text:
\section{Introduction}

A prevalent and useful version of unsupervised learning arises when
both the observed data and the latent variables are structured.
Examples range from hidden alignment variables in speech recognition
\cite{rabiner89tutorial} and machine translation
\cite{brownetal93,vogel96}, to latent trees in unsupervised parsing
\cite{paskin01bigrams,klein04induction,smith05induction,titov07latent},
and to pose estimation in computer vision \cite{ramanan05pose}.  These
techniques are all based on probabilistic models.  Their applicability
hinges on the tractability of (approximately) computing latent
variable expectations, thus enabling the use of EM
\cite{dempster77em}.  In this paper we show that a recently-developed
\emph{search-based} algorithm, \textsc{Searn}\ \cite{daume09searn} (see
Section~\ref{sec:searn}), can be utilized for unsupervised structured
prediction (Section~\ref{sec:unsearn}).  We show: (1) that under an
appropriate construction, \textsc{Searn}\ can imitate the expectation
maximization (Section~\ref{sec:em}); (2) that unsupervised \textsc{Searn}\ can
be used to obtain competitive performance on an unsupervised
dependency parsing task (Section~\ref{sec:gi}); and (3) that
unsupervised \textsc{Searn}\ naturally extends to a semi-supervised setting
(Section~\ref{sec:semi}).  The key insight that enables this work is
that we can consider the prediction of the (observed) input to be,
itself, a structured prediction problem.

\section{Structured Prediction} \label{sec:sp}

The \emph{supervised} structured prediction problem is the task of
mapping inputs $x$ to complex structured outputs $y$ (e.g., sequences,
trees, etc.).  Formally, let $\cX$ be an arbitrary input space and
$\cY$ be structure output space.  $\cY$ is typically assumed to
\emph{decompose} over some smaller substructures (e.g., labels in a
sequence).  $\cY$ comes equipped with a loss function, often assumed
to take the form of a Hamming loss over the substructures.  Features
are defined over pairs $(x,y)$ in such a way that they obey the
substructures (e.g., one might have features over adjacent label pairs
in a sequence).  Under strong assumptions on the structures, the loss
function and the features (essentially ``locality'' assumptions), a
number of learning algorithms can be employed: for example,
conditional random fields \cite{lafferty01crf} or max-margin Markov
networks \cite{taskar05mmmn}.

A key difficulty in structured prediction occurs when the output space
$\cY$, the features, or the loss, does not decompose nicely.  All of
these issues can lead to intractable computations at either training
or prediction time (often both).  An attractive approach for dealing
with this intractability is to employ a search-based algorithm.  The
key idea in search-based structured prediction is to first decompose
the output $y$ into a sequence of (dependent) smaller predictions
$y_1, \dots, y_T$.  These may each be predicted in turn, with later
predictions dependent of previous decisions.

\subsection{Search-based Structured Prediction} \label{sec:sbsp}

A recently proposed algorithm for solving the structured prediction
problem is \textsc{Searn}\ \cite{daume09searn}.  \textsc{Searn}\ operates by
considering each substructure prediction $y_1, \dots, y_T$ as a
classification problem.  A classifier $h$ is trained so that at time
$t$, given a feature vector, it predict the best value for $y_t$.  The
feature vector can be based on any part of the input $x$ and any
previous decision $y_1, \dots, y_{t-1}$.  This introduces a
chicken-and-egg problem.  $h$ should ideally be trained so that it
makes the best decision for $y_t$ \emph{given} that $h$ makes all past
decisions $y_1, \dots, y_{t-1}$ and all future decisions $y_{t+1},
\dots, y_T$.  Of course, at training time we do not have access to $h$
(we are trying to construct it).  The solution is to use an iterative
scheme.

\subsection{\textsc{Searn}} \label{sec:searn}

The presentation we give here differs slightly from the original
presentation of the \textsc{Searn}\ algorithm.  Our motivation for straying
from the original formulation is because our presentation makes more
clear the connection between our unsupervised variant of \textsc{Searn}\ and
more standard unsupervised learning methods (such as standard
algorithms on hidden Markov models).

Let $\cD^{\text{SP}}$ denote a distribution over pairs $(x,y)$ drawn
from $\cX \times \cY$, and let $\ell(y,\hat y)$ be the loss associated
with predicting $\hat y$ when the true answer is $y$.  We assume that
$y \in \cY$ can be decomposed into atomic predictions $y_1, \dots,
y_T$, where each $y_t$ is drawn from a discrete set $Y$.  A
\emph{policy}, $\pi$, is a (possibly stochastic) function that maps
tuples $(x, y_1, \dots, y_{t-1})$ to atomic predictions $y_t$.

The key ingredient in \textsc{Searn}\ is to use the loss function $\ell$ and a
``current'' policy $\pi$ to turn $\cD^{\text{SP}}$ into a distribution
over cost-sensitive (multiclass) classification problems
\cite{beygelzimer05reductions}.  A cost-sensitive classification
example is given by an input $x$ and a cost vector $\vec c = \langle
c_1, \dots, c_K \rangle$, where $c_k$ is the cost of predicting class
$k$ on input $x$.  Define by \textsc{Searn}$(\cD^{\text{SP}}, \ell, \pi)$ a
distribution over cost-sensitive classification problems derived as
follows.  To sample from this induced distribution, we first sample an
example $(x,y) \sim \cD^{\text{SP}}$.  We then sample $t$ uniformly
from $[1,T]$ and run $\pi$ for $t-1$ steps on $(x,y)$.  This yields a
partial prediction $(\hat y_1, \dots, \hat y_{t-1})$.  The input for
the cost sensitive classification problem is then the tuple $(x,\hat
y_1, \dots, \hat y_{t-1})$.  The costs are derived as follows.  For
each possible choice $k$ of $\hat y_t$, we defined $c_k$ as the
\emph{expected} loss if $\pi$ were run, beginning at $(\hat y_1,
\dots, \hat y_{t-1}, k)$ on input $x$.  Formally:
\begin{equation}
c_k = \Ep_{\hat
  y_{t+1}, \dots, \hat y_T \sim \pi} \ell(y, (\hat y_1, \dots, \hat
y_{t-1}, k, \hat y_{t+1}, \dots, \hat y_T))
\end{equation}
\textsc{Searn}\ assumes access to an ``initial policy'' $\pi^*$ (sometimes
called the ``optimal policy'').  Given an input $x$, a true output $y$
and a prefix of predictions $\hat y_1, \dots, \hat y_{t-1}$, $\pi^*$
produces a best next-action, $\hat y_t$.  It should be constructed so
that the choice $\hat y_t$ is optimal (or close to optimal) with
respect to the problem-specific loss function.  For example, if the
loss function is Hamming loss, the $\pi^*$ will always produce $\hat
y_t = y_t$.  For more complex loss functions, computing $\pi^*$ may be
more involved.

\begin{figure}[t]
\framebox{
\begin{minipage}[t]{7.7cm}
~{\bf Algorithm} \textsc{Searn}-Learn$(\cA, \cD^{\text{SP}}, \ell, \pi^*, \be)$

\begin{algorithmic}[1]
\STATE Initialize $\pi = \pi^*$
\WHILE{not converged}
\STATE Sample: $D \sim $\textsc{Searn}$(\cD^{\text{SP}}, \ell, \pi)$
\STATE Learn:  $h \leftarrow \cA(D)$
\STATE Update: $\pi \leftarrow (1-\be) \pi + \be h$
\ENDWHILE
\STATE Return $\pi$ without reference to $\pi^*$
\end{algorithmic}
\end{minipage}
}
\caption{The complete \textsc{Searn}\ algorithm.  It's parameters are: a cost-sensitive classification algorithm $\cA$, a distribution over structured problems $\cD^{\text{SP}}$, a loss function $\ell$, an initial policy $\pi^*$ and an interpolation parameter $\be$.}
\label{fig:searn}
\end{figure}

Given these ingredients, \textsc{Searn}\ operates according the algorithm
given in Figure~\ref{fig:searn}.  Operationally, the sampling step is
typically implemented by generating \emph{every} example from a fixed
structured prediction training set.  The costs (expected losses) are
computed by sampling with tied randomness \cite{ng00pegasus}.

If $\be = 1/T^3$, one can show \cite{daume09searn} that after at most
$2T^3 \ln T$ iterations, \textsc{Searn}\ is guaranteed to find a
solution $\pi$ with structured prediction loss bounded as:
\begin{equation} \label{eq:searn}
L(\pi) \leq L(\pi^*) + 2 \ell_\textrm{avg} T \ln T + c (1+\ln T)/T
\end{equation}
where $L(\pi^*)$ is the loss of the initial policy (typically zero),
$T$ is the length of the longest example, $c$ is the worse-case
per-step loss and $\ell_\textrm{avg}$ is the average multiclass
classification loss.  This shows that the structured prediction
algorithm learned by \textsc{Searn}\ is guaranteed to be not-much-worse than
that produced by the initial policy, \emph{provided} that the created
classification problems are easy (i.e., that $\ell_\textrm{avg}$ is
small).  Note that one can use \emph{any} classification algorithm one
likes.

\section{Unsupervised \textsc{Searn}} \label{sec:unsearn}

In unsupervised structured prediction, we no longer receive an pair
$(x,y)$ but instead observes only an input $x$.  Our job is to
construct a classifier that produces $y$, even though we have never
observed it.

\subsection{Reduction for Unsupervised to Supervised}

The key idea---one that underlies much work in unsupervised
learning---is that a good $y$ is one that enables us to easily recover
$x$.  This is precisely the intuition we build in to our model.  The
observation that makes this practical is that there is nothing in the
theory or application of \textsc{Searn}\ that says that $\pi^*$ cannot be
stochastic.  Moreover, there is not requirement that the loss function
depend on \emph{all} components of the prediction.  Our model will
essentially first predict $y$ and then predict $x$ based on $y$.
Importantly, the loss function is agnostic to $y$ (since we do not
have true outputs).

The general construction is as follows. Let $\cD^{\text{unsup}}$ be a
distribution over inputs $x \in \cX$ and let $\cY$ be the space of
desired latent structures (e.g., trees). We define a distribution
$\cD^{\text{sup}}$ over $\cX \times (\cY \times \cX)$ by defining a
sampling procedure.  To sample from $\cD^{\text{sup}}$, we first
sample $x \sim \cD^{\text{unsup}}$.  We then sample uniformly from the
set of all $\cY$ that are valid structures for $x$.  Finally, we
return the pair $(x, (y, x))$.  We define a loss function $L$ by
$L((y, x), (\hat y, \hat x)) = L^{\text{input}}(x, \hat x)$ where
$L^{\text{input}}$ is any loss function on the input space (e.g.,
Hamming loss). We apply \textsc{Searn}\ to the supervised structured
prediction problem $\cD^{\text{sup}}$, and implicitly learn latent
structures.

\subsection{Sequence Labeling Example}

To gain insight into the operation of \textsc{Searn}\ in the unsupervised
setting, it is useful to consider a sequence labeling example.  That
is, our input $x$ is a sequence of length $T$ and we desire a label
sequence $y$ of length $T$ drawn from a label space of size $K$.  We
convert this into a supervised learning problem by considering the
``true'' structured output to be a label sequence of length $2T$, with
the first $T$ components drawn from the label space of size $K$ and
the second $T$ components drawn from the input vocabulary.  The loss
function can then be anything that depends only on the last $T$
components.  For simplicity, we can consider it to be Hamming loss.
The construction of the optimal policy in this case is
straightforward.  For the first $T$ components, $\pi^*$ may behave
arbitrarily (e.g., it may produce a uniform distribution over the $K$
labels).  For the second $T$ components, $\pi^*$ always predicts the
true label (which is known, because it is part of the input).

An important aspect of the model is the construction of the feature
vectors.  It is most useful to consider this construction as having
two parts.  The first part has to do with predicting the hidden
structure (the first $T$ components).  The second part has to do with
predicting the observed structure (the second $T$ components).  For
the first part, we are free to use whatever features we desire, so
long as they can be computed based on the input $x$ and a partial
output.  For instance, in the HMM case, we could use the two most
recent label predictions and windowed features from $x$.

The construction of the features for the second part is, however, also
crucial.  For instance, if the feature vector corresponding to
``predict the $t$th component of $x$'' contains the $t$ component of
$x$, then this learning problem is trivial---but also renders the
latent structure useless.  The goal of the designer of the feature
space is to construct features for predicting $x_t$ that crucially
depend on getting the latent structure $y$ correct.  That is, the
ideal feature set is one for which you can predict $x_t$ accurately
\emph{if an only if} we have found the correct latent structure (more
on this in Section~\ref{sec:guarantees}).  For instance, in the HMM
case, we may predict $x_t$ based only on the corresponding label
$y_t$, or maybe on the basis of $y_{t-1},y_t,y_{t+1}$.  (Note that we
are not limited to the Markov assumption, as in the case of HMMs.)

In the first iteration of \textsc{Searn}, all costs for the prediction of the
latent structure are computed with respect to the initial policy.
Recalling that the initial policy behaves randomly when predicting the
latent labels and correctly when predicting the words, we can see that
these costs are all \emph{zero}.  Thus, for the latent structure
actions, \textsc{Searn}\ will not induce any classification examples (because
the cost of all actions is equal).  However, it will create example
for predicting the $x$ component.  For predicting the $x$s, the cost
will be zero for the correct word and one for any incorrect word.
These examples will have associated features: we will predict word
$x_t$ based \emph{exclusively} on $y_t$.  Remember: $y_t$ was
generated randomly by the initial policy.

In the \emph{second} iteration, the behavior is different.  \textsc{Searn}\
returns to creating examples for the latent structure components.
However, in this iteration, since the current policy is not longer
optimal, the future cost estimates may be non-zero.  Consider
generating an example corresponding to a (latent) state $y_t$.  For
some small percentage (as dictated by $\beta$) of the ``generate $x$''
decisions, the previously learned classifier will fire.  If this
learned classifier does well, then the associated cost will be low.
However, if the learned classifier does poorly, the the associated
cost will be high.  Intuitively, the learned classifier will do well
if and only if the action that labels $y_t$ is ``good'' (i.e.,
consistent with what was learned previously).  This, in the second
pass through the data, \textsc{Searn}\ \emph{does} create classification
examples specific to the latent decisions.

As \textsc{Searn}\ iterates, more and more of the latent prediction decisions
are made according to the learned classifiers and not with respect to
the random policy.

\section{Comparison to EM} \label{sec:em}

In this section, we show an equivalence between expectation
maximization in directed probabilistic structures and unsupervised
\textsc{Searn}.  We use mixture of multinomials as a motivating example
(primarily for simplicity), but the results easily extend to more
complicated models (e.g., HMMs: see Section~\ref{sec:hmm}).

\subsection{EM for Mixture of Multinomials} \label{sec:em-mm}

In the mixture of multinomials problem, we are given $N$ documents
$\vec d_1, \dots, \vec d_N$, where $\vec d_n$ is a vector of word
counts over a vocabulary of size $V$; that is, $d_{n,v}$ is the number
of times word $v$ appeared in document $n$.  The mixture of
multinomials is a probabilistic clustering model, where we assume an
underlying set of $K$ clusters (multinomials) that generated the
documents.  Denote by $\th_k$ the multinomial parameter associated
with cluster $k$, $\rho_k$ the prior probability of choosing cluster
$k$, and let $\vec z_n$ be an indicator vector associating document
$n$ with the unique cluster $k$ such that $z_{n,k} = 1$.  The
probabilistic model has the form:
\begin{equation} \label{eq:mmlik}
  p(\vec d \| \vec \th, \vec \rho) =
    \prod_n
      \frac {(\sum_v d_{n,v})!}
            {\prod_v d_{n,v}!}
        \sum_{\vec z_n} \prod_k \left[
          \rho_k
          \prod_v \th_{k,v}^{d_{n,v}}
          \right]^{z_{n,k}}
\end{equation}
Expectation maximization in this model involves first computing
expectations over the $\vec z$ vectors and then updating the model
parameters $\vec \th$:
\begin{small}
\begin{align}
\text{E-step:} &&
z_{n,k} &\varpropto \rho_k \prod_v \th_{k,v}^{d_{n,v}} \label{eq:em:z} \\
\text{M-step:} &&
\th_{k,v} &\varpropto \sum_n z_{n,k} d_{n,v}
&
;~~~ \rho_k &\varpropto \sum_n z_{n,k} \label{eq:em:rho}
\end{align}
\end{small}
In both cases, the constant of proportionality is chosen so that the
variables sum to one over the last component.  These updates are
repeated until convergence of the incomplete data likelihood,
Eq~\eqref{eq:mmlik}.

\subsection{An Equivalent Model in Searn} \label{sec:em-searn}

Now, we show how to construct an instance of unsupervised \textsc{Searn}\ that
effectively mimics the behavior of EM on the mixture of multinomials
problem.  The ingredients are as follows:
\begin{small}
\begin{itemize}
\item The input space $\cX$ is the space of documents, represented as word count vectors.
\item The (latent) output space $\cY$ is a single discrete variable in the range $[1,K]$ that specifies the cluster.
\item The feature set for predicting $y$ (document counts).
\item The feature set for predicting $x$ is the label $y$ and the total number of words in the document.  The predictions for a document are estimated word probabilities, not the words themselves.
\item The loss function ignores the prediction $y$ and returns the log loss of the true document $x$ under the word probabilities predicted.
\item The cost-sensitive learning algorithm is different depending on whether the latent structure $y$ is being predicted or if the document $x$ is being predicted:
\begin{itemize}
\item Structure: The base classifier is a multinomial na\"ive Bayes
  classifier, parameterized by (say) $h^m$
\item Document:  The base classifier is a collection of independent
  maximum likelihood multinomial estimators for each cluster.
\end{itemize}
\end{itemize}
\end{small}
Consider the behavior of this setup.  In particular, consider the
distribution \textsc{Searn}$(\cD^{\text{SP}}, \ell, \pi)$.  There are two
``types'' of examples drawn from this distribution: (1) latent
structure examples and (2) document examples.  The claim is that
\emph{both} classifiers learned are identical to the mixture of
multinomials model from Section~\ref{sec:em-mm}.

Consider the generation of a latent structure example.  First, a
document $n$ is sampled uniformly from the training set.  Then, for
each possible label $k$ of this document, a cost $\Ep_{\vec {\hat d}
  \sim \pi} l((y,\vec d_n), (k,\vec {\hat d}))$ is computed.  By
definition, the $\vec {\hat d}$ that is computed is exactly the
prediction according to the current multinomial estimator, $h^m$.
Interpreting the multinomial estimator in terms of the EM parameters,
the costs are \emph{precisely} the $z_{n,k}$s from EM (see
Eq~\eqref{eq:em:z}).  These latent structure examples are fed in to
the multinomial na\"ive Bayes classifier, which re-estimates a model
exactly as per the M-step in EM (Eq~\eqref{eq:em:rho}).

Next, consider the generation of the document examples.  These
examples are generated by $\pi$ first choosing a cluster according to
the structure classifier.  This cluster id is then used as the (only)
feature to the ``generate document'' multinomial.  As we saw before,
the probability that $\pi$ will select label $k$ for document $n$ is
precisely $z_{n,k}$ from Eq~\eqref{eq:em:z}.  Thus, the multinomial
estimator will effectively receive weighted examples, weighted by
these $z_{n,k}$s, thus making the maximum likelihood estimate exactly
the same as the M-step from EM (Eq~\eqref{eq:em:rho}).

\subsection{Synthetic experiments} \label{sec:hmm}

To demonstrate the advantages of the generality of \textsc{Searn}, we report
here the result of some experiments on synthetic data.  We generate
synthetic data according to two different HMMs.  The first HMM is a
first-order model.  The initial state probabilities, the transition
probabilities, and the observation probabilities are all drawn
uniformly.  The second HMM is a second-order model, also will all
probabilities drawn uniformly. The lengths of observations are given
by a Poisson with a fixed mean.

\begin{table*}[t]
\centering
\caption{Error rates on first- and second-order Markov data with 2, 5 or 10 latent states. Models
are the true data generating distribution (approximated by a first-order Markov model in the case of
âHMM2â), a model learned by EM, one learned by \textsc{Searn}\  with a na\"ive Bayes base classifier, and
one learned by \textsc{Searn}\  with a logistic regression base classifier. Standard deviations are given in
small text. The best results by row are bolded; the results within the standard deviation of the best
results are italicized.}
\begin{tabular}{|l|l|c|c|cc|}
\hline
Model &States &Truth &EM &\textsc{Searn}\ -NB &\textsc{Searn}\ -LR \\
\hline
1st order HMM  &K = 2  &0.227 {\tiny $\pm 0.107$} &\it 0.275 {\tiny $\pm 0.128$} &\bf 0.287 {\tiny $\pm 0.138$} &\it 0.276 {\tiny $\pm 0.095$} \\
1st order HMM  &K = 5  &0.687 {\tiny $\pm 0.043$} &\it 0.678 {\tiny $\pm 0.026$} &\it 0.688 {\tiny $\pm 0.025$} &\bf 0.672 {\tiny $\pm 0.022$} \\
1st order HMM  &K = 10 &0.806 {\tiny $\pm 0.035$} &\it 0.762 {\tiny $\pm 0.021$} &\it 0.771 {\tiny $\pm 0.019$} &\bf 0.755 {\tiny $\pm 0.019$} \\
\hline
2nd order HMM  &K = 2  &0.294 {\tiny $\pm 0.072$} &0.396 {\tiny $\pm 0.057$} &0.408 {\tiny $\pm 0.056$} &\bf 0.271 {\tiny $\pm 0.057$} \\
2nd order HMM  &K = 5  &0.651 {\tiny $\pm 0.068$} &0.695 {\tiny $\pm 0.027$} &0.710 {\tiny $\pm 0.016$} &\bf 0.633 {\tiny $\pm 0.018$} \\
2nd order HMM  &K = 10 &0.815 {\tiny $\pm 0.032$} &0.764 {\tiny $\pm 0.021$} &0.771 {\tiny $\pm 0.015$} &\bf 0.705 {\tiny $\pm 0.019$} \\
\hline
\end{tabular}
\label{tab:hmm}
\end{table*}

In our experiments, we consider the following learning algorithms: EM,
\textsc{Searn}\ with HMM features and a na\"ive Bayes classifier, and \textsc{Searn}\
with a logistic regression classifier (and an enhanced feature space:
predicting $y_t$ depends on $x_{t-1:t+1}$. The first \textsc{Searn}\ should
mimic EM, but by using sampling rather than exact expectation
computations.  The models are all first-order, regardless of the
underlying process.

We run the following experiment.  For a given number of states (which
we will vary), we generate $10$ random data sets according to each
model.  Each data set consists of $5$ examples with mean example
length of $40$ observations. The vocabulary size of the observed data
is always $10$. We compute error rates by matching each predicted
label to the best-matching true label and the compute Hamming loss.
Forward-backward is initialized randomly.  We run experiments with the
number of latent states equal to $2$, $5$ and $10$.\footnote{We ran
  experiments varying the number of samples \textsc{Searn}\ uses in $\{1, 2,
  5\}$; there was no statistically significant difference. The
  results we report are based on $2$ samples.}

The results of the experiments are shown in Table~\ref{tab:hmm}.  The
observations show two things.  When the true model matches the model
we attempt to learn (HMM1), there is essentially no statistically
significant difference between any of the algorithms. Where once sees
a difference is when the true model does not match the learned model
(HMM2). In this case, we see that \textsc{Searn}-LR obtains a significant
advantage over both EM and \textsc{Searn}-NB, due to its ability to employ a
richer set of features. These results hold over all values of $K$.
This is encouraging, since in the real world our model is rarely (if
ever) right.  The (not statistically significant) difference in error
rates between EM and \textsc{Searn}-NB are due to a sampling versus exact
computation of expectations.  Many of the models outperform ``truth''
because likelihood and accuracy do not necessarily correlate
\cite{liang08errors}.

\section{Analysis} \label{sec:guarantees}

There are two keys to success in unsupervised-\textsc{Searn}. The first key is
that the features on the $\cY$-component of the output space be
descriptive enough that it be learnable.  One way of thinking of this
constraint is that if we had labeled data, then we would be able to
learn well.  The second key is that the features on the
$\cX$-component of the output space be intrinsically tied to the hidden
component.  Ideally, these features will be such that $\cX$ can be
predicted with high accuracy if and only if $\cY$ is predicted
accurately.

The general--though very trivial--result is that if we can guarantee
that the loss on $\cY$ is bounded by some function $f$ of the loss on
$\cX$, then the loss on $\cY$ is guaranteed after learning to be
bounded by $f(L(\pi^*) + 2\ell_{\text{avg}}T_{\text{max}} ln
T_{\text{max}} + c(1 + ln T_{\text{max}})/T_{\text{max}})$, where all
the constants now depend on the induced structured prediction problem;
see Eq~\ref{eq:searn}.

One can see the unsupervised \textsc{Searn}\ analysis as justifying a small
variant on ``Viterbi training''--the process of performing EM where
the E-step is approximated with a delta function centered at the
maximum.  One significant issue with Viterbi training is that it is
not guaranteed to converge.  However, Viterbi training is recovered as
a special case of unsupervised \textsc{Searn}\ where the interpolation
parameter is fixed at $1$.  While the \textsc{Searn}\ theorem no longer
applies in this degenerate case, any algorithm that uses Viterbi
training could easily be retrofitted to simply make some decisions
randomly.  In doing so, one would obtain an algorithm that does have
theoretical guarantees.

\section{Unsupervised Dependency Parsing} \label{sec:gi}

The dependency formalism is a practical and linguistically interesting
model of syntactic structure. One can think of a dependency structure
for a sentence of length $T$ as a directed tree over a graph over $T +
1$ nodes: one node for each word plus a unique root node. Edges point
from heads to dependents. An example dependency structure for a $T =
7$ word sentence is shown in Figure~\ref{fig:deptree} . To date,
unsupervised dependency parsing has only been viewed in the context of
global probabilistic models specified over dependency pairs
\cite{paskin01bigrams} or spanning trees
\cite{klein04induction,smith05induction}.  However, there is an
alternative, popular method for producing dependency trees in a
supervised setting: shift-reduce parsing
\cite{nivre03parsing,sagae05shiftreduce}.

\begin{figure}
\centering
\includegraphics[width=0.45\textwidth]{deptree.pdf}
\caption{Dependency parse of a $T=7$ word sentence.}
\label{fig:deptree}
\end{figure}

\subsection{Shift-reduce dependency parsing}

Shift-reduce dependency parsing \cite{nivre03parsing} is a
left-to-right parsing algorithm that operates by maintaining three
state variables: a stack $S$, a current position $i$ and a set of arcs
$A$. The algorithm begins with $\langle S, i,A\rangle = \langle
\emptyset, 1, \emptyset \rangle$: the stack and arcset are empty and
the current index is $1$ (the first word).  The algorithm then
proceeds through a series of actions until a final state is reached. A
final state is one in which $i = T$, at which point the set $A$
contains all dependency edges for the parse.  Denote by $i|I$ a stack
with $i$ at the head and stack $I$ at the tail.  There are four
actions:

\vspace{-2mm}
\begin{small}
\begin{description}
\item[LeftArc:] $\langle t|S, i, A \rangle \longrightarrow \langle S,
  i, (i, t)|A \rangle$, so long as there does not exist an arc
  $(\cdot·, t) \in A$.  (Adds a left dependency to the arc set between
  the word $t$ at the top of the stack and the word $i$ at the current
  index.)

\item[RightArc:] $\langle t|S, i, A \rangle \longrightarrow \langle
  i|t|s, i + 1, (t, i)|A\rangle$, so long as there is no arc $(\cdot·,
  i) \in A$.  (Adds a right dependency between the top of the stack
  and the next input.)

\item[Reduce:] $\langle t|S, i,A \rangle \longrightarrow \langle S,
  i,A \rangle$, so long as there does exist an arc $(\cdot·, t) \in
  A$. (Removes a word from the stack.)

\item[Shift:] $\langle S, i, A\rangle \longrightarrow \langle n|S, i +
  1,A \rangle$. (Place item on stack.)
\end{description}
\end{small}
\vspace{-2mm}

This algorithm is guaranteed to terminate in at most $2T$ steps with a
valid dependency tree \cite{nivre03parsing}, unlike standard
probabilistic algorithms that have a time-complexity that is cubic in
$T$ \cite{mcdonald07complexity}.  The advantage of the shift-reduce
framework is that it fits nicely into \textsc{Searn}. However, until now, it
has been an open question how to train a shift-reduce model in an
unsupervised fashion. The techniques described in this paper give a
solution to this problem.

\subsection{Experimental setup}

We follow the same experimental setup as \cite{smith05induction},
using data from the WSJ10 corpus (sentences of length at most ten from
the Penn Treebank \cite{marcus93treebank}). The data is stripped of
punctuation and parsing depends on the part-of-speech tags, not the
words. We use the same train/dev/test split as Smith and Eisner:
$5301$ sentences of training data, $531$ sentences of development data
and $530$ sentences of blind test data.  All algorithm development and
tuning was done on the development data.

We use a slight modification to SearnShell to facilitate the
development of our algorithm together with a multilabel logistic
regression classifier, MegaM.\footnote{SearnShell and MegaM are
  available at \url{http://searn.hal3.name} and
  \url{http://hal3.name/megam}, respectively.} Our algorithm uses the
following features for the tree-based decisions (inspired by
\cite{hall06dependency}), where $t$ is the top of the stack and $i$ is
the next token: the parts-of-speech within a window of $2$ around $t$
and $i$; the pair of tokens at $t$ and $i$; the distance (discretized)
between $t$ and $i$; and the part-of-speech at the head (resp. tail)
of any existing arc pointing to (resp. from) $t$ or $i$. For producing
word $i$, we use the part of speech of $i$'s parent, grandparent,
daughters and aunts.

We use \textsc{Searn}\ with a fixed $\be = 0.1$.  One sample is used to
approximate expected losses.  The development set is used to tune the
scale of the prior variances for the logistic regression (different
variances are allowed for the ``produce tree'' and ``produce words''
features). The initial policy makes uniformly random decisions.
Accuracy is directed arc accuracy.

\subsection{Experimental results}

\begin{table}[t]
\centering
\caption{Accuracy on training and test data, plus number of iterations for a variety of dependency
parsing algorithms (all unsupervised except for the last two rows).}
\begin{tabular}{|l|c|c|r|}
\hline
Algorithm & Acc-Tr & Acc-Tst & \# Iter \\
\hline
Rand-Gen       & 23.5 {\tiny $\pm 0.9$}& 23.5 {\tiny $\pm 1.3$ }& \\
Rand-\textsc{Searn}\   & 21.3 {\tiny $\pm 0.2$}& 21.0 {\tiny $\pm 0.6$} & \\
\hline
K+M:Rand-Init  & 23.6 {\tiny $\pm 3.8$}& 23.6 {\tiny $\pm 4.3$}& 63.3 \\%{\tiny $\pm  9.2$}\\
K+M:Smart-Init & 35.2 {\tiny $\pm 6.6$}& 35.2 {\tiny $\pm 6.0$}& 64.1 \\%{\tiny $\pm 11.1$}\\
\hline
S+E:Length     & 33.8 {\tiny $\pm 3.6$}& 33.7 {\tiny $\pm 5.9$}& 173.1 \\%{\tiny $\pm 77.7$}\\
S+E:DelOrTrans1& 47.3 {\tiny $\pm 6.0$}& 47.1 {\tiny $\pm 5.9$}& 132.2 \\%{\tiny $\pm 29.9$}\\
S+E:Trans1     & 48.8 {\tiny $\pm 0.9$}& 49.0 {\tiny $\pm 1.5$}& 173.4 \\%{\tiny $\pm 71.0$}\\
\hline
\textsc{Searn}: Unsup  & 45.8 {\tiny $\pm 1.6$}& 45.4 {\tiny $\pm 2.2$}& 27.6 \\%{\tiny $\pm  3.7$}\\
\hline
S+E: Sup       & 79.9 {\tiny $\pm 0.2$}& 78.6 {\tiny $\pm 0.8$}& 350.5 \\%{\tiny $\pm 54.4$} \\
\textsc{Searn}: Sup    & 81.0 {\tiny $\pm 0.3$}& 81.6 {\tiny $\pm 0.4$}& 24.4 \\%{\tiny $\pm 2.6$}\\
\hline
\end{tabular}
\label{tab:dep}
\end{table}

The baseline systems are: two random baselines (one generative, one
given by the \textsc{Searn}\ initial policy), Klein and Manning's model
\cite{klein04induction} EM-based model (with and without clever
initialization), and three variants of Smith and Eisner's model
\cite{smith05induction} (with random initialization, which seems to be
better for most of their models). We also report an ``upper bound''
performance based on supervised training, for both the probabilistic
(Smith+Eisner model) as well as supervised \textsc{Searn}.

The results are reported in Table~\ref{tab:dep}: accuracy on the
training data, accuracy on the test data and the number of iterations
required. These are all averaged over $10$ runs; standard deviations
are shown in small print. Many of the results (the non-\textsc{Searn}\
results) are copied from \cite{smith05induction}. The stopping criteria for the
EM-based models is that the log likelihood changes by less than
$10e-5$.  For the \textsc{Searn}-based methods, the stopping criteria is that
the development accuracy ceases to increase (on the individual
classification tasks, not on the structured prediction task).

All learned algorithms outperform the random algorithms (except
Klein+Manning with random inits).  K+M with smart initialization does
slightly better than the worst of the S+E models, though the
difference is not statistically significant.  It does so needing only
about a third of the number of iterations (moreover, a single
S+E iteration is slower than a single K+M
iteration). The other two S+E models do roughly comparably in
terms of performance (strictly dominating the previous methods). One
of them (``DelOrTrans1'') requires about twice as many iterations as
K+M; the other (``Trans1'') requires about three times (but
has much high performance variance). Unsupervised \textsc{Searn}\ performs
halfway between the best K+M model and the best S+E model (it is
within the error bars for ``DelOrTrans1'' but not ``Trans1'').

Nicely, it takes significantly fewer iterations to converge (roughly
$15\%$). Moreover, each iteration is quite fast in comparison to the
EM-based methods (a complete run took roughly $3$ hours on a 3.8GHz
Opteron using SearnShell). Finally, we present results for the
supervised case. Here, we see that the \textsc{Searn}-based method converges
much more quickly to a better solution than the S+E model.
Note that this comparison is unfair since the \textsc{Searn}-based model uses
additional features (though it is a nice property of the \textsc{Searn}-based
model that it \emph{can} make use of additional features).
Nevertheless we provide it so as to give a sense of a reasonable
upper-bound. We imagine that including more features would shift the
upper-bound and the unsupervised algorithm performance up.

\section{A Semi-Supervised Version} \label{sec:semi}

\begin{figure}
\centering
\includegraphics[width=0.4\textwidth]{semisup.pdf}
\caption{Parsing accuracy for semi-supervised, supervised and unsupervised \textsc{Searn}.  X-axis is: (semi/sup) \# of labeled examples; (unsup) \# of unlabeled examples.}
\label{fig:semisup}
\end{figure}

The unsupervised learning algorithm described above naturally extends
to the case where some labeled data is available.  In fact, the only
modification to the algorithm is to change the loss function.  In the
unsupervised case, the loss function completely ignores the latent
structure, and returns a loss dependent only on the ``predict self''
task.  In the semi-supervised version, one plugs in a natural loss
function for the ``latent'' structure prediction for the labeled
subset of the data.

In Figure~\ref{fig:semisup}, we present results on dependency parsing.
We show learning curves for unsupervised, fully supervised and
semi-supervised models.  The x-axis shows the number of examples used;
in the unsupervised and supervised cases, this is the total number of
examples; in the semi-supervised case, it is the number of labeled
examples.  Error bars are two standard deviations.  Somewhat
surprisingly, with only five labeled examples, the semi-supervised
approach achieves an accuracy of over $70\%$, only about $10\%$ behind
the fully supervised approach with $5182$ labeled examples.
Eventually the supervised model catches up (at about $250$ labeled
examples).  The performance of the unsupervised model continues to
grow as more examples are provided, but never reaches anywhere close
to the supervised or semi-supervised models.

\section{Conclusions}

We have described the application of a search-based structured
prediction algorithm, \textsc{Searn}, to unsupervised learning. This answers
positively an open question in the field of learning reductions
\cite{beygelzimer05reductions}: can unsupervised learning be reduced
to supervised learning?  We have shown a near-equivalence between the
resulting algorithm and the forward-backward algorithm in hidden
Markov models. We have shown an application of this algorithm to
unsupervised dependency parsing in a shift-reduce framework. This
provides the first example of unsupervised learning for dependency
parsing in a non-probabilistic model and shows that unsupervised
shift-reduce parsing is possible.  One obvious extension of this work
is to structured prediction problems with additional latent structure,
such as in machine translation.  Instead of using the predict-self
methodology, one could directly apply a predict-target methodology.

The view of ``predict the input'' for unsupervised learning is
implicit in many unsupervised learning approaches, including standard
models such as restricted Boltzmann machines and Markov random fields.
This is made most precise in the wake-sleep algorithm
\cite{hinton95wakesleep}, which explicitly trains a neural network to
reproduce its own input.  The wake-sleep algorithm consists of two
phases: the wake phase, where the latent layers are produced, and the
sleep phase, where the input is (re-)produced.  These two phases are
analogous to the predict-structure phase and the predict-words phase
in unsupervised \textsc{Searn}.

\vspace{-1mm}
\paragraph{Acknowledgements.}
Thanks for Ryan McDonald and Joakim Nivre for discussions related to
dependency parsing algorithms.  Comments from 5 (!)  anonymous
reviewers were incredibly helpful.  This was partially supported by
NSF grant IIS-0712764.

\vspace{-1.4mm}

redpajama_set_name: RedPajamaArXiv


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All products can be combined with one another within the module.


(Victoria, 19 November 2012) – IntegrityBC is calling on the B.C. government to recommit to its 2009 ban on non-essential government advertising in the four months prior to voting day.
The organization made the call following growing public concerns over the government's new TV ads featuring Premier Christy Clark.
The memorandum goes on to define each category of permissible advertising.
"Since the Premier herself noted that most undecided voters don't make up their mind until the final days of the campaign, a ban on non-essential government advertising would seem a no-brainer," said IntegrityBC executive director Dermod Travis.
Under its 2009 rules, a government ban would apply as of January 8th. IntegrityBC called for it to apply on crown corporations as well.
While imposing a ban will do little to address growing cynicism over the current ad campaign – which many critics claim is simply political advertising masquerading as government ads – a four month pre-election ban would send an important signal that the government won't use the public purse for anything remotely partisan.
Nothing in the recent B.C. Court of Appeal decision on pre-election gag laws prevents the government from imposing such a ban in the lead-up to the 2013 election.
"While the Court of Appeal ruled that the government can't limit third-party advertising in a pre-election period, the government is still free to impose a limit on non-essential government advertising on itself, just as it did in 2009," said Travis.
IntegrityBC drew specific attention to the fact that the self-imposed 2009 government ban was for twice as long as what the government had imposed on third-parties in the lead-up to that year's election.


text:
Conrad at Anchor
These handsome uniform editions of Joseph Conrad with introductions by Morton Dauwen Zabel were published by Anchor Books in the early '60s, when that imprint, which under the leadership of Jason Epstein had pioneered the trade paperback format, was a legitimate competitor of Vintage and Penguin as a publisher of serious literature and non-fiction in paperback. The cover designs were by Diana Klemin, the art director for Anchor's parent company, Doubleday. The use of photographs rather than art works in several of these titles was, I think, a little unusual at the time.
In addition to these six there were two listed as being in preparation that apparently were never issued, at least in the same form: Tales of Conflict and Falk, and Other Tales of the Sea; Zabel's death in 1964 may have prompted their cancellation. Anchor published a number of other Conrad titles with different cover treatments as well, some of them featuring art work by Edward Gorey. Doubleday's association with the Polish-born Conrad was already at least a half-century old at the time. At some point in the cascading consolidation of the book industry the connection lapsed, although a number of Conrad titles remain available from Doubleday's sister imprints under the Random House umbrella.
Labels: Paperbacks
The Fourth Floor
The woman in apartment 4A has been roasting a chicken, which is nearly done. She lowers the oven door, pulls the rack out halfway with a potholder, and lifts the glass lid of the casserole by the knob. As one hand tilts the casserole slightly the other spoons the liquid that flows to the opposite side over the golden bird. She pricks the breast with the tines of a fork, noting the color of the juices that run down the sides and sizzle into the pot below. She replaces the lid and slides the rack back in, shuts the oven door, and sets the utensils down on the counter by the sink.
Her husband is in the next room, sitting in a green armchair. He is facing the kitchen doorway but only his legs and feet and the top of his head are visible, as he's reading a newspaper held spread out in both hands. There's a cut glass ashtray on the little table at his side, next to a porcelain lamp, but it's been wiped clean. Beyond him the window is open and the shade, pulled down nearly to the sill, is fitfully rising and falling back again, making a barely perceptible sound.
In apartment 4B an older woman is sitting alone at a formica kitchen table. She is dressed to go out, except for her hat which along with her purse is close at hand on the table before her. She's reading a book, and with one hand is absently fingering the bookmark that she's moved to the back pages while she reads. There's a narrow bookcase in one corner of the room. Most of the books are cookbooks or her own notebooks of recipes that she has cut out or been given by friends. Lying on top of the bookcase are a phone book and a mail order catalog of equal size. Over the doorway hangs an ornately carved and painted wooden clock with a motto in German.
The couple in 4C have been making love. The young man remains in bed, still wearing his undershirt, half in and half out of the disheveled bed covers. He's picked up a magazine he found lying on the nightstand and is flipping through its pages indifferently. There's a dresser along the opposite wall. Above a lamp, a hairbrush, and a tray containing a jar of cold cream and a few perfume bottles is a large mirror in which the man can see only his own head on the pillow and the headboard behind him. The woman has left the room and can't be seen.
The single gentleman in 4D is sitting at the desk where he has been typing all afternoon. He's smoking steadily and keeps a glass of neat whiskey within easy reach. He taps awkwardly but steadily on the keys, rarely pausing to think, pushing back the carriage with a flick of his hand at the end of each line. There's a pile of correspondence and newspapers in one corner of the desk; the letters have been opened, roughly, but the newspapers are still as tightly folded as when they arrived. At his feet is a box of books, carelessly packed; the only one that is visible is a dictionary, thumb-indexed but missing its jacket. Behind him on the floor is a worn brown leather valise, festooned with paper tags and labels from several countries, some of them in the Far East.
Labels: City
Jorge Luis Borges had already made a name for himself, at least in Argentine literary circles, as a poet and essayist years before he turned to the short fiction on which his broader international renown would largely come to be based. When he did so, like many an artist he began by borrowing from the masters, in his case with a volume of retellings and translations of tales gleaned from Mark Twain and the Thousand and One Nights as well as Herbert Asbury's Gangs of New York, the Encyclopedia Britannica (Eleventh Edition), and other, less familiar sources. Most of the stories and anecdotes in Historia universal de la infamia deal with some kind of treachery or violence, usually both, and together they make up a little guided tour of a few of the lower precincts of Hell, the ones reserved for those whose crimes can't be easily ascribed to mere misplaced love or folly.
The only declaredly original story, describing the fatal dénouement of a confrontation at knifepoint at a Buenos Aires dance hall, is "Hombre de la esquina rosada," the title of which is here translated, felicitously if not entirely accurately, as "Streetcorner Man." Most of the pieces were originally published, in many cases pseudonymously, in Argentine literary magazines and supplements in the 1930s; one of the headings used for their appearance was "Museo" ("Museum"), which seems particularly appropriate. The arts of collecting and of presentation are here given equal footing with the art of creation. Borges scrupulously provides the source for each piece, and if a few of those attributions turned out, on closer examination, to be fictions, who was to know?
In the preface to the 1954 edition, written with nearly two decades of hindsight, Borges was fairly dismissive about the volume's merits, but, in contrast to his attitude towards several early works that he actively sought to suppress, he evidently managed to come to terms with its shortcomings (which may well strike the reader as no shortcomings at all).
The very title of these pages flaunts their baroque character. To curb them would amount to destroying them; that is why I now prefer to invoke the pronouncement "What I have written I have written (John 19:22) and to reprint them, twenty years later, as they stand. They are the irresponsible game of a shy young man who dared not write stories and so amused himself by falsifying and distorting (without any esthetic justification whatsoever) the tales of others. From these ambiguous exercises, he went on to the laboured composition of a straightforward story – "Streetcorner Man" – which he signed with the name of one of his great grandfathers, Francisco Bustos, and which has enjoyed an unusual and somewhat mystifying success...
The theologians of the Great Vehicle point out that the essence of the universe is emptiness. Insofar as they refer to that particle of the universe which is this book, they are entirely right. Scaffolds and pirates populate it, and the word "infamy" in the title is thunderous, but behind the sound and fury there is nothing. The book is no more than appearance, than a surface of images; for that reason, it may prove enjoyable. Its author was a somewhat unhappy man, but he amused himself writing it; may some echo of that pleasure reach the reader.
As Borges had remarked in the preface to the first edition, the stories are not, and do not try to be, psychological.
The di Chiricoesque cover painting of this edition, in which nefarious characters from various eras -- probably intended to represent specific antiheroes from the tales themselves -- lurk among the cracking pillars of a looming colonnade, is by Peter Goodfellow, who created several other Borges covers for Penguin UK in the 1970s. The translations above are by Norman Thomas di Giovanni, who had the advantage of working closely with Borges himself. Sadly, his translation is out of print, having been replaced by one commissioned -- largely, it appears, for pecuniary reasons -- by the author's widow.
Labels: Borges, Tales, Translation
This wasn't quite the version of this song I went looking for, but having found it I have to admit it's not half bad. Helio, a veteran powerpop band from Seville, covers Peter Case's "Zero Hour," originally recorded by the Plimsouls.

redpajama_set_name: RedPajamaCommonCrawl


My favourite photos from our 2700 mile backpacking trip from Delhi to Mumbai via Amritsar, Gwalior, Orchha, Aurangabad, Hyderabad, Hampi and Goa.
The blog from our most recent trip from Delhi to Mumbai via Amritsar, Gwalior, Orchha, Aurangabad, Hyderabad, Hampi and Goa.
A blog about our Rajasthan, Delhi and Agra trip including lots of information on planning the trip, booking trains and planes, money worries, innoculations and so on.
Some of the photos from our tour of Rajasthan.
This is the journal which was typed up from the notes I took during our holiday. It's much more detailed and differs from the blog considerably, as the blog was a set of quickly written articles and lacks the depth of the journal.
This journal is hosted on www.indiamike.com (see below for more information).
IndiaMike.com is an invaluable resource web site devoted to information about living and travelling within India and all things related. I have absolutely no doubt that our 2009 trip wouldn't have been half as enjoyable without the information I learned from this web site and the community behind it.
Erail.in is a web site which tells you everything you need to know about trains, routes, times, availability. It's my go to place for planning train routes.


text:
<?php namespace Strana;

class InfiniteScroll {

    /**
     * @var ViewLoader
     */
    protected $viewLoader;

    /**
     * @var ConfigHelper
     */
    protected $configHelper;

    /**
     * @param ViewLoader $viewLoader
     * @param ConfigHelper $configHelper
     */
    public function __construct(ViewLoader $viewLoader, ConfigHelper $configHelper)
    {
        $this->viewLoader = $viewLoader;
        $this->configHelper = $configHelper;
    }

    /**
     * @param array $config
     * @return mixed
     *
     * Prepare infinite scroll JavaScript
     */
    public function getJs(Array $config = array())
    {
        $default = array(
            'container'             =>  '.container',
            'item'                  =>  '.item',
            'pagination'            =>  '.pagination',
            'next'                  =>  '.next a',
            'loader'                =>  'Loading ...',
            'triggerPageThreshold'  =>  5,
        );

        $data['config'] = array_merge($default, $config);

        return $this->viewLoader->load('infiniteScroll.php', $data);
    }
}

redpajama_set_name: RedPajamaGithub


text:
Ten Horrific Shipwrecks That Weren't the Titanic
10 Courageous and Clairvoyant Ghost-Hunting Canines
Ten of the Wildest Scientific Discoveries of 2022
10 Celebrities That Have Been Arrested at Their Own Performance
10 Incredible Historical (and Modern) Women You Should Know About
10 Bizarre Urban Legends That Are Related to Video Games
Weird Stuff | September 13, 2017
Top 10 Unbelievable Things Found Underwater
by Abigail Bingham
The world's oceans make up around 70 percent of this planet, and only 5 percent of them have been explored. That means that the vast majority of Earth is made up of deep, dark waters with mysteries waiting to be discovered.
Even with the small amount of ocean exploration that has been completed, some incredible places, animals, and objects have been found. Searching the depths of our oceans can give us insight into the past, the present, and sometimes, even the future.
Exploring these areas lets us see pieces of history and gives us ideas of other things that may be lurking in the deep. Here are 10 of the most unbelievable discoveries that have been made underwater. Can you imagine what else is out there?
10 Current Movement Of Tectonic Plates
Photo credit: Alexander Mustard
The North American and Eurasian tectonic plates have been spreading farther apart in recent years as the North American plate moves westward. This impressive occurrence can be seen both on land and by diving deep in between the two plates near Iceland.
This has been done by a number of underwater photographers and scuba divers. Alexander Mustard, in particular, is a diver and marine biologist who has explored this area and recounts his experience as an incredible one. He notes that visitors can become nauseated as a result of the "sheer walls and clear water."
Though the future cannot be completely foreseen, researchers can make predictions based on the current rate of plate motion, which is about 2.5 centimeters (1 in) per year.[1]
9 Ancient Underwater City Of Pavlopetri
Photo credit: greecebyagreek.com
Discovered in 1967 by Dr. Nic Flemming, this port town from the Neolithic era can be found off the southern coast of Greece. Ancient pottery was discovered at the site, giving archaeologists the impression that the town of Pavlopetri traded goods within the city and by sea.
According to Flemming in 2009, Pavlopetri is thousands of years older than most of the ancient underwater ruins that have already been discovered. The estimated area of the town covers about 100,000 square meters (1.1 million ft2). Its remote location likely preserved it from being disrupted by humans before the discovery.[2]
Neolithic pottery is one of the most notable finds in this underwater city. It provided a timeline for researchers to understand just how old these ancient ruins were. Originally, Pavlopetri was thought to be part of the Bronze Age. But after further exploration, the city was found to be more than 1,000 years older than previously thought.
8 Underwater River
Photo credit: University of Leeds
At the bottom of the Black Sea, there is an entire ecosystem with a running river, trees, and waterfalls! How does this work, though—a body of water running through another body of water?
It seems confusing, but it actually occurs because the water in the river is much saltier—and therefore denser—than the surrounding water in the Black Sea.
The high salinity of the river allows it to flow through the sea at a fast pace, creating riverbanks. The large amount of sediment running through the river also helps it to flow through the Black Sea.[3]
As if this wasn't cool enough, it is estimated that the river's large volume would make it the sixth largest river on land. Finding this wondrous little ecosystem within the Black Sea, which eventually drains into the Atlantic Ocean, could be crucial to researchers' understanding of our oceans as a whole.
This discovery can potentially help scientists better understand life underwater and the different conditions that allow phenomena like this to occur on our planet.
7 Zhemchug Canyon
Bering Sea
Photo credit: messagetoeagle.com
If you thought the underwater river was impressive, you'll love this underwater canyon. It is so huge that it can only be seen in its entirety from space. Crazy, right?
Found in the Bering Sea, Zhemchug is also the deepest underwater canyon. With a volume of 5,800 cubic kilometers (1,400 mi3) and a depth of 2.6 kilometers (1.6 mi), Zhemchug could fit the entire Grand Canyon inside it!
Submarines can travel through this deep-sea site as well as the different marine life found in the Bering Sea. Since Zhemchug Canyon is so ridiculously wide, the entire underwater canyon can only be seen if you are above the curve of the Earth.[4]
So unless you have connections that can get you off Earth and into a spaceship, then you might just have to look at pictures.
6 Bimini Road
Photo credit: Ancient Origins
One of the most popular tourist attractions in the Bahamas is Bimini Road, an underwater path discovered in the 1930s. The origin of this ocean feature is unknown, though many believe it to be a path leading to the lost city of Atlantis.
Sitting just 6 meters (20 ft) under the water's surface, Bimini Road is accessible to those curious enough to dive down and experience the mysterious formation for themselves.[5]
Possible origin stories for Bimini Road have been featured in different mystery pieces on Unexplained Mysteries, Unsolved Mysteries in the World, and more. The origin theories include ancient Egyptian divine protectors, the path to Atlantis, and a natural occurrence without any help from humans.
Wherever this road came from, it's an awesome sight to see.
5 'Britain's Atlantis'
Doggerland
Photo credit: stephenliddell.co.uk
While the original Atlantis waits to be found, a similar discovery has been made deep within the North Sea. "Doggerland" is the name of a huge landmass that became submerged in the ocean at least 8,500 years ago.
Researchers believe that the landform reached from Scotland to Denmark and was once occupied by mammoths. After the land was settled by Mesolithic humans, it eventually flooded over thousands of years. It has been called the "real heartland of Europe" by those who discovered it due to its large size and possible human community of hunter-gatherers (thought to be in the tens of thousands).
Doggerland is particularly impressive because it proves that the islands that are now Great Britain were once connected to the rest of Europe. Though it's not the real Atlantis, this underwater world is just as interesting. Climatologists, archaeologists, and geophysicists continue to map the entirety of these ancient ruins and are working to understand them more fully.[6]
4 Ruins Of Atlit Yam
Photo credit: amazingancient.com
Ancient cities found in the depths of the sea are always intriguing, no matter how many are discovered. The ruins of Atlit Yam are 8–12 meters (26–39 ft) deep in the Mediterranean Sea off the coast of Israel.
Discovered in 1984, the city is believed to belong to the Neolithic era and is one of the largest underwater settlements ever found. Exploration at Atlit Yam has produced the remains of houses, water wells, humans, and animals as well as objects, artifacts, and mysterious structures dating back thousands of years.
One of its most impressive structures is thought to be a ritual site consisting of huge stones arranged in a circle around what used to be a spring. Other interesting discoveries at Atlit Yam include a stone well used for water service and eventually as a disposal pit (due to the large number of remains found inside).[7]
Sixty-five sets of human remains were also located in and around the city, some providing the earliest known cases of tuberculosis. There were also bones of wild and domesticated animals, suggesting that those who occupied Atlit Yam both hunted and raised animals for food.
3 Black Smokers
Photo credit: Live Science
Certain chimneylike formations occur underwater when seawater meets magma. These hydrothermal vents are almost a type of hot spring that spits out jets of water and fluids reaching 370 degrees Celsius (700 °F) and higher.
Often called "black smokers" due to the color of their iron sulfide deposits, these vents have been found in various places in our oceans. But their northernmost location was discovered in the Arctic Circle between Norway and Greenland in 2008.
There are five vents in this cluster of black smokers. One of them is almost four stories tall, creating the largest sulfide deposit ever recorded. The discovery of this vent field was unexpected.[8] Researchers did not think that such a formation was possible in an area with such slow tectonic plate spreading. (That's how seawater makes its way through the Earth to collide with magma.)
These black smokers demonstrate just how unpredictable and impressive nature is, while hinting at the other mysteries that could be hiding below the water's surface.
2 Ghost Fleet
Chuuk Lagoon
Photo credit: atlasobscura.com
Shipwrecks are some of the most interesting things to find in the ocean. They tell a story of another time in history, give clues about what happened and who was there, or just look cool.
Chuuk Lagoon is found in the Caroline Islands in the Pacific Ocean, north of New Guinea and east of the Philippines. Here, the wreckage of countless Japanese ships and aircraft was discovered, having been destroyed in World War II.
The "Ghost Fleet" was the subject of a 1969 Jacques Cousteau film in which the wreckage was explored and the site began to be recovered. However, it is said that bodies still remain among the ships and planes and the site itself is partially visible from above the water's surface.[9]
The wreckage of Japan's Imperial Fleet is an incredible example of an underwater discovery that is not only intriguing but informational and historic as well.
1 The Great Blue Hole
With a name as daunting as this one, the Great Blue Hole of Belize should surely attract curious divers and thrill seekers—and it does. Found off the coast of Belize, this natural sinkhole is the largest ever recorded.
Made famous in 1971 by Jacques Cousteau, it has since developed into a tourist attraction. Visitors can take guided diving trips into the Great Blue Hole that was formed from a limestone cave during the last Ice Age hundreds of thousands of years ago.[10]
This impressive natural feature in the Caribbean Sea is 300 meters (980 ft) across and 125 meters (410 ft) deep, making it quite the accomplishment to dive the entire depth. (This is not recommended, and guided tours will only take you a fraction of the way down.)
Abigail is a 21-year-old lover of dogs, donuts, and everything Disney. She is a firm believer that there's nothing much better than a good book and a great Spotify playlist on a rainy day.
Read about more intriguing discoveries under the sea on 10 Bizarre Recent Ocean Discoveries and 10 Mysterious Underwater Anomalies.
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BAAG and the Civil Society Working Committee (CSWC) are please to introduce the 10 delegates representing Afghan Civil Society in Geneva. As delegates they will be responsible advocating key messages from civil society, influencing decision makers on policy priorities and two delegates (one male, one female) will be the Civil Society spokespeople selected to present a Civil Society statement on behalf of civil society on the 28th November during the main ministerial conference.
A refugee in Pakistan until age 16, Forozan is one of the youngest civil society delegates to the GCA. Having begun a career at the Ministry of Women's Affairs, she became a social activist through her work at NGOs and is now focused on human rights, peace and good governance from the perspectives of women and youth through her role at EPD. The NGO, which combines research, capacity building and advocacy, has established women's networks in 15 provinces and works in 10 provinces to build understanding of good governance among civil society. It helps to increase women's political and economic participation through agreement on quotas in official posts and by challenging cultural biases and mindsets. Forozan is advocating for investment to develop the industrial sector in Afghanistan to build a sustainable and self-reliant economy better able to withstand corruption and provide a future for the youth, and for the end to military targeting of the homes of suspected insurgents to avoid civilian casualties and subsequent suicide revenge attacks as part of a range of peace initiatives to take.
Languages: English, Dari/Persian, Pashto and Urdu.
The youngest member of the Civil Society delegation to the GCA and emerging young leader, Tamana is also a doctor. Through her work as a medical expert for AFSO evaluating forensic evidence from hymen ("virginity") tests in cases brought before the Kabul courts, Tamana has identified and highlighted flaws in the policy of virginity testing in prosecutions for so-called 'moral crimes'. This has helped to criminalise forced virginity testing, but she advocates its complete ban as an institutionalised form of gender-based violence and a human rights abuse. She is a lead researcher in a study across 14 Afghan provinces on victims of forced gynaecological examinations and their physical and psychological outcomes. She is also engaged on mental health, human rights and law as well as on reproductive health issues.
Freshta Karimi identifies access to justice and the rule of law as among the main challenges in Afghanistan. Co-founder and Director of DQG, which provides legal aid for civilians, capacity building for legal practitioners, also raises awareness on laws and advocates at policy level on access to justice and the rule of law. Through the work of her NGO which works in 16 provinces and her previous experience in the justice sector from 2003, Freshta has a good insight into the many issues Afghans face. She has provided input and advice on draft laws, including the Anti-Torture Law adopted in December 2017. She cites the poor implementation of laws, corruption and reform of the justice sector and unfair procedures that block justice as issues in need of urgent address for better governance.
Languages: English, Pashto and Dari/Persian.
A recipient of many prizes, including the 2008 International Women of Courage Award and one of Time magazine's 100 most influential people in 2009, Suraya Pakzad is a globally renowned women's rights activist for her lifelong work to help and protect Afghan women and girls from all forms of violence in the face of life-threatening abuse. She ran a covert school for girls in Kabul during the Taliban years which later evolved into the Voice of Women NGO. It runs five safe houses in western Afghanistan, providing refuge and legal services to more than 1000 women and girls subjected to various forms of violence, and works on gender equality and justice in 29 of the 34 Afghan provinces. Connecting the dramatic levels of violence against women to poverty and war, she calls on the international community to commit to tailored interventions focused on economic security, and for foreign interference in the Afghan conflict to end. Twice the subject of religious edicts ordering her assassination, Suraya lives under daily threat to her life. She is based in Herat.
A refugee in Pakistan for 18 years, Frozan worked for the Afghan government, international and national organizations before founding her NGO, PATH-O. Operating in northern Afghanistan, the NGO focuses on women, youth and peace and in particular, the implementation of UN SC Resolution 1325. Frozan highlights poverty and corruption as key factors of insecurity, the recruitment by anti-government forces of large numbers of returning Afghan refugees without homes, jobs or support, and the lack of women's economic empowerment and gender-based violence as major challenges in need of urgent address. She is calling on the international community to abandon the short-term and one-size-fits-all approach to support for Afghanistan and provide long-term assistance adapted to Afghan realities and for better support and protection of a civil society increasingly paralysed by intimidation, insecurity and obstruction from all sides.
A former refugee in Pakistan, Mohammad Naeem Ayubzada returned home to contribute to Afghanistan's reconstruction. He established an education centre for poor children on the outskirts of Kabul in 2002 believing education was key to Afghanistan's future progress. During eight years at the National Democratic Institute for International Affairs his work focused on governance, parliamentary engagement, election observation, gender mainstreaming and political party development. The experience led him to set up TEFA, an election observation body and network of civil society organizations which advocate for transparent elections. It conducted its first observation missions in the 2010 Afghan parliamentary elections. His organization has observed subsequent national and provincial elections, including the 2014 presidential elections and the first nationally organized parliamentary elections in 2018. Languages: English, Dari/Persian, Pashto, Urdu.
A rural development professional from Kabul with national and international experience, including the UN, Mohammad Zakir Stanikzai is focused on local governance, community development and strengthening civil society. He is the Executive Director of the Afghanistan Institute for Civil Society established by the Aga Khan Foundation in 2014 to help build a vibrant and competent civil society in the country. It aims to do this by certifying local NGOs using locally defined and internationally recognized standards to provide credible channels for development resourcing from donors and the government and to connect them to each other. He is urging the Afghan government to give civil society organizations the voice, space and support to do their work as it cannot tackle the challenges alone and need the ability of civil society to reach marginalized and rural communities to deliver programmes and to oversee public service.
Mohammad Shafaq's mission is to make sure no disabled person in Afghanistan is left behind. Through his work, Mohammad monitors the level of implementation of the nationally ratified UN Convention on the Rights of Persons with Disabilities. Although data on disability in Afghanistan is outdated and levels sure to have significantly grown since, at least one in five households in 2005 had a disabled person; 75% of people with disabilities lived in rural or poor semi-urban settings; mostly all were illiterate and without access to health services and education, and that 70% of disabled Afghans were unemployed. With an end to the conflict an absolute priority, Mohammad urges the international community to stop the privatisation and outsourcing of conflict which, by recruiting young Afghans, only benefits warlords and jihadis.
Samiullah Hamidee is a young social activist from Helmand Province and one of two civil society delegates to the GCA based outside of Kabul. Born, educated and a life-long resident of conflict-ridden Helmand province, he and his team at Bost University provide legal aid services to marginalised people and communities. Founder and Chairman of his own NGO which only works in Helmand, Samiullah promotes and strengthens democracy by advocating for unfettered access to information, transparency and accountability. Drawing upon his knowledge and understanding of Helmand Province where more than half of the districts are now under Taliban control, he believes the conflict is not a war of ideology or religion, but a war between haves and have nots that has now become a way of life for the young.
Languages: English, Dari/Persian and Pashto.
An Afghan refugee in the Pakistan city of Peshawar for 18 years, Raz Mohammad Dalili has won many international and national awards for his work as a peace educator. He believes that Afghanistan's future lies in changing the behaviour and mindset of its young through education and to invest wholeheartedly in poverty eradication. The peace education curriculum developed by his NGO and rolled out to 35,000 Afghan refugees in Peshawar before being extended to 120 schools in Kabul, is now being considered for a national rollout. The NGO also works with 400 religious leaders around the country to leverage their power and influence in the provinces to resolve local conflicts and build community peace. Raz believes the poor coordination on peace and development initiatives between the international community itself and with the Afghan government is helping to fuel the conflict itself. He also argues for greater pressure to be placed on supporters of the Taliban and terrorism, including neighbouring countries, to desist from interference.


1) Sunday, 18 June, is Father's Day. Let's celebrate by writing a blog post about our father, or another significant male ancestor (e.g., a grandfather).
2) What are three things about your father (or significant male ancestor) that you vividly remember about him?
1) Lyle was a small child as a youth and a small man as an adult. He was over-protected after his birth because his parents lost their first child, Devier David Carringer (1889-1890) at 9 months of age. The earliest photos of Lyle show him in short pants with curly hair.
I think that he started attending school at age 7 but did not graduate from San Diego High School until 1914 when he was age 22. When he entered the United States Marine Corps in 1917 at age 25, he was all of 5 feet 7 inches tall and weighed 121 pounds.
As a boy, he roamed all over San Diego from downtown to East San Diego walking, riding the streetcars or on a bicycle. He loved San Diego and attended many historic events and took photographs of some of them throughout his life.
While still a youth (age 14), he started working as a cash boy at Marston's Department Store in downtown San Diego in 1905. By 1917, he had worked as a cashier, mail order clerk, post office clerk and floorwalker. He worked as a clerk and as an accountant at Marston's for many years, and retired in 1960, after 55 years of service.
2) Lyle Carringer collected postage stamps and corresponded with other stamp collectors from all over the world. Over the years, friends from Pitcairn Island, New Zealand, Papua-New Guinea and Australia came to visit them. Family, friends and coworkers sent postcards from their travels all over the world. He was also an excellent photographer, and the family albums have many pictures of family groups, San Diego scenes, and of his daughter and grandsons. Lyle loved mechanical things - cars, cameras, and the like. Lyle was a hardy and enterprising man who liked to garden, do handyman work on the rental properties, and go for automobile rides in the countryside.
He was a kind, gentle and patient man, yet firm, who loved his wife and daughter very much, but was not a religious person, because his parents had rejected religion after the death of their first child. I don't recall seeing him angry, even once. He enjoyed his three grandsons and encouraged them to collect things (like stamps!), to explore their world, and to get an education.
"Mr. and Mrs. Lyle L. Carringer, 2130 Fern st., are never bored with their garden. They have four fish pools in it filled with finny pets that are always on the move. Sometimes they move out on the grass, but the Carringers chalk this up to playfulness and continue to enjoy the aquatic aspects of their home.
"The Carringer place is distinctly in the livable small garden or outdoor living room class. The pools are strategically located to invite folks outdoors for pleasure and relaxation. One pool, the smallest, is placed near the rear door of the house as a starting point. From there to a sunny larger pool planted to lilies and overhung with shrubs is a natural step.
"Other parts of the garden planted in flowers, cacti and succulents and several fruit trees are easily accessible from the second pool and then the Carringers and their friends naturally gravitate to a lath house furnished with chairs and tables.
"There are sources of amusement in the lath house. The remaining pools are located there. One is a rill in the heart of a pretty rockery and the other a shadowy oval presided over by a rice plant with curving stalks topped with leaves like giant hands. Directly above the pool a tree rests apparently on the water. The invisible stand which holds it up represents some clever household ingenuity. It was formerly a piano stool.
"A grill is built at the end of the lath house on a rock structure. The grill space can be filled with a decorative potted plant when not in use although the Carringers have found it a consistent entertainer.
"Next to the grill is a glass house annex with a surprise feature. On shelves are aquariums filled with tropical fish, paradise, moons, guppies and other fancy swimmers weaving about in grassy water jungles; blinking, gobbling and raising families in the most distracting style. The glass house is heated for them and each aquarium has individual radiators and thermometers for perfect comfort.
"Tropical aquariums are often kept indoors but the Carringers have theirs handy to the pools where fish life from goldfish to guppies can be observed. Certain of the tropicals are moved to the goldfish pools at favorable times for variety."
I remember the lath house and the glass house and the fish ponds in them, but I don't recall the other features. They moved out of that house in 1950 and built their house on Point Loma soon after.
I guess that's more than three stories, but I wanted to tell all of them.
We have tested syncing and it works well as confirmed by beta testers and some volunteer test drivers who tested as well. Syncing for beta testers is already open (they are testing preview builds of Test Drive 2.0) But we have decided to hold off opening syncing to test drivers until Monday until further tree safety features can be put in place.
We have implemented a number of new tree safety features that will help prevent accidental deletion of people from your trees. They include a delete warning message, proposed deletions appearing in red in the sync change log window, and requiring a button click to initiate syncing. All to call attention to deletions so they can't happen by mistake.
As noted, all beta testers have syncing on, and we hope to get test drivers syncing again by Monday with a modified build that has the new safety features added. We are also on track to introduce Test Drive 2.0 sometime next week and will start by testing it with a few thousand volunteers who would like to move up from the Original Test Drive.
This blog post will serve as a blog compendium for genealogy blog posts about activities at the SCGS Genealogy Jamboree 2017 in Burbank, California, held from 8 June to 11 June 2017.
Social media users are using the hashtag #SCGS2017 for this conference - search on Facebook, Twitter, Pinterest, Instagram and other social media outlets for additional information from the conference.
If you know of a genealogy oriented blog post about the 2017 SCGS Genealogy Jamboree Conference that is not on this list, please let me know in Comments to this post or in email at randy.seaver@gmail.com.
I am attending this years SCGS Genealogy Jamboree 2017 Conference and will try to update this post at least twice daily until the blogging ends.
First Published: Friday, 9 June 2017.
Last Updated: Wednesday, 21 June, 8 a.m. PDT.
I am in the 8th great-grandmothers and I'm up to Ancestor #1905, who is Mary HOLLOWAY (1641-1703) [Note: the earlier great-grandmothers and 8th great-grandfathers have been covered in earlier posts].
1904. Nathaniel Fitz Randolph, born before 15 May 1642 in Barnstable, Barnstable, Massachusetts, United States; died 21 November 1713 in Woodbridge, Middlesex, New Jersey, United States. He was the son of 3808. Edward Fitz Randolph and 3809. Elizabeth Blossom. He married November 1662 in Barnstable, Barnstable, Massachusetts, United States.
1905. Mary Holloway, born about 1641 in Sandwich, Barnstable, Massachusetts, United States; died 12 July 1703 in Woodbridge, Middlesex, New Jersey, United States.
* John Fitz Randolph (1663-1727), married (1) 1682 Martha Adams (1658-1702); (2) 1702 Sarah Potter (1667-1736).
* Isaac Fitz Randolph (1664-1694), married (1) 1690 Martha Bingley (1664-1691); (2) 1692 Ruth Higgins (1672-1709).
* Nathaniel Fitz Randolph (1666-1703), married 1692 Grace Hull (1672-1752).
* Joseph Fitz Randolph (1670-1718), married 1700 Isabel Laing (1675-1755).
* Edward Fitz Randolph (1672-1760), married 1704 Katherine Hartshorne (1682-1759).
* Martha Fitz Randolph (1674-????).
3810. Joseph Holloway, born about 1605 in Crewkerne, Somerset, England; died December 1647 in Sandwich, Barnstable, Massachusetts, United States. He married before 1638 in Probably Sandwich, Barnstable, Massachusetts, United States.
3811. Rose Allen, born about1609 in England; died about 1692 in Sandwich, Barnstable, Massachusetts, United States. She was the daughter of 7622. George Allen and 7623. FNU LNU.
* Joseph Holloway (1638-1692), married 1661 Mary Hull (1645-1692).
* Sarah Holloway (1643-1675), married 1662 Joseph Allen (1645-1704).
* Experience Holloway (1644-????0, married 1668 John Goodspeed (1645-1719).
* Hopestill Holloway (1646-1715), married 1665 Samuel Worden (1645-1716).
* Carl Boyer 3rd, Ancestral Lines, Third Edition (Santa Clarita, Calif. : the author, 1998), pages 306-7.


text:
filename: /packages/material-ui/src/FormControlLabel/FormControlLabel.js
---

# FormControlLabel API

The API documentation of the FormControlLabel React component. Learn more about the properties and the CSS customization points.

```js
import FormControlLabel from '@material-ui/core/FormControlLabel';
```

Drop in replacement of the `Radio`, `Switch` and `Checkbox` component.
Use this component if you want to display an extra label.

## Props

| Name | Type | Default | Description |
|:-----|:-----|:--------|:------------|
| checked | bool |  | If `true`, the component appears selected. |
| classes | object |  | Override or extend the styles applied to the component. See [CSS API](#css) below for more details. |
| control | element |  | A control element. For instance, it can be be a `Radio`, a `Switch` or a `Checkbox`. |
| disabled | bool |  | If `true`, the control will be disabled. |
| inputRef | union: func |
 object
 |  | This property can be used to pass a ref callback to the `input` element. |
| label | node |  | The text to be used in an enclosing label element. |
| labelPlacement | enum: 'end' |
 'start' |
 'top' |
 'bottom'
 | 'end' | The position of the label. |
| name | string |  |  |
| onChange | func |  | Callback fired when the state is changed.

**Signature:**
`function(event: object, checked: boolean) => void`
*event:* The event source of the callback. You can pull out the new value by accessing `event.target.checked`.
*checked:* The `checked` value of the switch |
| value | any |  | The value of the component. |

The `ref` is forwarded to the root element.

Any other properties supplied will be provided to the root element (native element).

## CSS

You can override all the class names injected by Material-UI thanks to the `classes` property.
This property accepts the following keys:

| Name | Description |
|:-----|:------------|
| root | Styles applied to the root element.
| labelPlacementStart | Styles applied to the root element if `labelPlacement="start"`.
| labelPlacementTop | Styles applied to the root element if `labelPlacement="top"`.
| labelPlacementBottom | Styles applied to the root element if `labelPlacement="bottom"`.
| disabled | Styles applied to the root element if `disabled={true}`.
| label | Styles applied to the label's Typography component.

Have a look at the [overriding styles with classes](/customization/components/#overriding-styles-with-classes) section
and the [implementation of the component](https://github.com/mui-org/material-ui/blob/master/packages/material-ui/src/FormControlLabel/FormControlLabel.js)
for more detail.

If using the `overrides` [key of the theme](/customization/themes/#css),
you need to use the following style sheet name: `MuiFormControlLabel`.

## Notes

The component can cause issues in [StrictMode](https://reactjs.org/docs/strict-mode.html).

## Demos

- [Checkboxes](/components/checkboxes/)
- [Radio Buttons](/components/radio-buttons/)
- [Switches](/components/switches/)


The inductive modem solution uses an OEM modem from Seabird and it works with any Seabird surface modem. The signals are induced into the cable using an inductive coil and the whole system comes with special mechanical clamps that holds the Aquadopp attached to the cable. A fin is included to avoid vortex induced oscillations.
The Aquadopp-IM comes in two versions, one designed for fixed diameter cables and one where it is possible to ajust the clamp size according to the cable diameter.
The first four units have been delivered to NOAA and to WHOI for further integration and testing.


Produced by Ben Affleck & Matt Damon.
Cancelled in 2017 after just one season.
Georgina Haig originally played the female lead in the pilot but left and Allison Miller was cast in her place.
Both Sean Teale and Douglas Nyback played together in the TV show "Reign".


text:
movie review Apr. 12, 2019
Her Smell Is a Bracing, Unnerving Depiction of Addiction, Narcissism, and Growth
By Emily Yoshida
Her Smell. Photo: Courtesy of TIFF
A low thrum runs throughout much of Her Smell. It's part of the Keegan DeWitt's unsettling score, but it could easily be mistaken for diegetic sound design, or even just the actual noise picked up in the low-ceilinged space under a rock venue. It starts out sounding like guitars and drums and screaming crowds filtered through several walls, but soon starts to sound like the churn of machinery. You can imagine gargantuan gears, like the ones that propelled the Titanic, on the other side of the grimy green room where Becky Something (Elisabeth Moss) terrorizes her bandmates, handlers, and anyone else who dares come within 20 feet of her. Over time, it occurs to you that the sound may only be there for Becky.
In five scenes spread across roughly a decade, Alex Ross Perry's film documents the fall and rise of Something She, the fictional '90s riot grrrl punk band for which Becky is the swaggering, tongue-wagging, bottle-smashing figurehead. Scenes of their ascendancy are all shown via home-movie footage, as Becky, bassist Marielle (Agyness Deyn) and drummer Ali (Gayle Rankin), still bright-eyed and giddy, freak out over their first Spin cover, their first gold record (which they promptly destroy). These clips come as welcome punctuation between the long, more or less real-time scenes that make up the body of the film. Perry's film depicts Becky and the band's fame as a kind of very long plastic playground tunnel slide, something windowless and garishly colored that one just hurtles down uncontrollably until it finally spits you out in the white light of day.
That light takes its time coming. For the almost unbearably tense first hour and a half or so, cinematographer Sean Price Williams (who captured neon-tinged anxiety so well in last year's Good Time) veers around the backstage areas and recording booths in which the band slowly implodes. The first scene, at a club show early in the band's decline, starts with a high-energy cover of the Only Ones' "Another Girl, Another Planet," giving us a glimpse of the band when everything is working and everything is fun. As soon as they leave the stage, that incessant thrum comes in and the cohesiveness ceases as Becky starts downing drink after drink. She stalks the halls looking for the next person to torment, an oversized Phantom of the Opera T-shirt draped on her figure, and it seems not entirely accidental: her narcissism and insecurity and substance abuse turns her into a toxic ghoul that haunts the theater, especially as the band's finances start to take a turn.
Later in the film Becky opportunistically trades up her exasperated bandmates for a trio of newer, fresher faces (Cara Delevingne, Ashley Benson, and Dylan Gelula) and she runs her hands over their fresh faces and even more freshly dyed hair, practically salivating at the idea of bending new people, who aren't sick of her shit yet, to her will. In so many ways, Becky exhibits all the traits of an abuser — manipulating those around her without even thinking about it. But the faces of the people she makes miserable — particularly the excellent Deyn and Rankin as her two original bandmates — are not people cowering from her out of fear but people exasperated that her performance, on and off the stage, has derailed their lives as well as hers. In the first scene, Becky's old flame Danny (Dan Stevens) brings their infant child to see her and get either custody or divorce papers signed — and the introduction of a baby could have easily turned into a metaphorical Last Bastion of Purity to be tossed around the scene like a hot potato (see: any number of cinematic babies in peril in the last year or so). But what the existence of that baby does to Becky isn't metaphorical at all — nothing so neat as redemption or proof of character. And Her Smell is all the more complex for that.
Much of Her Smell, especially these backstage scenes, border on unintelligible, with numerous exchanges getting lost in the chaos. I found this to be incredibly, teeth-grindingly effective — this is a thoroughly subjective depiction of mental illness and substance abuse, and the accurate relay of information often takes a backseat in the throes of such a state. Some may not have the stomach for it, and yet Moss's oscillating, energy-devouring performance and the real-time composition of Perry's scenes make it almost impossible to look away. But tellingly, the more anxiety-inducing scenes come at the end of the film, when Becky has clawed her way to a hard-won sobriety, and there's suddenly far more to lose than there ever was in those halcyon days bouncing around in label offices for the camera. In a climate in which more and more creators are compelled to discuss and depict disordered states onscreen, Her Smell is a rare project that feels holistic enough to actually capture such a state, not just refract it through an audacious formal exercise. The thrum goes away, but we spend the rest of the film anxiously fearing its return.
vulture homepage lede
her smell
Her Smell Is a Bracing, Unnerving Depiction of Addiction