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FALL DOWN ELEVATOR SHAFT HURTS 1
Twila Decker of The Sentinel StaffTHE ORLANDO SENTINEL
A hide-and-go-seek game at an abandoned Pine Hills mental hospital turned nearly deadly early Monday when a 23-year-old man fell down an elevator shaft, authorities said.
Afraid to get in trouble, Keith Murdock's three friends made an anonymous 911 call about 4:30 a.m., but rescuers were unable to find him until the friends called again two hours later.
During that time, the Kissimmee man was in critical condition on the floor of the Orlando Sunland Center, said Jim Solomons, Orange County sheriff's spokesman.
Murdock remained in critical condition late Monday at Orlando Regional Medical Center after surgery.
Murdock's three friends, Garry McCoy, 17, and Michelle Merriam, 18, and a 14-year-old, all of Kissimmee, have been charged with trespassing and burglary, Solomons said.
The Sunland Center is a former state mental hospital just southwest of the intersection of Silver Star and Hiawassee roads. The four-story hospital was shut down more than a decade ago amid charges of extreme patient neglect.
Pine Hills residents have complained that it has become an eyesore, attracting errant teenagers, crime and rodents and causing their property values to plummet.
"It doesn't surprise me one bit," resident Jeff Grzelak said of the fall. "We have been trying to get that torn down for 15 years but the state hasn't done anything."
Grzelak, who lives five houses away from Sunland, said a meeting to discuss the hospital already had been scheduled for 7 p.m. Wednesday at Barnett Park, 4801 W. Colonial Drive. Residents also are concerned about halfway houses and juvenile correctional facilities on the property.
Grzelak said the fall just proves his and his neighbors' point - that the hospital needs to be torn down immediately.
The towering white building is surrounded by fencing, razor wire and no trespassing signs. That hasn't kept people away. Trespassers have painted the walls with graffiti, broken windows and littered the grounds with beer bottles and other garbage.
"It has a legend of being haunted, like the stuff of Stephen King," Solomons said. "Naturally, it attracts kids who like to do that stuff - hide and scare each other."
Early Monday, Solomons said the thrill-seeking turned tragic when Murdock and three friends went to the building to play.
Solomons said the teenagers told investigators Murdock was hiding and trying to scare them. In the midst of their game, Solomons said, the teenagers heard a loud crash and discovered Murdock had fallen down the elevator shaft from the third floor.
Solomons said the friends told investigators they went to the Texaco station at the intersection of Hiawassee and Silver Star roads and made an anonymous 911 call. Then they returned to Kissimmee.
Rescue workers and deputy sheriffs searched the building for about 40 minutes but couldn't find the man, he said. Two hours later, the teenagers called again.
This time, they stayed on the line and led police to Murdock, who was airlifted to the hospital. Because of the seriousness of Murdock's injuries, Solomons said homicide detectives are investigating to make sure it was an accident.
"We're getting sketchy information," Solomons said, of the investigation. "One of the players isn't talking to us at all."
Editor's note: The former Sunland center in Pine Hills was not a mental hospital but rather a hospital for profoundly mentally and physically disabled adults and children.
2 WHO DIED IN RESCUE HONORED AS HEROES
Orlando Health employee invents pen sanitizer


Here, we are in conversation with Sebastian Herkner, one of today's most rising stars in design. He's possibly best known for his sublime Bell Table but he could be viewed as a renaissance man of sorts. He has designed such a wide range of objects, from chairs to tables, baskets to lighting, installations, interiors, clocks, rugs, vases and even containers. As you browse through is collection, it's hard to come across something you don't like and in fact I still have yet to do so. Herkner has chosen to stay in the town where he studied product design, Offenbach in Germany, but he is always on the road, either lecturing, designing or visiting other countries to better understand their design and manufacturing processes, and be inspired. His studio is small but his reach is far, having collaborated with Moroso, ClassiCon and others. Here, he speaks about the beauty of combining that which may seem incongruous.
Let's talk a little bit about how you grew up. I'm really curious with everybody that I speak to, when you started realizing that design was important, when it became important to you? Did you have a special object in your home that you felt was designed or were you disenchanted by what was around you, that maybe made it interesting to you?
I grew up on totally on the countryside in Germany, in a small village with 700 people and very slow tech so I was really outside growing up in the fields and the forests around. I think this was very important also for my work and I think in general it should be very interesting for kids also nowadays to go outside in nature, just watching the screens. It's something for your fantasy, it's something really for your senses also to get another experience to your real life outside. I think this was very important for my early career also to build outside really, to do something by hand because this is very important also for my work now.
Like I explained, to do something with your hand to shape something, I think these are my roots and I'm not really from a family based in architecture or designs. It was really driven by my interest on vacations that I saw some design classics from Scandinavia and then we visited the museum to get in contact. That time in the 80's and early 90's, I had no really idea about this profession, or that it's called industrial designer or product designer. At school also there was just, like doing fashion which was a fashion designer or an architect.
This changed, thanks God, the last years and also in the news and in the society in Germany, that it becomes more and more important, what a designer is doing. Also related, of course, to the new technology to the smart phones, and tablets, and so on that it's coming more and more important, the topic of design. I'm still more focused really on the product like furniture, lighting and accessories. We also starting now to really think about a place where people can feel comfortable and with nice products which become like companions for their life.
This small 700 person town, it's amazing it's really small, I mean 700 people, that's tiny. How long did you live there, and then when did you leave and what made you leave?
Actually, I lived so far 50% of my life there, so 18 years and, of course, my parents are still there and for me it's always very interesting to go back to see the difference to my city now where I live. Like Frankfurt in Germany or now I'm here in San Francisco or I just have been to Tokyo and other cities to see always these differences. They all have in common that we all have similar needs, for all life.
It's very interesting to observe the people in Japan, or in the small village, or also here at the west coast. What are their needs or how do they behave, sometimes there are differences, but nowadays more and more through globalization, we have more and more the same idea, and the same idea of freedom, and of our life. They are still the differences I can really catch and observe and with a good instinct and the interpretation there's the space to create new things.
You're always creating new things, it's incredible, how many different pieces there are. I love your website actually, I love how they just, as they're loading they come up and you don't know what to expect actually in those first few moments. Then suddenly a new piece comes and a new piece comes and think wow this person really definitely thinks about this a lot. Definitely has a lot in him.
What was the first piece you made, not that became popular, not that was put into production, but for you? When you first designed something do you remember what that was, when you first designed something?
Well, actually maybe it was not a design but I was really working when I was a teenager with a lot with materials, repairing things I found or put them together in a new typology, using two chairs to create another one. Things like this, so this was really my first contact and I think this is always very important to have this tests and to play with something to create something. At the end we work in a similar way, now we that we find a nice material, a new material, or we find a technology to create something with them, so it's always about a curiosity and to have a challenge or to have a vision to create something out of it.
Then to have the right partner who believes also in your vision and your idea and then it's always a good starting point to develop a design.
When you say partner, that's not, you have an assistant but you don't have a creative partner?
I have a team we are four, five people in the studio, and then we work for now international clients. They ask us to do a sofa, or to design a sofa, or to design lighting, or ways, or even an interior. This is always the starting point, sometimes we have a concept or an idea and then we go forward to a suitable company we think this could match to the collection.
How did you build your team? How did you choose who was the right fit for you?
Well, I started directly when I did my diploma in Offenbach where I started, there's also my studio. My studio was in the corner of my living room and it was 2006. Then it was growing step by step, with the first intern, this is like also some stories started here in a garage. It really started in a small space in the corner and then growing and then at the end, I just had the privacy in my sleeping room. The others was like a archive, or laboratory, or workshop for my designs.
Three years ago I moved one floor down to a formal leather workshop for leather bags, because Offenbach was the center for handbag production in Germany. There I got this space and I moved and now we are five people together with me.
Sounds like you have your own personal space to yourself, you're not sharing your apartment anymore.
Yeah, right I got it finally back.
I was thinking about your space, is it filled with beautiful things, where you live, your home?
My private home, yeah, well if it's beautiful or not some people maybe say it's too eclectic or it's a little bit too messy. It's not a white cube at the end, it's really a mix from own designs, designs from colleagues, or designers I really appreciate and like. There are classic pieces there are also, pieces by a big Swedish furniture store, so it's really a mix. This is very important, and a lot of souvenirs I bought on my vacations in Asia or South America or here, because I need all their things around me, and some of them are maybe ugly, some of them are nice, some of them became dusty because.
This is okay, but it's also with my design it's not just about to do beautiful things, I think it's more interesting to do things which are unique and maybe at the first moment you really have to stop and think about it. You have to really get in contact with them, and then the people later should decide if they like it or not. It would be too easy to do something which is like other things and then the people say, "Yeah, well it's like this, it's okay." Sometimes it's better to do something which is not so easy to understand, to give some energy to the product and then to get something successful.
I'm always careful of telling someone whose shown me something they've created that it's like something else, because while it's not an insult, but you can always tell when you say that to someone, the look on their face just gets disappointed. They don't want to be like something, they want to be theirs.
I think my designs are sometimes imitating, and on the other side for me it's very important always to put it upside down to go completely the other way. To swim against the river.
It's very important and plus it triggers new thoughts and I think that's exciting. Now why did you stay in Offenbach, what made you decide to stay there? I know you studied there, and I know you started your studio there, but you could have moved.
Actually I was also in the time between my pre diploma and diploma, I did a internship at Stella McCartney, so I used to live in London also for one year and really enjoyed the city. Now with the talks and fairs I'm really travelling a lot so, for me the best place is always where you feel really comfortable and home, and this is actually Offenbach where I studied.
I have my boyfriend there, my friends there around and it's a small city close by, really close there's just a river between is Frankfurt, which is a city with 750,000 people. A little bit bigger than my village, and I really like it there and I can travel quite easily from this area and I think you don't have to be in Berlin, Munich, New York to be more creative than in a smaller city.
The most important thing is that you really feel well with yourself and comfortable and have your positive energy and friends around, and on the other hand we get so much impact by travelling around or from social medias and other things. Nowadays there's no big reason to be in one of this very famous areas, and this is something I tell also students in China, or Thailand, or in Columbia which maybe have not the financial power to move to one of this big cities.
It's just important to have your own unique style to have your own design language, your philosophy, your power. You have to spend a lot of energy in it, but at the end if you use the right ways and nowadays with social medias you can get in contact with a lot of people worldwide. You have a chance to just have your success.
Thinking about you speaking to students, it's not that long ago you were a student yourself. You are so young in this field, I know that you're in your early 30's, but that's still very young. Do you notice that, do you notice your age, do you notice being young?
I was never interested really in the age, so I have a lot of friends who are in their 70's. Which is very interesting, because nowadays the people who are in their 70's are not like my grandparents at that time. I think we don't need to think in these clusters, I think it's really about communication and if you like someone it's the best way.
I was also thinking about the fact that, I think I had read that you had said that you need a quiet space to create. Living in a city would make it very hard to find that, but you need a quite space to really think about … to relate to design.
It's not just about the noise or the silence it's also sometimes about a whiter space, white walls. In my studio, also in the flat, it's sometimes it's very colorful with all this stuff I've found in the shelves. Sometimes it's good to have this white curtain to close in front of the shelf to have a white area to really think from the beginning and not to get some, all this information you see in the shelves.
It's really good to have a free mind to start to think about something. If I get a brief of a company, sometimes I get a idea in two weeks, or two days, but sometimes it takes two years, and I have to tell them, "No, sorry I don't have the super idea, or the idea you need or I feel comfortable." Deciding something is really about responsibility, it's something which can change the society, you see, especially here like products created here, in the area that it's changed our social life.
Then designers like me have to interact also observe these changes, so a bed nowadays is something different than 20 years ago. Now we use a bed in other ways, it's not just about sleeping, it's also about working late, so when I go to bed I check the last emails on social media. I need to sit maybe, so I need a back wall or something like wood back pillow, and also our seating on a sofa, or a working desk changed with all these new technologies.
There are still a lot of things to do, the thing about this, that's why I can't say we have enough chairs. New technologies, new materials, but also new behaviors with all this new surroundings or typologies on our working desks, needs new chairs and new ways of seating.
I read something that you had said about chairs, that you really have to think about the environment within which the chair is. Like is it going to be in the cinema, is it going to be in your home, is it going to be in, you didn't say this, but in a hospital, is it going to be in a kids room? Where's it going to be, because every chair is going to be radically different based on the context?
Even in a company there are different chairs. The secretary has another chair, then the CEO, or the person working or assembling somethings.
Let's talk about some of your pieces. Looking through them there were so many that I would have wanted. I'm actually pretty picky, so I was surprised. The glass table is so beautiful, and the colors are incredible. It sounds like you're always adding new colors to that collection. What are some of the pieces that you love? I know that sounds like a crazy question for someone that designs, it's hard to choose, they are like your children but what are some of them that stand out for you?
They are all kids, so I can't say that I prefer this one more than the other one, because some of them took three months, or two, others three years. They all have something in common, their approach for materials, craftsmanship, the mix of colors and materials, and that I know really where they are produced and how they are produced. In the case of the bell table, you mentioned, for German brand Clasicon it's produced in Bavaria in Germany.
Then I started to do design this table, I was thinking side tables and normally there's a metal structure and there's a glass plate on top. I wanted to say put it upside down, that's why the base of the table is made of glass, and the top is made of brass or copper. You have different versions and sizes also, and this is something I do with other products as well, to put it upside down to get this irritation. To get something really unique and strong.
For this table it was very difficult to find a glass maker, glass blower to produce this piece. Then we found this company which is more than 400 years old, and they are in 15th generation. At the end they just opened the workshops for school classes and people who were interested to see how glass production works, so it was like a stage or like in theater, they just did it for their visitors. Then I asked them if they can do it, and since that day in 2012, they produced this table, and the company's growing, so it also has a social sustainability and benefit. That there's this craft which has a new possibility now for this table, and a new avant garde design and I think this is very important. This is also something which made me very happy that I had this success also in this way.
This I do also with other products, so I do products also in Columbia, or in Zimbabwe in Africa. Like in Zimbabwe I went there for two weeks, to the north in an area close to Binga, to work on baskets with 20 woman. There was no tap water, no electricity but there was really passion and they interested to do something together and this was really an incredible moment.
How did that happen? How did you find them? What happened you were interested in basket weaving, just you were in Germany and you just suddenly, you thought wow basket weaving must amazing. Then what happened?
I was invited by European Union to do a project there and I was really curious to do but on the other hand I was also nervous. To get in contact, there was of course the barrier with the language which was not easy, and really to stay there. Two weeks ago I was on the furniture fair in Milan, which is completely the opposite, it's more or less a premium furniture show. Then you go to this very simple life in Zimbabwe.
Then you really have to talk to them, respect their tradition and how they live, and they sell like two baskets a month and earn $12, and they use the money for their kids to go to school to learn some English. To get their education and the man they go into the field to have the corn and protect the corn from the elephants. Then I asked for colors, but there was nothing and there's also no home depot you can go to buy something, and at the end they had not the money for this. I was looking for something which is easy to carry because some of the woman came 300 kilometers for this workshop.
At the end I found this packaging, the rice packaging, which are these plastic fiber bags and we used this plastic fiber, so there were some bags with red, and some with blue. We used this and mixed it with the natural fibers to get a very colorful basket but still respecting their tradition and pattern. This was really great experience, they were super happy about this very simple idea at the end, to think in a new way about the tradition and baskets.
It's an amazing experience, I mean really amazing experience. Particularly the way you've staged it, that it was right after Milan, and all the fanciness of Milan, and the money of Milan. Then to come to something like that, I just wouldn't expect it. Also it sounds like you were incredibly innovative, because since you didn't have access to something you managed to find the materials that you needed in the bags of the rice. That takes some thinking.
I think in this case it's really important to find something they can really afford and they can carry. Every woman buys rice there, every two weeks maybe one of this big bags. They have the material, so they can recycle also in this way, this was very interesting then to develop this product, and on the other hand it was super interesting to see that the men wore the European soccer tri-colors. They knew about the European Championship in soccer, so it's not like they're really in the bush, they really know about international things.
They also have their bank account on their mobile phone, which is not a smart phone a regular phone, but they can do their banking with the phone. Which was also interesting, we in Germany can't do our banking with the phone, at the moment, it will change now. You really have this big differences in the country it was really fantastic to see and to realize.
Wow what a highlight, pretty incredible for you to have really traveled the world before 40, coming from such a different background.
That's true I think travelling is really amazing, and I'm very thankful also to have this opportunity to go on one hand to the workshop, on the other hand to all these amazing cities to see. For a designer this is really the big pot with all the inspirations, and to see the mix of cultures and how cultures also mix like you can see here also in New York.
Now I was invited earlier this year as guest of honor at IMM Cologne fair to do the Das Haus. Das Haus is a special installation about living, about interior. I did it this year, and last year it was for example, [inaudible 00:27:37] and other designers. For me it was very important not just to do a house, for me it was really more important to do a statement, because what I mentioned we have the responsibility but we can have really also the freedom, opportunity to say something. All the creative minds can do it.
I wanted to do a house about hospitality about openness because in Europe, you know we have the crisis and the movement of refugees. This is really interesting, sometimes very sad, experience or situation now that you see, on one hand you see a lot of people welcoming them. On the other side you see the opposite because they are not curios or they have fear about their own future. I think we all need the curiosity and you see it in cities around the world that a mix of culture it works.
The same is also for me as a designer, it's so interesting to get the impact of other cultures to create new things. At Das Haus we did a house which was round, it was without strong walls. We used 600 square meters of curtains which were moving, changing the walls, opened the house, closed the house. It was about the perspective, the inside and outside, it was about the sensors. I wanted not to do a museum, I wanted to do really a space about communication where people stayed during the fair to communicate, to eat a soup we served. Really to enjoy the together, this was very important for me to have this statement.
It's very cool that you served soup too, it's kind of universal meal is soup. What kind of soup was it?
That kind it was a typical dish from Syria, so connected to refugees, and the soup also because of the sense of taste and smelling something, which was very important for me. To touch all the sensors, not just seeing and feeling, also to taste something, to hear something, and to smell something. There was also something very special which was a small highlight of sensors house, about smelling, it was the walls in the bathroom were covered with soap tiles. This is related to more or less to my studio because close to my studio there's a soap manufacturer producing soap for discount shops in Germany.
When we have lunch outside with my team, or barbecue with my friends in the evening, you can smell the daffodils one day and the other day you can smell the roses. I wanted to bring this experience to Das Haus so I asked the company if they can produce a special brick or tile made of soap, and we covered the two walls of the bathroom with the soap. It was very interesting also to watch the people walking by, because just maybe 30, 40% realized that the smell came from the soaps, from the wall. They thought it's maybe a fragrance, just from the supermarkets or fashion stores, which has the special fragrance.
Then they really enjoyed this experience that the smell came from this wall, and here I always mention, yeah this is something really do you have to remember you have to save in your head. You can't take a picture, you can't post it, because nowadays we go to concerts taking pictures, or just staring through the lens or through the mobile phone and we don't listen and watch the concert.
This is something really like with the soap, you have to remember and have to tell, you can't save it. I think this is an aspect which is very important nowadays, because we have this simple opportunity now with a smart phone to click, click, click to take pictures. We don't train or and use our head that much anymore.
Smell is one of those things, you smell something and you know you remember something from 20 years ago. Even from your early childhood it just comes back.
Exactly, smell is something very special, it's always connected to a sutiaton or to a person or to a material. I still remember the fragrance of my grandmother, which was also something to throw, so really if I smell this it connects me really to the situation with her and in my experience with her. I think this is something we don't use that much anymore, to smell somethings, we train more our sensors like to watch or to feel something. I think it's very special the sense of smelling.
It's great to incorporate that as a designer. You went to ICFF, is ICFF inspiring?
Of course, ICFF is also inspiring it's another fair, it's different to European fairs because the American style is different. The America interior style is different, and it's inspiring it's also for my point of view, it's really growing it's getting better and better. You have a lot of these small workshops and young designers from Brooklyn or from Washington showing their crafted things, lighting's.
You have the established designers like Lindsey Edelmann doing this amazing chandeliers, so I think it's very interesting to see and I really like also that there are differences to European style or also Asian style which are important. I don't want to have the same designs through everywhere, so this makes it more interesting. On the other hand European design is very important and successful also here in the States. Europe maybe is more the country for the crafted things and here it's more the technology, coming from here spreading over the world.
Worldwide you have this technologies developed here but on the other hand you have this furniture and crafted things [or pens 00:34:40], or whatever, all the cars produced in Europe.
So not what's next, like actually what's next, what's happening, what're you making next, but what dreams do you have next for yourself? Not what deals do you have with Clasicon, or any other company, but what do you have for yourself dream wise?
We're working a lot with new companies, we're doing also first interiors, which I really wanted to do. To think about the whole concept, about we doing department stores at the moment for example, so this is really something I really enjoy now with my team. On the other hand I want to travel the world to see different cultures, really to go this year to Japan for a holiday. To go another time to Africa which is an amazing continent because you see the milky way, which you can't see here anymore, not really because you have this light pollution.
Maybe if you go here to a desert on the way to Los Vegas maybe you can see it if there's no light. This was really something amazing to see it, so really to travel and to experience the world, and cultures, and people and different kinds of foods and music.


text:
\section{Introduction}

Deep convolutional neural network (CNN) architectures have a proven track
record of success in image classification \cite{Lenet}, \cite{Alexnet}, object detection \cite{Girshick2014}, \cite{Fastrcnn}, \cite{COCO}, semantic segmentation \cite{Deeplabv3}, \cite{Gscnn}, etc.
CNNs are also popular in human pose estimation.
For instance, DeepPose \cite{Deeppose} regresses the numerical coordinate locations of keypoints.
Fully convolutional networks such as \cite{Newell2016}, \cite{Chen2018}, \cite{Papandreou2017}, \cite{personlab}, \cite{Xiao2018}
predict keypoint heatmaps, which implicitly learn the dependencies between the human body parts.
All these studies rely on a CNN backbone, which unfortunately makes it
unclear how to explain the network's learning of the relationships between the different human body parts.
Moreover, the CNN models used for human pose estimation \cite{Newell2016}, \cite{Chen2018}, \cite{Papandreou2017}, \cite{personlab}, \cite{Xiao2018}
have very deep architectures, which makes it cumbersome to ascertain what each layer of the network learns and how it is related to the
final output predictions.
A schematic of the human keypoint detection is shown in Fig. \ref{fig:bottleneck} (a).
The task of keypoint detection inherently involves learning long-range dependencies: the network must
collect and associate body parts of the human in an image, and for this to be accurately learned, the
network must learn the relationships across sub-regions of the image, which is challenging to undertake in
purely convolutional neural network architectures.
Convolutional-based architectures require stacking multiple layers \cite{Simonyan2014}, \cite{Resnet}
in order to efficiently learn the local-to-global relationships.

Learning the long-range dependencies is critical for
robust predictions of human pose. In natural language processing (NLP) applications,
self-attention has a proven track record of success as the basic building block of
Transformer architectures to learn such local-to-global correspondences \cite{Vaswani2017}, \cite{BERT}, including for
long sequences.
The self-attention building block has also been extended to computer vision
applications by stacking Transformer blocks---the Vision Transformer (ViT)---by feeding image patches into the Transformer \cite{Dosovitskiy2020}
and learning the relationships between the patches. This demonstrated success of
self-attention as  the fundamental building block (i.e., in the Transformer)
has made it an alternative to the more classical convolutional architectures for many applications.
Recently, the Bottleneck Transformer \cite{BottleneckTransformers} was introduced
by blending CNN and MHSA layers. The standard ResNet \cite{Resnet} consists of multiple ``ResNet blocks"
where each of the block is a sequence of 1$\times$1, 3$\times$3 and 1$\times$1 convolutions. In the Bottleneck Transformer \cite{BottleneckTransformers},
the early ResNet blocks are retained as is, but in the last of the blocks, the 3$\times$3 convolution is replaced with a MHSA module.
A schematic of the MHSA block is shown in Figure \ref{fig:bottleneck} (b).
This MHSA performs a global ({\it all2all}) self-attention over a 2D feature map. The authors of \cite{BottleneckTransformers}
demonstrated strong performance on the ImageNet classification and COCO instance segmentation tasks.

\begin{figure*}[!h]
\centering%
(a)\includegraphics[width=5cm]{pose_schematic.eps} (b)\includegraphics[width=5cm]{botnet.eps} \\
\caption{(a) Schematic of the pose estimation problem. (b) Bottleneck Transformers: comparison of the ResNet block \cite{Resnet} and the MHSA block \cite{BottleneckTransformers}.}
\label{fig:bottleneck}%
\end{figure*}

Self-supervised learning (SSL) is a branch of machine learning that aims to learn useful features in
data without the use of a supervisory learning signal such as a label. Instead, one
often uses a Siamese-like framework with two identical neural network architectures---termed
``teacher" and ``student"---that see different augmented views of an input image and try to
match features using Knowledge Distillation \cite{SimCLR}, \cite{BYOL}, \cite{BarlowTwins}, \cite{SimCLR}, \cite{DINO}. Augmentations from the same source
image are a positive pair whereas from different sources form a negative pair. The objective here
is to maximize the similarity between positive pairs and minimize the same for negative pairs \cite{SimCLR}.
However, only positive pairs are used in many newer self-supervised learning algorithms such as BYOL \cite{BYOL},
Barlow Twins \cite{BarlowTwins}, SimSIAM \cite{SimSiam} and DINO \cite{DINO}. This works by Knowledge Distillation \cite{KnowledgeDistillation}.
Pre-training the network using SSL has the advantage of starting with superior weights vis-\'a-vis random weights, and this
forms the motivation for us to consider self-supervised pre-training.

While CNN architectures have a proven track record at
low-level feature extraction, they may not be robust enough for determining the local-to-global
correspondences as they need to be deep enough to have a large receptive field.
Inspired by the success of Bottleneck Transformers \cite{BottleneckTransformers}, in this study we
extend the idea to the problem of 2D human keypoint estimation on the COCO dataset \cite{COCO}.
We use hybrid backbone architectures combining (1) convolutional blocks, (2) MHSA bottleneck block(s) and (3) Transformer
Encoder layer. The early convolutional blocks extract low-level features, whereas the MHSA bottleneck block(s)
use them to extract mid-level features. Finally, the Transformer uses the mid-level features to learn
local-to-global relationships. Our architecture is similar in spirit to a recent paper
called Transpose \cite{Transpose}, however our architecture differs from them in that we use the
MHSA bottleneck transformer block as a mid-level feature extractor, which they did not consider.
We test the hypothesis that separating out early, mid and high level features with appropriate attention-based architectures helps in
better learning the local-to-global dependencies, as will be demonstrated in this paper.
The last attention layer of the Transformer Encoder layer acts as a feature aggregator from different sub-regions
of the image and combines them to learn the relationships between them.

In summary, our contributions are as follows:

\begin{enumerate}

\item{We use a hybrid architecture that combines Bottleneck Transformers with the vanilla Transformer Encoder to estimate human keypoints by predicting heatmaps. We term our model as BTranspose.}
\item{We consider self-supervised pre-training using DINO \cite{DINO} of our backbone architectures and this is found to improve the prediction accuracy.}
\item{We show attention map dependencies and compare them across different backbone architectures as well as with and without self-supervised pre-training. This provides insights on what exactly the attention layer of the MHSA and the Transformer Encoder focus on in an input image and how it relates to the final keypoint heatmaps predicted.}
\item{With self-supervised pre-training, our best performing BTranspose architecture achieves an AP of 76.4 and an AR of 79.2 on the COCO validation set, which
is competitive with Transpose \cite{Transpose} on AP with much fewer number of neural network parameters (10.1M for BTranspose versus 17.5M for Transpose).}

\end{enumerate}

\section{Related Work}
\subsection{Human pose estimation}

Deep CNNs have a proven track record at human pose estimation. However, locality and translation equivariance are inductive biases inherent in CNNs \cite{Lenet}, \cite{Alexnet}.
While CNNs are good at extracting low-level features from images, they have to be very deep in order to learn the local-to-global relationships between different
sub-regions of an image. Learning this relationship is vital to the task of human pose estimation as the location of the different human body parts must be learned to preserve
the human anatomy in the predictions. Many strategies have been proposed such as stacking \cite{Xiao2018}, \cite{Wei2016}, high-resolution representation \cite{Sun2019}
and multi-scale fusion \cite{Pfister2015}, \cite{Newell2016}, \cite{Chu2017}, \cite{Cheng2020} with a good amount of success.
Different neural network architectures have also been considered for human pose estimation: FPN \cite{FPN}, SimpleBaseline \cite{Xiao2018}, Hourglass Network \cite{Newell2016},
HRNet \cite{Sun2019}, RSN \cite{RSN}. Some of these architectures have $>$ 50M neural network parameters, which is hard to train and deploy. In contrast, we focus in this
study on lightweight neural network architectures ($\sim$ 10M parameters or fewer) for human pose estimation so that one can achieve real-time inference speeds.

\subsection{Multi-head self attention}
The basic building block of the vanilla Transformer is the multi-head self attention (MHSA) module \cite{Vaswani2017}.
Since the development of transformers for NLP applications, they have also percolated into computer vision applications.
For instance, DETR \cite{DETR} combines a ResNet-50 and a Transformer Encoder for object detection and reported high AP scores.
Bottleneck Transformers \cite{BottleneckTransformers} include the multi-head self attention module inside ResNet-like blocks and
have shown good performance for image classification and object
detection tasks; it forms the basis of the present study.
The Vision Transformer (ViT) \cite{Dosovitskiy2020} extended the idea of MHSA to image patches and has gained a lot of interest in the
computer vision community. DeiT \cite{DeiT} uses a distillation token for the student to learn from the teacher; they also introduce multiple
transformer architectures for computer vision applications: small, medium and large.
In this study, we extend \cite{Transpose} architecture by using a Bottleneck Transformer in the backbone network and demonstrate robust
predictions for the problem of 2D human pose estimation.

\subsection{Self-supervised learning}

Self-supervised learning (SSL) is the learning of representations in data without supervised labels and has gained a lot of interest in the computer
vision community recently. BYOL \cite{BYOL} uses two neural networks, referred to as online and target networks.
The online network is trained from an augmented view of an image to predict the representation of the target network of the same image under a different augmented view, using
the L2 loss function. The target network parameters are a slow-moving average of the online network.
DINO \cite{DINO} is similar in spirit as BYOL, but uses the cross-entropy loss instead.
Barlow Twins \cite{BarlowTwins} measures the cross-correlation matrix between the outputs of two identical networks fed with distorted versions of an image, and
makes it as close as possible to the identity matrix. This enforces the embedding vectors of augmented images to be similar, and concomittantly minimizes
the redundancy between the components of these vectors. Recently, \cite{Data2vec} use the same SSL framework for speech, NLP and computer vision, by
predicting latent representations of the full input data based on a masked view of the input in a self-distillation setup with standard Transformer architectures.
Inspired by the success of these different SSL methods for multiple applications, in this study we use the DINO method to pre-train the backbone architectures
in a SSL fashion on ImageNet images (i.e., without class labels) and transfer the learning to 2D human pose estimation.
We will demonstrate that this SSL pre-training consistently augments the final prediction AP values.

\subsection{Explainability of neural networks}

Explainability is essentially an effort to understand what the model learns from data to make predictions.
This is a key component in Deep Learning as otherwise neural network predictions are essentially black box in nature lacking an understanding of what the model infers from data.
Explainability and interpretability of a neural network is ascertaining what parts of an input are the most relevant to make predictions \cite{Samek2019}.
\cite{Li2014} undertake gradient descent in the input to find which patterns maximize a given unit.
For image classification task, \cite{Simonyan2013} compute a class saliency map, specific to a given image and class and show that such maps can be used
for weakly supervised object segmentation using CNN architectures. They also establish the connection between gradient-based visualization methods in CNNs
and deconvolutional networks.
\cite{Zeiler2014} introduced a novel visualization technique to provide insights into the function of intermediate feature layers in CNNs.
Transpose \cite{Transpose} also present dependency areas for keypoints for the 2D human pose estimation problem using
Activation Maximization \cite{Simonyan2013}. Explainability boils down to a sensitivity analysis problem of estimating which
region of an input maximizes the activation of a given neuron. Similar to \cite{Transpose}, we also undertake such dependency area estimation, but
the main difference being that we make this dependency analysis both at the mid (MHSA block) and high levels (Transformer Encoder) whereas they do so only at the high level.
We will demonstrate how this mid-level explainability is useful to better understand what the network learns.

\begin{figure*}[!h]
\begin{center}
  \includegraphics[width=1.0\linewidth]{BTranspose2D.eps}
\end{center}
   \caption{The BTranspose architecture: shown here is the C3A1 architecture used in our paper. The purple rectangle is the backbone used. The naming convention for the different architectures is bsed on the number of CNN and MHSA blocks in the blue rectangle inside the backbone. Finally, the output of the Transformer encoder is fed into a Deconvolutional layer followed by a 1$\times$1 convolutional layer to output 17 heatmaps.}
\label{fig:btranspose}
\end{figure*}

\section{Method}

The primary objective of this study is to make 2D human keypoint predictions from input images, and also explain global dependencies between the keypoint
predictions. Specifically, we explore different backbone architectures by combining Bottleneck Transformers \cite{BottleneckTransformers}
and vanilla Transformer encoder layer \cite{Vaswani2017}. The backbone architectures are pre-trained using Self-Supervised Learning (SSL) and we
will demonstrate that this strategy improves the prediction accuracy. Most of the architectures considered in this study are lightweight and have $\sim$ 10M parameters.

\subsection{Architecture}

Our neural network architecture consists of a backbone for feature extraction, followed by a head to predict 17 keypoint heatmaps.
A schematic of one of our architectures is presented in Figure \ref{fig:btranspose}.
We will now describe our neural network architectures, indicating the color used for each module in Figure \ref{fig:btranspose}.
The purple rectangle in Figure \ref{fig:btranspose} represents the backbone, which is pre-trained using self-supervised learning (SSL), and is now described in detail.

{\bf Backbone}
The backbone consists of three components: (1) early CNN block; (2) a Bottleneck Transformer \cite{BottleneckTransformers}; (3) a vanilla Transformer encoder layer \cite{Vaswani2017}.
The input image first goes through an early convolutional block (yellow in Figure \ref{fig:btranspose})
that consists of 7$\times$7 convolutional kernel, Batch Normalization and max pooling to downsample, identical to a ResNet-50 \cite{Resnet}.
The second module in the backbone is the Bottleneck Transformer (BT) \cite{BottleneckTransformers} with consists of
either 2 or 3 convolutional ResNet block groups \cite{Resnet}, followed by 1 or 2 multi-head self-attention (MHSA) block groups (see Figure \ref{fig:bottleneck}).
The convolutional ResNet blocks consist of 3$\times$3 convolutions sandwiched between
1$\times$1 convolutions (green in Figure \ref{fig:btranspose}), albeit the number of feature maps used in this study for these convolutional blocks are fewer than the standard ResNet.

This is followed by the MHSA blocks \cite{BottleneckTransformers} (refer to Figure \ref{fig:bottleneck}) with the
MHSA layer sandwiched between 1$\times$1 convolutional layers (orange in Figure \ref{fig:btranspose}).
The naming convention we will use in this paper is the number of such blocks in the BT.
For example, architectures with 2 CNN blocks and 2 MHSA blocks are termed C2A2; with 3 CNN blocks and 1 MHSA block are termed C3A1, etc.
The blue rectangle in Figure \ref{fig:btranspose} denotes the subset of the backbone that gives rise to the naming convention used:
for instance, since we use 3 conv blocks (green) and 1 MHSA block (orange), the architecture shown in the figure is C3A1.

The last module in the backbone is a standard Transformer encoder layer (blue in Figure \ref{fig:btranspose}) to capture the long-range
dependencies from the sequences using the standard query-key-value attention modules \cite{Vaswani2017}.
If the output of the MHSA block is $\mathbb{R}^{(d_1 \times H \times W)}$, it is first passed through a 1$\times$1 convolution to obtain a feature dimension $d$ and is
then flattened before feeding into the Transformer encoder. The dimension of the features input to the Transformer encoder is $\mathbb{R}^{(L \times d)}$, where $L = H \times W$.
This goes through $N$ attention layers and feed-forward networks (FFNs), similar to the vanilla Transformer \cite{Vaswani2017}.
Note that our architecture is somewhat similar to \cite{Transpose}, but they used vanilla CNN blocks followed by the Transformer encoder, whereas we use
BT followed by the vanilla Transformer encoder.
The motivation for choosing our architectures is that the CNN blocks in BT capture the low-level features, the MHSA blocks
use this information to capture mid-level features using attention, and the Transformer encoder will then use these mid-features to learn the global relationships.
Thus, our architecture uses self-attention both at the mid and high levels.

{\bf Head} A head is attached to the backbone. This head consists of a deconvolution layer (grey in Figure \ref{fig:btranspose})
to increase the size of the feature map from 32$\times$24 to 64$\times$48, and a simple 1$\times$1 convolutional layer (cream color in Figure \ref{fig:btranspose})
to output 17 human keypoint heatmaps of size 64$\times$48.

\subsection{DINO self-supervised pretraining}

The self-supervised pre-training of the backbone is undertaken using the DINO \cite{DINO} method, which uses self distillation from the
teacher to student networks. Augmented views of the input images are fed into the teacher and student networks, respectively.
The output features of the teacher and student are enforced to be similar with the cross-entropy loss, which is back-propagated into the
student network. The teacher network follows the student network by means of an Exponential Moving Average (EMA) update rule, i.e., a Momentum Encoder \cite{He2020}.
In addition, a centering operation is done to avoid collapse \cite{DINO}.

\section{Experiments}

{\bf Dataset}. For the Self-Supervised pre-training of the backbone architectures, we use ImageNet (images only, no labels).
For the 2D human pose estimation downstream task, all our training and inference is on the COCO \cite{COCO} dataset. COCO consists of 200k images in the wild, with $\sim$ 250k human instances.
Train2017 has 57k images and 150k person instances. Val2017 has 5k images. The evaluation metric is based on Object Keypoint Similarity (OKS) \cite{COCO} and
we compare Average Precision (AP) and Average Recall (AR) for the different models on the COCO validation set.

{\bf Training details}. The SSL pre-training strategy follows \cite{DINO} and uses 300 epochs.
For the 2D human pose estimation, we follow the top-down human pose estimation paradigm where a pre-trained object detector
obtains the bounding box around humans in an image, which is then extracted and
fed into the pose estimator. The training set consists of single person crops from the dataset. All input images are resized to 256 $\times$ 192.
The same data augmentation and training strategies from \cite{Sun2019}, \cite{Transpose} are used in this study. We use a total of 230 training epochs for
the 2D human pose estimation problem, and employ the L2 loss function.

{\bf Architecture details}. The architectures we use comprise of either 3 or 4 block groups \cite{BottleneckTransformers}
with the number of feature maps being [64, 96, 128, 256] for the block groups (in the same sequence),
which is fewer than the original BT used in \cite{BottleneckTransformers} or the ResNet-50. The number of blocks used in each group is
[3, 4, 6, 3], which is the same as the ResNet-50 and BT.
We vary the number of attention heads in the MHSA block and this takes the value 4 or 8 in this study.
The Transformer encoder uses a transformer dimension of 256 and a feed-forward dimension of 1024. The number
of transformer layers is $N$ = 4 (except in one ablation study where $N$ = 6), the number of heads in the Transformer is 8.
We use relative position embedding for height and width for the MHSA block, consistent with \cite{BottleneckTransformers}.
For the Transformer encoder, we use learnable position embeddings \cite{Vaswani2017}.

{\bf Naming convention}. As aforementioned, the naming convention used in this paper (e.g., C2A1, C2A2, C3A1) is based on the number of convolutional and MHSA blocks in the BT part of
the backbone (inside the blue rectangle shown in Figure \ref{fig:btranspose}).
When the number of attention heads in the MHSA block is 4, we add a ``(4)" to the model name; and similarly a ``(8)" when 8 heads are used in the MHSA block (note: the number
of attention heads in the Transformer encoder is 8 for all the cases considered in this paper).
In addition, when we pre-train with DINO, we add a suffix ``Dino" to the model name; if the suffix ``Dino" is not used, it is a model
trained on the 2D pose estimation task with random weights as initialization.

\subsection{Results on 2D human pose estimation}

We compare BTranspose with SimpleBaseline \cite{Xiao2018}, HRNet \cite{Sun2019} and Transpose \cite{Transpose} in Table \ref{table1} on the
COCO validation set.
Specifically, C3A1(4)-Dino is the best performing BTranspose model with an AP of 76.4 and AR of 79.2, and so the comparison is done with this.
C3A1(4)-Dino outperforms all the other models shown in Table \ref{table1} on AP and comes close to Transpose-H-A4/6 on AR.
Note that C3A1(4)-Dino only uses 10.1M neural network parameters, whereas Transpose-H-A4/6 use 17.5M.
C3A1(4)-Dino also outperforms Transpose-H-S which has number of parameters comparable to BTranspose.
Thus, with only 10.1M parameters, BTranspose is competitive with other pose estimation models.
Furthermore, at inference C3A1(4)-Dino has an FPS of 45, which is also comparable with other pose estimation models such as \cite{Transpose}.

\begin{table}
\begin{center}
\begin{tabular}{|c|c|c|c|}
\hline
{\bf Model} & {\bf AP} & {\bf AR} & {\bf \#Params} \\
\hline
SimpleBaseline-Res50 \cite{Xiao2018} & 70.4 & 76.3 & 34.0M \\
SimpleBaseline-Res101 \cite{Xiao2018} & 71.4 & 76.3 & 53.0M \\
SimpleBaseline-Res152 \cite{Xiao2018} & 72.0 & 77.8 & 68.6M \\
\hline
HRNet-W32 \cite{Sun2019} & 74.4 & 79.8 & 28.5M \\
HRNet-W48 \cite{Sun2019} & 75.1 & 80.4 & 63.6M \\
\hline
Transpose-R-A3 \cite{Transpose} & 71.7 & 77.1 & 5.2M \\
Transpose-R-A4 \cite{Transpose} & 72.6 & 78.0 & 6.0M \\
Transpose-H-S \cite{Transpose} & 74.2 & 78.0 & 8.0M \\
Transpose-H-A4 \cite{Transpose} & 75.3 & 80.3 & 17.3M \\
Transpose-H-A6 \cite{Transpose} & 75.8 & {\bf 80.8} & 17.5M \\
\hline
C3A1(4)-Dino (this study) & {\bf 76.4} & 79.2 & 10.1M \\
\hline
\end{tabular}
\end{center}
\caption{Comparison of AP and AR on the COCO validation set for different models. C3A1(4)-Dino achieves competitive results with fewer parameters.}
\label{table1}
\end{table}

\subsection{Ablation studies}

{\bf SSL pre-training versus random weight initialization}. Self-supervised learning (SSL) used as pre-training has a proven track record
of learning useful image representations from unlabeled data. To this end, we have also pre-trained our models on
ImageNet (images only, no lables) using the DINO \cite{DINO} method. We compare our pose estimation models with (1)
random weight initialization and (2) DINO pre-training on ImageNet. With pre-training, the neural network learns useful
low level features such as basic shapes and edges and how to aggregate information from them for the downstream pose
estimation task. Table \ref{table2} compares several BTranspose models with random initialization and
DINO pre-training.\footnote{training C2A2(4) went awry, probably due to poor weight initialization.}
All three BTranspose models C2A1(4)-Dino, C2A2(4)-Dino, C3A1(4)-Dino outperform when pre-trained using DINO, with +1.3 AP and +1.4 AR observed for
C3A1(4)-Dino compared to its random initialization counterpart C3A1(4). Pre-training using SSL obviously improves performance, even though the
pre-training was done on a completely different dataset (i.e., ImageNet), as basic features such as shapes and edges can still be learned which
is useful for the downstream pose estimation task. Furthermore, C3A1(4)-Dino outperforms both C2A1(4)-Dino and C2A2(4)-Dino, which suggests that
3 convolutional blocks help in learning better low-level features, before we proceed to processing the mid-level features in the MHSA block.

\begin{table}
\begin{center}
\begin{tabular}{|c|c|c|c|}
\hline
{\bf Model} & {\bf AP} & {\bf AR} & {\bf \#Params} \\
\hline
\multicolumn{4}{|c|}{random initialization} \\
\hline
C2A1(4) & 74.2 & 76.9 & 6.8M \\
C2A2(4) & 59.0 & 62.6 & 9.55M \\
C3A1(4) & 75.1 & 77.8 & 10.1M \\
\hline
\multicolumn{4}{|c|}{self-supervised pre-training}  \\
\hline
C2A1(4)-Dino & 75.2 & 78.0 & 6.8M \\
C2A2(4)-Dino & 75.0 & 77.8 & 9.55M \\
C3A1(4)-Dino & 76.4 & 79.2 & 10.1M \\
\hline
\end{tabular}
\end{center}
\caption{Random weight initialization versus self-supervised pre-training on ImageNet using DINO \cite{DINO}.}
\label{table2}
\end{table}

{\bf 4 versus 8 heads in the MHSA block}. For the MHSA block of the Bottleneck Transformer used in the backbone,
we considered either 4 or 8 self-attention heads (note: the Transformer encoder always uses 8 heads for all models; the analysis here is for the MHSA block only).
The MHSA block is responsible for learning mid-level features using attention, which are then passed on to the Transformer encoder.
The results are compared in Table \ref{table3} for C3A1 architecture for the bottleneck in the backbone.
With random weight initialization, 8 heads outperforms 4 heads, with +0.2 and +0.3 on AP and AR observed, respectively.
However, with SSL pre-training, 4 heads has better performance than 8: +0.4 on AP and +0.3 on AR.
We believe that more heads help training when the networks are trained from scratch with random weight initialization.
However, when SSL pre-training is used, the networks already learn useful image features and in this setting fewer heads in the MHSA block suffices.

\begin{table}[!htb]
    \begin{minipage}{.5\linewidth}
      \begin{tabular}{|c|c|c|}
      \hline
      {\bf Model} & {\bf AP} & {\bf AR}  \\
      \hline
      \multicolumn{3}{|c|}{random initialization} \\
      \hline
      C3A1(4) & 75.1 & 77.8  \\
      C3A1(8) & 75.3 & 78.1  \\
      \hline
      \multicolumn{3}{|c|}{self-supervised pre-training}  \\
      \hline
      C3A1(4)-Dino & 76.4 & 79.2  \\
      C3A1(8)-Dino & 76.0 & 78.9  \\
      \hline
      \end{tabular}
      \caption{4 versus 8 attention heads in the MHSA block. All models have 10.1M \#Params.}
      \label{table3}
    \end{minipage}%
    \begin{minipage}{.5\linewidth}
        \begin{tabular}{|c|c|c|c|}
        \hline
        {\bf Model} & {\bf AP} & {\bf AR} & {\bf \#Params}  \\
        \hline
        C3A1(4)-Dino & 76.4 & 79.2 & 10.1M  \\
        C3A1(4)-Dino-N6 & 76.3 & 79.1 & 11.67M \\
        C3A1(4)-Dino-Large & 76.7 & 79.3 & 23.82M \\
        \hline
        \end{tabular}
        \caption{Other architecture variants.}
        \label{table3b}
    \end{minipage}
\end{table}

{\bf Number of transformer encoder layers}. We consider another case where the number of transformer encoder layers is varied.
For all the cases considered thus far, we used N = 4 layers; we consider one case with N = 6 layers (called C3A1(4)-Dino-N6), with everything else
being the same. The results on the COCO validation set are presented in Table \ref{table3b}. Compared to the baseline
C3A1(4)-Dino case, AP and AR are -0.1 and -0.1, respectively. Thus, adding extra transformer encoder layers has no performance boost.

{\bf Larger network architecture}. The number of feature maps used in the CNN and MHSA blocks for all the cases considered thus far is
[64, 96, 128, 256]. We consider a larger network architecture with [64, 128, 256, 512] feature maps for the CNN and MHSA block groups---we name it C3A1(4)-Dino-Large---
whose backbone is the same as the original architecture used in \cite{BottleneckTransformers}. As before, this network is also pre-trained using DINO \cite{DINO} and the
results on the COCO validation set are presented in Table \ref{table3b}. AP and AR for C3A1(4)-Dino-Large are
+0.3 and +0.1 compared to C3A1(4)-Dino, which is an incremental improvement, albeit with 2X number of parameters.

{\bf Optimal transport loss function}. We also consider one training with the Sinkhorn loss function used in optimal transport \cite{Cuturi2013}
for the downstream pose estimation task, with the entropy regulation term $\epsilon$ = 0.05 and number of training iterations $n$ = 3.
On the COCO validation set the AP is 76.3 and AR is 79.0, which is -0.1 and -0.2 compared to C3A1(4)-Dino.
Thus, with the Sinkhorn loss BTranspose slightly underperforms. We believe the reason could be the two hyperparameters $\epsilon$ and $n$
may need to be calibrated, which is problem specific.

\begin{figure*}[!h]
\centering%
(a)\includegraphics[width=10cm]{attn265_C3A1_4_Dino_MHSA.eps} \\
(b)\includegraphics[width=10cm]{attn265_C3A1_4_Dino.eps} \\
(c)\includegraphics[width=10cm]{attn265_C3A1_4_MHSA.eps}  \\
(d)\includegraphics[width=10cm]{attn265_C3A1_4.eps} \\
\caption{Dependency areas for the predicted keypoints using models: (a \& b) C3A1(4)-Dino; (c \& d) C3A1(4). The dependency area is computed at the MHSA block in (a \& c) and at
the Transformer encoder layer in (b \& d). The predicted keypoints and the skeleton reconstruction is also shown in the first column of each image.}
\label{fig:da1}%
\end{figure*}

\subsection{Dependency areas}

We undertake a qualitative analysis to understand how the network makes
the final predictions by aggregating information. To this end, we visualize dependency areas from attention maps
using the final predicted keypoint as the query location, similar to \cite{Transpose}. But the analysis
differs from \cite{Transpose} in that they have attention layers only in the Transformer encoder, whereas in our study
we have 2 parts of the backbone network that have attention layers: the MHSA block in the bottleneck transformer and in the
final Transformer Encoder (TE) layer. The dependency areas in the MHSA block provides insights on the role of attention at the
mid-level features; whereas the same at the TE layer provides insights on the role of attention at the high-level features.

{\bf MHSA block versus Transformer encoder} We present the dependency areas for an input image comparing
C3A1(4)-Dino and C3A1(4) in Figure \ref{fig:da1}. It is interesting to note that at the MHSA level, the network has already learned to
focus on the human anatomy/shape, i.e., a rough segmented region of the human,
with many potential keypoint regions having high scores (yellow/red patches).
However, at the MHSA block (Figure \ref{fig:da1} a \& c), no strong correlation is evident between the
final keypoint predicted (white star in the figure) and the region where the MHSA block focuses on in the input image.
In the Transformer encoder attention dependency areas (Figure \ref{fig:da1} b \& d),
we see high values in the vicinity of the predicted keypoints.
This suggests that at the MHSA block, the network has learned to aggregate low-level information to construct mid-level features and
infer the basic human anatomy/shape, but has not yet learned which part of the human anatomy corresponds to the final keypoint of interst.
In contrast, at the Transformer encoder level, the network aggregates the mid-level understanding to learn local-to-global relationships, and so
arrives at the final predicted keypoint.

\begin{figure*}[!h]
\centering%
(a)\includegraphics[width=10cm]{attn265_C3A1_8_Dino_MHSA.eps} \\
(b)\includegraphics[width=10cm]{attn265_C3A1_8_Dino.eps} \\
\caption{Dependency areas for the predicted keypoints using C3A1(8)-Dino for the (a) MHSA block and (b) Transformer encoder layer.}
\label{fig:da2}%
\end{figure*}

{\bf Dependency area due to self-supervised pre-training.} Comparing C3A1(4)-Dino and C3A1(4) in Figure \ref{fig:da1}, it is evident that the
dependency areas are different. At the MHSA level, the dependency areas look relatively smaller for C3A1(4)-Dino than C3A1(4), so
pre-training potentially helps at the MHSA level as the early layers have learned low-level features better. At the Transformer encoder level, the
differences are subtle, suggesting that even though there may be multiple pathways by which the mid-level features are extracted in the MHSA block, at the
Transformer encoder layer they still get aggregated to form mid-to-high relationships to arrive at the final keypoint. More examples are presented in the
supplementary material.

{\bf 4 versus 8 attention heads in the MHSA block.} Next, we consider C3A1(8)-Dino and present the dependency areas for the predicted keypoints
in Figure \ref{fig:da2}. Here too the network has learned the basic human anatomy (i.e., a segmented sub-region of the human) at the MHSA block using attention (i.e., the mid-level), and
at the Transformer Encoder level it has learned to aggregate the mid-level information to infer the global relationships and predict the final keypoint.
Comparing Figures \ref{fig:da1} and \ref{fig:da2}, we observe that at the MHSA block 8 heads seems to result in a few dependency areas
that are partially outside the human sub-region, suggesting that 8 heads can confuse the network to some extent at the mid-level features.
However, at the Transformer Encoder level, the differenes are much less pronounced, suggesting that at the high-level features the Transformer Encoder has overcome some of the
confusion that exists at the mid-level. More examples are presented in the supplementary material.

{\bf Special Scenarios.} Next, we consider some special cases and compare the dependency areas. In Figure \ref{fig:da21} (a) we consider a horse rider with the left half of the human
occluded. At the MHSA block, the network has already learned the shape of the human, although some
regions of the horse are also attended to.
Comparing the dependency areas at the TE layer for the left elbow and left ankle (i.e., two occluded body parts)
we find the red patches (i.e., peak magnitudes) to be closer to the predicted keypoint location for C3A1(4)-Dino than for C3A1(4).
We consider the input image of a surfer in Figure \ref{fig:da21} (b). This is interesting as the human in the image is nearly horizontal, whereas most of the images
in the training are of vertically-oriented humans. At the MHSA block, the network has learned to focus on the sub-region of interest, albeit with
no correlation with the query keypoint. At the TE layer, the peak magnitudes are closer to the query keypoint for C3A1(4)-Dino than for C3A1(4)
suggesting that SSL pre-training helps in the network being more confident in its predictions.
In another special case, we consider two humans close to each other in the input image (see supplementary material). Here, the
MHSA block is unable to distinguish between the two humans, but has learned to segment the sub-region as a large patch.
The TE layer then aggregates this information to make the distinction between the two humans close to each other.

\begin{figure*}[h]
\centering%
(a) \includegraphics[width=5.5cm]{horserider.eps} (b) \includegraphics[width=5.5cm]{surfer.eps}
\caption{Dependency areas for the (a) horse rider and the (b) surfer. The white star denotes the predicted keypoint location.}
\label{fig:da21}%
\end{figure*}

\section{Conclusion}

For the task of 2D human pose estimation, we explored a model---BTranspose---by combining
Bottleneck Transformers with the vanilla Transformer Encoder (TE). We have attention layers in two regions of the backbone:
the MHSA block and in the Transformer encoder. Attention in the MHSA block focuses on the mid-level features, whereas attention in the
TE aggregates this information to learn high-level features.
Self-supervised pre-training of the backbone is found to improve the performance of the models for all the architectures considered in this study.
The model is lightweight and with only 10.1M parameters is able to compete with other state-of-the-art approaches on the COCO dataset.
Analyzing dependency areas reveals that the MHSA block learns the basic human anatomy/shape, but has not yet learned which
region of the human anatomy to focus on for the queried keypoint.
However, this mid-level information is then used by the Transformer encoder to learn the mid-to-high relationships and arrive at the final keypoint.
Analyzing some special scenarios, we find that the model pre-trained with SSL is slightly more confident in knowing where to focus on in the input image.
This is particularly useful for images with occluded human body parts, as well as when there are two humans very close to each other.
Overall, BTranspose is competitive with other 2D pose estimation models, is lightweight and provides insights into how the model aggregates information at
both the mid and high-levels.

\bibliographystyle{splncs}

redpajama_set_name: RedPajamaArXiv


text:
module IAFinance.Path
( config
) where

import Paths_iafinance

-- dir :: FilePath
-- dir = "res"

-- resource :: IO FilePath
-- resource = getDataFileName dir

config :: IO FilePath
config = getDataFileName "config.json"

{-

[3.3.5. Accessing data files from package code]
(https://www.haskell.org/cabal/users-guide/developing-packages.html)

-}

redpajama_set_name: RedPajamaGithub


Digital print of my original watercolor painting featuring a mermaid in silhouette. Choose between 8"X10" and 8"X8" square. Printed on "Rag" Fine Art Paper.
Materials used to create the original piece: cold-press watercolor paper, saltwater, rubbing alcohol, large grain sea salt, Grumbacher watercolor paints, masking fluid.


text:
using System;
using System.Collections.Concurrent;
using System.Collections.Generic;
using System.Linq;
using Umbraco.Core.Models.PublishedContent;
using Umbraco.Core.Services;

namespace Umbraco.Core.PropertyEditors.ValueConverters
{
    [DefaultPropertyValueConverter]
    public class FlexibleDropdownPropertyValueConverter : PropertyValueConverterBase, IPropertyValueConverterMeta
    {
        private static readonly ConcurrentDictionary<int, bool> Storages = new ConcurrentDictionary<int, bool>();
        private readonly IDataTypeService _dataTypeService;

        // TODO: Remove this ctor in v8 - the other one will be usable via IoC
        public FlexibleDropdownPropertyValueConverter() : this(ApplicationContext.Current.Services.DataTypeService)
        { }

        public FlexibleDropdownPropertyValueConverter(IDataTypeService dataTypeService)
        {
            Mandate.ParameterNotNull(dataTypeService, "dataTypeService");
            _dataTypeService = dataTypeService;
        }

        public override bool IsConverter(PublishedPropertyType propertyType)
        {
            return propertyType.PropertyEditorAlias.Equals(Constants.PropertyEditors.DropDownListFlexibleAlias);
        }

        public override object ConvertDataToSource(PublishedPropertyType propertyType, object source, bool preview)
        {
            return source != null
                       ? source.ToString().Split(new[] { ',' }, StringSplitOptions.RemoveEmptyEntries)
                       : null;
        }

        public override object ConvertSourceToObject(PublishedPropertyType propertyType, object source, bool preview)
        {
            if (source == null)
            {
                return null;
            }

            var selectedValues = (string[]) source;
            if (selectedValues.Any())
            {
                if (IsMultipleDataType(propertyType.DataTypeId, propertyType.PropertyEditorAlias))
                {
                    return selectedValues;
                }

                return selectedValues.First();
            }

            return source;
        }

        public Type GetPropertyValueType(PublishedPropertyType propertyType)
        {
            return IsMultipleDataType(propertyType.DataTypeId, propertyType.PropertyEditorAlias)
                       ? typeof(IEnumerable<string>)
                       : typeof(string);
        }

        public PropertyCacheLevel GetPropertyCacheLevel(PublishedPropertyType propertyType,
                                                        PropertyCacheValue cacheValue)
        {
            PropertyCacheLevel returnLevel;
            switch (cacheValue)
            {
                case PropertyCacheValue.Object:
                    returnLevel = PropertyCacheLevel.ContentCache;
                    break;
                case PropertyCacheValue.Source:
                    returnLevel = PropertyCacheLevel.Content;
                    break;
                case PropertyCacheValue.XPath:
                    returnLevel = PropertyCacheLevel.Content;
                    break;
                default:
                    returnLevel = PropertyCacheLevel.None;
                    break;
            }

            return returnLevel;
        }

        /// <summary>
        /// Determines if the "enable multiple choice" prevalue has been ticked.
        /// </summary>
        /// <param name="dataTypeId">The ID of this particular datatype instance.</param>
        /// <param name="propertyEditorAlias">The property editor alias.</param>
        /// <returns><value>true</value> if the data type has been configured to return multiple values.
        /// </returns>
        private bool IsMultipleDataType(int dataTypeId, string propertyEditorAlias)
        {
            // GetPreValuesCollectionByDataTypeId is cached at repository level;
            // still, the collection is deep-cloned so this is kinda expensive,
            // better to cache here + trigger refresh in DataTypeCacheRefresher
            return Storages.GetOrAdd(dataTypeId, id =>
            {
                var preVals = _dataTypeService.GetPreValuesCollectionByDataTypeId(id).PreValuesAsDictionary;

                if (preVals.ContainsKey("multiple"))
                {
                    var preValue = preVals
                                   .FirstOrDefault(x => string.Equals(x.Key, "multiple",
                                                                      StringComparison.InvariantCultureIgnoreCase))
                                   .Value;

                    return preValue != null && preValue.Value.TryConvertTo<bool>().Result;
                }

                //in some odd cases, the pre-values in the db won't exist but their default pre-values contain this key so check there
                var propertyEditor = PropertyEditorResolver.Current.GetByAlias(propertyEditorAlias);
                if (propertyEditor != null)
                {
                    var preValue = propertyEditor.DefaultPreValues
                                                 .FirstOrDefault(x => string.Equals(x.Key, "multiple",
                                                                                StringComparison
                                                                                .InvariantCultureIgnoreCase))
                                                 .Value;

                    return preValue != null && preValue.TryConvertTo<bool>().Result;
                }

                return false;
            });
        }

        internal static void ClearCaches()
        {
            Storages.Clear();
        }
    }
}


Live performance of 'Chapeau Melon' by French singer Anaïs and her backing band. The music is loosely based on Laurie Johnson's theme of 'The New Avengers'. The video contains two versions, both were recorded during the concert tour in 2009. The song can be found on the album 'The short version' of 2010.


text:
Q: Legacy code: window.parent.location.href="https:/home" I have inherited some code from our former developer and I found this piece of code.
<?php
   if($this->loginAction->isAuthenticated()){
?>
   <script type="text/javascript">
      window.parent.location.href="https:/home";
   </script>
<?php
}
?>

I have been looking on it for a while and I have no idea why he put there the shortcut for the URL. I think that the URL should be https://mysite.com/home. I basically don't know what does this code do.
Edit: I know that this code is redirection if user is authenticated, but I don't know what the strange URL(https:/home) is doing there. This code is working.
Thank you for any help.
Edit2: According to my colleague, this is one of the JS secrets:) Going to test it. This two lines of code should have equal functionality:
window.parent.location.href="https:/home";
window.parent.location.href="https://www.my-testing.com/home";

A: This is the URI and it is valid according to the spec here: https://www.ietf.org/rfc/rfc2396.txt in section 3 as quoted here: ( the bold and highlights are mine)  This says that the scheme https followed by the separator colon : then the rest is optional. Thus the single forward slash is likely interpreted as the root or "full" URI of "https://home" or simply the "/home" of the current provider.  Likely this then looks like "https://www.example.com/home" if your site is www.example.com (it isn't but that one is in the specifications as a reserved name as well) which is the DNS address of your site.  Note that if the scheme were missing as "//www.ourbug.net" then that would be translated into the current scheme in use (http or https for most resources)
Quote from the reference:
3. URI Syntactic Components
The URI syntax is dependent upon the scheme.  In general, absolute
   URI are written as follows:
  <scheme>:<scheme-specific-part>

An absolute URI contains the name of the scheme being used ()
   followed by a colon (":") and then a string (the ) whose interpretation depends on the scheme.
The URI syntax does not require that the scheme-specific-part have
   any general structure or set of semantics which is common among all
   URI.  However, a subset of URI do share a common syntax for
   representing hierarchical relationships within the namespace.  This
   "generic URI" syntax consists of a sequence of four main components:
  <scheme>://<authority><path>?<query>

each of which, except , may be absent from a particular URI.
   For example, some URI schemes do not allow an  component,
   and others do not use a  component.
  absoluteURI   = scheme ":" ( hier_part | opaque_part )

URI that are hierarchical in nature use the slash "/" character for
   separating hierarchical components.  For some file systems, a "/"
   character (used to denote the hierarchical structure of a URI) is the
   delimiter used to construct a file name hierarchy, and thus the URI
   path will look similar to a file pathname.  This does NOT imply that
   the resource is a file or that the URI maps to an actual filesystem
   pathname.
  hier_part     = ( net_path | abs_path ) [ "?" query ]

  net_path      = "//" authority [ abs_path ]

  abs_path      = "/"  path_segments"

A: Well, it appears to say if the login action is authenticated then redirect the user to a new url.
Seems like a bug to me.  I don't believe https:/home is anything more than a typo.

A: Try:
<script type=text/javascript>
   window.parent.location="https://www.my-testing.com/home";
</script>

redpajama_set_name: RedPajamaStackExchange


Studying of foreign-language competence at the present stage of higher pedagogical education development acquires the increasing relevance. In this paper the requirements of federal educational standards of pedagogical education and teacher' professional standard to forming students' foreign language competence mastering non-linguistic pedagogical specialties are specified. Analysis of understanding of professional and communicative foreign language competence by researchers has been performed; the author's structure of future pedagogues' foreign language competence is presented. The following subcompetences are distinguished as its structural components: linguistic, discursive, colloquial, pragmatic, information-technological, strategic, socio-cultural, personal-creative. The results of the foreign language competence level of the first and second year students of non-linguistic pedagogical specialties are shown. It is determined that the key structural elements of students' foreign language competence are within the advanced level of development. The strategic sub-competence is in a zone of optimal development level that makes actual creation of the psychological and pedagogical conditions for their development.


text:
Vicky Kaushal gets a picture clicked with Sam Manekshaw's statue
Actor Vicky Kaushal, who will be seen essaying the role in 'Sam Bahadur' in Meghna Gulzar's upcoming directorial, shared a picture posing with Sam Manekshaw's statue. 'Sam Bahadur' is a biopic based on the life and times of Field Marshal Sam Manekshaw, based on the life of India's first Field Marshal Sam Manekshaw, and his career in the Indian Army over four decades and multiple wars.
Vicky took to Instagram to share the picture where he is seen dressed in a black hoodie, a baseball cap and black sunglasses. For the caption, he wrote, "SAM."The film also stars Sanya Malhotra as Sam Manekshaw's wife Silloo and Fatima Sana Shaikh as former Prime Minister Indira Gandhi.
Directed by Meghna Gulzar, the film also stars Neeraj Kabi, Edward Sonnenblick, Richard Bhakti Klein, Saqib Ayub and Krishna Kant Singh Bundela, among others.
Tags: Vicky Kaushal , Bollywood , Entertainment , Mumbai , Actor , Cinema , Hindi Films , Movie , Mumbai News , Sam Manekshaw , Sam Manekshaw Statue

redpajama_set_name: RedPajamaCommonCrawl


OCS training can be as rigorous as Basic Training.
Those interested in becoming an officer in the U.S. Armed Forces after earning a four-year college degree or college credits plus military experience can do so by completing Officer Candidate School, or OCS. This type of training is appropriate for civilians as well as enlisted service members, according to the "Today's Military" website. The OCS application packet varies by service branch.
A civilian who wish to join the Armed Services and become an officer through the OCS must have at least a four-year college degree from a school recognized by the U.S. Department of Education and must meet the eligibility criteria of the respective service she wants to join. To attend OCS, the U.S. Army requires that you be a college graduate and begin training before your 30th birthday. To become an Army Reserve officer, you must enter the OCS program before your 33rd birthday. However, these age limits may vary by military branch.
An active service member who wishes to attend OCS to become an officer must have at least 60 college credits toward an undergraduate degree to enroll in the OCS program, according to the New York State Division of Military and Naval Affairs. However, applicants must have at least 90 credits to be commissioned or to enroll in an accelerated OCS program. Depending on the branch of service, an enlisted individual may need to also pass the Scholastic Aptitude Test or American College Test if he doesn't have a four-year degree at the time of application.
In addition to earning college credits or a degree, an individual who wants to enter the OCS program must be at least 18, a U.S. citizen or legal resident and earn a passing score on the Armed Forces Vocational Aptitude Battery. An active service member must also complete advanced individual training and pass the respective physical fitness test. An enlisted service member is not eligible to enroll in the OCS if she dis-enrolled or dropped out of a previous OCS program.
To ensure the health of future officers, civilians and active service members must pass a physical exam at a Military Entrance Processing Station or an approved medical station. Applicants must also be able to obtain security clearance by passing a background screen. Criminal history requirements in the Army may include not having any convictions from a civil or military court as an adult or minor, as well as not having a traffic violation that resulted in a fine of $250 or more.
Richards-Gustafson, Flora. "OCS Packet College Requirements." Synonym, https://classroom.synonym.com/ocs-packet-college-requirements-16617.html. Accessed 19 April 2019.


If Windows will not boot in either normal or safe mode, there may be a problem with the file system on your hard drive, or the hard drive itself. Microsoft provides a utility, checkdisk, that can help determine the state of your hard drive.
You must boot from the Vista installation or recovery disc that came with your computer. Insert your Vista installation DVD and then restart the computer.
If you are prompted to Press any key to boot from CD or DVD... do so. If you do not press a key before the message disappears, the computer will not start using the CD or DVD.
After your computer has booted from the disc, select your language and then press Next, as shown below.
Click on Repair your computer as shown below.
You must choose the operating system that you would like to repair. For most users there will only be one option listed. Select the operating system you wish to repair and click Next as shown below.
NOTE: If no operating system is available at this step, there is an issue with your Vista installation that cannot be repaired by running chkdsk.
The System Recovery Options will then appear, as shown below. To run checkdisk, you must click on Command Prompt.
A black window will appear, as shown below. This is the Command Prompt.
and press Enter. If Vista is installed on a drive besides the C: drive, you should replace C: with the appropriate drive letter.
Checkdisk will now run. The scan may take several hours to complete.
When the scan is complete, a report similar to the one below will display. If any errors appear, contact the DoIT Help Desk.
To leave the System Recovery tool and reboot your computer, type exit and press Enter. Remember to eject your Windows Vista CD or DVD before your system tries to boot from it.
Click on the Start Menu, All Programs, Accessories, right click on Command Prompt and click on Run as Administrator.
In order to run checkdisk to repair the hard drive, you should type the following: chkdsk C: /r and press Enter. If Vista is installed on a drive besides the C: drive, you should replace C: with the appropriate drive letter.


The Department of Pediatrics at the University of California, Davis School of Medicine is recruiting an academic pediatric infectious diseases faculty member. This appointment may be made between 80% and 100% (full time).
The position is open at the Assistant/Associate/Full Professor rank (commensurate with credentials) in the Clinical Pediatrics or Health Sciences Clinical Professor series. The candidates will be expected to participate in clinical care, teaching of medical students, residents and fellows, and research activities of the Department of Pediatrics and serve on certain departmental committees. The candidates will be expected to do clinical service at participating regional hospitals. We expect that this position will be an active participant in antimicrobial stewardship and infection prevention programs.


text:
Microsoft details four ways it's experimenting with haptic VR
By Colm Hebblethwaite
13 March 2018, 11:11 a.m.
Developers, Hardware
VR has gained so much traction in the last couple of years that its futuristic, sci-fi sheen has been diminished in the eyes of a lot of consumers. Most people now expect that they will be interacting with the technology in some form within the next decade.
The area of haptic feedback, having the ability to touch and feel virtual objects, is still likely to elicit shocked gasps from consumers. Unsurprisingly, there are currently a wealth of companies racing to trying to create the killer piece of haptic hardware.
A recent blog post, Microsoft's research team has detailed four distinct approaches to haptic controllers. The tech giant is trying to move past the current range of tactile devices, which are mostly limited to buzzes created by internal motors. As the blog states:
"There are many reasons why haptic is such a hurdle. Anyone who's been to the movies understands that the eye and the ear can be tricked; film after all, at 24 frames per second, isn't true motion. But haptic is different and represents a challenge many orders of magnitude larger in complexity. Some of the challenges lie in the area of hardware. Laboratory prototypes such as exoskeletons and other hand mounted devices tend to be cumbersome, both to fit to individual users as well as to don and remove."
Many of the current range of prototype devices that are known about are trying to simulate singular types of sensation such as heat or weight, and the complex mechanics are likely to render the devices expensive, fragile and cumbersome.
"What you really want is the impression of virtual shapes when you interact with objects in VR, not just the binary feedback you get from current devices," said Microsoft researcher Christian Holz.
"Our controllers render such shapes, giving your fingers and hands continuous and dynamic haptic feedback while you interact."
Here are the four prototypes detailed in the blog post.
The CLAW looks to extend the current paradigm for VR controllers to include haptic capabilities. The user's index finger is placed into a mount that uses a motor to move the finger in a way that simulates force feedback.
The controller can also adapt its haptic rendering based on sensing differences in the user's grasp and the virtual context in which they are interacting. This could allow for incredibly accurate grasping and grabbing sensations.
Here is a product introduction video.
Haptic Wheel
The Haptic Wheel looks to simulate the feeling of friction and different materials. The controller uses an actuated wheel that moves up and down in order to simulate running your finger over a surface, and spins in order to "render shear forces and motion as the user slides along a virtual surface."
The user's index finger rests in a groove in the wheel, so all of the haptic sensation is localised to their fingertip.
You can see the Haptic Wheel in action here.
These dual controllers aim to solve the problem of using both hands in virtual environments, such as to carry an object in both hands or using a device like a bow and arrow. Two controllers are connected with a range of sensors that are capable of dynamically altering the forces felt in the user's hand. So, for example, the hand pulling back the bow strings would feel a realistic build-up of pressure.
Watch a demonstration of the device here.
The team are also exploring ways to make virtual experiences accessible to all. The Canetroller allows people who use a cane in real life to transfer that ability to virtual environments.
The controller provides three kinds of haptic feedback to users. They will feel physical resistance from a programmable brake mechanism that makes it harder to move the cane along the floor, as well as vibrotactile feedback simulating the feel of hitting objects and 3D auditory effects.
Watch a demo video here.
Colm Hebblethwaite
Follow @@colmtechforge
https://www.virtualreality-news.net/
Colm is the editor of VR 360, with a mission to bring the most important developments in technology to both businesses and consumers.


The Data Request Form provided in this site may be used for sending the data requests together with a formal requesting letter in the letter head of the institution/office/ organisation to ensure the identity of the user.
Initial correspondences, such as availability of particular data or station etc may be sent via, e-mails.
Normally data are supplied on payment basis. Payment should be made through a Banker's DD favouring 'The Asst. Met (C&A), RMC, Chennai', payable at Chennai.
The amount mentioned in the Charge Intimation Letter (CIL) is ONLY for the data specified in the CIL and whatever may be the actual request. Because, CIL is prepared on the basis of availability of data and stations. Hence, users are instructed to go through the CIL carefully and if they are interested in getting the data mentioned therein then only they should make payment DD.
The amount paid against a data request is not refundable under any circumstances and hence before making any payment, users are requested to ensure themselves about the same.
Supply of data will be considered on case by case and DDGM, RMC, Chennai and ADGM (Research), Pune reserves the right to accept or decline any data request on public interest.
The data supplied to an user is only meant for the purpose mentioned in their request letter and in no case the data be parted and handed over to any other user for any other purpose.
Though maximum care is taken at various stages in collecting, scrutinizing and compiling the data, there could be some errors in the data due to instrumental and technical reasons.


text:
The Course Manager's Calendar – January
Although Stewart Brown grew up on the west coast of Scotland, he spent most of his greenkeeping career in the east. So, when an opportunity arose last year to head up the prestigious Western Gailes Golf Club, he jumped at the chance to go home.
What prompted you to move to Western Gailes?
"I knew the course from when I was young, so it was a chance to go back home to a Top 100 venue. But there's a big difference in the courses over here. The main thing is the difference in the weather – it's a lot wetter. The surfaces are pretty soft for a traditional links, so there was a lot to do on the course."
What were the first jobs you tackled when you arrived?
"The decision was taken to concentrate on moving water through the soil profile more quickly because we were struggling with soft surfaces and puddling. In the past, the greens had only been dressed with Fen Dress, a peat based material, which by its nature holds onto moisture. To help us create a free draining profile where the finer grasses can thrive, we needed to make some changes below ground. Historically, the club has used some sand from Hugh King here and there, mainly for bunkers and to make our divot mix. So we opted to use straight sand from Hugh King due to the quality and consistency of the product and being situated close to the quarry, it was extremely cost effective."
So the first challenge was improving drainage.
"The movement of water is the most important thing. We started off the process with drill-and-fill work using Hugh King's kiln-dried sand in order to create a link between the surface and the straight sand below. We used a machine so the drills were only going down about 12 inches. We had to use dried sand for that, but we also carried out home-made drill-and-fill work at deeper depths during which we poured the dried sand by hand. We were addressing issues further down in the profile initially but we also needed to look at the top of the profile with water sitting on the surface after heavy rain. We did some Graden work and that also required dried sand."
How important was Hugh King's sand to the project?
"It's a fundamental part of what we're trying to do here. We are trying to restore firm and fast conditions, and Hugh King's sand is a fundamental part of that."
It is quite a direct strategy.
"We used 80 tonnes of Hugh King's dried sand this year. There is also a longer-term strategy, but we had to start somewhere. We could have taken longer to do these things but the club wanted to get things moving."
And the results are…
"We've already seen a difference in our percolation rates and a reduction in puddling so the water is definitely moving through the profile quicker. There's a long way to go but we're heading down the right track."
You're also using Hugh King's Washed Dune sand for topdressing
"Yes. We're trying to get sand throughout the profile, but in a general sense we use topdressing to smooth out surfaces and create a firmer playing environment throughout the season. We use Hugh King sand on the greens, approaches and tees applying it every three or four weeks throughout the year. We back off during the busy periods during June, July and August, but we're trying to do more topdressing during the winter which should reduce the amount of applications during the playing season."
You've also brought in 40 tonnes of Hugh King's rootzone for construction. How have you found that?
"The decision was made to bring in some Hugh King rootzone, which is the ideal product for our tees and pathways. It is 80% sand, which is the main thing, but that 20% of soil helps with moisture retention and is similar to most of our current soil and rootzone. It means the rooting should be significantly better. We could lay the turf straight onto sand but we wouldn't get the same establishment. We need the soil to give the roots something to work with."
And how far on are you with your winter works?
"When we come back after Christmas, we should have finished more than half of our winter programme which involves rebuilding 27 bunkers and a couple of tees and some paths. After Christmas, we'll work on the back half of that plan and hopefully get it all finished by the end of February."
Comments Off on The Course Manager's Calendar – January
"We've never had a bad load of sand"
Charlie Macdonald, Course Manager at Crieff Golf Club