text:
\section{ALGORITHM}
\label{sec:Alg}
We are interested in joint inference on functional parameters
$x$ and quantile paramaters $\tau$.
Taking the normalization constant into account, the full
inference problem is given by
\begin{equation}
\label{eq:jointInf}
(\overline x, \overline \tau) = \argmin_{\mathbf{x},\tau} \left\{\rho_\tau(r(\mathbf{x})) + \log(c(\tau))\right\}.
\end{equation}
If the residual $r(\mathbf{x})$ is affine in $\mathbf{x}$, we can focus our analysis onto the tuple $(r,\tau)$.
Joint convexity of the objective~\eqref{eq:jointInf} in $(r,\tau)$ would be a sufficient condition
for joint inference to be well posed. We show however that this is necessarily false.
\begin{lemma}
\label{lem:notjointconv}
The function $f(r, \tau) = h(\tau) + \tau r$ cannot be jointly convex in $(r,\tau)$ for any $h$.
\end{lemma}
\begin{proof}
Without loss of generality, suppose we could find a twice differentiable $h(\tau)$ for the sum to be convex.
Then $f$ is also twice differentiable, with hessian
\[
\nabla^2 f = \begin{bmatrix}
\nabla^2 h(\tau) & 1 \\
1 & 0
\end{bmatrix}.
\]
This matrix is always indefinite, since its determinant is negative. This contradicts the
necessary condition for convexity of a twice differentiable function.
\end{proof}
Lemma~\ref{lem:notjointconv} can be immediately used to show that,  regardless of how large a quadratic lower
bound one can find for $\log c(\tau)$,
the function~\eqref{eq:jointRho} cannot be jointly convex in $(r,\tau)$, and consequently in $(\tau, x)$.
However, $f(r, \tau)$ is {\it biconvex} for any convex $h(\tau)$.
We therefore get a simple certificate of biconvexity for the joint inference problem.
\begin{lemma}
\label{lem:biconvexity}
A sufficient condition for biconvexity of
\begin{equation}
\label{eq:fullObj}
g(\mathbf{x}, \tau) := n\log(c(\tau)) + \sum_{i=1}^n \rho_\tau(y_i-\mathbf{a}_i^T \mathbf{x})
\end{equation}
in $(\mathbf{x},\tau)$ is $\nabla^2 c(\tau) > \kappa$.
\end{lemma}
\begin{proof}
For fixed $\mathbf{x}$, hence fixed $\mathbf{r}$, $\rho_\tau(\mathbf{r})$ is concave in $\tau$, with Hessian $-n \kappa$.
The necessary condition on $\log c(\tau)$ follows immediately.
\end{proof}
The condition in Lemma~\ref{lem:biconvexity} can be easily verified numerically using
the results of Section~\ref{sec:GradHess}.  Note also that Lemmas \ref{lem:notjointconv} and \ref{lem:biconvexity} will hold true as long as $\rho_\tau$ is convex in $\mathbf{x}$, although we proved them for the affine case. The simple structure of the objective in $\tau$ suggests an elegant
scheme for joint optimization of~\eqref{eq:fullObj}. Since for each fixed $\mathbf{x}$,
quantile inference boils down to minimizing a scalar convex objective,
we can use {\it variable projection}. Specifically, define
\begin{equation}
\label{eq:projObj}
\widetilde g(\mathbf{x}) = f(\mathbf{x}, \tau(\mathbf{x})),
\end{equation}
with $\tau(\mathbf{x}) = \argmin_\tau g(\mathbf{x},\tau)$. The problem fits into the scheme studied by~\cite{AravkinVanLeeuwen2012},
and in particular we have
\begin{equation}
\label{eq:varProj}
\nabla \widetilde g(\mathbf{x}) = [\partial_{x_i} f(\mathbf{x}, \tau) | _{\tau = \tau(\mathbf{x})}]_{i=1}^p
\end{equation}
In other words, to compute the gradient of the projected function, one needs only compute the partials with respect to
the elements of $\mathbf{x} \in \mathbb{R}^p$, and plug in the inferred value of $\tau$.
The resulting algorithm has the flavor of an alternating minimization method,
but in fact can be analyzed as a descent method for the projected function $\widetilde g(\mathbf{x})$.

\begin{algorithm}
  \label{alg:joint}
  Minimize $g(\mathbf{x},\tau)$ in~\eqref{eq:fullObj} over $\tau \in [0,1]$.
  \begin{enumerate}
  \item Initialize $\mathbf{x}^0 = 0$, $k = 0$, $\mbox{err} = 1$, $\mathbf{H}_k = \mathbf{I}$.
  \item While $\mbox{err} > \epsilon$
  \begin{enumerate}
  \item Compute $\tau^k = \argmin_\tau g(\mathbf{x}^k, \tau)$
  \item Set $\mathbf{d}^k =  \mathbf{H}_k^{-1}[\partial_{x_i} g(\mathbf{x}, \tau^k)]_{i=1}^p$
  \item Set $\mathbf{x}^{k+1} = \mathbf{x}^k - \alpha_k \mathbf{d}^k$ using line search
  \item Update $\mathbf{H}^k$ so that $\mathbf{H}^k > 0$.
  \item Set $\mbox{err} = \|\mathbf{d}^k\|$ and Increment $k$
  \end{enumerate}
  \item Output $(\mathbf{x}^k, \tau^k)$.
           \end{enumerate}
\end{algorithm}
In particular, the matrix  $\mathbf{H}^k$ is the Hessian approximation for the projected function $\widetilde g(\mathbf{x})$,
and $\mathbf{d}^k$ is a descent direction for this function.
There are many choices for Hessian update strategies.
For large-scale problems,  the L-BFGS strategy can be very effective.
Since $\tau$ lies in a compact set and $\rho_\tau(\mathbf{r}) \geq 0$, we have $\widetilde g(\mathbf{x})$ is bounded below,
and Algorithm~\ref{alg:joint} is guaranteed to find a stationary point for~\eqref{eq:fullObj}.

\subsection{Demonstration}
We provide a demonstration of the proposed approach in a linear regression setting where the data is generated as $y_i = \mathbf{a}_i^T \mathbf{x} + \nu$. A total of $100$ samples were generated by fixing $\mathbf{a}_i$ as a $2-$dimensional vector realized from a Gaussian random distribution, and $\mathbf{x} = [2 \text{ } 5]$. The noise $\nu$ is generated from a Laplacian distribution. The sign of the elements of the noise are varied from $100 \%$ positive to $100 \%$ negative. The coefficient vector $\mathbf{a}$ is recovered using least squares. We also perform the joint recovery of coefficients and quantile parameter using the proposed approach. Average recovery performance is evaluated using MSE with respect the true coefficients for $100$ repetitions of the experiment and tabulated in Table \ref{tab:rec_perf}. Clearly, the proposed approach is superior to least squares in coefficient recovery at all noise conditions. Further, the recovered quantile parameter is also reflective of the noise added: $\tau$ changes from high to low as the noise changes from fully positive to fully negative.

\begin{table}[htbp]
  \centering
  \caption{Average performance (MSE) with least squares and the proposed joint inference approach for recovering the coefficient and the quantile parameter.}
    \begin{tabular}{cccc}
    \toprule
    \textbf{\% Positive} & \textbf{Least sq.} & \textbf{Proposed} & \textbf{$\tau$} \\
    \midrule
    100   & 0.255 & 0.036 & 0.788 \\
    80    & 0.270 & 0.143 & 0.719 \\
    60    & 0.231 & 0.135 & 0.587 \\
    40    & 0.260 & 0.157 & 0.422 \\
    20    & 0.248 & 0.103 & 0.289 \\
    0     & 0.263 & 0.039 & 0.212 \\
    \bottomrule
    \end{tabular}%
  \label{tab:rec_perf}%
\end{table}%

For the same data, we also perform coefficient recovery at individual quantiles using plain quantile Huber regression without the proposed inference scheme for $\tau$ and plot the mean normalized quantile penalty. This is shown in Figure \ref{fig:lossfn_quants}. Clearly the $\tau$ at which the loss is minimized match well with the $\tau$ inferred by our proposed algorithm in Table \ref{tab:rec_perf}.

\begin{figure}
\centering
\includegraphics[width=7cm]{figures/loss_qr.png}
\caption{Mean normalized quantile penalty at various quantiles for different percentages of positive values in the residual - $0 \%$ (cyan),  $20 \%$ (magenta),  $40 \%$ (red),  $60 \%$ (green), $80 \%$ (blue), and $100 \%$ (black) for $\kappa = 1$. This is performed using plain quantile Huber regression without the proposed inference scheme. The minimum values are loss are respectively obtained at $\tau = \{0.79, 0.72, 0.59, 0.42, 0.29, 0.21\}$.}
\label{fig:lossfn_quants}
\end{figure}
\section{INTRODUCTION (Sasha, Jay, NRK)}
\label{sec:intro}

In supervised learning, we are interested in estimating the functional relationship between the random \textit{input} variables $\mathbf{x} = \{x_1, \ldots, x_p\}$ and the \textit{response} or \textit{output} variable $y$. The training sample $\{\mathbf{x}_i,y_i\}_{i=1}^n$ is provided and denoting the function that maps $\mathbf{x}$ to $y$ as $f(\mathbf{x})$, we estimate it such that the expected value of the \textit{loss}, $\rho(y,f(\mathbf{x}))$, is minimized over the joint distribution of all $(y, \mathbf{x})$.

Symmetric losses are used in a range of cases, including regression and robust regression.
While the quadratic loss is often the method of choice,
it is not robust with respect to the presence of outliers in the data
\citep{Hub,Gao2008,Aravkin2011tac,Farahmand2011} and may have
difficulties in reconstructing fast system dynamics, e.g. jumps in
the state values \citep{Ohlsson2011}.
In these cases, the Huber penalty
\citep{Hub}, Vapnik's $\epsilon$-insensitive loss, used in support
vector regression \citep{Vapnik98,Hastie01}.

These methods are sufficient when the data is homogenous;
however, when the data is heterogeneous, merely estimating the conditional mean
is insufficient, as estimates of the standard errors are often biased.
To comprehensively analyze such heterogeneous datasets, \textit{quantile
regression}~\cite{KB78} has become a popular alternative.
In quantile regression, one studies the effect of explanatory variables on the
entire conditional distribution of the response variable, rather than just on its mean value.
Quantile regression is therefore well-suited
to handle heterogeneous datasets~\cite{Buchinsky:1994}.
A sample of recent works in areas such as computational biology \cite{Zou08},
survival analysis~\cite{KG01}, and economics~\cite{KH01} serve as testament to
the increasing popularity of quantile regression.
Furthermore, quantile regression is \emph{robust to
outliers}~\cite{Koenker:2005}: the quantile regression loss is a piecewise
linear ``check function'' that generalizes the absolute deviation loss for
median estimation to other quantiles, and shares the robustness properties of
median estimation in the presence of noise and outliers.
A huberized extension of the quantile loss has recently been proposed
in the context of high dimensional regression~\cite{aravkin2014qh}.

The quantile loss and its huberized version are parameterized by a scalar
variable. While state of the art relies on cross-validation methods to obtain
reasonable values for this parameter, it is intuitively clear that we can infer
the degree of asymmetry in the loss in addition to the standard parameters of
interest. Especially in the large data setting, a large residual vector may inform
the degree of asymmetry.

The paper proceeds as follows. In Section~\ref{sec:QHQ} we introduce the quantile
and quantile huber loss, and specify their asymmetric parametrization.

- How is such an approach possible (concave loss wrt tau becomes convex in a statistical setting)

- goals of the joint inference

- Contributions

- computing gradients and Hessian

- providing an efficient algorithm for jointly inferring the quantile and the function parameters (variable projection) and showing why it makes sense to do this

- application - GBM extension
\section{APPLICATION TO GRADIENT BOOSTED MACHINES}
\label{sec:GBM}

Gradient boosted machines (GBM) are a machine learning paradigm where the key idea is to assume that the unknown function $f$ is a linear combination of several \textit{base learners} \cite{friedman2001greedy}. The base learners will be greedily trained by setting their target response to be the negative gradient of the current loss with respect to the current prediction. The base learner can be imagined to be the ``basis function'' for the negative gradient. Concretely, if we assume the function of interest
\begin{equation}
f(\mathbf{x}) = \sum_{j = 1}^m  \beta_j \psi_j(\mathbf{x} | \mathbf{z}_j)
\end{equation} where $\psi_j(\mathbf{x} | \mathbf{z})$ is the base learner parameterized by $\mathbf{z}$. The GBM proceeds by performing a stagewise greedy fit,
\begin{equation}
\label{eqn:greedy_update}
(\beta_j, \mathbf{z}_j) = \argmin_{\beta,\mathbf{z}} \sum_{i=1}^n
L(y_i, f_{j-1} (\mathbf{x}_i)+ \beta \psi_j(\mathbf{x}_i | \mathbf{z})),
\end{equation} where $L$ is the loss function and $f_{j-1}$ is the estimate of the function obtained at the previous iteration,
\begin{equation}
f_{j-1}(\mathbf{x}) = \sum_{t = 1}^{j-1}  \beta_t \psi_t(\mathbf{x} | \mathbf{z}_t).
\end{equation} The estimate of base learner parameters at iteration $j$ is obtained by setting its response $\widetilde{\mathbf{y}}$ to be the negative gradient
\begin{equation}
\label{eqn:neg_grad}
\widetilde{y}_i = -\left[ \frac{\partial L(y_i,f(\mathbf{x}_i))} {\partial f(\mathbf{x}_i)} \right]_{f(\mathbf{x}) = f_{j-1}(\mathbf{x})}
\end{equation} for all $i = \{1, \ldots, n\}$. The parameter $\mathbf{z}_j$ is updated using
\begin{equation}
\label{eqn:zupdate}
(\gamma, \mathbf{z}_j) = \argmin_{\gamma,\mathbf{z}} \sum_{i=1}^n
(\widetilde{y}_i -  \gamma \psi_i(\mathbf{x}_i | \mathbf{z}))^2.
\end{equation} The base learner coefficient $\beta_j$ is updated using (\ref{eqn:greedy_update}).

\begin{figure}[!h]
\centering
\begin{minipage}[b]{\linewidth}
 \centering
\includegraphics[width=7.5cm]{figures/forestfire.png}
\end{minipage}
\vfill
\begin{minipage}[b]{\linewidth}
 \centering
\includegraphics[width=7.5cm]{figures/concslump.png}
\end{minipage}
\vfill
\begin{minipage}[b]{\linewidth}
 \centering
\includegraphics[width=7.5cm]{figures/concdata.png}
\end{minipage}
\vfill
\caption{Quantile parameter $\tau$ estimated in each step of the proposed GBM algorithm: (top) Forest fires dataset, (middle) Concrete slump dataset and, (bottom) Compressive strength dataset.}
\label{fig:gbm}
\end{figure}

\begin{table}[t]
  \centering
  \caption{Performance of the proposed quantile GBM algorithm for $4$ UCI datasets at different noise levels and patterns. Regardless of the underlying noise pattern, our approach can identify the appropriate quantiles for recovering the data. }
    \begin{tabular}{|c|c|c|c|}
    \hline
    \textbf{Dataset} & $\boldsymbol{\sigma} $ & \textbf{\%pos./ \%neg.} & \textbf{MSE} \\
    \hline
    \multirow{7}{*}{Forest Fires} & 0     & -   & 0.364 \\
    \cline{2-4}
          & \multirow{3}{*}{2} & 75/25 & 1.17 \\
          &       & 50/50 & 1.12 \\
          &       & 25/75 & 1.15 \\
          \cline{2-4}
          & \multirow{3}{*}{4} & 75/25 & 2.25 \\
          &       & 50/50 & 2.21 \\
          &       & 25/75 & 2.31 \\

    \hline
    \multirow{7}{*}{Housing} & 0     & -   & 0.74 \\
    	\cline{2-4}
          & \multirow{3}{*}{2} & 75/25 & 1.39 \\
          &       & 50/50 & 1.44 \\
          &       & 25/75 & 1.32 \\
          \cline{2-4}
          & \multirow{3}{*}{4} & 75/25 & 2.08 \\
          &       & 50/50 & 2.19 \\
          &       & 25/75 & 2.15 \\
          \hline
    \multirow{7}{*}{Concrete Slump} & 0     & -   & 0.19 \\
    \cline{2-4}
          & \multirow{3}{*}{2} & 75/25 & 0.95 \\
          &       & 50/50 & 0.89 \\
          &       & 25/75 & 0.92 \\
          \cline{2-4}
          & \multirow{3}{*}{4} & 75/25 & 1.83 \\
          &       & 50/50 & 1.91 \\
          &       & 25/75 & 1.86 \\
          \hline
    \multirow{7}{*}{Comp. strength} & 0     & -  & 0.48 \\
    \cline{2-4}
          & \multirow{3}{*}{2} & 75/25 & 1.57 \\
          &       & 50/50 & 1.51 \\
          &       & 25/75 & 1.49 \\
          \cline{2-4}
          & \multirow{3}{*}{4} & 75/25 & 2.53 \\
          &       & 50/50 & 2.41 \\
          &       & 25/75 & 2.47 \\
    \hline
    \end{tabular}%
  \label{tab:perf}%
\end{table}%

\subsection{GBM with Quantile Parameter Inference}
GBMs with quantile losses \cite{zheng2012qboost} have been shown to perform better in inferring the quantiles compared to conventional quantile regression. They have a computational advantage, since they proceed by fitting functions greedily to negative gradients. Their hypothesis class is also much larger since each stage of the GBM can potentially use a different base learner. However, since they cannot automatically infer quantile parameters, we have to perform estimation over many values of $\tau$ in order to understand their performance profile and choose the ``best'' quantile parameter according to our requirements.

We propose to extend GBMs with quantile Huber losses to perform automatic inference of the base-learner parameters and the quantile parameters. The greedy stagewise regression (\ref{eqn:greedy_update}) modifies to
\begin{equation}
\label{eqn:greedy_update_quantile}
(\tau_j,\beta_j, \mathbf{z}_j) = \argmin_{\tau, \beta,\mathbf{z}} \sum_{i=1}^n
L (y_i, f_{j-1} (\mathbf{x}_i)+ \beta \psi_j(\mathbf{x}_i | \mathbf{z})).
\end{equation} In practice this update is first performed by computing the negative gradient in (\ref{eqn:neg_grad}), where the loss $L = \rho_{\tau_{j-1}}$. Here $\tau_{j-1}$ is the quantile parameter estimated in the previous iteration. The parameter $\mathbf{z}_j$ is computed using (\ref{eqn:zupdate}). The base learner coefficient $\beta_j$ and the quantile parameter $\tau_j$ are obtained using the minimization (\ref{eqn:greedy_update_quantile}) with $\mathbf{z}_j$ fixed. This is performed using our proposed algorithm \ref{alg:joint}. Note that the $\mathbf{x}$ in the algorithm corresponds to the one-dimensional parameter $\beta_j$ and $\tau$ corresponds to the quantile parameter $\tau_j$.

\subsection{Experimental Results}
We study the behavior of the proposed quantile GBM algorithm using from $4$ different datasets obtained from the UCI repository: (a) Housing ($506$ samples with $12$ input variables), (b) Compressive strength ($1030$ samples in $9$ input variables), (c) Concrete slump ($103$ samples with $7$ input variables) abd (d) Forest fire ($517$ samples with $10$ input variables). In each case, we used $75\%$ samples for training and the rest for validation, and all reported results were averaged over $10$ independent runs. We believe that the proposed quantile parameter estimation technique can infer the underlying noise model and thereby build robust regression models without user interference. In particular, the we study the regression performance and the behavior of the qunatile estimation process at different levels of positive and negative noise corrupting the data. We rescale all datasets to the range $(-1,1)$ and in each case, we add random Laplacian noise at $3$ different noise levels, $\sigma = \{0, 2, 4\}$ respectively. We fixed the parameter $\kappa = 0.05$, the number of estimators $m = 200$. Since the GBM framework is flexible to used with any base learner, we employed regression trees in our setup.

We evaluated the average Mean Squared Error (MSE) for each of the cases using our proposed quantile GBM algorithm, and they are  reported in Table \ref{tab:perf}. It can be observed from the results that the proposed quantile GBM algorithm can automatically infer the appropriate parameter $\tau$ in each iteration of the algorithm and hence can result in an effective ensemble model. This is verified by corrupting the response variable for a subset of samples with positive noise and the rest with negative noise, and evaluating the regression performance using a validation dataset. As the results show, regardless of the noise pattern considered, for a given $\sigma$, the quantile GBM technique produces very similar MSE. However, it must be noted that the optimal quantile parameter for each of the noise pattern settings is widely different. Figure \ref{fig:gbm} illustrates the quantile parameters chosen in each iteration of the GBM algorithm with different datasets. In particular, we show the results for the cases with completely positive, and completely negative noise components at $\sigma = 4$ and the noise-free case. There are two interesting observations: (a) higher $\tau$ values are chosen when data is corrupted by positive noise and lower $\tau$ values when it is corrupted by negative noise, (b) As the GBM algorithm proceeds towards convergence, it moves closer to the median quantile, indicating that the initial set of estimators in the ensemble have already recovered the underlying data, and the subsequent models added being modeling the noise components.

\section{INTRODUCTION}
\label{sec:intro}

In supervised learning, we are interested in estimating the functional relationship between the random \textit{input} variables $\mathbf{x} = \{x_1, \ldots, x_p\}$ and the \textit{response} or \textit{output} variable $y$. The training sample $\{\mathbf{x}_i,y_i\}_{i=1}^n$ is provided and denoting the function that maps $\mathbf{x}$ to $y$ as $f(\mathbf{x})$, we estimate it such that the expected value of the \textit{loss}, $L(y,f(\mathbf{x}))$, is minimized over the joint distribution of all $(y, \mathbf{x})$.

Symmetric losses are used in a range of cases, including regression and robust regression.
While the quadratic loss is often the method of choice,
it is not robust with respect to the presence of outliers in the data
\citep{Hub,Gao2008,Aravkin2011tac,Farahmand2011} and may have
difficulties in reconstructing fast system dynamics, e.g. jumps in
the state values \citep{Ohlsson2011}.
In these cases, the Huber penalty
\citep{Hub}, Vapnik's $\epsilon$-insensitive loss, used in support
vector regression \citep{Vapnik98,Hastie01}.

These methods are sufficient when the data is homogenous;
however, when the data is heterogeneous, merely estimating the conditional mean
is insufficient, as estimates of the standard errors are often biased.
To comprehensively analyze such heterogeneous datasets, \textit{quantile
regression}~\cite{KB78} has become a popular alternative.
In quantile regression, one studies the effect of explanatory variables on the
entire conditional distribution of the response variable, rather than just on its mean value.
Quantile regression is therefore well-suited
to handle heterogeneous datasets~\cite{Buchinsky:1994}.
A sample of recent works in areas such as computational biology \cite{Zou08},
survival analysis~\cite{KG01}, and economics~\cite{KH01} serve as testament to
the increasing popularity of quantile regression.
Furthermore, quantile regression is \emph{robust to
outliers}~\cite{Koenker:2005}: the quantile regression loss is a piecewise
linear ``check function'' that generalizes the absolute deviation loss for
median estimation to other quantiles, and shares the robustness properties of
median estimation in the presence of noise and outliers.
A huberized extension of the quantile loss has recently been proposed
in the context of high dimensional regression~\cite{aravkin2014qh}.

The quantile loss and its huberized version are parameterized by a scalar
variable. While state-of-the-art relies on cross-validation methods to obtain
reasonable values for this parameter, it is intuitively clear that we can infer
the degree of asymmetry in the loss in addition to the standard parameters of
interest. Especially in the large data setting, a large residual vector may inform
the degree of asymmetry.

The paper proceeds as follows. In Section~\ref{sec:QHQ} we introduce the quantile
and quantile Huber loss, and specify their asymmetric parametrization.
We then design an inference scheme for the asymmetry parameter by exploiting
the statistical distributions corresponding to the asymmetric losses of interest. In particular, we show
that the normalization constant, which can be ignored for standard quantile and quantile Huber regression,
plays a key role both for theoretical and practical aspects of the inference scheme.
We present a joint optimization method to obtain regression estimates and the
asymmetry parameter in Section~\ref{sec:Alg}. Finally, we present numerical examples
that exploit the proposed framework in the context of gradient boosted machines
in Section~\ref{sec:GBM}.

\section{QUANTILE AND QUANTILE Huber LOSS}
\label{sec:QHQ}
Quantile loss is an important learning tool for the high dimensional inference,
and a `huberized' version has recently been shown to perform even better in some settings~\cite{aravkin2014qh}.
In particular, while the {\it quantile Huber} shares the asymmetric features of the quantile loss,
it is smooth at the origin.

\begin{figure}[t]
\centering
\begin{minipage}{0.49\textwidth}
\begin{tikzpicture}
  \begin{axis}[
    thick,
    width=.45\linewidth, height=2cm,
    xmin=-2,xmax=2,ymin=0,ymax=1,
    no markers,
    samples=50,
    axis lines*=left,
    axis lines*=middle,
    scale only axis,
    xtick={0},

    ytick={0},
    ]
\addplot[red,domain=-2:0,densely dashed]{-.3*x};
\addplot[red,domain=0:+2,densely dashed]{.7*x};
  \end{axis}
\end{tikzpicture}
\begin{tikzpicture}
  \begin{axis}[
    thick,
    width=.45\linewidth, height=2cm,
    xmin=-2,xmax=2,ymin=0,ymax=1,
    no markers,
    samples=50,
    axis lines*=left,
    axis lines*=middle,
    scale only axis,
    xtick={0},

    ytick={0},
    ]
\addplot[red,domain=-2:0,densely dashed]{exp(-(-.3*x))};
\addplot[red,domain=0:+2,densely dashed]{exp(-(.7*x))};
  \end{axis}
\end{tikzpicture}

\begin{tikzpicture}
\vspace{-.05 in}
  \begin{axis}[
    thick,
    width=.44\linewidth, height=2cm,
    xmin=-2,xmax=2,ymin=0,ymax=1,
    no markers,
    samples=100,
    axis lines*=left,
    axis lines*=middle,
    scale only axis,
    xtick={-.24,.56},
    xticklabels={L, R},
    ytick={0},
    ]
\addplot[red,domain=-2:-2*0.3*0.4,densely dashed]{0.3*abs(x) - 0.4*0.3^2};
\addplot[blue,domain=-2*0.3*0.4:2*(1-0.3)*0.4]{0.25*x^2/0.4};
\addplot[red,domain=2*(1-0.3)*0.4:2,densely dashed]{(1-0.3)*abs(x) - 0.4*(1-0.3)^2};
\addplot[blue,mark=*,only marks] coordinates {(-.24,0.0550) (0.56,0.20)};
  \end{axis}
\end{tikzpicture}
\begin{tikzpicture}
\vspace{-.05 in}
  \begin{axis}[
    thick,
    width=.44\linewidth, height=2cm,
    xmin=-2,xmax=2,ymin=0,ymax=1,
    no markers,
    samples=100,
    axis lines*=left,
    axis lines*=middle,
    scale only axis,
    xtick={-.24,.56},
    xticklabels={L, R},
    ytick={0},
    ]
\addplot[red,domain=-2:-2*0.3*0.4,densely dashed]{exp(-(0.3*abs(x) - 0.4*0.3^2))};
\addplot[blue,domain=-2*0.3*0.4:2*(1-0.3)*0.4]{exp(-(0.25*x^2/0.4))};
\addplot[red,domain=2*(1-0.3)*0.4:2,densely dashed]{exp(-((1-0.3)*abs(x) - 0.4*(1-0.3)^2))};
\addplot[blue,mark=*,only marks] coordinates {(-.24,0.9464851) (0.56, 0.8187308)};
  \end{axis}
\end{tikzpicture}
\end{minipage}

\caption{Quantile $(\tau = 0.3)$  loss (top left) and associated density (top right);
quantile Huber $(\tau = 0.3)$ loss (bottom left) and associated density (bottom right).
The quantile Huber loss is obtained by smoothing the quantile loss at the origin.}
\label{fig:PLQFig}
\end{figure}
In this section, we define these two loss functions, and describe their characteristics and properties.
To enable the inference on the {\it quantile parameter} $\tau$, we develop a statistical
interpretation for quantile and quantile Huber densities, and find normalization constants for these densities.
We also illustrate the key role the normalization constants play in quantile inference.

For the scalar residual $r = y-f(\mathbf{x})$, the quantile loss (or {\it check function}) is defined as follows:
\begin{equation}
\label{en:quant_loss}
q_{\tau}(r) = (-\tau+1 [{r \geq 0}]) r,
\end{equation} where $1[.]$ is the indicator function.
Note that the quantile loss is same as the $\ell_1$ loss when $\tau = 0.5$.
We focus on two features: robustness to outliers (heavy tails), and
 sparsity of residual (non-smoothness at the origin).
A sparse residual corresponds to exact fitting of some of the data,
and this is unlikely to be desirable in most regression settings.
If we apply Moreau-Yosida smoothing (c.f.~\cite{RTRW}), we obtain use the \textit{quantile Huber}, which matches
the asymmetric slopes of the quantile outside the interval $[-\kappa\tau,
\kappa(1-\tau)]$, and is quadratic on this interval:
\begin{equation}
\label{quantileHuber}
\small
\rho_\tau(r) = \begin{cases}
\tau |r| - \frac{\kappa \tau^2}{2} & \text{if} \text{ } r < -\tau\kappa,\\
\frac{1}{2\kappa}r^2 & \text{if} \text{ } r\in [-\kappa \tau, (1-\tau)\kappa],\\
(1-\tau) |r| - \frac{\kappa(1-\tau)^2}{2}, & \text{if} \text{ } r > \quad (1-\tau)\kappa.
\end{cases}
\end{equation}
In a data fitting context, the quantile Huber
is more permissive, allowing small errors for all residuals, in contrast to the quantile loss
which forces a portion of the data to be fit exactly.
The quantile Huber becomes the Huber loss when $\tau = 0.5$.
Figure \ref{fig:PLQFig} shows the quantile $(\tau = 0.3)$ and quantile Huber $(\tau = 0.3)$ loss functions.
As $\kappa \rightarrow 0$, the quantile Huber converges to the quantile loss, so we can view the quantile loss as a
special case of the quantile Huber.
The loss for a multi-dimensional residual $\mathbf{r}$ is defined as the sum of losses for individual dimensions for both quantile and quantile Huber losses, i.e., $q_{\tau}(\mathbf{r}) = \sum_{i=1}^N q_{\tau}(r_i)$ and $\rho_\tau (\mathbf{r}) = \sum_{i=1}^N \rho_\tau (r_i)$

\subsection{Quantile Huber Density and Normalized Loss}
In order to perform inference of the quantile parameter $\tau$, we build a correspondence between densities that correspond to
the penalties of interest.
For a scalar residual, the we define the density function for the quantile Huber loss as follows:
\begin{equation}
p (r | \tau) = \frac{1}{c(\tau)} e^{-\rho_{\tau} (r)},
\end{equation} where $c(\tau)$ is the normalization constant:
\begin{equation}
c(\tau) = \int e^{-\rho_{\tau} (r)} dr.
\end{equation}
Quantile Huber loss now corresponds to the negative log of the density, ignoring the normalization constant $c(\tau)$.
If $\tau$ is fixed and known, ignoring $c(\tau)$ does not impact regression estimates.
However, in our case we are interested in $\tau$, and it becomes essential to consider $c(\tau)$.
The joint loss function $-\log(p(y | \tau))$ including the normalization constant is given by
\begin{equation}
\label{eq:jointRho}
\bar{\rho}_{\tau} (r) = \rho_{\tau} (r) + \log (c(\tau)).
\end{equation}The normalization constant $c$ can be obtained in
closed form by simple integration:
\begin{align}
\nonumber
c(\tau) =& \frac{1}{\tau} e^{-\frac{\kappa \tau^2}{2}} + \frac{1}{1-\tau} e^{-\frac{\kappa (1-\tau)^2}{2}}\\
&+ \sqrt{2 \pi \kappa} \left( F((1-\tau) \sqrt{\kappa}) - F(-\tau \sqrt{\kappa}) \right),
\end{align} where $F$ is the CDF of the standard normal distribution.

\subsubsection{Gradient and Hessian}
\label{sec:GradHess}

Optimization over $\tau$ can be done efficiently once we know the
gradient and the Hessian of~\eqref{eq:jointRho}. We show the necessary
derivations in this section, and present an algorithm for joint inference
of $(\tau, x)$ in Section~\ref{sec:Alg}.
The derivative of $\bar{\rho}_{\tau} (r)$ with respect to $\tau$ is
\begin{equation}
\frac{\partial \bar{\rho}_{\tau} (r)}{\partial \tau} =\frac{\partial \rho_{\tau} (r)}{\partial \tau}  + \frac{1}{c(\tau)} \frac{\partial c (\tau)}{\partial \tau}.
\end{equation} The first term can be immediately computed from~\eqref{quantileHuber}:
\begin{equation}
\frac{\partial \rho_{\tau} (r)}{\partial \tau}  = \begin{cases}
-r - \kappa \tau & \text{if} \text{ } r < -\tau\kappa,\\
0 & \text{if} \text{ } r\in [-\kappa \tau, (1-\tau)\kappa],\\
-r + \kappa(1-\tau), & \text{if} \text{ } r > \quad (1-\tau)\kappa.
\end{cases}
\end{equation} The derivative $c'(\tau)$ can also be computed in closed form:
\begin{align}
\label{eqn:cp}
 c'(\tau) = -\frac{1}{\tau^2}e^{-\frac{\kappa \tau^2}{2}} + \frac{1}{(1-\tau)^2}e^{-\frac{\kappa (1-\tau)^2}{2}}.
\end{align}

\begin{figure}
\centering
\includegraphics[width=7cm]{figures/loss_fns.png}
\caption{The concave un-normalized loss when added to the convex normalization constant, results in the final loss which is convex in $\tau$ for $\kappa = 1$. $10 \%$ of elements in the residual vector are positive, and the total loss is small for small $\tau$ values.}
\label{fig:lossfn}
\end{figure}

The second derivative of $\log(c(\tau))$ is given by
\begin{align}
\label{eqn:Hess2}
(\log(c(\tau))''  = \frac{c(\tau) c''(\tau) -(c'(\tau))^2}{c^2(\tau)},
\end{align} where $c'(\tau)$ is given in (\ref{eqn:cp}) and
\begin{align}
\nonumber
c''(\tau) &=  \left(\frac{\kappa}{\tau}+\frac{2}{\tau^3} \right) e^{-\frac{\kappa \tau^2}{2}}\\
\nonumber
&+\left(\frac{\kappa}{1-\tau}+\frac{2}{(1-\tau)^3} \right) e^{-\frac{\kappa (1-\tau)^2}{2}}.
\end{align} Further, since
\begin{equation}
\label{eqn:Hess1}
\frac{\partial^2 \rho_{\tau} (r)}{\partial \tau^2}  = \begin{cases}
- \kappa \tau & \text{if} \text{ } r \notin [-\kappa \tau, (1-\tau)\kappa],\\
0 & \text{otherwise},
\end{cases}
\end{equation}
we know $\frac{\partial^2 \bar{\rho}_{\tau} (r)}{\partial \tau^2}$ is just the sum of (\ref{eqn:Hess1}) and (\ref{eqn:Hess2}). The positivity of this Hessian provides us a \textit{certificate of convexity} for $\bar{\rho}_{\tau} (r)$ with respect to $\tau$. This quantity depends on the Huber parameter $\kappa$.
We empirically verified that minimum value of the second partial of $\bar \rho$ (over $\tau \in [0,1]$) decreases from $8$ to $6$ as $\kappa$ moves from $0$ to $1$,
so the objective function is strongly convex in $\tau$ for all of these values.
In contrast, the un-normalized loss, ${\rho}_{\tau} (r)$ given in (\ref{quantileHuber}) is clearly concave in $\tau$, with the minimum value of the second derivative given by $-\kappa$. Adding the normalization term gives rise to a convex problem in $\tau$, paving the way for an inference for both $x$ and $\tau$.
This idea is demonstrated in Figure \ref{fig:lossfn}, where only $10 \%$ of values in a $1000$-dimensional residual vector were set to be positive.
By definition, in this case the quantile Huber loss is expected to be small for small values of $\tau$.
The un-normalized loss is concave, and the minimum is {\it always} $\tau = 0$ when negative elements dominate in the residual, and {\it always} $\tau = 1$ when positive elements dominate. This behavior in principle cannot recover a true $\tau$ strictly in the range $(0,1)$, and can also adversely affect inference in $x$.
When the normalization term is added, the loss becomes convex and the minimum is obtained in the lower range of $\tau$ values. In addition, as shown in Figure \ref{fig:lossfn_diffnoise}, the minimizer $\overline\tau$ for the normalized loss shifts smoothly across $(0,1)$ as the noise changes from mostly negative to mostly positive.

\begin{figure}
\centering
\includegraphics[width=7cm]{figures/loss_diff_noise.png}
\caption{The un-normalized loss (dashed line) and normalized loss (solid line) for different percentages of positive values in the residual - $10 \%$ (black),  $25 \%$ (blue),  $50 \%$ (green),  $75 \%$ (red), and  $100 \%$ (magenta), for $\kappa = 1$. While minimizing the un-normalized loss can provide only a very coarse estimate of $\tau$ - either $0$ or $1$, using the normalized loss can provide a much finer estimate that changes smoothly with the actual $\tau$.}
\label{fig:lossfn_diffnoise}
\end{figure}

In the case of multidimensional residual $\mathbf{r}$, the total loss $\rho_\tau({\mathbf{r}})$ and the total normalized loss $ \bar{\rho}_{\tau} (\mathbf{r})$ is obtained as a sum over corresponding quantities at individual dimensions. Hence, the total gradient and Hessian are also obtained as a summation over the individual dimensions.

{\bf Remark}: Quantile loss is a special case of quantile Huber, since quantile Huber converges (pointwise) to the quantile loss as $\kappa \rightarrow 0$. The loss function, normalization constant, gradient and Hessian of the quantile loss can be obtained in the limit $\kappa \rightarrow 0$ from the corresponding expressions for quantile Huber. As mentioned before, the second derivative of quantile Huber is positive for $\kappa = 0$ and $\tau \in [0,1]$. Hence it is clear that quantile loss is strongly convex and $\tau$ can be inferred in a similar manner, as described in the next Section.
\subsubsection*{References}

redpajama_set_name: RedPajamaArXiv


text:
Цапля-голиаф, или исполинская цапля, или цапля-великан () — околоводная птица из семейства цаплевых.

Общая характеристика
Цапля-голиаф — самый крупный ныне живущий представитель семейства цапель (вымершая во II тысячелетии до н. э. цапля Ardea bennuides была больше). В высоту достигает 155 см, весит не менее 7 кг, размах крыльев — 210—230 см. Голова и шея каштаново-бурые, туловище — серо-бурое, подбородок белый. Клюв средней длины, очень мощный.

Распространение
Цапля-голиаф обитает на всей территории Африки южнее Сахары. Изредка залетает и гнездится в Азии. Взрослые птицы оседлы, молодые совершают перелёты от гнездовых территорий к кормовым.

Образ жизни
Цапли-голиафы живут на болотах, в уединённых от людей местах, никогда не селятся колониями. Кормятся рыбой весом от 30 г до 3,5 кг. Так же поедают земноводных, в том числе самых крупных особей африканской роющей лягушки, грызунов весом до 1 кг, и рептилий, в том числе мамб и варанов.

Фото

Примечания

Ссылки
 Heron Conservation: Goliath Heron Ardea goliath (Cretzchmar). The IUCN-SSC Heron Specialist Group.

Настоящие цапли
Птицы Африки
Животные, описанные в 1829 году

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text:
The Doctor Is Coming To Marvel
by Alanna Bennett
Frederick M. Brown/Getty Images Entertainment/Getty Images
One man has taken another step in the longstanding battle for the Iron Throne of Geekdom. That man would be former/forever Tenth Doctor, David Tennant, who is coming to Marvel thanks to a role in the upcoming Netflix series AKA Jessica Jones . It's yet another case of geeky cross-pollination, though fans should know that he will hardly be playing a character reminiscent of his beloved Ten. According to The Wrap, Tennant will play the series' villain, Killgrave. And, I mean, if that's not a dead-on villain name then I don't know what is.
Tennant is iconic among genre fans for his stint as the Tenth Doctor in BBC's Doctor Who. More recently, he's been the star of BBC's Broadchurch and its Americanized version Gracepoint. From his Doctor Who days, he is known as a generally affable being, so seeing him take a villainous turn will be an interesting shake-up. That's especially true given the phrasing being used to describe his character's role in the series. The Wrap describes him both as an "enigmatic figure" and one whose presence "is said to send 'shockwaves' through the universe of the superhero-turned-private detective."
Tennant is hardly the first beloved Brit to make the move to Marvel — heck, importing British actors is even more of a Marvel trope than hiring muscle-bound blonde men named Chris. With the exception of Benedict Cumberbatch's Doctor Strange, they're largely cast as villains — see Tom Hiddleston's Loki. He's not even the first former Doctor to be a Marvel villain: Predecessor Christopher Eccleston (Nine) was one of the baddies in Thor: The Dark World . I wouldn't be surprised if Matt Smith (Eleven) announced a Marvel contract sometime soon.
Yes, a Marvel role seems like practically a rite of passage for the British thespian seeking an international audience, much like being in the Harry Potter series used to be. And hey, Tennant's already done that, in one of his previous villainous roles. You gotta remember Barty Crouch Jr. and his fascinating tongue problem.
Seriously, what was up with that?
Image: secrethorcruxes/Tumblr
Rupert Grint Just Shared A *Major* Update About Ron Weasley's Future
By El Hunt
Twitter Is Overjoyed By Angela Bassett's Record-Making Oscar Nomination
By Maxine Harrison

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text:
Q: how to recover deleted ntfs patition with data entirely while installing ubuntu 13.04 I've installed ubuntu 13.04 onto my hp 2231tx computer. During installation all of my data was erased. I didn't know all of my three partitions would be deleted.
I was shocked after finding out that all of my personal data was erased. I didn't know what to do to resolve this problem so I search google for an answer.
I found a program called testdisk and I used it to recover about half of my data. Among this data weren't my personal photos and videos. Is there a way to recover the other half?

A: In short: no, your data cannot be recovered, and please learn from this experience and back up your data regularly, and also make sure you fully understand what you are doing to your system before you start. By and large, if you erased your partitions, you've erased your data.
The more you use the drive and write data to it, the less change any retrieval application will be able to recover anything, so in all honesty, if you've formatted the drive, installed Ubuntu and booted into it, the chances of you being able to recover anything are almost nil - I'm surprised the tool you chose was able to recover any data at all.

A: As others have said, you should use this as a learning experience about the importance of backups, particularly when doing anything risky like repartitioning the disk or installing a new OS.
That said, there's a chance that PhotoRec or a similar tool (there are some Windows-specific tools that are similar in broad strokes, but I don't have any URLs handy) may be able to recover some files. Be aware, though, that the longer you use the disk in its current state, the more likely it is that additional files will be irrecoverably damaged or erased. Thus, if you really need any files from that disk, STOP USING IT IMMEDIATELY! Use it only along with PhotoRec (or something similar), and be sure to use another physical disk as the recovery target.

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text:
Weitenhagen Hotels
Accommodation in Weitenhagen
See Weitenhagen hotels on a map
Where to stay in Weitenhagen
What's Weitenhagen like?
If you're looking for a place to get away, look no further than Weitenhagen. Whether you're planning to stay for a night or for the week, the area around Weitenhagen has accommodations to fit every need. Search for hotels in Weitenhagen with Hotels.com by checking our online map. Our map displays the areas and neighborhoods around all Weitenhagen hotels so you can see how close you are from landmarks and attractions, and then refine your search within the larger area. The best Weitenhagen hotel deals are here with our lowest price guarantee.
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How to Get to Weitenhagen
What is the closest airport to Weitenhagen?
• Peenemuende (PEF), 16.3 mi (26.2 km) from central Weitenhagen
Things to See and Do in Weitenhagen
What is there to see near Weitenhagen:
• Eldena Abbey (3.1 mi/4.9 km from the city center)
• University of Greifswald (3.4 mi/5.4 km from the city center)
• St. Nikolai Cathedral (3.3 mi/5.4 km from the city center)
• Greifswald Botanic Garden (3.5 mi/5.7 km from the city center)
• Strandbad Eldena (3.3 mi/5.3 km from the city center)
What is there to do near Weitenhagen:
• Freizeitbad Greifswald (2.5 mi/4 km from the city center)
• Theater Vorpommern (3.1 mi/4.9 km from the city center)
• Museumshafen Greifswald (3.4 mi/5.5 km from the city center)
• Pomeranian State Museum (3.2 mi/5.2 km from the city center)
• Heimattierpark Greifswald (3.6 mi/5.8 km from the city center)
When is the best time to visit Weitenhagen?
• Rainiest months: July, August, June, and May (average 2.43 inches of rainfall)


Ariel began at the young age of 5 with her father's ministry, Kent Henry Ministries, as he travelled to different churches all over America preaching, teaching, and training worship teams. Kent Henry, joined with Integrity Music, had been an instrumental piece in a new breed of worship music in the 70's and 80's. More than the words on pages, he mingled spontaneous worship with songs and praise with prayer, which revolutionized the Christian music world. With this lineage, over 10 years of combined experience in musical prayer and worship with Destiny Church of St. Louis, the International House of Prayer, and the hands-on training traveling with her father, Ariel has reached up to take the mantel of spontaneous prayer, worship, and the singing of The Word.
She has two completed albums with her singing and piano playing, along with a full band, available on iTunes, her website, and most music platforms. "Children's Worship" was her first recording experience at 6 years old followed by "Friend" at 8. However, "Come With Me," her own very first album, was completed at the age of 18 with 3 of her own original songs and 6 covers of other worship songs. Her most recent record, "Among the Branches," is a 12 song praise and worship album of original songs molded out of her personal time with the Lord along with her devotional and intercession worship sets at Destiny Church of St. Louis.
Ariel recently relocated to Southern California with her husband Jared. She joined with her father and brother, Matt Henry, to begin a new ministry, Carriage House Ministries. Their desire is to use the anointing God has placed on their lives and family to see people's hearts and lives changed by the presence of the Lord. Check out Carriage House's Website too!


text:
Abbott And Costello Meet Frankenstein
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Ian Jane
Released by: Universal Pictures
Released on: August 28, 2012.
Director: Charles Barton
Cast: Bud Abbott, Lou Costello, Bela Lugosi, Lon Chaney Jr., Glenn Strange
Purchase From Amazon
The Movie:
This beloved mix of monsters and mayhem directed by Charles Barton follows two baggage handlers, Chick Young (Bud Abbott) and Wilbur Grey (Lou Costello), who take receipt of two large cartons for a Mr. MacDougal (Frank Ferguson). The operator of a house of horrors attraction, MacDougal is unusually anxious to get his hands on the crates which he claims contain Dracula and his coffin and the body of the Frankenstein monster, two artifacts he can't wait to get into his attraction. Before they hand over the goods, Wilbur gets a phone call from Europe from a man named Larry Talbot (Lon Chaney Jr.) who tells them what's inside and not to hand them over to MacDougal but before the two can finish their conversation Talbot starts to change into something not quite human.
MacDougal, concerned that our heroes have damaged the goods, insist the they deliver and unload them for him so that he can have an insurance inspector on hand to make sure everything is as it should be. They oblige and soon find out that Talbot wasn't kidding about the contents. Wilbur sees Dracula (Bela Lugosi – the only other time he'd play Dracula in a film outside of the 1931 original) come out of his coffin and later sees the Frankenstein Monster (Glenn Strange) lumbering around but Chick doesn't believe him. Eventually Dracula turns into a bat and flies to a castle where he meets Dr. Sandra Mornay (Lenore Aubert), who has been flirting with Wilbur to coerce him into helping her and who is going to help him put a new brain into the Frankenstein monster and in return Dracula will help Sandra out with her work. Complicating matters further is the presence of Joan Raymond (Jane Randolph), an insurance investigator who has also been flirting with Wilbur to get him to help her, which sends Chick into a bit of a spat. When all involved wind up a masquerade ball held at Sandra's castle, just as Talbot flies across the Atlantic from England, the monsters run amok!
Eighty minutes of screwy physical comedy, snappy dialogue and creepy monsters, Abbott And Costello Meet Frankenstein remains one of the greatest horror comedy hybrids ever made. While some might lament the fact that Glenn Strange dons the Frankenstein's monster makeup for the film (no Karloff, of course he famously declined the offer), he does a fine job lumbering around and smashing through things while Lugosi is, not surprisingly, great as Dracula and seemingly having a lot of fun playing the part. Chaney is great as the Talbot/Wolfman character, playing his part completely straight here but eventually getting involved in a funny chase gag with Wilbur, while both Lenore Aubert and Jane Randolph are beautiful as the two female leads. The real stars though? Abbott and Costello, of course. Pairing them with the most recognizable movie monsters of all time was a great idea and the two really get the chance to show off their collective knack for slapstick and clever, funny conversations and arguments.
The film shows a fair bit of style in certain spots – the animated introduction (courtesy of Walter Lantz) involving a pair of skeletons is charming and quirky as are the bits of animation used in the movie to show Dracula turning into the bat. The castle sets are shadowy and dark, making for the perfect spot for a pair of goofballs to mix it up with some monsters – and of course, there's that revolving door that proves to be such a pain. There are plenty of sight gags throughout the movie, be it Wilbur's balancing act atop a giant carton Chick is trying to level on the floor or Wilber's demonstrations of Dracula's hypnosis technique, and they all just add to the fun. The movie flies by at a very quick pace, a relentlessly entertaining picture that has lost none of its charm and quirky appeal.
Video/Audio/Extras:
Universal presents Abbott And Costello Meet Frankenstein in an AVC encoded 1.33.1 fullframe transfer that looks pretty good considering the film's age. The black and white photography holds up well in high definition and shows good detail and contrast and fairly strong black levels as well. Close up shots, such as when the camera zooms in towards Dracula's eyes, show the most detail and texture but medium and long distance shots fare well too. There's a fair bit of grain throughout the presentation, just as you'd expect, and some softness here and there that stems back to the original photography but increased texture is evident in shots like the one showing the pillow in Dracula's coffin and the scenes in which Larry Talbot transforms into a werewolf. Some minor noise reduction might have been applied but it's not distracting nor does it eliminate the abundance of grain. There isn't much in the way of serious print damage to note, just a few minor specks here and there and a couple of 'blink and you'll miss it' scratches, while the animated bits that appear in the film, primarily the opening scene but also the scenes in which Dracula turns into a bat, show strong, clean lines.
The only audio option for the feature is an English language DTS-HD Master Audio 2.0 Mono track, though optional subtitles are provided in English SDH, French and Spanish. The audio is a bit on the flat side but otherwise fine – it's probably about what you'd expect from an older mono mix. Hiss and distortion are never a problem and the levels are well balanced. The score sounds good and the dialogue is easy to understand. It's not a particularly complex mix, but it doesn't need to be and it suits the movie just fine.
Some of these extras are going to look familiar to those who have owned the previous DVD release, as they've been carried over from that disc. First up is a commentary from film historian Gregory W. Mank who does a very strong job of exploring the history of the film and providing loads of detail on those who made it. He covers the involvement of the various cast and crew who worked on the production, relays some interesting stories about things that happened on the set during the making of the movie and offers up quite a bit of interesting trivia about the film as well as providing some fun details about the makeup and effects work that we see in the feature.
Also carried over from that DVD release is the documentary Abbott And Costello Meet The Monsters: The Making Of Abbott And Costello Meet Frankenstein, which clocks in at over half an hour and is hosted by David J. Skal. Primarily made up of interviews with Lou Costello's daughter Christine, film historian Ron Palumbo, Bela Lugosi Jr. and filmmaker/monster man extraordinaire Bob Burns this documentary does cover some of the same ground as the commentary but has the added bonus of the visuals, so we get a good look at some of Burns' memorabilia from the film and very cool stills and clips.
Rounding out the extras are the film's original theatrical trailer and two of the Universal 100th Anniversary featurettes that have been included on some of the other releases in the line: 100 Years Of Universal: The Lot and 100 Years Of Universal: Unforgettable Characters. Menus and chapter selection are also included as is a digital copy and a standard DVD copy. For those interested in the packaging, the Blu-ray case fits inside a nice slipcase (that flips open to provide some basic trivia on the film and the studio) featuring identical front cover and slightly different back cover art
The Final Word:
While it would have been nice to see some more extras included here, Universal has otherwise done a pretty respectable job bringing this classic horror comedy to Blu-ray. The transfer is a nice step up from the previous DVD release and the film holds up incredibly well, more than a half a century after it was made.
Click on the images below for full sized Blu-ray screen caps!
DVD And Blu-ray Reviews A-F
Tags : abbott and costello meet frankenstein, bela lugosi, blu-ray review, bud abbott, dracula, dvd review, frankenstein, glenn strange, lon chaney jr., lou costello, the wolfman, universal monsters, universal picutres
Mike Howlett - 08-18-2012, 09:35 AM
That just jumped to the top of my need-it list!
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I don't think I can handle any more Zoom interviews.
Matt H. 01-13-2021 06:18 PM
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Gary Banks 01-13-2021 09:44 AM
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Back then I hated 3rd grade so much because I hated my teacher. My mom help me get through the week...
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MY DEAR KILLER stands waaayyy above the other two films in this set. Terrific, haunting story made...
Andrew Monroe 01-12-2021 09:38 AM
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text:
Les épreuves d'équitation aux Jeux olympiques d'été de 2024 à Paris sont le saut d'obstacles, le dressage et le concours complet, disputées à titre individuel et par équipes.

Épreuves

Six titres issus de trois disciplines sont disputés à l'occasion de ces Jeux, ainsi qu'indiqué dans le tableau ci-contre.

L'équitation est un sport mixte où hommes et femmes concourent dans la même catégorie.

Lors du dressage, le cavalier et son cheval exécutent une série de figures au cours d'une reprise dans une carrière. Un jury attribue des notes en fonction de critères techniques, d'aisance et de fluidité.

Le saut d'obstacles est un parcours chronométré que le cavalier et sa monture doivent effectuer le plus rapidement possible. Il comprend différents obstacles constitués de barres. Quand l'une d'elles tombe lors du passage du cheval, cela entraîne une pénalité.

Le concours complet regroupe trois épreuves : le dressage, le saut d'obstacles et le cross-country. Cette épreuve est un long parcours comportant des obstacles naturels et artificiels variés à franchir dans un temps imparti.

Organisation

Qualifications

200 cavaliers sont qualifiés pour les Jeux : 60 au dressage, 75 au saut d'obstacles et 65 au concours complet.

Dans le détail ci-après, les groupes olympiques FEI sont :
 A : Europe du Nord-Ouest ;
 B : Europe du Sud-Ouest ;
 C : Europe de l'Est et centrale / Asie centrale ;
 D : Amérique du Nord ;
 E : Amérique centrale et du Sud ;
 F : Afrique et Moyen-orient ;
 G : Asie du Sud-Est / Océanie.

Dressage
15 équipes de 3 cavaliers se qualifient pour l'épreuve par équipes. En individuel, il y a 60 places, dont 45 sont attribuées aux cavaliers qualifiés par équipes.

Saut d'obstacles
20 équipes de 3 cavaliers se qualifient pour le concours par équipes. En individuel, il y a 75 places, dont 60 sont attribuées aux cavaliers qualifiés par équipes.

Concours complet
16 équipes de 3 cavaliers se qualifient pour la compétition par équipes. En individuel, il y a 65 places, dont 48 sont attribuées aux cavaliers qualifiés par équipes.

Tableau récapitulatif des nations participantes

Site des compétitions

Les épreuves d'équitation ont lieu dans le parc du château de Versailles : une carrière avec des tribunes d'une capacité de  places sera installée à l'ouest du Grand Canal.

Calendrier

Résultats

Podiums

Tableau des médailles

Notes et références

Notes

Références

Annexes

Articles connexes
 Liste des médaillés olympiques en équitation
 Chronologie des sports équestres

Liens externes
Le site officiel du Comité international olympique
Site officiel des Jeux olympiques de Paris 2024


Most of the rideable trails are in the Northern section of Lane Cove National Park (LCNP). Stretching from Macquarie Uni in the South, Thornleigh Park in the North, & Cheltenham rail station in the West.
The trails in the North section range from rocky fire trails, sealed commuter cycle trails in the South, & roughly concreted steep sections in the NW.
Heading out from the start point of Vimiera Rd, head towards the M2 underpass, then down steep sealed commuter bike path to Lane Cove River, where you can join the Great North trail. From here you can head in a number of directions along a variety of firetrails.
Notes from a recent rider "There are some good hill climb training hills too. Mostly the trails follow the river. I rode up to where the trail ends at Ramsay Ave in Comenarra and then back tracked a bit to continue up another steep hill to finish at Browns Waterhole.
This trail is good for some variety and reminds me most of Cascades in terms of fitness, length and skill required. The whole time I was riding I only saw 1 person walking, so it seems to be pretty forgotten by everyone else too.
There were a few other trails off to the side, but I haven't checked them out yet, so can't comment on what they are like. "
A scenic ride that takes in the variety of vegetation types and offers great harbour and city views and nice spots to stop for picnics.
Follow the Lane Cove River from Pennant Hills Park (Hornsby Council Park) and take the management trail south downhill through to Devlins Creek in South Turramurra and Browns Water Hole through to Pymble. For a loop, travel back along the Comenarra Parkway. There are some tricky sections with steep areas but a good surface.
The Southern section of LCNP between Delhi Road and Lane Cove Road has no trails that can be ridden by mountain bikes. There is tarmac riding though.
For the off-road bit, I rode from the north side of Lane Cove Road (across from Lady Game Drive intersection) and headed northwest up the Lane Cove River. It starts with a nice downhill with a few good sized water bars to jump on the way down. At the bottom of the first downhill is a creek crossing and a sign asking you to hike a bike. Unless you are able to jump up 1 metre rocks, you will need to walk the next uphill section anyway. This hike a bike section goes for about 400m.
After that you can ride again. The trail is smooth and flowing in places, rough and rocky in others and sandy in others.


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text:
Arandon-Passins é uma comuna francesa na região administrativa de Auvérnia-Ródano-Alpes, no departamento de Isère. Estende-se por uma área de 26.14 km².

Foi criada em 1 de janeiro de 2016, a partir da fusão das antigas comunas de Passins (sede da comuna) e Arandon.

Comunas de Isère


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text:
Cellular responses to protein production in the filamentous fungus Trichoderma reesei
Tiina Pakula
BA3402 Protein production
Research output: Chapter in Book/Report/Conference proceeding › Conference abstract in proceedings › Scientific
The filamentous fungus Trichoderma reesei is known as an efficient producer of a variety of extracellular enzymes, the major products being cellulases and hemicellulases e.g. cellobiohydrolases, endoglucanases, Beta-glucosidases, xylanases, and hemicellulose side-chain cleaving enzymes. Altogether, analysis of the genome sequence has revealed over 200 genes classified in glycoside hydrolase gene families. T. reesei has potential to produce extracellular proteins in very large quantities, and it has been used as an industrial host organism for production of both the fungal enzymes as well as for heterologous proteins. Production of secreted proteins in large quantities or production of the heterologous proteins originating from distantly related organisms challenge the capability of the cells to fold and transport the proteins, and are known to provoke stress responses in the cell. Impaired protein folding in the endoplasmic reticulum (ER) activates the unfolded protein response pathway (UPR) which result in induction of a number of genes involved e.g. in folding, glycosylation and transport. The fungal cells have also a feed-back mechanism to reduce the load in the secretory pathway by negative transcriptional regulation of genes encoding the major secreted proteins. The availability of the genome sequence information has made it possible to apply genome-wide approaches in studies of the cellular responses to protein production under different conditions. Specifically, we have compared the effects of production of two different heterologous proteins, human tPA and Melanocarpus albomyces laccase, in T. reesei using proteome and transcriptome data. The analysis showed a clear difference between the responses induced by the proteins, the main difference being in the induction of the UPR pathway. Furthermore, in order to obtain information on protein production at different physiological conditions we have carried out transcriptome and proteome analysis of carbon-limited chemostat cultures of T. reesei under different conditions, e.g. at different specific growth rates and cell density.
3rd European Federation of Biotechnology Conference
Physiology of Yeasts and Filamentous Fungi PYFF3
3rd European Federation of Biotechnology Conference : Physiology of Yeasts and Filamentous Fungi - Helsinki, Finland
VTT Symposium
PYFF3
http://www.vtt.fi/inf/pdf/symposiums/2007/S245.pdf
Fingerprint Dive into the research topics of 'Cellular responses to protein production in the filamentous fungus Trichoderma reesei'. Together they form a unique fingerprint.
Trichoderma Medicine & Life Sciences
Fungi Medicine & Life Sciences
Cellulases Medicine & Life Sciences
Unfolded Protein Response Medicine & Life Sciences
Proteome Medicine & Life Sciences
Genome Medicine & Life Sciences
hemicellulase Medicine & Life Sciences
Pakula, T. (2007). Cellular responses to protein production in the filamentous fungus Trichoderma reesei. In 3rd European Federation of Biotechnology Conference: Physiology of Yeasts and Filamentous Fungi PYFF3 (pp. 40). [T29] VTT Technical Research Centre of Finland. VTT Symposium No. 245 http://www.vtt.fi/inf/pdf/symposiums/2007/S245.pdf
Pakula, Tiina. / Cellular responses to protein production in the filamentous fungus Trichoderma reesei. 3rd European Federation of Biotechnology Conference: Physiology of Yeasts and Filamentous Fungi PYFF3. Espoo : VTT Technical Research Centre of Finland, 2007. pp. 40 (VTT Symposium; No. 245).
@inbook{0ce85cecb1d94ec498cb9ccceec8dbbb,
title = "Cellular responses to protein production in the filamentous fungus Trichoderma reesei",
abstract = "The filamentous fungus Trichoderma reesei is known as an efficient producer of a variety of extracellular enzymes, the major products being cellulases and hemicellulases e.g. cellobiohydrolases, endoglucanases, Beta-glucosidases, xylanases, and hemicellulose side-chain cleaving enzymes. Altogether, analysis of the genome sequence has revealed over 200 genes classified in glycoside hydrolase gene families. T. reesei has potential to produce extracellular proteins in very large quantities, and it has been used as an industrial host organism for production of both the fungal enzymes as well as for heterologous proteins. Production of secreted proteins in large quantities or production of the heterologous proteins originating from distantly related organisms challenge the capability of the cells to fold and transport the proteins, and are known to provoke stress responses in the cell. Impaired protein folding in the endoplasmic reticulum (ER) activates the unfolded protein response pathway (UPR) which result in induction of a number of genes involved e.g. in folding, glycosylation and transport. The fungal cells have also a feed-back mechanism to reduce the load in the secretory pathway by negative transcriptional regulation of genes encoding the major secreted proteins. The availability of the genome sequence information has made it possible to apply genome-wide approaches in studies of the cellular responses to protein production under different conditions. Specifically, we have compared the effects of production of two different heterologous proteins, human tPA and Melanocarpus albomyces laccase, in T. reesei using proteome and transcriptome data. The analysis showed a clear difference between the responses induced by the proteins, the main difference being in the induction of the UPR pathway. Furthermore, in order to obtain information on protein production at different physiological conditions we have carried out transcriptome and proteome analysis of carbon-limited chemostat cultures of T. reesei under different conditions, e.g. at different specific growth rates and cell density.",
author = "Tiina Pakula",
isbn = "978-951-38-6313-5",
series = "VTT Symposium",
booktitle = "3rd European Federation of Biotechnology Conference",
note = "3rd European Federation of Biotechnology Conference : Physiology of Yeasts and Filamentous Fungi, PYFF3 ; Conference date: 13-06-2007 Through 16-06-2007",
Pakula, T 2007, Cellular responses to protein production in the filamentous fungus Trichoderma reesei. in 3rd European Federation of Biotechnology Conference: Physiology of Yeasts and Filamentous Fungi PYFF3., T29, VTT Technical Research Centre of Finland, Espoo, VTT Symposium, no. 245, pp. 40, 3rd European Federation of Biotechnology Conference , Helsinki, Finland, 13/06/07. <http://www.vtt.fi/inf/pdf/symposiums/2007/S245.pdf>
Cellular responses to protein production in the filamentous fungus Trichoderma reesei. / Pakula, Tiina.
3rd European Federation of Biotechnology Conference: Physiology of Yeasts and Filamentous Fungi PYFF3. Espoo : VTT Technical Research Centre of Finland, 2007. p. 40 T29 (VTT Symposium; No. 245).
T1 - Cellular responses to protein production in the filamentous fungus Trichoderma reesei
AU - Pakula, Tiina
N2 - The filamentous fungus Trichoderma reesei is known as an efficient producer of a variety of extracellular enzymes, the major products being cellulases and hemicellulases e.g. cellobiohydrolases, endoglucanases, Beta-glucosidases, xylanases, and hemicellulose side-chain cleaving enzymes. Altogether, analysis of the genome sequence has revealed over 200 genes classified in glycoside hydrolase gene families. T. reesei has potential to produce extracellular proteins in very large quantities, and it has been used as an industrial host organism for production of both the fungal enzymes as well as for heterologous proteins. Production of secreted proteins in large quantities or production of the heterologous proteins originating from distantly related organisms challenge the capability of the cells to fold and transport the proteins, and are known to provoke stress responses in the cell. Impaired protein folding in the endoplasmic reticulum (ER) activates the unfolded protein response pathway (UPR) which result in induction of a number of genes involved e.g. in folding, glycosylation and transport. The fungal cells have also a feed-back mechanism to reduce the load in the secretory pathway by negative transcriptional regulation of genes encoding the major secreted proteins. The availability of the genome sequence information has made it possible to apply genome-wide approaches in studies of the cellular responses to protein production under different conditions. Specifically, we have compared the effects of production of two different heterologous proteins, human tPA and Melanocarpus albomyces laccase, in T. reesei using proteome and transcriptome data. The analysis showed a clear difference between the responses induced by the proteins, the main difference being in the induction of the UPR pathway. Furthermore, in order to obtain information on protein production at different physiological conditions we have carried out transcriptome and proteome analysis of carbon-limited chemostat cultures of T. reesei under different conditions, e.g. at different specific growth rates and cell density.
AB - The filamentous fungus Trichoderma reesei is known as an efficient producer of a variety of extracellular enzymes, the major products being cellulases and hemicellulases e.g. cellobiohydrolases, endoglucanases, Beta-glucosidases, xylanases, and hemicellulose side-chain cleaving enzymes. Altogether, analysis of the genome sequence has revealed over 200 genes classified in glycoside hydrolase gene families. T. reesei has potential to produce extracellular proteins in very large quantities, and it has been used as an industrial host organism for production of both the fungal enzymes as well as for heterologous proteins. Production of secreted proteins in large quantities or production of the heterologous proteins originating from distantly related organisms challenge the capability of the cells to fold and transport the proteins, and are known to provoke stress responses in the cell. Impaired protein folding in the endoplasmic reticulum (ER) activates the unfolded protein response pathway (UPR) which result in induction of a number of genes involved e.g. in folding, glycosylation and transport. The fungal cells have also a feed-back mechanism to reduce the load in the secretory pathway by negative transcriptional regulation of genes encoding the major secreted proteins. The availability of the genome sequence information has made it possible to apply genome-wide approaches in studies of the cellular responses to protein production under different conditions. Specifically, we have compared the effects of production of two different heterologous proteins, human tPA and Melanocarpus albomyces laccase, in T. reesei using proteome and transcriptome data. The analysis showed a clear difference between the responses induced by the proteins, the main difference being in the induction of the UPR pathway. Furthermore, in order to obtain information on protein production at different physiological conditions we have carried out transcriptome and proteome analysis of carbon-limited chemostat cultures of T. reesei under different conditions, e.g. at different specific growth rates and cell density.
M3 - Conference abstract in proceedings
SN - 978-951-38-6313-5
T3 - VTT Symposium
BT - 3rd European Federation of Biotechnology Conference
T2 - 3rd European Federation of Biotechnology Conference
Pakula T. Cellular responses to protein production in the filamentous fungus Trichoderma reesei. In 3rd European Federation of Biotechnology Conference: Physiology of Yeasts and Filamentous Fungi PYFF3. Espoo: VTT Technical Research Centre of Finland. 2007. p. 40. T29. (VTT Symposium; No. 245).


text:
THE I-PHYC SOLUTION
ABOUT ALGAE
I-Phyc News
I-Phyc's tech to treat farm slurry and produce fertiliser trialled
The UK's water treatment industry has been keeping a keen eye on an upcoming technology that promises to revolutionise the way we treat waste water, all without the use of harsh chemicals. New tech to treat farm slurry and produce fertiliser trialled - Farmers Weekly...
I-Phyc's Team Building Away Day
As we are sure you are all aware the team at I-Phyc are very passionate about the environment so they decide to help a local river clean the water ways and have agreed to test their water quarterly to make sure it is of a good quality. The local newspaper The Frome...
Balancing the risks of new and old Phosphorus removal technologies: is it really better the devil you know?
All sectors are working to become more sustainable and reduce the impact they have on the environment. For the water industry, though, all this change has to be undertaken while continuing to deliver proper operations, safe drinking water and controlled effluent...
Mellby Gård and Feralco water initiative leads to first investment in UK wastewater challenger I-Phyc
At the start of 2022, Mellby Gård and Feralco launched a joint investment initiative to scale-up sustainable water treatment solutions. Now, this initiative has made its first investment in UK-based company I-Phyc. I-Phyc have developed a carbon negative technology...
'Green' water treatment firm raises further £2.3m
I-Phyc – the Mercia-backed company behind a nature-based wastewater treatment solution – has raised a further £2.3m to establish itself as the technological leader in 'circular' solutions to remove wastewater contaminants. The latest investment was led...
Natural Water Treatment: The Way Forward
An overabundance of phosphorus in water can cause a number of adverse ecological effects. Traditionally, chemicals have been used for P removal. But a new process of removing phosphorus from wastewater using algae is becoming more and more established. Here,...
The true cost of new P-removal infrastructure
Every few decades, infrastructure needs to be upgraded. This is true in every industry, but for sectors with annual budgets, like the water industry, it can be hard to justify the long-term investment required to replace obsolete infrastructure that is no longer fit...
The power of micro algae in water treatment
I-Phyc has established an approach to wastewater treatment that harnesses the natural power of micro algae. Here, Dr Thea Ekins-Coward, Chief Scientific Officer at I-Phyc, provides an overview of the organisations' technology and dives deep into the applications of...
Today we celebrate all women working in very male dominated positions, but we are especially celebrating the women that work at I-Phyc and specifically out Chief Scientific Officer Dr Thea Ekins-Coward. Overcoming severe injuries in a lab accident in 2016, she has...
Severn Trent selects algae-based phosphorus removal technology for rural plant
Severn Trent is using I-Phyc's algae-based method of removing phosphorus from wastewaterat Croxton Kerrial, owing to its efficacy, sustainability and its ability to work with variable flowrates. Read more here -...
I-Phyc Raises £700k for roll-out
Thank you to Mercia for their continued support of I-Phyc. GreenTech water treatment firm raises £700k for roll-out - Mercia
Green power: Harnessing algae to clean water and fight climate change
Exclusive interview: H2O Global News Editor Sion Geschwindt spoke with Industrial Phycology Business Development Manager, Andrew Best, about the company's unique approach to waste water treatment....
Announcement regarding Dr. Dan Murray
Dr. Dan Murray, who founded the company in 2012 and now the company's CTO, has decided to re-focus his incredible talents in other areas of algal science and will be leaving full-time employment at I-Phyc. Dan had the foresight, drive and ambition to develop a...
I-Phyc in Drain Trader UK
I-Phyc has been talking to Drain Trader UK about how the water industry can get to Net Zero with a little help from Algae and I-Phyc… Read more here.
150sec's Top 20 UK Impact Startups in 2021 features I-Phyc
Some great innovation happening in the UK which is truly making a big social impact in 2021. Check out Russell Bright & Industrial Phycology. https://150sec.com/150secs-top-20-impact-startups-in-the-uk-in-2021/16278/
I-Phyc featured in Institute of Water Magazine
I-Phyc has been featured in the latest issue of the quarterly Institute of Water Magazine. The article centres on the pros and cons of different artificial and natural wastewater treatment techniques, including the algae-based methods employed by I-Phyc. Read more on...
I-Phyc in Scope Newsletter
Last month Peter Vale, Severn Trent Water UK and UKWIR (UK Water Industry Research) presented to European Sustainable Phosphorus Platform the I-Phyc technology of removing unwanted nutrients from municipal wastewater with Low/Zero carbon footprint. We are mentioned in...
I-Phyc speaking at the WWT Future of Phosphorus Removal in Wastewater Conference
Sensors for Water Interest Group picked up on the fact that we are speaking at the WWT Future of Phosphorus Removal in Wastewater conference. Come and be part of the conversation....
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Get today's predication for Poorattathi nakshatram based on Indian Vedic Astrology. It has forecasts on health, emotional life, personal life, profession, travel and luck for Thursday, 25 April 2019 .
You'll remain active. You immunity levels will be high. You will experience a new wave of energy and a new optimism today. You can start a new diet or new exercise pattern today.
An atmosphere of camaraderie and universal cheerfulness prevails. You will appreciate and enjoy the good things of life.
Today is a good time to tend to personal relationships where you can express yourself in a charming way. You will be able to enjoy a quiet and reflective time with your partner. You will also enjoy physical comforts of various kinds, nice clothes, good food etc.
A number of opportunities in various fronts will open up and you shall take full advantage of them. Following your instincts will lead to improvement in finances. Your enthusiasm will enable you to finish the work on time. It will be a satisfying day for you.
You may have travel for completing some important work. Don't get friendly with strangers.
Today is one of the lucky days when destiny smiles at you. It shall take you to places you have never dreamt of.


Interested in the Catholic Faith? Whether you are Baptized or not, are just starting to learn about the Catholic Church or have looked into joining the Church for years, we welcome you! Catholics enter into full communion though the Sacraments of Baptism, Holy Eucharist, and Confirmation. Baptized Catholics who have been away from the Church or have not received these Sacraments are welcome to join our classes as well. Please contact the Parish Office at (630) 462-5000 for more information.
The Bible Timeline: The Story of Salvation is a fascinating Bible study that will take you on a journey through the entire Bible. Go deep into each period of salvation history and discover the amazing story woven throughout all of Scripture. Using a unique color-coded system,you will see how all the major people, places, and events of the Bible reveal the remarkable story of our faith. This Bible Study will will meet Tuesdays beginning on April 9 at 7pm in the Adult Formation room of the Faith Center. Please register in advance online or with John Ellison at jellison@stmichaelcommunity.org or (630) 462-5045. A suggested donation of $35 covers the cost of supplies.
If you have had questions as to why Catholics do what we do, we invite you to join us to learn more about our rich Catholic traditions as we offer a program called Credo, which means I BELIEVE. Credo is designed as a Rite of Christian Initiation for Adults, RCIA, but all Catholics are welcome! Come to learn what we believe and why, along with the biblical foundation. Credo is written and presented by the Dominican Friars and the Thomistic Institute of Washington, DC. This Bible Study will will meet Tuesday mornings beginning on April 30th at 9am in the Adult Formation room of the Faith Center. Please register in advance with John Ellison at jellison@stmichaelcommunity.org or (630) 462-5045. Suggested donation of $30 for workbook and $10 for Catechism.
Communio is an evening of Adoration and Prayer in the church that takes place on the first Friday of every month beginnning at 7PM. A guest speaker opens the evening with a short reflection, followed by Eucharistic Adoration and confessions, concluding with fellowship. We hope you can join us! Communio kicks off our monthly "Prayer First: Forty Hours Devotion" every first Friday, Saturday, and Sunday in the Adoration Chapel. To sign up for a monthly hour, please sign up here!
Study the Bible Readings, be challenged by your Faith, enjoy excellent men's fellowship, be encouraged in your vocation. Every Saturday, 7-8am in the St. Michael Conference Room. For more information, contact: chicagomensbiblestudy@gmail.com.


The ability to transport produced hydrocarbons has always been an important factor in the successful development of an oil or natural gas field, both onshore and offshore. Historically, pipelines have been the most common means used to transport crude oil produced in the Gulf of Mexico (GOM) Outer Continental Shelf (OCS). The technology and methods involved in pipeline transport of product from offshore oil and gas fields located in the shallower waters of the Gulf of Mexico OCS have evolved to the level of routine and commonplace. Much of the same technology and methods would be employed in the deep waters beyond the edge of the continental slope; however, the operating environment for deepwater pipelines differs from the operating environment of pipelines on the shelf. The current extent of oil pipeline infrastructure in the GOM is shown on Figure 1. As of June 2001, there were approximately 44,218 km (27,569 mi) of pipeline on the seafloor of the GOM. Most of these pipelines support shelf and near-shelf facilities; a small percentage supports deepwater operations.
This course presents a general summary of pipeline information and issues in the Gulf of Mexico. The collected information is not intended as an in-depth review of pipeline activities and issues, but rather general information for decision makers, regulatory agencies, and other interested parties. This paper has also been prepared to serve as a general reference document for environmental assessments prepared on specific pipeline applications.


text:
Trump Officials Approve Tennessee's Controversial Request To Revamp Medicaid Funding
Kaiser Health News | By Phil Galewitz
Published January 9, 2021 at 7:00 AM EST
Seema Verma, chief administrator of the Centers for Medicare & Medicaid Services, says the changes in the way Medicaid is funded and regulated in Tennessee "could be a national model moving forward."
With less than a dozen days left in power, the Trump administration on Friday approved a radically different Medicaid financing system in Tennessee. With this move, the federal government is for the first time granting a state broader authority in the operation of its health insurance program for the poor without interference from Washington, allowing Tennessee to make decisions on such issues as whether to add new benefits or eligibility categories or spend Medicaid dollars outside of health care, if it thinks that would help enrollees.
Instead of the open-ended federal contribution to the funding of Medicaid in all other states — which increases with higher enrollment and associated health costs — Tennessee will instead get a capped amount of money, via an annual block grant.
The annual funding cap will increase if enrollment grows. What's different is that, unlike with other states, Medicaid funding to Tennessee won't automatically keep up with rising expenses.
The approval is a 10-year "experiment," according to Friday's announcement by Seema Verma, administrator of the federal Centers for Medicare and Medicaid Services. The approach has been pushed for decades by conservatives who say states too often chafe under strict federal guidelines about enrollment and coverage. They think they can find ways to provide care more efficiently, she says.
But Michele Johnson, executive director of the Tennessee Justice Center sees the move as a step backward for the state's Medicaid program.
"No other state has sought a block grant, and for good reason," Johnson says. "It gives state officials a blank check and creates financial incentives to cut health care to vulnerable families."
Tennessee is one of 12 states that have not approved expanding Medicaid under the Affordable Care Act – a decision that's left tens of thousands of working adults in Tennessee without health insurance.
Whether this change in the way Tennessee's Medicaid program is funded will stick is an open question. The incoming Biden administration is likely to oppose the move, but to unravel it, officials would need to set up a review that includes a public hearing.
Implementing such a fundamental change via an executive branch action rather than getting Congress to amend Medicaid law is also likely to be met with court challenges.
In any event, the changes to the state's program will take months to implement because the shift in policyrequires final approval by the legislature, andofficials in Tennessee must also negotiate quality of care targets with the administration.
TennCare, the state's Medicaid program, says the new system would give it unprecedented flexibility to decide who is covered and what services it will pay for.
Under the agreement, TennCare will have a specified annual spending cap, to be based on historical spending, inflation and predicted future enrollment changes. If the state can operate the program at a lower cost than the cap and maintain or improve quality, the state then shares in the savings.
Trump administration officials say the approach adds incentive for the state to save money, unlike the current system, in which increased state spending is matched with more federal dollars. If Medicaid enrollment grows, the state can secure additional federal funding. If enrollment drops, it will get less money.
"This groundbreaking waiver puts guardrails in place to ensure appropriate oversight and protections for beneficiaries, while also creating incentives for states to manage costs — while holding them accountable for improving access, quality and health outcomes," Verma said. "It's no exaggeration to say that this carefully crafted demonstration could be a national model moving forward."
The agreement is somewhatdifferent from traditional block grants championed by conservatives, the CMS notes, since it allows Tennessee to get more federal funding as enrollment grows. And while the state is given flexibility to increase benefits, it can't on its own cut benefits.
However, opponents — including most advocates for low-income Americans — say the approach will threaten care for the 1.4 million people in TennCare — a group that includes children, pregnant women and the disabled. Federal funding covers two-thirds of the cost of the program.
Democrats have fought back against block grant Medicaid proposals since the Reagan administration and most recently in 2018 as part of Republicans' failed effort to repeal and replace major parts of the Affordable Care Act. Even some key Republicans opposed the idea because it would cut billions in funding to states, and that would make it harder to help the poor.
The approval of Tennessee's request comes as Medicaid enrollment is at its highest-ever level.
More than 76 million Americans are covered by the state-federal health program — a million more than when the Trump administration took charge in 2017. Enrollment has jumped by more than 5 million in the past year as the economy slumped with the pandemic.
Medicaid, part of President Lyndon B. Johnson's "Great Society" initiative of the 1960s, is an entitlement program in which the government pays each state a certain percentage of the cost of care for anyone eligible for the health coverage. As a result, the more money states spend on Medicaid, the more they get from Washington.
The newly approved demonstration project in Tennessee calls for CMS to work with the state to set spending targets that will increase at a fixed amount each year.
The plan also includes a "safety valve" to increase federal funding in the case of unexpected increases in enrollment.
"The safety valve will maintain Tennessee's commitment to enroll all eligible Tennesseans with no reduction in today's benefits for beneficiaries," according to a CMS written statement.
Tennessee has committed to maintaining coverage for eligible beneficiaries and existing services while staying under the spending targets.
In exchange for taking on this financing approach, the state will receive a range of operating flexibilities from the federal government, as well as up to 55% of the savings generated on an annual basis when spending falls below the aggregate spending cap and the state meets certain quality targets, yet to be determined.
The state can spend that money on various health programs for residents, plus areas that Medicaid funding typically doesn't cover, such as improving transportation and education and employment for enrollees.
Ten years is an unusually long time for a federally granted waiver of its rules, but the Trump administration has approved other long-term experiments in recent years to give states more flexibility.
"The block grant is just another example of putting politics ahead of health care during this pandemic," says Johnson of the Tennessee Justice Center. "Now is absolutely not the time to waste our energy and resources limiting who can access health care."
State officials in Tennessee applauded the approval.
"It's a legacy accomplishment," says Gov. Bill Lee, a Republican. "This new flexibility means we can work toward improving maternal health coverage and clearing the waiting list for developmentally disabled."
"This means we will be able to make additional investments in TennCare without reduction in services and provider cuts."
is a nonprofit, editorially independent program of the Kaiser Family Foundation, and is not affiliated with Kaiser Permanente.KHN chief Washington correspondent Julie Rovner contributed to this report.
Copyright 2021 Kaiser Health News. To see more, visit .
Phil Galewitz


Where you from? How old are you?
Born 03/07/1975. I grew up in Queensland, Australia, between a place called Maroochydore on the sunshine coast on weekends and Brisbane during the week. My nana and uncle both lived in Sydney so I would come down for holidays as a child. I moved to Sydney in 1998 for art and skateboarding mostly.
When did you consider yourself an artist? Was it through skateboarding or before?
I guess it all over lapped. Like when you start drawing on grip tape with paint pens, walking into skateboard shops and getting blown away by the graphics was inspiring. I know it sounds cliché but I do think it (art) chooses you; you don't just wake up one day and decide I want to be an artist.
As a kid, I always liked figuring out how stuff worked, pulling stuff apart and putting it back together, problem solving, which applies also to making art. My mother was very interested in the arts and painted a bit, so I grew up getting taken to the museums and galleries, which might of got me interested, but at the end of the day its either in you or its not.
I did do oil painting classes at age 14 or something like that, it was cool drinking tea, eating biscuits and painting but by then skateboarding had already taken total control over my life and I'd rather be skating, so I quit.
Years later I broke my ankle really bad in a few places and I couldn't skateboard for a long time. I was in and out of hospital with all these operations. That's when the window of Art making really opened, it was around then I started doing a few skateboard graphics and I was painting a lot. I studied art for two years at Morningside College of Art, just to get all the nuts and bolts. My ankle never healed properly, so I put most of my energy into painting.
How did skateboarding begin for you?
I saw that movie Back to the Future with Michael J Fox in it skating, within weeks it seemed every kid in my neighborhood had a skateboard – so I'd borrowed theirs.
Then a couple of years later it was blocking off streets in the suburbs, building jump ramps and skating the streets and schools. All around Australia at that time you had all these gnarly old concrete bowls, rough as guts and kinked to hell, the older guys ripped em to bits.
I had a second hand vision jinx mini first. Then I got a new Blue Tommy Guerrero dagger board for Christmas 1987 the one with Bonite through the ply layers. I was hyped.
Many crews through different times, it was forever changing. For me skateboarding in the mid 90's was a golden era; Brisbane, Gold Coast and Sydney sort of merged for a bit. There was a school called Kedron High and everyone would meet there for a bit or we would skate the city or travel interstate.
Mainly Kicking it with Shaun Caulfield, Gibbo, Manch, Bundy, Wade Burkett, Mike O'Meally, Westy, Andrew Currie, Rick, Moondog, Dion Kovac, Adrian Powell, and Al Boglio those times stand out most to me.
Who were some of your early influences in both skateboarding and art?
Skateboarding friends and mostly the early street skating stuff in the videos – Future Primitive, Natas in Streets of Fire and of course, Video Days.
Art influences would be, Ian Fairweather, Brett Whiteley, Raymond Pettibon, Chris Johanson, Bob Dylan just to name a few.
What would you consider one of your favorite pieces?
When did you meet Gonz?
What was it like knowing you were doing a guest deck for Krooked?
Pretty stoked to be honest,it's like if you were in band and you just got the cover of rolling stone magazine. I've always liked the stuff Mark has done.
Did that affect your normal process in any way?
Not really, I normally attack a canvas without much planing and try to let things just flow.Given the deck shape makes things a bit harder,trying to stay within boundaries, so I painted a larger canvas and cropped it in.I wanted the board to look like a painting.
Lots of Bob Dylan talk. Highest Ollie comps on the lounge room carpet while listening to the Fine Young Cannibals. Painting on large canvases in the garage.
Mainly another solo show for later this year, moving studios, some artwork for two charity auctions, and a little project called Japan National with my girlfriend.