text:
\section*{Acknowledgements}
We thank members of the UCSB NLP Lab and the anonymous reviewers for their valuable comments and suggestions.
\section{Conclusion}
We proposed generative models for blends, clippings, and reduplicatives, three dominant word-formation phenomena in slang. Our models are distinguished by their simplicity, adept use of linguistic and structural constraints, easy to implement and yield state of the art performance.

Our work suggests several directions for future research. First, our blending model can be extended to handle relatively rare insertions, incorporate the language model and the length model in a unified reinforcement learning framework optimizing a joint reward. Second, we do not investigate the complementary problem of de-blending. Moreover, we note that our evaluation of blends is based on an assumed gold standard. It would be useful to also characterize our blending model based on a human evaluation. Third, the gap between the performance of our clipping model and the upper bound (by the oracle) opens up the question of developing more nuanced models for clipping perhaps using deeper linguistic cues like stress patterns. Fourth, our models do not incorporate relatively rare types of reduplicative formation (like \emph{schm} reduplicatives) suggesting yet another direction for research. Yet another open question is whether a global model can effectively model all the above phenomena. Finally, in our work, we focus on only developing models for word formation in English slang. However, such word formation patterns are also evident in other languages \cite{vstekauer2012word} and it is an open question as to whether similar models generalize to other languages as well.

Finally, our work potentially enables the development of several applications some of which include brand name generators, and rich conversational agents that are not only passive agents but can actively contribute to the evolution of language varieties.

Altogether our work has implications for the broader fields of Internet Linguistics and natural language understanding especially in the context of slang formation.
\section{Datasets and Definitions}
Here, we define the extra-grammatical morphological phenomena modeled and describe the datasets used for our experiments and analysis.
\citet{mattiello2013extra} argues that slang exhibits extra-grammatical morphological properties that distinguish them from the standard variety and identified four broad word formation phenomena\footnote{These categories are not exhaustive and several slang words and non-standard expressions do not fall into any of the above categories.} described below:
\begin{enumerate}
	\item \textbf{Alphabetisms} are shortenings of a multi-word sequence.
	Examples include \texttt{lol} from \texttt{laugh out loud} or \texttt{YOLO} from \texttt{you only live once}.
	They can be further sub-categorized into two types based on their pronunciation although the distinction may not always be clear: (a) \emph{Acronyms} are pronounced using the regular reading rules (for example. YOLO) (b) \emph{Initialisms} are pronounced letter by letter (for example. BBC).
	\item \textbf{Blends} or portmanteaus, are formed by merging parts of existing words.
	For example, \texttt{edutainment} is a blend of \texttt{education} and \texttt{entertainment}.  Prior work notes that blend formation does not exhibit rigid rules but only demonstrates affinities towards certain patterns of formation \cite{mattiello2013extra} suggesting learning based approaches to modeling blends \cite{deri2015make,gangal2017charmanteau}.
	\item \textbf{Clippings} are constructed by  shortening words (lexemes). For example, \texttt{berg} is a clipping of \texttt{iceberg}, \texttt{gym} is a clipping of \texttt{gymnasium} and \texttt{ammo} is a clipping of \texttt{ammunition}.
	Based on the portion that is being clipped, clippings are sub-categorized into three types: (a) \textsc{Back} clipping where the beginning of the word (lexeme) is retained (like \texttt{brill} from \texttt{brilliant}) (b) a \textsc{Fore} clipping, where the end of a word is retained (like \texttt{choke} from \texttt{artichoke}) and (c) A \textsc{Compound} clipping (\texttt{adman}), a clipping of a compound word (\texttt{advertisment man}).

	\item \textbf{Reduplicatives} are word pairs constructed by either repeating a word (\texttt{boo boo}) or by alternating certain vowels or consonants so that they are phonologically similar (\texttt{clickety-clackety, teenie-weenie, itsy-bitsy}).
\end{enumerate}

In our work, we propose generative models using a data-driven approach towards generating blends, clippings ,and reduplicatives.
We do not consider generative models for alphabetisms since a majority of them can be trivially generated by picking the first letter of each word making up the acronym (for example. \texttt{laugh out loud} $\rightarrow$ \texttt{lol}).
We now outline the datasets considered:
\begin{enumerate}
    \item \textbf{Blends.} We consider a gold standard dataset $\mathcal{D}_{knight}$ of $400$ blends constructed by \cite{deri2015make} from Wikipedia as well as a larger list of $1624$ blends manually compiled by \cite{gangal2017charmanteau} called $\mathcal{D}_{large}$, a superset of $\mathcal{D}_{knight}$. We define $\mathcal{D}_{blind}=\mathcal{D}_{large}-\mathcal{D}_{knight}$.
    \item \textbf{Clippings.} We consider a list of $576$ human curated clippings constructed by \citet{mattiello2013extra} for our analysis of clippings. These were manually collected from a variety of sources including prior work and dictionaries like the \emph{Oxford English Dictionary} and the \emph{Merriam-Webster online dictionary}.
    \item \textbf{Reduplicatives.} We consider a dataset of a total of $337$ reduplicatives manually constructed by \citet{mattiello2013extra} also collected from prior work and online dictionaries\footnote{We note that the dataset released by \cite{mattiello2013extra} is the largest human curated dataset of slang word formation we are aware of. Furthermore, large crowd sourced data-sets like Urban Dictionary are a poor fit for such modeling since they do not explicitly contain annotations by linguistic experts.}.
\end{enumerate}

\section{Experiments}
Having described our models and evaluation metrics in the previous section, we proceed to evaluate our models empirically and describe our results.
\paragraph{Blends} We train our model on $\mathcal{D}_{knight}$ and evaluate on the $\mathcal{D}_{blind}$ dataset as in \cite{gangal2017charmanteau} comparing against previous methods \cite{deri2015make,gangal2017charmanteau}. We set the number of hidden units to $50$ with a dropout probability of $0.5$\footnote{All hyper-parameters were chosen using a validation set.}. We use \textsc{Adam} \cite{kingma2014adam} optimizer with an initial learning rate of $0.001$ to train the model for $500$ epochs with early stopping over a validation set.

Tables \ref{tab:f1_gold_knight} and \ref{tab:f1_gold_blind} show the mean edit distance of our predictions from the target blend. First, the evaluation on the $\mathcal{D}_{knight}$ dataset compares the performance of our models against previous baselines. Note that just using our basic model \textsc{CopyCat -(LSTM + LM)} outperforms the baseline proposed by \cite{deri2015make} ($1.59$ vs $1.49$). Furthermore, observe that even our vanilla model (LSTM) significantly outperforms the equivalent ``FORWARD'' models by \cite{gangal2017charmanteau}\footnote{We obtain the numbers for these baselines from the latest released data and predictions at https://github.com/vgtomahawk/Charmanteau-CamReady.} that use greedy and beam-search decoding ($1.90$ and $2.37$ vs $1.75$).  Moreover, observe that our model even achieves almost equivalent performance to the ``FORWARD'' state-of-art model which uses exhaustive decoding ($1.37$ vs $1.33$). Furthermore, our model achieves competitive performance with the more complex models proposed by \textsc{Gangal-Backward} which use exhaustive decoding. We emphasize that we can achieve competitive performance using a simpler model without using exhaustive decoding.  Similar observations can also be made for the evaluation of the $\mathcal{D}_{blind}$ data set (our best model yields a score of $1.91$ vs $1.77$). Altogether these observations suggest that even simple models with effective modeling of linguistic structure can perform competitively and even outperform overly complex models (see Table \ref{tab:qualitative_blends} for a few example predictions).

\begin{table}[htb!]
\centering
	\begin{tabular}{l|l}
	\textbf{Model} & \textbf{Distance} \\
	\hline
	 \textsc{Knight} & 1.59  \\
	 \hline
	 \textsc{Gangal-Forward (Greedy)} & 1.90 \\
	 \textsc{Gangal-Forward (Beam)} & 2.37 \\
	 \textsc{Gangal-Forward (SOTA)}$^\dagger$ & 1.33 \\
	 \textsc{Gangal-Backward (SOTA)}$^\dagger$ & 1.12 \\
	 \hline
	 \textsc{CopyCat-(LSTM)} & 1.75 \\
	 \textsc{CopyCat-(LSTM + LM)} &  1.49 \\
	 \textsc{CopyCat-(LSTM + LM + Len)} & \textbf{1.40} \\
	 \textsc{CopyCat-(LSTM + LM + Len)}$^\dagger$ & \textbf{1.37} \\
	\hline
	\end{tabular}
	\caption{$10$-fold cross validation performance of our blending model \textsc{CopyCat} in terms of edit distance (lower is better) on $\mathcal{D}_{knight}$ dataset. $\dagger$ indicates ensemble approach using sub-samples of training data consistent with previous work. Our simpler model yields competitive performance (especially compared to the state of art forward model) without the need for exhaustive decoding (which the state of art uses), uses a smaller learn-able parameter set while effectively using linguistic insights into the blending process.}
	\label{tab:f1_gold_knight}
\end{table}

\begin{table}[htb!]
\centering
	\begin{tabular}{l|l}
	\textbf{Model} & \textbf{Distance} \\
	\hline
	 \textsc{Knight} & 2.10  \\
	 \hline
	 \textsc{Gangal-Forward (SOTA)}$^\dagger$ & 1.76 \\
	 \textsc{Gangal-Backward (SOTA)}$^\dagger$ & 1.77 \\
	 \hline
	 \textsc{CopyCat-(LSTM)} & 2.27 \\
	 \textsc{CopyCat-(LSTM + LM)} &  2.13 \\
	 \textsc{CopyCat-(LSTM + LM + Len)} & \textbf{1.98} \\
	 \textsc{CopyCat-(LSTM + LM + Len)}$^\dagger$ & \textbf{1.91} \\
	\hline
	\end{tabular}
	\caption{Performance of our blending model \textsc{CopyCat} in terms of edit distance (lower is better) on $\mathcal{D}_{blind}$ dataset. $\dagger$ indicates ensemble approach using sub-samples of training data consistent with previous work. Our simpler model yields competitive performance without the need for exhaustive decoding, uses a smaller learn-able parameter set while effectively using linguistic insights into the blending process. To ensure the comparison is fair, numbers for the baselines were obtained by filtering the released predictions for these models to the same set of words our models were evaluated on.}
	\label{tab:f1_gold_blind}
\end{table}

\paragraph{Clippings} We consider the dataset of clippings introduced by \cite{mattiello2013extra} and report the mean edit distance ($\mu$) on a set of $173$ clippings in Figure \ref{fig:clipping_edit_distance} (see Table \ref{tab:qualitative_clipping} as well). First, both \textsc{ClipPhone} ($\mu=3.39$) and \textsc{ClipGraph} ($\mu=2.65$) outperform the naive baseline ($\mu=4.6$). Second, based on the empirical distribution, it is almost impossible to predict whether a word has a one or two syllables ($0.49$ vs $0.46$ significant only at $\alpha>0.2$) clipping and incorrect guesses critically affect the downstream performance. In the absence of such information, just clipping to one syllable yields better performance. However, when this information is exactly known (\textsc{ClipPhone(O)}), we note an improvement as expected ($\mu=2.79$). Finally, \textsc{ClipGraph} shows a small but not significant advantage over methods working explicitly in the phoneme space which are prone to errors by imperfect conversion.
Finally, the upper bound on the performance is substantially better than our models ($\mu=0.79$) suggesting scope for future improvements.

\begin{figure}[]
	\includegraphics[scale=0.5, width=\columnwidth]{figs/clipping_edit_distance_all.pdf}
	\caption{\textbf{Evaluation of clipping models} (lower is better). The \textsc{Naive} model is the performance lower bound. Both \textsc{ClipPhone} and \textsc{ClipGraph} substantially outperform the baseline. Estimating whether a given word has one or two syllable clipping is a major challenge hindering \textsc{ClipPhone} since both cases are equally likely from empirical estimation. Using an oracle on the number of syllables (\textsc{ClipPhone(O)}) improves performance. \textsc{ClipGraph} which operates purely in grapheme space performs as well as \textsc{ClipPhone(O)}. \textsc{G2PGold} denotes a upper bound when using our P2G model.}
	\label{fig:clipping_edit_distance}
\end{figure}

\begin{table}[htb!]
\small
\centering
	\begin{tabularx}{\linewidth}{l|l|l|l}
	 \textbf{Input} & \multicolumn{2}{c}{\textbf{Our models}} & \textbf{Gold} \\
	  & \textbf{\textsc{ClipPhone}} & \textbf{\textsc{ClipGraph}} & \textbf{Clipping} \\
	\hline
	\texttt{cocaine} & \texttt{coke} & \texttt{coca} & \texttt{coke} \\
	\texttt{juvenile} & \texttt{juve} & \texttt{juve} & \texttt{juvey} \\
	\texttt{amelia} & \texttt{umm} & \texttt{amel} & \texttt{mel} \\
	\texttt{alfred} & \texttt{fred} & \texttt{fred} & \texttt{fred} \\
	\texttt{kid video} & \texttt{kidvid} & \texttt{kivid} & \texttt{kidvid} \\
	\hline
	\end{tabularx}
	\caption{Exemplary predictions from clippings models. We can generate one/two syllable clippings, as well as compound clippings. Note the effect of incorrect P2G conversion for \texttt{amelia} as \texttt{umm} which is pronounced similar to \texttt{ame}. The current G2P model does not incorporate stress. It is possible that incorporating stress into the model can address this scenario.}
	\label{tab:qualitative_clipping}
\end{table}

\begin{table}[htb!]
\centering
\small
	\begin{tabularx}{\linewidth}{X|l|l|l}
	\textbf{Input} & \textbf{Prediction} & \textbf{Gold} & \textbf{Dist}\\
	\hline
	\texttt{jizz\#disney} & \texttt{jizzney} & \texttt{jizzney} & 0 \\
	\texttt{scum\#fuzz} & \texttt{scuzz} & \texttt{scuzz} & 0 \\
	\texttt{phone\#neck} & \texttt{phoneck} & \texttt{phoneck} & 0 \\
	\texttt{woman\#romance} & \texttt{womance} & \texttt{womance} & 0 \\
	\texttt{piss\#mishap} & \texttt{pisshap} & \texttt{pisshap} & 0 \\
	\texttt{pass\#asshole} & \texttt{passhole} & \texttt{passhole} & 0 \\
	\texttt{spike\#angel} & \texttt{spangel} & \texttt{spangel} & 0 \\
	\texttt{man\#amazon} & \texttt{mamazon} & \texttt{manazon} & 1 \\
	\texttt{awkward\#sauce} & \texttt{awkwauce} & \texttt{awksauce} & 1 \\
	\texttt{bed\#orgasm} & \texttt{bergasm} & \texttt{bedgasm} & 1 \\
	\hline
	\end{tabularx}
	\caption{Exemplary predictions from our simple blends model which suggests our model effectively captures blending phenomena by incorporating linguistic constraints.}
	\label{tab:qualitative_blends}
\end{table}

\paragraph{Reduplicatives} We evaluate our model on a held-out test set of $50$ reduplicatives obtained using the manually compiled dataset by \cite{mattiello2013extra}. We evaluate two flavors of our model: (a) \textsc{Our(NoDup)} where we disallow generating duplicates (which are trivial to generate) and (b) \textsc{Our}, the full-fledged model where duplicate reduplicatives are allowed. We report the mean MIR for each model over $10$ independent runs in Figure \ref{fig:redup_eval}. Our model consistently outperforms the baselines (\textsc{Let}, and \textsc{Let(Cond)}) by at-least $8$ percentage points suggesting it adeptly captures patterns in reduplicative formation. Finally,  we examine the inferred probability distributions to gain insights into the linguistic phenomena in reduplicative formation few of which we outline: (a) The most common reduplicative types are  \textsc{Duplicate}, \textsc{VowelEx} followed by \textsc{ConEx}. (b) Vowel \texttt{i} is more  likely to be replaced by \texttt{a} and \texttt{o} and (c) Consonant \texttt{t} is much more likely to be replaced by \texttt{w} and \texttt{l} (like in \texttt{teenie-weenie}).  We leave a comprehensive analysis of these patterns to future work.

\begin{figure}[htb!]
		\includegraphics[width=\columnwidth]{figs/redup_model_generator_eval_gold_test_mir.pdf}
	\caption{Performance of our reduplicative models based on the MIR metric (higher is better). Our models \textsc{Our(NoDup)} and \textsc{Our} consistently outperform baselines by at-least $8$ percentage points ($0.86$ vs. $0.78$).}
	\label{fig:redup_eval}
\end{figure}
\section{Introduction}
Linguistic analysis of slang has traditionally received little attention with some arguing that research on slang be assigned to an \emph{``extra-linguistic darkness''} \cite{labov1972some}. However, \citet{eble2012slang} argues that the emergence of social media has predominantly increased the usage of slang and non standard forms and \emph{``slang is now worldwide the vocabulary of choice of young people''}\footnote{While the definition of slang is a controversial issue, we adopt a broad definition including non-standard expressions.}. This increasing pervasiveness has recently motivated research on slang and the linguistic phenomena it manifests.
Most notable are the works of \cite{mattiello2005pervasiveness,mattiello2008introduction,mattiello2013extra}, who argues that slang exhibits extra-grammatical properties that distinguish it from the standard form.

Specifically, linguistic phenomena like \emph{alphabetisms}, \emph{blending}, \emph{clippings}, and \emph{reduplicatives} abound in slang (see Table \ref{tab:crown_jewel})\footnote{While such phenomena are likely present in several languages, in this work we restrict ourselves to slang in English.}.
Note the rich and varied word formation patterns ranging from simple abbreviations like \texttt{dink} to more complex combinations like \texttt{lambortini}, a blend of \texttt{lamborghini} and \texttt{martini}.
Note further, that even within a particular class like \textsc{Blends} there are variations in what portions of the components are retained.
\begin{table}[t!]
\small
\centering
\begin{tabularx}{\columnwidth}{l|l|l}
\textbf{Word} & \textbf{Derived From} & \textbf{Type} \\ \midrule
dink & double income no kids & alphabetism \\
lambortini & lamborghini + martini & blend \\
diamat & dialectical + materialism & blend \\
tude & attitude & clipping (fore) \\
brill & brilliant & clipping (back) \\
teenie-weenie & teenie & reduplicative \\
yik-yak & yik & reduplicative \\ \midrule
\end{tabularx}
\caption{Sample words depicting word formation in slang. Note the rich variation across different types.}
\label{tab:crown_jewel}
\end{table}
These word formation mechanisms are not only attractive from a linguistic standpoint in deepening our understanding of slang but also have applications spanning the development of rich conversational agents and tools like brand name generators \cite{ozbal2012computational}. While such phenomena have been qualitatively studied by \citet{mattiello2008introduction,mattiello2013extra}, computational models for their generation have not been proposed.

In this paper, we propose the first simple models for generating blends, clippings, and reduplicatives\footnote{While there has been some prior work on computationally modeling blends, generative models for clippings and reduplicatives have not been outlined.}.
Our models incorporate linguistic insights coupled with data-driven analysis to model the above phenomena. In line with ``Occam's razor'', we strive for simplicity.
The simplicity of our models not only implies better generalization, more robust estimation of parameters in the wake of small dataset sizes, and better interpret-ability but also yields state of the art performance.

Specifically, we show that by exploiting structural constraints, blend formation can be modeled as a simple \emph{sequence labeling} problem
as opposed to prior work which models it as a \emph{general sequence to sequence} problem. This view enables the use of a simple LSTM model to yield competitive performance. Similarly, we propose the first probabilistic generative models for clippings and reduplicatives effectively incorporating phonetic constraints. In a nutshell, our contributions are:
\begin{enumerate}
    \item \textbf{Generative models.}  We propose simple models for generating blends, clippings and, reduplicatives with state of the art performance.
    \item \textbf{Linguistic Insights.} We reveal linguistic insights into these phenomena which we incorporate into the generative models.
    \item \textbf{Resources.} We release all our models and the compiled datasets to aid further research \footnote{Code and data with reproducible results is available at \texttt{https://github.com/viveksck/simplicity}}.
\end{enumerate}
\section{Models and Methods}
We now describe our proposed models to generate blends, clippings, and reduplicatives.  We precisely formulate the problem,  specify our models and comprehensively outline our evaluation.
\subsection{Blends}
\paragraph{Problem Formulation}
Given a string $C=C_{1}\#C_{2}$, consisting of components $C_{1}$ and $C_{2}$, we seek to combine them to yield the blend $B$.
For example, given $C_1=\texttt{brad}$ and $C_2=\texttt{angelina}$ such that $C=\texttt{brad\#angelina}$, we seek to generate the blend \texttt{brangelina}.

\paragraph{Existing Models}  \cite{deri2015make} proposed a model to generate blends using multi-tape finite state transducers.  Most recently, \cite{gangal2017charmanteau} (the current state of the art) model this as a general sequence to sequence learning problem and propose a neural encoder-decoder architecture with attention to outperform the model by \cite{deri2015make}.  However, this model fails to effectively exploit the inherent structure and linguistic constraints of blending. One implication is an exploration of an overly complex hypothesis space with a small amount of training data making it harder to generalize.
A second implication is that the decoding phase uses exhaustive generation. In fact, the best model \emph{exhaustively} generates all candidate strings, where the first part is a prefix of $C_{1}$ and the second part is a suffix of $C_{2}$ and scores them to pick the best candidate while using a backward model learned to generate the components given the blend.  In contrast, we propose a more straightforward model that explicitly incorporates inherent linguistic constraints entirely obviating the need for decoding using exhaustive candidate generation yet yielding competitive performance.

\subsubsection{Linguistic Insights into Blend Formation}
\label{sec:insights}
While one can model the problem of learning to blend as a variable length sequence to sequence learning problem (akin to machine translation), we argue that incorporating structural constraints yields a different view of modeling the problem that can enable better generalization given the small amount of training data. We motivate this by observing the following constraints:
\begin{enumerate}
    \item \textbf{Blend length and vocabulary constraints.} First, we observe that a majority of blends ($99.0\%$ in $D_{knight}$ and $92.4\%$ in $D_{blind}$) are formed by using only characters present in the original components.  Second, the length of the blends in these cases is at-most the length of the components.
    \item \textbf{Fixed length input output representation.} The blend $B$ can thus be encoded as a string of the exact same length as $C$ by noting that $B$ only contains characters copied from $C$ or deleted from $C$. Specifically, we represent $B$ by $E(B)$ denoting the sequence of \emph{copy} (\textbf{C}) and \emph{delete} (\textbf{D}) operations needed to transform $C$ to $B$. $E(B)$  can easily be computed by the edit distance function between $C$ and $B$. For example, \texttt{brangelina} is encoded as \texttt{CCCDDDCCCCCCC}. Since $E(B)$ has the same length of $C$, we can now model this as a \emph{fixed length sequence labeling} problem rather than a variable length sequence to sequence learning problem completely obviating the need for the \emph{``encoder-decoder''} architecture for this large class of blends.
\end{enumerate}

\textbf{Equivalent Problem Definition} Given a string $C=C_{1}\#C_{2}$, consisting of components $C_{1}$ and $C_{2}$, learn $E(B)$, a labeling of each character in $C$ from the label set $\{\textbf{C}, \textbf{D}\}$.

\subsubsection{Proposed model: \textsc{CopyCat}}
\begin{figure}[t!]
	\includegraphics[scale=0.5, width=\columnwidth]{figs/lstm.PNG}
	\caption{Our simple LSTM sequence labeling model for blends. Our model is considerably simpler both in complexity of the parameter space and in terms of implementation compared to the encoder-decoder model proposed by \cite{gangal2017charmanteau} which modeled the problem as a machine translation problem.}
	\label{fig:lstm}
\end{figure}
\paragraph{Model Architecture} Inline with work on neural sequence labeling \cite{wang2015part,plank2016multilingual}, our model uses a single layer long short term memory (LSTM) \cite{graves2012supervised} and an embedding layer as depicted in Figure \ref{fig:lstm}. Since the size of the training set is relatively small, we reduce the number of learn-able parameters by using pre-trained character embeddings frozen during training. In particular, we use $50$ dimensional character embeddings from \cite{gangal2017charmanteau} obtained by training an LSTM language model on an English Dictionary. The implementation of the LSTM layer is described by \cite{graves2012supervised,wang2015part} and therefore omitted here. The output layer is a soft-max over the label set \{\textbf{C, D}\}.

\paragraph{Candidate Generation}
Our model outputs probability scores over the label set for each element in the sequence. As in previous works \cite{gangal2017charmanteau}, we use the output to generate an ordered candidate set $\mathcal{T}$. To construct $\mathcal{T}$, we use a simple top-K decoder which selects the $k$ most probable label sequences. Finding the $k$ most probable tag sequences from the soft-max outputs can be cast as finding the top $k$ shortest simple paths in a directed acyclic graph which can be efficiently solved using \cite{yen1971finding,eppstein1998finding}. Note that the greedy decoder which just picks the most likely label at each position is a special case with $k=1$. While the number of candidates generated by \cite{gangal2017charmanteau} depends on the size of $C$, our model generates a constant number of candidates ($k=5$) regardless of the input $C$.

\begin{table*}[htb]
\centering
\small
	\begin{tabularx}{\linewidth}{l|X|X|X|X}
	\textbf{Input} & \textbf{LSTM} & \textbf{LSTM + LM} & \textbf{LSTM + LM + LEN} & \textbf{Gold standard} \\
	\hline
	\texttt{brad\#angelina} & \texttt{brngelina} & \texttt{brangelina} & \texttt{brangelina} & \texttt{brangelina} \\
	\texttt{animated\#matrix} & \texttt{animtrix} & \texttt{animatrix} & \texttt{animatrix} & \texttt{animatrix} \\
	\texttt{merkel\#sarkozy} & \texttt{merkrkozy} & \texttt{merkkozy} & \texttt{merkkozy} & \texttt{merkozy} \\
	\texttt{dramatic\#drastic}  & \texttt{draastic} & \texttt{drastic} & \texttt{dramastic} & \texttt{dramastic} \\
	\texttt{kentucky\#indiana} & \texttt{keiana} & \texttt{keana} & \texttt{keniana} & \texttt{kentuckiana} \\
     \texttt{employability\#agility} & \texttt{emplglity} & \texttt{emplgility} & \texttt{employgility} & \texttt{employagility} \\
	\hline
	\end{tabularx}
	\caption{Set of examples demonstrating the effect of incorporating the language model and length model when ranking candidates revealing insights into blend formation. Incorporating the language model generally improves the fluency of the blend while the length model helps generate blends with lengths close to the target.}
	\label{tab:ranking_models}
\end{table*}

\paragraph{Candidate Ranking and Selection} While the list of candidates in $\mathcal{T}$, can be used to make a prediction we note that re-ranking these candidates can result in better performance and thus consider multiple ranking strategies:
\begin{enumerate}
    \item \textbf{LSTM.} We consider only the scores as obtained by the LSTM with no re-ranking.
    \item \textbf{LSTM + LM.} We augment the score of each candidate to include both the score of the LSTM as well as its score according to a character level language model where the language model is trained on a large amount of unsupervised text \footnote{We use words from the CMU pronouncing dictionary.}.
    \item \textbf{LSTM + LM + LEN.} Figure \ref{fig:blends_reg_plot} shows a least squares fit to the length of the blends versus the length of its component, suggesting a strong correlation between these two variables. We capture this notion through a probabilistic model. Specifically, we model $\mathbf{Pr}(Blend_{len}|Component_{len})$ by fitting a Bayesian Ridge Regression model to the training data and score each candidate on this model as well. Finally, we combine the scores obtained for the LSTM, the language model and the length model uniformly to yield the final score for each candidate.
\end{enumerate}
In each of the above cases, we pick the topmost candidate as our prediction.

\begin{figure}[htb!]
	\includegraphics[scale=0.5, width=\columnwidth]{figs/components_blend_regplot.pdf}
	\caption{Best fit between blend length and the length of the input string, implying that one can infer the length of the blend from the length of the components using a regression model.}
	\label{fig:blends_reg_plot}
\end{figure}

Table \ref{tab:ranking_models} illustrates the effects of each of these ranking strategies on an exemplary set of strings. While ranking using the raw scores of the LSTM yields blends which are close, observe that the LSTM alone does not capture ease of pronunciation effectively. For example, the top ranking candidate \texttt{brngelina} is relatively unlikely under both a character language model and a phoneme language model. Incorporating scores from the language model results in much more natural blends like \texttt{brangelina}. To observe the effect of the length model, note that for \texttt{kentucky\#indiana} the blends obtained by LSTM and LSTM + LM are too short (\texttt{keiana} and \texttt{keana}). Incorporating the length model boosts scores for candidates closer to the target length yielding \texttt{keniana} which is closer to the target \texttt{kentuckiana}.

\subsubsection{Evaluation} We compare our model to previous methods namely \cite{deri2015make} and \cite{gangal2017charmanteau}. Inline with \cite{gangal2017charmanteau}, we evaluate our model on $\mathcal{D}_{knight}$ and $\mathcal{D}_{blind}$ (consisting of $1078$ instances)\footnote{We discard relatively rare instances with insertions.}. For evaluating on $\mathcal{D}_{knight}$, we use $10$-fold cross-validation. For evaluating on $\mathcal{D}_{blind}$ we train our model on $\mathcal{D}_{knight}$ and report the mean score on the test-set obtained using $10$ random splits of the training data.  As in previous work, our metric is the edit distance between the predicted blend and the true blend.

\begin{comment}
\begin{figure}[]
	\begin{subfigure}{0.48\linewidth}
		\includegraphics[width=\columnwidth]{figs/blends_prefix_length.pdf}
		\caption{Length of prefix}
		\label{fig:blends_prefix_length}
	\end{subfigure}
	\begin{subfigure}{0.48\linewidth}
		\includegraphics[width=\columnwidth]{figs/blends_suffix_length.pdf}
		\caption{Length of suffix}
		\label{fig:blends_suffix_length}
	\end{subfigure}
	\caption{Performance of our models for generating reduplicatives on both datasets.}
	\label{fig:blends_insight}
\end{figure}
\end{comment}

\subsection{Clippings}
\paragraph{Problem Formulation} Given a word $w$, learn a model that can generate its clipping $c$. For example, given the word \texttt{administration} we would like the model to output the clipping \texttt{admin}.
\paragraph{Proposed Models}
\begin{figure}[tb!]
	\includegraphics[scale=0.5, width=\columnwidth]{figs/clippings_num_syllables.pdf}
	\caption{Distribution of the number of syllables in clippings. Most clippings have at-most 2 syllables but it is a challenge to infer whether a given word has a one or two syllable clipping.}
	\label{fig:clipping_num_syllables}
\end{figure}

We motivate our models by presenting two insights into linguistic properties of clippings first noted by \cite{mattiello2013extra}. First, most clippings are back clippings while fore clippings are relatively rare. Second, most clippings tend to have at most two syllables (see Figure \ref{fig:clipping_num_syllables}). The first insight guides our model in determining whether to retain a prefix or a suffix of the original word. The second insight guides our model in determining how much to retain (or clip). In particular, we can capture this in two ways: (a) Working in the phoneme space and (b) Learn a function to predict the length of the clipping (which encodes the number of syllables implicitly).

\subsubsection{\textsc{ClipPhone}}
\textsc{ClipPhone} operates by mapping the word $w$ to a sequence of phonemes, explicitly clipping in the phoneme space and mapping the phoneme sequence back to the grapheme space. In particular, the model can be described as follows: \textbf{(1)} Let $\theta$ and $\pi$ represent multinomial distributions over clipping types and the number of syllables respectively. \textbf{(2)} Represent the word $w$ as a sequence of phonemes  $\mathcal{P}$ and identify each syllable. \textbf{(3)} Draw a sample $l$ from $\pi$ to represent the number of syllables in the clipping and the type $t$ by drawing a sample from $\theta$. \textbf{(4)} If $t\in \{\textsc{Back, Fore}\}$, clip $\mathcal{P}$ to have exactly $l$ syllables by selecting the appropriate length prefix or suffix depending on clipping type $t$ to make exactly $l$ syllables represented by $\mathcal{P}_{clip}$. If $t$ is a \textsc{Compound}, clip each word recursively and concatenate the outputs. \textbf{(5)} Map $P_{clip}$ back to grapheme space to yield the clipping. \textbf{(6)} The parameters of $\theta$ and $\pi$, both multinomial distributions can be estimated from observed data via maximum likelihood estimators.
\paragraph{Phoneme-based representation}
Given  $w$, we obtain its phoneme representation using the pre-trained state-of-art neural model \textsc{G2PSeq2Seq} \cite{yao2015sequence}\footnote{G2PSeq2Seq has a PER of $5.45\%$ on CMUDict.}. For example, the word \texttt{captain} is mapped to the following sequence of phonemes $\mathcal{P}$ given by \texttt{K AE P T AH N}.
\paragraph{Clipping in the phoneme space} We identify the syllable boundaries in $\mathcal{P}$ by looking for vowel phonemes and clip $\mathcal{P}$ until it contains the desired number of syllables. For example, a one syllable clipping of \texttt{K AE P T AH N}  is \texttt{K AE P T} since \texttt{AH} is the beginning of the second syllable ($\mathcal{P}_{clip}=\texttt{K AE P T}$).
\paragraph{Mapping clipping back to graphemes}
Finally, given the clipped phoneme sequence $\mathcal{P}_{clip}$, we map it back to the grapheme space by learning a sequence to sequence model to map a phoneme sequence to its grapheme sequence. We follow the same model architecture as used in \textsc{G2PSeq2Seq} \cite{yao2015sequence} but just flip the input and output sequences, thus learning a model to map phonemes back to graphemes. Finally, we quantify the effectiveness of this model explicitly in our evaluation by establishing an upper-bound on the expected performance using this model.
\subsubsection{\textsc{ClipGraph}} As we will demonstrate empirically, \textsc{ClipPhone} poses the following challenges: Since a given phoneme sequence can map to multiple grapheme sequences, explicitly mapping to phoneme space and back introduces errors and loss of fidelity \footnote{\texttt{K AE P T} can be mapped to \texttt{capped} or \texttt{capt}.}. This is compounded by noting that whether $w$'s clipping should have one or two syllables is hard to predict (since both occur in almost equal proportions empirically, see Figure \ref{fig:clipping_num_syllables}). Furthermore, deciding whether the clipping should end in a vowel or a consonant and determining the length is yet another challenge. As an alternative, we propose \textsc{ClipGraph} where we seek to directly learn a model to predict the length of the clipping directly given $w$, obviating the need to work in phoneme space. \textsc{ClipGraph} works as follows: \textbf{(1)} Let $\theta$ represent multinomial distributions over clipping types and let $\pi$ be a model that predicts the length $l$ of the clipping ${w}_{clip}$ given $w$. \textbf{(2)} Draw a sample from $\theta$ to get the type of clipping $t$. \textbf{(3)} Retain the appropriate $l$-length prefix or suffix of $w$ based on the type $t$ handling compound words recursively. \textbf{(4)} We use Ridge regression to learn $\pi$ from the training data and estimate $\theta$ using its maximum likelihood estimator.

\paragraph{Evaluation} To evaluate our models, we consider a naive baseline \textsc{Naive} which clips $w$ to one of its prefixes or suffixes randomly. For \textsc{ClipPhone}, we also consider one (\textsc{ClipPhone-1Syl}) and two (\textsc{ClipPhone-2Syl}) syllable clippings. Since \cite{jamet2009morphophonological} notes that determining whether a word has a one or two syllable clipping is extremely challenging, we also consider a model with an oracle (\textsc{ClipPhone(O)}) on the number of syllables in the clipping to quantify this. Finally,  we establish an upper bound (\textsc{G2P-Gold}) on the performance of any method using our learned P2G model. \textsc{G2P-Gold} maps the gold standard $G$ to phoneme space and maps the resulting phoneme sequence back to yield $\hat{G}$ as the predicted clipping. We use the edit distance between the predicted clipping and the gold standard as the evaluation metric.

\subsection{Reduplicatives}
\label{sec:method_redups}
\paragraph{Problem Formulation}
Given a word $v$, we seek to generate a word $w$ such that $v.w$ is a reduplicative. For example, given the word \texttt{flip}, we would like to generate \texttt{flop} or \texttt{flap} to yield the reduplicatives \texttt{flip-flop} or \texttt{flip-flap}.

\paragraph{Proposed Model}
We motivate our model from a linguistic standpoint by referring to observations by \cite{mattiello2013extra} that a large fraction of reduplicatives can be formed by either (a) Duplicating the word \textsc{Duplicate} (\texttt{boo-boo}) (b) Exchanging the initial vowel \textsc{VowelEx} (\texttt{bing-bong}) and (c) Exchanging the initial consonant \textsc{ConEx} (\texttt{teenie-weenie}). Other patterns include adding a consonant (\texttt{artsy-fartsy}) or adding \texttt{schm/shm} (\texttt{moodle-schmoodle}). In our work, we propose a generative model for the three dominant forms of reduplication mentioned above. Broadly, our model captures the notion that vowels and consonants display strong replacement preferences. For example, the vowel \texttt{i} is much more likely to be replaced with \texttt{a} than \texttt{u} (instances like \texttt{clip-clap, wishy-washy}). Similarly the consonant \texttt{t} is much more likely to be replaced by \texttt{w} (as in \texttt{teenie-weenie, tinky-winky}). We incorporate these insights into our generative model as follows: \textbf{(1)} Let $\theta$ be a distribution over the three different types of reduplicatives. Let $\phi_{v}, \psi_{c}$ be distributions over letters that replace vowel $v$ and consonant $c$ respectively. \textbf{(2)} Sample the type of reduplicative $t$ generated from $\theta$. \textbf{(3)} If $t$ is \textsc{Duplicate}, set $w$ to $v$ and return $w$ as the reduplicative component. \textbf{(4)} If $t$ is \textsc{VowelEx}, find the first vowel $x$ with non-zero replacement probability. Sample the replacement $z$ from $\phi_{x}$ and replace $x$ with $z$. If $t$ is \textsc{ConEx}, find the first consonant $c$ with non-zero replacement probability and sample the replacement $z$ from $\psi_{c}$. Replace $c$ with $z$. \textbf{(5)} Return the edited string as the second component of the reduplicative. \textbf{(6)} The parameters of multinomial distributions $\theta$, $\phi_{v}$, and $\psi_{c}$ are estimated via MLE estimates from the data.

\paragraph{Evaluation}
We consider two baseline models (a) \textbf{\textsc{Let}} Uniformly replace a letter with another letter in $v$ to return $w$. (b) \textbf{\textsc{Let(Cond)}} Uniformly replace a letter (vowel or consonant) with a letter from its class. Since reduplicatives are characterized by phonologically similar sounds, merely using edit distance as a metric for evaluation would be ineffective. For example, even the ill-rhyming \texttt{flip-flsp} has the same edit distance as the correct reduplicative \texttt{flip-flop}. Thus, we use a distance measure (MIR) defined over the phoneme space \cite{hixon2011phonemic} which effectively captures the similarity of two phonetic sequences by modeling the affinities between pairs of phonemes using a point access mutation matrix and is superior over metrics like PER (phoneme error rate).
\section{Supplemental Material}

\end{document}

\section{Related Work}
Blends, clippings and reduplicatives have been studied from a linguistic standpoint \cite{thun1963reduplicative,murata1990ab,merlini2008extra,hladky1998notes,hamans1997clippings,fandrych2008submorphemic,moehkardi2016patterns,ungerer2007word,beal1991toy,algeo1977blends,smith2014nehovah,shaw2014emergent,beliaeva2014unpacking,broad2016emergent,renner2013cross,gries2004isn,gries2004shouldn}. Most relevant is the work of \cite{mattiello2005pervasiveness,mattiello2008introduction,mattiello2013extra} who argues that slang is pervasive on the Internet, suggests its extra-grammatical nature and outlines some  phonological and morphological properties. Specifically, these phenomena are discussed followed by a qualitative analysis on a manually compiled dataset of $1580$ words from various sources. Recently \cite{deri2015make} and \cite{gangal2017charmanteau} study the problem of learning to blend and derive data-driven computational models for the task. \cite{deri2015make} propose a model based on finite state transducers. \cite{gangal2017charmanteau} outperform \cite{deri2015make} by modeling the problem of generating blends as a variable length sequence to sequence learning problem and propose a neural encoder-decoder based model. We differ from all of these works in several ways. In contrast to the blending model proposed by \citet{gangal2017charmanteau}, our model is simpler with lesser parameters, does not require using an encoder-decoder framework, or exhaustive decoding and yet yields competitive performance on a large class of blends.  We also propose the first computational generative data-driven models for clippings and reduplicatives while capturing phonetic similarity as well and evaluate our models quantitatively.

redpajama_set_name: RedPajamaArXiv


Amanda Gouws is a professor of political science at the University of Stellenbosch and a commissioner for the Commission for Gender Equality. Her research deals with women and citizenship, representation and the women's movement. She regularly writes for Die Burger. She writes here in her personal capacity.


COET's work is closely aligned with and supported by the Department of Conservation (DOC). COET and DOC's Grand and Otago Skink (GAOS) Recovery Programme are formal partners with corresponding objectives to promote public education and advocacy, support captive breeding, and provide protected sanctuaries for wild populations of grand and Otago skinks.
COET is taking a lead in community education and has recently hosted a joint community field day at the Mokomoko dryland sanctuary with DOC. The GAOS team was involved in the assessment of this sanctuary site as suitable habitat for Otago skinks and provided assistance with permits. DOC was able to quarantine the captive born skinks which were to be translocated into the sanctuary in order to screen them for diseases and parasites as well as conducting general health checks of the animals.
Details of DOCs recovery programme for grand and Otago skinks are summarised below. For information on skink biology and factors that could be contributing to their decline, refer to Flora & Fauna.
DOC undertook a 3 year experimental management programme (2006-2008) at Macraes Flat, in Otago, to identify a viable management option for the recovery of the endangered grand and Otago skinks. The research involved the establishment of mammal proof fences and extensive trap lines around known skink habitat and existing skink populations to assess the impact of invasive mammals on these populations. Results from the research have shown that eradication or intensive control of introduced mammalian predators can reverse the decline in skink numbers. Grand and Otago skink numbers within the managed areas at Macraes Flat have doubled over a 3-4 year period.
Due to the proven success of management techniques, protection and monitoring of grand and Otago skinks in the east of their range continues at Macraes Flat. The GAOS team also monitors isolated skink populations in the west of their range near Wanaka.
A range of invasive mammalian predators have been implicated in the decline of grand and Otago skinks including cats, stoats, ferrets, weasels and hedgehogs. Predators are controlled via trapping and exclusion fences at Macraes Flat. A wide range of trap types (including both kill traps and live trapping) are used in combination with a variety of baits (eggs, rabbit and fish) to target a broad range of predator species. The trapping network was expanded from 2100ha to 4660ha in 2008/09 and includes both conservation estate and private land. Experimental mammal proof fences have been constructed to exclude predators. These fences have a small mesh size, a hood to prevent animals climbing over the top and a buried skirt to prevent animals from burrowing underneath. All predators were removed from within the fenced areas and regular monitoring is carried out to ensure that predators do not recur. Two fenced areas have been constructed; one at an area known as Wildlife (20 ha: gully habitat) was built in winter 2005 and one at Redbank (9 ha: ridgetop habitat) was established in winter 2007.
The responses of skink populations to current management techniques are monitored across the Macraes Flat area. Monitoring is also carried out in the west near Wanaka to assess the status of populations there. Both Otago and grand skinks have been monitored in the wild since 2005 using Photo-Resight (P-R) methods (aka photo Mark-Recapture). This method relies on the fact that individual Otago and grand skinks are recognisable by the unique markings and patterns on the side of their head, these markings do not change considerably as the skinks age. By revisiting the same sites over a period of time the GAOS team can build up a catalogue of individuals that occur at each site. Statistical methods are then employed to estimate survival and population change for skinks using the P-R method at each key site.
Management is not currently practical for both species across the whole of their ranges, so it is essential that the genotypes of the grand and Otago skinks are secured. Both species have disjunct ranges with isolated populations in the east and west of their range and thus genetic diversity from both populations must be protected. A captive management programme has been developed in co-operation with private breeders and zoological institutions to protect and maintain the diversity of skinks and as insurance from extinction in the wild. A captive husbandry manual has also been developed to aid captive management by producing guidelines for grand and Otago skink husbandry.
Community involvement in skink recovery and ecological restoration of drylands is essential to the long-term success of the programme. The grand and Otago skink team has hosted community days, university and conference open days and have visited many local schools to deliver education and advocacy programmes. The grand and Otago skink recovery programme also relies on good relationships with many neighbouring landowners for access to sites.
A number of collaborative projects are conducted by Landcare Research, the University of Otago and numerous volunteers in association with the GAOS programme. Research includes the wider effects of the GAOS trapping network on the ecosystem, the relationship between skink populations and vegetation and landuse and movements of Otago skinks (via radiotracking).


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The news that Donald Trump has appointed right-wing warmonger John Bolton as his national security adviser is an ominous signal that the world could become an even more dangerous place. Bolton replaces General H.R. McMaster, a career officer seen as "too moderate" among the most rabid supporters of the President.
Until now, Bolton was most infamous as George W. Bush's ambassador to the United Nations in 2005-2006, and for his previous role as the State Department official in charge of arms control, during the period when lies about Iraq's non-existent weapons programs were invented to justify the US-British invasion of 2003.
But wait, there's more. As a bitter critic of the US-Iran nuclear agreement, Bolton has repeatedly urged war against Iran, which could spark catastrophic global consequences. He supported imperialism's 2011 assault on Libya and regime-change efforts in Syria, both of which have created widespread chaos and death in the North Africa-Middle East region. With John Bolton back in the White House, the planet is a big step closer to nuclear disaster.


text:
iPhone brings new technologies
Apple chose to take its products to the next level by "making the experience of using it even simpler, more useful, and more enjoyable."...
The Late Night Special
By Anthony Smith
The Panther Grill, Prairie View's on-the-go retail dining room, now stays open until 11 p.m. Under a new formed contract The Panther...
Black Cat Cafe welcomes artistic community of Prairie View
By Sylvania E. Omokaro and Erin Rausaw
On Monday, Oct. 14, 2013, The Black Cat Café took place in a dimly lit ballroom on the 2nd floor of the MSC. There stood many m...
University Village welcomes new residents to luxury amenities
By Celia Hinkson
The fall 2013 semester welcomes University View, the hottest new place to live on Prairie View A&M's campus. This luxurious and private...
Kappa Alpha Psi hosts health week on campus
Last week the Zeta Beta chapter of Kappa Alpha Psi Fraternity Inc. held a Health Week that promoted its health initiative called ...
Numbers don't lie: dropout awareness
By Shalun Wells
"I think people come to Prairie View for the wrong reasons. Students get caught up in the "college life" and lose focus, which reflects...
The repetitive fashion sense is at an all time high as males wear their freshly re-released retro Jordan's, crisp army-camouflage...
Area students come together to mentor middle school pupils
Students from Prairie View A&M University and rival school Texas Southern University were in sync to assist in mentoring and raising...
Cooler temperatures bring cuddling weather
By Melissa Adams
What is your impression of the approaching "Cuffing Season?" The nights are starting to get cooler and the days are becoming shorter. A...
Leading ladies of Prairie View A&M University
There are two powerful ladies this year on the hill working to create change for Prairie View A&M University. Breanna Green, Miss...
KPVU kicks off TV station
In Prairie View, Texas, students, faculty and staff are ready with creative content and will begin programming for PV Media Channel 55...
#PV17 Invasion
By Rania Dotson
Freshmen converged on Prairie View A&M University grounds with their clothes, shoes, furniture, and more. Not only were their utilities...

redpajama_set_name: RedPajamaCommonCrawl


19 backers pledged $10,075 to help bring this project to life.
I'm Denys Ivanichek and I'd like to welcome you to my third Kickstarter project.
With the support of the Kickstarter community and fellow photographers, we have built two lenses already: Petzvar version one, a shutterless 120mm f/3.8 lens that is available in Pentacon Six, Hasselblad V, Mamiya 645 and Pentax 645 mounts, and the Petzvar version two, a 120mm f/4 lens built around vintage V-mount C lenses with an integrated Synchro Compur shutter for Hasselblad 500 and 200 series cameras.
The lens has uncorrected chromatic aberrations that makes it somewhat soft. Highlights "glow" and the skin appears velvet like.
All lenses from my previous projects were made with uncoated optical elements. I made that choice in order to pursue my goal to keep the lens as close to antique petzvals as it can be.
Making coated lens was in my mind since the very beginning of the Petzvar project. With this, my third Kickstarter campaign, I would like to introduce you to my new lens, the C Petzvar 105/3.2, where "C" stands for "Coated".
The C Petzvar 3.2/105 is a coated version of Petzvar 3.8/120, made for convenient use on video cameras. It has native Pentacon Six mount and can be adapted to virtually any full frame camera with available adapters.
The lens I'm offering as reward will have a positive lock mount, or PL mount, allowing the lens to be used with movie cameras such as Arriflex, and digital video cameras such as Arri Alexa and other cameras which can use the PL mount, but if you would like to use it with different camera, just remove P6 to PL adapter and replace it with any other P6 adapter. Or you can let me know and I will send the lens to you with the adapter of your choice such as P6-Nikon or P6-Canon etc.
To make the lens more convenient for videography, the C Petzvar 105/3.2 lens comes equipped with follow focus gear and de-clicked aperture control ring.
This lens was primarily designed for medium format photography and therefore can be used on cameras with a smaller sensor size. The most swirly outer part of the image will be cropped.
Whilst the C Petzvar 105/3.2 will use many similar components to the current version one and version two lenses, some modification and new tooling will still be necessary. Also, I need your support to order new tooling for the lens elements of the C Petzvar 105/3.2.
The C Petzvar 105/3.2 isn't the only new PL mount lens in this new Kickstarter campaign. I am also happy to bring you a specially modified Nikkor 2.8/35mm with shift control for PL mount as part of this campaign!
The idea of building this lens for PL mount with a shift movement belongs to a friend of mine, Mr. Mike Fourman, founder of Kiev Camera.
This is a normal 35mm f/2.8 Nikkor lens that we have added shift movement and PL mount. Just another idea for your consideration.
Since this is not my first Kickstarter project, as a rewards for your pledges I am also offering the original version one and two Petzvar lenses as part of this campaign.
If you missed your opportunity last time, or the time before, you can order them right here.
This lens if for medium format cameras with P6 bayonet mount such as Pentacon Six, Praktisix, Exacta 66, Kiev 60 ( or 6C), Kiev 88 CM or other cameras with the appropriate adapters.
This lens is for Hasselblad cameras of 200 and 2000 series wich have a focal plane shutter. This lens is shutterless and therefore it can't be used on 500 series cameras. As with the P6 version above, this lens may be mounted on other (focal plane shutter) cameras with a suitable adapter.
This lens is for Pentax 645 mount cameras. It can be mounted on film cameras and digital as well. Like all of our other lenses, this lens is entirely manual and won't autofocus. Also, there is no electric connection to the camera body.
This lens is for Mamiya 645 mount cameras. It can be mounted on film cameras and digital as well. Like all of our other lenses, this lens is entirely manual and won't autofocus. Also, there is no electric connection to the camera body.
This is the C Petzvar 3.2/105 coated lens for cameras with PL mount, the new lens that I hope you will help me to build this time!
6. The Petzvar 4/120 with Synchro-Compur shutter for Hasselblad 500 series.
This is the Petzvar 4/120 in Synchro-Compur shutter. This lens can be used on 500, 200 and 2000 series Hasselblad cameras, or other cameras with suitable adapter. Please, note, when the lenses with internal central leaf shutter are mounted on focal plane shutter cameras, the camera is should be set to "C" mode and the fastest shutter speed available (used the lens shutter) will be 1/500 sec.
There are always a risk of unforeseen supply issues, or delays involved with manufacture or shipping. I gained valuable experience whilst delivering my first two Kickstarter projects.
You can be certain that just like my other projects, all rewards to project backers will be fulfilled and I will tirelessly work to deliver this lens to each and every backer!
Early bird special. You will receive shutterless Petzvar 3.8/120 lens with one of the following mounts: Pentacon Six, Hasselblad V, Mamiya 645 or Pentax 645. Please, let me know your preference.
You will receive the Petzvar 3.8/120 lens with Pentacon Six mount.
Petzvar 3.8/120 with Hasselblad V mount.
You will receive the Petzvar 3.8/120 lens with Hasselblad V mount for 200 series cameras with focal plane shutters.
Petzvar 3.8/120 with Pentax 645 mount.
You will receive the Petzvar 3.8/120 lens with Pentax 645 mount.
Petzvar 3.8/120 with Mamiya 645 mount.
You will receive the Petzvar 3.8/120 lens with Mamiya 645 mount.
C Petzvar 3.2/105 lens with PL mount.
You will receive coated C Petzvar 3.2/105 lens with PL mount.
Petzvar 4/120 in Compur for Hasselblad.
You will receive Petzvar 4/120 lens in Compur shutter for Hasselblad cameras of 500 and 200 series.


text:
Wind-Turbine Makers Press for Green Mandates
By Russell Gold
Sept. 22, 2009 12:01 am ET
Wind-turbine makers say growth in their industry could dramatically slow unless the federal government requires more electricity come from renewable energy.
New federal stimulus grants helped restart a stalled wind-power industry, but Vic Abate, a General Electric Co. vice president in charge of its wind-turbine business, said orders for wind turbines to be built in 2012 and thereafter have been "extremely light."


Australia's first royal commission into family violence has made a comprehensive 227 recommendations in its landmark report to the Victorian parliament, making it the most extensive document ever handed down on how to how to prevent and respond to the issue.
Minutes after the report was published, the Victorian premier, Daniel Andrews, repeated his pledge to introduce all its recommendations, even though he acknowledged it would cost "many hundreds of millions of dollars" and some would take years to implement.
"The budget will be delivered in just a couple of weeks time," he said. "Obviously here are some things that can't wait. We will move quickly on those things that simply can not wait.
Rosie Batty, the campaigner against domestic violence and mother of Luke Batty, whose killing by his father was one of the attacks that prompted the royal commission, said she was "thrilled" by the report.
Within the recommendations there is an emphasis on breaking down a siloed system to increase transparency and cooperation between sectors, as well numerous recommendations to hold the performance of those sectors to account.
Removing the burden from victims in getting help and placing accountability on the shoulders of perpetrators is also a strong focus of the report.
There is also a role for the state government in lobbying the federal government for change, the report says.
Andrews established the family violence royal commission in February last year. The report was handed by the commission to the Victorian governor, Linda Dessau, on Tuesday, and released on Wednesday.
Several of the recommendations deal with creating a more comprehensive information-sharing regime between police, courts, family service providers and other parts of the family violence sector, including a recommendation the government establish a secure central information point led by Victoria police and which stores databases from the Department of Health and Human Services and the Department of Justice.
It would mean that when risk assessments are being made about the likelihood of a perpetrator reoffending, their history of involvement with a range of services are made available to those charging with assessing that risk.
Throughout the royal commission's hearings, several experts described a system where the full history of a perpetrator's involvement with courts and the justice system was not immediately accessible to police and magistrates, leading to weakened intervention orders and an underestimation of risk to victims and children.
Even a perpetrator's involvement with different police commands was not always easily accessible to other police officers or magistrates, the commission heard.
There is also a focus on allowing victims of family violence to remain in their own homes so that they are not forced to uproot themselves and often children as they seek out safety in crisis accommodation or homeless shelters.
Instead the commission has recommended the government should give priority to allowing victims to safely stay in or return to their own homes by offering rental and mortgage subsidies and by monitoring perpetrators through the police and justice systems. This means the government also needs to expand the number and range of crisis and accommodation services, the report says, with priority given to rural and remote areas.
Victims should not be forced to spend more than six weeks in crisis accommodation, including motels and refuges, the commission recommends. To achieve this, a family violence housing implementation taskforce comprising senior representatives from the public and commercial housing sectors as well as family violence specialists should be established within a year, the report says.
A strong emphasis has been placed on accountability by the commission. The Victorian minister for housing, disability and ageing should report annually to the parliamentary committee on family violence on the extent of housing shortages for family violence victims, progress in meeting benchmarks including ensuring women are moved into permanent housing within six weeks, and proposals on how to meet demand and shortfalls.
The government and police should regularly audit their compliance with the code of practice for investigating family violence, the report says, and the results of these audits should be made public.
The recommendations aim to take the burden away from victims in having to navigate through the court, police and social service systems by calling on the Victorian government to introduce support and safety hubs in 17 regions of the state.
Australians are being told that gender inequality is the root cause of domestic violence. But is it?
These hubs would be a kind of one-stop-shop for victims to safely receive police referrals, make contact with family violence services, establish a safety plan and risk assessment for further violence, and book victims into crisis accommodation and support services for children.
The court system should be overhauled within five years so that all family violence cases be heard within specialist family violence courts and the magistrates' court should consider capping lists of family violence-related matters to take pressure off an overwhelmed system that struggles to properly consider each case.
Family violence victims, especially those caring for children, should be able to give remote evidence so that they don't experience the trauma and at times, safety risk of facing their perpetrator in court, the report says. Where this is not possible safety rooms in courts should be established so victims do not have to face their perpetrators as they wait for their court matter to come up.
A review of interventions for perpetrators, including behaviour change programs, and the strength of those programs should be carried out by an expert committee, the commission recommends, while the attendance of men to counselling service and behaviour change programs should be monitored.
Men's behaviour change programs need to be comprehensively reformed so that they are based on best practice and international evidence, the report recommends, with evidence given during the commission's hearings that there was little evaluation carried out of the programs and it was difficult to establish if they worked.
Behind the Lines podcast: what can we expect from the report of Victoria's royal commission into family violence?
There is a role for the Victorian government to encourage the commonwealth to consider a Medicare number for family violence counselling, the report says, and through its membership of the Australian Health Workforce Ministerial Council, encourage the council to improve family violence training standards for general practitioners.
The Victorian government should also lobby the Council of Australian Governments (Coag) to encourage the federal government to amend the national credit code to include family violence as a ground for financial hardship.
Numerous other recommendations have been made in the report focused on maintaining and improving the financial security of family violence victims, with perpetrators often disempowering their victims by controlling their finances. There are numerous recommendations made for specific groups of victims, including the elderly, men, LGBTI victims, Aboriginal and Torres Strait Islanders, and those from ethnically diverse communities. Within two years, the Victorian government should expand support programs for Indigenous victims and provide adequate funding for Aboriginal-controlled health organisations within one year.
Representatives from the family violence sector, including the chief executive of Domestic Violence Victoria, Fiona McCormack, will hold a press conference at noon to respond to the report.
In a statement issued shortly before his scheduled press conference, Andrews reiterated his commitment to implementing every one of the report's recommendations.
"I refuse to look back in 10 years' time and admit that we could have done more to save lives," he said.


Mine site rehabilitation has been integrated into the early planning process, even preceding the start of mine operations, and is now an ongoing consideration throughout the mines lifetime both from a technical and a financial standpoint.
Year 4-7 Teacher Resource ... Rehabilitation Process Each year mine pits that have had the ore removed and roads that are no longer needed are rehabilitated.
process of rehabilitation of mine sites sunstonenri.com. Mine rehabilitation in the Australian minerals . Mine rehabilitation in the Australian minerals the rehabilitation of mine sites in the event that Mine rehabilitation ... Chat Online .
New Zealand coal mining company Solid Energy has developed a new method of using biosolids to rehabilitate old mining sites. By loading replaced topsoil with nutrients, post-mining plant growth can be accelerated and the severe environmental effects of the mining process can be greatly reduced.
Mining Rehabilitation Fund Guidance. Mining Rehabilitation Fund Act 2012 ... rehabilitation works on legacy abandoned mine-sites throughout the state. ... process of lodging disturbance data on behalf of the tenement holder.
Full-Text Paper (PDF): Assessment of Rehabilitation Completion Criteria for Mine Closure Evaluation ... MREMP does facil itate in the changing mining process, where significant changes to the site .
Major mining houses have the financial resources to employ consultants and teams of experts to investigate the factors impacting the cost of environmental rehabilitation. In some cases, indicative numbers have been calculated and accepted.
legislation needs to be amended to ensure that rehabilitation of mine sites is a standard mandatory practice that forms an integral part of the overall mining process.
Reclamation Processes. As part of the life cycle of a surface coal mine, completed mine areas must undergo rehabilitation. When mining ends, operators must restore » Learn More. Newmont Mining. Email Alerts You may automatically receive Newmont Mining Corporation financial information by email.
PILBARA IRON ORE PROJECT REHABILITATION AND REVEGETATION MANAGEMENT ... Progression of mine rehabilitation ... Ltd Rehabilitation and Revegetation Management Plan .
Challenges associated with mine closure are acute in mature mining countries: the number of operating coal mines in South Africa has declined by almost half, from 112 in 1986, to approximately 65 in 2004 1 (Mohring et al. , 2001; Phillips, pers. comm., 2005).
Mine rehabilitation. Modern mine rehabilitation aims to minimize and mitigate the environmental effects of modern mining, which may in the case of open pit mining involve movement of significant volumes of rock. Rehabilitation management is an ongoing process, often resulting in open pit mines being backfilled.
Unfortunately, mine rehabilitation was not always a consideration during historic mining operations. In 1974, the NSW Government established the Derelict mines program to address legacy mine issues. $3.3 million was allocated to the Derelict mines program in the 2015-16 financial year, for the purpose of remediating derelict mine sites.


weatherData is an R package that fetches weather data - like temperature, pressure, humidity, wind speed, and more - from the Web as a clean data frame.
The package is hosted on GitHub, or it can be installed from CRAN.
Location: which city, zipcode or weather station are you interested in?
Time interval: over what period of time do you want to pull data from?
Level of detail: how many records per time interval?
Type of weather metric: are you examining temperatures? Precipitation? Or something else?
In addition to pulling data, the package also offers a number of preloaded datasets that are ready for instant analysis.
Visit the weatherData page here.


The Android tablet market is a bit dead right now, and it doesn't look like it's coming back anytime soon. Google has pretty much given up on the segment and so have most brands. However, Huawei is still putting out some solid tablets for those who want to consume media on a bigger display without going the Apple route. The MediaPad M5 Lite is their latest offering, and it's a budget take on the more powerful and more expensive M5 Pro model.
The Huawei MediaPad M5 Lite is rocking mid-range specs, with a lower end Kirin 659 processor, which is about the equivalent of a Qualcomm Snapdragon 625 or 630, along with a choice of 3GB of RAM and 32GB of storage or 4GB of RAM and 64GB of storage. I've been testing the lower end model with 3GB of RAM, but the storage is of no concern as there is a microSD card slot.
Just like its higher end brother, the highlight of this tablet is the quad Harman Kardon speaker setup. Two speakers face up and two face down, producing some seriously impressive sound. It lacks low end, but it gets very loud and remains clear. It's great for media consumption, something tablets are made for. And unlike its higher end brother, the M5 Lite actually comes with a headphone jack! I know, it's shocking to hear that a media consumption device with a focus on audio has a headphone jack. It always seems that paying less gets you more in the audio department. It isn't a high power, audiophile experience (and the Histen audio effects should be turned off if you like pure audio) but it's a regular headphone jack that you'd find on any other phone or tablet.
Otherwise you're getting many of the features from the M5 Pro, like the USB-C port, a (very quick) fingerprint reader, and the same sizable 7,500 mAh battery.
Unlike its big brother, this tablet has a 1920×1200 display. It's a step down in resolution, especially at this size, but it still looks very nice.
Text is relatively sharp unless you look really close, and at this size your face won't really be against the tablet. Colors are not very accurate but they're vibrant and strong. However, this example has a bit of a green tint to it. The panel just has an odd tint to it, where whites don't quite look white. This may be a defect and adjusting the color temperature in settings helped a lot, but it isn't perfect. However, unless you're comparing it to another device, you likely won't notice too much. It remains a good panel.
This may be the Lite version of the standard MediaPad M5 tablet, but it sure doesn't feel like it. The front is a slab of black glass with nicely rounded edges. The rear is all one piece of aluminum with the only thing betraying the lower price being the plastic "cap" lining the top. This is for signal and is likely cheaper than a milled antenna line.
The construction is solid, with no flex or bend when you twist it. The buttons are clicky and feel great. The SD card tray is made of plastic inside, unlike the higher end model, but this doesn't make a difference.
If you don't have both side by side, you'd likely never be able to tell if this was the cheaper model.
Here you'll find exactly what you'd expect from a Huawei tablet: the companies EMUI skin on top of Android 8.0 Oreo. It's not running Pie in 2019, which is a bit embarrassing, and we wouldn't hold our breath for an update to Pie anytime soon. Huawei doesn't have the best record with updates when it comes to devices other than flagships.
However, the software experience is still pretty decent. Oreo is fairly recent and the EMUI experience is still leagues ahead of what it used to be. And on a tablet, some of the additions are definitely appreciated. It's not too far off from stock outside of visuals, and you get additions like a customizable navbar (you can move the buttons to the sides or keep them in the center on the fly with a swipe), smart resolution that will turn down screen resolution to save battery when needed, and Histen sound effects (if you consider that a good thing).
Since the review has started, it has gotten a few updates and it now resides on the February 2019 security patch, so security updates aren't particularly awful. However it is fairly new, so this might drop off later in 2019.
Rocking a Kirin 659 processor and 3GB of RAM, this isn't exactly a high end tablet. The processor is decidedly mid-range and 3GB of RAM isn't plenty (though it's plenty adequate). Thus performance can't be expected to reach flagship levels.
However, this tablet is fairly snappy. Unlike its higher end brothers, you will notice the occasional dropped frame from animations, but app loading and switching is pretty quick. Scrolling with a lot of apps loaded into the memory can also get a bit choppy, but again the performance is strong and satisfactory for the price.
Gaming isn't perfect but it's pretty good. Games like Asphalt can be played with high quality graphics with decent framerates, with drops during intense moments like hitting other cars (and setting off particle effects and pieces flying off). PUBG is perfectly playable on balanced settings (as playable as it gets on mobile, where it's never smooth). Just be sure to fire up Game Suite and set it to gaming mode, which will give you those extra frames. Don't worry, battery life doesn't tank when gaming anyway.
Surprisingly, this budget tablet comes with a stylus in the box. It's not the same stylus as the one in the MediaPad M5 Pro box though, but it's not all that different. The nicer stylus charges over USB-C, has two buttons, and has a nicer feeling tip. This one uses a AAAA battery like the Surface Pro 4 stylus and has no extra buttons, but it's still a one piece metal design.
As far as navigating, drawing, and playing games, it works great. It's a similar active tech to the Samsung S Pen so you can expect great performance and accuracy. How useful is it? Unless you're doing drawing, not very. There's also no place to store it so you'll likely end up forgetting it at home most of the time. We're glad to see it included, but whether anyone will use it is another story.
It's a tablet camera. What do you want to hear about it? Images are noisy, soft, and often blurry. Bright light is washed out thanks to poor dynamic range. Is it awful? No, not really.
Should you be using it? Nope. It's a tablet, not a phone. Don't bring it to the Grand Canyon expecting photos worth great memories.
Thanks to the beefy 7,500 mAh battery, this tablet tends to last quite a while even during hard use. Despite the bright display and relatively powerful speakers, watching YouTube or Netflix isn't going to substantially drop the battery life throughout the day.
Standby time could be better, as coming back to the tablet after a few days often reveals a low or dead battery after some initial use. Its hibernating state, even with few apps, isn't the greatest. However this is easily offset by its supported 18W fast charging and convenient USB-C port placement on the side rather than the bottom.
The Android tablet market is a bit barren, so competition isn't running wild by any means. The closest competitor to the MediaPad M5 Lite seems to be the Samsung Galaxy Tab A 2018, which has a similar display size and resolution, storage, RAM, and Android version. But it ships with a Snapdragon 450, a 200mAh smaller battery, microUSB, and a $30 higher MSRP. Looking at it from that perspective, the M5 Lite is a great value.
However the MediaPad M5 8.4-inch is only $20 more and features much higher end specs, so if you're okay with a smaller tablet, that's a better choice. But if you're set on a 10-inch tablet, the M5 Lite is an excellent choice for media consumption. The speakers seal the deal. You can pick one up here for $299.99! And don't forget to pick up a case or screen protector to protect your investment.


text:
Ormond Beach Observer Friday, Dec. 20, 2019 4 weeks ago
Volusia County launches nurse triage program
The team of eight nurses will handle non-emergency calls right from the county's 911 dispatch.
With the recent implementation of the nurse triage program inside the Volusia County Sheriff's Office Communication Center, the county is hopeful that its EMS service and ambulance availability will improve as the program tackles non-emergency calls.
The program began on Dec. 9, and is active from 7 a.m. to 7 p.m. Monday-Friday. A total of eight nurses — one full-time lead nurse and seven part-timers — make up the triage, all of whom went through 280 hours of training over 14 weeks with VCSO Communications Center staff to learn how to handle and dispatch 911 calls. The program was part of the recommendations the Volusia County Council approved back in February to improve EMS.
"Ultimately, our goal with this program is to integrate mobile healthcare to the community," Volusia County EMS Director Jason Brady said.
The program, which has cost $284,000, has the team of nurses handling calls involving flu symptoms, earaches, localized infections and others. Some of the recent calls handled by nurses include a resident complaining of pink eye and another who got a fish hook stuck on his hand.
"Before we went live, those calls would get an ambulance and even a firetruck dispatched to them with no pre-screening, or very little pre-screening," said Pam Cawood, lead EMS triage nurse.
So far, her team is averaging about 13 calls a day. The nurses ask a set of questions and gauge what care is needed, the severity of the injury or health concern, and from there they recommend where the patient should seek care. Options include an urgent care center, a follow-up with a primary care physician, or self-care instructions.
Finding the appropriate treatment facility
Two things had to fall into place for the program to come to fruition: support from the local hospitals, and a consolidated 911 dispatch. It has achieved both. The part-time nurses are from AdventHealth, Halifax Health and HCA Healthcare.
VCSO Communications Director Glenn said without a consolidated dispatch, a program like this would have been much harder to incorporate.
"With us, it was pretty much a no-brainer to do this," Lopez said. "It makes logical sense."
EMS Clinical Services Manager Michael Vincent, Medical Director Peter Springer, EMS Director Jason Brady, Lead Triage Nurse Pam Cawood, Debbie Rego and Glenn Lopez of VCSO Communications,PIO Kevin Captain.Photo by Jarleene Almenas
Interim County Community Information Director Kevin Captain said an ambulance today is an "emergency department on wheels." If that unit is busy responding to a non-emergency call, that could hinder the ability to respond to a time-sensitive emergency.
"If you're having a heart attack, you want to make sure that ambulance is available — not being used by a low acuity injury, taking a patient to the hospital that could otherwise have gone to an urgent care," Captain said.
However, if a resident truly wants an ambulance, they'll still get it. County Medical Director Peter Springer said they're not discounting anyone's injury. In a year, ambulances respond to between 60,000 to 80,000 calls. Out of that number, Springer said around 10,000 to 20,000 don't necessitate a hospital transfer.
"We want the public to know we're not abandoning them," Springer said. "We're actually just trying to find the appropriate places for them to be treated. It's not always an emergency department."
'A work in progress'
Volusia County is the first to implement a nurse triage program withs its 911 dispatch in the state, though not the first in the country. Cities like Las Vegas and Washington D.C. have programs in place.
Springer said this was a "work in progress," as the county will continue tweaking the program as they begin to gather data on high peak times and areas. In the future, the county will like to build on its paramedicine by introducing in-home patient care.
For now, Michael Vincent, EMS clinical services manager said they're looking to train paramedics for critical care. The first class will happen around March.
"There's really no limit to what we can do," Brady said. "We're really pioneering this to be a highly innovative EMS system."
Calendar: See a show at the Performing Arts Center
A Year in Review: What 2019 was like in Ormond Beach.


text:
Final-10 2 3 4 5 6 7 8 9 10 R H E
NY Yankees 0 0 0 1 2 0 1 0 1 5 9 0
W: C. Holmes (7-4) L: K. Ort (1-2)
4:15 PM PT5:15 PM MT6:15 PM CT7:15 PM ET19:15 ET23:15 GMT7:15 4:15 PM MST6:15 PM EST6:45 PM VEN3:15 UAE (+1)5:15 PM CT, September 22, 2022
Yankee Stadium, New York City, New York Attendance: 43,123
Judge falls just short of 61, Yanks clinch playoff berth
MLB Headlines
Braves extend Snitker through 2025
P Fairbanks, Rays agree to 3 years, $12M
Mauer to go into Twins Hall of Fame
A's, 1B Aguilar agree to 1 year, $3M
Rangers give deals to Kennedy, Frazier
Astros hire Braves VP Brown as GM
Athletics trade LHP Irvin to Orioles
A's running out of time to find new home
RHP Voth, Orioles avoid arbitration
Detroit pitcher Herbert dies at 93
(AP Photo/Frank Franklin II)
By RONALD BLUM
AP Baseball Writer
NEW YORK (AP) Aaron Judge turned around a fastball in the ninth inning, and the already-standing crowd of 43,123 at Yankee Stadium erupted in noise, wondering, hoping he had hit a record-tying 61st home run and clinched a playoff berth with one mighty swing.
Seconds later, groans arose in unison. Kike Hernandez made the catch a step in front of the fence, 404 feet from the plate.
Judge remained one shy of the American League record set by New York slugger Roger Maris in 1961. But the Yankees did accomplish goal No. 1: Josh Donaldson's 10th-innng RBI single gave them a 5-4 win over the Boston Red Sox and clinched their sixth straight playoff berth and 24th in 28 years.
"A great accomplishment. A lot of hard work over the course of the season to get to this point," said Judge, who also threw out a runner at second base to help hold off Boston in the ninth. "But I think you could ask anybody in this room: The job's not finished. We have an ultimate goal of going out there and winning our division, and setting ourselves up for the postseason. And this is step No. 1, step No. 1 of many steps to come."
Coming within a few feet of his fourth walk-off home run this year, Judge went without a long ball for the second straight game since hitting No. 60 to match Babe Ruth's 1927 season.
Judge has 13 games remaining. He walked three times and struck out once before his drive off Matt Barnes just to the right of straightaway center.
"I thought it would have been pretty showy to drop it at Monument Park out there," Yankees manager Aaron Boone said.
Judge didn't think it was gone, not on the first night of autumn with the game-time temperature a crisp 68 degrees.
"I just got underneath it a little bit," he said. "A pretty windy night, so I was hoping maybe it was blowing out at the time I was hitting, but just missed it."
The ball left the bat at 113 mph, the hardest-hit by anyone in the game.
"I go out there and get caught up in the history of what he could potentially be doing and I start tiptoeing around the at-bat, I'm probably going to hang something," Barnes said.
Before each of the 26 pitches to Judge, Yankee Stadium's usual cacophony was replaced by a silence more familiar at a church or golf course.
"Literally, you could hear a pin drop," Donaldson said. "It's kind of spooky, really."
Jameson Taillon, who pitched six scoreless innings, said Judge's pursuit altered teammates' routines.
"There's guys that are inside and stuff watching the game," Taillon said, "and whenever he comes up, everyone runs outside to watch the at-bat because no one wants to miss it."
Baseball Commissioner Rob Manfred was on hand. Judge's fame has extended beyond the sport - he appears on the cover of the Oct. 3 issue of The New Yorker.
After an 0-for-2 night, Judge remained on track for an AL Triple Crown with a .316 batting average and 128 RBIs. Boston's Xander Bogaerts is second in batting at .314 after going hitless in five at-bats.
With the score 4-all, Tommy Pham led off the ninth with a one-hopper off the right-field wall against Clay Holmes (7-4). Judge played the carom and from the warning track threw a 92.3 mph strike to shortstop Isiah Kiner-Falefa for the tag. It was Judge's sixth assist of the season.
Then in the 10th, with pinch-runner Marwin Gonzalez at second as the automatic runner, Kaleb Ort (0-2) intentionally walked Gleyber Torres before Donaldson grounded a single just past diving third baseman Rafael Devers and into left.
New York grounded into four double plays but had built a 3-0 lead on Kyle Higashioka's sacrifice fly and Giancarlo Stanton's 28th homer, a two-run shot against Michael Wacha.
Triston Casas hit a solo homer and pinch-hitter Reese McGuire delivered a three-run drive for Boston in a four-run seventh off Clarke Schmidt. Harrison Bader had a tying sacrifice fly in the eighth after Stanton's leadoff infield single and a stolen base by Tim Locastro - he was called out initially but a video review reversed the decision.
Donaldson gave a speech to teammates after the Yankees secured their record 58th postseason appearance with their major league-high 16th walk-off win of the season, one shy of the team standard set in 1947.
"Welcome back to the playoffs," he told them.
New York has a 7 1/2-game lead in the AL East over second-place Toronto, on track for a first-round bye. Boone became the first manager to reach the postseason in each of his first five seasons, according to the Elias Sports Bureau.
"We're in the dance and we got a chance now," he said.
CHOPPY CHAPMAN
Aroldis Chapman had another shaky outing for the Yankees in relief, walking two batters with one out in the eighth - the second on four pitchers.
TRAINER'S ROOM
Red Sox: 2B Trevor Story (bruised left heel) went back on the 10-day injured list, retroactive to Monday, and 1B/3B Bobby Dalbec was recalled from Triple-A Worcester.
Yankees: LHP Zack Britton (Tommy John surgery) was activated from the 60-day IL and LHP Wandy Peralta (back) was put on the 15-day IL, retroactive to Monday.
New York ace Gerrit Cole (12-7, 3.41) starts against Boston LHP Rich Hill (7-7, 4.70) on Friday night.
More AP MLB: https://apnews.com/MLB and https://twitter.com/AP-Sports


Get excited about the 2016 Volvo XC90. With just over 30,000 miles on the odometer, this 4 door sport utility vehicle prioritizes comfort, safety and convenience. Under the hood you'll find a 4 cylinder engine with more than 200 horsepower, and for added security, dynamic Stability Control supplements the drivetrain. The engine breathes better thanks to a turbocharger, improving both performance and economy. All of the premium features expected of a Volvo are offered, including: front and rear reading lights, a power seat, fully automatic headlights, telescoping steering wheel, lane departure warning, cruise control, and more. For drivers who enjoy the natural environment, a power moon roof allows an infusion of fresh air. Volvo also prioritized safety and security by including: head curtain airbags, front side impact airbags, traction control, brake assist, anti-whiplash front head restraints, a security system, an emergency communication system, and 4 wheel disc brakes with ABS. You'll never lose visibility with rain sensing wipers, which activate automatically when the drops start to fall. This vehicle has achieved Certified Pre-Owned status, by passing Volvo's comprehensive certification process, including a comprehensive 130-point inspection! Our knowledgeable sales staff is available to answer any questions that you might have. They'll work with you to find the right vehicle at a price you can afford. Stop in and take a test drive!


The Department of Music curriculum is based on a synthesis of music scholarship, performance, and composition. A distinguished faculty serve undergraduate and graduate students. Many of our graduates successfully continue their education in the best graduate schools and move into a variety of related careers. Department resources include a diverse collection of instruments and facilities, such as an extensive music library and an electronic music studio.
Undergraduate majors can pursue a Bachelor of Arts degree in Music, focusing their studies in either performance, composition or history, theory and ethnomusicology. In the first two years of study, all students take the same lower-division course work, which should include one or more years of applied music study; courses typically include music history and ensemble performance as well. In their third and fourth years, students choose upper-division courses along one of the three specialty tracks, concluding their studies with a required senior project. All tracks include music history, applied music study, and ensemble performance. Students may declare as a music major without auditioning.
Music offers graduate programs leading to the Ph.D. and M.A. degrees. Candidates for a Doctor of Philosophy degree can specialize in the fields of composition and theory, musicology, or ethnomusicology. The department also offers a Master of Arts degree in conducting.
Our students speak about their experiences here.