months, I've been working on getting the biggest name I know attached to it. The week before our pitch, we got him. He looked at what we were going in with and said that he was happy to be part of it and if it lands, we can hammer out the details then.
I researched the person we were pitching to. She was a guest on a comedy podcast and spent an entire hour talking about the pitching process. In that interview, I also found out that she, like DJ, is from Indiana and her wife, like me, is from Michigan. She and I both nerd out over character actors too. So I knew we had a conversation opener and I wasn't going to have to overly explain things like if I said, "Oh this character is a little bit like Miller from the movie Repo Man, played by Tracey Walter", she was going to know who Tracey Walter is. For the record, many of the scripts I've written have characters that I imagine Tracey Walter playing. I know why Jonathan Demme puts him in every movie, because Tracey Walter is a national treasure! I once told DJ that I would be more excited about meeting Tracey Walter than I would be about meeting George Lucas or Harrison Ford. I think Tracey Walter lives within a mile of me too. And if you think every time my girlfriend and I walk around the neighborhood, I'm not on a constant look out for Tracey Walter watering his plants, then you're wrong!
Axel went in to pitch on another show three weeks prior to our meeting and told us what the executive asked from them. DJ and I wanted to make sure we were over prepared. We had a pilot script. We had ideas for story beats for our first season and I knew what I wanted to happen in the final episode of the entire series. But DJ and I detailed out episode by episode the entire first season of our show. We knew the story beats and what themes each episode would explore. A friend of mine who works in printing made us a really nice pitch packet too.
The idea of writing stories with an overall point of view or theme is something that I would accidentally sometimes hit, but other times missed completely. DJ and I have a mutual friend in comedian turned television writer Nick Anthony who really hammered into us the idea that we sometimes missed that. So when DJ and I started the conversation about each episode of our show, we started with what was the theme for that episode. It really helped us figure out where the other characters were going to be in their individual arcs.
The night before our pitch, DJ and I walked home around five miles from West Hollywood to the far eastern side of Hollywood really exploring every character we created. We knew their strengths, their weaknesses, their desires, their backgrounds, and their secrets.
So going into our pitch I knew we created something cool, timely, we have a big name attached and we were prepared.
The meeting was great. I told her that I enjoyed the podcast she was on and we found out her dad and sister both went to Purdue, same as DJ. Her wife and my girlfriend are from the same small suburb of Detroit. She asked how DJ, Axel and I knew each other. DJ and I have a fun origin story where we both showed up early at Nick Anthony's home to go do a show in San Diego. Nick wasn't there yet. I was parked on the street waiting. A guy pulled up on the other side of the street, got out of his car, put a large pizza on the roof and just ate the entire thing in front of me. I couldn't take my eyes off of it. He was so full of unadulterated and gluttonous joy. From that moment I wish I could be that guy's friend. That guy turned out to be DJ. We hit it off that night and have been friends ever since. Axel and I met at San Diego Comic Con last year and our origin story gave me a chance to really talk about how much I love his work and why he was the perfect person to design our show.
Then she wanted to know about our characters. DJ and I were able to speak passionately and deeply about each of them. I like writing female characters. This show features two really strong female characters. The main character is, in some ways, man boy with arrested development not completely unlike DJ or myself. One of the women in the show is his daughter, the other is basically his boss. Both are strong. Neither of them are bitchy. Both of them are just as cool and quick as he is. They're strong women, but they're perfect. They're deep and rich characters. I can see how it would be an easy trap for a guy writing a woman to over compensate by trying to make her too perfect. Ours aren't at all. They both have real human foibles. Also, I'm hoping I just used the word "foibles" correctly.
At the end of our meeting she asked us what our roles would be if the show were picked up. I explained that when Garth Ennis created the comic book Preacher he gave artist Steve Dillon co-creator credit because he felt the look of it was just as important as the story. I believe in that. When I asked Axel to draw the characters, I told him very little. And aside from changing the hair color on one of them, I didn't have any changed. I love his art. DJ and I are great at writing jokes and character, but we still struggle with story and conflict. An analogy a script reader gave us of a screenplay that we wrote was that there's "a lot of icing, but not much cake". That's fair. So I said DJ and I care a lot about our characters, but if the show were picked up, it would probably be best if we were teamed with a producer who was better at breaking stories. So I think that showed that we were flexible and going to be easy to work with.
There's another show, which I can't name, where the creators were fired because they were way too difficult and had very unrealistic expectations of what they were entitled to. Hollywood is a city built on collaborative art. Nothing here is a single vision. You have to be willing to trust others to help you create the best product possible.
According to the podcast our executive was on, because she sees so many people, her pitch meetings rarely go more than thirty minutes. Ours went 45. She laughed a lot and seemed engaged. There's nothing I would have done differently about our meeting. We gave her our book with the character art and descriptions as well as additional information about the show. We'll know more in a couple weeks.
Yes, at this stage a hundred things would have to happen for this to be a show any of us would ever get to see. We're going to move forward and continue pitching to other places too. But on the flipside of this meeting, I thought it would be nice to share some of the tips that I learned.
Know as much about the person you're pitching as you can. As they say, "knowledge is power".
Be prepared to explain why what you're pitching is important to you personally and why you're the best person to write it.
Television is a medium based on characters. Make sure you have interesting ones and you can talk about them.
As with any speech writing, pepper in a joke or two if it suits the mood. I don't think I'd try to make anyone laugh if I were pitching Schindler's List: The Series.
Keep it pithy, punk. Metallica songs are like three hours long and feel like they include every idea that everyone in the band had. In the time it takes to listen to two Metallica songs, you could listen to seventeen Ramones songs. Be a Ramone. Leave them wanting more.
Don't just talk. Listen. We were prepared to talk nonstop for 15-20 minutes, but were totally able to let our exec lead the pitch in a much more conversational and informal manner.
Television is a flexible art. We didn't get any notes in this first meeting, but my mentor has. He digests them all and if he has complaints, he does it later to his manager, not in the room.
Know what you're talking about inside and out. DJ and I know every in and out of our show where it stands right now. And if you had a question for us that we didn't know the answer to, we'd make something up. We have an alien character who is the last of his kind. What planet is he from? Zaphodbrock. He's a Zaphoid from the planet Zaphodbrock. I just made that up. It's one part Hitchhiker's Guide to the Galaxy and one part late singer of Gwar. If you wanted me to talk about Zaphodbrock, I could. And DJ could jump in and we'd piggy back on each other's ideas. Executives want to feel safe. The words, "I don't know", has never instilled security in anyone.
If you can't come up with ten items, don't just make stuff up to fill time or space. See #5.
So there you go. I'm far from an expert, but I know a week or two ago I'd love to have this information compiled in one place. I hope this helps or at least makes for some interesting reading.
Inspired by an article on Hip in Detroit about how to be a good music fan, I decided to write about what it takes to be a good comedy audience.
Comedy Pro Tip: The Tortoise and the Hare.
Maybe it's nerves or the adrenaline rush of doing what we've waited all day long to do, but many comedians tend to talk a bit too fast on stage. Comedian and owner of the Komedy Korner, Leo DuFour once suggested when I got off stage that I slow down and enjoy my time like I would a delicious meal. Maybe that was a health tip and I'm forgetting that I was scarfing down a Poutine platter at the time….it was Canada after all. Let me find another example.
One of my best friends asked me after a show why I don't speak on stage like I speak normally off stage. He was right. I didn't believe in my material at the time so on stage I would yell and ram my jokes down the throats of the audience as quickly as I could.
This past weekend I was at the Ann Arbor Comedy Showcase with my friend Nate Fridson. I've only seen Nate once since he moved to New York about a year or so ago. He churned out a ton of new material. It was really nice watching him. I was nervous going into the weekend since it had been a good month since I did more than 15 minutes of time in one set. Maybe stand up is like riding a bike. I haven't ridden a bike in years and I'm worried how my first attempt would be.
The shows ended up going pretty well. With the exception of about four or five minutes on being an uncle, I'm not doing any material from my CD that I released just about a year ago. I have a pretty good track record at the Showcase so I took advantage of that trust to try out some new pieces. Most of them worked.
This week marks the start of two months of road work. The boredom I feel from doing the same jokes over and over again tends to go away when I'm in new cities. I know everything will be brand new to them. This week I'll be at the Skyline Comedy Cafe in Appleton, Wisconsin. It's a great club and I'm really looking forward to it.
Earlier in the week I did a live episode of WTF with Marc Maron. That was pretty awesome. I know Marc has his reputation, but he's been super cool to me. I was nervous for the interview, but it went fairly well. We dug a little more into my personal life than I would have wanted, but that's the nature of the show. After that I went over to the UCB Theatre and did a set on Comedy Bang Bang. Zach Galifianakis closed that show. Backstage he seemed like a genuinely good guy. That made me happy. Eric Andre was there too. He was just super nice and charming. It really does seem like the only dicks you encounter in this business are the people at the bottom who are bitter being stuck there. The higher up you go, the nicer people seem to be.
I closed out my LA trip with a set on The Meltdown and Meltdown Comics. That show was simply amazing. It's a small room, packed full of comedy super fans. The line up is always great. I was so honored that my Jonah Ray let me be part of it. Through my years I've met a lot of people who I don't get to see nearly as much as I'd like. Jonah is one of those guys. He's another guy who in addition to being a really good comedian, is also a hell of a nice person.
Sean Patton from New Orleans closed the Meltdown show and was simply amazing. I worked with Sean here in Michigan and thought he was great. Earlier this week though, that greatness was on a whole new level. At the risk of sounding like a broken record, another super awesome person too. Hopefully Sean and I will be able to do some shows together in 2013. He's going to be on Maron's television show for IFC next year. I have a feeling that's about the time that he's going to blow up and become a household name at least with comedy nerds.
From start to finish, last week was a blast! Enjoy some clips from Nate, Jonah and Sean.
Through comedy I know a few people. Through her work, Lesley knows a few people as well. In my mind, here's basically how I thought this would work.
Lesley and I wrote a really good screenplay. We let some people read it and give us feedback. We wrote a better second draft. We had some funny people over to read through it and we took notes on the comedy beats and how the words flowed. We wrote an even better third draft. We registered the script with the WGA. Then we started contacting people.
Our script is basically Bridesmaids meets The Social Network. It's really funny. Paul Feig, the director of Bridesmaids, was a stand up comedian in Detroit, just like me. A mutual friend gave me his e-mail address a long time ago and we wrote back and forth a couple times. He was super nice and supportive. So, with this third draft in hand, I wrote him and asked him if he could point me towards someone who could read it. I know there are all sorts of legal issues with asking him to read it himself. I waited for a response. And waited. And waited. And waited.
Over the past couple of weeks I've been in LA to do some comedy stuff. I always had a couple thumb drives in my pocket with copies of the script in case I ran into someone that could do something with it. I ended up performing on Comedy Bang Bang at the UCB Theater. It's a show produced by the great Scott Aukerman, who in addition to creating Comedy Bang Bang, has been making a living as a writer since about 1994. I got to pick his brain for a little bit after the show.
He told me that a response from Paul would be a long shot because even though I worded my e-mail carefully, the potential for lawsuits would be too risky. Even if he did write back, it was unlikely that he'd feel comfortable passing on a thing he hasn't read himself. Yeah, that's a big Catch 22. He can't read it because of legal issues. He can't pass it on because it's good because he can't read it. Scott said the way to get it out there is to basically treat the script like I treat my stand up and let the work speak for itself. I need to let people in Hollywood read it and generate a buzz. Eventually the buzz will get around and someone will take interest. There's a part in the script that would be perfect for TJ Miller. I know him a little, so maybe I'll try to get it in his hands.
The more likely scenario Scott said was that someone will notice me from my stand up and want to be my agent. They'll ask if I have a script and when I tell them about this thing that Lesley and I created, it'll make me 20 times more valuable. Great. Well, as good as I feel I am as a stand up, no one in nine and a half years has asked to be my agent yet.
Discouraging? Yeah. A long time ago and older comedian asked to take me out to lunch so he could pick my brain. He was retired now and wanted to make a run of it as a comedian. He was a nice guy who had been public speaking for longer than I've been alive. He was comfortable on stage, but every joke he told came straight from a book. I told him that the only way he could make a go of it as a comedian was to write his own material. He didn't want to hear that and got angry. I couldn't tell him what he wanted to hear. I told him what he needed to hear. Scott Aukerman didn't tell me what I wanted to hear. There is no secret backdoor into the writing pool of Hollywood. He told me what I needed to hear. There is no secret backdoor into the writing pool of Hollywood.
So where does that leave us? Lesley has a couple connections too, but they feel like really long shots. To make the script speak for itself, I think the plan is to do what I would do with my comedy. For comedy, a lot of people enter festivals so our peers from all over take notice of us and hopefully spread the word. I think Lesley and I are going to start entering the script into different script writing contests and see what happens. Maybe it isn't as good as we think it is. I really like it. When I pitch it to people, they seem to like it. They laugh. If there's one thing I've learned in nine and a half years in comedy it's the difference between a kind and a genuine laugh. I think they laugh genuinely.
So that's it for now. You're up to speed. I'll let you know when something happens. If something happens.
When Lesley and I initially started writing our script, we were writing it with the intent to find a local production company to shoot it for us with us in the leads. Once we changed our minds on that, we dream cast it so that we'd both be writing towards the same vision.
I know a lot of writers say they don't do this, but it helped me a lot. Like with writing jokes, most of it happens for me while I'm lying in bed at night unable to sleep. I'll just lay there with ideas racing through my head until I have to jump up and find a notebook. Sometimes ideas hit me first thing in the morning, which honestly, beating the alarm to jump out of bed with some creative inspiration is a great way to start the day!
Writing with someone in mind helped me find the character's voice fairly easily. Final Draft has a profile on Facebook where every day they have some sort of writing tip or question. Recently they posed a question asking if you were to block out the names of the characters in your script, would you still be able to tell who they are?
Once we hammered out our first draft, we assigned each other characters and we went through the script focusing on each piece of those lines trying to make sure there was a flow. One thing that came from this that I liked was I had one of the characters often times refer to another by a nickname. I think this was a nice little touch to show that they had some sort of history. The unfortunately short lived television show The Middle Man did this too. The lead was named Wendy Watson. Her partner called her Dub Dub. A little touch like that was nice because it not only showed a little bit of familiarity, but it was so out of character for the straight laced Middle Man to use a nickname that it really humanized him. It gave his character a little more depth.
Another thing Lesley and I did that helped was we had our friends over to read through the script assigning them different roles. Some lines didn't flow as smoothly out of other mouths, so as we did the readthru I took notes on how the actor initially wanted to read a line.
I tend to flip sentences. Not like Yoda, but a little bit like that. Right now, I'm totally blanking on an example. Hmm. I guess that was an example in itself. Where a lot of people may have said "I'm totally blanking on an example right now." I lead with the "right now". I think I'm better at that now. A long time ago a friend asked me where I originally hail from because he found my sentence structure so foreign.
So there you go. That's a little insight in our process in trying to give our characters unique voices. Does anyone else have any tips?


This Texas woman celebrates her 84th birthday; she received a replacement heart valve that was made with porcine heart tissue. A diagram of the human heart shows its four one-way valves (white flaps).
Diseases of the heart and circulatory system are a leading cause of death in the U.S. and a leading area of research for biomedical engineers. Heart valve diseases, including valve stenosis, valvulitis and valve prolapse, can be fatal if the valve is not replaced. Engineers and physicians work together to design valves made of materials that the human body accepts and function for as long as possible, and that require the least invasive procedures for implantation.
Identify the four valves in the human heart.
Describe the function of the heart valves.
Explain three different diseases that can weaken or damage human heart valves.
Explain the pros and cons of different materials used to build artificial heart valves.
Basic knowledge about the heart and the circulatory system.
Many different types of valves exist, but all are devices that control or direct the flow of fluids. What are examples of valves in your home? (Listen to student answers.) You have plumbing valves such as the tap for your tap water. Washing machines and dishwashers have valves also. The valves in your heart are similar to the valves in your home, except they control the flow of blood instead of water.
Our heart valves allow blood to flow through them in one direction only. The four valves are the aortic valve, the mitral valve, the pulmonary valve and the tricuspid valve. Every time the muscles in the heart contract to pump blood, certain valves open and others close to make sure the blood is only pumped in the correct direction. All of the valves have leaflets or flaps that are the moving pieces of the valve. When a valve opens, its leaflets separate to allow blood flow, and when the valve closes, its leaflets come together to block the blood flow.
When a person is unwell, diseases can affect the heart valves so they do not work as well as they should. We will learn about these different diseases, which can all be fatal if the damaged valve is not replaced. We will also discuss pros and cons of different types of replacement valves that are designed by biomedical engineers. In theory, the best artificial valve would not require open heart surgery, be made of materials that do not cause blood to clot and last for a person's lifetime. All things we are about to learn!
What diseases can affect the heart valves and endanger a person's health? Three such diseases are valve prolapse, valve stenosis and valvulitis.
Valve prolapse is a condition in which the leaflets become floppy or stretched out, allowing blood to regurgitate, or flow back in the wrong direction. Regurgitation can result in the heart increasing its workload, meaning pumping harder, to keep enough blood flowing through the body.
Valve stenosis is calcium build-up in the valve leaflets, causing them to stiffen and fail to open completely. When the heart beats, blood flow is slowed down causing pressure to build in certain heart chambers. Over time, this can thicken the heart wall, as well as enlarge and weaken the heart.
Valvulitis is the inflammation or swelling of a valve. This is most commonly caused by another disease called rheumatic fever, and less frequently by bacterial endocarditis and syphilis. Eventually, an inflamed valve can degenerate or its leaflets become stiff and calcified, leading to valve stenosis.
Once a patient has a disease that impairs his/her heart valves, often his/her best chance is to have the affected valve replaced. Biomedical engineers have developed a few different types of artificial valves and each type has pros and cons. Purely mechanical artificial valves are made with metal, wire and plastics that are foreign to cells in the human body. Blood cells do not like the presence of foreign materials, and their presence often leads to increased chances of fatal blood clots. As a result, patients who receive these types of artificial valves must take blood thinning medications and lower their levels of physical activity. On the other hand, these artificial valves do not degrade, meaning they will work for the rest of patients' lives without needing to be replaced. Other types of artificial valves are made with real animal tissue; these biological types are more accepted by the human body and do not lead to blood clotting. Because of this, people with these implants can maintain normal lifestyles and be active. The downside is that these artificial valves degrade and only last about 10 years.
Replacing the original heart valve or an artificial heart valve is a traumatic experience, requiring open heart surgery. Biomedical engineers are currently working on designing artificial heart valves that could be placed in the body similar to how stents are implanted (as we will see in a short video clip), avoiding open heart surgery.
Example artificial heart valves (left to right): a biological valve (using human or porcine tissue), a mechanical valve, and recently FDA-approved tri-leaflet valve using bovine tissue. The far right valve is made of cow tissue attached to a stainless steel mesh frame with a polyester wrap and is inserted into the body through a small opening made in a leg artery.
When designing artificial valves, biomedical engineers consider many factors. What do you think would be important factors? (Listen to student ideas.) In theory, the best artificial valve would not require open heart surgery, be made of materials that does not cause blood to clot and lasts for a person's lifespan.
Following is suggested slide-by-slide narration to accompany the PowerPoint presentation, as well as additional, more in-depth background information for the teacher.
Slide 2: (Review, as necessary, the path of blood flow in the human heart.) The blood enters the heart from the body through the superior vena cava and the inferior vena cava. Then the blood enters the right atrium chamber of the heart. The blood then moves through the tricuspid valve (shown as two white flaps) into the right ventricle chamber of the heart. Then the blood moves through the pulmonary valve (shown as two white flaps) into the pulmonary artery (one on each side of the heart). The blood re-enters the heart through the pulmonary veins (two on each side of the heart), and travels into the left atrium. The blood then passes through the mitral valve (shown as two white flaps) and into the left ventricle chamber of the heart. The blood then moves through the aortic valve (shown as two white flaps) and into the aorta.
Slide 3: In this cross-section drawing, we can see the four heart valves that are present in mammalian hearts. The four valves in your heart are the tricuspid valve, the pulmonary valve, the mitral valve and the aortic valve. The tricuspid valve is located between the right atrium and the right ventricle; the pulmonary valve is located between the right ventricle and the pulmonary arteries; the mitral valve is located between the left atrium and the left ventricle; and the aortic valve is located between the left ventricle and the aorta. The mitral and tricuspid valves are atrioventricular (AV) valves located between the atria and the ventricles. Two semilunar (crescent-shaped, like a half-moon) valves, the aortic and pulmonary valves, are located in the arteries leaving the heart. The mitral valve is the only bicuspid valve in the human heart.
The valves open and close with the movements of the heart. When the ventricles contract, the pulmonary valve and the aortic valve open so the blood can be pushed out in the correct direction, while the tricuspid valve and the mitral valve close, so the blood cannot slip back into the atria.
Slide 4: (Play the embedded Heart Valve Surgery – operation for replacement heart valves, a 2:31-minute animation video available at YouTube: http://www.youtube.com/watch?v=G5S0yQhK42s.) This animation shows how the valves open and close with the heart's motion. It also shows surgery to replace a damaged mitral valve, with options for a mechanical or biological valve. The stringy-looking material attached to the tricuspid and mitral valves are tendons attached to the ventricle walls; they assist in opening and closing these valves at the appropriate times.
Open heart surgery to replace a mitral valve.
Slide 5: Many pathologies can afflict the heart valves. The following three primary conditions can be caused by disease or be inherited and can affect the heart valves and endanger a person's life: valve prolapse, valve stenosis and valvulitis.
Slide 6: Replacement valves can be made of animal tissue (such as porcine pericardium) or be purely mechanical. Purely mechanical (top left example) valves outlast the patient, but cause thrombosis (clotting) unless the person takes blood thinning medication and lives a more sedentary lifestyle. Most young patients who need heart valve replacement go with this option. Older patients typically have animal tissue valves installed (top right example). These valves only last about 10 years, but operate just like normal heart valves so the person can be active. Getting a valve replaced is a traumatic process and involves open heart surgery (lower right). Biomedical engineers are designing new surgical techniques and valves that are less invasive (lower left picture is a prototype valve that is installed similar to how a stent is implanted, which can be seen in the video, next slide).
Slide 7: (Play the embedded Edwards Sapien transcatheter heart valve animation, a 1:52-minute video available at YouTube: http://www.youtube.com/watch?v=GAmq6ccC4Ws.) This animation shows how a new type of valve is implanted to replace a diseased aortic valve with aortic stenosis. This new valve is not yet on the market and is installed without open heart surgery, using a much less-invasive procedure.
Saving a Life: Heart Valve Replacement - Students use cardboard boxes, classroom construction materials and marbles to design, construct, implant and test prototype replacement bicuspid mitral valves for hypothetical patients' hearts. They learn more about the pros and cons of different artificial valve solutions from the testing and scoring of their heart valve designs.
Today we learned all about how our heart valves operate, diseases that can damage our heart valves, and the artificial valves that biomedical engineers have created. We learned the pros and cons for each type of replacement valve, which are issue that engineers and physicians must consider. The design and creation of replacement valves is an example of how engineering can improve and save lives.
heart valve: A one-way valve that allow blood to flow through it in one direction. Four valves are present in mammalian hearts. They open and close depending on different pressures on each side of them.
open heart surgery: A surgery performed on the exposed heart while a heart-lung machine pumps and oxygenates the blood and diverts it from the heart.
stent: A slender tube of plastic or sprung metal mesh placed inside a hollow tube to open it or keep it open. For example, used in surgery to provide support to prevent blood vessels from closing, especially after they have just been unclogged.
Pre/Post-Lesson Quiz: Administer The Circulatory System Quiz, a 10-question pre- and post- assessment of content knowledge to determine students' prior knowledge of the subject matter. Administer the same quiz again, after lesson conclusion, to ascertain students' knowledge gain.
Divide the class in half and facilitate a classroom debate on whether to use a metal and plastic heart valve or a valve made from animal tissue for two patient scenarios. Have Patient #1 be a 75-year-old man and the Patient #2 be an 11-year-old girl. Have each student write down one reason to defend his/her side of the debate and share it with the class. To help students organize their points for the discussion, have them complete this table to evaluate the different valve replacement options.


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Home >> A News >> The Need for and Challenge of Bringing More Dual-Language Teachers into Classrooms
The Need for and Challenge of Bringing More Dual-Language Teachers into Classrooms
The U.S. Hispanic population accounts for 56 percent of the country's population growth since 2000, according to the Pew Research Center. What this means for schools is that roughly five million or one out of every 10 school children are classified as English language learners (ELLs).
Many of these students are immigrants or first-generation from Spanish-speaking countries and while they might know some English, it may not be the primary language spoken at home. This elevates the potential for them to easily fall behind in school. Because of this, the need for bilingual or dual-language teachers continues to rise in many parts of the country.
While states with a history of having large Hispanic populations such as Texas, California, and Florida have seen their Hispanic populations continue to grow, states in the Pacific Northwest and Midwest have also seen an incredible boom in the children of Spanish-speaking immigrants. Both Washington and Oregon are in the top 10 states that have the highest ELL student density, according to a 2015 study by the Migration Policy Institute.
Districts have looked to hiring short-term teachers from Mexico and Spain to fill the need, though there's a push towards incubating bilingual educators stateside. "We have a staffing shortage," Amaya Garcia, who has researched the need for more dual-language teachers in Washington and Oregon, was told by a district employee. "We've done trips to Puerto Rico, we have brought in visiting international teachers, but that's still not meeting the need that we have. So we decided that we need to grow them ourselves."
The Portland School District teamed with Portland State University in 2016 to foster its own educator talent pool, the PPS & PSU Dual Language Teacher Partnership. The university already had its own bilingual teaching pathway in place and now helps screen applicants for 28 fellowship slots in the city's public schools. Those who are selected must pass a language fluency exam, gain admission to one of the school's education programs, and hold a bachelor's degree. Those who are accepted take on roles as either substitutes or paraprofessionals while completing their graduate studies.
Oklahoma City is working to address its own dual-language teacher shortage through the Bilingual Teacher Pipeline Project. Currently, the program has 34 bilingual paraprofessionals who are studying to become certified teachers and committed to staying in the school district.
The program launched in January of last year, with the goal of providing financial support to working bilingual paraprofessionals in the district while they study to become certified teachers. Upon completion of all requirements, there is an understanding that they will be hired by the school district as bilingual teachers and placed in a classroom of their own. In order to be accepted into the program, applicants must agree to work a minimum of three years for the district upon completion of certification. Oklahoma City Public School District currently has a student population that is 52 percent Hispanic.
Oddly enough, should the Trump administration carry through with putting an end to DACA, the country's need for more bilingual teachers could become even greater with potentially 20,000 teachers being forced to leave the country.
Article by Joel Stice, Education World Contributor
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Hot Diggity Dog Build grammar skills using this activity and answer key for an article about one of America's favorite foods, the hot dog! Click for a PDF (portable document format) printable version of this Every-Day Edit activity. Scroll down or click for work sheet text and answer key. Click for our archive of Every-Day Edit activities from previous weeks. Click for Ideas for Using Every-Day Edit in the classroom. Activity Key Uncorrected Text Did you now that July is national Hot Dog Month. The average American eat 70 hot dogs each year. New York City is the hot dog "capital." More then 105 million dogs was sold there in 2004. Mustard is the topping of choice among people who are more than 35 years old, but most younger eators prefer ketchup. Some people even like they're hot dogs served in ice cream! The largest hotdog ever made was 1,996 feet long Answer Key Did you know that July is National Hot Dog Month? The average American eats 70 hot dogs each year. New York City is the hot dog "capital." More than 105 million dogs were sold there in 2004. Mustard is the topping of choice among people who are more than 35 years old, but most younger eaters prefer ketchup. Some people even like their hot dogs served in ice cream! The largest hot dog ever made was 1,996 feet long. Article by Gary Hopkins Education World® Editor in Chief Copyright © 2005 Education World 07/2005
Every-Day Edits: Hot Diggity Dog
Lesson Plan: Unfamiliar Multisyllabic Words Subject: ELA- Reading Grade: 5 Lesson Objective: To read unfamiliar multisyllabic words in and out of context Common Core Standard: CCSS.ELA-LITERACY.RF.5.3.A- Use combined knowledge of all letter-sound correspondences, syllabication patterns, and morphology (e.g., roots and affixes) to read accurately unfamiliar multisyllabic words in context and out of context. Materials: Printable Sudent Worksheet Handout Starter: Say: When you come to a word that you do not know when you are reading, what do you do? (Allow the students to answer.) Main: Say: We all find words that we do not know when we are reading. There are different ways that we can figure out a word that we do not know. You can sound the word out. You are reading books that are harder and longer now and sounding out a word is not as easy as it used to be! You should start by figuring out the syllable patterns. You know that every syllable has a vowel sound. The different syllable patterns are closed syllables and open syllables. A closed syllable ends with a consonant and usually has a short vowel sound. For example, plan-et. An open syllable ends with a long vowel sound. For example, pa-per. In addition to the syllables in a word, you should think about the affixes. An affix is added to the beginning or end of a root word to form different words. For example, the root word scope can be changed to microscope or telescope by adding different affixes. Now, you are going to be doing a worksheet so that you can practice reading difficult words. You will read the paragraph. There are words that are underlined in the paragraph, those are the difficult words. After you have read the whole paragraph and tried the hard words, you are going to find a partner and take turns reading the list of words at the bottom of the worksheet to each other. Does anyone have any questions? Feedback: Say: Who would like to share how to read the words? (Allow the students to share and go over the answers.) Written by Kimberly Greacen, Education World® Contributing Writer Kimberly is an educator with extensive experience in curriculum writing and developing instructional materials to align with Common Core State Standards and Bloom's Taxonomy. Copyright© 2019 Education World
Lesson Plan: Unfamiliar Multisyllabic Words-Grade 5
Lesson Plan: Drawing Inferences Subject: ELA- Reading Grade: 5 Lesson Objective: To explain what part of a text means while drawing inferences about that text Common Core Standard: CCSS.ELA-LITERACY.RL.5.1- Quote accurately from a text when explaining what the text says explicitly and when drawing inferences from the text. Materials: Printable Student Worksheet Handout Starter: Say: What are some situations in which you draw inferences? (Allow the students to answer.) Main: Say: We all draw inferences. We do it every day when we are listening to someone speaking, when we are reading and when we are watching other people. An inference is a conclusion that we make using things that we already know (prior knowledge) and new information. An example of an inference is a mother giving her young child blueberries to try. The child puts the blueberry in its mouth, chews it and then spits it out. The mother can infer that the child does not like the blueberry. Sometimes when we are reading, the text does not tell us exactly what we need to know. When that happens, we need to draw an inference. A good strategy for drawing inferences is to think "It says, I know, so…" When you do this, it helps you to think about what the text is telling you and what you already know. You are then able to draw an inference. Sometimes, when you are explaining how you found the information that used when drawing your inference, you need to quote the text that you are using. When quoting a text, you must use the author's exact words and you should always use quotation marks. If you are quoting a text from a book, you should always include the page number so that others can find the quote. Now, you are going to be reading a few paragraphs from a book. After you have finished reading, there are questions for you to answer. Remember to use the inference strategy and remember how to quote a text. Does anyone have any questions? Feedback: Say: Who would like to share their answers? (Allow the students to share and go over the answers.) Written by Kimberly Greacen, Education World® Contributing Writer Kimberly is an educator with extensive experience in curriculum writing and developing instructional materials to align with Common Core State Standards and Bloom's Taxonomy. Copyright© 2019 Education World
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