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The 26th of March
by Fifth Estate Collective
National days of protest March 25-26 constituted the largest concerted world-wide action for peace in history. Demonstrations to protest the war in Vietnam took place in 30 countries, according to the National Coordinating Committee to End the War in Vietnam—initiator of the campaign.
In the United States, the protests surpassed those called by the NCV last October, indicating that the anti-war movement has grown significantly in response to the Johnson Administration's escalation of U.S. intervention in Vietnam.
"The international days of protest were a monumental achievement for the anti-war movement," a spokesman for the coordinating committee told the Guardian from the NCC's headquarters at 420 W. Washington Ave., Madison, Wis.
"People in great numbers have moved since October beyond questioning the war to actively opposing it," the spokesman said. "Despite news blackouts and other harassments, the demonstrations exceeded all our expectations. We have shaken the government into the realization that to many, many Americans this is a deplorable war; and this is only the start.
"The major success of March 25-26 in the United States is that we are demonstrably reaching the grass roots. We are reaching new people. In nearly all the reports we've received from various groups throughout the country that participated in the action, there was a common theme: new people, bigger demonstrations."
Although complete accounts are not yet in, protests were conducted in at least 100 U.S. cities, communities and campuses. The NCC estimates that as many as 125 areas were involved all told as compared with 93 known actions last October. In almost every case, the protests were larger.
In New York City, a crowd of no less than 50,000 persons participated in a march down Fifth Avenue and a rally in Central Park—the greatest peace action of its kind in America.
In Chicago, 5,000 marched; in San Francisco, 6,500; in Detroit, nearly 2,000; in Washington, 1,200 picketed the White House; in Los Angeles, 8,000 are estimated to have attended a 12-hour teach-in at UCLA; in Denver, 800.
And in Sidon, Miss. (population 410), 30 people attended a prayer meeting for peace in Vietnam—one of nine such meetings in small Mississippi communities.
As well as being larger in numbers, the demonstrations were distinctly more explicit in their opposition to the war than those five months ago. "Bring the troops home now," an exceptionally controversial slogan at one time, especially to liberals, was accepted as a valid position in most of the protests.
Overseas, the NCC reported, an estimated 10,000 persons marched in Paris, one of 77 French cities and communities where protest actions were reported to have taken place. In Italy, a crowd estimated at more than 20,000 attended an anti-war rally in Rome during a cold rain. Demonstrations were held throughout West Germany and in East Germany also. In London there was a torchlight parade; in Auckland, New Zealand, more than 1,000 people attended a rally.
In the U.S., counter-demonstrations and heckling—in general—appeared to have declined considerably since last October. Overt pro-war sentiment and hysteria seemed confined to obvious far-rightists. And there was little talk of organizing war parades in reaction to the peace activities, in contrast to widespread proposals of this kind after the first international days of protest.
Official Washington, which responded to the last protests with threats against some of the groups involved and innuendoes that the demonstrations were "communist-inspired," was relatively silent this time. The Administration, no doubt aware that the massive outpouring lends support to growing congressional opposition to the escalation of the war, apparently did not choose to recognize the demonstrations, in hopes that they would generally be ignored.
The National Coordinating Committee, which is no more than an office in Wisconsin and a small staff of young activists, organized the world-wide action in less than three months. The decision to hold the second international days of protest was made at the NCC convention in Washington late in November. Work began a month later. The NCC was founded eight months ago as a loose coordinating body to bring together and give some direction to the resurgent American peace movement. As many as 150 peace, student and political groups are affiliated.
Starting with this issue, pages three and four of the Fifth Estate will function as the Detroit Committee to End the War in Vietnam's newsletter, We hope that Fifth Estate readers and Newsletter readers will enjoy the change.


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As representatives of the public for the School of Marine and Atmospheric Sciences (SoMAS), members of the Dean's Council are the primary external force moving the School of Marine and Atmospheric Sciences to the realization of its opportunities for mission fulfillment and service to its constituencies. Working with the Dean and his designated representatives, the Dean's Council provides advice and guidance in the areas of planning, advancement, organization, and operations. The current Dean's council members are:
Sarah Chasis Senior Attorney and Director, Oceans Program, Natural Resources Defense Council
Rosalind Walrath Board, New York League of Conservation Voters.
Michael Halpern President, East Hampton Beach Preservation Society
Laurie J. Landeau, VMD Associate Director, Aquavet; Marinetics, Inc. and Co-founder, Marinetics
Robert J. Maze, Ph. D. Co-founder and Vice President of Marinetics, Inc.
Jane Ross Vice President, Alfred and Jane Ross Foundation, Inc.
Dieter von Lehsten Co-chair, Town of Southampton Sustainability Advisory Committee
Michael White Environmental Law Practice
Michael J. Zeitlin CEO, Aqumin LLC
Ex Officio:
Paul Shepson, Ph. D. Dean, School of Marine and Atmospheric Sciences
Deborah Lowen-Klein Assistant Vice President for Advancement, Stony Brook University

redpajama_set_name: RedPajamaCommonCrawl


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What type of congenital disorder is trisomy 21?
Is trisomy 21 congenital?
Is trisomy 21 an autosomal disorder?
Is trisomy 21 a neurodevelopmental disorder?
What type of anomaly is Down syndrome?
Is Edwards Syndrome genetic?
Is Patau syndrome genetic?
Is Trisomy 21 autosomal dominant or recessive?
What is an autosomal?
Which are types of autosomal trisomy conditions?
Is Downs syndrome in DSM?
What is other specified neurodevelopmental disorder?
What is the DSM-5 code for Down syndrome?
What are 4 common congenital anomalies of a child with Down syndrome?
Is Down syndrome aneuploidy or polyploidy?
What is the difference between Mosaic Down syndrome and Down syndrome?
Trisomy 21: About 95% of people with Down syndrome have Trisomy 21. With this type of Down syndrome, each cell in the body has 3 separate copies of chromosome 21 instead of the usual 2 copies. Translocation Down syndrome: This type accounts for a small percentage of people with Down syndrome (about 3%).
Trisomy 21 is the most common chromosomal anomaly in humans, affecting about 5,000 babies born each year and more than 350,000 people in the United States. Also known as Down syndrome, trisomy 21 is a genetic condition caused by an extra chromosome.
Trisomy 21 is the commonest autosomal trisomy in humans. The extra chromosome 21 may cause a variety of congenital malformations and medical problems, especially affecting the heart, growth, and learning.
Down syndrome, also known as Trisomy 21, is a neurodevelopmental condition that results from the presence of full or partial extra copy of chromosome 21.
IT IS SURPRISING: What is the outcome of meiosis ck12?
Trisomy 21, also known as Down syndrome, is a condition characterized by a distinctive pattern of minor and major anomalies associated with excess chromosome 21 material.
Edwards syndrome is a genetic condition in babies that causes severe disability. It is caused by an extra copy of chromosome 18 and babies born with the condition usually do not survive for much longer than a week.
Patau's syndrome is a serious rare genetic disorder caused by having an additional copy of chromosome 13 in some or all of the body's cells. It's also called trisomy 13.
An autosomal recessive disorder requires two copies of the abnormal gene for the disease or trait to develop. But Down Syndrome itself is neither dominant nor recessive. Trisomy 21 is an extra copy of chromosome 21.
"Autosomal" means that the gene in question is located on one of the numbered, or non-sex, chromosomes. "Dominant" means that a single copy of the disease-associated mutation is enough to cause the disease. This is in contrast to a recessive disorder, where two copies of the mutation are needed to cause the disease.
The most common types of autosomal trisomy that survive to birth in humans are:
Trisomy 21 (Down syndrome)
Trisomy 18 (Edwards syndrome)
Trisomy 13 (Patau syndrome)
Trisomy 9.
Trisomy 8 (Warkany syndrome 2)
DSM-5 neurocognitive disorder criteria can be used reliably in a Down syndrome population and has higher concurrence with clinical judgement than the older DSM-IV and ICD-10 criteria.
IT IS SURPRISING: What is the number of chromatids in each?
• 315.8 Other specified neurodeveopmental disorder
A category that pertains to characteristic symptoms of a neurodevelopmental disorder causing significant impairment but does not fulfill the criteria of a specific diagnostic class.
Unspecified Neurodevelopmental Disorder DSM-5 315.9 (F89)
Fourteen (2%) of the cases with DS had an obstructive anomaly of the renal pelvis, including hydronephrosis. The other most common anomalies associated with cases with DS were syndactyly, club foot, polydactyly, limb reduction, cataract, hydrocephaly, cleft palate, hypospadias and diaphragmatic hernia.
You are likely familiar with one example of aneuploidy. Down Syndrome is a disorder that results from an extra copy of 1 chromosome. The most common cause of Down Syndrome is an extra copy of chromosome 21.
Down syndrome is a genetic disorder that results in an extra copy of chromosome 21. People with mosaic Down syndrome have a mixture of cells. Some have two copies of chromosome 21, and some have three. Mosaic Down syndrome occurs in about 2 percent of all Down syndrome cases.


Purple Passion Rhododendron is covered in stunning clusters of lightly-scented purple trumpet-shaped flowers with dark red spots at the ends of the branches in mid spring. It has burgundy-spotted dark green foliage which emerges green in spring. The glossy narrow leaves turn an outstanding coppery-bronze in the fall. The fruit is not ornamentally significant.
Purple Passion Rhododendron is an open multi-stemmed evergreen shrub with an upright spreading habit of growth. Its relatively coarse texture can be used to stand it apart from other landscape plants with finer foliage.
Purple Passion Rhododendron will grow to be about 6 feet tall at maturity, with a spread of 6 feet. It tends to be a little leggy, with a typical clearance of 1 foot from the ground, and is suitable for planting under power lines. It grows at a slow rate, and under ideal conditions can be expected to live for 40 years or more.


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Fax Line: (312) 948-3340
Featured this Month:
Keeper of Memories
Wednesday, November 28, 2018 by Cynthia Waderlow MSE, LCSW
I'd like to extend some brief thoughts about family grief through the holidays. There is a lot written on the subject to be found on the internet and various bereavement books. No wonder, because holiday traditions have "normal" and "what we always do" baked into them. When a loved one central to the family has died from suicide, these days can be approached with perhaps too much hope that they will help us feel better, or only dread or confusion.
Remembering Paul
Wednesday, August 07, 2013 by Steve Moore
Early in NBC's broadcast of the 2008 Ironman Hawaii, the narrator says that there is a time cutoff for the swim and failing to make it will result in a competitor being removed from the race. As he is speaking, a man hurries out of the water and up some steps, stumbles a few feet and collapses. Most viewers probably thought: "That was a bad, unprepared swimmer." They are right. I am a bad swimmer and I wasn't as prepared as I should have been. But there is more to the story than can be shown in ten seconds of television.
Grief and Family Development: When Children Refuse Counseling
Wednesday, August 07, 2013 by Deborah R. Major, Ph.D., L.C.S.W
Lately, we have heard parents express concerns over what they should do if children refuse counseling following a family member's suicide. We understand that concern, given the research that examines the characteristics of child survivors of suicide.* We recently heard about an adult survivor who lost a parent as a child and who told her mother at the time that she did not want to attend counseling. Today this same survivor is asking her mother why she was not made to attend counseling anyway, recognizing in retrospect how much she needed it. This story speaks to the gradual accretion of developmental competence and maturity around decision-making, competence that young children do not have, which adds to the complexity of helping them make good choices.
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Home / Academics / Internships / NOC Internships
NOC Internships
NOC Student Completes Internship at Blackwell United Way
The Northern Oklahoma College Internship Program continues to prepare students for the work place as NOC student Cindy Love recently completed an internship with the United Way in Blackwell.
Love, a 1975 graduate from Ponca City, has been working at the United Way in Blackwell under the tutelage of executive director Charlene Flanery.
Her primary duties include maintaining accounts in QuickBooks, prepare and make deposits at financial institution, reconcile bank statements, prepare financial reports, and assist in any other way that is needed.
She currently owns a tax preparation business in Ponca City.
What does Love plan to do with the new skills?
"I plan on adding bookkeeping/accounting services to my current tax business," Love said.
Love said the internship works concurrent with her NOC classwork.
"In one of my current classes I am learning to use QuickBooks," Love said. "This internship has given me an opportunity to put my classroom knowledge of QuickBooks and accounting to practice in the business world."
What has been the best part about the internship?
"Besides the fact that this is an amazing chance to get first-hand experience in my chosen field, it has allowed me the opportunity to help a non-profit organization at the same time, Love added.
Love said her experience with her internship supervisor has been terrific.
"I report to Charlene Flanery, executive director for United Way of Blackwell," Love said. "Charlene (Flanery) has been great to work for, she made me feel welcome from the first day I walked in. She has allowed me take over handling the accounting records for the organization, while always being available if assistance is needed."
Flanery said she is appreciative of the partnership of the NOC Internship Program.
"Blackwell United Way is very thankful to have the opportunity to work with NOC by providing an internship opportunity for business student, Cindy Love," said Blackwell United Way Executive Director Charlene Flanery. "Cindy is working closely with me learning non-profit management, accounting, and bookkeeping skills. Skills such as Quickbooks data input, preparing financial reports, keeping accurate records of donor pledges, coordinating and working with board members has allowed her to use her business education in a real – life setting.
"She is very knowledgeable, easy to work with, and takes initiative when performing her tasks," Flanery added. "Although her internship is nearing the end, she has been a pleasure to work with and we wish her the best in her future studies and endeavors."
Northern Oklahoma College Business Division Chair Cara Beth Johnson said, "Cindy is a phenomenal student who brings a wealth of knowledge and experience to her internship with the Blackwell United Way."
Johnson, who oversees the business internships added, "The ability to put her academic skills in accounting and Quickbooks to work in a real world setting is invaluable to her future professional endeavors, as well as a great benefit to the United Way. The Northern Oklahoma College Business Division couldn't be more proud of the work that she's doing."
For more information regarding NOC Internships, contact Rae Ann Kruse at 580.628.6341.
Posted on Thu, January 18, 2018 by Rae Kruse


There's a problem I've been having, a problem that runs through my writing like some kind of insidious thread, that, once pulled, begins to unravel all those carefully constructed sentences and metaphors. Several problems, really, but a unique one in particular that I struggle with: that of making characters relate-able and sympathetic to my readers. There's a dark streak in me; I know it's there; I'll admit it. It's the side of human nature that has always fascinated me, that has drawn me like that proverbial moth to that proverbial flame--the dark side of the human condition that, in my struggle to be as open-minded in my view of the world as possible, I have begun to under-appreciate. I have been known to say (most often at parties after a few) that all people are capable of acts some might label as "unspeakable". That's right, ALL PEOPLE. We are all capable of the act of murdering another human being and we've all had thoughts--fantasies even--about doing it. I firmly believe this to be true; we are animals; we can be broken down; our facades of civility, our protective masks worn in our world of human-constructed society, can be worn away--scratched away like the silvery stuff on a lottery ticket that shows us that we really have lost the race; we can be stripped down and driven to perform those "unspeakable" acts by the pressures of our base instincts versus our socially-constructed needs. What's worse, we can begin to lose it without even knowing it.
I wrote a story, a few months back, a flash fiction piece, in about an hour, in a random moment of inspiration, and MicroHorror published it: I Used to Find Things. It's about a young boy who plays by himself in the woods behind his house and starts to see some bad things and feel some bad things about the woods. I used to play by myself in the woods behind my house growing up in much the same way. What struck me, is a comment left on the site by jsorensen that mentions the underlying plot in the piece as "the developing dark side of the boy." This got me wondering--am I writing about the birth and growth of my own "dark side"? To some extent, I believe this to be true--at least, in the developing awareness of my personal dark side. The problem is, I didn't see this element in the story when I wrote it. I saw the boy as an innocent stumbling into a dangerous world. Jsorensen goes on to say, "a part of me can empathize with the boy but hopefully not too much…".
The problem I have, then, is that when I want to write from the point of view of a disturbed character (which I guess I do since a lot of my characters seem to have this problem and what does this say about me?) I still have to be able to make him or her a rational human being with compassion and traits that make people able to feel for my character. How do you make someone feel for a character who murders somebody or purposefully steals and does a lot of drugs and stuff like that? The answer: you have to show him/her doing kind things that contrast with their detestable ones and show how they began to do these things. The keyword is, of course, "show". If you show your character doing something horrible and then just say 'by the way this is the reason,' you're not doing your job as a writer and storyteller. My issue is in accessing the 'dark side of humanity' while still evoking compassion in my readers.


As part of the prestigious Geneva Health Forum in Switzerland the International Summit of Cooperatives held a workshop on the development of health care cooperatives in Africa.
Dr Adriano Leite Soares provided an overview of the history and development of Unimed, the world's largest health care cooperative organisation with 113 000 affiliated physicians and a diverse network of healthcare institutions, including hospitals and clinics.
Gabriel Gbedjissokpa of the Panafrican Cooperative Conference spoke about Benin's cooperative clinics, while Jean-Pierre Girard, a consultant working with the Summit and a lecturer at Université du Québec à Montréal, provided some background on the different types of health care and social services cooperatives and mutuals around the world.


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\subsection{Experimental Results}

\begin{figure*}[t!]

\noindent\begin{minipage}[b]{1\columnwidth}%
\begin{minipage}[c]{0.44\columnwidth}%
\begin{figure}[H]
\begin{centering}
\includegraphics[width=1.03\columnwidth]{results_barchart_eccv22}\vspace{1em}
\par\end{centering}
\caption{GAP's universality across different baselines and datasets.\label{fig:comparing_against_baselines_gqa_vqav2}}
\end{figure}
\end{minipage}\hspace{1.5em}%
\begin{minipage}[c]{0.52\columnwidth}%
\begin{figure}[H]
\begin{centering}
\includegraphics[width=0.9\columnwidth]{all_datasets_subsets_cvpr22}\medskip{}
\par\end{centering}
\caption{GAP improves generalization capability with limited access to grouthtruth
answers.\label{fig:ban_less_supervision}}
\end{figure}
\end{minipage}%
\end{minipage}

\end{figure*}

\paragraph{Enhancing VQA performance }

We compare GAP against the VQA models based on the UpDn baseline that
utilize external priors and human annotation on VQA v2. Some of these
methods use internal regularization: \emph{adversarial regularization}
(AdvReg){\small{} }\citep{ramakrishnan2018overcoming}, \emph{attention
alignment} (Attn. Align) \citep{selvaraju2019taking}; and some use
human attention as external supervision: \emph{self-critical reasoning}
(SCR) \citep{wu2019self} and \emph{HINT} \citep{selvaraju2019taking}.
While these methods mainly aim at designing regularization schemes
to exploit the underlying data generation process of VQA-CP datasets
\citet{agrawal2018don} where it deliberately builds the train and
test splits with different answer distributions. This potentially
leads to overfitting to the particular test splits and accuracy gains
do not correlate to the improvements of actual grounding \citep{shrestha2020negative}.
On the contrary, GAP does not rely on those regularization schemes
but aims at directly improving the learning of attention inside any
attention-based VQA models to facilitate reasoning process. In other
words, GAP complements the effects of the aforementioned methods on
VQA-CP (See the supplemental materials).

Table \ref{tab:Comparing-att-reg-methods} shows that our approach
\emph{(UpDn+GAP)} clearly has advantages over other methods in improving
the UpDn baseline. The favorable performance is consistent across
all question types, especially on ``Other'' question type, which
is the most important and challenging for open-ended answers \citep{teney2021unshuffling,teney2020value}.

Compared to methods using external attention annotations (\emph{UpDn+SCR},
\emph{UpDn+HINT}), the results suggest that GAP is an effective way
to use of attention priors (in both learning and inference), especially
when our attention priors are extracted in an unsupervised manner
without the need for human annotation.

\paragraph{Universality across VQA models}

GAP is theoretically applicable to any attention-based VQA models.
We evaluate the universality of GAP by trialing it on a wider range
of baseline models and datasets. Fig. \ref{fig:comparing_against_baselines_gqa_vqav2}
summarizes the effects of GAP on UpDn, MACNet and BAN on the large-scale
datasets VQA v2 and GQA.

It is clear that GAP consistently improves upon all baselines over
all datasets. GAP is beneficial not only for the simple model UpDn,
but also for the multi-step model (MACNet) on which the strongest
effect is when applied only in early reasoning steps where the attention
weights are still far from convergence.

Between datasets, the improvement is stronger on GQA than on VQA v2,
which is explained by the fact that GQA has a large portion of compositional
questions which our unsupervised grounding learning can benefit from.

The improvements are less significant with BAN which already has large
capacity model at the cost of data hunger and computational expensiveness.
In the next section, we show that GAP significantly reduces the amount
of supervision needed for these models compared to the baseline.

\begin{table}[t]
\centering{}\caption{Grounding performance of the unsupervised RE-image grounding when
evaluated on out-of-distribution image-caption Flickr30K Entities
test set. \textbf{R}ecall@$k$: fraction of phrases with bounding
boxes that have IOU$\protect\geq$0.5 with top-$k$ predictions.\label{tab:flickr30k_performance}}
\begin{tabular}{|l|c|c|c|c|}
\hline
{\footnotesize{}Model} & {\footnotesize{}R@1} & {\footnotesize{}R@5} & {\footnotesize{}R@10} & {\footnotesize{}Acc.}\tabularnewline
\hline
\hline
{\footnotesize{}$\enskip$Unsupervised RE-image grounding} & {\footnotesize{}14.1} & {\footnotesize{}35.6} & {\footnotesize{}45.5} & {\footnotesize{}45.4}\tabularnewline
\hline
{\footnotesize{}$\enskip$Unsupervised grounding w/o REs} & \multirow{1}{*}{{\footnotesize{}12.0}} & {\footnotesize{}33.0} & {\footnotesize{}42.9} & {\footnotesize{}44.3}\tabularnewline
\hline
{\footnotesize{}$\enskip$Random alignment score (10 runs)} & {\footnotesize{}6.6} & {\footnotesize{}28.4} & {\footnotesize{}43.3} & {\footnotesize{}40.7}\tabularnewline
\hline
\end{tabular}
\end{table}

\paragraph{Sample efficient generalization}

We examine the generalization of the baselines and our proposed methods
when analyzing sample efficiency with respect to the number of annotated
answers required. Fig. \ref{fig:ban_less_supervision} shows the
performance of the chosen baselines on the validation sets of VQA
v2 (left column) and GQA dataset (right column) when given different
fractions of the training data. In particular, when reducing the
number of training instances with groundtruth answers to under 50\%
of the training set, GAP considerably outperforms all the baseline
models in accuracy across all datasets by large margins. For example,
when given only 10\% of the training data, GAP performs better than
the strongest baseline BAN among the chosen ones by over 4.1 points
on VQA v2 (54.2\% vs. 50.1\%) and nearly 4.0 points on GQA (51.7\%
vs. 47.9\%). The benefits of GAP are even more significant for MACNet
baseline which easily got off the track in the early steps without
large data. The results strongly demonstrate the benefits of GAP in
reducing the reliance on supervised data of VQA models.

\subsection{Model Analysis\label{subsec:Model-Analysis}}

\paragraph{Performance of unsupervised phrase-image grounding }

To analyze the unsupervised grounding aspect of our model, (Sec.~\ref{sec:Grounding}),
we test the grounding model trained with VQA v2 on a mock test set
from caption-image pairs on Flickr30K Entities. This out-of-distribution
evaluation setting will show whether our unsupervised grounding framework
can learn meaningful linguistic-visual alignments.

The performance of our new unsupervised linguistic-visual alignments
using the query grammatical structure is shown in the top row of Table~\ref{tab:flickr30k_performance}.
This is compared against the alignment scores produced by the same
framework but without breaking the query into REs (Middle row) and
the random alignments (Bottom row). There is a 5 points gain compared
to the random scores and over 1 point better than the question-image
pairs without phrases, indicating our linguistic-visual alignments
is a reliable inductive prior for attention in VQA.

\begin{table*}
\begin{quotation}
\noindent\begin{minipage}[t]{1\columnwidth}%
\hspace{-1.5em}%
\begin{minipage}[t]{0.4\columnwidth}%
\begin{flushleft}
\begin{table}[H]
\centering{}\caption{VQA performance on VQA v2 validation split with different sources
of attention priors.\label{tab:grounding_scores_comparisons}}
\begin{tabular}{|c|>{\raggedright}p{4cm}|c|}
\hline
\multirow{1}{*}{No.} & \multirow{1}{4cm}{Models} & \multirow{1}{*}{Acc.}\tabularnewline
\hline
\hline
1 & $\enskip$UpDn baseline & 63.3\tabularnewline
\hline
2 & $\enskip$+GAP w/ uniform-values vector & 63.7\tabularnewline
3 & $\enskip$+GAP w/ random-values vector & 63.6\tabularnewline
4 & $\enskip$+GAP w/ supervised grounding & 64.0\tabularnewline
\hline
5 & $\enskip$+GAP w/ unsup. visual grounding & 64.3\tabularnewline
\hline
\end{tabular}\vspace{0.47cm}
\end{table}
\par\end{flushleft}%
\end{minipage}\hspace{0.8em}%
\begin{minipage}[t]{0.58\columnwidth}%
\begin{flushright}
\begin{table}[H]
\centering{}\caption{Ablation studies with UpDn on VQA v2.\label{tab:Ablation-studies}}
{\small{}}%
\begin{tabular}{|l|c|}
\hline
Models & \multirow{1}{*}{Acc.}\tabularnewline
\hline
\hline
1.$\enskip$UpDn baseline, $\beta^{\prime}\equiv\beta$ ($\gamma(\theta)\equiv1.0$) & 63.3\tabularnewline
\hline
\textbf{Attention as priors} & \tabularnewline
2.$\enskip$w/ $\beta^{\prime}\equiv\text{\ensuremath{\beta^{*}}}$($\gamma(\theta)\equiv0.0$) & 60.0\tabularnewline
\textbf{Effects of the direct use of attention priors} & \tabularnewline
3.$\enskip$+GAP w/o 1st stage fine-tuning & 63.9\tabularnewline
4.$\enskip$w/ 1st stage fine-tuning with attention priors & 64.0\tabularnewline
\textbf{Effects of the gating mechanisms} & \tabularnewline
5.$\enskip$+GAP, fixed $\gamma(\theta)\equiv0.5$ & 64.0\tabularnewline
6.$\enskip$+GAP (multiplicative gating) & 64.1\tabularnewline
\textbf{Effects of using visual-phrase associations} & \tabularnewline
7.$\enskip$+GAP (w/o extracted phrases from questions) & 63.9\tabularnewline
\hline
8.$\enskip$+GAP (full model) & 64.3\tabularnewline
\hline
\end{tabular}
\end{table}
\hspace{-1.2em}
\par\end{flushright}%
\end{minipage}\vspace{-1.4cm}
\end{minipage}
\end{quotation}
\end{table*}

\paragraph{Effectiveness of unsupervised linguistic-visual alignments for VQA}

We examine the effectiveness of our attention prior by comparing it
with different ways of generating values for visual attention prior
$\beta^{*}$ on VQA performance.  They include: (1) UpDn baseline
(no use of attention prior) (2) uniform-values vector and (3) random-values
vector (normalized normal distribution), (4) supervised grounding
(pretrained MAttNet \citep{yu2018mattnet} on RefCOCO \citep{kazemzadeh2014referitgame}),
and (5) GAP. Table \ref{tab:grounding_scores_comparisons} shows results
on UpDn baseline. GAP is significantly better than the baseline and
other attention priors (2-3-4). Especially our unsupervised grounding
gives better VQA performance than the supervised one (Row 5). This
surprising result suggests that pre-trained supervised model could
not generalize out of distribution, and is worse than underlying grounding
phrase-image pairs extracted unsupervisedly.

\paragraph{Ablation studies}

To provide more insights into our method, we conduct extensive ablation
studies on the VQA v2 dataset (see Table \ref{tab:Ablation-studies}).
Throughout these experiments, we examine the role of each component
toward the optimal performance of the full model. Experiments (1,
2) in Table \ref{tab:Ablation-studies} show that UpDn model does
not perform well with either only its own attention or with the attention
prior itself. This supports our intuition that they complement each
other toward optimal reasoning. Rows 5,6 show that a soft combination
of the two terms is necessary.

Row 7 justifies the use of structured grounding. It shows that phrase-image
grounding gives better performance than question-image pairs only.
In particular, the extracted RE-image pairs improves performance
from 63.9\% to 64.3\%. This clearly demonstrates the significance
of the grammatical structure of questions as an inductive bias for
inter-modality matching which eventually benefits VQA.

\begin{table}[t]
\centering{}\medskip{}
\caption{Grounding scores of UpDn before and applying GAP on GQA validation
split.\label{tab:quantitative_results}}
\begin{tabular}{|l|c|c|c|}
\hline
{\footnotesize{}Model} & {\footnotesize{}Top-1 attention} & {\footnotesize{}Top-5 attention} & {\footnotesize{}Top-10 attention}\tabularnewline
\hline
\hline
{\footnotesize{}UpDn baseline} & {\footnotesize{}14.50} & {\footnotesize{}27.31} & {\footnotesize{}35.35}\tabularnewline
\hline
{\footnotesize{}UpDn + GAP} & \multirow{1}{*}{{\footnotesize{}16.76}} & {\footnotesize{}29.32} & {\footnotesize{}36.53}\tabularnewline
\hline
\end{tabular}
\end{table}

\paragraph{Quantitative results}

We quantify the visual attentions of the UpDn model before and after
applying GAP on the GQA validation set. In particular, we use the
grounding score proposed by \citep{hudson2019gqa} to measure the
correctness of the model's attentions weights comparing to the groundtruth
grounding provided. Results are shown in Table \ref{tab:quantitative_results}.
Our method improves the grounding scores of UpDn by 2.26 points (16.76
vs. 14.50) for top-1 attention, 2.01 points (29.32 vs. 27.31) for
top-5 attention and 1.18 points (36.53 vs. 35.35) for top-10 attention.
It is to note that while the grounding scores reported by \citep{hudson2019gqa}
summing over all object regions, we report the grounding scores attributed
by top-$k$ attentions to better emphasize how the attentions shift
towards most relevant objects. This analysis complements the VQA performance
in Table \ref{tab:grounding_scores_comparisons} in a more definitive
confirmation of the role of GAP in improving both reasoning attention
and VQA accuracy.

\begin{figure*}[t]
\begin{centering}
\begin{minipage}[t]{0.92\textwidth}%
\begin{center}
\begin{minipage}[t]{0.48\textwidth}%
\begin{flushleft}
\hspace{-3mm}\includegraphics[width=1.01\columnwidth]{vis_question_10482274_groundings}
\par\end{flushleft}%
\end{minipage}\enskip{}{\color{blue}\vrule}\,%
\begin{minipage}[t]{0.48\textwidth}%
\begin{flushright}
\includegraphics[width=1\columnwidth]{vis_question_10482274_attentions}\hspace{-3mm}
\par\end{flushright}%
\end{minipage}
\par\end{center}%
\end{minipage}
\par\end{centering}
\caption{Qualitative analysis of GAP. (a) Region-word alignments of different
RE-image pairs learned by our unsupervised grounding framework. (b)
Visual attentions and prediction of UpDn model before (left) vs. after
applying GAP (right). GAP shifts the model's highest visual attention
(green rectangle) to more appropriate regions while the original puts
attention on irrelevant parts.\label{fig:Qualitative-results}}
\end{figure*}

\paragraph{Qualitative results}

We analyze the internal operation of GAP by visualizing grounding
results on a sample taken from the GQA validation set. The quality
of grounding is demonstrated in Fig. \ref{fig:Qualitative-results}(a)
with the word-region alignments found for several RE-image pairs.
With GAP, these good grounding eventually benefits VQA models by guiding
their visual attentions. Fig. \ref{fig:Qualitative-results}(b) shows
visual attention of the UpDn model before and after applying GAP.
The guided attentions were shifted towards more appropriate visual
regions than attentions by UpDn baseline.

\section{Introduction}

\input{intro.tex}

\section{Related Work \label{sec:Related-Work}}

\input{related.tex}

\section{Preliminaries\label{sec:Preliminary}}

\input{prelim.tex}

\section{Methods\label{sec:Methods}}

\input{method.tex}

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

\input{exps.tex}

\section{Conclusion \label{sec:Conclusion}}

\input{conclusion.tex}

{\small{}\bibliographystyle{unsrtnat}

\subsection{Structures for Linguistic-Visual Alignment\label{sec:Grounding}}

\textbf{Grammatical structures for grounding}. The task of \emph{Linguistic-visual
Alignment} aims to find the groundings between the linguistic entities
($\eg$, query words $L=\left\{ l_{i}\right\} _{i=1}^{T}$ in VQA)
and vision entities ($\eg$, visual regions $V=\left\{ v_{j}\right\} _{j=1}^{N}$
in VQA) in a shared context. This requires the interpretation of individual
words in the complex context of the query so that they can co-refer
to the same concepts as image regions. However, compositional queries
have complex structures that prevent state-of-the-art language representation
methods from fully understanding the relations between semantic concepts
in the queries \citep{reimers2019sentence}. We propose to better
contextualize query words by \emph{breaking a full query into phrases
that refer to simpler structures, }making the computation of word-region
grounding more effective\emph{. }These phrases are called \emph{referring
expressions} (RE) \citep{mao2016generation} and were shown to co-refer
well to image regions \citep{kazemzadeh2014referitgame}. The VQA
image-query pairing labels are passed to the REs of such query. We
then ground words with contextualized embeddings within each RE to
their corresponding visual regions. As the REs are nested phrases
from the query, a word can appear in multiple REs. Thus, we obtain
the query-wide \emph{word-region grounding} by aggregating the grounding
of REs containing the word. See Fig.~\ref{fig:Groundeing_illustration}
for an example on this process.

\begin{figure}[t]
\begin{centering}
\includegraphics[width=0.58\columnwidth]{grounding}
\par\end{centering}
\caption{The query is parsed into a constituency parse tree to identify REs.
 Each RE serves as a local context for words. Words within each RE
context are grounded to corresponding image regions. A word can appear
in multiple REs, and thus its final grounding is averaged over containing
REs, serving as inductive prior for VQA.\label{fig:Groundeing_illustration}}
\end{figure}

We extract query REs using a constituency parse tree $\mathcal{T}$\citep{cirik2018using}.\footnote{Berkeley Neural Parser \citep{kitaev2018constituency} in our implementation.}
In this structure, the query is represented as a set of nested phrases
corresponding to subtrees of $\mathcal{T}$. The parser also provides
the grammatical roles of the phrases. For example,  the phrase ``the
white car'' will be tagged as a \emph{noun-phrase }while ``standing
next to the white car'' is a\emph{ verb-phrase}. As visual objects
and regions are naturally associated with noun-phrases, we select
a set $E=\left\{ E_{r}\right\} $ of all the noun phrases and wh-noun
phrases\footnote{noun phrases prefixed by a pronoun, $\eg$, ``which side'', ``whose
bag''.} as the REs.

We denote the $r^{th}$ RE as $E_{r}\hspace{-1mm}=\hspace{-1mm}\{w_{i}\hspace{-1mm}\mid\hspace{-1mm}w_{i}\in\mathbb{R}^{d}\}_{s_{r}\leq i\leq e_{r}}$
where $s_{r}$ and $e_{r}$ are the start and end word index of the
RE within the query $L=\left\{ l_{i}\right\} _{i=1}^{T}$. It has
length $m_{r}=e_{r}-s_{r}+1$. We now estimate the correlation between
words in these REs and the visual regions $V=\left\{ v_{j}\mid v_{j}\in\mathbb{R}^{d}\right\} _{j=1}^{N}$
by learning the neural association function $g_{\delta}(V,E_{r})$
of parameter $\delta$ that generates a mapping $A_{r}^{*}\in\mathbb{R}^{m_{r}\times N}$
between words in the RE and the corresponding visual regions.

We implement $g_{\delta}\left(.\right)$ as the dot products of a
contextualized embedding of word $w_{i}$ in $E_{r}$ with the representations
of regions in $V$, following the scaled dot-product attention \citep{vaswani2017attention}.

\textbf{Unsupervised training.} To train the function $g_{\delta}\left(.\right)$,
we adapt the recent contrastive learning framework \citep{gupta2020contrastive}
for phrase grounding to learn these word-region alignments from the
RE-image pairs in an unsupervised manner, i.e. without explicit word-region
annotations. In a mini batch $\mathcal{B}$ of size $b$, we calculate
the positive mapping $A_{r}^{*}=\left(a_{r,i,j}^{*}\right)\in\mathbb{R}^{m_{r}\times N}$
on one positive sample (the RE $E_{r}$ and the image regions $V$
in the image that is paired with it) and $(b-1)$ negative mappings
$\overline{A_{r,s}^{*}}=\left(\overline{a_{r,s,i,j}^{*}}\right)\in\mathbb{R}^{m_{r}\times N}$
where $1\leq s\leq(b-1)$ from negative samples (the RE $E_{r}$ and
negative image regions $V_{s}^{\prime}=\{v_{s,j}^{\prime}\}$ from
images that are not paired with it). We then compute linguistic-induced
visual representations $v_{i}^{*}\in\mathbb{R}^{d}$ and $\overline{v_{s,i}^{*}}\in\mathbb{R}^{d}$
over regions for each word $w_{i}$:

\begin{alignat}{1}
v_{i}^{*} & ={\textstyle \sum_{v_{j}\in V}}\text{norm}_{j}\left(a_{r,i,j}^{*}\right)W_{v}^{\top}v_{j},\\
\overline{v_{s,i}^{*}} & ={\textstyle \sum_{v_{s,j}^{\prime}\in V_{s}^{\prime}}}\text{norm}_{j}\left(\overline{a_{r,s,i,j}^{*}}\right)W_{v^{\prime}}^{\top}v_{s,j}^{\prime},
\end{alignat}
\\
where ``$\text{norm}_{j}$'' is a column normalization operator;
$W_{v}\in\mathbb{R}^{d\times d}$ and $W_{v^{\prime}}\in\mathbb{R}^{d\times d}$
are learnable parameters. We then push them away from each other by
maximizing the linguistic-vision InfoNCE \citep{oord2018representation}:

\begin{equation}
\mathcal{L}_{r}(\delta)=\mathbb{E}_{\mathcal{B}}\left[\sum_{w_{i}\in E_{r}}\textrm{log}\left(\frac{e^{\left\langle W_{w}^{\top}w_{i},v_{i}^{*}\right\rangle }}{e^{\left\langle W_{w}^{\top}w_{i},v_{i}^{*}\right\rangle }\hspace{-0.25em}+\hspace{-0.25em}\sum_{s=1}^{b-1}e^{\left\langle W_{w}^{\top}w_{i},\overline{v_{s,i}^{*}}\right\rangle }}\right)\right].
\end{equation}

This loss maximizes the lower bound of mutual information $\textrm{MI}(V,w_{i})$
between visual regions $V$ and contextualized word embedding $w_{i}$
\citep{gupta2020contrastive}.

Finally, we compute the word-region alignment $A^{*}\in\mathbb{R}^{T\times N}$
by aggregating the RE-image groundings:

\begin{equation}
A^{*}=\frac{1}{|E|}{\textstyle \sum_{r=1}^{|E|}}\tilde{A_{r}^{*}},
\end{equation}
where $\tilde{A_{r}^{*}}\in\mathbb{R}^{T\times N}$ is the zero-padded
matrix of the matrix $A_{r}^{*}$.

Besides making grounding more expressive, this divide-and-conquer
strategy has the extra benefit of augmenting the weak supervising
labels from query-image to RE-image pairs, which increase the amount
of supervising signals (positive pairs) and facilitate better training
of the contrastive learning framework.

The discovered grounding provides a valuable source of priors for
VQA attention. Existing works \citep{qiao2018exploring,selvaraju2019taking}
use attention priors to regulate the gradient flow of VQA models during
training, hence only constraining the attention weights indirectly.
Unlike these methods, we directly guide the computation of attention
weights via two pathways: through pre-training them without answers,
and by refining in VQA inference on a case-by-case basis.

\subsection{Pre-training VQA Attention \label{sec:Pretraining}}

A typical VQA system seeks to ground linguistic concepts parsed
from the question to the associated visual parts through cross-modal
attention. However, this attention mechanism is guided only indirectly
and distantly through sparse training signal of the answers. This
training signal is too weak to assure that relevant associations can
be discovered. To directly train the attention weights to reflect
these natural associations, we pre-train VQA models by enforcing the
attention weights to be close to the alignment maps $A^{*}$ discovered
through unsupervised grounding in Sec.~\ref{sec:Grounding}.

For joint attention VQA models, this is achieved through minimizing
the Kullback-Leibler divergence between vectorized forms of the VQA
visual attention weights $A$ and the prior grounding scores $A^{*}$:

\begin{align}
\mathcal{L}_{\text{pre-train}} & =KL(\text{norm}\circ\text{vec(}A^{*})\parallel\text{norm}\circ\text{vec}(A)),\label{eq:reconstruction_loss_matrix}
\end{align}
where $\text{norm}\hspace{-0.5mm}\circ\hspace{-0.5mm}\text{vec}$
flattens a matrix into a vector followed by a normalization operator
ensuring such vector sums to one.

For VQA marginalized attention models, we first marginalize $A^{*}=\left(a_{i,j}^{*}\right)\in\mathbb{R}^{T\times N}$
into a vector of visual attention prior:
\begin{equation}
\beta^{*}=\frac{1}{T}{\textstyle \sum_{i=1}^{T}}\text{norm}_{j}(a_{i,j}^{*}).
\end{equation}
The pre-training loss is the KL divergence between the attention weights
and their priors:
\begin{align}
\mathcal{L}_{\text{pre-train}} & =KL(\beta^{*}\parallel\beta).\label{eq:reconstruction_loss}
\end{align}

\subsection{Attention Refinement with Attention Priors \label{sec:refinement}}

\subsubsection{Marginalized attention refinement}

Recall from Sec.~\ref{sec:Preliminary} that a marginalized attention
VQA model computes linguistic attention over $T$ query words $\alpha\in\mathbb{R}^{T}$
and visual attention over $N$ visual regions $\beta\in\mathbb{R}^{N}$.
In this section, we propose to directly refine these attentions using
attention priors $A^{*}=\left(a_{i,j}^{*}\right)\in\mathbb{R}^{T\times N}$
learned in Sec.~\ref{sec:Grounding}. First, $A^{*}$ is marginalized
over rows and columns to obtain a pair of attention priors vectors
$\alpha^{*}\in\mathbb{R}^{T}$ and $\beta^{*}\in\mathbb{R}^{N}$:

\begin{align}
\alpha^{*} & =\frac{1}{N}\sum_{j=1}^{N}\text{norm}_{i}(a_{i,j}^{*});\:\beta^{*}=\frac{1}{T}\sum_{i=1}^{T}\text{norm}_{j}(a_{i,j}^{*}).\label{eq:marginalizing_vis_scores}
\end{align}

We then refine $\alpha$ and $\beta$ inside the reasoning process
through a gating mechanism to return refined attention weights $\alpha^{\prime}$
and $\beta^{\prime}$ in two forms:

\smallskip{}

\noindent
\begin{tabularx}{\linewidth}{@{}XX@{}}

\emph{Additive form:}
\begin{align}
\label{eqn:additive_combine}
\begin{split}
\alpha^{\prime} =\lambda\alpha+\left(1-\lambda\right)\alpha^{*},\\
\beta^{\prime} =\gamma\beta+\left(1-\gamma\right)\beta^{*}.
\end{split}
\end{align}

&
\emph{Multiplicative form:}
\begin{align}
\label{eqn:mul_combine}
\begin{split}
\alpha^{\prime} =\textrm{norm}\left(\left(\alpha\right)^{\lambda}\left(\alpha^{*}\right)^{\left(1-\lambda\right)}\right),\\
\beta^{\prime} =\textrm{norm}\left(\left(\beta\right)^{\gamma}\left(\beta^{*}\right)^{\left(1-\gamma\right)}\right),
\end{split}
\end{align}

\end{tabularx}\\
where ``norm'' is a normalization operator; $\lambda\in(0,1)$ and
$\gamma\in(0,1)$ are outputs of learnable gating functions that decide
how much attention priors contribute per words and regions. Intuitively,
these gating mechanisms are a solution to maximizing the agreement
between two sources of information: $\alpha^{\prime}=\text{argmin}\left(\lambda*D(\alpha^{\prime},\alpha)+\left(1-\lambda\right)*D(\alpha^{\prime},\alpha^{*})\right),$
where $D(P_{1},P_{2})$ measures the distance between two probability
distributions $P_{1}$ and $P_{2}$. When $D\equiv$ Euclidean distance,
it gives Eq.~(\ref{eqn:additive_combine}) and when $D\equiv$ KL
divergence between the two distributions, it is Eq.~(\ref{eqn:mul_combine})
\citep{heskes1998selecting} (See the Supplement for detailed proofs).
The same intuition applies for the calculation of $\beta^{\prime}$.

The learnable gates for $\lambda$ and $\gamma$ are implemented as
a neural function $h_{\theta}\left(.\right)$ of visual regions $\bar{v}$
and the question $q$:

\begin{equation}
\lambda=h_{\theta}\left(\overline{v},q\right).\label{eq:lambda_gating_func}
\end{equation}
For simplicity, $\overline{v}$ is the arithmetic mean of regions
in $V$.

For multi-step reasoning, we apply Eqs.~(\ref{eqn:additive_combine},
\ref{eqn:mul_combine}) step-by-step. Since each reasoning step $k$
is driven by an intermediate controlling signal $c_{k}$ (Sec.~\ref{sec:Preliminary}),
we adapt the gating functions to make use of that signal:

\begin{equation}
\lambda_{k}=p_{\theta}\left(c_{k},h_{\theta}\left(\bar{v},q\right)\right).\label{eq:lambda_gating_func_macnet}
\end{equation}

\subsubsection{Joint attention refinement}

In joint attention VQA models, we can directly use matrix $A^{*}=\left(a_{ij}^{*}\right)\in\mathbb{R}^{T\times N}$
without marginalization. With slight abuse of notation, we denote
the output the modulating gate for attention refinement as $\lambda\in(0,1)$
sharing similar role with the gating mechanism in Eq.~(\ref{eq:lambda_gating_func}):

\begin{align}
A^{\prime} & =\begin{cases}
\lambda A+\left(1-\lambda\right)B^{*} & \text{(add.)}\\
\textrm{norm}\left(\left(A\right)^{\lambda}\left(B^{*}\right)^{\left(1-\lambda\right)}\right) & \text{(multi.)}
\end{cases}\label{eq:joint_attn_refine}
\end{align}
where $B^{*}=\text{norm}_{ij}\left(a_{ij}^{*}\right)$.

\subsection{Two-stage Model Training \label{sec:training}}

We perform a two-step pre-training/fine-tuning procedure to train
models using the attention priors: (1) unsupervised pre-training VQA
without answer decoder with attention priors (Sec.~\ref{sec:Pretraining}),
and (2) fine-tune full VQA models with attention refinement using
answers, i.e. by minimizing the VQA loss $-\log P(y\mid q,\mathcal{I})$.

\part*{Supplementary material}

In this supplementary material, we provide extra details on the proposed
Grounding as Attention Priors (GAP) method and analysis. They include:
\begin{itemize}
\item Implementation details of the Language and visual embedding (Sec.
\ref{sec:Preliminary} main text)
\item Mathematical proof on the meaning of the attention refinement formulations
(Sec. \ref{sec:refinement} main text).
\item Implementation of the Neural gating functions (Eqs. \ref{eq:lambda_gating_func}
and \ref{eq:lambda_gating_func_macnet} main text).
\item Experiment details including datasets and baselines.
\item Additional experimental results.
\item Additional qualitative analysis.
\end{itemize}

\section{Method details}

\subsection{Language and Visual Embedding}

\paragraph{Textual embedding }

Given a length-$T$ question, we first tokenize it into a sequence
of words and further embed each word into the vector space of 300
dimensions. We initialize the word embeddings with the popular pre-trained
vector representations in GloVe \citep{pennington2014glove}.

To model the sequential nature of the query, we use bidirectional
LSTMs (BiLSTMs) taking as input the word embedding vectors. The BiLSTMs
result in hidden states $\overrightarrow{h_{i}}$ and $\overleftarrow{h_{i}}$
at a time step $i$ for the forward pass and backward pass, respectively.
We further combine every pair $\overrightarrow{h_{i}}$ and $\overleftarrow{h_{i}}$
into a single vector $l_{i}=\left[\overrightarrow{h_{i}};\overleftarrow{h_{i}}\right]$,
where $\left[;\right]$ indicates the vector concatenation operation.
The contextual words are then obtained by gathering these combined
vectors $L=\left\{ l_{i}\mid l_{i}\in\mathbb{R}^{d}\right\} _{i=1}^{T}$.
The global representation $q$ of the query is a combination of the
ends of the LSTM passes $q=\left[\overrightarrow{h_{1}};\overleftarrow{h_{S}}\right]$.

For our grounding framework with contrastive learning, we use a contextualized
word representation extracted by a pre-trained BERT language model
\citep{devlin2018bert} for each word $w_{i}$ in an extracted RE.
These contextualized embeddings are found to be more effective for
phrase grounding \citep{gupta2020contrastive}.

\paragraph{Visual embedding }

Visual regions are extracted by the popular object detection Faster
R-CNN \citep{ren2015faster} pre-trained on Visual Genome \citep{krishna2017visual}.
We use public code\footnote{https://github.com/MILVLG/bottom-up-attention.pytorch}
making use of the Facebook Detectron2 v2.0.1 framework\footnote{https://github.com/facebookresearch/detectron2}
for this purpose. For each image, we extract a set of $N$ RoI pooling
features with bounding boxes $\left\{ \left(a_{j},b_{j}\right)\right\} _{j=1}^{N}$,
where $a_{j}\in\mathbb{R}^{2048},b_{j}\in\mathbb{R}^{4}$ are appearance
features of object regions and bounding box's coordinators, respectively.
We follow \citep{yu2017joint} to encode the bounding box's coordinators
into a spatial vector of 7 dimensions. We further combine the appearance
features with the encoded spatial features by using a sub-network
of two linear transformations to obtain a set of visual objects $V=\left\{ v_{j}\mid v_{j}\in\mathbb{R}^{d^{\prime}}\right\} _{j=1}^{N}$,
where $d^{\prime}$ is the vector length of the joint features of
the appearance features and the spatial features. For ease of reading
and implementation, we choose the linguistic feature size $d$ and
the visual feature size $d^{\prime}$ to be the same.

\subsection{Meaning of Attention Refinement Mechanisms}

In this section, we will give a proof that our choices for attention
refinement in Eqs. (\ref{eqn:additive_combine}, \ref{eqn:mul_combine}
and \ref{eq:joint_attn_refine}) in the main paper are the optimal
solutions with respect to some criteria for probability estimate aggregation.

Let us consider the generic problem where a system has multiple estimates
$\left\{ P_{i}\left(x\right)\right\} _{i=1}^{n}$ of a true discrete
distribution $P\left(x\right)$ from multiple mechanisms with corresponding
degrees of certainty $\lambda=\left\{ \lambda_{i}\right\} _{i=1}^{n}$.
We first normalize these certainty measures so that they sum to one:
$\lambda_{i}\geq0,\,\sum_{i}\lambda_{i}=1$. We aim at finding a common
distribution $P^{\prime}\left(x\right)$ aggregating the set of distributions
$\left\{ P_{i}\left(x\right)\right\} $ subject to a item-to-set distance
$D$:

\begin{equation}
P^{\prime}\left(x\right)=\underset{P(x)}{\mathrm{argmin}}D_{\lambda}\left(P\left(x\right);\left\{ P_{i}\left(x\right)\right\} _{i=1}^{n}\right).
\end{equation}
This problem can be solved for particular choices of the set-distance
function $D$ that measures the discrepancy between $P\left(x\right)$
and the set $\left\{ P_{i}\left(x\right)\right\} _{i=1}^{n}$ under
the confidence weights $\lambda=\left\{ \lambda_{i}\right\} _{i=1}^{n}$.
We consider several heuristic choices of the function $D$:

\textbf{Additive form:}

If we define the distance to be Euclidean distance from $P\left(x\right)$
to each member distribution $P_{i}\left(x\right)$ of the set, then
the minimized term $D$ becomes

\begin{equation}
D_{\lambda}\left(P\left(x\right);\left\{ P_{i}\left(x\right)\right\} _{i=1}^{n}\right)=\frac{1}{2}\sum_{i}\lambda_{i}\sum_{x}\hspace{-0.5mm}\left(\hspace{-0.5mm}P\left(x\right)\hspace{-0.5mm}-\hspace{-0.5mm}P_{i}\left(x\right)\hspace{-0.5mm}\right)^{2}.
\end{equation}
Here, we minimize $D_{\lambda}$ w.r.t. $P\left(x\right)$:

\[
\partial_{P}D=\sum_{i}\lambda_{i}\left(P\left(x\right)-P_{i}\left(x\right)\right).
\]
By setting this gradient to zero, we have $P^{\prime}\left(x\right)=\sum_{i}\lambda_{i}P_{i}\left(x\right)$.
This explains for the additive form of our attention refinement mechanism
in Eqs. (\ref{eqn:additive_combine} and \ref{eq:joint_attn_refine}
(upper part)) in the main paper where we seek a solution that best
agrees with the grounding prior and the model-induced probability
. \\

\textbf{Multiplicative form:}

If we define $D$ as the weighted sum of the KL divergences between
$P\left(x\right)$ to each member distribution $P_{i}\left(x\right)$
of the set:
\begin{align}
D_{\lambda}\left(P\left(x\right);\left\{ P_{i}\left(x\right)\right\} _{i=1}^{n}\right) & =\sum_{i}\lambda_{i}\textrm{KL}\left(P\left(x\right)\parallel P_{i}\left(x\right)\right)\\
 & =\sum_{i}\lambda_{i}\sum_{x}P\left(x\right)\textrm{log}\frac{P\left(x\right)}{P_{i}\left(x\right)}.
\end{align}
Here, we minimize a Lagrangian with the multiplier $\eta$ of $D_{\lambda}$
w.r.t. $P\left(x\right)$, $L=D+\eta\left(\sum_{x}P\left(x\right)-1\right):$\vspace{-0.3em}

\begin{align}
\partial_{P}L & =\sum_{i}\lambda_{i}\left(\text{log}\frac{P\left(x\right)}{P_{i}\left(x\right)}+1\right)+\eta\\
 & =\text{log}P\left(x\right)-\sum_{i}\lambda_{i}\text{log}P_{i}\left(x\right)+1+\eta\\
 & =\text{log}P\left(x\right)-\text{log}\prod_{i}P_{i}\left(x\right)^{\lambda_{i}}+1+\eta.
\end{align}
Setting this gradient to zero leads to:
\begin{equation}
P^{\prime}\left(x\right)=C\prod_{i}P_{i}\left(x\right)^{\lambda_{i}},
\end{equation}
where $C$ is a calculable constant to normalize $P^{\prime}(x)$
such that its components sum to one. This explains for the multiplicative
form of our attention refinement mechanism in Eqs. (\ref{eqn:mul_combine}
and \ref{eq:joint_attn_refine} (lower part)) in the main paper.

\subsection{Neural Gating Functions}

Here we provide the details of implementation choices for the neural
gating functions in Eqs. (\ref{eq:lambda_gating_func} and \ref{eq:lambda_gating_func_macnet}).
In particular, we use element-wise product between embedded representations
of the input $\overline{v}\in\mathbb{R}^{d}$ and $q\in\mathbb{R}^{d}$
as following:\vspace{-0.3em}

\begin{align}
\lambda & =h_{\theta}\left(\overline{v},q\right)\\
 & =\sigma\left(w_{\lambda}^{\top}\left(\text{ELU}\left(\left(W_{v}^{\top}\overline{v}+b_{v}\right)\varodot\left(W_{q}^{\top}q+b_{q}\right)\right)\right)\right),
\end{align}
where $w_{\lambda}\in\mathbb{R}^{d},\,W_{v}\in\mathbb{R}^{d\times d},\,W_{q}\in\mathbb{R}^{d\times d}$
are learnable weights, $b_{v},b_{q}$ are biases, $\sigma$ is the
sigmoid function and $\varodot$ denotes the Hadamard product. ELU
\citep{clevert2015fast} is a non-linear activation function.

For multi-step reasoning, we additionally takes as input the intermediate
controlling signal $c_{k}\in\mathbb{R}^{d}$ at reasoning step $k$.
Output of the modulating gate in Eq. \ref{eq:lambda_gating_func_macnet}
in the main paper is given by
\begin{equation}
\lambda_{k}=p_{\theta}\left(c_{k},h_{\theta}\left(\overline{v},q\right)\right),
\end{equation}
where\vspace{-0.3em}
\begin{align}
h_{\theta}\left(\overline{v},q\right) & =W_{h}^{\top}\left(\text{ELU}\left(\left(W_{v}^{\top}\overline{v}+b_{v}\right)\varodot\left(W_{q}^{\top}q+b_{q}\right)\right)\right),\\
p_{\theta} & =\sigma\left(w_{\lambda}^{\top}\left(\text{ELU}\left(c_{k}\varodot h_{\theta}\left(\overline{v},q\right)\right)\right)\right).
\end{align}
Here $W_{h}\in\mathbb{R}^{d\times d},\,W_{v}\in\mathbb{R}^{d\times d},\,W_{q}\in\mathbb{R}^{d\times d},\,w_{\lambda}\in\mathbb{R}^{d}$
are learnable weights, and $b_{v},b_{q}$ are biases.

\section{Experiment details}

\subsection{Datasets}

\paragraph{VQA v2}

is a large scale VQA dataset entirely based on human annotation and
is the most popular benchmark for VQA models. It contains 1.1M questions
with more than 11M answers annotated from over 200K MSCOCO images
\citep{lin2014microsoft}, of which 443,757 questions, 214,354 questions
and 447,793 questions in train, val and test split, respectively.

We choose correct answers in the training split appearing more than
8 times, similar to prior works \citep{teney2018tips,anderson2018bottom}.
We report performance as accuracy calculated by standard VQA accuracy
metric: $\text{min}(\frac{\text{\#humans that provided that answer}}{3},1)$
\citep{antol2015vqa}.

\paragraph*{GQA}

is currently the largest VQA dataset. The dataset contains over 22M
question-answer pairs and over 113K images covering various reasoning
skills and requiring multi-step inference, hence significantly reducing
biases as in previous VQA datasets. Each question is generated based
on an associated scene graph and pre-defined structural patterns.
GQA has served as a standard benchmark for most advanced compositional
visual reasoning models \citep{hudson2019gqa,hu2019language,hudson2019learning,shevchenko2020visual}.
We use the balanced splits of the dataset in our experiments.

\subsection{Baseline Models}

\paragraph{Bottom-Up Top-Down Attention (UpDn)}

UpDn is the first to introduce the use of bottom-up attention mechanism
to $\Problem$ by utilizing image region features extracted by Faster
R-CNN \citep{ren2015faster} pre-trained on Visual Genome dataset
\citet{krishna2017visual}. A top-down attention network driven by
the question representation is used to summarize the image region
features to retrieve relevant information that can be decoded into
an answer. The UpDn model won the VQA Challenge in 2017 and became
a standard baseline $\Problem$ model since then.

\paragraph{MACNet}

MACNet is a multi-step co-attention based model to perform sequential
reasoning where they use $\Problem$ as a testbed. Given a set of
contextual word embeddings and a set of visual region features, at
each time step, an MAC cell learns the interactions between the two
sets with the consideration of their past interaction at previous
time steps through a memory. In particular, an MAC cell uses a controller
to first compute a controlling signal by summarizing the contextual
embeddings of the query words using an attention mechanism. The controlling
signal is then coupled with the memory state of the previous reasoning
step to drive the computation of the intermediate visual attention
scores. At the end of a reasoning step, the retrieved visual feature
is finally used to update the memory state of the reasoning process.
The process is repeated over multiple steps, resembling the way humans
reason over a compositional query. In our experiments, we use a Pytorch
equivalent implementation\footnote{https://github.com/tohinz/pytorch-mac-network}
of MACNet instead of using the original Tensorflow-based implementation.
We choose the number of reasoning steps to be $6$ in all experiments.
For experiments with GAP, we only refine the attention weights inside
the controller (linguistic attention) and the read module (visual
attention) at the first reasoning step where the grounding prior shows
its best effect in accelerating the learning of attention weights,
hence leads to the best performance overall.

\paragraph{Bilinear Attention Networks (BAN)}

BAN is one of the most advanced VQA models based on low-rank bilinear
pooling. Given two input channels (language and vision in the VQA
setting), BAN uses low-rank bilinear pooling to extract the pair-wise
cross interactions between the elements of the inputs. It then produces
an attention map to selectively attend to the pairs that are most
relevant to the answer. BAN also takes advantage of a multimodal residual
networks to improve its performance by repeatedly refining the retrieved
information over multiple attention maps. We use its official implementation\footnote{https://github.com/jnhwkim/ban-vqa}
in our experiments. In order to make the best judgments of the model's
performance with our attention refinement with grounding priors, we
remove the plug-and-play counting module \citep{zhang2018learning}
in the original implementation.

Regarding the choice of hyper-parameters, all the experiments regardless
of the baselines are with $d$=512. The number of visual objects $N$
for each image is $100$ and the maximum number of words $T$ in a
query is set to be the length of the longest query in the respective
dataset. We train all the models using Adam optimizer with a batch
size of $64$. The learning rate is initialized at $10^{-4}$ and
scheduled with the warm up strategy, similar to prior words in VQA
\citep{jiang2018pythia}. Reported results are at the epoch that gives
the best accuracy on the validation sets after $25$ training epochs.

\subsection{Additional Experimental Results}

Apart from the experimental results in Sec. \ref{sec:Experiments}
in the main paper, we provide additional results on VQA-CP2 dataset
\citep{agrawal2018don} to support our claim that GAP complements
related works with regularization schemes. We choose RUBi \citep{cadene2019rubi}
as a representative bias reduction method for VQA with a general linguistic
debiasing technique and yet effective on VQA-CP2 dataset. Table \ref{tab:VQACP-performance}
presents our experimental results with UpDn baseline. As clearly seen,
even though linguistic biases are not the main target, GAP still shows
consistent improvements on top of both UpDn baseline and UpDn+RUBi
baseline. It is to emphasize that applying the regularization by RUBi
for linguistic bias considerably hurts the performance on VQA v2 even
though RUBi largely improves performance on VQA-CP2 test split. GAP
brings the benefits of pre-computed attention priors and rejects the
damage caused by the regularization effects by RUBi to maintain its
excellent performance on VQA v2 while slightly improving the baseline's
performance on VQA-CP2. Looking more closely at the results per question
type on VQA-CP2 (Row 1 vs. Row 2, and Row 3 vs. Row 4), GAP shows
its universal effect on all question types with the strongest effect
on ``Other'' question type which contains open-ended arbitrary questions.
On the other hand, RUBi (Row 3 vs. Row 1) shows its significant impact
only on binary questions ``Yes/No'' but considerably hurts ``Number''
and especially ``Other'' question types. This reveals that the regularization
scheme in RUBi is overfitted to ``Yes/No'' questions specifically
due to the limitation of the data generation process behind this dataset.

The analysis in this Section is consistent with our results in Figure
4 in the main paper and is clearly evident to GAP's universal effects
in improving VQA performance. The additional results with RUBi shown
in this Section also state GAP's complementary benefits upon the use
of the learning regularization methods targeting only a specific type
of data as such in VQA-CP2.

\begin{table}[t]
\caption{Performance on VQA v2 val split and VQA-CP2 test split with UpDn baseline.\label{tab:VQACP-performance}}

\centering{}%
\begin{tabular}{|c|c|c|c|c|c|c|c|c|}
\hline
\multirow{2}{*}{{\small{}Model}} & \multicolumn{4}{c|}{{\small{}VQA-CP2 test}} & \multicolumn{4}{c|}{{\small{}VQA v2 val}}\tabularnewline
\cline{2-9} \cline{3-9} \cline{4-9} \cline{5-9} \cline{6-9} \cline{7-9} \cline{8-9} \cline{9-9}
 & {\small{}Overall} & {\small{}Yes/No} & {\small{}Number} & {\small{}Other} & {\small{}Overall} & {\small{}Yes/No} & {\small{}Number} & {\small{}Other}\tabularnewline
\hline
\hline
{\small{}UpDn baseline} & {\small{}40.6} & {\small{}41.2} & {\small{}13.0} & {\small{}48.1} & {\small{}63.3} & {\small{}79.7} & {\small{}42.8} & {\small{}56.4}\tabularnewline
{\small{}UpDn+GAP} & {\small{}40.8} & {\small{}41.2} & {\small{}13.2} & {\small{}48.3} & {\small{}64.3} & {\small{}81.2} & {\small{}44.1} & {\small{}56.9}\tabularnewline
\hline
{\small{}UpDn+RUBi} & {\small{}48.6} & {\small{}72.1} & {\small{}12.6} & {\small{}46.1} & {\small{}62.7} & {\small{}79.2} & {\small{}42.8} & {\small{}55.5}\tabularnewline
{\small{}UpDn+RUBi+GAP} & {\small{}48.9} & {\small{}72.2} & {\small{}12.8} & {\small{}46.4} & {\small{}64.2} & {\small{}81.4} & {\small{}44.3} & {\small{}56.3}\tabularnewline
\hline
\end{tabular}
\end{table}

\subsection{Additional Qualitative Analysis}

\begin{figure*}[p]
\begin{centering}
\begin{minipage}[t]{0.92\textwidth}%
\begin{center}
\hspace{-5mm}%
\begin{minipage}[t]{0.47\textwidth}%
\begin{flushleft}
\hspace{-3mm}\includegraphics[width=1.02\columnwidth]{vis_question_05244818_groundings}
\par\end{flushleft}%
\end{minipage}\enskip{}{\color{blue}\vrule}\,%
\noindent\begin{minipage}[t]{0.41\textwidth}%
\begin{flushright}
\includegraphics[width=0.99\columnwidth]{vis_question_05244818_attentions}\hspace{-3mm}
\par\end{flushright}%
\end{minipage}\hspace{-5mm}
\par\end{center}%
\end{minipage}
\par\end{centering}
\caption{Qualitative analysis of GAP with UpDn baseline. (a) Region-word alignments
of different RE-image pairs learned by our unsupervised grounding
framework. (b) Visual attentions and prediction of UpDn model before
(left) vs. after applying GAP (right). GAP shifts the model's highest
visual attention (green rectangle) to more appropriate regions while
the original puts attention on irrelevant parts.\label{fig:Qualitative-results-updn}}
\end{figure*}
\begin{figure*}[p]
\begin{centering}
\begin{minipage}[t]{0.92\textwidth}%
\begin{center}
\hspace{-5mm}%
\begin{minipage}[t]{0.47\textwidth}%
\begin{flushleft}
\hspace{-3mm}\includegraphics[width=1.02\columnwidth]{vis_question_02750167_groundings}
\par\end{flushleft}%
\end{minipage}\enskip{}{\color{blue}\vrule}\,%
\noindent\begin{minipage}[t]{0.41\textwidth}%
\begin{flushright}
\includegraphics[width=0.85\columnwidth]{vis_question_02750167_attentions}\hspace{-3mm}
\par\end{flushright}%
\end{minipage}\hspace{-5mm}
\par\end{center}%
\end{minipage}
\par\end{centering}
\caption{Qualitative analysis of GAP with MACNet baseline. (a) Region-word
alignments of different RE-image pairs learned by our unsupervised
grounding framework. (b) Visual attentions and prediction of MACNet
model before (left) vs. after applying GAP (right). Visualized attention
weights are obtained at the last reasoning step of MACNet.\label{fig:Qualitative-results-macnet}}
\end{figure*}

Fig.6 in the main paper provides one case of visualization on the
internal operation of our proposed method GAP as well as its effect
on VQA models. We provide more examples here for UpDn baseline (Fig.
\ref{fig:Qualitative-results-updn}) and MACNet baseline (Fig. \ref{fig:Qualitative-results-macnet})
with the same convention and legends.

In each figure, left subfigures present the linguistic-visual alignments
learned by our unsupervised grounding framework. Right subfigures
compare the visual attentions before and after applying GAP. In all
cases across two different baselines (UpDn and MACNet), GAP clearly
helps direct the models to pay attention to more appropriate visual
regions, partly explaining their answer predictions.

redpajama_set_name: RedPajamaArXiv


Until a couple of years ago Jeremy was the only clogmaker in the country still using the traditional knives to hand cut clog soles and make traditional British clogs. As was traditional in the mill towns of Lancashire and Rural Wales I have a pair of working clogs and a pair of posh clogs, I also have a pair of his shoes and sandles so I am a bit of a fan.
The knives are not easy to use but once mastered give tremendous controled power and leverage.
British clogs always had a wooden sole (alder, birch or sycamore) and leather uper unlike the Dutch and French sabot. There is a lot of understanding that goes into making a clog that is comfortable to walk in rather than just stand still in and Jeremy's just work.
That film was taken at St Fagan's museum in Cardiff whilst Jeremy was teaching Geraint who now makes clogs there. So now we have at least two makers hand cutting soles, the craft is still endangered but perhaops not quite so critically so.
This is Jeremy's website though I must say he is notoriously difficult to buy a pair of clogs from, you have to nag for a while.


Brett and Nazim open up the listener mailbag to answer questions posed by listeners and update previous cases that have since been decided. The topics this week include Holt v. Hobbs, smart phone technology, why DUI checkpoints are acceptable under the 4th amendment, and the possible scope of the Confrontation Clause decision. Due to the general breadth of this episode, we split it into two, so the remaining topics will be covered next week. Same Breyer time, same Breyer Channel.
The thesis of this week's episode is that taxes are nothing to be trifled with. First, Brett and Nazim discuss the many ways that citizens have failed trying to make income taxes unconstitutional. Afterward, Brett and Nazim discuss Comptroller of Maryland v. Wynn, in which the Court is asked to determine if a State is required to credit taxes paid to other states under the Dormant Commerce Clause. Finally, Brett's wife Jess comes on to confuse the issue entirely.
This week's episode covers Ohio v. Clark, which asks whether or not a teacher may testify on behalf of a three year old child who was the only witness to a child abuse case under the Confrontation Clause. This presents a good example of how the law can complicate an objective view of a bad situation, or how a sensitive topic can otherwise deny a citizen's Constitutional rights. Either way, Brett and Nazim get real awkward debating the issue somewhere around the 35 minuute mark, so get ready for that.
This week's episode discusses the legal issues surrounding the police in Ferguson, MS and Staton Island, NY. The goal here was to take a strictly legal and objective overview of the issues in each case, specifically what a grand jury does, why you can't sue the government and what the Constitution says about police use of deadly force, without getting too deep into the political issues that made up most of the media coverage. Let's call this one better late than never.


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The Ultimate Gentlemen's Weekend at the Cosmopolitan of Las Vegas
By Jared Paul Stern
Jul. 22nd, 2019
The concept of a "cool" resort / casino in Las Vegas simply didn't exist before The Cosmopolitan opened in 2010. Rat Pack nostalgia aside, even the most lavish properties were, and still are, decidedly cheesy. With a focus on luxury, art and design, and above all a wicked sense of humor, The Cosmopolitan changed Vegas overnight and, as it approaches its 10th anniversary, has continued to keep things fresh and vibrant. And with 3,000 guest rooms and suites, it still manages to feel like a boutique hotel, which is no small feat in itself. In addition to the gaming and the accommodations, the Cosmo also has the coolest bars, clubs, restaurants, shops, and entertainment venues, which have been carefully curated rather than merely designed to generate as much revenue as possible in the usual Vegas way.
The resort recently unveiled a multi-year, multimillion-dollar redevelopment, assuring it will enter its second decade in style. The multiphase project saw all 3,000-plus guest rooms remodeled last year, designed in partnership with New York-based Virserius Studios. Highlights include modern artwork, custom textiles, "statement" wall coverings, ambient lighting, user-friendly technology and a more energy-conscious approach overall. Rooms and suites are spacious, with stylish living, dining, work and bedroom areas, and boast terrific skyline views from the upper floors. The prevailing aesthetic is posh yet playful. The completion of the work offered an opportune time to catch up on the Cosmopolitan's latest enhancements in the form of an "Ultimate Gentlemen's Weekend" involving the following:
the Barbershop
The Barbershop Cuts & Cocktails
The Cosmopolitan is all about elevated, unexpected experiences, and so there's more than meets the eye to what looks like an ordinary, if beautiful designed barbershop on the main floor. There you can get a variety of cuts ranging from "clean and classic to wild and rebellious," a beard trim or straight razor shave courtesy of master barber Jose Sosa and his team. They'll even pour you a small-batch bourbon to enjoy while you get cleaned up. Come evening, however, you'll notice an awful lot of people going in through a door marked Janitor. And when you investigate you'll find a swanky Prohibition-style speakeasy that could be straight out of The Great Gatsby, but a bit more rock'n'roll. The cocktails here and throughout the property, especially at its signature multistory Chandelier bar, are impeccable.
The Cosmopolitan's owners scoured the most famous and sought-after foodie favorites in America to assemble this collection of casual food and drink purveyors. The offerings include District Donuts, Sliders and Brew from New Orleans; Ghost Donkey mezcal bar from NYC; Hattie B's Hot Chicken from Nashville; and Lardo and Pok Pok Wing from Portland. That's in addition to Eggslut, the Cosmo's cult all-day breakfast joint, a juice bar, bakery and more.
Boulevard Penthouses
The Cosmopolitan's most stunning penthouse suites are not available to rent; they're gifted to the very highest of high rollers. A number of world-famous interior designers worked on different spaces so no two are exactly the same. Ranging in size from 2,000 to 5,000 square feet, lavish doesn't begin to describe them, but they're all done in excellent taste and usually boast at least one eye-popping design element, be it a lucite grand piano or bespoke billiards table. You can, however, get access to the property's Chelsea Penthouse if you have deep pockets. Butler service is standard at both.
Dinner at Momofuku
It can be extremely difficult to decide which restaurant to dine in; The Cosmopolitan boasts enough outstanding ones to make a small city proud. There's something to suit every taste, but the standout for us is Momofuku Las Vegas from celebrity chef David Chang, the first Momofuku restaurant on the West Coast. While Korea and Japan exert the most influence on the menu and there's a definite street food gone gourmet vibe, Chang's cuisine can't be pigeonholed that easily. His version of surf and turf, for instance, involves fried chicken and caviar. The architecture is equally impressive; the dining room is a mélange of neon, glass, white oak, raw oiled steel, and concrete, as suits a dining concept that was named the "most important restaurant in America" by Bon Appétit. Other must-try dining experiences at the property include Blue Ribbon, the cult bistro from New York City; Scarpetta, the much-loved Italian eatery also from NYC; Zuma, the cool contemporary Japanese restaurant from Miami; and Rose.Rabbit.Lie., a modern supper club where entertainment is always on the menu.
One of the few experiences worth going off property for, Exotics Racing is the original, best rated and world's largest supercar driving experience, just outside of the Strip in the Las Vegas desert. The company lets you choose from over 50 exotic cars, 25 race cars and 25 go-karts, and you pay per lap to customize the experience you want. You can also create your own multicar package and drive up to 19 of them. Lamborghini, Ferrari, Porsche, McLaren, Aston Martin – they've got them all. You can also get a full photo and video package to commemorate the experience.
The Talon Club
Gaming at the Talon Club
The Talon Club is the Cosmopolitan's high roller retreat, and has the air of an ultra-exclusive gentlemen's lounge. There are public and private areas of the elite enclave. You need an established $500,000 line of credit with the property to open a private, reserved game in the Talon Club, and in The Reserve, which is off-limits to onlookers, the number jumps to $2 million, or $1 million just to gain entry. The public areas require a minimum bet of $500 to play at most tables, which include baccarat, poker, blackjack, and roulette. The Talon Club has its own selection of ultra-rare spirits as well as its own chef to cater to the whims of its top customers.
One of the hottest nightclubs in Vegas, Marquee, which originated in New York City, is easy to access for Cosmo guests (just be sure to observe the dress code). In addition to top international house music DJs presiding over the dance floor with its soaring windows looking over the Vegas Strip, you'll find the Boom Box, a mixed-format zone, and The Library, a VIP boîte. The Dayclub has a beach club vibe with a pool full of beautiful people that becomes an extension of the nightclub in the evening. The Grand Cabanas, each with its own private spa and infinity plunge pool, are the VIP sections here.
Maverick Helicopters
Maverick Helicopter Tour
The other off-property adventure that is not to be missed is a nighttime tour of the Vegas Strip from the air. The tour starts off with a glass of champagne to prepare you for a view that only high rollers usually get to see, from the iconic New York-New York skyline to the Eiffel Tower, the Bellagio's famous fountains and the Luxor pyramid. The company calls its ECO-Star helicopters "the limousine of the sky" and they are not wrong. Maverick also has a number of other tour options if you're feeling more adventurous.
The Pool District
Of course a resort of this caliber has more than one pool. In addition to the Marquee Dayclub pool, there are two others. The Boulevard Pool is geared more towards luxurious socializing with upbeat music and "intimately spaced" lounge chairs and daybeds, while the Chelsea Pool is an escape from the excitement of the strip, a stylish oasis above it all where you can lounge the day away. And overlooking the Marquee Dayclub pool are the Cosmopolitan's exclusive Bungalows, three-story lofts include spacious living areas infinity plunge pool and rooftop terraces.
Private Gulfstream Jet
If you merit one of the Cosmo's poshest penthouses, you need to arrive in style. Perhaps the Cosmopolitan's coolest new feature is the private jet is has acquired for its elite casino clientele. The Gulfstream aircraft, painted in the property's signature colors, features a lavish, contemporary interior bedecked in genuine leather that can accommodate up to 16 guests. In-flight menus will offer standout items from The Cosmopolitan's renowned Restaurant Collection, curated by the resort's Executive Chef, Bryan Fyler, alongside an extensive wine, champagne and cocktail selection.
The Sahra Spa
As this is Vegas, you'll like be recovering at some point. Or just looking to prepare for an epic night. The Sahra Spa and Hammam is the place to do it. Inspired by the desert and its legendary healing powers, the spa offers every service imaginable in a beautifully designed space that's a soothing cocoon of wood, stone and water. More than just a spa its a wellness retreat that can whisk you worlds away from the controlled chaos outside. Massages are a must but the hydrotherapy offerings and hammam, which includes a giant stone slab heated from within "as if by an eternal sun" are worth the price of admission alone.
Stitched Shirt Fitting & Scotch Locker
Stitched belies the notion that Vegas is locked in a losing battle with bad taste. This gentleman's oasis blends the classic with the contemporary, with a touch of irreverence that makes it perfect for the Cosmo. Top-drawer custom tailoring is the order of the day, from shirts to suits and tuxedos, with world-renowned fabrics master tailors, stylists, and other sartorial experts on hand to exceed your expectations every time. Designed as a place "where a man can be a man," there's also a VIP section of sorts in the back of the store where Scotch tastings courtesy of The Balvenie and other intimate events are frequently hosted. There you'll find some bespoke lockers filled with precious liquors, some with famous names including athletes, actors and DJs engraved on brass plates. They are not mere tributes but in fact boldface names who have reached a very exalted status indeed.
Jared Paul Stern
Jared Paul Stern, JustLuxe's Editor-at-Large, is the Executive Editor-at-Large at Maxim magazine and has written for the Wall Street Journal, New York Times, the New York Times' T magazine, GQ, WWD, Vogue, New York magazine, Details, Hamptons magazine, Playboy, BlackBook, the New York Post, Man of the World, and Bergdorf Goodman magazine among others. The founding editor of the Page Six magazine, he has also served as a judge for everything from the International Best Dressed List to the Ford Supermodel of the World contest. He currently contributes to A Continuous Lean, AskMen, CNN Style, Pursuitist, Airows and InsideHook, among others. ...(Read More)
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Idlewild: A Brand New Compilation From TMBG!
Idlewild: A Compilation is an ultra-vivid illustration of the band's prodigious output and singular musical vision. From the trance-inducing "Am I Awake?" to the haunting biography-in-miniature of "Tesla," the scope of Idlewild is as wide as it is beguiling.