Margins matter. The more AMN Healthcare Services (NYSE: AHS) keeps of each buck it earns in revenue, the more money it has to invest in growth, fund new strategic plans, or (gasp!) distribute to shareholders. Healthy margins often separate pretenders from the best stocks in the market. That's why we check up on margins at least once a quarter in this series. I'm looking for the absolute numbers, so I can compare them to current and potential competitors, and any trend that may tell me how strong AMN Healthcare Services's competitive position could be.
Here's the current margin snapshot for AMN Healthcare Services over the trailing 12 months: Gross margin is 28.6%, while operating margin is 5.8% and net margin is 2.1%.
Unfortunately, a look at the most recent numbers doesn't tell us much about where AMN Healthcare Services has been, or where it's going. A company with rising gross and operating margins often fuels its growth by increasing demand for its products. If it sells more units while keeping costs in check, its profitability increases. Conversely, a company with gross margins that inch downward over time is often losing out to competition, and possibly engaging in a race to the bottom on prices. If it can't make up for this problem by cutting costs -- and most companies can't -- then both the business and its shares face a decidedly bleak outlook.
Here's the margin picture for AMN Healthcare Services over the past few years.
Over the past five years, gross margin peaked at 28.3% and averaged 27.4%. Operating margin peaked at 5.9% and averaged 4.5%. Net margin peaked at 2.8% and averaged -4.4%.
TTM gross margin is 28.6%, 120 basis points better than the five-year average. TTM operating margin is 5.8%, 130 basis points better than the five-year average. TTM net margin is 2.1%, 650 basis points better than the five-year average.
With recent TTM operating margins exceeding historical averages, AMN Healthcare Services looks like it is doing fine.


Andrew Gibbs has been called an "innovator in the world of design" as the founder, CEO, and Editor-in-Chief of Dieline, founded in 2007.Andrew started Dieline Awards in 2009 to formally recognize the world's best consumer product packaging design. The awards competition was recognized as a "global phenomenon" in its first year.In 2010, he founded Dieline Conference, an annual conference for creative professionals in the packaging design and branding industry.He now serves as Creative Director and Programming Partner for HOW Design Live, one of the largest annual gatherings of creatives in the world.In 2014, he became a faculty member at ArtCenter College of Design in Pasadena, CA teaching Packaging Design. Andrew was previously named the Editor-in-Chief of HOW Design Magazine in 2015, making him the youngest editor of a design magazine ever.In 2018, Andrew became a Climate Reality Leader after training under Al Gore at the 2018 Climate Reality Corps. He joined A Plastic Planet, a U.K. based non-profit that is dedicated to dramatically reducing the amount of single use plastic used in the packaging industry. Andrew speaks and consults worldwide on the topics of packaging design and sustainable packaging.


Red Clay is an Atlanta-based leading consultancy for the utility industry, delivering technical and business solutions on Customer Information Systems, Customer Experience, Meter Data Management Systems, Mobile Field applications, Lodestar, Load Profiling & Settlement along with our industry-leading SecureGrid security solutions. With over eighteen years of industry experience, it is our mission to integrate your vision and create solutions that exceed expectations today and prepare you for tomorrow. Let us make your vision our mission.


For their very first limited edition video game screen print, Skuzzles has teamed up with acclaimed artist Tom Whalen to release this epic Sonic the Hedgehog poster! This officially licensed screen print pays tribute to SEGA's most iconic video game, Sonic The Hedgehog, and includes Sonic, Tails, Knuckles and Dr. Eggman. Whalen really knocked it out of the park on this one, don't you think!?!
Sonic the Hedgehog by Tom Whalen is a 12"x36" hand numbered 6 color screen print. The Regular Purple Edition is limited to 250 pieces and will retail for $45, while the Variant Metallic Blue Edition is limited to 150 pieces and will retail for $55. Both versions go on sale at 1pm EST today, Wednesday, June 14th, at the Skuzzles online store.


text:
Metallica Launch Vinyl Club Subscription
metallica.com
Metallica have unveiled their latest endeavor: their own official vinyl club.
"As huge music fans, the band has been super excited to experience the resurgence of vinyl and the enjoyment of collecting, exploring and connecting with other fans mining for lost treasures," declares a press release announcing the club.
The new subscription-based service will be available only to members of Metallica's Fifth Member fan club. Vinyl subscribers will each receive a personalized membership card along with four 7" records. Fans can choose whether they receive the records together at once or spread throughout the year. Material will include "rare cuts, demos and rough mixes, and live rarities -- none of which have ever been released on vinyl before." In some cases, "additional collectibles" will accompany the releases.
This is just the latest in a long list of distinctive Metallica ventures. Beer, whiskey, timepieces and ski gear are some of the many products to bear the band's name in recent years.
Memberships for the inaugural run of Metallica's vinyl club are available now at the band's website. The sign-up period closes on March 31 and will not reopen until 2021. A year's subscription costs $49.99.
The vinyl club announcement comes as Metallica continue to manage what has been an eventful -- if frustrating -- period for the band. Frontman James Hetfield checked himself into rehab in September 2019, resulting in a slew of canceled tour dates for the group. They earlier announced they would be headlining five different U.S. festivals in 2020, only to pull out of two of them due to Hetfield's continuing recovery.
Still, Hetfield now seems to be on the mend, recently appearing at a Los Angeles-area museum to discuss his classic-car collection.
Source: Metallica Launch Vinyl Club Subscription
Filed Under: Metallica

redpajama_set_name: RedPajamaCommonCrawl


text:
\section{Introduction}
\label{introduction}
Convolutional neural networks (CNNs) \cite{lecun1989backpropagation}
have demonstrated impressive performance success in
a wide variety of image recognition problems
\cite{krizhevsky2012imagenet,he2016deep,liu2016deepfashion}.
Two common problems are
{\em single-label} (one class label per image)
and
{\em multi-label} (multiple class labels per image)
classification.
Whilst both problems have the {\em same learning objective} of
inducing a multi-class classifier CNN model through supervised training,
their standard objective learning functions are {\em rather different}.

Specifically, in single-label classification learning,
we often adopt the \texttt{softmax regression} (SR)
learning algorithm.
This is based on the per-sample single-label and class-exclusion assumptions \cite{bridle1990probabilistic}.
For multi-label classification
in which a data sample may be tied with multiple class labels,
we instead adopt
the \texttt{logistic regression} (LR) learning algorithm
\cite{Bishop}.
Without the ``single-label'' and ``class-exclusion'' prior,
LR considers per-sample prediction of all individual class labels
{\em independently}.

It is intuitive that single-label classification
is a special case of multi-label classification.
Hence, LR should be applicable
for single-label classification. Surprisingly, despite that both SR and LR
have been extensively studied and exploited
for learning single-label and multi-label classification {\em independently}, their comparison in deep learning of single-label classification has never been investigated systematically in the literature to our knowledge.
A few fundamental questions remain unclear:
How advantageous is the {\em de facto standard} choice SR
over LR for single-label classification on earth?
Is LR possibly competitive with or even superior to SR?

In this work, we investigate the potential and validity of
{\em the Logistic Regression learning algorithm
	for single-label multi-class classification}
in theory and practice.
We observe that
although SR has an advantage
of conducting class discriminative learning
via posing a competition mechanism between
the ground-truth and other classes per training sample,
it may simultaneously distort the underlying data manifold
geometry which may in turn hurt the model generalisation capability
\cite{belkin2006manifold}.
This is because in SR
all non-ground-truth classes are identically pushed away
from the labelled ground-truth class in a homogeneous fashion
with the class-to-class correlation ignored in model learning.
On the contrary, LR avoids this limitation
due to that each class is modelled independently
as a separate binary classification task \cite{Bishop}.
This allows the intrinsic inter-class geometrical correlation
to emerge naturally in a data-mining manner.

In light of the above theoretical merit,
we particularly study the efficacy of LR for single-label
classification learning.
Empirically, we found that
the vanilla LR indeed yields less discriminative and generalisable
models
on image classification tasks in most cases.
With in-depth LR loss and gradient analysis,
we identify that the {\em negative class distraction} problem turns out to be
the major model learning barrier.

We make three {\bf contributions}:
{\bf (1)}
We investigate the fundamental characteristics of SR and LR in single-label multi-class classification learning.
This is an essential and under-studied problem
in the literature to our knowledge.
{\bf (2)}
We identify the negative class distraction problem in LR
as the main obstacle for single-label classification learning,
and propose two optimisation focus rectified LR learning algorithms in
a hard mining principle as effective solvers.
{\bf (3)}
We conduct extensive evaluations on two single-label multi-class classification based image recognition tasks, coarse-grained object categorisation and fine-grained zero-shot person instance identification, by using {\em five} standard benchmarks.
The results show that our methods perform on par with or outperform the standard
algorithm SR.
We further validate the effectiveness of our method
in clothing attribute classification with extremely sparse labels per data sample.

\section{Related Work}
\label{sec:rel}
With the recent surge of interest in
neural networks like CNNs,
image classification by deep learning
has gained massive attention and remarkable success
\cite{krizhevsky2012imagenet,
	girshick2015fast,dong2017multi,liu2016deepfashion,lin2017focal,dong2017class}.
We have witnessed significant advances in many aspects
including
network architecture improvement \cite{he2016deep},
nonlinear activations \cite{maas2013rectifier},
layer designs \cite{lin2013network},
regularisation techniques \cite{srivastava2014dropout},
optimisation algorithms \cite{kingma2014adam},
and data augmentation \cite{krizhevsky2012imagenet}.

Essentially, these existing methods are mostly used and
grounded on the well-established learning algorithms
such as softmax regression (SR)
\cite{luce2005individual,bridle1990probabilistic}
and
logistic regression (LR)
\cite{little1974existence,mor1990ranking}.
Impacted by the traditional design principles \cite{goodfellow2016deep,krishnapuram2005sparse}, LR is often used to produce the prediction output
of multi-label multi-class classification models \cite{liu2016deepfashion,chua2009nus},
whilst SR to that of single-label multi-class classification models
\cite{krizhevsky2012imagenet,russakovsky2015imagenet} in current deep learning practice.

Although taking {\em different} learning formulations,
both SR and LR algorithms aim to train a multi-class neural network classifier
which, once trained, is able to predict the top one or multiple
class label(s) of new samples at test time.
One reason for this design discrepancy is that in SR,
the single-label constraint
facilitates the learning of a multi-class classifier.
Although lacking the single-label class prior,
LR has a merit of individually learning per-class distributions
and better maintaining the class manifold structures
\cite{Bishop}.
In spite of that, LR is however {\em rarely} chosen to
learn single-label multi-class
classification by existing methods,
leaving its potential efficacy for image recognition remaining unknown in
deep learning.
To fill this gap, we systematically study this ignored problem,
identify and address a negative class distraction (NCD) problem.

The NCD problem is concerned with imbalance learning with
a particular focus on positive and negative classes per training sample.
It is therefore related to
the conventional class imbalanced learning problem \cite{japkowicz2002class,weiss2004mining,he2009learning,dong2018imbalanced,huang2016learning}.
Whilst sharing some theoretical concept in general,
the NCD problem in our context is fundamentally different
because it is {\em independent} of the {\em training data distribution} which
however is the core problem existing class imbalanced learning methods aim to address.
Differently, the NCD problem is underpinned in the {\em target class} space,
occurring in the multi-class joint optimisation process on each training sample.
A larger class space leads to a more serious NCD problem.
In other words, the NCD problem remains
even with {\em absolutely balanced} (equally sized) training samples
per class.
Besides, LR bias correction has been extensively studied
\cite{king2001logistic,schaefer1983bias,qiu2013logistic,heinze2002solution} but
still focusing on the data imbalance issue,
rather than the NCD problem as considered in this work.

\section{Delve Deep into Deep Learning Classification}
\label{sec:loss_analysis}
{Supervised deep learning algorithms} learn to classify input images into target class labels,
given a training set of $n$ image-label pairs
$\mathcal{D}=\{(\bm{x}_{i}, {y}_{i})\}_{i=1}^{n}$
where ${y}_{i} \subseteq \mathcal{Y} = \{1,2,\cdots,K\}$ specifies
the ground-truth label set of image $\bm{x}_{i}$
with one (single-label)
or multiple (multi-label) class(es) associated.
There are totally $K$ possible classes.
Supervised learning of such multi-class classifiers is generally conducted based on
estimating probabilistic class distributions ${\bm{p}}$ of training images with
the element $p_k = p(k \; | \; \bm{x})$, $k \in \mathcal{Y}$.

\begin{figure*}[th]
	\centering
	\includegraphics[width=0.9\linewidth]{vis.pdf}
	\caption{
		Colour-coded t-SNE feature embedding of 5 CIFAR100 object classes
		produced by
		(a) SR,
		(b) LR,
		and
		(c) the proposed SS-LR (Eq \eqref{eq:LR_att})
		loss functions. It is evident that
		in the feature embedding space by the vanilla LR,
		different classes are poorly distinguishable with severe cross-class
		boundary overlap.
		In contrast, our SS-LR
		yields more discriminative feature embedding
		by addressing the NCD problem involved in model optimisation.
		Best viewed in colour.
	}
	\label{fig:vis}
\end{figure*}

\noindent{\bf Probability Distribution Estimation. }
To make $p_k$ represent valid probability values,
{one} common approach to normalising individual $p_k$ is to
apply the logistic (or sigmoid) function \cite{little1974existence,mor1990ranking}
to squash the raw output
$\bm{z} = \phi (\bm{x} \;|\;\bm{\theta})$
into the interval $(0,1)$ as:
\begin{equation}\small
\begin{aligned}
p_k = p(k \; | \; \bm{x};\bm{\theta})
=
\sigma(\bm{z})_k =
\frac{1}{1+e^{-z_k}} \\
= \frac{e^{z_k}}{e^{z_k}+1},
\quad k \in \mathcal{Y} = \{1,2,\cdots,K\}
\label{eq:sigmoid}
\end{aligned}
\end{equation}
where $\bm{\theta}$ denotes the model parameters
that project an input $\bm{x}$ into a logit space $\bm{z}$
via a to-be-learned mapping $\phi$.

In essence, Eq \eqref{eq:sigmoid} models a Bernoulli distribution for each individual class (a binary-value random variable)
{\em independently},
i.e. the model learns to predict the positive probability $p(1 \; | \: \bm{x})$ for each class $k$.
Therefore, it naturally fits the {\em multi-label} classification scenario:
Each image sample can be associated with multiple (an unknown number) class labels
in any possible combinatorial ways.
Eq \eqref{eq:sigmoid} is known as \texttt{Logistic Regression} (LR).

{\em Single-label} classification is another common scenario
in which only one class label is outputted for a single sample.
This implicitly assumes a mutual exclusion relationship between all classes.
Hence, we can further require the {\em entire} vector $\bm{p}$ as a multi-class probability distribution:
$\sum_{k=1}^K p_k = 1$ and $p_k \geq 0$.
To that end,
the softmax function is often employed \cite{luce2005individual,peterson1989new,bridle1990probabilistic}.
Formally, we exponentiate
and normalise the logit $\bm{z}$ to obtain a valid probability vector ${\bm{p}}$ as:
\begin{equation}
\begin{aligned}
p_k = p(k \; | \; \bm{x};\bm{\theta})
=
h(\bm{z})_k
= \frac{e^{z_k}}{\sum_{j=1}^K e^{z_j}}, \\
\text{with} \;\; k \in \mathcal{Y} = \{1,2,\cdots,K\}
\end{aligned}
\label{eq:softmax}
\end{equation}
Eq \eqref{eq:softmax} models a
categorical
 distribution of a discrete variable over
multi-classes {\em collectively} and {\em inter-dependently}.
Eq \eqref{eq:softmax} is called \texttt{Softmax Regression} (SR).

\noindent {\bf Learning Objective Function. }
To perform supervised classification learning,
we usually employ the principle of maximum likelihood
that attempts to match the model distribution with
the data empirical distribution by a cross-entropy measurement
\cite{goodfellow2016deep}.
The specific learning objective function
relies on the regression form of the model' s prediction.

In case of multi-label classification,
the objective function for maximum likelihood learning is formulated as:
\begin{equation}
\footnotesize
\mathcal{L}_{\text{LR}}(\bm{x}, y)  = - \sum_{k=1}^{K}
\Big (q_{k} \log(p_{k}) +
(1-q_{k})\log (1-p_{k}) \Big )
\label{eq:LR}
\end{equation}
This objective function aggregates the
negative log-likelihood of all class-wise Bernoulli distributions.

In case of single-label classification,
we directly use the cross-entropy between
the ground-truth class distribution $\bm{q}$ of the training datum
and the predicted class distribution $\bm{p}$ of the model
to form the objective function.
$\bm{q} \!=\! \delta_{k,y}$ is Dirac delta which equals to 1
if $k\!=\!y$, and 0 otherwise.
The learning objective function
is written as:
\begin{equation}
\mathcal{L}_\text{SR}(\bm{x}, y) = - \sum_{k=1}^{K}
q_k \log p_k =-\log {p_y}
\label{eq:SM}
\end{equation}

\noindent {\bf Remarks. }
In essence, the key of model learning
is
to induce the target multi-class feature embedding space.
A {\em generalisable} feature space should be characterised by
an accurate
inter-class manifold structure.
Given a training sample $\bm{x}$, SR enforces a {\em competition}
between the ground-truth and other classes
to learn the model discrimination capability:
the softmax output always sums to 1, subject to that an increase in the estimation of one class necessarily corresponds to a decrease in the estimation of others.
Whilst this competition significantly helps learn discriminative inter-class boundaries, it may distort the underlying inter-class manifold structure
therefore potentially hurting the model generalisation capability \cite{belkin2006manifold},
since SR treats all non-ground-truth classes {\em identically}
by pushing them away from the ground-truth class in a homogeneous manner.
In contrast, LR learns to induce the inter-class manifold structure
from the training data, enabling a natural emergence of the underlying multi-class manifold geometry.

\begin{figure*}[th]
	\centering
	\includegraphics[width=1\linewidth]{loss_comp.pdf}
	\caption{
		Negative class distraction effect of LR
		on Tiny ImageNet.  .
		(a) Overall training loss values;
		The loss of (b) the positive class and (c) the negative classes;
		(d) Gradient ratio of positive to negative classes.
	}
	\label{fig:obs}
\end{figure*}

\section{
	Focus Rectified Logistic Regression}
\label{sec:method}

\subsection{The Negative Class Distraction Problem}

{Despite the theoretical merit of LR,
	directly using the vanilla LR often leads to
	inferior model performance than SR, as shown in Fig \ref{fig:vis} (b).
	Why does this happen?
	To examine this problem in LR,
	we track and analyse the training loss and gradient quantities (the blue curves in Fig \ref{fig:obs}).
	We observe that at the beginning of model training,
	the LR loss drops dramatically until near 0 (see Fig \ref{fig:obs} (a)).
	Further decomposing the LR loss into two parts:
	the positive class loss on the ground truth class (see Fig \ref{fig:obs}(b))
	and negative class loss on all non-ground-truth classes (see Fig \ref{fig:obs}(c)),
	we find that at early training iterations,
	(1) the starting negative class loss is far larger than the starting positive class loss  (e.g. 140 vs 0.4); and
	(2) the positive class loss increases {\em unexpectedly}, while the negative class loss drops fast.
	This suggests that at the early training stage, the overall LR loss is dominated by negative classes therefore the positive class is largely ignored.
	We call this effect as {\em negative class distraction} (NCD).

	The NCD problem is intrinsic to single-label multi-class classification.
	Specifically, suppose a $K$-class setting,
	each training sample $\bm{x}$ has only one positive class (the ground-truth label $y$)
	but $(K\!-\!1)$ negative classes.
	With the vanilla LR learning objective (Eq \eqref{eq:LR}), the positive class obtains insufficient attention, especially when $K$ is large.
	Therefore, the training is hinted by the severe learning bias towards the negative classes.
	Such a biased loss composition deceives the model to converge towards some poor local minima with the negative classes well satisfied
	(Fig \ref{fig:obs}(c))
	whilst the positive class largely ignored (Fig \ref{fig:obs}(b)).
	This can be further justified by the nearly zero gradient ratio of positive to negative classes
	 (Fig \ref{fig:obs})(d)).
	 The NCD problem similarly exists in multi-label classification with sparse labels per sample (Fig \ref{fig:losstrack}).}

{Often, the inherent learning difficulty and velocity of different classes
	can be distinct, as indicated in the accuracy variety over object classes \cite{deng2009imagenet}.
	Hence, treating all negative classes per sample identically as
	Eq \eqref{eq:LR} may be {\em not} optimal,
	and selecting important ones to learn is likely to be more effective.
	Crucially, this helps mitigate the NCD problem
	as more learning focus is assigned to the positive class.
	Inspired by such consideration,
	we formulate two negative class selection mechanisms
	to rectify the biased learning focus of the vanilla LR
	in a hard mining principle:
	A training sample $\bm{x}$ is more (less)
	informative to hard (easy) classes.}

\subsection{Rectification by Negative Class Hard Selection}

We confine the learning focus to non-trivial (hard) other than all negative classes.
Specifically, we use the predicted probability $p_k$ as the
hardness measurement to rank $(K\!-\!1)$ negative classes in the descending order.
We then choose the top negative classes to formulate the objective loss function as:
\begin{equation}
\mathcal{L}_{\text{LR}}^\text{hs}(\bm{x}, y)  =
- \log(p_y)
- \alpha \sum_{k \in f_{hs}(m|\bm{p}, y)}
\Big(\log(1 - p_{k}) \Big)
\label{eq:LR_sel}
\end{equation}
where the hard selection function $f_{hs}(m|\bm{p}, y)$ returns the
top $m\%$ negative classes with highest prediction probabilities.
We add a balancing weight $\alpha$ to trade-off
positive and negative classes, inspired by
the cost-sensitive learning \cite{akbani2004applying,he2009learning}.

Adjusting $m \in [0, 100]$ allows us to modulate the focus rectification degree:
with a training sample, we learn the decision boundaries of
$m\%$ most confusing negative classes along with the positive class.
We empirically found that $m\!=\!25$ is satisfactory.
When $m\!=\!100$, we attend all negative classes
with a cost-sensitive trade-off between the positive and all negative classes.
The weight $\alpha$ can be intuitively set as inversely proportional
to the selected negative class number: $\alpha\!=\!\frac{\beta}{\floor{m\% (K-1)}} < 1$
where $\beta$ is a hyper-parameter ($\beta\!=\!10$ in our experiments).
We call this {\em negative class Hard Selection} based LR formulation as \textbf{\em HS-LR}.

\subsection{Rectification by Negative Class Soft Selection}

An alternative to HS-LR is a soft selection of negative classes. Formally, we employ a hardness (probability) adaptive weight $(p_{k})^r$ to each negative class as:
\begin{equation}
\mathcal{L}_{\text{LR}}^\text{ss}(\bm x)  =
- \log(p_y)
- \alpha \sum_{k=1, k \neq y}^{K}
\Big( (p_{k})^r \log(1 - p_{k}) \Big)
\label{eq:LR_att}
\end{equation}
where $r \geq 0$ is the attending parameter
that controls the rate of attending hard {negative} classes
and disregarding easy {negative} classes.
{Note that} when $r\!=\!0$, it is equivalent to HS at $m\!=\!100$.
Growing $r$ makes the {focus}
modulating effect {like-wisely} increase.
In our experiments, we found that $r$ is not sensitive {in a reasonable range of } and $r\!=\!2$ {is selected in our main experiments}
(Fig \ref{fig:gamma}).
We set $\alpha\!=\!\frac{\beta}{K-1}$ since all negative classes
are considered.

Our soft selection mechanism achieves the effect of hard class mining
in this manner:
If a negative class $k$ is a hard
class w.r.t. $\bm{x}$
and receives a higher probability $p_k$,
its weight $(p_{k})^r$ is larger and hence more attention is assigned.
When the value of $p_k$ is small
which suggests an easy negative class,
the learning attention will be close to 0 and the quantity
of class $k$ is significantly down-weighted.
We call this {\em negative class Soft Selection} based LR formulation as \textbf{\em SS-LR}.

The soft selection
principle has been used in other
methods, e.g. Entropy-SGD \cite{chaudhari2016entropy}
and focal loss \cite{lin2017focal}.
Entropy-SGD tackles a different problem
of seeking better local minima.
The focal loss is more similar to
our SS-LR (Eq \eqref{eq:LR_att}) but differs in a number of
fundamental aspects:
(1) Focal loss solves the {\em global} training data sample imbalance
whilst SS-LR deals with the {\em local} sample-wise negative class distraction,
independent of the training data distribution over classes.
(2) Focal loss is built on the softmax regression,
whilst SS-LR is formulated based on the logistic regression.
(3) Focal loss aims to suppress easy training instances in the {\em sample} space
whilst SS-LR handles the per-sample negative classes in the {\em class} space.

\begin{figure}[th]
	\centering
	\includegraphics[width=0.9\linewidth]{dataset.pdf}
	\caption{
		Example images of benchmark datasets evaluated.
	}
	\label{fig:datasets}
\end{figure}

\section{Experiments}
\label{sec:exp}

We evaluated the proposed method on
(1) single-label object classification,
(2) person instance identification, and
(3) multi-label clothing attribute recognition.
For each test, we performed 10 independent runs and reported the average result.
Note that,
outperforming existing best performers
by extra complementary techniques
is not the focus of our evaluations.
Rather, the key is to investigate the model generalisation performance
of the same network model learned by
the SR and LR objective functions
in a fair test setting.

\subsection{Single-Label Object Image Classification}
\label{sec:exp_obj}
\noindent {\bf Datasets. }
We used three single-label object classification benchmarks.
\textbf{\em\small CIFAR10} and \textbf{\em\small CIFAR100} \cite{krizhevsky2009learning}
both have $32\!\times\!32$ sized images from 10 and 100 classes, respectively.
We adopted the benchmarking 50,000/10,000 train/test image split on both.
\textbf{\em\small Tiny ImageNet (Tiny200)} \cite{deng2009imagenet}
contains
110,000 64$\times$64 images from 200 classes.
We followed the standard 100,000/10,000 train/val setting.
These datasets present varying class numbers,
thus giving a spectrum of different single-label model test scenarios.
Example images of these datasets are given in Fig \ref{fig:datasets}.

\noindent{\bf Experiment setup. }
We carried out all the following experiments in TensorFlow \cite{abadi2016tensorflow}.
We tested {three} varying-capacity networks: ResNet-32
(32 layers with 0.7 million parameters) \cite{he2016deep},
WideResNet-28-10
(28 layers with 36.5 million parameters)
\cite{zagoruyko2016wide},
and DenseNet-201 (20 million parameters) \cite{huang2017densely}.
We adopted the top-1 classification accuracy in our evaluations.
We used the standard SGD with momentum for model training.
We set
the initial learning rate to 0.1,
the momentum to 0.9,
the weight decay to $10^{-4}$,
the batch size to 128/64{/128} for CIFAR/Tiny200{/ImageNet},
the epoch number to 300.
We set the parameter $m$ (Eq \eqref{eq:LR_sel})
in the range of $[25,75]$ and
$r=2$ (Eq \eqref{eq:LR_att}) ($r=50$ for ImageNet) by a grid search on the validation dataset.
Data augmentation includes horizontal flipping and translation.
All models compared used the same training/test data
for fair comparative evaluations.

\begin{table}[th]
	\centering
	\caption{
		Evaluation on single-label object image classification.
		{Metric}: Top-1 accuracy rate (\%).
	}
	\label{tab:obj_clf}
	\begin{tabular}{r||c |c| c}
		\hline
		Base Net &
		\multicolumn{3}{c}{{\em ResNet-32} \cite{he2016deep}}
		\\
		\hline
		Dataset &{CIFAR10} & {CIFAR100}  &{Tiny200} \\\hline
		SR
		& 92.5    & 68.1    & 50.2 \\
		\hline
		LR
		&\bf 93.0     & 64.3    & 45.9 \\
		\hline

		\bf HS-LR
		&\bf 93.0 & 68.9 & 50.7
		\\ \hline
		\bf SS-LR
		&\bf 93.0 &\bf 69.1 &\bf  56.0
		\\
		\hline \hline

		Base Net &
		\multicolumn{3}{c}{{\em WideResNet-28-10}  \cite{zagoruyko2016wide}}
		\\
		\hline
		Dataset & {CIFAR10} & {CIFAR100}  &{Tiny200} \\\hline
		SR
		& 95.3      & 81.0     & 57.4      \\
		\hline
		LR
		& 95.0      & 79.0    & 55.2     \\
		\hline
		\bf HS-LR
		& 95.3 &79.0 &59.0
		\\ \hline
		\bf SS-LR
		&\bf 96.0  &\bf 81.2 &\bf  61.0      \\
		\hline
	\end{tabular}
\end{table}

\noindent{\bf Evaluation. }
Table \ref{tab:obj_clf} compares the single-label object
categorisation performances
between the SR and LR function variants
using the small ResNet-32 and large WideResNet-28-10 architectures.
We make these observations:
{\bf (1)} When the class number increases,
the vanilla LR suffers a more severe NCD problem
and yields much weaker performances than SR.
For example, on CIFAR10 with 10 classes,
LR performs on a par or even slightly better.
However, LR is clearly inferior on Tiny ImageNet with 200 classes
(On the other hand, this also simultaneously implies a good potential of LR
since the results are not far worse).
{\bf (2)}
The proposed LR variants
notably improve the performance
and outperform the SR,
especially on tasks with more classes.
This indicates that once the NCD problem is properly solved (Fig \ref{fig:obs}),
LR can be a stronger formulation
for single-label classification learning.
This observation is rarely made in the literature
where SR dominants the learning of single-label classification models.
{\bf (3)}
The Soft Selection (SS) strategy consistently yields
the best model generalisation,
suggesting the advantages of exploiting all negative object classes
in a hardness adaptive manner.
{\bf (4)}
Both small and large nets benefit from the proposed \text{LR} algorithms,
indicating that our method is generically applicable to
different CNN architectures.

\subsection{Fine-Grained Person Instance Identification}
\label{sec:exp_reid}

\noindent {\bf Datasets. }
We used two popular person instance identification ({a.k.a.,} person re-identification) benchmark datasets in our experiments.
The \textbf{\em\small Market-1501} \cite{zheng2015scalable}
has 32,668 images of 1,501 different identities (ID)
captured from 6 outdoor camera views.
We followed the standard 751/750 train/test ID split.
The \textbf{\em\small DukeMTMC} \cite{ristani2016MTMC}
consists of 36,411 images of 1,404 IDs from 8 camera views.
We adopted the benchmarking 702/702 ID split as \cite{zheng2017unlabeled}.
Unlike normal image classification,
person re-identification (re-id) is a more fine-grained
recognition problem of matching person instances across non-overlapping camera views. It is more challenging due to the inherent zero-shot learning knowledge transfer from seen classes (IDs) to unseen classes in deployment,
i.e. no overlap between training and test classes.

\noindent{\bf Experiment setup. }
We tested two nets, with variant capacities, often used in existing re-id methods:
ResNet-50 \cite{he2016deep}
(50 layers with 25.6 million parameters),
and MobileNet \cite{howard2017mobilenets}
(28 layers with 3.3 million parameters).
We adopted two standard performance metrics in the {\em single query} mode:
the Cumulative Matching Characteristic accuracy (Rank-$1$ rate)
and mean Average Precision (mAP).
We used the Adam optimiser \cite{kingma2014adam}, and
set the initial learning rate to 0.0003,
the momentum to 0.9,
the weight decay to $10^{-4}$,
the batch size to 32, and
the maximum epoch number to 300.
We trained all methods without using
complex tricks in order to focus the evaluation on
comparing SR and LR algorithms.

\begin{table}[h!]
	\centering
	\caption{Evaluation on person instance identification.}
	\label{tab:reid}
	\begin{tabular}{r|cc|cc}
		\hline
		Base Net &
		\multicolumn{4}{c}{{\em ResNet-50} \cite{he2016deep}}
		\\
		\hline
		Dataset
		& \multicolumn{2}{c|}{Market-1501}
		& \multicolumn{2}{c}{DukeMTMC}
		\\ \hline
		Metric (\%)
		& Rank-1 & mAP    & Rank-1 & mAP
		\\ \hline
		SR
		& 83.3 & 65.8
		& 73.7 & 54.9 \\
		\hline
		LR
		&81.4   & 65.0
		&  72.2  &  54.6
		\\
		\hline
		\bf HS-LR &\bf 87.1&\bf 70.7&\bf 77.9&\bf 60.1  \\

		\hline
		\bf SS-LR
		& 85.8  & 69.7
		& 76.7   &  58.2  \\
		\hline
		\hline

		Base Net &	\multicolumn{4}{c}{{\em MobileNet} \cite{howard2017mobilenets}}
		\\
		\hline
		Dataset
		& \multicolumn{2}{c|}{Market-1501}
		& \multicolumn{2}{c}{DukeMTMC} \\ \hline
		Metric (\%)
		& Rank-1 & mAP    & Rank-1 & mAP
		\\ \hline
		SR & 71.7  & 50.0
		& 57.0&35.8
		\\ \hline
		LR & 51.5  & 34.3
		& 43.9  & 27.5
		\\ \hline
		\bf HS-LR
		&\bf 76.4 &\bf 54.1 &\bf 63.7   &\bf 42.5
		\\ \hline
		\bf SS-LR
		& 74.0 & 53.7 &62.9  &  41.5
		\\ \hline
	\end{tabular}
\end{table}

\noindent{\bf Evaluation. }
Table \ref{tab:reid} shows the performance comparisons of SR
and LR methods on person re-id.
We have the following observations:
{\bf (1)}
Unlike generic object classification (Table \ref{tab:obj_clf}),
the vanilla LR and SR produce very
similar generalisation performances when using ResNet-50.
The plausible reason is that, re-id has the less stringent requirement
of well fitting the model to training classes
since the test classes are entirely new and unseen to model training.
{\bf (2)}
Both proposed LR algorithms
improve the model performance,
suggesting that the NCD problem still matters
in cross-class recognition.
{\bf (3)}
Hard selection (HS) turns out to be the best strategy,
as opposite to object classification (Table \ref{tab:obj_clf})
where SS is the best performer.
This indicates that
using every training sample to
learn all classes is not necessarily superior,
which may negatively affect the modelling capacity of mining
fine-grained discriminative information
among a large number of training classes (751 on Market-1501, and 702 on DukeMTMC).

To validate the statistical significance of our model's performance,
we conducted a Wilcoxon signed-rank test on the DukeMTMC results using MobileNet.
The test verifies that the improvements in accuracy and mAP rates are statistically significant at the 5\% significance level.

\subsection{Clothing Attributes Recognition}
\label{sec:exp_attr}

Apart from single-label classification,
we evaluated our LR methods
on the multi-label classification specially with only a few labels per instance, which also suffers
a similar NCD problem.
The SR is not applicable in this test.

\noindent {\bf Dataset. }
We evaluated a large scale multi-label clothing attribute dataset
\textbf{\em \small DeepFashion} \cite{liu2016deepfashion}.
This dataset has 289,222 images labelled with 1,000 fine-grained clothing attributes
with a 209,222/40,000/40,000 train/val/test benchmark setting.
Each image is associated with {\em extremely} sparse labels, $3$ out of 1,000 in average.
The training set is also highly class imbalanced (733:1),
therefore presenting a very challenging
multi-label classification task.
We adopted the standard
multi-label classification setting
without using auxiliary types of supervision such as
key-points and clothing category
as used in \cite{liu2016deepfashion}.

\begin{table}[!h]
	\centering
	\caption{Evaluation on multi-label attribute recognition.}
	\label{tab:df_balance}
	\begin{tabular}{r|cc|cc}
		\hline
		Base Net &
		\multicolumn{4}{c}{{\em ResNet-50} \cite{he2016deep}}
		\\
		\hline
		\multirow{2}{*}{Metric (\%)}
		& \multicolumn{2}{c|}{Accuracy}
		& \multicolumn{2}{c}{mAP}
		\\ \cline{2-5}
		& Per Img    & Per Cls & Per Img    & Per Cls
		\\ \hline
		LR
		& 64.8 & 50.1  & 21.6  & 2.5
		\\ \hline

		\bf HS-LR
		&\bf74.3 &\bf59.0 &31.5 &\bf9.3
		\\ \hline

		\bf SS-LR
		&73.9 & 58.7 &\bf34.4 &9.2
		\\
		\hline
		\hline

		Base Net &
		\multicolumn{4}{c}{{\em MobileNet} \cite{howard2017mobilenets}}
		\\ \hline
		\multirow{2}{*}{Metric (\%)}
		& \multicolumn{2}{c|}{Accuracy}
		& \multicolumn{2}{c}{mAP}
		\\ \cline{2-5}
		& Per Img    & Per Cls & Per Img    & Per Cls
		\\ \hline
		LR & 62.4&51.9&23.4&4.7
		\\ \hline
		\bf HS-LR &\bf 72.2 &\bf57.4&28.1&\bf7.2
		\\ \hline
		\bf SS-LR & 72.0&55.8&\bf31.0& 6.5
		\\  \hline
	\end{tabular}
\end{table}

\noindent{\bf Experiment setup. }
We similarly tested two nets:
ResNet-50 \cite{he2016deep}, and
MobileNet \cite{howard2017mobilenets}.
We adopted two standard performance measurement criteria:
mean Average Precision (mAP)
and balanced classification accuracy \cite{dong2018imbalanced,huang2016learning}.
The latter is particularly designed
to remedy the performance evaluation bias towards
the majority classes of imbalanced data.
For each metric, we evaluated per-image and per-class model performances
of top-5 class predictions.
We used the Adam optimiser \cite{kingma2014adam},
with
the learning rate of 0.0001 {for the first 45 epochs and 0.00001 for the last 5 epochs,}
the weight decay of 0.00004,
the momentum of 0.9,
and the batch size of 32.

\begin{figure}[h]
	\centering
	\includegraphics[width=0.9\linewidth]{df_loss.pdf}
	\captionof{figure}{Training loss on DeepFashion. }
	\label{fig:losstrack}
\end{figure}

\noindent{\bf Evaluation. }
Table \ref{tab:df_balance} shows the clothing attribute classification performances
of different LR variants.
It is observed that:
{\bf (1)}
Our LR methods are significantly superior
to the vanilla algorithm, which is consistent with
the results on single-label object classification
and person re-id.
{\bf (2)}
Hard and soft selection strategies perform
similarly across different nets and metrics.
These results suggest the generic advantages
of our approach in multi-label classification,
confirming the existence of NCD.
Moreover, our method also notably outperforms
the state-of-the-art result (54.5 per-class accuracy)
in the same test setting obtained by \cite{dong2018imbalanced},
further validating the efficacy of our approach.

Figure \ref{fig:losstrack} shows the
loss converging process during training.
Similar to single label object classification (Fig \ref{fig:obs}),
the vanilla LR is clearly hurt by the NCD problem.
In contrast, SS-LR and HS-LR achieve a more stable and healthy model learning process
by adaptively hard mining negative classes.

\subsection{Further Analysis and Discussion}
\label{sec:ablation}

{\bf Learning Focus Rectification. }
We employ the gradient ratio of positive to negative classes to explicitly reveal the model learning focus (the higher ratio, the more focus on the positive class and vice versa). By comparing two lines in Fig \ref{fig:obs} (d), it is clear
that with the vanilla LR,
the positive class is highly distracted by
negative classes throughout model training.
The proposed SS-LR formulation effectively raises the
learning focus of positive classes in training
and therefore mitigating the NCD problem.

\begin{figure}[h]
	\centering
	\includegraphics[width=0.8\linewidth]{C100_speed_.pdf}
	\captionof{figure}{Evaluating the converging rate on CIFAR100.}
	\label{fig:test1}
\end{figure}

\noindent{\bf Convergence Rate. }
Figure \ref{fig:test1} compares the convergence rate of
SR and LR
on CIFAR100.
It is shown that
all learning algorithms have very similar convergence speeds,
suggesting that our method does not sacrifice the training efficiency
whilst yielding favourable performance advantages.

\begin{figure}[th]
	\centering
	\includegraphics[width=0.9\linewidth]{HSvsSS.pdf}
	\captionof{figure}{
		Sensitivity evaluation of $m$ in HS-LR.
	}
	\label{fig:HSvsSS}
\end{figure}

\noindent{\bf Parameter Analysis. }
We analysed the parameter sensitivity of
HS ($m$ in Eq \eqref{eq:LR_sel}) and
SS ($r$ in Eq \eqref{eq:LR_att}) designs.
Fig \ref{fig:HSvsSS} shows that
a good selection of $m$ is important, and
a high value of $m$ is preferred for object classification
but hurts the performance of person re-id.
This is consistent with the earlier observation
that re-id needs to mine fine-grained discriminative information
by concentrating the learning attention more on the most confusing negative classes.
As shown in Fig \ref{fig:gamma},
$r$ is not sensitive to the model performance ($r\!=\!2$ in the main experiments),
rendering SS a favourable choice over HS
in terms of parameter selection.

\begin{figure}[h]
	\centering
	\includegraphics[width=0.6\columnwidth]{gamma_abl.pdf}
	\captionof{figure}{Sensitivity evaluation of $\gamma$ in SS-LR.}
	\label{fig:gamma}
\end{figure}

\noindent{\bf Hard Mining for SR. }
How is the performance of the proposed focus rectified hard mining on SR? We additionally applied the same HS formulation (Eq \eqref{eq:LR_sel}) to SR,
and tested two cases: (1) With MobileNet on DukeMTMC, we obtained 54.9\%/34.9\% Rank-1/mAP vs 57.0\%/35.8\% by the standard SR. (2) With WRN-28-10
on CIFAR100, no performance change, both at 80.0\%. This suggests SR does not suffer from the same NCD problem as LR.

\section{Conclusion}
In this work, we have extensively investigated the validity and advantages of
the logistic regression (LR) learning algorithms
for training single-label multi-class neural network classifiers,
a standard technique conventionally employed for multi-label classification model learning.
This is motivated by our in-depth analyses of softmax regression (SR) and LR in learning properties and their correlation.
We identified the negative class distraction problem
and proposed two rectification solutions using a hard mining idea.
Extensive experiments on both
coarse-grained object classification
and fine-grained person re-identification and spare attribute recognition tasks
show the performance effectiveness of the proposed LR algorithms
over the standard choice SR.

\section{Acknowledgements}
This work was partly supported by the China Scholarship Council, Vision Semantics Limited, the Royal Society Newton Advanced Fellowship Programme (NA150459), and Innovate UK Industrial
Challenge Project on Developing and Commercialising Intelligent Video Analytics Solutions for
Public Safety (98111-571149).
\bibliographystyle{aaai}

redpajama_set_name: RedPajamaArXiv


text:
Home / Burgundy 2021 En Primeur | Tradition and Innovation
The 2021 vintage highlights the challenges facing the current generation of Burgundian vignerons. However, far from being deflated and downhearted, I found tasting this vintage exhilarating and exciting. As they prepare for the future, there is an extraordinary energy, sense of adventure and innovation amongst this new cohort. Respectful of their forebears, traditions, and reputation as some of the finest viticulturalists in the wine world, they are also fully aware that with such radical seasonal and climatic changes they must adapt and move forward.
Respect is the word of the moment. Today's growers know they are only short-term incumbents, and it is their duty to preserve their land and the region's reputation for the next generation.
It all starts with the land. Burgundy's fame originates from its unique patchwork quilt of vineyards and the classification system that surrounds it. Terroir is everything in Burgundy. It was this that the Cistercian monks and their predecessors understood so well, when they mapped out the differences between the Grands and Premiers Crus sites and so on. If the land loses this identity, Burgundy becomes nothing.
The respect and preservation of this identity has been the driving force for the new direction in their vineyard management. Gone are the days of systematic treatments in the vineyards. The philosophy of biodynamic viticulture is no longer considered the domaine of the outliers but plays a crucial part in the protection and regeneration of the soil and its surrounding environment, flora and fauna. Many of our estates follow these practices and, whilst others are not necessarily certified as organic, the majority of them all follow an organic code of practice.
VIEW ALL BURGUNDY 2021 WINES
TIMING & TECHNIQUES
Vineyard work is also changing. Timing and techniques of pruning were the buzz words this autumn. With less rigorous winters, growers appreciate the importance of deferring pruning until as late as possible, to delay the vines' development in case of a harsh spring frost. Sebastien Cathiard is not alone in saying that before 2021 he used to start in December and now waits until January before tackling his Hautes Côtes vineyards and will certainly not complete his best located vineyards in the Côte de Nuits before the end of March.
The age-old system of low cane training is also under the spotlight. Morgan Truchetet of Domaine Truchetet, one of our new domaines this year, is experimenting with higher trained vines as heralded by Lalou Bize-Leroy. This encourages the upward growth of the vegetation which aids both photosynthesis and air flow through the vineyards protecting against disease. Thomas Bouley in Volnay concurs that this could be the future in some locations.
the physical work is also changing at a rate of knots. The return of horses in the vineyard is not for romantic reasons. Horse-drawn ploughing reduces the soil compaction that is such a concern with tractors and allows for more precise ploughing between rows. Alternatively, we now see minute caterpillar-tracked machines working rows as opposed to large-wheeled tractors, again minimising compaction.
Whilst every grower agrees that high-quality fruit is paramount for making great wine, it goes without saying that the decisions made in the winery during vinification are also of critical importance. These contribute the final part of the recipe.
The past few years have seen an increasing trend towards the traditional practice of whole bunch vinification. This gives an additional nuance to the scent and texture of a red wine and can also lower acidity levels, important in 2021.
However, destalking machines have never been more advanced and allow for a previously unseen precision and purity of primary fruit flavours. There is universal agreement that gentle handling and treatment of the grapes during the vinification is of the utmost importance. Infusion was the word of the year. We should all be excited that 2021 sees a further step in the pursuit of elegance and grace in Burgundian wines, rather than power and muscle.
These are exciting times. The new generation are most definitely not resting on their laurels and there is so much to look forward to.
BURGUNDY 2021 EN PRIMEUR HOMEPAGE


text:
Jason Patric to Star in WAYWARD PINES Season 2
Jason Patric has joined the cast of Fox's Wayward Pines Season 2 premiering this upcoming Summer 2016. Jason Patric is best known for The Lost Boys (1987) and Narc (2002). M. Night Shyamalan had reassured fans that Wayward Pines was just a one season event, so I hope that they have a well-baked screenplay to work with. I enjoyed the first season of Wayward Pines, and I hope that the series does not suffer while trying to cash in on the success of the TV series.
Press Release: Jason Patric ("Rush," "Narc") has been cast in a leading role in WAYWARD PINES, the hit psychological thriller event series returning for a second season this summer on FOX. Patric will portray DR. THEO YEDLIN, a confident, driven surgeon whose leadership skills will prove invaluable to the residents of Wayward Pines.
From executive producer M. Night Shyamalan ("The Sixth Sense," "The Visit") and based on the world created by author Blake Crouch in his international best-selling series of books, the 10-episode, second season will pick up after the shocking events of Season One, with the residents of Wayward Pines battling against the iron-fisted rule of the First Generation. Dr. Theo Yedlin - a new resident of Wayward Pines - awakens from suspended animation and finds himself in the middle of this rebellion, as he tries to understand what Wayward Pines really is and help preserve the endangered human race.
"Jason is an incredibly grounded and believable actor," said Shyamalan. "I'm so excited for audiences to see what he brings to the world of WAYWARD PINES."
Patric's feature film debut came in 1987, when he appeared in the comedy-thriller "The Lost Boys." He then starred in the war drama "The Beast," "After Dark, My Sweet" and "Rush," which earned him critical acclaim. He most recently completed production on the film "The Yellow Birds," opposite Jennifer Aniston and Toni Collette. Patric's previous feature credits include "Geronimo: An American Legend," "The Journey of August King," "Sleepers" and "Your Friends and Neighbors," which was the first feature Patric produced for his production company, Fleece. He also starred in the feature films "Downloading Nancy," "Expired," "My Sister's Keeper" and "The Losers," as well as "Narc" and "The Alamo" for director John Lee Hancock. On Broadway, Patric starred as "Brick" in "Cat on a Hot Tin Roof." Some of his other theater credits include Neil LaBute's "Bash," "Beirut," "Out of Gas on Lover's Leap," "The Tempest," "Henry V" and "Love's Labor Lost." Additionally, Patric starred in the Broadway revival of the Tony- and Pulitzer Prize-winning play "That Championship Season," with Chris Noth, Kiefer Sutherland, Brian Cox and Jim Gaffigan, directed by Gregory Mosher. The play, originally presented in 1972, was written by Patric's father, Jason Miller.
Season One of WAYWARD PINES ranked as Summer 2015's No. 1 broadcast scripted series among Adults 18-49, averaging a 2.2/8 in the key demo. The series - about a Secret Service agent on a mission to find two missing federal agents in a sleepy Northwest town, and the shocking results of his investigation - ranked among Summer 2015's Top 10 broadcast programs overall among Adults 18-49. The series earned a multi-platform average audience of 9.4 million, which represents a +145% increase versus its Live+Same Day audience - the largest multi-platform lift versus Live+Same Day ever for a FOX drama.
WAYWARD PINES is a production of 20th Century Fox Television. The series is executive-produced by Donald De Line ("Green Lantern," "The Italian Job"), Ashwin Rajan ("Devil," "The Visit"), Blake Crouch and M. Night Shyamalan. Mark Friedman ("Believe," "The Forgotten") also serves as executive producer and showrunner. "Like" WAYWARD PINES on Facebook at facebook.com/WaywardPines. Follow the series on Twitter at @WaywardPinesFOX and join the discussion using #WaywardPines. See photos and videos on Instagram by following @Wayward Pines.
Wayward Pines (2015)
Directed by Ti West, Tim Hunter, Jeff T. Thomas, Zal Batmanglij
Official Synopsis: Secret Service agent Ethan Burke arrives in the bucolic town of Wayward Pines, ID, on a mission to find two missing federal agents. But instead of answers, Ethan's investigation only turns up more questions. What's wrong with Wayward Pines? Each step closer to the truth takes Ethan further from the life he knew, from the husband and father he was, until he must face the terrifying reality that he may never get out of Wayward Pines alive.
Cast: Djimon Hounsou, Toby Jones, Christian Tessier, Carla Gugino, Chad Krowchuk, Tim Griffin, Melissa Leo, Josh Helman, Hope Davis, Juliette Lewis,…
Genres: Thriller, Drama, Sci-Fi, Mystery
Source: dailydead.com
FOX's Wayward Pines Season 1


text:
Employee Advice
Kevin Wilford
About the founder of Assist Legal Services
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Kevin's clients appreciate his practical no-nonsense advice, his friendly approach and the flexibility that comes with working with a small dynamic and approachable legal consultancy. He has a 2:1 (honours) law degree from Sheffield University; he is married (to Juliet) and has three children and has the fortune to support Newcastle United!
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Despite double and triple dip recessions the West End has never looked healthier. The number of people paying to see a London musical or play is constantly rising, and new shows continue to open each month, each fighting above the noise to be the commercial and critical success of the season. This growth however means that the London theatregoer is presented with a wide selection of shows that are vying for their hard earned money, and with premium ticket prices slowly edging up towards the £100 mark, choosing which show to see has become much more of an investment.
There surely cannot be a Londoner who has not heard of the hit Broadway musical The Book of Mormon in some form. Whether it be their formidable presence on social media, their regular Evening Standard take overs or the iconic images on escalators and buses, 'Mormon-Mania' has certainly taken over the city. Even those who do not keep up with the comings and goings of West End shows are bound to have noticed the efforts employed by the marketing team to make sure The Book of Mormon is the show everyone is talking about this spring, but what is it about their campaign that makes them stand out from the other dozens of shows equally fighting for our attention?
The answer surely has to do with the show (or for the purpose of this discussion, the 'brand') being a wholly American one. Unlike the majority of major shows in the West End, The Book of Mormon is not based on a film, nor a collection of hits from well-loved musicians, but is instead an original book musical, the likes of which is sadly rarely seen on the commercial West End. True, the show relies somewhat on the writers association with 'South Park', but this is as exclusive as it is appealing, given that the popularity of the TV series is incomparable with what it is in the USA. What Producers of the show have done is create the first modern day 'brand' out of an original musical, that is as effective as it is overwhelming. Broadway is a much more cut-throat commercially dependent industry than its UK counterpart, and is not blessed with the public financial support that blesses many UK venues. There is no Arts Council, there are few subsidised theatres and no National Theatre to exhume £18million from the tax-payers each year. This results in a more competitive industry, where shows, especially big budget musicals, need to fight for survival and are quickly closed if targets are not met.
The Book of Mormon opened in the 2011 Broadway season and was the runaway success of the year. Even in the first two weeks of previews it was playing to 95% capacity audiences, which escalated to 102% after just three weeks, and has remained at that level ever since (the additional 2% takes into account the standing room tickets that are only sold when every seat has been sold). Two years on, it continues to perform 8 times a week to sold out crowds, and the average price of a ticket is creeping up to over $200, resulting in weekly grosses in excess of $1.6million. This success is hard to ignore, and obviously one which producers hope to replicate when the show opens officially in London's West End on 21 March 2013.
Critical praise: The New York Times' Ben Brantley, who holds so much significance to the success of a production in a way some UK reviewers desperately try to emulate called it 'the best musical of this century'. This was matched by publications from around the world, adding to the hype and giving it the 'must see' label.
Tony Awards: The most highly coveted awards in worldwide theatre awarded the production 14 nominations and 8 awards, including the 'big three' of Best Musical, Best Original Score and Best Book. After the awards telecast, the show's Grammy Award Winning Cast Album rocketed into the US Billboard charts at #3, becoming the first musical album to do so in four decades.
Word of Mouth: Perhaps the slowest burner in terms of ticket sales, but the most effective method of conversion. People are more likely to see a show if recommended by a friend, and this show has been the talk of dinner parties, bus rides and the school gate pick-up.
Despite all of these factors, I believe the marketing of the show has had the most significant effect on ticket sales. The same model is currently being used in London to promote the European premiere, but has it achieved the same effect? Are UK audiences ready to be sold a 'brand' when wanting to choose an evening at the theatre? The proof as they say will be in the pudding, but for now we can examine the methods used by producers to market the show to see exactly how this marketing machine has taken off and left other shows trailing behind in its shadow. These three things are particularly effective especially within the theatre community, but any producer worth their salt knows that real world marketing is the most difficult nut to crack. Theatre can not sustain itself by relying on a small group of people – for a show to succeed, the marketing must reach beyond the 'traditional theatre goer' and attract those who didn't know they possibly could be fans of a brand new musical.
As Mama Rose tells Louise as she is preparing to take off her clothes for the first time in public, 'always leave them wanting more'. Since the hype of the Broadway production was so great it was only a matter of time until London audiences were treated to this theatrical delight. Rather than fast-tack the show to ride on the back of the Tony wave, UK Producer Sonia Friedman clearly thought carefully about when to bring the show to London. After teasing newspaper columnists with the news of it coming in 2012, creators Trey Parker and Matt Stone seemed adamant that a transfer wouldn't happen until they had finished work on the 16th series of 'South Park'. By this time however people knew the Mormons were coming, and online speculation grew as to where, when and how it would land. Whether the 2012 Summer Olympics proved to be too much of a hurdle is unclear, but news of the show's opening was leaked to the Daily Mail, before details were drip-fed to fans. This scramble for information only heightened the anticipation, which was at fever pitch by the time the official announcement was later made.
The artwork for the show has become one of the most iconic in recent history, and has helped the brand become an international phenomenon. Not since the days of the 80s mega musicals has a production's art work been as highly regarded in the 'normal' advertising markets. Unlike other Broadway to West End transfers such as Wicked (2006) and Avenue Q (2006) whose artwork went through subtle changes during the Atlantic crossing, The Book of Mormon stuck with their black on white figure and signature doorknob for the UK market. The usual methods were employed with takeovers of buses, cabs and escalators reaching out to the average Londoner, but instead of using the title of the show, a clever tag line, "The Mormons are Coming" was used to add to the ever bubbling anticipation. Although it kept commuters talking, some were confused at the campaign, with some thinking that was the name of the show, even after posters were replaced with the 'Now in Previews' strap-line.
House Full – Or Is It?
How do you make something instantly attractive? By making it unobtainable. The term 'Sold Out' has become almost synonymous with the show since tickets went on sale last year, with the previews being the first tickets to go. Whilst this seems to be a current trend by West End producers to hopefully boost interest in a show, this method certainly worked at the Prince of Wales. Added to the fact that tickets were only released one month at a time, genuine panic buying ensued as theatre fans booked up to see the show as soon as they could. Magazine and TV features claiming the show was set to be the biggest opening of the year created a 'must see' mentality, as no one wanted to be left behind in the rush. The show has faced some online criticism for claiming it is sold out when tickets were readily available online. Although it is certainly true that tickets are hard to come by, especially in the early months, the confusion was somewhat detrimental in the early stages and people saw through the marketing ploy. At least producers were almost telling the truth, unlike those at Viva Forever who proudly claimed in the media that the Spice Girls musical at the Piccadilly Theatre was unavailable for months, when in reality hundreds of discounted seats were left available for each performance. The ease of online booking systems means that such bold statements have to be carefully controlled, as audiences are only ever a couple of clicks away from disproving such claims. Whilst releasing tickets for The Book of Mormon at a monthly pace worked initially, the system proved somewhat confusing for both ticket retailers and consumers, and as of Friday 20 March the booking period has been extended into 2014.
One of the biggest headline grabbing stunts occurred before the show's marquee had even been completed. Producers used Twitter and Facebook to reveal that a surprise preview performance was going to happen on Monday 25th February, the day before the previously announced first preview. All tickets for this sneak peak would be priced at £20, with unreserved seating meaning the keenest Mormon fans would be rewarded for their loyalty. The catch was that to stand a chance to secure a ticket potential audience members had to wait in line outside the box office, which opened at 12 noon. Lines started from 10pm the night before, and it wasn't long before the producers had exactly what they were after – a line wrapping around the block full of converted fans raring up for a bargain. As the lines continued to build, stretching more than 500 feet, the news cameras came to find out exactly what it was about the show that had people waiting for hours in the freezing cold. The most interesting thing that this publicity stunt proved was the breadth of support base the show had already attracted. This wasn't the 14 year old Idina Menzel fans that regularly line up outside the Victoria Palace for returns at Wicked but the crowd included the most diverse collection of ages, races, genders and personality.
Anyone who works in advertising or marketing has come to know the power of the hashtag. Twitter is quickly becoming one of the most viral forms of social media, and hashtags are a way to group together what people are talking about at any one point. On leaving the Prince of Wales Theatre ushers are handing out business cards which encourage audience members to share their thoughts online by using #LoveMormon. These have been collected together to create a Twitter collage on their website LoveMormon.com, and audience members have been encouraged to find their own tweets in the picture. When shows have long preview periods, social media can either be a friend or a foe, and this is an example of producers using the voices of the masses to keep the hype bubbling before the more 'official' reviews are considered. Printed adverts including Twitter reviews are being used in Time Out and the Evening Standard, adding another interactive aspect to the show, speaking directly to the audience and marketing the 'brand' as one that is endorsed by peers and the theatre-going public in 140 characters, rather than (to use the words of Jennifer Saunders) 'five balding men' in a national newspapers. Rather than coming across as a publicity attempt to be 'down with the kids', this direct link between audiences and the show has set the bar high for future, and current productions.
Twitter's older and somewhat wayward relative Facebook has also been utilised to the max, and the show's official London page now has over 30,000 'likes'. To reach this level of social media esteem, Producers incentivised users with the offer of free tickets and competitions to those who shared their posts, widening their reach even further into the online stratosphere. If the American model is to be used as a marker, this involvement is set to continue, with fans earning 'points' based on their level of activity that can then be cashed in for a range of exclusive goodies, as well as tickets. The team in charge of the show's social media activity certainly knows how to engage fans, with daily posts, pictures, videos and comments that have helped the Broadway page attract almost 400,000 'likes' which continues to grow exponentially. Exclusive priceless gifts such as tickets to a dress rehearsal and signed memorabilia were a perfect way to reward fans and make them do most of the work for you. Online and social media endorsement is one of the most effective, and cheapest methods of marketing, but also one that many productions fail to handle well.
After the show formally opens on March 21, many would think that from here on in the hard work is done and the 'brand' will continue to sell itself. In many cases this may be true, but in a heavily saturated market that relies on new audiences and seasonal traffic, the hard work is far from over. Some may argue that this marketing method has been so overwhelming that it has the potential to create the opposite effect, as other hit Broadway transfers such as Once the Musical and A Chorus Line have been left somewhat in the dark. Some within the industry have criticized this method, calling it too commercial and brand focuses, saying it has removed the art from theatre industry. Although I agree with this on some level, I feel ticket sales and box office receipts will say otherwise, as The Book of Mormon will prove to be the biggest West End hit of the year, and will set the marketing bench-mark for future 'brands' to compete against.


A group of 12 students from the Universidad de las Americas (UDLA) in Ecuador are spending three weeks at Framingham State University taking an intensive course on the Art of Music Video Production offered in cooperation with the Communication Arts Department.
For many of the students, it is their first opportunity to visit the United States.
"It's been wonderful to experience the culture and have the opportunity to share my knowledge with my friends," says senior Paola Herrera.
The intensive course runs for four hours a day, plus location shooting, and will culminate in the completion of a music video, according to Paulina Donoso, the director of the Multimedia and Visual Production Department at UDLA.
The video, Finger Poppin' Time, will feature the band Doo Wop Deville, who performed on campus last spring, and includes Art and Music Professor Lisa Burke and her husband. The students have been staying on campus in Linsley Hall during the visit and will be making day trips to Boston, Cape Cod, and New York City.
FSU President Javier Cevallos met Donoso when she was a master's degree student at Kutztown University, where he previously served as president. The two kept in touch over the years and when Donoso reached out about bringing her students up to FSU, Cevallos was on board.
The class includes two local students, including Weston resident Janet Macy.


Just to be sure, the climate will probably not be "quite" the #1 topic in the elections but it will come close. The more important topic will be the sustainability of their welfare, pension, healthcare systems.
The mainstream press doesn't hide its hostility but hasn't hidden the expected good aspect of today's polls, either. The Financial Times wrote Finland's populists find favour with anti-green agenda. The Finns Party is painted as clones of the French yellow vests which seems extremely inaccurate to me. Why? For example, the Finns Party's chairman – who became anti-left due to trips to the Soviet Union as a kid – is a scholar who has taught Old Church Slavonic, no kidding, show me the French yellow vests who speak that 9th century Slavic language. ;-) The hot blonde woman who is the Finns' presidential candidate is a trained teacher of a sort.
The New York Times admitted the Finns Party's strong enough support in the article The Right's New Rallying Cry in Finland: 'Climate Hysteria'. A pretty nice title. The Times reveal that the Finns Party were strengthened by others – who foolishly decided it was a good idea to brag about the climate panic. When this topic was made important by the alarmists, the Finns Party naturally became the recipient of most of the skeptical votes. I think that if the topic were made "really primary", which it's not, the Finns Party could approach 50% in this way.
Reports indicate that the Finnish media actually haven't demonized or ostracized the Finns Party and its position towards the climate etc.
Except for the Finns Party, all the Finnish politicians are more or less drowning in the mud of the climate change propaganda. The Green League wants to ban carbon by 2030, others by 2045, ... you can imagine that the rhetoric of numerous politicians is somewhere in between "insane" and "faded away insane". The Finns Party is the only exception and they mostly care about the increases of prices – of food and other things (I've read that Northern Finns need to travel a lot and are worried about all restrictions on transportation) – that would follow from any "climate targets". Yes, in this sense, their primary motivation to be skeptics is analogous to the Yellow Vests'.
But opinions and passions don't necessary copy the material interests too accurately. The Finns Party have the chance to be the silver winners – but it's even possible that they will turn to the strongest party, assuming that the right-wing gains are being artificially suppressed in the surveys, as we have seen in the U.S., U.K., and elsewhere.
The Heavy Pochondriac's parody of "Volare Cantare" is about Czech gypsies who fly to Helsinki to apply for asylum – probably relevant for the elections today.
There's some material common sense about many Finns' climate skepticism: the cold weather. The average annual temperature in Helsinki is 5 °C, it's worse elsewhere, and some 15 °C of global warming would clearly be good news for Finland if possible. Even lots of regular workers are sane enough to understand that the painting of 1.5 °C of a hypothetical warming as the end of the world is just a silly fairy-tale.
Finland is obviously a rich enough, full-blown capitalist country, with over $45,000 of GDP per capita etc. At some point, Nokia was a global giant in the tech industry – these days it's a rather small $40 bilion company but it (plus HMD) may become a giant again. (Nokia has announced some violation of rules done by Alcatel-Lucent that the firm acquired a few years ago and a big fine or other trouble may arrive; some HMD Nokia phones were also sending some data to China, due to a mistake (?) in firmware update.) It runs trade surpluses and has other healthy traits. But the strength of the Finns Party indicates some kind of a spirit that makes the Finns close to the post-communist countries like mine.
This has some reasons. First, linguistically, Finns are closest to Estonia which has been a part of the Soviet Union, and they belong to the Finno-Ugric linguistic family along with the Hungarians (although Finnish and Hungarian are not mutually intelligible). But Finland itself, while a non-Germanic "province of Sweden" for a very long time (which made Finland a bit analogous to Czechia), was also one of the capitalist countries that were keeping the most peaceful relationships with the Soviet Union during the Cold War.
While the Soviet Union allowed the normal, full-blown capitalist system in Finland during the Cold War, Finland appreciated the ease with which the Soviet Union could conquer Finland, and they sort of cooperated and also had a significant trade with the Soviet bloc. I remember some softly propagandist articles in my favorite science-and-technology-for-youth (VTM) journal that was praising Finns for that approach in the 1980s. Note that like Sweden, Finland is still outside NATO even today. But in the past, Finland was way more pro-Soviet than Sweden, despite the latter's being a "prototype of the Western socialism".
I think that the people who are worried about the ideological transformations of the contemporary West should learn much more European geography. That's true especially for the Americans who often imagine Europe to be one homogeneous chunk – or at most a collection of two chunks, the Western and Eastern Europe. But there are many more subtleties you should know. Some countries, like Norway and Switzerland, are outside the European Union which has some consequences. Others, inside the EU, have various reasons to resemble Central European post-communist countries in various ways. That includes Finland, Austria, and perhaps Denmark – but the character of the proximity is different in each case. And the post-communist countries significantly differ from each other as well.
Good luck to the Finns who are choosing their Parliament today.


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How To Live Cancer Free – "Bob Wright and The American Anti-Cancer Institute (AACI)"
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Bill Henderson interviews Bob Wright, founder and CEO of the American Anti-Cancer Institute about his vision of a campus near Seattle dedicated to education and research of natural cancer healing methods and healing of people like you using these methods. This is an exciting project which you can become part of through donation of both your money and time. Bob has organized a professional money-raising campaign to raise the $125 million needed to fulfill this dream. It is starting with the proceeds from the sale of his book Killing Cancer — Not People.
"They were not helped by conventional or holopathic medicine," says Bob Wright of his parents' passing and his initial motivation to start the American Anti-Cancer Institute. After the death of his own parents and watching many others suffer through similar fates, Bob Wright decided to become a fighting force for the side of "alternative" medicine.
"When did it become 'alternative' to do the natural thing and actually have an opportunity to heal your body? That's as conventional as it gets!" says Wright. But what alternatives are there for someone with Cancer? What other answers could their been when all the so-called experts are pointing to surgery and chemotherapy?
Bob Wright and Bill Henderson talk about their struggles as advocates for alternative healing. "Doctors don't heal, they treat," says Henderson. But how can someone with Cancer explain to their families and friends that they're turning their backs on conventional treatment when society has been conditioned to think of doctors as the master authority on wellness?
In this insightful visionary episode, Bob Wright and host Bill Henderson discuss their personal experiences with helping thousands of patients to heal themselves without radiation, as well as the ethics of alternative healing. And in the Cancer-in-the-News segment, Bill discusses iodine and its ability to protect you from the hazards of radiation. This is an episode you won't want to miss!
How To Live Cancer Free
How To Live Cancer Free with Bill Henderson Bill is a best-selling author on both Amazon and Booklocker, has written three books on natural cancer healing. His latest book "Cancer-Free – Your Guide to Gentle, Non-toxic Healing (Third Edition)" has...
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Based in our hometown of Buffalo, the law firm of Cantor Dolce and Panepinto is dedicated to helping working families get the justice they deserve. Their attorneys represent victims suffering from personal injuries of all kinds, from car crashes to construction accidents.
To demonstrate their legal expertise, credibility and history of success, they needed a website that was clean, professional and trustworthy. That's where we came in. We redesigned the entire site to highlight the firm's capabilities and experience while creating an interface that was simple for potential clients to navigate. We utilized responsive design to ensure potential clients could easily access the information they needed from any device, from phones to desktop computers.


One thing I have come to expect on this trip is the unexpected. Be that monkeys going at it like, er monkeys on the streets of Calcutta or men eating plates of noodles while taking a dump in China.
Precious little surprises me these days but my last meal in Palermo, last night, came pretty close as I chowed down appreciatively on a bowl a pasta with a sauce made from veal and potatoes prepared lovingly for us by Valentino, a Sicilian post-op transsexual.
The day began with breakfast during which Claudio, the owner of my B&B warned me not to eat too much as he was going to take me on an afternoon foodie crawl of the local neighbourhood. He put his finger to his cheek and twisted it, which he told me is the Sicilian way of expressing how delicious something is, or will be.
At the appointed time, we headed the short walk to the market area of Vuruccio and began our eating expedition with a local favourite, plates of octopus, simply boiled in salted water and served in the traditional Palermo way, with head split open so the inside forms a coating.
After taking the chance to have yet another Pani Ca Meusa (lung and spleen sandwich) we stopped at a small storefront specialising in Fritti Di Mare, where Claudio picked up a container of mixed little delights to take away before taking me around the corner to introduce me to another of his friends, one of the last producers of Bottarga in the city, who proudly held up the sack of fish eggs he was heading off to dry.
By now, it was time for lunch and we stopped at a neighbourhood restaurant, with four small tables out front, called Caffe Antico where Claudio's friend, Giovanni prepares what's considered some of the best local food in the city.
I never saw a menu, so can't tell you what else was on offer, but I was more than happy with a plate of spaghetti (always spaghetti, I was told) with a sauce made from fresh sea urchins, lots of oil and garlic.
Everyone seems to know Claudio and, as we sat, ate and drank a bottle of Nero D'Avola we were joined by a constant stream of friends each of whom protested that they were far too busy to sit down but then did exactly that as they joined us for a glass of wine and a cigarette. At the end of the meal, Giovanni came out to join us bringing some homemade semi freddo and canoli and, even more welcome, a bottle of the local grappa.
Cue long afternoon of getting frighteningly pissed in the company of a motley crew of chain smoking Sicilian men until I was high as a kite and reeled back to the B&B to sleep it off.
A couple of hours later, I was awoken by a loud rapping on my bedroom door to find Claudio, bright as a button, informing me that we were going to head out again for an evening tour of the old city, but not before supper which was courtesy of his upstairs neighbour, Valentino, the transsexual, who had prepared pasta for me, Claudio and his girlfriend.
It was one of the more unusual offers I have had on the trip, but Valentino's flat was lovely, warm and welcoming and the pasta, linguine served with a sauce of veal thickened with potatoes, was delicious and just what I needed to blot up some of the afternoon's over indulgences.
Afterwards, we all went for the promised walk which perked me up enough to have one final indulgence, another Palermo speciality of fresh fruit smothered in granita, after which, I headed back for the couple of hours sleep I was going to be allowed before heading for the airport and to Rome.


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Big Oil is awash in rainbows for Pride Month
Kate Yoder — June 4, 2021 0 comment
June is Pride Month, and Corporate America won't let you forget it. Every year around strawberry season, social networks is flooded with logo designs dressed up in rainbow colors, a program of public assistance for the LGBTQ neighborhood. But those beautiful logo designs are frequently implicated of being a type of "rainbow-washing" — marketing spin that improves a business's social justice cred, with little compound behind it.
This week, the American Petroleum Institute gotten on the rainbow-colored bandwagon, including the traditional gradient to its logo design on Facebook, Twitter, and Instagram. (The oil and gas market's greatest lobbying group isn't keen on TikTok, obviously.) One corner of the Twitter universe was not having API's modification of appearance, with reactions varying from the woozy face to the vomit face .
Before rainbow-washing, there was "greenwashing," another type of marketing spin. It all began with some unclean towels in the 1980s. An ecologist called Jay Westerveld created the term after checking out Fiji, where he saw a note in a resort asking consumers to get their towels to protect the oceans and reefs ("help us help our environment!"). The paradox struck Westerveld, who understood that the resort was broadening despite the ecological repercussions. "I don't think they really cared all that much about the coral reefs," he later on informed the Guardian.
Soon after his journey to Fiji, Westerveld composed an essay in 1986 about what he called "greenwashing" — a play on the "whitewash" metaphor for glossing over vices — and the term captured on rapidly amongst ecologists. Around the very same time, Chevron put out commercials that looked like low-budget nature documentaries, revealing a grizzly bear awakening from hibernation to a lavish meadow that was when an oil expedition zone and a fox leaving from a coyote by delving into an oil pipeline, an evident effort to burnish the business's ecological record. The project went on to win an Effie marketing award, in addition to ending up being the requirement for greenwashing.
Over the years, the -cleaning suffix has actually been utilized to explain other business PR techniques. The 2010s brought "pinkwashing," when a few of the business slapping pink ribbons on whatever for breast cancer awareness were offering items connected to breast cancer. More just recently, "woke-washing" has actually become a catchall term to describe brand names attempting to burnish their track record with numerous social justice platitudes.
The oil and gas market has actually been implicated of all sorts of woke-washing, from Chevron putting out a Black Lives Matter declaration to a Shell gasoline station including an apostrophe to its logo design, becoming She'll for International Women's Day. And API's rainbow logo design wasn't the market's only venture into Pride Month. Chevron put out a tweet with the hashtag #ChevronPride, commemorating the 30 year anniversary of its PRIDE staff member network: "To celebrate, we're holding a series of joyful events that highlight intersectionality and honor our personal identities and experiences that make each one of us unique." BP and Phillips 66 likewise tweeted Pride declarations.
LGBTQ employees in the oil and gas market have actually reported harassment, and oil business have actually taken procedures to attend to discrimination (in Exxon's case, carrying out policies it when opposed). But even with high marks on the Human Rights Campaign's business equality index, an effort to judge business' LGBTQ policies, oil majors are getting called out for rainbow-washing. Chevron, for example, has actually funneled more than $6 million to PACs working to keep GOP leaders in Congress — much of whom have actually obstructed legislation on environment modification and LGBTQ rights. Last year, prior to the governmental election, Republicans voted to keep their 2016 platform, which reduces the hazard of environment modification and likewise called for a restriction on same-sex marital relationship.
Companies are significantly feeling the requirement to reveal their assistance for progressive causes — however when individuals begin poking into what's taking place behind the scenes, a few of those rainbow logo designs lose their shine.
This story was initially released by Livescience.Tech with the heading Big Oil is awash in rainbows for Pride Month on Jun 4, 2021.
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One way to live Matthew 25: Learn how to be a healing presence
by Bill Tammeus
A small c catholic
We are losing the man I will call Dan.
A valued employee of the same company for 30 years, he retired a few years ago. Until a year or two ago, Dan, the husband of a friend, was engaged with life, busy in his woodworking shop, playing with a gaggle of grandchildren, traveling and supporting the professional work his wife still does.
Today, he mostly sits in an easy chair and watches TV, emitting a low laugh -- not quite a giggle -- no matter what the appropriate reaction should be to what he's watching. His laugh means something is funny or sad or terrifying or crazy or meaningless. Sometimes it just means he's there.
There's no official diagnosis for what's battering him, though one immediately thinks of Alzheimer's disease. But it seems more complicated than that. He can still walk, though his gait is unsteady and he's had spells when he fell a lot. His rubber-soled shoes squeak as he crosses the hardwood floors, so it's easy to keep track of him.
Sometimes he can engage in conversation and make quite good sense. But these aren't long exchanges. The joy for his wife is that Dan seems happy, is not violent, doesn't appear anxious to live in any way other way than the way he's now living.
My wife and I spent a few days with them recently in the state where they live just because we wanted to see Dan before he slipped away beyond cognitive reach of us. We brought with us no answers to Dan's situation, no words that would take away the pain, the angst his wife feels, no stack of books full of advice.
All we brought was ourselves. We hoped our presence would be our present.
It's no imaginative, stunning insight to say that sometimes the best gift we can give is our presence. But as much as that idea has become conventional wisdom -- especially for people in faith communities -- it seems to be said more than it's done.
My experience tells me that people continue to be nervous about inserting themselves into difficult situations involving illness or injury because they fear they will say the wrong words, do the wrong thing or be unwelcome. It also tells me that sometimes when people stir up the courage to make what I will call a pastoral visit, they overwhelm the recipient with unneeded words, allegedly good advice and way too much of themselves.
In either case -- fear resulting in inaction or too much action resulting in awkwardness -- no real good is accomplished.
What our churches and other worshipping communities need to do is teach members how to be a healing presence. Often, that will require nothing more than sitting quietly and holding a hand. Or active listening. That's when we simply let the one in pain or that person's caregiver unload about what's happening and what it feels like.
That's some of what my wife and I did while we were visiting. We had several good conversations with Dan's wife about how she's coping with all this and what the future might hold. And we sometimes just sat in Dan's TV room and watched whatever show he was watching, making occasional small talk.
In Matthew 25, Jesus urges people to engage in pastoral care. He puts into the mouth of the people he's addressing this question: "When did we see you sick or in prison and come to you?" His answer, of course, convicts us: "When you have done it for one of the least of these brothers and sisters of mine, you have done it for me."
I've never liked the English word "least" there because it seems to create a sort of caste system. But I'm a fan of the idea that we need not bring gold, frankincense or myrrh to the wounded. Like the shepherds in Bethlehem, we can just bring ourselves.
[Bill Tammeus, a Presbyterian elder and award-winning former faith columnist for The Kansas City Star, writes the daily "Faith Matters" blog for the Star's website and a monthly column for The Presbyterian Outlook. His latest book is Woodstock: A Story of Middle Americans. Email him at wtammeus@gmail.com.]
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Source URL (modified on 08/03/2017 - 8:10am): https://www.ncronline.org/blogs/small-c-catholic/one-way-live-matthew-25-learn-how-be-healing-presence