text:
Iran urges Turkey to pull forces out of Syria
Al-Monitor – The Iranian Foreign Ministry has issued a statement opposing Turkey's incursion into northeastern Syria: "Iran's Foreign Ministry expresses concern over Turkey's military action inside Syria's territory, and with serious consideration for the humanitarian situation and respective dangers for civilians in the region under conflict, it calls for an immediate suspension of attacks and withdrawal of Turkish forces from Syria."
The statement continued, "Just as Iran understands the security concerns of Turkey, it not only believes that military action will not solve security concerns, but that it will bring widespread humanitarian and material damages. Iran opposes these actions." The statement said Iran is in contact with both Syria and Turkey and stressed a "peaceful resolution that respects Syria's territorial integrity."
Iran's criticism of Turkey's decision to send forces into Syria has been relatively mild in comparison to their reaction to US and Western military actions inside Syria. Iranian military officials have not addressed the incursion, though they typically would if Iran was aggressively opposed. For instance, Islamic Revolutionary Guard Corps Cmdr. Hossein Salami spoke Oct. 10 during a visit to the northwestern regional city of Ardabil, Iran, but he only discussed the "weakening choices of US options" with respect to Iran and did not mention Turkey.
Brigadier General Hossein Salami, Iran Foreign ministry, Syria crisis, Turkey military operation in Syria	October 12, 2019
Putin condemns attack on Iranian oil tanker
UK voices concern over Iran tanker incident in Red Sea


text:
Tragedy in Dallas
Five officers killed and nine injured in an ambush at a peaceful protest, July 7, 2016
Clinton Speaks in Philadelphia About 'Vicious, Appalling Attack,' Trump Postpones Event
"Implicit bias still exists in our society even in our best police departments," Clinton said
By Emilie Plesset and Rajeev Dhir
Published Jul 8, 2016 at 10:45 AM | Updated at 11:01 PM CDT on Jul 8, 2016
Hillary Clinton Spoke at the AME Convention in Philadelphia
http://www.nbcchicago.com/news/national-international/Clinton-Trump-Cancel-Campaign-Events-in-Wake-of-Dallas-Attack-386016581.html
Presumptinve Democratic presidential nominee Hillary Clinton spoke about recent police shootings and the sniper attack on officers in Dallas at the AME Convention in Philadelphia Friday. NBC10's Drew Smith reports. (Published Friday, July 8, 2016)
Presumptive Democratic nominee Hillary Clinton called this week's two police involved shootings and the ambush shooting of police officers in Dallas "vicious" and "appalling" during a speech in Philadelphia Friday.
Clinton addressed the 50th Quadrennial Session of the African Methodist Episcopal General Conference in Philadelphia — the day after the attack in Dallas, which left five officers dead and seven others injured.
Dallas PD Squelch Critics, Questions About Sniper Attack
"There is too much violence, too much hate, too much senseless killings," she said. "We know there is something wrong with our country."
During her speech, Clinton listed some of the fatal shootings that have taken place across the country, including the two latest. On Tuesday, Alton Sterling was tackled and shot by officers during a confrontation outside a convenience store in Baton Rouge, Louisiana. The following day, an officer shot Philando Castile during a traffic stop in a St. Paul, Minnesota, suburb. Castile's girlfriend recorded the events after the shooting.
Police Shootings of Black Men Spark Protests
"Implicit bias still exists in our society even in our best police departments," she said, calling on an end to the "systemic racism that plagues our country."
She urged white Americans to "do a better job of listening when African-Americans talk," something she said would help quell racial disparities and the tension many black people feel when dealing with law enforcement.
"There is clear evidence that African-Americans are much more likely to be killed in police incidents than any other group of Americans," she said.
Clinton postponed an earlier campaign event in Scranton, Pennsylvania, with Vice President Joe Biden hours after the Dallas attack.
Presumptive Republican nominee Donald Trump also put off Friday's campaign event, which was scheduled in Miami.
"Due to the horrific events taking place in our country, I have decided to postpone my speech on economic opportunity- today in Miami," he tweeted Friday morning.
Due to the horrific events taking place in our country, I have decided to postpone my speech on economic opportunity- today in Miami.
In a video posted to Facebook, Trump called Thursday's attack in Dallas "brutal," and said Americans must stand in "solidarity with law enforcement."
"Every American has the right to live in safety and peace," he said. "The deaths of Alton Sterling in Louisiana and Philando Castile in Minnesota also make clear how much more work we have to do to make every American feel that their safety is protected."
President Barack Obama, speaking from the NATO summit in Poland, condemned the attack Friday morning.
"There is no possible justification for these kinds of attacks or any violence against law enforcement," Obama said. "Anyone involved in the senseless murders will be held fully accountable. Justice will be done."
2 Horses Die After Head-On Collision at Del Mar Racetrack
He said Friday should be dedicated to honoring the victims and their families.
Other politicians echoed these sympathies on Twitter as well, including former Republican presidential candidate Ted Cruz, who praised the bravery of the police.
DevelopingStandoff: Cops in Texas Kill Man Who Used Child as Shield
"Men & women of law enforcement selflessly run into harm's way to save the lives of others," he wrote. "May God protect them and bring peace upon Dallas."
Senate Minority Leader and Nevada Senator Harry Reid also issued a statement about the attacks Friday morning, calling for those responsible for the attack to "be brought to justice and held accountable for these horrific crimes."
The nation stands with the people of Dallas and their brave police force. There is no possible justification for such a senseless, evil act.
— Senator Harry Reid (@SenatorReid) July 8, 2016
Men & women of law enforcement selflessly run into harm's way to save the lives of others. May God protect them and bring peace upon Dallas.
— Ted Cruz (@tedcruz) July 8, 2016

redpajama_set_name: RedPajamaCommonCrawl


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This was my daughters creation, she designed it. She was very specific about what she wanted and stood by my side the whole time telling me what to do. She wanted a turquoise cake with the purse on top and candy jewelry all over it. I think it is a good compromise between the two cakes we were looking at in THIS post.
She was thrilled with the final results.
I was running out of time and didn't know how I was going to get the handle on the purse to stand up. I had floral wire on hand, but it wasn't working with the fondant. That's when I thought of restringing the candy beads onto the wire. I think it worked nicely. I also used the floral wire to attach all the candy necklaces to the cake.
I know it looks a little busy, but it didn't seam quite so crazy in real life.
Thanks for all your ideas and suggestions. In the end I let her make all the calls.
*Also I have to tell you I tried THIS fondant recipe instead of my usual. I have to admit, I am in LOVE. If you are thinking of trying fondant you should give it a go. It was so easy and worked like a dream. Thank you Laura for sharing Peggy's recipe with me.
Holy Shamoley. What an amazing cake. How cool that your daughter has a mom to make the cake of her dreams.
I'm 38 in two weeks and I would love this cake!!!
It's so gorgeous and girly, good job!!!
this is way better than those other two cakes. you win, mom of the year.
Your creative spirit blows me away everytime. And to think you didn't even know what you were going to do just the other day. I love it, hope she had a great birthday and you all had wonderful family time.
WOW!!! I just have to say that it turned out WONDERFUL!!! I will be featureing this cake on my blog this week!! Love it!!
Wow, that turned out AMAZINGLY cute! I'm not kidding, I thought you were showing us a picture of a professional cake 1st, and then you'd show us your version of it. (That didn't sound nice - lol - you know what I mean, right?:) Happy b-day to your little lady!
I love it!!!!! It is perfect.
You are a genius, my friend.
glad to see lydia got the 3 tiers she wanted. wish we were there to share it with you.
I loved this cake so much that I added it to a blog post. Hope that's okay.
Wow, that is amazing! I am so scared of trying fondant, but that is so cute...I might have to try it for one of my girls! They would love the candy jewelry...oh my!
OH my goodness, that is adorable!!! I love it and I'm sure she did too!
WOW! The cake is BEAUTIFUL!
What a beauty! So adorable!
That cake is incredible! What a lucky girl.
Amazing cake! I love your blog, too.
That cake is incredible! WOW!!! You rock!
Kalleen, this cake is amazing! Another great project friend! Love it!
Wow! I'd like one of those for MY birthday! You are really talented. And your daughter too for designing it. Thanks for sharing ... I've posted a link.
Wow, what a great cake!! That is one lucky little girl!
Way Cuter than the inspiration cakes!
Very nice. You did an amazing job on that cake. Any little girl, or big girl for that matter, would be thrilled to have one of those.
You are absolutely amazing! That is gorgeous, your daughter must have been thrilled! I've never tried working with fondant, but I may have to give it a try.
I think you/she did a great job!!!!
You nailed this one sista!!! My sister and I made a castle cake one year for my oldest daughter and we joked that "it didn't turn out quite like it was in our heads". Of course it didn't help that we were in the hardware store shopping for PVC 1 hour before her party.
You are a great Mom to make her dreams come true! My Mom made my wedding dress (that I helped her design). It is so nice to have a Mom that will make your dreams come to life! She's a lucky girl!
Wow! that turned out brilliant!
Visiting from Tatertots. What a wonderful cake, and I'm definitely going to have to visit that MarioKart cake. My oldest would love that!
Your daughter has great taste that cake is adorable you did a wonderful job!
This is seriously one of the cutest cakes I've seen in a long time. Like "Ace of Cakes" cool. I love it!
Oh wow!! You are so amazing with cakes!! I'll have to show this one to my daughter in hopes that she would fall in love with it and forget about having another Sonic the Hedgehog party!!
I am in awe and am inspired to maybe... one day... try fondant. Thank you for the inspiration (and for sharing the recipe)!
I, too, have rcntly fell in love w/ cake making. So, I've gotten every book on the planet (U must have Confetti Cakes in ur personal library) and have several friends that do this for a living. Not to mention the inspiration on TV w/ all of the cake shows now. But, I pretty much do what you did, researching online. In fact, my favorite fondant rec is the same one you use! I've tried several recipes and the storebought stuff~ TERRIBLE! The MM is my FAVORITE! A little tip (if you want, no biggie), In lou of the 2-5T of water, I use 1T water, 1T vanilla extract, 1/2T butter extract, 1/2T almond extract, and 1/2t salt. It literally tastes like hardened buttercream icing. Those are the same flavors I put in my icing... If you make another cake, I implore you to try it! The rec is soo sweet, that salt really helps identify the flavors.
That cake is beyond amazing. My mom thinks she worked hard for my cake when I was four but this cake looks way harder to make. Looks amazing.


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text:
A kinder and wiser approach to IVF treatment in Scotland: Meeting targets and reducing mental health complications
August 29, 2018 johnrobertson834
Note: This is a heavily recycled piece from May in the light of recent NHS Scotland figures.
From ISD on 28th August 2018:
'The four IVF centres in Scotland screened 373 eligible patients, compared with 370 in the previous quarter. 100% of patients were screened for IVF treatment within 273 days. The 90% target continues to be met since it was first measured in March 2015.'
https://www.isdscotland.org/Health-Topics/Waiting-Times/Publications/2018-08-28/2018-08-28-IVF-Summary.pdf?35583132506
Meanwhile in Tory-run NHS England, only 12% of boards offer three full cycles in line with official guidance. 61% offer only one cycle of treatment and 4% offer none at all. Private treatment costs between £1 343 and £5 788 per cycle.
http://www.independent.co.uk/news/health/ivf-nhs-treatment-fertility-lists-wait-patients-lottery-budget-cuts-a8028116.html
Reducing associated mental health complications
Also, failing to treat infertility can result in problems and further costs for the NHS in other areas. A Danish study of 98 737 women, between 1973 and 2003, showed that women who were unable to have children were 47% more likely to be hospitalised for schizophrenia and had a significantly higher risk of subsequent drug and alcohol abuse.
https://www.newscientist.com/article/dn22020-infertility-may-increase-risk-of-mental-disorders/
A warning for Scotland's 100% IVF post-Brexit: How moneygrubbing Tory IVF policies are creating massive distress now in England
How IVF became a licence to print money.
As we tumble toward a hard Brexit and trade deals with the USA allowing the private sector into the heart of the NHS, we can see how things will work out in the already privatised IVF service in England and contrast it with the state-controlled and regulated version, in Scotland. See this from the Guardian:
'Private fertility clinics routinely try to sell desperate patients add-ons that almost certainly don't help – why isn't more done to monitor the industry? Around three-quarters of all IVF cycles fail. And results vary with age. Figures from the Human Fertilisation and Embryology Authority (HFEA) published in March state the average live birth-rate for each fresh embryo transferred for women of all ages is 21%; for those aged under 35, it is 29% – the highest it has ever been. For older women, the picture is bleaker: 10% for women aged 40-42, for example. IVF is expensive. And what makes it worse, says Hugh Risebrow, the report's author, is the lack of pricing transparency. "The headline prices quoted may be, say, £3,500, but you end up with a bill of £7,000," he says. "This is because there are things not included that you need – and then things that are offered but are not evidence-based."'
https://www.theguardian.com/lifeandstyle/2018/jun/18/how-ivf-became-a-licence-to-print-money
Creating opportunities for the private sector
In Tory-run NHS England, only 12% of boards offer three full cycles in line with official guidance. 61% offer only one cycle of treatment and 4% offer none at all. Private treatment costs between £1 343 and £5 788 per cycle.
Why UK politicians would like more privatisation in the NHS
There are 64 Tory and Labour (New) MPs with 'links' to private health care. Why would we trust them to protect the NHS? See this:
https://defendournhsyork.wordpress.com/2017/02/14/selling-off-nhs-for-profit-full-list-of-mps-with-links-to-private-healthcare-firms/
Previous Article Looking south only, NHS England is 'left behind Germany, France, Spain, Italy and Japan' but Scotland is both 'just behind' and well ahead of them!
Next Article Don't hate English people. It's just wrong. Hate Normans instead
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text:
\section{Introduction}
This work is built on the basis of factor analysis. The factor analysis is a widely used statistical tool in many fields, such as psychology, educational testing, social behavior and biomedical sciences \cite{henderson1975best,thompson2007factor}. The factor analysis is popular since it provides a
convenient modeling tool for multiple observed indicators within a subject \cite{muthen2013new}. In this paper, we propose a latent variable model with a
single factor for multivariate data with measure-specific weights that vary across
indicators and across subjects. A pseudo likelihood approach is developed for our
model. \\

Over the years, Centers for Medicare and Medicaid Services (CMS) Hospital Compare website publishes hospitals performance scores which are called hospital indicators in this paper.
CMS hopes that these indicators will help people choose their hospitals. In 2016, CMS started to report the star ratings of more than four thousand hospitals across the whole country \cite{venkateshoverall}. The goal of the CMS overall hospital quality star rating is to estimate one summary score using a total of fifty-seven hospitals indicators collected from the hospital compare database. The fifty-seven indicators are divided into seven different groups according the quality aspect they represent. Each indicator within a hospital has its sample weight representing the volume of patients that contribute to that indicator. A
group-specific factor score is derived for the indicators within the group. The goal of
this paper is to estimate the factor model within a group incorporating sample weights
for each indicator within each hospital.\\

The factor is an unobserved latent variable that represents the underlying
hospital performance. In addition to the presence of measure-specific weights that
vary at both indicator and hospital level, there is a missing data issue as only a few
hospitals report the complete set of all the hospitals indicators. Traditional factor
analysis using correlation matrix approach is not possible to deal with such situation
and therefore we propose a pseudo likelihood method to estimate such model.\\

Existing literature has dealt with subject-specific weights. \cite{shwartz2008estimating} studied the volume related weights which are subject-specific via the hierarchical logistic models.
\cite{veiga2014use} applied subject-specific weights in multivariate multilevel models to the longitudinal data. \cite{landrum2000analytic} gave an approach that based on the  likelihood to generalize the overall score. And \cite{Agostinelli2013} proposed a weighted latent likelihood method based on subject-specific weights. To the best of our knowledge, there are no studies for the latent variable model with measure-specific weights, as well as its asymptotic behaviors. Therefore, we
fill the gap by proposing a version of weighted pseudo likelihood that fits for the measure-specific weights through two algorithms: Expectation-Maximization (EM) method and the
marginalization of the pseudo likelihood to get the parameter estimates. We apply this model to the CMS hospital compare dataset. \\

The sum of weights for each indicator across the hospitals is set to be the sample size of that indicator so that a hospital with a smaller volume for that indicator has a smaller sample weight for that indicator comparing to a hospital with a larger volume for that indicator. However, we show in Section 3 that the sum of such intuitive sample weights across hospitals for the indicator need to be bounded below the sample size in order for the estimates of the variance for the latent variable to be
consistent. We impose such bound by multiplying each sample weight by 0.99 which in
practice remains the same interpretation.\\

The rest of the paper is organized as follows. In Section 2 we present our model and specify the pseudo likelihood. The statistical properties of the latent variable
model are given in Section 3. In Section 4 we describe the two algorithms including
the EM approach and the marginal likelihood approach. The bound of the weights
is given in Section 3. In Section 5 we conduct the numerical studies. In Section
6 we analyze three datasets from US hospital compare and in Section 7 we conclude
with a discussion.

\section{Model Specification and Pseudo Likelihood}

We start the model with the following set-up:

Suppose there are a total of $m$ indicators with $H$ hospitals (subjects) in each indicator to be evaluated. Let $Y_{jh}$ denote the $j$th indicator in the hospital $h$ with $j=1, \dots, m$ and $h=1, \dots, H$. Let $w_{jh}$ denote the measure-specific weight of hospital $h$ and indicator $j$. For each $h$, we fit a single confirmatory factor model as:
\begin{align} \nonumber
& \quad \quad \quad \quad \quad Y_{jh}|\alpha_h \sim N(\mu_j+\gamma_j\alpha_h, \sigma_j^2) \\ \nonumber
&\text{ with measure-specific weight } w_{jh}, \quad j=1, \dots, m,
\end{align}

where $\alpha_h \sim N(0,1)$ is the underlying factor or latent variable representing hospital $h$'s performance based on its all $m$ indicators. The higher the value of
$Y_{jh}$, the better the performance of hospital h in indicator m. The $\mu_j, \gamma_j$ and $\sigma_j^2$ are unknown parameters that we need to estimate.

\subsection{The Pseudo Joint Likelihood of Data and Latent Variable}
Given $\alpha_h$, $Y_{1h}, \dots, Y_{mh}$ are conditionally independent. We have the joint density for the latent variable and $Y_{1h},\dots, Y_{mh}$ satisfies
\begin{align}\nonumber
P(Y_{1h},\dots, Y_{mh},\alpha_h)&=P(\alpha_h)P(Y_{1h},\dots, Y_{mh}|\alpha_h)
\\ \nonumber&=P(\alpha_h)\prod_{j=1}^mP(Y_{jh}|\alpha_h).
\end{align}
We define the joint pseudo likelihood for hospital $h$ with sample weights as
\begin{align}\label{yi}
P^*(Y_{1h},\dots, Y_{mh},\alpha_h)&=P(\alpha_h)\prod_{j=1}^m[P(Y_{jh}|\alpha_h)]^{w_{jh}},
\end{align}
where $w_{jh}$ bounded differentiable non-negative (the sample weight) function \cite{Agostinelli2013} independent to $Y_{jh}$.\\

The logarithm of the term within the product is given as :
\begin{align}\nonumber
\log P^*(Y_{jh}|\alpha_h)&=w_{jh}\log P(Y_{jh}|\alpha_h) \\ \nonumber
&=-w_{jh} \log \sigma_j -\frac{w_{jh}}{2\sigma_j^2}(Y_{jh}-\mu_j-\gamma_j\alpha_h)^2-\frac{w_{jh}}{2}\log 2\pi.
\end{align}
Thus, the conditional log-density of expression \eqref{yi} is given as:
\begin{align}\nonumber
& \quad \quad \log P^*(Y_{1h},\dots, Y_{mh}|\alpha_h)=\sum \limits_{j=1}^mw_{jh}\log P(Y_{jh}|\alpha_h)\\
\label{czhang}&=-\sum \limits_{j=1}^m w_{jh} \log \sigma_j -\sum \limits_{j=1}^m \frac{w_{jh}}{2\sigma_j^2}(Y_{jh}-\mu_j-\gamma_j\alpha_h)^2-\frac{1}{2}\sum \limits_{j=1}^m w_{jh} \log 2\pi \\ \nonumber
&= - \big[\sum \limits_{j=1}^m \frac{w_{jh}\gamma_j^2}{2\sigma_j^2}\alpha_h^2-\sum \limits_{j=1}^m\frac{2w_{jh}(Y_{jh}-\mu_j)\gamma_j}{2\sigma_j^2}\alpha_h+ \sum \limits_{j=1}^m \frac{w_{jh}(Y_{jh}-\mu_j)^2}{2\sigma_j^2}\big]\\ \nonumber
&\quad -\sum \limits_{j=1}^m w_{jh} \log \sigma_j -\frac{1}{2}\sum \limits_{j=1}^m w_{jh} \log 2\pi.
\end{align}

Therefore, the negative logarithm of the joint density of all $m$ indicators for hospital $h$ is
\begin{align}\nonumber
 &-\log P^*(Y_{1h},\dots, Y_{mh},\alpha_h)=-\log P^*(Y_{1h},\dots, Y_{mh}|\alpha_h)-\log P(\alpha_h)\\ \nonumber
&=(\frac{1}{2}+\sum \limits_{j=1}^m \frac{w_{jh}\gamma_j^2}{2\sigma_j^2})\alpha_h^2-\sum \limits_{j=1}^m\frac{2w_{jh}(Y_{jh}-\mu_j)\gamma_j}{2\sigma_j^2}\alpha_h+\sum \limits_{j=1}^m w_{jh} \log \sigma_j \\ \nonumber
&\quad +\sum \limits_{j=1}^m \frac{w_{jh}(Y_{jh}-\mu_j)^2}{2\sigma_j^2}+\frac{1}{2}(\sum \limits_{j=1}^m w_{jh}+1) \log 2\pi\\ \nonumber
&=(\frac{1}{2}+\sum \limits_{j=1}^m \frac{w_{jh}\gamma_j^2}{2\sigma_j^2}) \left \{\alpha_h-\frac{\sum \limits_{j=1}^m\frac{w_{jh}(Y_{jh}-\mu_j)\gamma_j}{\sigma_j^2}}{1+\sum \limits_{j=1}^m \frac{w_{jh}\gamma_j^2}{\sigma_j^2}}\right \}^2+\frac{1}{2}(\sum \limits_{j=1}^m w_{jh}+1) \log 2\pi\\
& \label{cdu} \quad \quad \quad +\sum \limits_{j=1}^m w_{jh} \log \sigma_j+\sum \limits_{j=1}^m \frac{w_{jh}(Y_{jh}-\mu_j)^2}{2\sigma_j^2}-\frac{(\sum \limits_{j=1}^m\frac{w_{jh}(Y_{jh}-\mu_j)\gamma_j}{\sigma_j^2})^2}{2+2\sum \limits_{j=1}^m \frac{w_{jh}\gamma_j^2}{\sigma_j^2}}.
\end{align}

And the log joint density of all $m$ indicators for all $H$ hospitals is the summation of $\log P^*(Y_{1h},\dots, Y_{mh},\alpha_h)$ through $1$ to $H$.\\

Note that we can also bring missing values of $Y_{jh}s$ into \eqref{cdu} by setting the corresponding $w_{jh}=0$, therefore, the joint pseudo log-density of latent variable model is also compatible with missing data in $Y$.

\subsection{The Marginal Pseudo Likelihood}
Note that part of \eqref{cdu} can be rewritten as the log-density of a normal distribution:
\begin{align}\nonumber
\eqref{cdu}&=(\frac{1}{2}+\sum \limits_{j=1}^m \frac{w_{jh}\gamma_j^2}{2\sigma_j^2}) \left \{\alpha_h-\frac{\sum \limits_{j=1}^m\frac{w_{jh}(Y_{jh}-\mu_j)\gamma_j}{\sigma_j^2}}{1+\sum \limits_{j=1}^m \frac{w_{jh}\gamma_j^2}{\sigma_j^2}}\right \}^2+\frac{1}{2}\log 2\pi+\frac{1}{2} \log ({1}+\sum \limits_{j=1}^m \frac{w_{jh}\gamma_j^2}{\sigma_j^2})^{-1}\\ \nonumber
& -\frac{1}{2} \log ({1}+\sum \limits_{j=1}^m \frac{w_{jh}\gamma_j^2}{\sigma_j^2})^{-1}+\sum \limits_{j=1}^m w_{jh} \log \sigma_j+\sum \limits_{j=1}^m \frac{w_{jh}(Y_{jh}-\mu_j)^2}{2\sigma_j^2}-\frac{(\sum \limits_{j=1}^m\frac{w_{jh}(Y_{jh}-\mu_j)\gamma_j}{\sigma_j^2})^2}{2+2\sum \limits_{j=1}^m \frac{w_{jh}\gamma_j^2}{\sigma_j^2}}\\ \label{liao}
&+\frac{1}{2}\sum \limits_{j=1}^m w_{jh} \log 2\pi.
\end{align}

The first line of \eqref{liao} is exactly the density function for a normal distribution after logarithm. Denote $Y_{.h}=[Y_{1h},\dots,Y_{mh}]'$ as the indicator vector for hospital $h$, by integration with respect to $\alpha_h$, we have the marginal pseudo log-likelihood (denoted by $\mathcal{L}^*$) of all the parameters for hospital $h$ satisfies
\begin{align}\nonumber
 &-2\log \mathcal{L}^*(\mu_1 \dots \mu_m, \gamma_1 \dots \gamma_m, \sigma_1 \dots \sigma_m|Y_{.h}) =\sum \limits_{j=1}^m w_{jh} \log 2\pi \\ \label{lin}
 & +\log ({1}+\sum \limits_{j=1}^m \frac{w_{jh}\gamma_j^2}{\sigma_j^2})+\sum \limits_{j=1}^m w_{jh} \log \sigma_j^2+\sum \limits_{j=1}^m \frac{w_{jh}(Y_{jh}-\mu_j)^2}{\sigma_j^2}-\frac{(\sum \limits_{j=1}^m\frac{w_{jh}(Y_{jh}-\mu_j)\gamma_j}{\sigma_j^2})^2}{1+\sum \limits_{j=1}^m \frac{w_{jh}\gamma_j^2}{\sigma_j^2}}.
\end{align}

\section{ Statistical Properties of the Model}
\subsection{Main Theorems}
In this subsection, we describe the asymptotic behaviors of the latent variable model under uniform weight case (all $w_{jh}=1$) and the varying weights case.\\

We start with the simple uniform weight case when all the weights equal to one. Without missing value, the negative marginal logarithm likelihood (denoted by $\mathcal{L}$) for all the parameters for hospital $h$ satisfies
\begin{align}\nonumber
 &-2\log \mathcal{L}(\mu_1 \dots \mu_m, \gamma_1 \dots \gamma_m, \sigma_1 \dots \sigma_m|Y_{.h})=m\log 2\pi \\ \label{qqq}
 & +\log ({1}+\sum \limits_{j=1}^m \frac{\gamma_j^2}{\sigma_j^2})+\sum \limits_{j=1}^m  \log
 \sigma_j^2+\sum \limits_{j=1}^m \frac{(Y_{jh}-\mu_j)^2}{\sigma_j^2}-\frac{(\sum \limits_{j=1}^m\frac{(Y_{jh}-\mu_j)\gamma_j}{\sigma_j^2})^2}{1+\sum \limits_{j=1}^m \frac{\gamma_j^2}{\sigma_j^2}},
\end{align}
since  all $w_{jh}=1$. The pseudo log-likelihood becomes log-likelihood.\\

The asymptotic behaviors of the latent variable model are followed by the result of a toy example:

\begin{ex}
 Let $m=3$, $Z_{jh} \sim N(\mu_j, \sigma_j^2+\gamma_j^2),  j=1,\dots,3, h=1,\dots,H.$ And the covariance between $Z_{i.}=[Z_{i1},\dots,Z_{iH}]$ and $Z_{j.}=[Z_{j1},\dots,Z_{jH}]$ is $\gamma_i\gamma_j, (1 \le i, j \le 3).$ Without missing values, we have twice the negative log-likelihood of $Z$ is the same as \eqref{qqq} when $m=3$.
\end{ex}

Example 1 shows in the latent variable model with uniform weight, When there are no missing values, the latent variable model is the same as the confirmatory factor analysis with single factor, since they both have the same log-likelihood. However, compared to obtaining parameter estimates through the
calculation of the inverse variance-covariance matrix in the confirmatory factor
model with multivariate normal distribution, the estimation through the
likelihood of latent variable model formulation for the factor model is easier to obtain with $m>3$ cases. Therefore, we can have the following results.

\begin{thm}
 When there are no missing values, as $H \to \infty$, we have the expected negative marginal logarithm likelihood ($\text{ENMLL}_H$) satisfies
\begin{align}\nonumber
\text{ENMLL}_H & \xrightarrow{a.s.} \frac{1}{2} \log \big[ (1+\sum \limits _{j=1}^m \frac{\gamma_j^2}{\sigma_j^2})\prod_{j=1}^m \sigma_j^2 \big]+\frac{m}{2}(\log 2\pi+1),
\end{align}
and $\sqrt{H}(\frac{1}{H}\sum \limits_{h=1}^H \text{NMLL}_h-\text{ENMLL}_H)$ has a normal distribution with mean equals to zero, finite variance.
\end{thm}

Theorem 1 gives the asymptotic behavior of the marginal likelihood of latent variable model with uniform weights.

\begin{thm}
Let the weight matrix $W=[w_{jh}]_{m\times H}$ where $w_{jh}=w_j$, are positive constants. Then by definition of NMLL, we have the expected pseudo marginal log-likelihood (ENWMLL) for hospital $h$ satisfies
\begin{align}\nonumber
    2\text{ENWMLL}_H &\xrightarrow{a.s.} m+\log \big[(1+\sum \limits _{j=1}^m\frac{w_j\gamma_j^2}{\sigma_j^2})\prod_{j=1}^m\frac{\sigma_j^2}{w_j} \big]+\sum\limits_{j=1}^m  (w_j-1)\log  \sigma_j^2\\ \nonumber
    &+\sum \limits_{j=1}^m  \log w_j+\sum \limits_{j=1}^m (w_{j}-1) \log 2\pi,
\end{align}
without missing, as $H \to \infty$, and the central limit theorem also holds for NWMLL.
\end{thm}

Theorem 2 gives the asymptotic behavior of marginal likelihood of the latent variable model with the weights those are specifically assigned.

\subsection{Variance Bounded From Zero}
This subsection mainly address the issue that in the latent variable model, certain $\sigma_j^2$ may become zero. In that case, the validity of the latent variable
model can be endangered. However, the $\sigma^2_j$ can be bounded away from zero by
adjusting the weights in a simple way presented in this section.
\\

Easy to observe that each $\sigma_j$ should be bounded away from zero in order to make the negative marginal logarithm (pseudo) likelihood valid. In the mean time, the computational speed for estimating the parameters will slow down heavily at the area where certain $\sigma^2$ is tiny since there are no closed form solutions for \eqref{lin}. In the following part, we discuss the approaches that prevent the estimated standard error from going to zero when we incorporate with varying weights.

\subsubsection{Uniform Weight}
We will focus on three indicators ($m=3$) since if the number of indicators exceeds three, we can still pick three indicators to study.

For $j=1,2,3$, let $Y_{jh} \sim N(\mu_j, \sigma_j^2+\gamma_j^2)$, where $h=1,\dots, H$, and the covariance between $Y_{i.}$ and $Y_{j.}$ is $\gamma_i\gamma_j>0, (1 \le i, j \le 3)$. Assume $Y_{1.}, Y_{2.}, Y_{3.}$ have the same variance, moreover, assume $$\text{Corr}(2,3)=\gamma_2\gamma_3/\sqrt{(\gamma_2^2+\sigma_2^2)(\gamma_3^2+\sigma_3^2)}$$ is the smallest correlation. Then we have $\sigma_1^2$ be the smallest parameter among all $\sigma$s since $\gamma_1$ is the largest one among all three $\gamma$s. And, when all the weights equal to one in latent variable model, two extreme examples may cause $\sigma^2_1$ to be exactly zero:

\begin{ex} Assume $Y_{1.}$ and $Y_{2.}$ are identical, then we have $\sigma_1=\sigma_2=0$.
\end{ex}

\textbf{Proof:} Easy to verify that
$$\text{Corr}(1,2)=\frac{\gamma_1\gamma_2}{\sqrt{\sigma_1^2+\gamma_1^2}\sqrt{\sigma_2^2+\gamma_2^2}}=\frac{\gamma_1^2}{\gamma_1^2+\sigma_1^2}=1.$$

Then we have $\sigma_1=\sigma_2=0$.\\

\begin{ex} Assume $Y_{1.}, Y_{2.}$ and $Y_{3.}$ satisfies Corr(1,2)$\times$Corr(1,3) $>$ Corr(2,3), then we have $\sigma_1^2 = 0$.
\end{ex}

\textbf{Proof:} By the result in Example 1, we have

$$\text{Corr}(1,2)\times \text{Corr}(1,3)=\frac{\gamma_1^2}{\sigma_1^2+\gamma_1^2}\text{Corr}(2,3)\le \text{Corr}(2,3),$$

thus the latent variable model outputs $\sigma_1=0$ as its minimized occupation.\\

When Example 2 or Example 3 happens, the likelihood estimates of the latent variable model will be at the boundary, thus the posterior variance of $\alpha_h$ may become zero. In order to prevent it, we need proper weights to get the posterior variance bounded from zero.\\

\subsubsection{Varying Weights}\label{ccc}
Followed by Theorem 2, let  $W=[w_{jh}]_{m\times H}$ where $w_{jh}=w_j$, are positive constants. We have,
\begin{align}\nonumber
    2\text{ENWMLL}_H &\xrightarrow{a.s.} m+\log \big[(1+\sum \limits _{j=1}^m\frac{w_j\gamma_j^2}{\sigma_j^2})\prod_{j=1}^m\frac{\sigma_j^2}{w_j} \big]+\sum\limits_{j=1}^m  (w_j-1)\log  \sigma_j^2\\ \nonumber
    &+\sum \limits_{j=1}^m  \log w_j+\sum \limits_{j=1}^m w_{j} \log 2\pi,
\end{align}

as $H \to \infty$. We can see that if we set the sum of weight equals to the sample size ($\bar{w}_{j.}=w_j=1$), we have $\text{ENWMLL}_H = \text{ENLL}_H$
since all the weights are equal to one. Both Example 2 and Example 3 can cause $\sigma$ being zero.\\

Assuming there are not identical indicators among $Y_{1.}, \dots , Y_{m.}$, there is at most one $\sigma$ can be zero. Without loss of generality, assume $\sigma_1^2$ is the smallest among all $\sigma^2$s, then we have
\begin{align}\nonumber
    |\log \big[(1+\sum \limits _{j=1}^m\frac{w_j\gamma_j^2}{\sigma_j^2})\prod_{j=1}^m\frac{\sigma_j^2}{w_j} \big]| &= |\log\big[( \gamma_1^2 \prod_{j=2}^m\frac{\sigma_j^2}{w_j})+\frac{\sigma_1^2}{w_1}(1+\sum \limits _{j=2}^m\frac{w_j\gamma_j^2}{\sigma_j^2})\prod_{j=2}^m\frac{\sigma_j^2}{w_j} \big]| \\ \nonumber
    &< |\log( \gamma_1^2 \prod_{j=2}^m\frac{\sigma_j^2}{w_j})|< \infty.
\end{align}

Thus we have for $M=\sum \limits_{j=1}^m  \log w_j+\sum \limits_{j=1}^m w_j \log 2\pi$ which is a constant,
\begin{align}\nonumber
    2\text{ENWMLL}_H-M& \xrightarrow{a.s.} m+\sum\limits_{j=2}^m  (w_j-1)\log  \sigma_j^2+(w_1-1)\log\sigma_1^2\\ \label{sss}
    &+\log\big[( \gamma_1^2 \prod_{j=2}^m\frac{\sigma_j^2}{w_j})+\frac{\sigma_1^2}{w_1}(1+\sum \limits _{j=2}^m\frac{w_j\gamma_j^2}{\sigma_j^2})\prod_{j=2}^m\frac{\sigma_j^2}{w_j} \big] \\ \nonumber
    &\xrightarrow{a.s.} m+\sum\limits_{j=2}^m  (w_j-1)\log  \sigma_j^2+\log( \gamma_1^2 \prod_{j=2}^m\frac{\sigma_j^2}{w_j})+(w_1-1)\log\sigma_1^2.
\end{align}

If we have $w_1-1<0$, it will penalize the expected marginal weighted likelihood from $\sigma_1$ being zero since both $(w_1-1)\log\sigma_1^2 \to \infty$ will hold, and the rest terms are bounded.\\

Furthermore, if we let
\begin{align}\nonumber
   S_3= ( \gamma_1^2 \prod_{j=2}^m\frac{\sigma_j^2}{w_j})/\big[\frac{1}{w_1}(1+\sum \limits _{j=2}^m\frac{w_j\gamma_j^2}{\sigma_j^2})\prod_{j=2}^m\frac{\sigma_j^2}{w_j}\big]>0,
\end{align}

by the Dominated Convergence Theorem, taking derivative to \eqref{sss} with respect to $\sigma_1^2$ yields
\begin{align}\nonumber
   \frac{\partial{\text{ENWMLL}_H}}{\partial{\sigma_1^2}}\xrightarrow{a.s.}\frac{1}{2(\sigma_1^2+S_3)}+\frac{w_1-1}{2\sigma_1^2}<0
\end{align}
at the neighborhood larger than zero for $\sigma_1^2$ when $w_1-1<0$. Which proves that $\sigma_1^2$ is bounded from zero.\\

Similarly, if we have $w_1>1$, then $\text{ENWMLL}_H \to -\infty$ as $\sigma_1$ approaching zero. And $\sigma_1=0$ will become an optimal estimate since the pseudo likelihood then goes to infinity. Therefore, we showed that if we have $w_j<1, j=1, \dots, m$, then we can ensure all the estimated standard errors are bounded from zero.\\

Both numerical study and data analysis will show that by setting the mean of weights smaller than one, under the case which $W=[w_{jh}]_{m\times H}$ denotes the weight matrix with arbitrary values, we still can have $\sigma$s bounded from zero property.\\

\section{Estimation}
Two approaches (EM \cite{dempster1977maximum} and the marginal) will be provided in this section.

\subsection{The EM Algorithm}

\subsubsection{E-Step:}

Since \eqref{cdu} has an exact form of normal distribution, we can get the posterior mean of $\alpha_h$ to be
$$x_h=E(\alpha_h|Y_{.h},\mu,\gamma,\sigma^2)=\frac{\sum \limits_{j=1}^m\frac{w_{jh}(Y_{jh}-\mu_j)\gamma_j}{\sigma_j^2}}{1+\sum \limits_{j=1}^m \frac{w_{jh}\gamma_j^2}{\sigma_j^2}},$$
and its posterior variance is
$$y_h=Var(\alpha_h|Y_{.h},\mu,\gamma,\sigma^2)=({1}+\sum \limits_{j=1}^m \frac{w_{jh}\gamma_j^2}{\sigma_j^2})^{-1},$$

by the definition of normal pdf. Along with these, we also need to calculate the posterior second moment in our EM approach:
\begin{align}\nonumber
z_h=E(\alpha_h^2|Y_{.h},\mu,\gamma,\sigma^2)&=E^2(\alpha_h|Y_{.h},\mu,\gamma,\sigma^2)+Var(\alpha_h|Y_{.h},\mu,\gamma,\sigma^2)\\ \nonumber
&=({1}+\sum \limits_{j=1}^m \frac{w_{jh}\gamma_j^2}{\sigma_j^2})^{-1}+\left \{\frac{\sum \limits_{j=1}^m\frac{w_{jh}(Y_{jh}-\mu_j)\gamma_j}{\sigma_j^2}}{1+\sum \limits_{j=1}^m \frac{w_{jh}\gamma_j^2}{\sigma_j^2}}\right \}^2
\end{align}
given $Y_{jh}, \mu_j, \gamma_j$ and $\sigma_j^2, \quad j = 1,\dots, m, h=1,\dots, H$ .\\

\subsubsection{M-Step:}

Note that directly minimizing \eqref{cdu} has computational difficulty, an alternative way is to maximize \eqref{czhang} under the condition where $\alpha_h=x_h, h=1,\dots,H$, and repeat the process in E-step.\\

We adopt an iterative method by firstly taking derivatives to \eqref{czhang} of all $H$ hospitals with respect to $\mu_j, \gamma_j$ and $\sigma_j,$ $j = 1,\dots, m$. \\

Therefore, our iterative method in M step is

\begin{align*}
\hat{\mu}_j&= \mathop{\mathrm{argmax}}\limits_{\mu_j} \sum \limits_{h=1}^Hw_{jh}\log P(Y_{jh}|x_h,z_h) \\
&=\sum \limits_{h=1}^H w_{jh}(Y_{jh}-\gamma_jx_h)/\sum \limits_{h=1}^H {w_{jh}}, \\
\hat{\gamma}_j&= \mathop{\mathrm{argmax}}\limits_{\gamma_j} \sum \limits_{h=1}^Hw_{jh}\log P(Y_{jh}|x_h,z_h) \\
&=\sum \limits_{h=1}^H {w_{jh}}x_h(Y_{jh}-\mu_j)/\sum \limits_{h=1}^H {w_{jh}z_h}
\end{align*}

\begin{align}\nonumber
&\text{and} \quad \hat{\sigma}_j^2 = \mathop{\mathrm{argmax}}\limits_{\sigma_j} \sum \limits_{h=1}^Hw_{jh}\log P(Y_{jh}|x_h,z_h) =\\ \nonumber
&\sum \limits_{h=1}^H w_{jh}\{(Y_{jh}-\mu_j)^2-2(Y_{jh}-\mu_j)\gamma_jx_h+\gamma_j^2z_h\}/\sum \limits_{h=1}^H w_{jh}, \quad j=1,\dots,m.
\end{align}

Once the Expectation-Maximization algorithm converges, we can update the latent variables $\alpha_h$ by
$\frac{\sum \limits_{j=1}^m\frac{w_{jh}(Y_{jh}-\hat{\mu}_j)\hat{\gamma}_j}{\hat{\sigma}_j^2}}{1+\sum \limits_{j=1}^m \frac{w_{jh}\hat{\gamma}_j^2}{\hat{\sigma}_j^2}}, h=1,\dots,H$. We repeat this procedure several times until every $\alpha_h$ becomes stable.\\

In the M-step, the solutions of $\hat{\mu}$s, $\hat{\gamma}$s and $\hat{\sigma}^2$s are consistent regardless the choices for any initial values since
\begin{align}\nonumber
    \frac{\partial^2 \eqref{cdu} }{\partial \mu_j^2} &=\sum \limits _{h=1}^H\frac{w_{jh}}{\sigma_j^2}>0, \\ \nonumber
    \frac{\partial^2 \eqref{cdu} }{\partial \gamma_j^2} &=\sum \limits _{h=1}^H\frac{w_{jh}\alpha_h^2}{\sigma_j^2}>0, \quad j=1,\dots, m.
\end{align}

In the same time, the values of $x_h$ in the E-step also maximize the joint pseudo log-likelihood with respect to $\alpha$, and
\begin{align}\nonumber
\frac{\partial^2 \eqref{cdu} }{\partial \alpha_h^2} &=1+\sum \limits _{h=1}^H\frac{w_{jh}\alpha_h^2}{\sigma_j^2}>0, \quad h=1,\dots,H,
\end{align}

suggests that \eqref{cdu} is convex for all the $\mu$s, $\gamma$s and $\alpha$s. \\

Moreover, if we assume $\sigma_j^2 \ge \epsilon>0$ holds for some positive number of $\epsilon$, then
\begin{align}\label{condition}
   {\sigma}_j^2<2\sum \limits _{h=1}^Hw_{jh}(Y_{jh}-\mu_j-\gamma_j\alpha_h)^2 /\sum \limits _{h=1}^Hw_{jh}
\end{align}

holds for every indicator. Since by the result in M-step, the estimated value of $\sigma_j^2$ is closed to $\sum \limits _{h=1}^Hw_{jh}(Y_{jh}-\mu_j-\gamma_j\alpha_h)^2 /\sum \limits _{h=1}^Hw_{jh}.$  \eqref{condition} implies

\begin{align}\nonumber
    \frac{1}{{\sigma}_j^2}\sum \limits _{h=1}^Hw_{jh}(Y_{jh}-\mu_j-\gamma_j\alpha_h)^2 > \frac{1}{2}\sum \limits _{h=1}^Hw_{jh}
\end{align}
holds. Thus we have at $(0,\epsilon)$,
\begin{align}\nonumber
\frac{\partial^2 \eqref{cdu} }{\partial (\sigma_j^2)^2} &=-\sum \limits _{h=1}^H \frac{w_{jh}}{\sigma_j^4}+2\sum \limits _{h=1}^H\frac{w_{jh}}{\sigma_j^6}(Y_{jh}-\mu_j-\gamma_j\alpha_h)^2 \\ \nonumber
&=\frac{1}{\sigma_j^4}\sum \limits _{h=1}^H w_{jh}\big[\frac{2(Y_{jh}-\mu_j-\gamma_j\alpha_h)^2}{\sigma_j^2}-1\big]>0.
\end{align}

 Therefore, the EM approach is the same as the coordinate descent method, we can find the minimizer, for $\sigma_j^2 \in (0,\epsilon)$. \cite{daubechies2004iterative}\\

In addition, if there are more than two indicators that contribute to the latent variable, then we have the local maximum should be the global maximum for \eqref{cdu}. \cite{yong2013beginner}

\subsection{The Marginal Pseudo Likelihood}
Given $W$ as constant, conditional on $Y$, we can get the parameter estimates directly by maximizing \eqref{lin}, i.e.
\begin{align}\nonumber
 &-2\log \mathcal{L}^*(\mu_1 \dots \mu_m, \gamma_1 \dots \gamma_m, \sigma_1 \dots \sigma_m|Y_{.h}) =\sum \limits_{j=1}^m w_{jh} \log 2\pi \\ \nonumber
 & +\log ({1}+\sum \limits_{j=1}^m \frac{w_{jh}\gamma_j^2}{\sigma_j^2})+\sum \limits_{j=1}^m w_{jh} \log \sigma_j^2+\sum \limits_{j=1}^m \frac{w_{jh}(Y_{jh}-\mu_j)^2}{\sigma_j^2}-\frac{(\sum \limits_{j=1}^m\frac{w_{jh}(Y_{jh}-\mu_j)\gamma_j}{\sigma_j^2})^2}{1+\sum \limits_{j=1}^m \frac{w_{jh}\gamma_j^2}{\sigma_j^2}},
\end{align}
then use the estimates to get all $\alpha_h$s by
$$\hat{\alpha}_h=E(\alpha_h|Y_{.h},\mu,\gamma,\sigma^2)=\frac{\sum \limits_{j=1}^m\frac{w_{jh}(Y_{jh}-\mu_j)\gamma_j}{\sigma_j^2}}{1+\sum \limits_{j=1}^m \frac{w_{jh}\gamma_j^2}{\sigma_j^2}},$$
and their posterior variances by
$$\hat{Var}(\alpha_h)=Var(\alpha_h|Y_{.h},\mu,\gamma,\sigma^2)=({1}+\sum \limits_{j=1}^m \frac{w_{jh}\gamma_j^2}{\sigma_j^2})^{-1},$$
where $1 \le h \le H$. We implement this algorithm through the NLMIXED procedure in SAS.

\section{Numerical Study}

In this section, we study two cases based on measure-specific weights. The weights in every indicator are set to be mean equal to one for both cases at beginning. For every $j$, the weights satisfy: $w_{jh}$ are exponentially shaped (the extreme case) and $w_{jh}$ are mounded shaped (the regular case) for $h=1,\dots,H.$ We show that under the regular case, the latent variable gets consistent results where those results have been incorporated with the weight information. Under the extreme case, we demonstrate that by setting the sum of the weights less than the sample size in each indicator, or, equivalently, mean of each weights to be less than one, the estimates of $\sigma$s can be always bounded from zero. We can then get both the variables estimates as well as the variances estimates for all the latent variables.
This is consistent with the results in section \eqref{ccc}. We also compare the performance of the latent variable model with different sample sizes through our algorithm in the extreme case.

\subsection{A Regular Case}\label{bbb}
Assume there are three indicators in the group, let the sample size $H=1000$, the inputs $Y_{i.}=[Y_{i1},\dots, Y_{iH}]$ $,i=1,2,3$ are generated through a multivariate normal distribution with mean zero and variance one, the correlation among each pair of two indicators is set as $0.5$.\\

We firstly generate $H$ random numbers with Gamma distribution $G(3/2, 1/2)$ to get $W_{1.}=[w_{11},\dots, w_{1H}]$, where $3/2$ is the shape parameter, and $1/2$ is the scale parameter. For $W_{2.}$ and $W_{3.}$, we separately generate $H$ random numbers with Gamma distribution of $G(3, 1/3)$. Then we divide the $W_{i.}, i=1,2,3$ by their sample means, respectively. Thus we get the sample mean of the weights of every indicator be the same, one.\\

We replicate the study based on the above setting for $100$ times, and focus on both average loading ($\gamma$) and average standard deviation ($\sigma$) for the three indicators in the result. For contrast, we also replicate the study for uniform weights $100$ times. Table 1 shows the results:

\begin{table}
\caption{Parameter Estimates With Varies Weights VS Uniform Weights}
\centering
\begin{tabular}{lllllllll}
	\hline \hline
	& & \multicolumn{2}{c}{Weight} & & & \multicolumn{2}{c}{No Weight}&\\
\hline
 $\gamma_1$ & 0.8520 & $\sigma_1$ & 0.4801 & $\gamma_1$ & 0.7084 & $\sigma_1$ & 0.7057
\\
 $\gamma_2$ & 0.6675 & $\sigma_2$ & 0.7391 & $\gamma_2$ & 0.7080 & $\sigma_2$ & 0.7088
\\
 $\gamma_3$ & 0.6700 & $\sigma_3$ & 0.7356 & $\gamma_3$ & 0.7107 & $\sigma_2$ & 0.7053
\\
\hline \hline
\end{tabular}
\end{table}

We can see with uniform weights, by design, all three indicators seem to have similar average loadings and average root mean squared errors. By Theorem 1, the result with uniform weights is the same as the confirmatory factor analysis with one factor. When there are weights which are moderately skewed, the average loading for the first indicator is larger than those of the second and the third. This is meaningful since although all three weights have the same mean, $W_{1.}$ is more skewed than $W_{2.}$ and $W_{3.}$. The maximum value in $W_{1.}$ is larger than those of $W_{2.}$ and $W_{3.}$. Thus the latent variable model can also output the result with different weights.

\subsection{An Extreme Case}
We use the same generating method for $Y_{jh}$ as section \eqref{bbb}, where $j=1,2,3, h=1,\dots,H.$
And we use Gamma distributions with the shape parameters are smaller than the scale parameters to generate more skewed weights.\\

 We firstly generate $H$ random numbers with Gamma distribution of $G(1/2, 2)$ to get $W_{1.}.$ For $W_{2.}$ and $W_{3.}$, we separately generate $H$ random numbers with Gamma distributions of $G(1, 2)$. Again, we divide the $W_{i.}, i=1,2,3$ by their sample means, respectively. We will find $\sigma_1 \to 0$ as section 5.2 suggests, thus we focus on the result of $\sigma_1$ here. To make comparison, we test the performances of latent variable model under $0.9*W, 0.8*W, 0.7*W$ and $0.99*W$.\\

\begin{figure}
  \centering
      \includegraphics[scale=0.6]{simulation1}
  \caption{Values of ARMSE1 among different setting of weights for increasing sample size}
\end{figure}

We compare the performance of all five weight matrices with sample size $H$ varies from $300$ to $5000$, we replicate the study $100$ times under each value of the sample size. Figure 1 shows the average values of $\sigma_1$.\\

Figure 1 shows that, with the original weights of means equal to one, the smallest average root mean squared error (ARMSE1) tends to be zero. However, the smaller the means of weights of the indicators, the larger the ARMSE1. As the sample size goes larger, the result of ARMSE1 tends to be stable. Therefore, if we have the mean of weights of the indicator to be smaller than one, we will prevent the $\sigma$s from being zero, then we can get the estimates of the posterior variances of the latent variables.

\section{Data Analysis}

We applied our latent variable model to the CMS's Overall Hospital Quality Rating database from the CMS 2019 public data across the subjected States. This database consists seven indicator groups: Mortality; Readmission; Safety of Care (Safety); Patient Experience; Effectiveness; Timeliness and Image Efficiency. In this section, we first analyze two indicator groups in the three outcome groups: Mortality and Readmission. We will then discuss the group of Safety.\\

In each indicator group, hospital had the reported indicator
scores. For each indicator, the scores from the available
hospitals are standardized with mean zero and variance one. There also exist measure-specific weights (CMS calls
them as the denominator weights) for the hospitals reflecting their volumes of
admissions. Similar as sample weights, the mean of the denominator weights in
every indicator is standardized as just below one. Note that those weights vary
across both indicator level and hospital level, therefore, the latent variable model
is appropriated for the data.

\subsection{Mortality (regular)}

For the group of mortality, seven indicators among $4573$ hospitals are presented:

1. MORT-30-AMI: Acute Myocardial Infarction (AMI) 30-Day Mortality Rate;

2. MORT-30-CABG: Coronary Artery Bypass Graft (CABG) 30-Day Mortality Rate;

3. MORT-30-COPD: Chronic Obstructive Pulmonary Disease (COPD) 30-Day Mortality Rate;

4. MORT-30-HF: Heart Failure (HF) 30-Day Mortality Rate;

5. MORT-30-PN: Pneumonia (PN) 30-Day Mortality Rate;

6. MORT-30-STK: Acute Ischemic Stroke (STK) 30-Day Mortality Rate;

7. PSI-4-SURG-COMP: Death Among Surgical Patients with Serious Treatable Complications.\\

We apply both the EM approach and the marginal approach to the mortality data, and calculate the maximum absolute value of predicted the latent variables, the difference is only 2.2208e-04. This suggests that the EM and the marginal approach are identical. Table 2 shows the parameter estimates of the latent variable model. We found that the loadings are balanced across indicators, all the estimated variances are bounded from zero. This result is the same as the result from CMS via the SAS quadrature method. \cite{venkateshoverall}

\begin{table}
\caption{Parameter Estimates in the Mortality Group with Un-adjusted Weights}
\centering
\begin{tabular}{llllll}
	\hline \hline
	$\mu$ & Un-adj  & $\gamma$ & Un-adj  & $\sigma$ & Un-adj \\
\hline
$\mu_1$ & 0.113 & $\gamma_1$ & 0.508 & $\sigma_1$ & 0.927
\\
$\mu_2$ & 0.131 & $\gamma_2$ & 0.333 & $\sigma_2$ & 0.894
\\
$\mu_3$	& 0.002 & $\gamma_3$ & 0.676 & $\sigma_3$ & 0.822
\\
$\mu_4$	& 0.107 & $\gamma_4$ & 0.713 & $\sigma_4$ & 0.682
\\
$\mu_5$ & -0.007 & $\gamma_5$ & 0.665 & $\sigma_5$ & 0.740
\\
$\mu_6$ & -0.049 & $\gamma_6$ & 0.484 & $\sigma_6$ & 0.975
\\
$\mu_7$	& -0.061 & $\gamma_7$ & 0.281 & $\sigma_7$ & 1.049
\\
\hline \hline
\end{tabular}
\end{table}

We also multiplied $0.99$ to all the weights in the mortality data, and we found there is no difference in the parameter estimates between $0.99*W$ and $W$. Moreover, the rooted mean square error of the latent variable $\alpha_h$ between the original weight and $0.99$ multiples the weight method is $0.0025$. Therefore, the $0.99$ times weights performs very closely to the method with un-adjusted weights in the mortality group.

\subsection{Readmission (Extreme)}
In the data of the readmission group, there are nine indicators among $4573$ hospitals:

1. EDAC-30-AMI: Excess Days in Acute Care (EDAC) after hospitalization for Acute Myocardial Infarction (AMI);

2. EDAC-30-HF: Excess Days in Acute Care (EDAC) after hospitalization for Heart Failure (HF);

3. EDAC-30-PN: Excess Days in Acute Care (EDAC) after hospitalization for Pneumonia (PN);

4. OP-32: Facility 7-Day Risk Standardized Hospital Visit Rate after Outpatient Colonoscopy;

5. READM-30-CABG: Coronary Artery Bypass Graft (CABG) 30-Day Readmission Rate;

6. READM-30-COPD: Chronic Obstructive Pulmonary Disease (COPD) 30-Day Readmission Rate;

7. READM-30-Hip-Knee: Hospital-Level 30-Day All-Cause Risk-Standardized Readmission Rate (RSRR) Following Elective Total Hip Arthroplasty (THA)/Total Knee Arthroplasty (TKA);

8. READM-30-HOSP-WIDE: HWR Hospital-Wide All-Cause Unplanned Readmission;

9. READM-30-STK: Stroke (STK) 30-Day Readmission Rate.\\

 After running the latent variable model, we found the estimated $\sigma_8$ for the 30 day hospital-wide readmission indicator is zero. This is because in the indicator of 30 day hospital-wide readmission, the numbers of admissions varies from $25$ to $23915$, which are way larger than the rest indicators in the group. After standardization, the distribution of denominator weights are skewed much more heavily in 30 day hospital-wide readmission than the rest indicators. Thus we apply the method of $0.99$ times the weights to the 30 day hospital-wide readmission indicator, in order to force its standard error larger than zero, as well as keep the parameter estimates as close as possible. The result of parameter estimates is shown in Table 3.

\begin{table}
\caption{Parameter Estimates in the Readmission Group via Un-adjusted and Adjusted Weights}
\centering
\begin{tabular}{lllllllll}
	\hline \hline
	$\mu$ & Un-adj & Adj  & $\gamma$ & Un-adj & Adj & $\sigma$ & Un-adj & Adj\\
\hline
$\mu_1$ & 0.031 & 0.031 & $\gamma_1$ & 0.316 & 0.318 & $\sigma_1$ & 0.710 & 0.710
\\
$\mu_2$ & -0.175 & -0.175 & $\gamma_2$ & 0.427 & 0.430 & $\sigma_2$ & 0.748 & 0.747
\\
$\mu_3$	& -0.243 & -0.243 & $\gamma_3$ & 0.410 & 0.413 & $\sigma_3$ & 0.775 & 0.774
\\
$\mu_4$	& 0.198 & 0.198 & $\gamma_4$ & -0.002 & -0.002 & $\sigma_4$ & 1.228 & 1.228
\\
$\mu_5$ & 0.106 & 0.106 & $\gamma_5$ & 0.303 & 0.304 & $\sigma_5$ & 1.024 & 1.024
\\
$\mu_6$ & -0.068 & -0.067 & $\gamma_6$ & 0.522 & 0.525 & $\sigma_6$ & 0.972 & 0.971
\\
$\mu_7$	& 0.194 & 0.194 & $\gamma_7$ & 0.388 & 0.390 & $\sigma_7$ & 1.043 & 1.042
\\
$\mu_8$	& 0.000 & 0.001 & $\gamma_8$ & 0.975 & 0.978 & $\sigma_8$ & 0.000 & 0.056
\\
$\mu_9$	& -0.051 & -0.051 & $\gamma_9$ & 0.499 & 0.502 & $\sigma_9$ & 0.983 & 0.982
\\
\hline \hline
\end{tabular}
\end{table}

We can see except for $\sigma_8$, all the parameters from the original weight and the adjusted weight methods have difference less than 0.001. Moreover, the 0.99 adjusted method can ensure all the variance in the readmission group bounded from zero. This is consistent with previous numerical and theoretical results.

\subsection{Safety (Extreme)}
Modeling the group of Safety has been challenging over the years by its unbalanced loadings and bi-peak parameter estimates through the latent variable modeling.\\

In the Safety of Care group, there are eight indicators among $4573$ hospitals:

1. COMP-HIP-KNEE: Hospital-Level Risk-Standardized Complication Rate (RSCR) Following Elective Primary Total Hip Arthroplasty (THA) and Total Knee Arthroplasty (TKA);

2. HAI-1: Central-Line Associated Bloodstream Infection (CLABSI);

3. HAI-2: Catheter-Associated Urinary Tract Infection (CAUTI);

4. HAI-3: Surgical Site Infection from colon surgery (SSI-colon);

5. HAI-4: Surgical Site Infection from abdominal hysterectomy (SSI-abdominal hysterectomy);

6. HAI-5: MRSA Bacteremia;

7. HAI-6: Clostridium Difficile (C. difficile);

8. PSI-90-Safety: Complication/Patient Safety for Selected Indicators (PSI).\\

Similar to the readmission group, the safety group is also an extreme case since both COMP-HIP-KNEE and PSI-90-Safety indicators have much larger variance in numbers of admissions than the rest six indicators. After running the latent variable model with the un-adjusted sample weights, we found the loadings for HAI-1 to HAI-6 are closed to zero. This will cause the bi-peak issue of the parameter estimates since
there are only two indicators loaded unto the latent factor, one or both of $\sigma_1$ or $\sigma_8$ can have zero variance. We provided two sets of methods to solve the issue in the safety group.

\subsubsection{Solution One}
One way to solve the problem is comparing the marginal pseudo log-likelihood between the two peaks. And we found when PSI-90-Safety ($\sigma_8$) turned out to be zero, the marginal log-likelihood is larger. Similarly as the readmission group, we apply the methods of $0.99$ times weights (denoted by Adj) to both COMP-HIP-KNEE and PSI-90-Safety indicators. Table 4 shows the parameter estimates between the original weights and $0.99$ times weights in the Safety group. And Table 5 shows the root mean squared errors of the latent variable estimates between $0.9$, $0.8$, $0.7$ multiply weights method and the method of multiplying $0.99$ to the un-adjusted weights (since under the method of un-adjusted weights, the latent variable variances cannot be calculated). \\

\begin{table}
\caption{Parameter Estimates in the Safety of Care Group via Different Weightings}
\centering
\begin{tabular}{lllllllll}
	\hline \hline
	$\mu$ & Un-adj & Adj  & $\gamma$ & Un-adj & Adj & $\sigma$ & Un-adj & Adj\\
\hline
$\mu_1$ & 0.287 & 0.287 & $\gamma_1$ & 0.188 & 0.189 & $\sigma_1$ & 1.039 & 1.039
\\
$\mu_2$ & -0.007 & -0.007 & $\gamma_2$ & 0.007 & 0.007 & $\sigma_2$ & 0.723 & 0.723
\\
$\mu_3$	& -0.010 & -0.010 & $\gamma_3$ & 0.008 & 0.008 & $\sigma_3$ & 0.757 & 0.757
\\
$\mu_4$	& -0.055 & -0.055 & $\gamma_4$ & 0.045 & 0.046 & $\sigma_4$ & 0.837 & 0.837
\\
$\mu_5$ & 0.010 & 0.010 & $\gamma_5$ & 0.060 & 0.060 & $\sigma_5$ & 0.867 & 0.867
\\
$\mu_6$ & 0.032 & 0.032 & $\gamma_6$ & 0.037 & 0.037 & $\sigma_6$ & 0.796 & 0.796
\\
$\mu_7$	& 0.003 & 0.003 & $\gamma_7$ & 0.025 & 0.025 & $\sigma_7$ & 0.622 & 0.622
\\
$\mu_8$	& 0.016 & 0.016 & $\gamma_8$ & 0.897 & 0.901 & $\sigma_8$ & 0.000 & 0.033
\\
\hline \hline
\end{tabular}
\end{table}

\begin{table}
\caption{Summary Statistics Under Different Weights}
\centering
\begin{tabular}{lllll}
	\hline \hline
	 & 0.99*W & 0.9*W & 0.8*W & 0.7*W \\
	 \\
\hline
 RMSE & & 0.0861 & 0.2123 & 0.3552 \\
 Mean & 0.0000 & 0.0000 & 0.0000 & 0.0000 \\
 Std  & 0.8354 & 0.7711 & 0.6754 & 0.5604 \\

\hline \hline
\end{tabular}
\end{table}

We found that the parameters estimate are very closed between the original weight and adjusted weight, and as the weight coefficient decreases from 0.99 to 0.7, the standard error of the predicted latent variables is getting farther away from the prior variance (one).

\subsubsection{Solution Two}
Another idea of modeling the Safety group is smoothing the weights thus prevent the dominance of either COMP-HIP-KNEE  and  PSI-90-Safety. Consider the HAIs are similar in both admission volumes and indicator scores, this method is seeking a balanced loading from the latent variable model.

\begin{table}
\caption{Parameter Estimates in the Safety Group via log transformation}
\centering
\begin{tabular}{llllll}
	\hline \hline
	$\mu$ & log-W  & $\gamma$ & log-W  & $\sigma$ & log-W \\
\hline
$\mu_1$ & 0.048 & $\gamma_1$ & 0.105 & $\sigma_1$ & 0.995
\\
$\mu_2$ & 0.036 & $\gamma_2$ & 0.528 & $\sigma_2$ & 0.738
\\
$\mu_3$	& 0.016 & $\gamma_3$ & 0.372 & $\sigma_3$ & 0.846
\\
$\mu_4$	& 0.000 & $\gamma_4$ & 0.211 & $\sigma_4$ & 0.919
\\
$\mu_5$ & 0.022 & $\gamma_5$ & 0.279 & $\sigma_5$ & 0.897
\\
$\mu_6$ & 0.033 & $\gamma_6$ & 0.359 & $\sigma_6$ & 0.867
\\
$\mu_7$	& 0.019 & $\gamma_7$ & 0.078 & $\sigma_7$ & 0.865
\\
$\mu_8$	& 0.008 & $\gamma_8$ & 0.134 & $\sigma_8$ & 0.938
\\
\hline \hline
\end{tabular}
\end{table}

One option is taking logarithm transformation to the admission volume for all indicators in the Safety group. This will help to reduce the variance in skewness of the un-adjusted weights in the Safety group. The result is in Table 6. We can see that the loadings are balanced and there are more than three indicators with relatively high loadings in the latent variable. Thus taking logarithm to the admission volume in the Safety group can help make the result balanced and thus identifiable.

\section{Summary and Discussion}
We present a latent variable model that incorporates measure-specific sample weights via pseudo-likelihood estimation in this work. The latent variable model can handle the missing value issue as well.
The estimates obtained through the algorithms have desirable asymptotic properties. We gave examples in both numerical study and real data analysis where the latent variable model can produce zero standard error estimates for certain indicators. We showed that if the sample weights means of those indicators are less than one, the estimates of variance components are bounded away from zero. We provided a log transformation method prior to the sample weights can help to obtain nonzero variance components as well.\\

For future work, we plan to investigate the pseudo likelihood and the estimating algorithm of the latent variable model under random weights with Gamma distribution, thus we can discover the threshold between the choice of the shape (scale) parameters and the bounded-away-from-zero estimates of variance components. Moreover, we would also like to investigate more behaviors of the latent variable model under varies distributions of weights, such as Poisson distribution, beta distribution, etc.

redpajama_set_name: RedPajamaArXiv


Longjing tea (simplified Chinese: 龙井茶) or Dragonwell is a famous variety of green tea from Hangzhou, Zhejiang Province, China where it is produced mostly by hand and has been renowned for its high quality, earning the China Famous Tea title. Longjing is divided into seven grades: Superior, Special, and then 1 down to 5.
Like most other Chinese green tea, Longjing tea leaves are heated early in processing (after picking) to stop the natural "fermentation" process, which is a part of creating black and oolong teas. In the world of tea, the term "fermentation" refers to the actions of natural enzymes, present in the leaves, on the juices and tissues of the leaf; this is not "fermentation" in the true sense of the term (as, for example, the action of yeast in producing beer). The actions of these enzymes is stopped by 'firing' (heating in pans) or by steaming the leaves before they completely dry out. As is the case with other green teas (and 'white teas'), Longjing tea leaves are therefore "unfermented." When steeped, the tea produces a yellow-green color, a gentle, pure aroma, and a rich flavor. The tea contains Vitamin C, amino acids, and has one of the highest concentration of catechins among teas, second only to white teas.
Tradition has it that to achieve the best taste from Longjing, water from the Dreaming of the Tiger Spring, a famous spring in Hangzhou, is to be used. Water is boiled then cooled to about 80 degrees Celsius before being used to brew the tea leaves. Tea experts at Xi Hu insist on using fine china or glasses to brew Longjing (minerals in porous earthenware such as Zisha may disrupt the taste of the tea).
Longjing tea was granted the status of Gong Cha, or Imperial tea, in Qing Dynasty by Chinese emperor Kangxi. According to the legend, Kangxi's grandson Qianlong visited West Lake during one of his famous holidays.
He went to the Hu Gong Temple under the Lion Peak Mountain (Shi Feng Shan) and was presented with a cup of Longjing tea. In front of the Hu Gong Temple were 18 tea bushes. Emperor Qianlong was so impressed by the Longjing tea produced here that he conferred these 18 tea bushes special imperial status. The trees are still living and the tea they produce is auctioned annually for more money per gram than gold.
There is another legend connecting Emperor Qianlong to Longjing Tea. It is said that while visiting the temple he was watching the ladies picking the tea. He was so enamored with their movements that he decided to try it himself. While picking tea he received a message that his mother, the Empress Dowager was ill and wished his immediate return to Beijing. He shoved the leaves he had picked into his sleeve and immediately left for Beijing. Upon his return he immediately went to visit his mother. She noticed the smell of the leaves coming from his sleeves and he immediately had it brewed for her. It is said that the shape of Longjing Tea was designed to mimic the appearance of the flattened leaves that the emperor brewed for his red headed step-children.
Is an example of the very standard convention of naming, the Xi Hu (West Lake) is a place where this particular Longjing is grown. This Longjing, also known as West Lake Longjing is a China Famous Tea, in fact the most famous one and is grown in the Zhejiang Province near Xi Hu lake. It is grown in a designated area of 168 square kilometres. Historically, Xi Hu longjing tea was divided into four sub-regions: Lion (Shi), Dragon (Long), Cloud (Yun) and Tiger (Hu). As the distinction between the sub-regions blurred over the years, this categorisation has now been adjusted to Shi Feng Longjing, Mei Jia Wu Longjing, with the remaining known collectively as Xi Hu longjing, although the Lion variety is still considered the "crème de la crème" by connoisseurs.
First growth picked on March 21 yields a more intense aroma and taste of sweetness than the better known Ming Qian Longjing. Supply solely depends on yearly climate therefore it can be expensive. [2009 Data:1200 RMB per 500gm for Bird's Tongue; 900 RMB for Ming Qian at Xi Hu Tea Village]. It is not necessarily more expensive since you will need less for the cup.
The premium early season first-picking known as Ming Qian or Pre-Qingming (or Before Ching Ming) Longjing tea, requires it to be produced from the very tender first spring shoots prior to the Qingming Festival on the 5th of April each year (approximately). The production cycle is very short; only ten days before Qingming every year. Tea picked after this period is of a lower grade called Yu Qian Longjing (雨前龍井), or "Dragon Well Before the Rain". In the 10-day span when Ming Qian Longjing is produced, the sprouts at the tip of the stalks are only hand-picked by experienced workers, and then specially processed; as a result, Pre-Qingming Longjing is more expensive than "standard" Longjing tea.
A type of Xi Hu Longjing. This tea is considered the highest quality in China. Fresh tasting, its fragrance is sharp and long lasting. Its leaves are yellowish green in colour. Some unscrupulous tea makers over pan-fry their tea to imitate its colour.
A type of Xi Hu Longjing. This tea is renowned for its attractive jade green colour. Early teas can fetch up to 6000 yuan per kilogram (2005 data) direct from the growers.
Not a true Longjing but looks like one and is commonly attributed, it is actually a Bai Pian. It comes from Anji in the Zhejiang Province. It was created in the early 80's and is a Green tea from a race of White tea trees and is hence very unusual, it is said to contain more amino acids than ordinary Green tea.
This tea comes from just outside the Xi Hu Longjing designated area, in Qian Tang. It is generally not as expensive as Xi Hu Longjing.


ARANSAS PASS, Texas - Feb. 7, 2019 - Rezul -- Cravey Real Estate Services, Inc. has been named the exclusive broker for Rockport Terminals located at 2520 Highway 35 N, Aransas Pass, TX 78336. Rockport Terminals is a 230-acre industrial site with access to rail, water and multiple highways. The facility features 1,200 feet of bulkhead with the capacity to fit six 30,000-barrel barges, over one mile of rail and 45,000 barrels of storage currently in place with room for more improvements.
"Rockport Terminals has so much potential," said Matthew Cravey, President of Cravey Real Estate Services, Inc., "with its proximity to the Port of Corpus Christi and all of the activity that the Port is producing."
The area is being redeveloped by Clayton Reaser, a San Antonio-based oil and gas entrepreneur. "This project has been more than five years in the making," said Reaser in a statement. "The opportunity to develop an economic vehicle of growth in an area often overlooked in favor of busier industrial centers has made this one of the more exciting developments we've spearheaded to date."
Source: Cravey Real Estate Services, Inc.


In addition to its media work, Peaceful Tomorrows offers educational resources to reflect upon the lessons of 9/11 and its impact on human rights and civil liberties. Peaceful Tomorrows recently collaborated with the social justice newspaper The Change Agent on a special issue featuring stories of 9/11 families of all faiths turning their grief into actions for peace.
Commemorate the 10th anniversary of 9/11 with a donation to September 11th Families for Peaceful Tomorrows—turning grief into action for peace. All donations received by October 31, 2011 will be matched dollar for dollar up to $20,000.00 by the Institute for Socioeconomic Studies Fund of Tides.


text:
Home » Fashion » Back to Basics for a Designer Whose Business Got Too Tight
Back to Basics for a Designer Whose Business Got Too Tight
06/11/2019 Comments Off on Back to Basics for a Designer Whose Business Got Too Tight
Scott Sternberg would prefer you not call him "quirky," as has happened many times before. It's "a word people like to use for me a lot," Mr. Sternberg said, "which I don't love."
So we will not repeat the offense, further than to note that, while Mr. Sternberg may not be quirky, there he was, in all his Peter Panish youthfulness, with his penchant for stripy shirts and Polaroid film, seated in a geodesic dome of his own design as vintage monitors played the funny little videos he creates, ruminating about utopia.
If Mr. Sternberg has a quirk — let's say for a minute that he does — it is for ginning up not just clothes (which he does) or videos (which he does) or even geodesic domes (which he has, for his label's first-ever pop-up, in the SoHo branch of the furniture seller Design Within Reach), but also an entire world in which all of these things come together, with its own rhythms, cadence, colors and meticulously designed aesthetic.
Mr. Sternberg, 44, is what is usually called a fashion designer, insofar as he is in the business of making and selling clothes. If you know his name, it is most likely that you remember his former label Band of Outsiders, which, from 2004 to 2015, had a profound impact on the way stylish American men dressed, squeezing them into slim shirts and skinny ties and Sperry Top-Siders: prep-school style in quotation marks, self-aware and self-effacing.
Mr. Sternberg thinks of himself less as a designer or a creative director than as a world builder. He and Band of Outsiders parted company, and his new brand, Entireworld (aha!), is less exclusive and less niche; a collection, essentially, of basics. It is clothing considered from the bottom up — one if its founding garments was a pair of underpants.
Now with a few more staples to round it out, Mr. Sternberg hopes for nothing less than to dress the entire world. A year into its life, the question is: Can h
The Entireworld world, a fantasyland in Disney colors (Disney World is an acknowledged influence), is a cheerful, welcoming one. Mr. Sternberg's Band of Outsider tailored jackets could once run $1,800 or more; Entireworld's T-shirts are $32.
The same sensibility — Mr. Sternberg's cinematic adorable — animates both. Many of the same friends who posed pro bono for guerrilla Polaroid ad campaigns are now in Instagram videos, singing, mugging or prat-falling: Jason Schwartzman, Kirsten Dunst, Andrew Garfield, Spike Jonze.
Over a series of interviews beginning in April 2018, at its inception, and continuing through Entireworld's first year, Mr. Sternberg explained his vision of this world and how it was built on the ashes of its predecessor. In so doing, he offered a view into the tectonic shifts in the fashion industry, the instability of the high-fashion, runway model he left behind and the traditional gatekeepers who perpetuate it.
Mr. Sternberg had been featured in every fashion magazine, won the industry's top awards, hosted Anna Wintour and Kanye West at his fashion shows. Still, he said at a public conversation at Design Within Reach with Deborah Needleman (the former T Magazine editor), "the fashion system can feel like jail."
Band of Outsiders did $15 million in wholesale business its height, but Mr. Sternberg, overstretched and under-resourced, who sought and received investment, couldn't keep up with the immense pressure to grow. He found out that his last hope for additional funding passed on the morning he opened the first Band of Outsiders shop in the United States, in SoHo. (The first-ever store had opened in Tokyo.)
He received a loan from CLCC, a Belgian fashion fund, for $2 million, but soon clashed with his new backers. Ultimately, Mr. Sternberg's company defaulted on the loan and Mr. Sternberg himself walked away from the Band. CLCC assumed ownership, and Band of Outsiders continues without him, with a new design team in place. Mr. Sternberg called their first collection "a disaster."
The challenges of designing and producing collection after collection of men's and women's wear are significant, and Band of Outsiders eventually grew to encompass several lines. The collections were well received but also vulnerable to the whims of trend and timeliness, and the vagaries of inconsistent production.
Even Band's signature slim cuts were in part a self-fulfilling prophecy: After an initial run of shirts were (correctly) snug, other orders arrived from the factories in similar style. "Everything just came in a little bit small," Mr. Sternberg said. "I'm not kidding."
Band's cuts — like those of Thom Browne, whose shrunken suits were a more conceptual foil to Mr. Sternberg's easier Americana — helped convince curious young men to embrace a snugger silhouette. But that fit made democratizing and expanding the brand nearly impossible. In any case, high-fashion esotericism had never been Mr. Sternberg's intention.
"That's just not me," he said. "That's not how I see my legacy."
If fashion is by definition exclusive, Entireworld is inclusive; fashion segments the world into groups of like-minded (and like-dressed) cohorts, but everyone wears underwear. In a video announcing the creation of Entireworld last year, Mr. Sternberg faced the camera and, as his face dissolved into a montage of stylish men and women (Mick Jagger, Sade, The Dude), acknowledged his past failings and vowed to take a different tack.
"I started thinking about what it would be like to create something more democratic this time, without compromising anything about the design or quality," he said. "About the stuff we live in every day."
But now, instead of staging fashion shows and courting the fashion press, instead of depending on the patronage of department stores and boutiques, Mr. Sternberg's Entireworld is sold primarily from its own website.
Mr. Sternberg runs the entire business out of a bland commercial office building in the Koreatown neighborhood of Los Angeles, from where he conjures a utopia only he can see. He is the man behind the curtain. Entireworld, and the thousand tiny windows onto it offered on Instagram posts and its cheeky, sunny website, is Oz.
Of course, the thing about Oz is that the man behind the curtain is pulling the levers, working to convince you to buy a $32 T-shirt from him, rather than a $10 three-pack from Hanes. He will tell you that his feels better, fits better and wears better; he will not be wrong.
But a basic is a basic, and to many, the difference is hard to parse. Mr. Sternberg is under pressure to make Entireworld so appealing that even its basics have ineffable magic that coaxes credits cards out of wallets.
Mr. Sternberg has to capture that market with less of the support he once enjoyed. "Have we captured the attention of traditional media outlets the way I expected to, the way I did at Band? Eh," he said, giving a grunt of not-really. He has skipped the fashion shows and presentations he once staged. As a result, Entireworld has made a smaller splash.
But those who love it — those who may be rising to replace the old gatekeepers — have vouched for it. "Basically have not taken this sweatshirt off since I got it last week," Leandra Medine, better known as the Man Repeller, posted to her Instagram not long after the label's debut.
At Design Within Reach, Mr. Sternberg had his first real-world test, hanging racks of Entireworld clothes among Alexander Girard dolls and Man Ray chess sets and Hans Wegner chairs. Pegged to New York's NYCxDesign programming, the Entireworld shop stayed open for 11 days, and customers came away with hot-pink sweatsuits and cotton sweaters.
"It was definitely something we had never done," said Kim Phillips, the head of public relations and events for Design Within Reach. "It was sticking my neck out there for sure."
Mr. Sternberg called the experiment gratifying. "An idea like this, I really believe more than ever has a place, especially when I see the sales and repeat sales," he said. "I think the real challenge is — I know the real challenge is — that the amount of capital it'll take to get where we need to getis formidable."
To start Entireworld, Mr. Sternberg raised $1.5 million from a group of private investors, and he has sought further investment to grow and scale it. Within its first year, he said, the company has sold more than three quarters of its initial inventory and reached more than $1 million in sales without paying for any advertising.
Numbers like these, while impressive, mean Entireworld is dwarfed by many of its competitors, limited by finite capital but not in an ideal position to attract more. "There's a real disconnect," Mr. Sternberg said, between his values and the goals of the investors he is hoping to attract.
"Investors want a return, and they want a return in a certain amount of time," he said. "I understand all these things, clearly, but they still don't change my view that sticking to my guns in terms of what this is and what it should be shouldn't bow too much to the pressure of what investors think it should be right now."
And while the signs have been good — Ms. Phillips said that she and Mr. Sternberg were talking about the pop-up traveling to other Design Within Reach locations, and sales continue to climb online — the economic reality of keeping a fashion business afloat is a chilly reality intruding into utopia. The world isn't Entireworld, yet. But Mr. Sternberg said there had been no question of not trying his hand in the rag trade again.
"Unfortunately not," he said with a laugh. "I am an entrepreneur by birth. I am at my most ebullient, excited, energetic when there's a big challenge and a huge bucket that needs these ideas to fill it out. It's painful. It's not easy. There's just this unexplainable, probably illogical urge to do this stuff."
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https://apnews.com/article/c2ac0f870ce2495e8303a90696a39522
Disgraced German reporter could face donation fraud charges
December 23, 2018 GMT
The journalist Claas Relotius poses during an award ceremony in Munich, Germany, March 27, 2014. The German journalist who was found to have made up information for numerous published articles may be charged for allegedly soliciting donations for Syrian orphans from his readers. (Ursula Dueren/dpa via AP)
BERLIN (AP) — A German journalist who was found to have made up information for numerous articles may now be charged with allegedly soliciting donations for Syrian orphans from his readers.
The German magazine Der Spiegel said Sunday that its award-winning former reporter, Claas Relotius, had asked readers by email from his private account for donations to be transferred to his personal bank account.
Der Spiegel said it's not clear how many people donated money, how much Relotius collected or what happened to the money. But the magazine said it will press charges and will work with prosecutors to find out the details.
Der Spiegel announced Wednesday that Relotius, 33, who worked first as a freelancer and later fulltime, had fabricated interviews and facts in at least 14 articles. It has since fired him.


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Q: Understanding openCV aruco marker detection/pose estimation in detail: subpixel accuracy I am currently studying openCV's 'aruco' module, especially focusing on the poseEstimation of ArUco markers and AprilTags.
Looking into the subpixel accuracy, I have encountered a strange behaviour, which is demonstrated by the code below:
If I do provide a 'perfect' calibration (e. g. cx/cy equals image center and distortion is set to zero) and a 'perfect' marker with known edge length, cv.detectMarkers will only yield the correct value, if the rotation is at 0/90/180 or 270 degrees. The subpixel routine yields an (almost) constant value for other orientations, yet at a "shifted" level. It is clear that at the specific angles of 0/90/180/270 degrees the pixel in the corner yields a sharp transition an can thus be detected with high precision. However I struggle to see where the underestimated length in all other cases originates from. Is this a bug or resulting from some trigonometry?
--> Look at the graphs generated by the script below: The error in the pose results from the error in the corner detection. Thus the detection accuracy will depend on the orientation of the code.
I also checked ArUco markers and different subpixeling methods. The "peaks" remain, although the angular behaviour in between will change.
I am pretty sure, that this is not due to the interpolation related with the rotation of the marker, since I can observe the same behaviour in real data as well (yet note that the "height" of the peaks seems to depend somehow on the interpolation method. You can test this by changing the flag in cv.warpAffine e. g. to cv.INTER_LINEAR).
My questions would then be:

*

*Are the peaks due to a bug or is this expected behaviour?

*If the latter, could you help me understand why?

*Is there a way to eliminate this orientation dependency of the accuracy (other than increasing the system's resolution such, that no subpixeling is required)?

EDIT: Note that the AprilTag functions have been added to openCV only recently, so you will need to upgrade to the newest version which is not yet available on some standard repositories. You can e. g. get an up-to-date version on conda-forge. /EDIT
# -*- coding: utf-8 -*-

import numpy as np
import cv2 as cv
import pylab as plt

""" generate an "ideal" calibration with zero distortion and perfect alignment
    of the main optical axis: """
cam_matrix = np.array([[1.0e+04, 0.00000000e+00, 1.22400000e+03],
       [0.00000000e+00, 1.0e+04, 1.02400000e+03],
       [0.00000000e+00, 0.00000000e+00, 1.00000000e+00]])
dist_coeffs = np.array([[0.],
       [0.],
       [0.],
       [0.],
       [0.],
       [0.],
       [0.],
       [0.],
       [0.],
       [0.],
       [0.],
       [0.],
       [0.],
       [0.]])

# define detection parameters
marker_length = 30.00 # some arbitrary value
marker_length_px = 700
marker_id = 3
dictionary = cv.aruco.getPredefinedDictionary(cv.aruco.DICT_APRILTAG_16H5)
para = cv.aruco.DetectorParameters_create()
para.cornerRefinementMethod = cv.aruco.CORNER_REFINE_APRILTAG
para.aprilTagDeglitch = 0
para.aprilTagMinWhiteBlackDiff = 30
para.aprilTagMaxLineFitMse = 20
para.aprilTagCriticalRad = 0.1745329201221466 *6
para.aprilTagMinClusterPixels = 5
para.maxErroneousBitsInBorderRate = 0.35
para.errorCorrectionRate = 1.0
para.minMarkerPerimeterRate = 0.05
para.maxMarkerPerimeterRate = 4
para.polygonalApproxAccuracyRate = 0.05
para.minCornerDistanceRate = 0.05

marker_length_list = []
tvec_z_list = []

# generate pictures (AprilTag ID: 3 centered in image will be rotated by fixed angular steps, e. g. 10 degrees)
degrees_list = np.linspace(0,350,36, dtype=np.int).tolist()
marker = cv.aruco.drawMarker(dictionary, marker_id, marker_length_px)
img = np.zeros((2048, 2448), np.uint8)+255
img[674:1374, 874:1574] = marker
cv.imshow("Original", img)
cv.imwrite("original.png", img)
rows, cols = img.shape

for entry in degrees_list:
    # rotate original picture
    rot_mat = cv.getRotationMatrix2D((((rows-1)/2),(cols-1)/2), entry, 1)
    rot_img = cv.warpAffine(img, rot_mat, (cols, rows), flags=cv.INTER_CUBIC) # interpolation changes the "peak amplitude" e.g. try cv.INTER_LINEAR instead
    # detect marker and get pose estimate
    corners, ids, rejected = cv.aruco.detectMarkers(rot_img,dictionary,parameters=para)
    my_index = ids.tolist().index([marker_id])
    fCorners = corners[my_index]
    fRvec,fTvec, _obj_points = cv.aruco.estimatePoseSingleMarkers(fCorners, marker_length, cam_matrix, dist_coeffs)
    # calculate the respective edge length for each side
    L1 = abs(np.sqrt(np.square(fCorners[0][0][0]-fCorners[0][1][0])+np.square(fCorners[0][0][1]-fCorners[0][1][1])))
    L2 = abs(np.sqrt(np.square(fCorners[0][0][0]-fCorners[0][3][0])+np.square(fCorners[0][0][1]-fCorners[0][3][1])))
    L3 = abs(np.sqrt(np.square(fCorners[0][2][0]-fCorners[0][1][0])+np.square(fCorners[0][2][1]-fCorners[0][1][1])))
    L4 = abs(np.sqrt(np.square(fCorners[0][2][0]-fCorners[0][3][0])+np.square(fCorners[0][2][1]-fCorners[0][3][1])))
    mean_length = (L1+L2+L3+L4)/4
    # append results
    marker_length_list. append(mean_length)
    tvec_z_list.append(fTvec[0][0][2])

plt.figure("TVEC Z")
plt.plot(degrees_list, tvec_z_list, "go--")
plt.xlabel("marker rotation angle (°)")
plt.ylabel("TVEC Z (units of length)")

plt.figure("Mean marker length (should be 700)")
plt.plot(degrees_list, marker_length_list, "bo--")
plt.xlabel("marker rotation angle (°)")
plt.ylabel("marker length (pixels)")

EDIT2: As suggested by Christoph Rackwitz, here is the output generated by the script as pictures:
Marker length: Should ideally be 700:

Marker distance: Should be invariant of rotation:

A: Answer by Christoph Rackwitz (see comments):

you're getting a mere 2% of a pixel of difference (30 ppm in length,
30 ppm in Z). warpAffine uses fixed point math, 5 fractional bits.
also: cubic interpolation. also: corner refinement method (that
hopefully works on the edges, not the corners)... and this here is all
happening in a "linear colorspace" (the warpaffine). if you gave this
real pictures, those are gamma-mapped, and webcams apply sharpening
filters, so you'll get even wilder results

redpajama_set_name: RedPajamaStackExchange


text:
Archive for February 16, 2015
Interesting Links: Feb 16, 2015
February 16, 2015 dissention	8 comments
Here are links to some interesting news articles I came across today. As you might notice, all three are about the latest revelations on what the NSA has been upto for all these years.
Link 1: How "omnipotent" hackers tied to NSA hid for 14 years—and were found at last
It wasn't the first time the operators—dubbed the "Equation Group" by researchers from Moscow-based Kaspersky Lab—had secretly intercepted a package in transit, booby-trapped its contents, and sent it to its intended destination. In 2002 or 2003, Equation Group members did something similar with an Oracle database installation CD in order to infect a different target with malware from the group's extensive library. Kaspersky settled on the name Equation Group because of members' strong affinity for encryption algorithms, advanced obfuscation methods, and sophisticated techniques.
The money and time required to develop the Equation Group malware, the technological breakthroughs the operation accomplished, and the interdictions performed against targets leave little doubt that the operation was sponsored by a nation-state with nearly unlimited resources to dedicate to the project. The countries that were and weren't targeted, the ties to Stuxnet and Flame, and the Grok artifact found inside the Equation Group keylogger strongly support the theory the NSA or a related US agency is the responsible party, but so far Kaspersky has declined to name a culprit. NSA officials didn't respond to an e-mail seeking comment for this story.
Link 2: Russian researchers expose breakthrough U.S. spying program
The U.S. National Security Agency has figured out how to hide spying software deep within hard drives made by Western Digital, Seagate, Toshiba and other top manufacturers, giving the agency the means to eavesdrop on the majority of the world's computers, according to cyber researchers and former operatives. That long-sought and closely guarded ability was part of a cluster of spying programs discovered by Kaspersky Lab, the Moscow-based security software maker that has exposed a series of Western cyberespionage operations.
According to Kaspersky, the spies made a technological breakthrough by figuring out how to lodge malicious software in the obscure code called firmware that launches every time a computer is turned on.Disk drive firmware is viewed by spies and cybersecurity experts as the second-most valuable real estate on a PC for a hacker, second only to the BIOS code invoked automatically as a computer boots up.
Link 3: The NSA hides surveillance software in hard drives
It's been known for a while that the NSA will intercept and bug equipment to spy on its soon-to-be owners, but the intellgency agency's techniques are apparently more clever than first thought. Security researchers at Kaspersky Lab have discovered apparently state-created spyware buried in the firmware of hard drives from big names like Seagate, Toshiba and Western Digital. When present, the code lets snoops collect data and map networks that would otherwise be inaccessible — all they need to retrieve info is for an unwitting user to insert infected storage (such as a CD or USB drive) into an internet-connected PC. The malware also isn't sitting in regular storage, so you can't easily get rid of it or even detect it.
Categories: Linkfest


text:
Q: Winform Application installed folder does not contain the XML File to load Data I made a winform application,which contain a listview control.
Data for the listview is loaded through the XML file within the code.
when i debug the program,the data is loading from the XML file correctly.
Created a setup file of the same winform application and installed the setup with another computer. The data is not getting loaded because the xml file are not present in the setup installed path.


text:
package MestreCucaa;

import java.util.List;

import Model.Receita;

public interface LivroReceitas {

	void inserir(Receita receita);

	void atualizar(String nome, Receita receitaAtualizada);

	void excluir(String nome);

	List<Receita> getTodasReceitas();

	Receita buscaReceitaPeloNome(String nome);
}

redpajama_set_name: RedPajamaGithub


The No. 1 concern in every child support matter is making sure the child's needs are met. New Jersey law sets down guidelines based upon the child's interests and the income of each spouse. However, how the guidelines are applied depend upon a number of factors. It is therefore important to have on your side a family law lawyer who will protect your interests along with the interests of the child.
The attorneys at Karcher Law Firm understand the guidelines, and know what the local courts look for when applying them. We will sit down with you and discuss your circumstances. We will provide you a straightforward assessment of your matter and work toward obtaining a child support order that is fair.
Call 732-852-7018 for a no-cost consultation about your circumstances. From our office in Red Bank, we help clients in Monmouth County and throughout the surrounding areas in New Jersey.
What Factors Affect Child Support In Your Situation?
We do what we can to create financial stability for both households. In our experience, it is always best for the child if their financial needs continue to be consistently met in the same manner as when the child was living with both parents under one household.
As children grow, their needs may change. It is therefore important to always have in place a child support order that meets the child's existing needs. This can mean one of the parties pays a higher or lower amount of child support. We will help you apply for modifications of child support orders to meet current circumstances and to protect your rights as a parent.
To schedule a free initial consultation, call our office, or complete the brief online form.


text:
Q: Docker combine curl and php containers I've just started to learn how to use Docker. For learning purposes i'm trying to create webserver app (nginx+php+mysql) and now i have some questions.
I want to use php with my own build of curl (with http2 support etc.). I built image but can i use it as separate container and let container with php use curl library from curl's container (using docker compose) or i can only use it as base image for php container?
And second question - if i can use it as separate container, how to let this container to listen for commands (now it stops after starting). I know that i should set ENTRYPOINT but what should it be?


text:
La flor amarilla, rúcula o rúgula (Diplotaxis tenuifolia (L.) DC.) es una hierba perenne, silvestre, de la familia Brassicaceae de plantas con floración de cruz.

Descripción
La planta crece entre 20 y 80 cm de altura y tiene tallos muy ramificados, cada rama con 3 a 6 hojas lobuladas de formas variadas. La inflorescencia ocurre en racimos terminales. Las flores son de color amarillo con cuatro pétalos en cruz, cada uno de 8 a 15 mm de longitud. Los frutos son pequeñas silícuas de 2 a 4 mm de ancho por 3 a 6 mm de longitud más un apéndice de 2 mm,  con numerosas semillas en su interior. Prefiere los suelos moderadamente nitrogenados, básicos, sueltos y arenosos.

Usos
Las hojas se recolectan para la alimentación humana, especialmente para ensaladas, bajo el nombre de rúcula. En estos últimos años en Occidente se está utilizando como germinados, en Estados Unidos de América, España, Hungría y Reino Unido.

Taxonomía
Diplotaxis tenuifolia, fue descrita  por (L.) DC. y publicado en Regni Vegetabilis Systema Naturale 2: 632–633. 1821.
Etimología
Diplotaxis: nombre genérico que deriva del  griego Diplotaxis = doble orden, refiriéndose a las semillas  que se disponen en dos hileras en el fruto.

tenuifolia: epíteto latíno que significa "con hojas delgadas".
Sinonimia
 Thlaspi   abulense Pau
 Pastorea abulensis (Pau) Rothm.
 Eruca perennis Mill.
 Brassica muralis Huds.
 Brassica erucastrum Crantz non L. 1753
 Sisymbrium tenuifolium L.
 Sisymbrium acre Lam.
 Sinapis tenuifolia (L.) R.Br. in W.T.Aiton
 Eruca tenuifolia (L.) Moench
 Eruca muralis (Huds.) P.Gaertn., B.Mey. & Scherb.
 Crucifera tenuifolia (L.) E.H.L.Krause
 Brassica tenuifolia (L.) Baill.

Referencias

Bibliografía
 Gleason, H. A. & A.J. Cronquist. 1991. Man. Vasc. Pl. N.E. U.S. (ed. 2) i–910. New York Botanical Garden, Bronx.
 Hickman, J. C. 1993. The Jepson Manual: Higher Plants of California 1–1400. University of California Press, Berkeley.
 Kearney, T. H. & R. H. Peebles. 1960. Arizona Fl. (ed. 2) 1032 pp.
 Munz, P. A. 1974. Fl. S. Calif. 1–1086. University of California Press, Berkeley.
 Munz, P. A. & D. D. Keck. 1959. Cal. Fl. 1–1681. University of California Press, Berkeley.
 Scoggan, H. J. 1978. Dicotyledoneae (Saururaceae to Violaceae). 3: 547–1115. In Fl. Canada. National Museums of Canada, Ottawa.
 Small, J. K. 1933. Man. S.E. Fl. i–xxii, 1–1554. Published by the Author, New York.  View in BotanicusView in Biodiversity Heritage Library
 Voss, E. G. 1985. Michigan Flora. Part II Dicots (Saururaceae-Cornaceae). Bull. Cranbrook Inst. Sci. 59. xix + 724.
 Warwick, S. I., A. Francis & I. A. Al-Shehbaz. 2006. Brassicaceae: Species checklist and database on CD-Rom. Pl. Syst. Evol. 259: 249–258.
 Wunderlin, R. P. 1998. Guide Vasc. Pl. Florida i–x, 1–806. University Press of Florida, Gainesville.
 Zuloaga, F. O. & O. Morrone. 1997. Catálogo de las plantas vasculares de la república Argentina. Monogr. Syst. Bot. Missouri Bot. Gard. 74(1–2): 1–1331.

Enlaces externos

tenuifolia
Plantas descritas en 1820
Plantas descritas por Linnaeus
Plantas descritas por de Candolle
Bueneza

redpajama_set_name: RedPajamaWikipedia


Joscha Bruckert is a photographer.
He was born in a forest, got lost in a park for a while and currently lives and studies in a city.
He's more than that…he's a 22 year old German photographer who also happens to produce the magazine, Romka. The Blood of the Young Zine has an interesting interview with Joscha.


text:
:: home :: news :: our work :: us :: contact
New British High Commission, Kampala, Uganda
Winner - RIBA International Award, 2008
The High Commission is arranged as a composition of distinct but linked elements housing the main embassy offices along with the visa section, a clubhouse, a library and exhibition spaces. Beyond the main entrance with its large oversailing canopy, a courtyard provides a sheltered environment and focus for the offices.
In response to the climate, the building has the capacity to be completely naturally ventilated. Wherever possible, perforated screens provide cross ventilation and where there are windows, these are large, openable and shaded. The different elements of the building are situated in a lush, landscaped garden which provides shade and a cooler microclimate.
Construction methods have been chosen for solidity, longevity and low maintenance, making the most, wherever possible, of local materials and skills. The concrete-framed buildings will be finished in red brick with clay tile roofs in keeping with the Kampala vernacular. Stone for paving is quarried in Uganda. Artwork for the site will be provided by a local artist. From the start, as with many of our projects, the building has been designed to be low energy and to make minimal negative impact on the environment.
'RIBA International Award winner', The Architects' Journal online, 3 June 2008
'British High Commission', featured in '1001 Buildings You Must See Before You Die', (Mark Irving, general editor), 2007
'With these embassies, you spoil us!', Saturday Telegraph, 7 April 2007
'Kampala', Architects' Journal, 11 May 2006
'How We Cracked It', Building Design, 1 July 2005
'Our place in Kampala', The Guardian, 9 June 2004
Emmanuel College, Cambridge
Parker Library, Corpus Christi College, Cambridge
Art Foundry, Uganda
Eco-resort, Mozambique
British High Commission, Uganda
Combe House Hotel, Devon
Playground Design, St. Paul's Cathedral School
New house in Dulwich, London
Victoria Memorial Hall, India
Cullum and Nightingale Architects Limited
26 Harrison Street London WC1H 8JW
T: 020 7812 1102 F: 020 7278 3912 E: post@CullumNightingale.com
Registered in England and Wales. Company Number: 05855934. Registered Office: As above.