text:
\section{Introduction}\label{introduction}}

According to an often-quoted but never cited definition, ``the goodness
of fit of a statistical model describes how well it fits a set of
observations. Measures of goodness of fit typically summarize the
discrepancy between observed values and the values expected under the
model in question.''\footnote{This quote appears verbatim on Wikipedia
  and countless books, papers, and websites.} Goodness of fit measures
vary with the goals of those constructing the statistical model.
Inferential goals may emphasize in-sample fit while predictive goals may
emphasize out-of-sample fit. Prior information may be included in the
goodness of fit measure for Bayesian models, or it may not. Goodness of
fit measures may include methods to correct for model overfitting. In
short, goodness of fit measures the performance of a statistical model
against the ground truth of observed data. Fitting the data well is
generally a necessary---though not sufficient---condition for trust in a
statistical model, whatever its goals.

Of course, goodness of fit is only one concern in statistical modeling.
Researchers may trade some goodness of fit for ease of interpretation.
For example, many accurate and robust predictive models are
non-parametric ``black boxes'', making inference {[}human interpretation
of model predictions{]} difficult. Researchers may trade off some
goodness of fit for interpretability by selecting a more restrictive
parametric model. If the emphasis is on prediction, researchers may
trade some in-sample fit for predictive robustness. This is cited as one
motivation for using the Bayesian latent Dirichlet allocation (LDA)
topic model over the frequentist probabilistic latent semantic analysis
(pLSA). It is alleged that pLSA tends to overfit its training sample,
making its estimates fragile to the introduction of new data (Blei, Ng,
and Jordan 2003). Under certain conditions, pLSA and LDA are equivalent
models, however (Girolami and Kabán 2003).

Goodness of fit manifests itself in topic modeling through word
frequencies. It is a common misconception that topic models are
fully-unsupervised methods. If true, this would mean that no
observations exist upon which to compare a model's fitted values.
However, probabilistic topic models are ultimately generative models of
word frequencies (Blei, Ng, and Jordan 2003). The expected value of word
frequencies in a document under a topic model is given by the expected
value of a multinomial random variable. The that can be compared to the
predictions, then, are the word frequencies themselves. Most goodness of
fit measures in topic modeling are restricted to in-sample fit. Yet some
out-of-sample measures have been developed (Buntine 2009).

\hypertarget{probabilistic-topic-models}{%
\section{Probabilistic Topic Models}\label{probabilistic-topic-models}}

Probabilistic topic models are a family of stochastic models for
estimating abstract ``topics'' in a set of documents. Many methods have
been developed to provide a flexible family of topic models. Some
include frequently available metadata about documents, such as the time
of the publication (Blei and Lafferty 2006), or other metadata about the
document or author (Roberts et al. 2014). Most probabilistic topic
models are Bayesian, though probabilistic latent semantic analysis
(pLSA) is frequentist. Without loss of generality, all probabilistic
topic models model the document-generating process as a mixture of
multinomial distributions.\footnote{The terms ``multinomial'' and
  ``categorical'' are often used interchangeably in the topic modeling
  literature. A multinomial distribution is the sum of categorical
  distributions---or multiple trials. To avoid confusion, this paper
  uses multinomial and specifies the number of trials, even if there is
  only one trial.} Probabilistic topic models estimate parameters of an
idealized stochastic process for how words get onto the page. Instead of
writing full, syntactically-coherent, sentences, the author samples a
topic from a multinomial distribution and then, given the topic, samples
a word. The process for a single draw of the \(n\)-th word for the
\(d\)-th document, \(w_{d,n}\), is

\begin{enumerate}
\def\arabic{enumi}.{\arabic{enumi}.}
\tightlist
\item
  Sample \(z_{d,n}\sim\) \(\text{Multinomial}(1,\boldsymbol\theta_d)\)
\item
  Sample \(w_{d,n}\sim\)
  \(\text{Multinomial}(1,\boldsymbol\phi_{z_{d,n}})\)
\end{enumerate}

The variable \(z_{d,n}\) is latent. The author repeats this process
\(Nd\) times until the document is ``complete''. For a corpus of \(D\)
documents, \(V\) unique tokens, and \(K\) latent topics, the goal is to
estimate two matrices: \(\boldsymbol\Theta\) and \(\boldsymbol\Phi\).
The \(d\)-th row of \(\boldsymbol\Theta\) comprises
\(\boldsymbol\theta_d\), above. And the \(k\)-th row of
\(\boldsymbol\Phi\) comprises \(\boldsymbol\phi_k\).\footnote{LDA
  estimates these parameters by placing Dirichlet priors on \(\theta_d\)
  and \(\phi_k\).} The document term matrix (DTM)---\(\mathbf{W}\)---can
be thought of as the result of repeated sampling from
\(\boldsymbol\Theta\) and \(\boldsymbol\Phi\). In expectation we have
the following relationship:\footnote{This relationship holds for
  frequentist models. For Bayesian models, where there are priors on
  \(\boldsymbol\Theta\) and \(\boldsymbol\Phi\), the law of total
  expectation applies. This is the method used in the Appendix.}

\begin{align}
  \mathbb{E}(\mathbf{W}) &= \mathbf{n} \odot \Theta \cdot \Phi
\end{align}

Above, \(\mathbf{n}\) is a \(D\)-length vector whose \(d\)-th entry is
the number of terms in the \(d\)-th document and \(\odot\) denotes
elementwise multiplication.

\hypertarget{evaluation-metrics-for-topic-models}{%
\section{Evaluation Metrics for Topic
Models}\label{evaluation-metrics-for-topic-models}}

Most research on topic model evaluation has focused on presenting
ordered lists of words that meet human judgement about words that belong
together. For each topic, words are ordered from the highest value of
\(\boldsymbol\phi_k\) to the lowest (i.e.~the most to least frequent in
each topic). In Chang et al. (2009) the authors introduce the ``intruder
test.'' Judges are shown a few high-probability words in a topic, with
one low-probability word mixed in. Judges must find the low-probability
word, the intruder. They then repeat the procedure with documents
instead of words. A good topic model should allow judges to easily
detect the intruders.

Coherence metrics attempt to approximate the results of intruder tests
in an automated fashion. Researchers have put forward several coherence
measures. These typically compare pairs of highly-ranked words within
topics. Rosner et al. (2014) evaluate several of these. They have human
evaluators rank topics by quality and then compare rankings based on
various coherence measures to the ranking of the evaluators. They
express skepticism that existing coherence measures are sufficient to
assess topic quality. In an ACL paper, Lau, Newman, and Baldwin (2014)
find that normalized pointwise mutual information (NPMI) is a coherence
metric that closely resembles human judgement.

Measuring goodness of fit in topic models has received less attention.
The primary goodness of fit measures in topic modeling are likelihood
metrics. Likelihoods, generally the log likelihood, are naturally
obtained from probabilistic topic models. Likelihoods may contain prior
information, as is often the case with Bayesian models. If prior
information is unknown or undesired, researchers may calculate the
likelihood using only estimated parameters. Researchers have used
likelihoods to select the number of topics (Griffiths and Steyvers
2004), compare priors (Wallach, Mimno, and McCallum 2009), or otherwise
evaluate the efficacy of different modeling procedures. (Asuncion et al.
2009) (Nguyen, Boyd-Graber, and Resnik 2014) A popular likelihood method
for evaluating out-of-sample fit is called perplexity. Perplexity
measures a transformation of the likelihood of the held-out words
conditioned on the trained model. However, Chang et al. (2009),
researchers have eschewed such goodness of fit metrics. This author
believes this is a mistake which---in part---motivates this research
paper.

Researchers have used also used topic models as classifiers. These
evaluations employ precision and recall or the area under a receiver
operator characteristic (ROC) curve (AUC) on topically-tagged corpora
(Asuncion et al. 2009). The most prevalent topic in each document is
taken as a document's topical classification.

Though useful, current evaluation metrics in topic modeling are
difficult to interpret, are inappropriate for use in topic modeling, or
cannot be produced easily. Intruder tests are time-consuming and costly,
making intruder tests infeasible to conduct regularly. Coherence is not
primarily a goodness of fit measure. AUC, precision, and recall metrics
mis-represent topic models as binary classifiers. This misrepresentation
ignores one fundamental motivation for using topic models: allowing
documents to contain multiple topics. This approach also requires
substantial subjective judgement. Researchers must examine the
high-probability words in a topic and decide whether it corresponds to
the corpus topic tags or not.

Likelihoods have an intuitive definition: they represent the probability
of observing the training data if the model is true. Yet properties of
the underlying corpus influence the scale of the likelihood function.
Adding more documents, having a larger vocabulary, and even having
longer documents all reduce the likelihood. Likelihoods of multiple
models on the same corpus can be compared. (Researchers often do this to
help select the number of topics for a final model.)(Griffiths and
Steyvers 2004) Topic models on different corpora cannot be compared,
however. One corpus may have 1,000 documents and 5,000 tokens, while
another may have 10,000 documents and 25,000 tokens. The likelihood of a
model on the latter corpus will be much smaller than a model on the
former. Yet this does not indicate the model on the latter corpus is a
worse fit; the likelihood function is simply on a different scale.
Perplexity is a transformation of the likelihood for out-of-sample
documents. The transformation makes perplexity less intuitive than a raw
likelihood. Perplexity's scale is influenced by the same factors as the
likelihood.

\hypertarget{the-coefficient-of-determination-r2}{%
\section{\texorpdfstring{The Coefficient of Determination:
\(R^2\)}{The Coefficient of Determination: R\^{}2}}\label{the-coefficient-of-determination-r2}}

The coefficient of determination is a popular, intuitive, and
easily-interpretable goodness of fit measure. The coefficient of
determination, denoted \(R^2\), is most common in ordinary least squares
(OLS) regression. However, researchers have developed \(R^2\) and
several pseudo \(R^2\) measures for many classes of statistical models.
The largest value of \(R^2\) is 1, indicating a model fits the data
perfectly. The formal definition of \(R^2\) (below) is
interpreted---without loss of generality---as the proportion of
\emph{variability} in the data that is explained by the model. For
linear models with outcomes in \(\mathbb{R}_1\), \(R^2\) is bound
between 0 and 1 and is the proportion of \emph{variance} in the data
explained by the model (Neter et al. 1996). Even outside of the context
of a linear model, \(R^2\) retains its maximum of 1 and its
interpretation as the proportion of explained variability. Negative
values of \(R^2\) are possible for non-linear models or models in
\(\mathbb{R}_M\) where \(M > 1\). These negative values indicate that
simply guessing the mean outcome is a better fit than the
model.\footnote{In this author's experience, negative values indicate an
  error in one's code, rather than an exceptionally poor fitting model.}

\hypertarget{the-standard-definition-of-r2}{%
\subsection{\texorpdfstring{The Standard Definition of
\(R^2\)}{The Standard Definition of R\^{}2}}\label{the-standard-definition-of-r2}}

For a model, \(f\), of outcome variable, \(y\), where there are \(N\)
observations, \(R^2\) is derived from the following:

\begin{align}
  \bar{y} &= \frac{1}{N}\sum_{i=1}^{N}y_i\\
  SS_{tot.} &= \sum_{i=1}^N{(y_i-\bar{y})^2}\\
  SS_{resid.} &= \sum_{i=1}^N{(f_i-y_i)^2}
\end{align}

Thus, the standard definition of \(R^2\) is a ratio of summed squared
errors.

\begin{align}
    R^2 \equiv 1 - \frac{SS_{resid.}}{SS_{tot.}}
\end{align}

\hypertarget{a-geometric-interpretation-of-r2}{%
\subsection{\texorpdfstring{A Geometric Interpretation of
\(R^2\)}{A Geometric Interpretation of R\^{}2}}\label{a-geometric-interpretation-of-r2}}

\(R^2\) has a geometric interpretation as well. \(SS_{tot.}\) is the
total squared-Euclidean distance from each \(y_i\) to the mean outcome,
\(\bar{y}\). Then \(SS_{resid.}\) is the total squared-Euclidean
distance from each \(y_i\) to its predicted value under the model,
\(f_i\). Recall that for any two points
\(\mathbf{p}, \mathbf{q} \in \mathbb{R}_M\)

\begin{align}
    d(\mathbf{p},\mathbf{q}) = \sqrt{\sum_{i=1}^M{(p_i - q_i)^2}}
\end{align}

where \(d(\mathbf{p}, \mathbf{q})\) denotes the Euclidean distance
between \(\mathbf{p}\) and \(\mathbf{q}\). \(R^2\) is often taught in
the context of OLS where \(y_i, f_i \in \mathbb{R}_1\). In that case,
\(d(y_i, f_i) = \sqrt{(y_i - f_i)^2}\); by extension
\(d(y_i, \bar{y}) = \sqrt{(y_i - \bar{y})^2}\). In the multidimensional
case where \(\mathbf{y}_i, \mathbf{f}_i \in \mathbb{R}_M; M > 1\), then
\(\bar{\mathbf{y}} \in \mathbb{R}_M\) represents the point at the center
of the data in \(\mathbb{R}_M\).\footnote{In the one-dimensional case,
  \(y_i , f_i \in \mathbb{R}_1\), \(SS_{resid.}\) can be considered the
  squared-Euclidean distance between the \(n\)-dimensional vectors \(y\)
  and \(f\). However, this relationship does not hold when
  \(\mathbf{y}_i , \mathbf{f}_i \in \mathbb{R}_M ; M > 1\).}

We can rewrite \(R^2\) using the relationships above. Note than now
\(\bar{\mathbf{y}}\) is now a vector with \(M\) entries. The \(j\)-th
entry of \(\bar{\mathbf{y}}\) is averaged across all \(N\) vectors. i.e
\(\bar{y}_j = \frac{1}{N} \sum_{i=1}^{N} y_{i,v}\). From there we have:

\begin{align}
    \bar{\mathbf{y}} &= \frac{1}{N} \sum_{i=1}^{N} \mathbf{y}_i \\
    SS_{tot.} &= \sum_{i=1}^N{d(\mathbf{y}_i, \bar{\mathbf{y}}})^2\\
    SS_{resid.} &= \sum_{i=1}^N{d(\mathbf{y}_i, \mathbf{f}_i)^2}\\
    \Rightarrow R^2 & \equiv 1 - \frac{SS_{resid.}}{SS_{tot.}}
\end{align}

Fig. 1 visualizes the geometric interpretation of \(R^2\) for outcomes
in \(\mathbb{R}_2\). The left image represents \(SS_{tot.}\): the red
dots are data points (\(\mathbf{y}_i\)); the black dot is the mean
(\(\bar{\mathbf{y}}\)); the line segments represent the Euclidean
distance from each \(\mathbf{y}_i\) to \(\bar{\mathbf{y}}\).
\(SS_{tot.}\) is obtained by squaring the length of each line segment
and then adding the squared segments together. The right image
represents \(SS_{resid.}\): the blue dots are the fitted values under
the model (\(\mathbf{f}_i\)); the line segments represent the Euclidean
distance from each \(\mathbf{f}_i\) to its corresponding
\(\mathbf{y}_i\). \(SS_{resid.}\) is obtained by squaring the length of
each line segment and then adding the squared segments together.

\begin{figure}
\centering
\includegraphics{geometric_graphic-1.pdf}
\caption{Visualizing the geometric interpretation of R-squared:
corresponds to an R-squared of 0.87}
\end{figure}

The geometric interpretation of \(R^2\) is similar to the
``explained-variance'' interpretation. When \(SS_{resid.} = 0\), then
the model is a perfect fit for the data and \(R^2 = 1\). If
\(SS_{resid.} = SS_{tot.}\), then \(R^2 = 0\) and the model is no better
than just guessing \(\bar{y}\). When \(0 < SS_{resid} < SS_{tot}\), then
the model is a better fit for the data than a naive guess of
\(\bar{\mathbf{y}}\). In a non-linear or multi-dimensional model, it is
possible for \(SS_{resid.} > SS_{tot.}\). In this case, \(R^2\) is
negative, and guessing \(\bar{\mathbf{y}}\) is better than using the
model.

\hypertarget{extending-r2-to-topic-models}{%
\subsection{\texorpdfstring{Extending \(R^2\) to Topic
Models}{Extending R\^{}2 to Topic Models}}\label{extending-r2-to-topic-models}}

An \(R^2\) for topic models follows from the geometric interpretation of
\(R^2\). For a document, \(d\), the observed value, \(\mathbf{y}_d\), is
a vector of integers counting the number of times each token appears in
\(n_d\) draws. The document's fitted value under the model follows that
\(\mathbf{y}_d = \mathbf{w}_d\) represents the outcome of a multinomial
random variable. The fitted value is

\begin{align}
  \mathbf{f}_d =
    \mathbb{E}(\mathbf{w}_d) =
    n_d \odot \boldsymbol\theta_d \cdot \boldsymbol\Phi
\end{align}

The center of the documents in the corpus, \(\bar{\mathbf{y}}\), is
obtained by averaging the token counts across all documents. From this
we obtain \(R^2\).

\begin{align}
    \bar{\mathbf{y}} &= \frac{1}{D}\sum_{d=1}^{D}\mathbf{y}_d\\
    SS_{tot.} &= \sum_{d=1}^D{d(\mathbf{y}_d, \bar{\mathbf{y}})^2}\\
    SS_{resid.} &= \sum_{d=1}^D{d(\mathbf{y}_d, \mathbf{f}_d)^2}\\
    R^2 &\equiv 1 - \frac{SS_{resid.}}{SS_{tot.} }
\end{align}

\hypertarget{pseudo-coefficients-of-variation}{%
\subsection{Pseudo Coefficients of
Variation}\label{pseudo-coefficients-of-variation}}

Several pseudo coefficients of variation have been developed for models
where the traditional \(R^2\) is inappropriate. Some of these, such as
Cox and Snell's \(R^2\) (Cox and Snell 1989) or McFadden's \(R^2\)
(McFadden, Tye, and Train 1977) may apply to topic models. As pointed
out in \emph{UCLA's Institute for Digital Research and Education},
(Bruin 2006)

\begin{quote}
These are `pseudo' R-squareds because they look like R-squared in the sense that they are on a similar scale, ranging from 0 to 1 (though some pseudo R-squareds never achieve 0 or 1) with higher values indicating better model fit, but they cannot be interpreted as one would interpret an OLS R-squared and different pseudo R-squareds can arrive at very different values.
\end{quote}

The empirical section of this paper calculates an uncorrected McFadden's
\(R^2\) for topic models to compare to the standard (non-pseudo)
\(R^2\). McFadden's \(R^2\) is defined as

\begin{align}
    R^2_{Mc} & \equiv 1 - \frac{ ln( L_{full} ) }{ ln( L_{restricted} ) }
\end{align}

where \(L_{full}\) is the estimated likelihood of the data under the
model and \(L_{restricted}\) is the estimated likelihood of the data
free of the model. In the context of OLS, the restricted model is a
regression with only an intercept term. For other types of models (such
as topic models), care should be taken in selecting what ``free of the
model'' means.

For topic models, ``free of the model'' may mean that the words were
drawn from a simple multinomial distribution, whose parameter is
proportional to the relative frequencies of words in the corpus overall.
This is the specification used for the emperical analysis in this paper.

\hypertarget{empirical-evaluation-of-topic-model-r2}{%
\section{\texorpdfstring{Empirical Evaluation of Topic Model
\(R^2\)}{Empirical Evaluation of Topic Model R\^{}2}}\label{empirical-evaluation-of-topic-model-r2}}

This paper performs three analyses to empirically evaluate \(R^2\) for
topic models. Two analyses use Monte Carlo-simulated corpora and one
uses a corpus of grants awarded through the Department of Health and
Human Services. This latter corpus was obtained from the National
Institutes of Health \emph{NIH ExPORTER} database. (``Welcome to
Exporter,'' n.d.) The first analysis uses simulated corpora to observe
how the properties of training corpora influence \(R^2\). The second
analysis uses these same simulated corpora to compare \(R^2\) as
commonly-defined to McFadden's \(R^2\) in the context of topic modeling.
The final analysis compares the \(R^2\) values of various models
constructed on the NIH corpus.

\hypertarget{monte-carlo-simulated-corpora}{%
\subsection{Monte Carlo-Simulated
Corpora}\label{monte-carlo-simulated-corpora}}

It is possible to simulate corpora that share some key statistical
properties of human-generated language using the functional form of a
topic model. To do so, one must set \(\boldsymbol\beta\) such that its
entries are proportional to a power law. The result is a corpus whose
relative term frequencies follow Zipf's law of language (Zipf 1949). The
derivation of this result is included in the appendix.

This method generates a corpus of \(D\) documents, \(V\) tokens, and
\(K\) topics through the following stochastic process:

\begin{enumerate}
\def\arabic{enumi}.{\arabic{enumi}.}
\tightlist
\item
  Initialize
  \(\\ \boldsymbol\phi_k \sim \text{Dirichlet}(\boldsymbol\beta)\\\)
  \(\boldsymbol\theta_d \sim \text{Dirichlet}(\boldsymbol\alpha)\)
\item
  Then for each document draw \(\\ n_d \sim \text{Poisson}(\lambda)\)
\item
  Finally, for each document draw the following \(n_d\) times
  \(\\ z_{d,n} \sim \text{Multinomial}(1, \boldsymbol\theta_d)\\\)
  \(w_{d,n} \sim \text{Multinomial}(1, \boldsymbol\phi_{z_{d,n}})\)
\end{enumerate}

The words for document \(d\) are populated by sampling with replacement
from \(z_{d,n}\) and \(w_{dk,n}\) for \(n_d\) iterations. The parameters
\(V\), \(K\), and \(\lambda\) may be varied to adjust the corpus
properties for the number of tokens, topics, and average document length
respectively. Adjusting the shape and magnitude of \(\boldsymbol\alpha\)
and \(\boldsymbol\beta\) affect the concentration of topics within
documents and words within topics respectively.

\hypertarget{latent-dirichlet-allocation}{%
\subsection{Latent Dirichlet
Allocation}\label{latent-dirichlet-allocation}}

This paper uses Latent Dirichlet Allocation (LDA) and collapsed Gibbs
sampling to estimate topic models from actual data. LDA---possibly the
most popular topic model---is a Bayesian hierarchical model that places
Dirichlet priors on \(\boldsymbol\theta_d, \forall d\) and
\(\boldsymbol\phi_k, \forall k\) (Blei, Ng, and Jordan 2003).

A limitation with LDA is that the number of topics in the corpus must be
specified a priori where no prior knowledge often exists. Nevertheless,
LDA's empirical utility has been overwhelmingly demonstrated. For LDA
models fit in this paper, symmetric parameters are used for both prior
distributions. Each entry of \(\boldsymbol\alpha\) is 0.1; each entry of
\(\boldsymbol\beta\) is 0.01. Models are trained using Gibbs sampling
from the \emph{textmineR} package for the R language (Jones, n.d.).

\hypertarget{comparing-r2-to-mcfaddens-r2}{%
\subsection{\texorpdfstring{Comparing \(R^2\) to McFadden's
\(R^2\)}{Comparing R\^{}2 to McFadden's R\^{}2}}\label{comparing-r2-to-mcfaddens-r2}}

McFadden's pseudo \(R^2\) is calculated for all simulated corpora for
comparison to the standard \(R^2\). McFadden's \(R^2\) is a ratio of
likelihoods. Various methods exist for calculating likelihoods of topic
models (Buntine 2009). Most of these methods have a Bayesian perspective
and incorporate prior information. Not all topic models are Bayesian,
however. And in the case of simulated corpora, the exact data-generating
parameters are known a priori. As a result, likelihoods calculated for
this paper follow the simplest definition: they represent the
probability of observing the generated data, given the (known or
estimated) multinomial parameters of the model. The ``model-free''
likelihood assumes that each document is generated by drawing from a
single multinomial distribution. The parameters of this ``model-free''
distribution are proportional to the frequency of each token in the
data.

\hypertarget{empirical-properties-of-r2-for-topic-models}{%
\subsection{\texorpdfstring{Empirical Properties of \(R^2\) for Topic
Models}{Empirical Properties of R\^{}2 for Topic Models}}\label{empirical-properties-of-r2-for-topic-models}}

The \(R^2\) for topic models has the following empirical properties: It
is bound between \(-\infty\) and 1. \(R^2\) is invariant to the
\textit{true} number of topics in the corpus. \(R^2\) increases with the
\textit{estimated} number of topics using LDA; this indicates that there
might be a risk of overfit from a model with too many topics (discussed
in more detail in the next section). \(R^2\) decreases as the vocabulary
size of the corpus increases. \(R^2\) increases as the average document
length increases. (See Fig. 2, Fig. 3, Fig. 4, and Fig. 5.)

\begin{figure}
\centering
\includegraphics{k_plot-1.pdf}
\caption{Varying the number of topics on a simulated corpus}
\end{figure}

\begin{figure}
\centering
\includegraphics{l_plot-1.pdf}
\caption{Varying average document length on a simulated corpus}
\end{figure}

\begin{figure}
\centering
\includegraphics{v_plot-1.pdf}
\caption{Varying vocabulary size on a simulated corpus}
\end{figure}

\begin{figure}
\centering
\includegraphics{d_plot-1.pdf}
\caption{Varying number of documents on a simulated corpus}
\end{figure}

Most of these empirical properties were obtained by using simulated
corpora, as described earlier in the paper, with one exception. The
default parameter settings for Monte Carlo simulation are \(K = 50\)
topics, \(D = 2{,}000\) documents, \(V = 5{,}000\) tokens, and
\(\lambda = 500\) for the average document length, which is distributed
\(\text{Poisson}(\lambda)\). Each parameter was varied, holding other
parameters constant. In each case, Monte Carlo simulation generates a
document term matrix for the corpus while the parameters for generating
the documents are known. \(R^2\) is calculated for each simulated
corpus, using the population parameters. These \(R^2\) metrics represent
a best-case scenario, avoiding misspecification and other pathologies
present with topic model estimation algorithms.

McFadden's pseudo \(R^2\) is also calculated for these simulated data.
McFadden's \(R^2\) is generally lower than the standard \(R^2\).
McFadden's \(R^2\) is subject to the common problem of many pseudo
\(R^2\) measures; its true upper bound is less than one. This makes
sense. If McFadden's \(R^2\) were to equal 1, then the likelihood of the
data would be 1 which is impossible. It is never the case that a
likelihood will equal 1. Given the scale of linguistic data, the
likelihood will always be significantly less than 1. Therefore, a scale
correction measure is needed, making McFadden's \(R^2\) more
complicated.

McFadden's \(R^2\) increases slightly with the number of true topics;
this is problematic. When the scale of an \(R^2\) varies with
\textit{known} properties of the data, such as the number of documents,
vocabulary size, average document length, etc. scale correction measures
are possible. However, when the metric varies with an \textit{unknown}
property, such as the number of latent topics, then a scale correction
is not possible. For this reason alone, McFadden's \(R^2\) is
undesirable. Other properties of McFadden's \(R^2\) are consistent with
the properties of the standard \(R^2\)

These properties of \(R^2\) suggest a change in focus for the topic
modeling research community. Document length is an important factor in
model fit whereas the number of documents is not. When choosing between
fitting a topic model on short abstracts, full-page documents, or
multi-page papers, it appears that more text is better. Second, since
model fit is invariant for corpora over 1,000 documents (our lower bound
for simulation), taking a sample from a large corpus may yield
reasonable estimates of the larger corpus's parameters.\footnote{Estimating
  appropriate sample sizes and strategies based on properties of the
  text and desired analyses is a fruitful area of future research.} The
topic modeling community has heretofore focused on scalability to large
corpora of hundreds-of-thousands to millions of documents. There has
been less focus on document length.

\hypertarget{comparison-of-simulated-data-with-the-nih-corpus}{%
\subsection{Comparison of Simulated Data with the NIH
Corpus}\label{comparison-of-simulated-data-with-the-nih-corpus}}

\(R^2\) increases with the \textit{estimated} number of topics using
LDA. This indicates a risk of model overfit with respect to the number
of estimated topics. To evaluate the effect of \(R^2\) on the number of
estimated topics, LDA models were fit to two corpora. The first corpus
is simulated, using the parameter defaults: \(K = 50\), \(D = 2{,}000\),
\(V = 5{,}000\), and \(\lambda = 500\). The second corpus is on the
abstracts of 1,000 randomly-sampled research grants for fiscal year 2014
in the National Institutes of Health's ExPORTER database. In both cases,
LDA models are fit to the data estimating a range of \(K\). For each
model, \(R^2\) and the log likelihood are calculated. The known
parameters for this NIH corpus are \(D = 1{,}000\), \(V = 8,751\), and
the median document length is 222 words. This vocabulary has standard
stop words removed,\footnote{175 English stop words from the snowball
  stemmer project.
  http://snowball.tartarus.org/algorithms/english/stop.txt} is tokenized
to consider only unigrams, and excludes tokens that appear in fewer than
2 documents.

The same likelihood calculation is used here, as described above. This
likelihood calculation excludes prior information typically used when
calculating the likelihood of an LDA model. This is perhaps not the
optimal method for calculating likelihoods when using a Bayesian model.
However, this prior information is excluded here for two reasons. First,
it is consistent with the method used for calculating McFadden's \(R^2\)
earlier in the paper. Second, this log likelihood can be calculated
exactly. LDA's likelihood is contained within an intractable integral.
Various approximations have been developed (Buntine 2009). However,
there is some risk that a comparison of an approximated likelihood to
\(R^2\) may be biased by the approximation method.

\begin{figure}
\centering
\includegraphics{sim_figure-1.pdf}
\caption{Comparison of R2 and log likelihood for LDA models fit on a
simulated corpus}
\end{figure}

\begin{figure}
\centering
\includegraphics{nih_figure-1.pdf}
\caption{Comparison of R2 and log likelihood for LDA models fit on the
NIH corpus}
\end{figure}

Fig. 6 and Fig. 7 depict \(R^2\) and the log likelihood over a range of
estimated \(K\) for both corpora. Fig. 6 corresponds to the simulated
corpus. Fig. 7 corresponds to the NIH corpus. From both images, we see
that \(R^2\) and the log likelihood both increase with the number of
estimated topics. The scale of \(R^2\) is comparable between the
simulated corpus and the NIH corpus, in spite of their differences in
size. In the case of the simulated corpus, we know that the true number
of topics is 50. However, this is not clear from observing \(R^2\) or
the log likelihood. It does not appear that \(R^2\) or this ``raw''
likelihood can help find the true number of topics.

Chang et al. (2009) observe that there may be a trade-off between model
fit and human interpretation. Specifically, they find that humans can
more easily interpret models with fewer topics. This may be true. As
discussed in an earlier section, non-\(R^2\) goodness of fit measures
are not readily comparable across corpora and models. Because \(R^2\) is
comparable, it is now possible to quantify how much goodness of fit is
lost when K is lowered. This does not address any issues arising from a
pathological misspecification of the model, an ``incorrect'' K, for
example. In the case of the NIH corpus, \(R^2\) goes from 0.27 to 0.2
when the number of estimated topics is lowered from 200 to 100.

\hypertarget{conclusion}{%
\section{Conclusion}\label{conclusion}}

\(R^2\) has many advantages over standard goodness of fit measures
commonly used in topic modeling. Current goodness of fit measures are
difficult to interpret, compare across corpora, and explain to lay
audiences. \(R^2\) does not have any of these issues. Its scale is
effectively bounded between 0 and 1, as negative values (though
possible) are rare and indicate extreme model misspecification. \(R^2\)
may be used to compare models of different corpora, if necessary.
Scientifically-literate lay audiences are almost uniformly familiar with
\(R^2\) in the context of linear regression; the topic model \(R^2\) has
a similar interpretation, making it an intuitive measure.

The standard (geometric) interpretation of \(R^2\) is preferred to
McFadden's pseudo \(R^2\). The effective upper bound for McFadden's
\(R^2\) is considerably smaller than 1. A scale correction measure is
needed. Also, it is debatable which likelihood calculation(s) are most
appropriate. These issues make McFadden's \(R^2\) complicated and
subjective. However, a primary motivation for deriving a topic model
\(R^2\) is to remove the complications that currently hinder evaluating
and communicating the fidelity with which topic models represent
observed text. Most problematically, McFadden's \(R^2\) varies with the
number of \textit{true} topics in the data. It is therefore unreliable
in practice where the true number of topics in unknown.

One result of this research indicates that further study is needed on
the relationship between goodness of fit and document length, the number
of documents in a corpus, and vocabulary size. Results reported in this
paper demonstrate that document length is a considerable factor in model
fit, whereas the number of documents (above 1,000) is not. If robust,
this result indicates that the topic modeling community may need to
change focus away from scaling estimation algorithms for large corpora.
Instead, more effort should be put towards obtaining high-quality data.
Also, studying the relationship between these parameters, along with the
number of topics, may facilitate the development of an adjusted \(R^2\),
guarding against model overfit.

Lack of consistent evaluation metrics has limited the use of topic
models as a mature statistical method. The development of an \(R^2\) for
topic modeling is no silver bullet. However it represents a step towards
establishing consistency and rigor in topic modeling. This paper
proposes reporting \(R^2\) as a standard metric alongside topic models,
as is typically done with OLS.

\newpage

\hypertarget{appendix}{%
\section{Appendix}\label{appendix}}

Below derives the expected term frequency of a corpus whose terms are
generated by the stochastic process modeled by Latent Dirichlet
Allocation. This process is

\begin{enumerate}
\def\arabic{enumi}.{\arabic{enumi}.}
\tightlist
\item
  Initialize
  \(\\ \boldsymbol\phi_k \sim \text{Dirichlet}(\boldsymbol\beta)\\\)
  \(\boldsymbol\theta_d \sim \text{Dirichlet}(\boldsymbol\alpha)\)
\item
  Then for each document draw \(\\ n_d \sim \text{Poisson}(\lambda)\)
\item
  Finally, for each document draw the following \(n_d\) times
  \(\\ z_{d,n} \sim \text{Multinomial}(1, \boldsymbol\theta_d)\\\)
  \(w_{d,n} \sim \text{Multinomial}(1, \boldsymbol\phi_{z_{d,n}})\)
\end{enumerate}

The expected term frequencies of a corpus generated with the above
process are proportional to \(\boldsymbol\beta\)---the parameter for the
Dirichlet prior for terms over topics. This implies that for a simulated
corpus to follow Zipf's law, then \(\boldsymbol\beta\) must be
proportional to a power law.\footnote{There may be other implications as
  well. The author plans to explore these in future research.}

Let's start by carrying the expected value through both sides of
equation (11), above, using the law of total expectation.

\begin{align*}
  \mathbb{E}(\mathbf{w}_d)
    &= \mathbb{E}\left(n_d \odot
      \boldsymbol\theta_d \cdot \boldsymbol\Phi\right)\\
    &= \mathbb{E}\left(n_d
      \begin{pmatrix}
        \sum_{k = 1}^K \theta_{d,k} \cdot \phi_{k,1}\\
        \sum_{k = 1}^K \theta_{d,k} \cdot \phi_{k,2}\\
        ...\\
        \sum_{k = 1}^K \theta_{d,k} \cdot \phi_{k,V}\\
      \end{pmatrix}
    \right)\\
    &= \mathbb{E}(n_d)
      \begin{pmatrix}
        \mathbb{E}(\sum_{k = 1}^K \theta_{d,k} \cdot \phi_{k,1})\\
        \mathbb{E}(\sum_{k = 1}^K \theta_{d,k} \cdot \phi_{k,2})\\
        ...\\
        \mathbb{E}(\sum_{k = 1}^K \theta_{d,k} \cdot \phi_{k,V})\\
      \end{pmatrix}\\
    &= \mathbb{E}(n_d)
      \begin{pmatrix}
        \sum_{k = 1}^K \mathbb{E}(\theta_{d,k}) \mathbb{E}(\phi_{k,1})\\
        \sum_{k = 1}^K \mathbb{E}(\theta_{d,k})\mathbb{E}(\phi_{k,2})\\
        ...\\
        \sum_{k = 1}^K \mathbb{E}(\theta_{d,k}) \mathbb{E}(\phi_{k,V})\\
      \end{pmatrix}
\end{align*}

The last step, above, is due to indpendence of \(\boldsymbol\theta_d\)
and \(\boldsymbol\phi_k\) \(\forall d,k\).

Before carrying on, let's note two more relationships:

\begin{enumerate}
\def\arabic{enumi}.{\arabic{enumi}.}
\tightlist
\item
  \(\boldsymbol\phi_k \sim \text{i.i.d. Dirichlet}(\boldsymbol\beta)\)
  means that
  \(\mathbb{E}(\boldsymbol\phi_i) = \mathbb{E}(\boldsymbol\phi_j)\)
  \(\forall i,j \in \{1,2,..,K\}\)
\item
  The expected value of a Dirchlet random
  variable---\(\mathbf{X}\)---with parameter \(\boldsymbol\delta\) is
  \(\mathbb{E}(\mathbf{X}) = \frac{1}{\sum_{m=1}^M\delta_m}\cdot\boldsymbol\delta\)
\end{enumerate}

From number 1., above, we can pull \(\mathbb{E}(\phi_{.,k})\) outside of
the summation. And we can carry through the expected values using
definition in 2., above.

\begin{align*}
  \mathbb{E}(\mathbf{w}_d)
    &= \mathbb{E}(n_d)
      \begin{pmatrix}
        \mathbb{E}(\phi_{k,1}) \sum_{k = 1}^K \mathbb{E}(\theta_{d,k}) \\
        \mathbb{E}(\phi_{k,2})\sum_{k = 1}^K \mathbb{E}(\theta_{d,k})\\
        ...\\
        \mathbb{E}(\phi_{k,V})\sum_{k = 1}^K \mathbb{E}(\theta_{d,k}) \\
      \end{pmatrix}\\
    &= n_d
      \begin{pmatrix}
        \frac{\beta_1}{\sum_{v=1}^V\beta_v}
          \sum_{k = 1}^K \frac{\alpha_k}{\sum_{k = 1}^K \alpha_k} \\
        \frac{\beta_2}{\sum_{v=1}^V\beta_v}
          \sum_{k = 1}^K \frac{\alpha_k}{\sum_{k = 1}^K \alpha_k} \\
        ...\\
        \frac{\beta_V}{\sum_{v=1}^V\beta_v}
          \sum_{k = 1}^K \frac{\alpha_k}{\sum_{k = 1}^K \alpha_k} \\
      \end{pmatrix}\\
    &= n_d
      \begin{pmatrix}
        \frac{\beta_1}{\sum_{v=1}^V\beta_v}
          \frac{\sum_{k = 1}^K \alpha_k}{\sum_{k = 1}^K \alpha_k} \\
        \frac{\beta_2}{\sum_{v=1}^V\beta_v}
          \frac{\sum_{k = 1}^K \alpha_k}{\sum_{k = 1}^K \alpha_k} \\
        ...\\
        \frac{\beta_V}{\sum_{v=1}^V\beta_v}
          \frac{\sum_{k = 1}^K \alpha_k}{\sum_{k = 1}^K \alpha_k} \\
      \end{pmatrix}\\
    &= n_d
      \begin{pmatrix}
        \frac{\beta_1}{\sum_{v=1}^V\beta_v} \cdot 1 \\
        \frac{\beta_2}{\sum_{v=1}^V\beta_v} \cdot 1  \\
        ...\\
        \frac{\beta_V}{\sum_{v=1}^V\beta_v} \cdot 1  \\
      \end{pmatrix}\\
   &= \frac{n_d}{\sum_{v=1}^V\beta_v}\boldsymbol\beta \\
   &\propto \boldsymbol\beta
\end{align*}

The end result is that the expected term frequency of a single document
is proportional to \(\boldsymbol\beta\)---the Dirichlet parameter for
terms over topics.

The term frequency for the whole corpus is the sum of the term
frequencies for each document. Specifically

\begin{align*}
  \mathbf{w} = \sum_{d=1}^D\mathbf{w}_d
\end{align*}

The expected value under the model, then, can be carried through.

\begin{align*}
  \mathbb{E}(\mathbf{w})
    &= \mathbb{E}\left(\sum_{d=1}^D\mathbf{w}_d\right) \\
    &= \sum_{d=1}^D\mathbb{E}(\mathbf{w}_d)\\
    &= \sum_{d=1}^D \frac{n_d}{\sum_{v=1}^V\beta_v}\boldsymbol\beta\\
    &= \frac{\sum_{d=1}^D n_d}{\sum_{v=1}^V\beta_v}\boldsymbol\beta\\
    &\propto \boldsymbol\beta
\end{align*}

\newpage

\hypertarget{references}{%
\section*{References}\label{references}}
\addcontentsline{toc}{section}{References}

\hypertarget{refs}{}
\leavevmode\hypertarget{ref-asuncion2009smoothing}{}%
Asuncion, Arthur, Max Welling, Padhraic Smyth, and Yee Whye Teh. 2009.
``On Smoothing and Inference for Topic Models.'' In \emph{Proceedings of
the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence},
27--34. AUAI Press.

\leavevmode\hypertarget{ref-blei2006dynamic}{}%
Blei, David M, and John D Lafferty. 2006. ``Dynamic Topic Models.'' In
\emph{Proceedings of the 23rd International Conference on Machine
Learning}, 113--20. ACM.

\leavevmode\hypertarget{ref-blei2003latent}{}%
Blei, David M, Andrew Y Ng, and Michael I Jordan. 2003. ``Latent
Dirichlet Allocation.'' \emph{Journal of Machine Learning Research} 3
(Jan): 993--1022.

\leavevmode\hypertarget{ref-bruin2006faq}{}%
Bruin, J. 2006. ``FAQ. What Are Pseudo-R-Squareds.'' UCLA: Academic
Technology Services, Statistical Consulting Group. Retrieved~\ldots{}.

\leavevmode\hypertarget{ref-buntine2009estimating}{}%
Buntine, Wray. 2009. ``Estimating Likelihoods for Topic Models.'' In
\emph{Asian Conference on Machine Learning}, 51--64. Springer.

\leavevmode\hypertarget{ref-chang2009reading}{}%
Chang, Jonathan, Sean Gerrish, Chong Wang, Jordan L Boyd-Graber, and
David M Blei. 2009. ``Reading Tea Leaves: How Humans Interpret Topic
Models.'' In \emph{Advances in Neural Information Processing Systems},
288--96.

\leavevmode\hypertarget{ref-cox1989analysis}{}%
Cox, DR, and EJ Snell. 1989. \emph{Analysis of Binary Data}. Vol. 32.
CRC Press.

\leavevmode\hypertarget{ref-girolami2003equivalence}{}%
Girolami, Mark, and Ata Kabán. 2003. ``On an Equivalence Between Plsi
and Lda.'' In \emph{SIGIR}, 3:433--34.

\leavevmode\hypertarget{ref-griffiths2004finding}{}%
Griffiths, Thomas L, and Mark Steyvers. 2004. ``Finding Scientific
Topics.'' \emph{Proceedings of the National Academy of Sciences} 101
(suppl 1). National Acad Sciences: 5228--35.

\leavevmode\hypertarget{ref-textminer}{}%
Jones, Tommy. n.d. ``TextmineR.'' \url{https://www.rtextminer.com}.

\leavevmode\hypertarget{ref-lau2014machine}{}%
Lau, Jey Han, David Newman, and Timothy Baldwin. 2014. ``Machine Reading
Tea Leaves: Automatically Evaluating Topic Coherence and Topic Model
Quality.'' In \emph{Proceedings of the 14th Conference of the European
Chapter of the Association for Computational Linguistics}, 530--39.

\leavevmode\hypertarget{ref-mcfadden1977application}{}%
McFadden, Daniel, William B Tye, and Kenneth Train. 1977. \emph{An
Application of Diagnostic Tests for the Independence from Irrelevant
Alternatives Property of the Multinomial Logit Model}. Institute of
Transportation Studies, University of California.

\leavevmode\hypertarget{ref-neter1996applied}{}%
Neter, John, Michael H Kutner, Christopher J Nachtsheim, and William
Wasserman. 1996. \emph{Applied Linear Statistical Models}. Vol. 4. Irwin
Chicago.

\leavevmode\hypertarget{ref-nguyen2014sometimes}{}%
Nguyen, Viet-An, Jordan Boyd-Graber, and Philip Resnik. 2014.
``Sometimes Average Is Best: The Importance of Averaging for Prediction
Using Mcmc Inference in Topic Modeling.'' In \emph{Proceedings of the
2014 Conference on Empirical Methods in Natural Language Processing},
1752--7.

\leavevmode\hypertarget{ref-roberts2014structural}{}%
Roberts, Margaret E, Brandon M Stewart, Dustin Tingley, Christopher
Lucas, Jetson Leder-Luis, Shana Kushner Gadarian, Bethany Albertson, and
David G Rand. 2014. ``Structural Topic Models for Open-Ended Survey
Responses.'' \emph{American Journal of Political Science} 58 (4). Wiley
Online Library: 1064--82.

\leavevmode\hypertarget{ref-rosner2014evaluating}{}%
Rosner, Frank, Alexander Hinneburg, Michael Röder, Martin Nettling, and
Andreas Both. 2014. ``Evaluating Topic Coherence Measures.'' \emph{arXiv
Preprint arXiv:1403.6397}.

\leavevmode\hypertarget{ref-wallach2009rethinking}{}%
Wallach, Hanna M, David M Mimno, and Andrew McCallum. 2009. ``Rethinking
Lda: Why Priors Matter.'' In \emph{Advances in Neural Information
Processing Systems}, 1973--81.

\leavevmode\hypertarget{ref-nih}{}%
``Welcome to Exporter.'' n.d. \url{https://exporter.nih.gov/}.

\leavevmode\hypertarget{ref-zipf1949human}{}%
Zipf, George Kingsley. 1949. ``Human Behavior and the Principle of Least
Effort.'' Addison-Wesley Press.

\end{document}

redpajama_set_name: RedPajamaArXiv


Do you have a car? Then you may have face lots of problem due to the battery. Basically, the issue of cells occurs in the cold weather, when the battery consumes too much. Therefore, the question is that what can people do when they find their car battery low? Well, its best solution is the portable car battery charger. It depends on the car's battery that which best portable car battery charger would be the best option. Even you will also get the best and effective options online which you can easily buy online. Here are some facts about the portable car battery charger.
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Check out the reverse polarity protection feature in the charger which makes it better.
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It should be very easy to use and also safe.
Check out the safety timer of the charger, which can help you to save the power.
The weight of the charger should not be too much heavy, 2.3 pounds is an ideal weight.
Stability of the charger will give you the best outcomes and Rubber base, and external mounting holes are accommodating in this process.
The clamps of the charger should be spark proof.
If the charger has a LED indicator, then it will really help you to diagnose the battery and give you information.
Well, we have covered all the main points those will help the customers to buy the ideal car battery charger for their car. It is beneficial especially if you are in the habit of forgetting turn off the headlight of the vehicle. Therefore, it will keep the battery of car full.


text:
In Chinatown, a new clinic offers homeless first step to housing
Published: May. 9, 2018 at 7:50 PM HST|Updated: May. 10, 2018 at 10:05 AM HST
(Image: Hawaii News Now)
CHINATOWN (HawaiiNewsNow) - Honolulu police are on the verge of launching an urgent care clinic in Chinatown, where homeless people can see a doctor for free.
The pilot project combines health care with an offer of housing.
In April, signs went up offering free medical care in the building next to HPD's substation in Chinatown.
The Joint Outreach Center won't be fully operational until June. But word about the clinic is already getting out.
Over the past month, a pair of volunteer doctors have treated more than 100 patients, several of whom are now in shelter or detox.
"This is a walk-in clinic. They just need to show up and we'll take care of them regardless of insurance or not," said Andy Mounthongdy, executive director of the Hawaii Homeless Healthcare Hui.
While the clinic is open to the public, its mission is to help the homeless by providing an option other than the ER. An average trip to the emergency room at the Queen's Medical Center costs taxpayers $4,000.
"Here, as far as coming to this clinic, it's probably only going to cost $100 to $200," said Mounthongdy.
Most of the of the patients who've been coming in are in need of wound care.
After seeing the doctor, additional staff from multiple homeless service providers are on hand to offer housing and help with issues associated with mental illness and addiction.
"We've never had such a big partnership with community service providers, medical providers and law enforcement," said David Abitbol, the CHOW Project's policy and contracts manager.
"There's already a lot of people who are homeless in this area. And what we're here to do is take them off the street," said the executive director of the Institute for Human Services, Connie Mitchell. "If police find someone in need of attention they can just bring them right over."
Drs. Josh Green and Scott Miscovich both volunteer their time at the clinic.
All other costs of running of are covered by grants from a number of organization, including The Queen's Medical Center, HMSA and Island Hospice.
The president of the Chinatown Business and Community Association says she's excited to welcome the clinic to the neighborhood.
"I think it's a bright spot for Chinatown to have a center here that people can go instead of calling the ambulance constantly," said Chu Lan Shubert-Kwock.
Once the clinic is fully operational, HPD wants to hear from its neighbors in Chinatown about how the project is going.
"I'm very excited about this location," said police Lt. Mike Lambert. "I hope it will serve as a blue print to other neighborhoods and perhaps other agencies."

redpajama_set_name: RedPajamaCommonCrawl


text:
Ditlev Conrad Reventlow (6. april 1708 – 4. marts 1794 i Celle) var en dansk gehejmekonferensråd.

Biografi
Han var søn af Ditlev Reventlow og Anna Margrethe von Jessen, blev immatrikuleret 1724 ved universitetet i Kiel, blev 1734 kammerjunker, var 1738-57 landfoged i Neuenburg i Oldenburg, blev 1747 konferensråd, 1750 kammerherre, 1754 landråd, 1765 overhofmester hos dronning Sophie Magdalene, blev 29. januar 1766 Hvid Ridder og samme år gehejmeråd. Oktober 1767 blev han ved grev Bothmers afgang beskikket til overhofmester hos dronning Caroline Mathilde, men afgik efter næppe et års tjeneste, da han afløstes af Christian Frederik Holstein og udnævntes til gehejmekonferensråd 1768. Hengivenheden for dronningen førte ham til Celle, hvor han i stor stilhed henlevede resten af sit liv. Han døde 4. marts 1794.

Familie
Reventlow ægtede 2. april 1748 i Achim Kirche Veronica Margaretha Clementine von Klinkowström (4. februar 1708 i Hannover - 2. december 1773 i Celle), datter af datter af general Balthasar von Klinkowström. Parret er begravet i en muret gravhvælving i samme kirke; han i en hvid marmorsarkofag, forsynet med det reventlowske våben, indskrift og ved fodenden en Psyke i siddende stilling; hun i en sarkofag af sort marmor med det forenede reventlow-klinkowströmske våben og indskrift.

Kilder
  Biografi af Louis Bobé
 Danmarks Adels Aarbog 1939.

Ditlev Conrad
Gehejmekonferensråder fra Danmark
Hofmestre fra Danmark
Kammerherrer fra Danmark
Kammerjunkere fra Danmark
Personer i Dansk Biografisk Leksikon
Riddere af Dannebrog (1671-1808)
Danskere i 1700-tallet

redpajama_set_name: RedPajamaWikipedia


Facebook got its start on the campus of Harvard University 15 years ago this month as was a way for students to communicate.
All these years later, it is a worldwide social media giant and has changed how we interact, even on Valentine's Day.
Back in 2013, Mike Stevens expressed skepticism and found old traditions still seem to work just fine.


A brown textured mug handmade from stoneware clay by Yuichiro Suzuki of Inima pottery. This is a good size for coffee or tea. The glaze has a slightly mottled appearance with the textures of the glaze through nicely. This gives a quirky twist on a functional object.
Overall a mug that can be used on a daily basis. They would also look great in a grouping as they all have different textures and have their own character.


He was skinny, his Hanes t-shirt nearly swallowing him up as it hung down past his waist over his dark sweat pants that pooled around the China Town slippers on his feet. "How lucky are we?!" He peels a few slices off the block of government issue cheese, a long, unnaturally orange-colored rectangle housed in a cardboard tube. I was as amused as I was confused.
He could tell I wanted more, "Well, the last place I lived we didn't ever really have bread. It was always moldy. And the place before that, we NEVER had cheese, that was like, a luxurious thing and all, and then before that the stove aint never worked…" He smeared both sides of the bread with the contents of an oversized tub of margarine, filled the two slices of bread with at least five pieces of cheese, and gently set it in a hot pan that screamed a victorious sizzle in response. "See!" the sound thrilled him, "We got, like…MAD cheese up in here, they don't even care when I use like a grip of slices. And look at all this butter! This thing HUGE! and we got bread for days!" he claps his hands, thrilled at the bounty that the group home kitchen provided.
I think about this, about the conversation I'd had the night before as I watch him finish up his grilled cheese. I realize that gratitude and happiness have nothing to do with circumstances. You can decide to focus on every great thing in your life, no matter how small, or you can decide to focus on what's broken. It's your choice.
I used Fort George Omegatex in the filling, and to wash it all down with.
Oh yeah! I want to devour that whole stack in all of it's cheesy bacon glory!
I love your stories! Makes each recipe EXTRA special and this one is quite special- gave me the feels. We all experience good and bad but the outlook you choose and how you react is definition of your character. God bless that little boy. Grilled cheese is such a great comfort food and making it from scratch, well makes it that much more fancy! 🙂 I will have to try. You made my day, Thank you!
Wow you truly are a beeroness! This sandwich is out of this world! Oh how I wish I could just take it right off the screen!
you are a seriously amazing person. I think I have a crush on you.
ok. So I had a crush on you when I thought you were just this hot, smart girl who loves beer and can cook. Now you're a good person too?? I'm helpless.
This looks amazing! So want to try this. Not to post something unrelated but I keep getting emails about all these great sounding recipes and when I click on the link or just come to this website this is the newest blog. Where are all the newest recipes/ blogs posted?
It might be your browser. It comes up just fine for me on my end. I tried a few other computers and it seems fine. Are you using explorer?


text:
Former SEAL Eddie Gallagher is now peddling clothing line, supplements
By Yaron Steinbuch
Eddie Gallagher AP
Navy SEAL pleads guilty in strangulation death of Green Beret
Retired Navy SEAL bragged about 'breaching the Capitol': video
Navy SEALs rescue American hostage in Nigeria
Navy SEAL who killed bin Laden slams Trump for retweeting QAnon conspiracy theory
Retired Navy SEAL Eddie Gallagher, who was convicted of posing for a photo with the corpse of a young ISIS terrorist, has been hawking his line of clothing and endorsing nutritional supplements since President Trump helped clear him, according to a report.
Gallagher's case made headlines after the president intervened on his behalf when he faced war-crimes charges.
The 40-year-old, who was acquitted of murdering the 17-year-old jihadist, was demoted to petty officer first class and had his pay cut after his conviction for posing with the body while serving as a platoon leader in Iraq.
Trump, who called Gallagher "one of the ultimate fighters" during a rally in Florida, stepped in to reverse his punishment and restore his rank of chief petty officer.
Last month, Gallagher also met with Trump at his Mar-a-Lago resort in Florida to thank him for taking up his controversial case.
The former SEAL has capitalized on his notoriety by amassing social media followers, and peddling T-shirts that read "stay salty" and hooded sweatshirts featuring pockets designed to hold beer bottles, The New York Times reported.
He also promotes veteran-owned coffee beans and muscle-building supplements as he makes appearances in conservative gatherings and touts the Trump administration.
"Trump is a master of casting, and Gallagher is a perfect fit," Paul Rieckhoff, founder of Iraq and Afghanistan Veterans of America and host of the Angry Americans podcast, told The Times.
"He's handsome, he's heroic, he's got a beautiful wife. He's a Rambo version of the same story Trump has been telling over and over: The deep state is trying to screw you, the media is bad, and the rich people don't understand you. But I'll stick up for you."
Rieckhoff noted that Gallagher's ties to the presidents can also help him cash in on book deals, speaking engagements and other business ventures.
Gallagher and his wife also have set up an online store selling shirts that mock the Navy and the SEALs who testified against him — calling them "mean girls."
The Times recently published an account of testimony from three SEALs who called Gallagher "freaking evil" and a "psychopath" in videos obtained by the newspaper.
Gallagher declined through his lawyer, Timothy Parlatore, to speak with The Times about his projects, including his apparel line, Salty Frog Gear.
In an email, Parlatore told the paper: "Chief Gallagher is happy to respond to inquiries by legitimate journalists looking to publish factual pieces, but has no time for propagandists who put out knowingly false statements masquerading as fact."
Filed under donald trump , navy seals , war crimes , 1/1/20
2 snowboarders killed in British Columbia avalanche


If you want to know why Egyptian women had beautiful and healthy hair, Suzy Vidal will give away their secret: castor oil! For congested eyes and sties, follow Suzy's grandmother's remedy: apply a child's urine on closed lids. The Jasmine necklace does not always come up smelling of roses – there are lice, cockroaches and warts aplenty. But the book also features traditions, recipes, a catalogue of picturesque Arabic words and expressions and untold other clues to this colourful, quirky but vanished world.
The author Suzy aka Sultana has two names because her headstrong mother refused to name her after her mother-in-law Sultana, as was the custom. Suzy was brought up on the fourth floor of a Cairo hotel because the family could not afford to rent a downtown apartment. Nevertheless the Extadey Hotel was a paradise for children, and Suzy sketches vivid portraits of the family's neighbours and friends. The hotel's permanent guests reflected the cosmopolitan and multilingual make-up of Egypt before the war – with its Armenians, Jews and Greeks. During World War 2 the US army requisitioned part of the hotel, but indulged the residents with sweets and treats.
With the stormclouds gathering over Egypt's Jewish community, it is interesting that Suzy's family – Italian passport–holders with no Italian - were subject to the same harassment and pressure as other Jews. While they were not expelled overnight like French and British nationals, the Vidals received no assistance from the Italian consulate. When the time came to leave in 1957, the family managed to smuggle out Suzy's mother's jewellery in a Player's cigarette packet, but their capital disappeared with a dishonest 'passeur'.
" Only mountains don't meet." This saying repeated in Suzy Vidal's book expresses the hope that her large family – scattered to the four corners of the earth - would once again come together after their uprooting from Egypt. But life was a slog and money tight for the refugees, now in Israel, Australia, Brazil and Italy. The close-knit community they had known in Cairo had gone forever. Some never recovered from the shock – Suzy's parents both died in penury in their 50s.
A graduate of the American University in Cairo, Suzy had to abandon her dreams of becoming a university professor in order to become a shorthand typist. Their exile in Italy was a series of bitter experiences and disappointments as the family struggled to make ends meet. But this book is a fitting tribute to Suzy's family and community, and a lasting reminder of their roots to the next generation.
Where did she go to exile?
We went to Milano Italy as we had an Italian passport. But our descent into poverty was awful till the day my father found a job with former Egyptian Jews.I, in the meanwhile I was studying Italian and working as a teacher at night school.
I remember my students asking:"Why are your eyes so sad?"
It is diffcult to be a refugee especially when Italians tell you: "you deserve what you got, afterall you killed Christ!"
But we had to lift up our heads and go onwards!
Did you leave Italy after that?
How many homelands do Palestinians need?
Will Labour be the new Sephardi party?
What's happened to the Aleppo Great Synagogue?
What is the solution to the refugee problem?
"We came penniless to Israel from Damascus"


text:
/* Generated by camel build tools - do NOT edit this file! */
package org.apache.camel.component.grpc;

import java.net.URISyntaxException;
import java.util.Collections;
import java.util.HashMap;
import java.util.HashSet;
import java.util.Map;
import java.util.Set;

import org.apache.camel.spi.EndpointUriFactory;

/**
 * Generated by camel build tools - do NOT edit this file!
 */
public class GrpcEndpointUriFactory extends org.apache.camel.support.component.EndpointUriFactorySupport implements EndpointUriFactory {

    private static final String BASE = ":host:port/service";

    private static final Set<String> PROPERTY_NAMES;
    private static final Set<String> SECRET_PROPERTY_NAMES;
    static {
        Set<String> props = new HashSet<>(31);
        props.add("serviceAccountResource");
        props.add("synchronous");
        props.add("jwtSubject");
        props.add("trustCertCollectionResource");
        props.add("forwardOnCompleted");
        props.add("jwtIssuer");
        props.add("bridgeErrorHandler");
        props.add("routeControlledStreamObserver");
        props.add("keyPassword");
        props.add("host");
        props.add("maxMessageSize");
        props.add("flowControlWindow");
        props.add("jwtSecret");
        props.add("keyResource");
        props.add("method");
        props.add("autoDiscoverClientInterceptors");
        props.add("exchangePattern");
        props.add("streamRepliesTo");
        props.add("userAgent");
        props.add("consumerStrategy");
        props.add("jwtAlgorithm");
        props.add("negotiationType");
        props.add("lazyStartProducer");
        props.add("port");
        props.add("service");
        props.add("maxConcurrentCallsPerConnection");
        props.add("forwardOnError");
        props.add("producerStrategy");
        props.add("authenticationType");
        props.add("exceptionHandler");
        props.add("keyCertChainResource");
        PROPERTY_NAMES = Collections.unmodifiableSet(props);
        Set<String> secretProps = new HashSet<>(2);
        secretProps.add("jwtSecret");
        secretProps.add("keyPassword");
        SECRET_PROPERTY_NAMES = Collections.unmodifiableSet(secretProps);
    }

    @Override
    public boolean isEnabled(String scheme) {
        return "grpc".equals(scheme);
    }

    @Override
    public String buildUri(String scheme, Map<String, Object> properties, boolean encode) throws URISyntaxException {
        String syntax = scheme + BASE;
        String uri = syntax;

        Map<String, Object> copy = new HashMap<>(properties);

        uri = buildPathParameter(syntax, uri, "host", null, true, copy);
        uri = buildPathParameter(syntax, uri, "port", null, true, copy);
        uri = buildPathParameter(syntax, uri, "service", null, true, copy);
        uri = buildQueryParameters(uri, copy, encode);
        return uri;
    }

    @Override
    public Set<String> propertyNames() {
        return PROPERTY_NAMES;
    }

    @Override
    public Set<String> secretPropertyNames() {
        return SECRET_PROPERTY_NAMES;
    }

    @Override
    public boolean isLenientProperties() {
        return false;
    }
}

redpajama_set_name: RedPajamaGithub


text:
Q: Show that $T \in \mathcal{L}(\mathbb{R}^4)$ with det $T<0$ has at least two distinct eigenvalues. a) Suppose that $T \in \mathcal{L}(\mathbb{R}^4)$ satisfies det $T < 0$. Show that $T$ has at least two distinct eigenvalues.
b) Find a $T \in \mathcal{L}(\mathbb{C}^4)$ where det $T < 0$ such that $T$ has only one eigenvalue.
For a) I was thinking of trying to show a contradiction when $T$ has at most one distinct eigenvector, but I don't know where to go from here. Any help would be great.

A: Let $P$ be the characteristic polynomial of $T$, $P(X)=(X-a)(X-b)(X-c)(X-d)$ where $a,b,c,d$ are eigenvalues, and $\det(T)=abcd$ suppose that $P$ has only complex eigenvalues, since the coefficients of $P$ are real $a$ and its conjugate $\bar a$ are eigenvalues, we deduce that $P=(X-a)(X-\bar a)(X-b)(X-\bar b)$ and $\det(T)=(|a||b|)^2$ contradiction. This implies that $P$ has a real eigenvalue $a$, if $a$ is the unique real value, $\det(T)=a^4>0$ contradiction.
For $b$ consider the $u=\sqrt[4]{-1}$ and $T(x)=ux$.

A: (a) The characteristic polynomial $\chi$ has degree $4$ and so $\chi(x)>0$ for $x$ large. Now $\chi(0)=\det T <0$. Therefore $\chi$ has a real root. Since roots come in conjugate pairs, $\chi$ has at least two real roots. They are different because otherwise $\chi(x)>0$ for all $x$.

redpajama_set_name: RedPajamaStackExchange


text:
Walt Disney: The Incredible Journey Of Dreams
October 24, 2018 weupdatedLeave a Comment on Walt Disney: The Incredible Journey Of Dreams
Early Life Of Walt Disney :
Early childhood of Walt Disney was never close to being as rosy and colorful as the movies he made later on in life. Born in Chicago to Elias Charles Disney and Flora Call Disney, Walt's childhood was interrupted when the family had to move to Missouri first and then Kansas. With barely any money to spare, the Disney brothers had to work several odd jobs to make ends meet, a task which left them with little to no time for school.
The big change to Walt's life came in the form of the Great War. A patriot by birth, Walt immediately joined the army, but was rejected because he was only a mere boy of 16. However, he finally became a part of the army by driving an ambulance bus. Through his time in the war, he realized his true passion lay in the magical world of theater, acting and drawing.
The Beginning Of Career :
The war had a great impact on Walt's life. It left a mark on him so much that the first caricatures he drew were of political figures, a feat which was clearly not a good idea. One of Walt's brothers, Roy, was the first man who introduced him to the world of cartoons, mystery and beauty! In July 1923, Walt sold his camera, his creations and his hard work and moved to Hollywood. Once he reached, he moved in with his uncle Robert Disney and set out to look for a job. Unfortunately, no one believed in Walt's dreams of becoming a director and as a result, every studio turned him down.
The first seeds for Disney were sown when Walt, Roy and Ub Iwerks came together to form the first version of Disney. Called the Disney Brothers' Studio, this company became home to the many creations for which Walt became famous. It was only after multiple rejections that Walt decided to turn to animation. The first success for Walt came in the form of a series called Alice Comedies, or what is now popularly known as Alice In Wonderland. The massive success of the Alice series resulted in the creation of a new project called Oswald the Lucky Rabbit and from then onward, there was absolutely no looking back for Walt.
Creation Of Mickey Mouse :
Even with the huge success of Oswald, Walt was not happy. He wanted a character that was sympathetic, personal and full of potential. An idea was slowly brewing in this creative man's head and slowly, after months of contemplation, the character of Mickey Mouse came to be! This particular character had a live voice, a back story and a dream with which everyone could relate. With such a specific idea for Mickey Mouse, Walt could not find a specific voice to fit this character. Despite several auditions, no one made the right impact and after several failed attempts, the creator decided to voice the character himself. Walt first came up with the idea of Mickey Mouse, he wanted to name him as Mortimer Mouse.
In the year 1932, Walt discovered a new invention in the form of technicolor while working on a project called Flowers and Trees. With the brilliant idea of re-shooting the project with this technology, Walt created the first ever successful animated series for Disney! Nothing could stop Walt after this success and post that, his touch was like the mythical Midas touch.
At the age of 62, Walt had amassed over 31 Academy Awards and was famous for creating not only amazing cartoons, but also for making brilliant feature films.
Tagged Walt DisneyWalt Disney life story
Google will release a software update soon to fix the Pixel 3 photo saving issue
Virat Kohli Scores 10,000 Runs in ODI Cricket became the fastest Player
Top 5 Life Lessons of Richard Branson
October 29, 2018 weupdated
The Mind Blowing Story Of Warren Buffett And some Unknown Facts
October 2, 2018 weupdated
Alibaba co-founder Jack Ma will retire from the company next week
September 8, 2018 September 29, 2018 weupdated


ENMU is proud to announce Microsoft Office is being offered for free to all ENMU staff, faculty and students while they are enrolled at ENMU. All staff, faculty and students will now be able to install Microsoft Office 365 on 5 devices for free. For detailed instructions on how to obtain your copy follow the instructions below to install on a PC or Mac.
Uninstall all MS Office applications from your computer and restart.
Log in using your Ruidoso Portal username and password.
Click on the Outlook 365 link on the left side of the page.
Once your mail comes up, click on the cog/gear in the upper right-hand corner.
Click on Office 365 from the drop down menu.
Click on Settings on the left side of the page.
NOTE: If you would like to use an older version of Office, you can select office 2010 from the bottom of the page.
You will be able to install Office 365 on up to 5 personal devices. It will remain operational as long as you are a student, faculty or staff member at ENMU.
NOTE: It is recommended that you use a fast Internet connection because the install can take some time to complete. If you have any questions about installing Office 365, please contact the Ruidoso IS Department at ENMURuidoso.ServiceDesk@enmu.edu.


Save the date! Tuesday February 19, 2019, 5:30-10 pm, Buckingham VA.
The Poor People's Campaign and the Climate Reality Project in collaboration with the community of Union Hill will bring Reverend Barber and former V.P. Al Gore to visit with and speak to Union Hill,, site of the proposed Atlantic Coast Pipeline compressor station. All will be welcome. Details to follow. See Facebook page for full listing of sponsors.
This entry was posted in Compressor Stations, Events on February 7, 2019 by Ellen Bouton.
A powerful statement from the Union Hill community of Buckingham County, VA after their press conference on Saturday February 2, 2019, following Virginia Governor Ralph Northam's decision to stay in office.
"For us who live or work in Union Hill, Buckingham, Virginia, the revelations that Gov. Ralph Northam engaged in racist behavior in medical school over thirty years ago are less troubling than his most recent actions, inactions, and silence about environmental racism related to the massive fracked gas compressor station to be built in Union Hill.
"When Gov. Northam removed two of the 5 Air Pollution Control Boards in the middle of a decision about that compressor station air permit after they raised questions of environmental [in]justice for that siting — his actions spoke loudly about his unwillingness to make donor sacrifices for his PAC required to make racial justice a reality in Virginia.
"His support of the actions of Virginia Department of Environmental Quality to push through the controversial air permit for Union Hill speaks loudly that he is not committed to racial justice in our state. For, the VDEQ spokesman publicly erased the existence of Union Hill's African American majority and labeled its history of "no significance." That history is of former slaves building a freedmen community on plantations lands where they were once enslaved. And, whose present day residents are their descendants threatened by the carcinogenic cocktail of most harmful air emissions of Virginia's ACP compressor station.
"Tellingly, Gov. Northam not only ignored the unanimous decision by his Advisory Council on Environmental Justice for him to issue a moratorium on the ACP and MVP pipelines until environmental justice reviews were taken, but disbanded that body of citizen experts to create a "new" EJ council with seats for the very corporate polluters whose toxic emissions were the reason for the EJ council formation.
"We ask you to consider the direct lines we see between the actions of a young man and his actions and silence when elected to the most powerful office in Virginia. It is not a matter of Democrat or Republican in state that was the capital of the Confederacy. It is a question of making the hard, ethical choices to do the daily work of restorative justice toward racial justice in Virginia.
See press coverage in the Daily Progress on February 2, 2019.
This entry was posted in Compressor Stations, Environmental Impact, Environmental Justice, Press Releases, Virginia government on February 3, 2019 by Ellen Bouton.
The Board apparently relied solely on Dominion's promises of good air quality, DEQ's promises to monitor emissions closely, and on Dominion's census figures on minority residents of the area (census figures that even DEQ staff have said are inaccurate).
Board members Richard Langford and Ignacia Moreno agreed that siting the compressor station in Union Hill raised environmental justice issues. But Langford voted in favor of the permit because he said he believed emissions would be within the accepted limits so there would be no disproportionate effects. After speaking about environmental justice issues, Moreno voted in favor because she believes the state will closely monitor health impacts. Although Board member Nicole Rovner had asked difficult questions in previous meetings, she voted in favor of the permit without any further explanation or comment. Lastly, William Ferguson of Newport News did not address environmental justice or air quality issues, but argued in favor of the entire ACP because he believes the proposed spur to Hampton Roads will provide natural gas for economic development there.
Read press coverage of the meeting and the vote in the Washington Post, the Richmond Times-Dispatch, and in the Virginia Mercury.
This entry was posted in Air Quality, Compressor Stations, DEQ, Environmental Justice, Virginia government on January 8, 2019 by Ellen Bouton.
Rubin and Bleicher are the two Air Board members that Northam removed after the November 2018 meeting when the Board postponed its vote. Although neither has said how they would vote (if they could), both raised questions in that November meeting.
The state Department of Environmental Quality and Dominion (which insists its data is unbiased) have said that the area around the proposed facility is predominantly white, but they used broad census data instead of an actual head count, and showed a sparsely populated area with a 37% minority concentration.
Read Rebecca Rubin's opinion column, also in the January 7 Washington Post, Is Virginia interested in environmental justice? We're about to find out.
This entry was posted in Air Quality, Certificates of Approval, Compressor Stations, DEQ, Dominion, Environmental Justice, Virginia government on January 7, 2019 by Ellen Bouton.
TODAY, Friday January 4, 2019: Comments to the Air Pollution Control Board are due by 11:59 pm. Write now!
TOMORROW, Saturday January 5, 2019: Friends of Nelson Annual Membership Meeting, 6-10 at Rockfish Valley Community Center. Bring your potluck dish, your dancing shoes, and come celebrate with us!
This entry was posted in Air Quality, Compressor Stations, Events, Take Action on January 4, 2019 by Ellen Bouton.


text:
Q: Reverse engineering Newton's approximation of impact depth I recently learned about Newton's approximation of impact depth, Depth = (Projectile Density / Target Density) * Length of the projectile. Where length and depth is measured in meters, and density is grams per cubic cm. This equation assumes that the projectile is moving faster than the speed of sound (in air I believe) relative to the target.
This is all fine and dandy for basic calculations.
What if the object is moving 3 times the speed of sound? My intuition says the impact depth would be 3 times as well. But I would rather get confirmation on my assumption.


text:
Crystal Palace fixtures for the 2016/17 Premier League season as the Eagles start the new season at home
Dates, kick-off times and full schedule for the Eagles can be found here as Alan Pardew's side aim for another top 10 finish
James WhalingSports Journalist
Alan Pardew will be looking to plot Palace to glory (Image: Paul Gilham)
Here are Crystal Palace's fixtures for the 2016-17 Premier League season.
It was certainly a season of two halves for the Eagles last term, with a superb start to the season preceding a plunge down the table.
They staved off the threat of relegation in the end and there was almost a happy ending in the FA Cup final , only for Palace to come up just short.
But when will those big games in the Selhurst cauldron take place next term?
Here are the fixtures - PLUS get all the reaction in our live blog here.
13/08/2016 15:00 Crystal Palace v West Bromwich Albion
20/08/2016 15:00 Tottenham Hotspur v Crystal Palace
27/08/2016 15:00 Crystal Palace v A.F.C. Bournemouth
10/09/2016 15:00 Middlesbrough v Crystal Palace
17/09/2016 15:00 Crystal Palace v Stoke City
24/09/2016 15:00 Sunderland v Crystal Palace
01/10/2016 15:00 Everton v Crystal Palace
15/10/2016 15:00 Crystal Palace v West Ham United
22/10/2016 15:00 Leicester City v Crystal Palace
29/10/2016 15:00 Crystal Palace v Liverpool
05/11/2016 15:00 Burnley v Crystal Palace
19/11/2016 15:00 Crystal Palace v Manchester City
26/11/2016 15:00 Swansea City v Crystal Palace
03/12/2016 15:00 Crystal Palace v Southampton
10/12/2016 15:00 Hull City v Crystal Palace
13/12/2016 20:00 Crystal Palace v Manchester United
26/12/2016 15:00 Watford v Crystal Palace
31/12/2016 15:00 Arsenal v Crystal Palace
02/01/2017 15:00 Crystal Palace v Swansea City
14/01/2017 15:00 West Ham United v Crystal Palace
21/01/2017 15:00 Crystal Palace v Everton
31/01/2017 19:45 A.F.C. Bournemouth v Crystal Palace
04/02/2017 15:00 Crystal Palace v Sunderland
11/02/2017 15:00 Stoke City v Crystal Palace
25/02/2017 15:00 Crystal Palace v Middlesbrough
04/03/2017 15:00 West Bromwich Albion v Crystal Palace
11/03/2017 15:00 Crystal Palace v Tottenham Hotspur
18/03/2017 15:00 Crystal Palace v Watford
05/04/2017 19:45 Southampton v Crystal Palace
08/04/2017 15:00 Crystal Palace v Arsenal
15/04/2017 15:00 Crystal Palace v Leicester City
22/04/2017 15:00 Liverpool v Crystal Palace
29/04/2017 15:00 Crystal Palace v Burnley
06/05/2017 15:00 Manchester City v Crystal Palace
13/05/2017 15:00 Crystal Palace v Hull City
21/05/2017 15:00 Manchester United v Crystal Palace
Premier League fixtures for 2016/17 season
Premier League fixtures
Giovani Lo CelsoTottenham set to break transfer record again to land £70million Giovani Lo CelsoSpurs made Tanguy Ndombele their most expensive ever buy earlier this summer, but the Argentina international is set to top that figure
Manchester United FCMan Utd and Leeds fans involved in mass brawl during pre-season friendly in PerthThe teams don't play each other very often, but this hasn't weakened the rivalry between the two
Film festivalBFI London Film Festival 2019 dates, film line-up, tickets, opening galaThe BFI London Film Festival 2019 will arrive later this year - here is everything you need to know


text:
package com.mowitnow.boot.rest.domain.model;

/**
 * Created by Amine Sagaama on 13/11/2016.
 */
public class LawnMower {

    private Location location;

    private Track track;

    public LawnMower(Location location, Track track) {
        this.location = location;
        this.track = track;
    }

    public Track getTrack() {
        return track;
    }

    public Location getLocation() {
        return location;
    }

    public boolean moveForward() {
        return location.forward(track.getMax(), track.getBorders());
    }

    public void turnRight() {
        location.turnRight();
    }

    public void turnLeft() {
        location.turnLeft();
    }

    public void executeCommands(String commands) {
        StringBuilder output = new StringBuilder();
        for (char command : commands.toCharArray()) {
            switch (command) {
                case 'A':
                    moveForward();
                    break;
                case 'D':
                    turnRight();
                    break;
                case 'G':
                    turnLeft();
                    break;
            }
        }
    }
}


text:
Common State Standards | Three C-s of Education Petition Campaign | CA History/Social Studies Framework
AB 2544 | Resolutions in Support of the CA Campaign | Testimony to Commission on No Child Left Behind
The Campaign works with state and local governments, school boards, and other agencies to examine existing policies addressing civic education and generates and supports new policies to strengthen civic education statewide.
Policy Committee Chair: Gary Dei Rossi, San Joaquin County Office of Education
Senate Bill 1458 introduced by Darrel Steinberg was signed into law by Governor Brown on September 25, 2012. In summary, beginning in 2016, a high school's Academic Performance Index (API) will be comprised of no more than 60 percent of standardized tests scores and more weight is to be given to Science and History. This provides an opportunity for civic education to play a larger role in how schools are assessed. For the full text of the bill click here.
Civic Education in California: Policy Recommendations: A report on the status of civic-education policy in California and recommendations.
Assembly Concurrent Resolution No. 30, introduced by Assembly Member McCarthy, was signed into law in June, 2005. The bill urges the State Board of Education and all local school governing bodies in California to examine current practice and develop plans to increase and broaden emphasis on principles and practices of democracy.
Common State Standards Update
The California Campaign for the Civic Mission of Schools supports the development of state standards in History/Social Studies and Science. For more information please contact Marshall Croddy (marshall@crf-usa.org or 213.316.2113). Below you will find letters that were sent to the National Governor's Association and the Council of Chief State School Officers asking them to replicate the process used to adopt Common Core State Standards for English and Mathematics for the adoption of standards for the other two academic disciplines, Social Studies and Science.
Letters of Support for Common State Standards
CA Campaign AJR 39
CRF Support Ltr AJR 39
CA Campaign Common Core
LACOE Common Core
Three C-s of Education Petition Campaign
Remind all policymakers of the essential and historic role schools play in providing the knowledge, skills and disposition for informed and engaged citizenship!
CA History/Social Studies Framework Update
The California Campaign for the Civic Mission of Schools would like to state its support of Senate Bill1278 sponsored by Senator Mark Wyland. The Campaign believes that it is critical to get the History/Social Studies framework revision process back on track. Therefore we support SB 1278 which requires the state board to adopt a revised curriculum framework and evaluation criteria for instructional materials in history-social science and science in 2011.
Sample Letter: Senate Bill 1278
CA History/Social Studies Framework Letters of Support
California Council for the Social Studies (CCSS)
Fresno County Office of Education
Los Angeles County Office of Education
CA Campaign
Constitutional Rights Foundation (CRF)
AB 2544 Update
We are disappointed to report that AB 2544, sponsored by Assembly Members Mullins and DeSaulnier, requiring the State Superintendent of Public Instruction to create a model civic education professional development plan for California was not enacted in this legislative session. Though it passed the Assembly easily by 71-3, sailed through Senate Education Committee with a 9-0 vote, and enjoyed broad support from more than a dozen statewide education groups, representing thousands of members, it died in the Senate Appropriations Suspense file with many other bills as a result of the state budget crisis. We are actively working to learn more and to evaluate next steps.
California Assembly Bill 2544
Sample Letter of Support for AB 2544 (MS Word)
Letter of Support – from California PTA
Letter of Support – from California School Board Association
Letter of Support – from Center for Civic Education
Letter of Support – from Sacramento County Office of Education
Six Promising Approaches in Civic Education
Civic Mission of Schools Report from the Carnegie Foundation of New York
Democracy for Some: The Civic Opportunity Gap in High School
San Francisco Chronicle (March 9, 2008): Voter turnout spotlights educational need
California Survey of Civic Education
Resolutions passed in support of the California campaign:
Alameda County Board of Education
California State PTA
California County Superintendents Educational Services Association
Kern County Board of Education
Montebello Unified School District
Pleasanton Unified School District
San Bernardino County Board of Education
San Diego County Office of Education
San Joaquin County Board of Education
San Luis Obispo County Board of Education
Santa Barbara County Office of Education
Siskiyou County Board of Education
Trinity County Office of Education
Testimony to Commission on No Child Left Behind, April 2006:
Letter from Dr. Darline Robles
California Campaign Testimony
A project of Constitutional Rights Foundation in collaboration with the
Center for Civic Education and the Alliance for Representative Democracy.
This project is made possible by generous grants from the Carnegie Corporation of New York and the Skirball Foundation


The Indian Crescent Society of Australia (ICSOA) organised a seminar on Unani Medicine (also known as Greco-Arabic Medicine) for health professionals on Thursday 25 October 2018 at Himalaya Restaurant in Sydney.
The event was attended by more 50 Australian health professionals, GPs, specialists and allied health professionals from diverse backgrounds from South Asia and the Middle East.
Professor Syed Ziaur Rahman, MBBS MD PhD, Professor of Clinical Pharmacology at JN Medical College of Aligarh Muslim University in India was the keynote speaker at this seminar and spoke on the topic of "Unani Medicine-history and future directions".
Professor Rahman is currently visiting Sydney for a short period primarily to received Distinguished Alumni Award from Western Sydney University where he completed his PhD a few years ago.
Professor Syed Ziaur Rahman has been working in the field of pharmacology for the last 21 years. He works in the field of traditional medicine with particular reference to Unani Medicine.
Professor Ziaur Rahman is the founder trustee and treasurer of Ibn Sina Academy of Medieval Medicine and Sciences which is doing much work in India to preserve the history of Unani Medicine and conduct education and research in this area.
During his address, Professor Ziaur Rahman introduced Unani Medicine with its etymology and definition given by Ibn Sina. He discussed Traditional Medicine versus Streamline Medicine.
For some, Unani medicine is still main medicine and opt Allopathy when in need of surgical crisis and emergency.
Professor Ziaur Rahman said that the foundation of Unani was laid by Greek physicians like Aesclepius, Hippocrates and Galen, but later on developed and promoted by Arab Physicians like Al Razi, Ibn Sina and Ibn Rushd.
That is why in some countries, Unani Medicine is also known as Greco-Arab Medicine.
Professor Ziaur Rahman elaborated that still the six basic principles of life (Sitta-e-Zaruria) viz Change of Air, Physical activity and relaxation, Diet (safe drinking and food habits), healthy pattern of Sleep and wakefulness, Proper Excretion and retention of fluids and regular Mental activities, are the basic rule of treatment by Unani physicians (called Hakim).
Medicine is prescribed only whenever badly necessary.
This theory is indirectly having partial similarity with the accepted definition of health as a state of physical, mental and social wellness.
Professor Ziaur Rahman discussed the fundamentals of Physic (Umoor-e-Tabiyyah) in detail saying that as per Unani Philosophy, If anyone one of these is lacking, then human body will not exist: Arkan: Basic Elements; Mizaj: Temperament; Akhlat: Humour; Aaza: Parts of body; Arvah: Soul; Quwa: Faculty of power and Af-aal: Function.
He further elaborated how Unani drugs are classified and the ways of diagnosis and treatment options.
Professor Ziaur Rahman in the second part of his talk told about the Ibn Sina Academy of Medieval Medicine and Sciences, which his family created and established in Aligarh (India). He also talked about the collection and importance of this Academy in India and Abroad.
During the third part of the lecture, Professor Ziaur Rahman introduced "Dawakhana Tibbiya College of Aligarh Muslim University". He showed pictures of its new buildings and all its Unani products.
His talk was followed by an interactive question-answer session which generated a lot of interest.
Dr Fasihuddin Khan, Secretary of ICSOA, was the MC for this program that commenced with the recitation of Quran by Mr Abbas Chelat, treasurer of ICSOA.
He mentioned that Unani Medicine provided affordable alternative primary care treatment options for the masses in India and employment generation as well, but now it's in decline. Traditional medicine globally is emerging, and we can take advantage of the positive aspects of traditional medicine.
He highlighted ICSOA health committee's initiatives in the last two years such as community education seminars on heart disease prevention, tackling childhood obesity, blood donation awareness and speech and language development in children.
"Our aim is to network with other organisations and health professionals from different communities to implement various health promotion and early intervention initiatives for the broader Australian society," he said.
Towards the conclusion of the program, Professor Syed Ziaur Rahman was presented with a certificate of appreciation by ICSOA recognising his contribution towards health care.
Dr Habib Bhurawala (MBBS MD FRACP DCH), based in Sydney is a consultant general paediatrician currently working at Nepean & Blue Mountains hospitals in Sydney. Originally from Gujarat state in India he moved to Australia to gain further training in Paediatrics in 2002. He has strong interest in community work especially health education/promotion.


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June 29, 2020 September 27, 2020 gilgongorecords
Tone Glow on Sandy Ewen's 'You Win' LP
Sandy Ewen, a Brooklyn-based sound artist and architect, creates subtly transportive improvisations on her first solo LP, You Win. Her work employs a wide variety of different extended techniques to explore the infinite possibilities of sound made by the guitar. The album features two sets recorded in 2018 that each stem from the type of airy crackling that's often dubbed ASMR, but the magic of the record lies in its unraveling. Ewen embraces a musical process based on change that comes in slow-motion—it's a restrained change that blooms in waves. "You Win," the title track, exemplifies this ideology as it travels in sections from crunching sounds to erratic guitar distortion to high-pitched chirping. These shifts in sound space move seamlessly, so much so you almost forget how the sound has come and gone. This subtlety manifests in other ways too, like on "Face Topography," where sound is delicate and tactile, through gargling pitches and abrasive scratching against the strings of a guitar.
While tangibility is a central part of the music Ewen makes on You Win, she takes the concept a step further in her visual recreations of the improvisations featured on the album. These surrealist visuals, informed by her expertise in architecture, launch her music into its own virtual reality that's gradually, but steadily, engulfing. Like the music, the videos work in slow-motion, gently weaving through absurdist scenery and moving deeper into mysterious realms. The eccentric reality Ewen imagines is hypnotic, a place where total immersion is the cause rather than the effect. —Vanessa Ague
https://toneglow.substack.com/p/020-our-favorite-albums-april-june
Available NOW on Gilgongo Records: https://gilgongorecords.storenvy.com/
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Didymus the Blind
Of Alexandria, b. about 310 or 313; d. about 395 or 398
Didymus the Blind, of Alexandria, b. about 310 or 313; d. about 395 or 398, at the age of eighty-five. Didymus lost the use of his eyes when four years old, yet he became one of the most learned men of his period. He prayed earnestly in his youth, we are told by Rufinus, not for the sight of his bodily eyes, but for illumination of the heart. He admitted to St. Anthony that the loss of his sight was a grief to him; the saint replied that he wondered how a wise man could regret the loss of that which he had in common with ants and flies and gnats, and not rather rejoice that he possessed a spiritual sight like that of the saints and Apostles. St. Jerome indeed habitually spoke of him not as "the blind" but as "the Seer". Didymus studied with ardor, and his vigils were long and frequent, not for reading but for listening, that he might gain by hearing what others obtain by seeing. When the reader fell asleep from weariness, Didymus did not repose, but as it were chewed the cud (says Rufinus) of what he had heard, until he seemed to have inscribed it on the pages of his mind. Thus in a short time he amassed vast knowledge of grammar, rhetoric, logic, music, arithmetic, and geometry, and a perfect familiarity with Holy Scripture. He was early placed at the head of the famous catechetical school of Alexandria, over which he presided for about half a century. St. Athanasius highly esteemed him. The orator Libanius wrote to an official in Egypt: "You cannot surely be ignorant of Didymus, unless you are ignorant of the great city wherein he has night and day been pouring out his learning for the good of others." He is similarly extolled by his contemporaries and by the historians of the following century. Rufinus was six years his pupil. Palladius visited him four times in ten years (probably 388-398). Jerome came to him for a month, in order to have his doubts resolved with regard to difficult passages of Scripture. Later ages have neglected this remarkable man. He was a follower of Origen, and adopted many of his errors. Consequently, when St. Jerome quarrelled with Rufinus and made war on Origenism, he ceased to boast of being a disciple of Didymus and was ashamed of the praise he had formerly given to the "Seer". When Origen was condemned by Justinian and then by the Fifth General Council, Didymus was not mentioned. But he was anathematized together with Evagrius Ponticus in the edict by which the Patriarch Eutychius of Constantinople gave effect to the decree of the council; and he was (perhaps in consequence of this) included in the condemnation of Origenists by the sixth and seventh councils. But this censure is to be taken as applying to his doctrine and not to his person. It has had the unfortunate effect of causing the loss to us of most of his very numerous writings, which, as the works of a supposed heretic, were not copied in the Middle Ages.
Didymus always remained a layman. The idea that he was married rests on a mistaken identification of him with a Didymus to whom one of the letters of St. Isidore of Pelusium is addressed. He seems on the contrary to have lived the life of an ascetic, although in the city and not in the desert. A curious story was told by him to Palladius. One day, when dwelling on the thought of Julian as a persecutor, and on this account having taken no food, he fell asleep in his chair and saw white horses running in different directions, while their riders cried out: "Tell Didymus, today at the seventh hour Julian died; arise and eat, and inform Athanasius, the bishop, that he also may know it." Didymus noted the hour and the month and the week, and it was even so.
Doctrine.—Didymus was one of the principal opponents of Arianism. His Trinitarian and Christological doctrine is perfectly orthodox; one may even say that he is more explicit than St. Athanasius as to the Unity in Trinity and the Divinity of the Holy Ghost. He has combined the theological vocabulary of St. Athanasius with that of the younger generation, Basil and Gregory Nazianzen. He continually uses the formula treis hupostaseis, mia ousia, which St. Athanasius admitted in his later years, and which has become the Catholic watchword. Didymus has been credited with the invention of this formula, and Leipoldt is in favor of the attribution, whereas K. Holl rejects it. Until the fourth century the Greek-speaking Church had no means of expressing the doctrine of of the Trinity. The use of hupostasis to express the Latin persona was in itself a clumsy device, for Didymus agrees with St. Jerome (who rejected the expression) that philosophically ousia and hupostasis are synonyms. Didymus, however, carefully safeguarded his doctrine from any wrong interpretation. His work on the Holy Spirit is preserved only in the Latin translation made by St. Jerome. It is free from the reproach of "economy" which attaches to the more famous work of St. Basil, who avoided (as he himself admits) calling the Holy Ghost "God". A yet more important work is the "De Trinitate", the three books of which are preserved almost entire; it was composed after 379. A treatise against the Manichans is also nearly complete. Of the exegetical fragments, those on the Psalms are the most important. A commentary on the Catholic Epistles is known to us through the Latin translation made by a certain Epiphanius for Cassiodorus. Didymus comments on II Peter, and elsewhere frequently quotes that Epistle, although in one place he declares it to be spurious (falsata—the Greek is lost). In his commentaries Didymus shows himself to be much influenced by Origen, both in his care for the text and the grammar, and in his wide allegorizing, but of Origenistic heresies the traces in extant works are slight. He seems to have held the preexistence of the soul. The doctrine of the "restitution of all things" is attributed to him by St. Jerome; but he speaks very often of eternal punishment, though he seems to teach that the fallen angels and even Satan himself are saved by Christ. He is fond of explaining that God's punishments are remedial. He deliberately rejects some of Origen's views, and in his Trinitarian and Christological teaching is wholly uninfluenced by his great predecessor. The style of Didymus is poor and careless. He is gentle in controversy. His earnestness and piety sometimes supply the place of the eloquence and energy which he lacks.