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South Belfast News February 25, 2012
"We aim to connect with the local community through schools and organisaitons to do things for them and with them." – Johnny Graham
There's a line in the film Stand By Me that always got me wondering: "The Women's Auxiliary barfed all over the Benevolent Order of Antelopes." Who are these people and just what do they do? Until last week I wondered the same about Rotary Clubs. While I never really found out about the Women's Auxiliary and the Benevolent Order of Antelopes, I'm delighted to report that Rotary Clubs are thriving and doing a great job too.
I recently had the opportunity to sit in on a meeting of the Rotary Club of Belfast West and have a chat with two of its members. While the title infers it is a club solely for West Belfast, don't be fooled.
"We are not territorial, we are not constituency based," explained Johnny Graham. "Belfast West means west of the Lagan so much of our activity is in a great part of Belfast."
A fair point, considering we met with these rotarians at the Belfast Boat Club in the south of the city. Every week the members gather at the Boat Club where they lunch, hear from guest speakers and give updates on the organisation's business plan.
So what exactly does the organisation do? We asked the President of Rotary Club of Belfast West, Mary Graham.
"Rotary International is the world's first service club organisation currently comprising more than 1.2 million male and female business, professional, and community leaders who volunteer their time, talents, professional skills and energy to improving the lives of people in their local community and others around the world.
"Rotarians are expected to help the community that they live in and also people in other parts of the world that are in need. As a member of a Rotary Club, you have the opportunity to give something back, to give hope to those less fortunate and to make lives worthwhile and fulfilled. Sometimes this help can be charitable giving and sometimes it can be hands-on. We are involved with a number of charities both at home and internationally."
And the evidence is there to see. This particular club has worked with numerous organisations and lent a hand to the Lapland Trust, the substance misuse charity FASA and many schools in the area, to name a few.
"Although we raise money for charity, it's not our raison d'être," says Johnny. "As well as the several fundraisers per year, we aim to connect with the local community through schools and organisations to do things for them and with them."
Johnny adds that joining the Rotary Club has enabled him to give something back to the community.
"In my life I always found that I was too busy for charity, now I feel I can give something back. I think it's nascent in all of us to want to help others.
"I think it's fair to say there has been a loss of community spirit, less people go to church, that doesn't mean they are bad people, it can be difficult to stay connected, we can fulfill that with the work we do."
As President, Mary is keen to highlight the outstanding selfless work they do and to attract more members.
"I want to build on the progress we have made in the last few years in terms of the rejuvenation of the club," she says. "My aims for the year are to further improve communications, increase membership, more service activities, fellowship and fun.
"I love the club and I love that in any country in the world, you can walk in to a Rotary Club meeting, we even had an American here today.
"We have great respect for differing opinions and have witnessed speakers and members talking about anything from the Marx brothers to sea explorers.
"The only pre-requisite is that you share our values."
The future certainly looks bright for this club and Mary is keen to meet all challenges in the year ahead.
"I have every confidence as we look forward to 2011/2012 that The Rotary Club of Belfast West, will continue to rise to the challenges ahead and will continue to live up to our motto: Service above Self."
You can contact the Rotary Club via post at:
Rotary Club of Belfast West,
Belfast Boat Club,
12 Lockview Road
Belfast,BT9 5FJ
Tags: Rotary Club of Belfast West, Rotary Clubs
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Watsonia is a scenic north-eastern suburb surrounded by parks and reserves including the Gresswell Forest Wildlife Reserve. Train services depart to the city from historical Watsonia Station on the Hurstbridge line. The Greensborough bypass and the Eastern Freeway offer swift access to the city, and bus services travel frequently to both Greensborough Plaza and Northland Shopping Centre.
Diamond Village Shopping Centre, an eclectic shopping strip, provides residents with an IGA supermarket, chemists, cafes, butchers and bakeries, and as well as specialty stores such as a tobacconist, a wool shop and a clothing boutique. Just a short walk from the shopping village you will find the Elder Street Reserve, a beautiful community park with play equipment, tennis courts and an oval. These facilities are often used for football, cricket, soccer and basketball.
Watsonia is home to some outstanding schools, such as Watsonia Primary School, Greensborough College and Loyola College, an independent Catholic school. It is also only a short distance away from the Bundoora campus of La Trobe University. Its flourishing natural backdrop makes Watsonia a perfect location for growing young families, students and professionals looking for a quiet escape only thirty minutes from the city.
The following data, without any explanation, is just that, raw data. So in order to help you research the property market, we have outlined what this demographic and real estate information means and how it can be interpreted to help you make a smarter decision when buying or selling your next house or unit in watsonia, Victoria.
Here we have outlined the median value for all sales for each year over a period of 5 years. This is a more realistic timeframe when gauging how houses and units in watsonia have performed, and it usually indicates how they will perform in the future.
Median values less than 3 months old are often not always accurate (due to incomplete data) and therefore cannot be relied upon for long-term watsonia property price predictions.
Request a free market Basic Report for watsonia to get a general idea of property prices in your area, or a more specific CMA Report for the property you own.


Click Here for information about our next sale.
Changes in Sheriff Sales from HB 390 go into effect with sales starting November 14, 2016.
Foreclosure sales are conducted on Monday�s at 10:00 AM at the Huron County Sheriff�s Office located at 255 Shady Lane Drive, Norwalk, Ohio.
Information about each specific sale will be published on Tuesdays in the legal section of the Norwalk Reflector.
The sales are conducted like an auction. Sheriff Sales require a deposit at the time of the sale based on the amount of the appraisal.
An appraisal of $10,000.00 or less will require a deposit of $2,000.00. An appraisal of more than $10,000.00 but less than or equal to $200,000.00 with require a deposit of $5,000.00. An appraisal of more than $200,000.00 will require a deposit of $10,000.00. The Balance of the purchase price is due upon confirmation of the sale. If the purchaser fails to pay the balance of the purchase price the deposit shall be forfeited to the Plaintiff for the costs associated with the advertisement and resale of the property and for interest charges.
If the subject property is residential and the purchaser is the judgment creditor then the purchaser does not need to make a sale deposit.
Tax Sales require 100% of the purchase price due at the time of the sale.
Payments can be made via cash, personal check, money order or cashier�s check.
Public notice is hereby given that all such real property to be sold at public auction may be subject to liens that may not be extinguished by the sale. The purchaser shall be responsible for costs, allowances and taxes that the proceeds from the sale are insufficient to cover. Purchasers of any such real property are urged to contact the attorney listed in each case and to search the indexes kept by the county recorder, clerk and treasurer to determine if any liens exist.
There may or may not have been an inside inspection of the property and the Sheriff is not liable for the condition of the property upon confirmation of the sale. The Sheriff�s Office nor any affiliates have access to the inside of said property.
Be advised that properties sold with a no minimum bid requirement can be redeemed by the Judgement Creditor and First Lienholder within 14 days of the sale by paying the full purchase price to the Clerk of Court. They will then be considered the successful purchaser of the sale.
Please contact the Huron County Sheriff�s Office for updated information about each sale at 419-668-6912 and speak to the civil office.
Please download forms, complete and return in person or by e-mail to sheriffsales@huroncountysheriff.org.
The 'Remote Bid Form' can only be used by used by the plaintiff's attorney.
Click Here for the Sheriff's Sale FAQ.


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15/08/2014 Publications
Money Can't Buy Love but Can it Buy Peace? Evidence from Northern Ireland
Tilman Brück and Neil Ferguson published a working paper on the effectiveness of a large-scale peace-building programme in Northern Ireland. In 1998, the Good Friday Agreement concluded a period of violence in Northern Ireland yet the scars of the conflict remained prevalent in the political landscape. Rival communities remained divided, economic performance was poor and intercommunity tensions frequently manifested. In a bid to reinforce progress towards a peaceful and stable society, over €1bn of public money was spent between 2000 and 2006 on small-scale community and business ventures. Despite the scale of expenditure, however, little rigorous effort has been made to test the success of the programmes. Splitting Northern Ireland into 582 electoral wards, we merge individual-level on perceptions of neighbourhood quality from the British Household Panel Survey with detailed PEACE II accounts. Noting potential selection and omitted variables biases, we implement two-stage random effects models and show that neither level of spending, nor number of projects, in a region is associated with improvements in perceptions of neighbourhood quality.
Brück, T. and N. Ferguson (2014). "Money Can't Buy Love but Can it Buy Peace? Evidence from the EU Programme for Peace and Reconciliation". HiCN Working Papers, Nr. 177. The full paper can be found here.

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What Does an LPN Do?
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Home » Blog » What is an Average LPN Salary?
What is an Average LPN Salary?
by Your LPN Guide
The salary for a Licensed Practical Nurse (LPN) can vary depending on factors such as the individual's level of experience, education, and location. According to data from the Bureau of Labor Statistics (BLS), the median annual wage for LPNs in the United States was $47,480 as of May 2020. However, LPN salaries can range from around $39,000 per year to over $60,000 per year depending on these factors.
Experience: LPNs with more experience tend to earn higher salaries than those who are just starting out in their careers.
Education: LPNs who have completed additional education, such as a bachelor's degree in nursing, may earn a higher salary than those with only an LPN certificate.
Location: LPN salaries can vary significantly depending on the location in which the individual works. For example, LPNs in urban areas tend to earn higher salaries than those in rural areas. Additionally, there can be a range in LPN salaries from state to state. Those in Alabama may make in a range of $39,000 to $40,000 per year, while those in Veront may make up to $51,000. Additionally, LPNs in California can make in the salary range of $62,000 to $64,000 per year and LPNs in New York may make up to between $49,000 to $52,000 per year.
It's worth noting that the BLS data represents the national average and that actual LPN salaries may vary significantly depending on the specific employer and other factors. It's a good idea to research salary data for LPNs in your specific area to get a more accurate idea of what you can expect to earn.
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WELCOME BACK, CAMBY – MARCUS SAYS HE IS READY FOR RAPS
By Paul Schwartz
May 1, 2001 | 4:00am
Marcus Camby was out there, rebounding free throws, when – more revealing than any words – he offered a silent, but telling glimpse into his state of mind.
The wounds from a terrible ordeal are not fully healed, but the process is underway, and Camby yesterday returned to the Knicks, in body and, more importantly, in spirit. Ending his one-game, self-prescribed hiatus, Camby will start at center tomorrow night in Toronto, hoping to contribute to a Game 4 victory that would close out the Raptors and propel the Knicks into the second round of the playoffs for the 10th consecutive season.
"I've been through a lot, my family has been through a lot," Camby said. "It's just a matter of time, me getting my head right, make sure my family situation is stable, which it pretty much is. Now I can just concentrate on basketball."
This sounded and looked more like the Camby the Knicks have come to know and appreciate. The day after he collected 18 rebounds in a series-opening victory, Camby's world was rocked when his mother and two sisters were held hostage at their Connecticut home. Three nights later, Camby tried to play through his shock and grief and was a shell of himself, adding next to nothing (two points, two rebounds in 31 zombie-like minutes) during a 20-point Knicks loss.
"No matter how hard I tried my mind just couldn't let me get through it," he said.
Camby left the Knicks for three days, suffering from symptoms related to post-trauma stress syndrome. He remained with his family and, after initially deciding he would not watch, flipped on the television Sunday and saw the Knicks, without him, beat the Raptors 97-89 at Air Canada Center.
"It was tough watching it, just wanting to be out there, but the guys responded," he said.
Before Camby left the team, he was told by Jeff Van Gundy not to return until he felt he could play effectively. Minutes before yesterday's 10:30 a.m. workout, Camby arrived at Purchase College, and immediately, his teammates could tell he was on the road to recovery.
"From the first time I saw him he looked very energetic and back to normal," Latrell Sprewell said. "That was nice to see. I think he's back to normal now, it looks that way. He's talkative, he's joking and being the Marcus of old."
Observed Rice, "I think he's going to be all right and in turn, I think we're going to be all right as well. He congratulated everyone on a good game the other day and let everyone know that he's ready. You can kind of sense a little bit of relief, that he's heading toward back being himself."
During a meeting yesterday with Camby, Van Gundy stressed that he needs to know if his center's mindset changes between now and game time. "He's got to be honest with me and let me know that, so we don't waste any minutes," Van Gundy said.
Unless he experiences a sudden relapse, Camby says he's moving forward. He spent virtually the entire three days away from the Knicks inside the family home, where one of his sisters was sexually assaulted during the hostage situation. It is believed Camby underwent some form of professional counseling, and he said his entire family has recently been pushing him to play.
Upon his return, Camby said he went out of his way to show his teammates he was on the mend. "It's been depressing, I can't lie," Camby said. "But with me, and with anybody who goes through any problems, you got to get back to your normal life, starting doing things you're used to doing. Coming in here, just trying to smile and laugh, just trying to let the guys know I'm all right, let's not talk about what happened, let's just move on and try to close out Toronto. That was my feeling coming in here, making sure everybody was upbeat."
As for his family, Camby added, "They're hanging in there. Each day is a struggle but we tend to lean on each other. It's going to take some time, don't get me wrong, nothing's going to change overnight or in a week, but we're going to get through this together."
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Home MAGAZINE STORIES Monsoon
Teresa Posyniak: Ponderosa
These Tiny Helpers
An exhibition by Calgary artist Katie Ohe and four of her former students demonstrates the power of a generous mentor.
by Amy Gogarty
Katie Ohe, "Monsoon," 2006
polished welded steel, automobile paint and internal mechanical rotary system, dimensions variable (collection of artist, courtesy of Herringer Kiss Gallery, Calgary; photo by Byron Dauncey)
Monsoon, an exhibition of work by Katie Ohe and four of her former students at what's now the Alberta University of the Arts, belies the old adage that "those who can, do; those who can't, teach." If anything, Ohe's more than 50-year career indicates that doing is often the best form of teaching.
On view at Griffin Art Projects in Vancouver through Dec. 15, this first survey of Ohe's work outside Alberta introduces her sculptures alongside works by Isla Burns, Evan Penny, Christian Eckart and Robin Arseneault, artists with their own national and international reputations.
"Monsoon," 2019, installation view at Griffin Art Projects, Vancouver (photo by Byron Dauncey)
Much credit goes to the curator, Katherine Ylitalo, who recognized the degree to which Ohe's creative vision and mentorship have shaped Canadian art. The works on view share little in terms of facture or appearance. Instead, Ylitalo points to underlying affinities such as craftsmanship, material sensibility, perceptual ambiguity and radiant energy.
Born in 1937, Ohe studied at the Alberta College of Art in Calgary, where she began teaching in the 1960s. She has produced numerous public commissions and private works and has received many important awards, including, most recently, the Lieutenant Governor of Alberta Distinguished Artist Award and the Alberta Order of Excellence. She retired from teaching in 2016, but continues to work in her studio and will be the subject of a major retrospective at Calgary's Esker Foundation in early 2020.
Katie Ohe, "Venetian Puddle," 1977
chrome plated steel, 10" x 38" x 36" (collection of artist, courtesy of Herringer Kiss Gallery, Calgary; photo by Byron Dauncey)
In addition to their visual enticements, Ohe's works are marvelously engineered. Many are interactive, and viewers are invited to set them into motion. Monsoon consists of 10 white spheroids, each of which rotates around a central bearing. When all 10 are sent spinning, they call to mind a bank of clouds seen from space, harbingering a deluge.
Sliding on hidden bearings, the sinuous silvery layers of Venetian Puddle suggest water rippling as rain falls. The two chromed components of Horizontal Loop, 1973, glide smoothly in opposite directions, curling and doubling in the reflective base.
Kate Ohe, "Doodle Tower," 2018
aluminum, steel and automobile paint, 27" x 5"x 5" (collection of artist, courtesy of Herringer Kiss Gallery, Calgary; photo by Byron Dauncey)
The exhibition includes three of Ohe's small wall-mounted works, which she calls "offerings." Produced over 20 years, each begins as a cup small enough to be held in two hands. Some are miniature versions of larger sculptures, while others are playful experiments. The latest, Doodle Tower, arranges a tangled pile of steel loops atop the cup, a resolution that has led to new work on a larger scale.
Ohe documents many of her completed works in the form of small, brightly coloured screen prints, which serve as her annual year-end cards. Highly prized and collected by recipients, a number of these charming images are displayed at the gallery entrance. As a collection, they offer valuable insights into her thinking.
Isla Burns, "Puja Kadali," 2012
steel and wood, 78" x 20" (collection of artist, courtesy of Wallace Gallery, Calgary; photo by Byron Dauncey)
Isla Burns was born and raised in India, where spiritual monuments are part of public life. Coming to Canada after studies in Scotland, she recalls that Ohe took her seriously, encouraging her to pursue the physical challenge of large-scale metal sculpture.
Puja Kadali pays homage to the Hindu spirituality Burns witnessed as a child. The columnar work is crowned with bananas, which are used in the festival to dress the goddess Lakshmi in preparation for her remarriage to Ganesh. Set on an antique wooden column, the gracefully arching bananas are like fingers reaching skyward in prayer.
Evan Penny, "Male Torso Model," 2016
silicone, hair, pigment, aluminum and wood, 19" x 9" x 7.5" (AP 2/2, collection of artist, courtesy of TrépanierBaer Gallery, Calgary; photo by Byron Dauncey)
Evan Penny's high-real figures and heads explore subjectivity and perception. He points to the example set by Ohe, who would often work late into the night alongside her students. In addition to imparting a fierce work ethic, she gave him permission to entertain doubt and ambiguity.
Male Torso Model hangs from the ceiling by a chain, suggesting extremes of both vulnerability and violence. The silicone form is rendered in high realism with paint and implanted hair, yet it is saved from gruesomeness by clear references to classical sculpture. Chilling and fascinating, the work resonates with pathos and gravitas.
Christian Eckart, "Curved Monochrome Painting - 5th Variation #2005-1," 2002
extreme-effect acrylic urethane on hand-formed aluminum panel, 84" x 33" x 4" (collection of artist, courtesy of TrépanierBaer Gallery, Calgary; photo by Byron Dauncey)
Christian Eckart traces his interest in formal and spiritual aspects of the sublime in part to his early exposure to Ohe's work, which he sees as invoking "implied or inferred infinities." Now living in Houston, he investigates religious art traditions using contemporary materials and formats.
Curved Monochrome Painting - 5th Variation #2005-1 is an elongated aluminum lozenge painted with layers of iridescent auto paint. Concave and altar-like in form, the painting reflects the viewer and surrounding gallery in reverse. Colours flash from plum to orange to green as the viewer shifts position, creating a meditative space of radiance and calm.
Robin Arseneault, "Squeeze," 2018
archival print on Entrada Rag Bright, mounted on DiBond, 60" x 40" (collection of artist, courtesy of Jarvis Hall Gallery, Calgary; photo by Byron Dauncey)
Robin Arseneault works across media and formats, addressing uncertainty, absurdity and abjection. In 2018, she attended a residency in Medicine Hat, Alta., where she photographed the nearby Red Rock Coulee. In Squeeze, Drip and Divide, a series of photo-based prints, she pairs details of Rodin sculptures, re-photographed from older art books, with close-up images of landforms.
Time-sculpted rocks meet the rough-cut stone of the unfinished sculptures, creating intriguing abstract shapes and patterns. More than a clever play, these uncanny works, which are beautiful in their production, invoke the body in the land and the potent energy of landscape in the figural works.
As Ohe has said: "The aesthetic experience is a spiritual experience of communion, of sharing. It is the essence of my work." This sums up the experience of encountering her work and recognizing the generosity of spirit that motivates a true teacher. ■
Monsoon: Robin Arseneault, Isla Burns, Christian Eckart, Katie Ohe and Evan Penny is on view at Griffin Art Projects in Vancouver from Sept. 14 to Dec. 15, 2019.
PS: Worried you missed something? See previous Galleries West stories here or sign up for our free biweekly newsletter.
Griffin Art Projects
1174 Welch St, North Vancouver, British Columbia V7P 2R5 View Map
Hours Sat noon - 5 pm, and by appointment
Isla Burns Evan Penny Christian Eckart Robin Arseneault Katie Ohe Katherine Ylitalo Wallace Galleries Ltd. Jarvis Hall Gallery Herringer Kiss Gallery TrepanierBaer Gallery 22 October 2019 issue


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Nick Romary
Justin DiSanto
Marcus Anderson
Kate Romary
May 8, 2019 Justin DiSanto
Nick Senzel Era Has Begun in Cincinnati
The reds should have started the year with Nick Senzel. My opinion doesn't matter but the same deal with Fernando Tatis and Pete Alsonso, coming out of Spring Training, it should ALWAYS be your best 25 players currently who can compete with the best. Senzel was one of the top 25 players for the Reds coming out of Spring Training and was told to marinate in the minors a little and he did. He marinated for a total of eight games and didn't play the best but the Reds needed help after a very slow start.
Then came the second overall pick of the 2016 MLB draft (which I bet the Phillies had back as they drafted Mickey Moniak who has just reached Double-A and has not yet reached his projected status yet) and in his second big league game went deep with a walk and then two days later the man went deep TWICE out of the leadoff spot! Sounds like me back in the college days dropping bombs out of the leadoff spot just for shits and gigs. Through five starts, Senzel has hit in four different spots in the lineup. As high as leadoff and as low as sixth thus proving the man is versatile and has a unique set of skills that can contribute at any spot in the lineup. The man can run, hit for average, and obviously drop bombs when needed.
Senzel is versatile with the bat, as is what I just explained, and is as equally as versatile with the glove. Senzel was drafted as a third baseman out of Tennessee and has had to honor of playing third, short, second, and center field professionally. So far with the big league club Senzel has started in center over as they already as showing more trust with Senzel out there then the Jesse Winker experiment last year where he had a -14 of defensive runs saved which is "the number of runs above or below average the player was worth based on the number of plays made." Senzel so far is sitting pretty with a nice 1.000 fielding percentage and has already made a couple nice plays patrolling center.
So far, through five games, Senzel is 4-20 :), with three bombs, three RBI's, four runs, a stolen base and and an on base percentage of .333 and a slugging percentage of .650 which equals out to a nice .983 OPS. The talent is there and it's obvious. Last year, Senzel suffered a tore tendon in his right index finger that cost him his season. Before getting injured, Senzel played 44 games and batted .310/.378/.509 with 12 doubles, six home runs, two triples, 23 runs, 25 RBI's, and eight steals. Thus proving his track record that he can do it all. Just have to be patient as he acclimates himself to the big league level.
This will not be the last you hear of Senzel from me. He is young, fun to watch play, and is capable of a highlight day in and day out whether it be defensively or with the bat. This will be a fun man to watch play baseball in the next few years with an All-Star appearance likely in his future. Stay tuned for an exciting year on a not so exciting team at the moment.
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Posted on August 10, 2015 Written by saraNo Comments on Jensen has joined Instagram!
Due to issues with the sites host I wasn't able to post this news until today, but on Saturday 08 August Jensen officially joined Instagram!
If that wasn't cute enough, later that same day Jensen's wife, Danneel, also joined Instagram! She shared a cute picture of Jensen and JJ as her first post on the social media site.
If that wasn't cute enough Danneel's profile picture is an adorable picture of herself and Jensen outside Jo's Coffee, Austin – the picture has been added to the gallery and can be viewed by following the link below.


PIX File Extension – What is .PIX file and How to open it?
Pix file extension has PCI Geomatics Database file type, developed by PCI Geomatics, which is associated with the "gis files" category.
Geomatica is a geomatics application for analyzing and processing geographic photos and images. It saves geomatics database file with this extension. They include spatial data split in multiple image channels. Can be used for storing large as well as small geospatial data sets, for example, collections of satellite photos. Geomatica software is available in Core and Prime versions.


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Q: Create Object Name with Array I want to create object name using arrays. How can I do it?
For example :
String dizi ={"person1","person2","person3"};
Person dizi[0] = new Person();

A: The technique you are trying to use works in some languages, especially interpreted ones. It uses the language symbol table as a map.
In Java, you can construct your own map:
Map<String,Person> myMap = new HashMap<String, Person>();
myMap.put(dizi[0], new Person());

and access it using:
myMap.get(dizi[0])

A: You cannot change the type of object stored in the array from String to Person. Instead, you will need two separate arrays, and supposing you have a Person(String) constructor, loop over first array to fill second array:
String[] names = {"person1","person2","person3"};
Person[] persons = new Person[3];
for (int i = 0; i < 3; i++)
    persons[i] = new Person(names[i]);

redpajama_set_name: RedPajamaStackExchange


Let's face it - web design and web development are industries that are changing as we speak. These trends dictated that every single website should be mobile-friendly as well as responsive.
Just think about the smartphones and the fast pace of the Internet over the past few years. Nowadays, there are more mobile devices than people on the planet. Browsing the Web is nowadays more exciting and convenient than it is on a computer.
So, having a mobile-friendly and responsive website is the ticket to success. Not only Google likes and promotes mobile-friendly websites among the top search results. Also, customers engage with mobile websites and interact with them in a better way.
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Once you download the app, the work can start. The application is easy to use and works in a simple way. You can resize and rearrange and move blocks - hence, customize your website in an easy way.
With Mobirise, you can also upload pictures directly to the website builder and re-size the graphics as you see them fit. The control panel features a few main buttons and icons. They work seamlessly when making a website. When you finish customizing your website, Mobirise lets you publish it by hitting the 'Publish' button. Or, you can save the website as a file in your disk drive and upload it on a FTP server or even save it on Google Drive. Amazing, isn't it?
The best part about Mobirise is that it is extremely easy to master it. You can build website blocks, use various techniques to customize texts, images and blocks. This lets you engage in a bold and minimalistic experience that is different from any generic website builder.
In a nutshell, Mobirise is the best shortcut in designing a professional website for any purpose. Are you tired, unwilling or don't know how to create a website in the quickest way possible?
Mobirise is definitely for you. The app lets you build websites on-the-go. It also ensures that each website fits every screen, is functional and engaging.
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For December I made a papier mache birthday card for my dad, a abstract fabric monster for a 5 month old, and a felt and button bird to wear to a party for myself.
The card is all recycled paper and stickers I had left over from a project from 6 years ago. The monster is made from felt and cotton (part of a summer dress that is being recycled). It has soft corners so that it can be grabbed easily by small hands or shoved in to a mouth since the baby is teething. Stuffed with organic natural fibers!
Now the bird is an all purpose accessory that I had pinned to my belt. Where it will land in the future is unknown. Maybe on a headband or part of a package for gift giving, or part of a wall decor. Perhaps it will fly into some craft project yet to be started! Until then it is room decor and lovely with its antique buttons.
Love the papier mache card! Very original! I used to love to do papier mache but haven't done it in years. I wouldn't have thought to do a card though. Great idea!


text:
\section{Introduction}

After receiving paper reviews, authors may optionally submit a rebuttal to address the reviewers' comments, which will be limited to a {\bf one page} PDF file.  Please follow the steps and style guidelines outlined below for submitting your author response.

Note that the author rebuttal is optional and, following similar guidelines to previous CVPR conferences, it is meant to provide you with an opportunity to rebut factual errors or to supply additional information requested by the reviewers. It is NOT intended to add new contributions (theorems, algorithms, experiments) that were not included in the original submission. You may optionally add a figure, graph or proof to your rebuttal to better illustrate your answer to the reviewers' comments.

Per a passed 2018 PAMI-TC motion, reviewers should not request additional experiments for the rebuttal, or penalize authors for lack of additional experiments. This includes any experiments that involve running code, e.g., to create tables or figures with new results.  \textbf{Authors should not include new experimental results in the rebuttal}, and reviewers should discount any such results when making their final recommendation. Authors may include figures with illustrations or comparison tables of results reported in the submission/supplemental material or in other papers.

The rebuttal must adhere to the same blind-submission as the original submission and must comply with this rebuttal-formatted template.

\subsection{Response length}
Author responses must be no longer than 1 page in length including any references and figures.  Overlength responses will simply not be reviewed.  This includes responses where the margins and formatting are deemed to have been significantly altered from those laid down by this style guide.  Note that this \LaTeX\ guide already sets figure captions and references in a smaller font.

\section{Formatting your Response}

{\bf Make sure to update the paper title and paper ID in the appropriate place in the tex file.}

All text must be in a two-column format. The total allowable width of the text
area is $6\frac78$ inches (17.5 cm) wide by $8\frac78$ inches (22.54 cm) high.
Columns are to be $3\frac14$ inches (8.25 cm) wide, with a $\frac{5}{16}$ inch
(0.8 cm) space between them. The top margin should begin
1.0 inch (2.54 cm) from the top edge of the page.  The bottom margin should be
1-1/8 inches (2.86 cm) from the bottom edge of the page for $8.5 \times
11$-inch paper; for A4 paper, approximately 1-5/8 inches (4.13 cm) from the
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Please number all of your sections and any displayed equations.  It is important
for readers to be able to refer to any particular equation.

Wherever Times is specified, Times Roman may also be used.  Main text should be
in 10-point Times, single-spaced. Section headings should be in 10 or 12 point
Times.  All paragraphs should be indented 1 pica (approx. 1/6 inch or 0.422
cm).  Figure and table captions should be 9-point Roman type as in
Figure~\ref{fig:onecol}.

List and number all bibliographical references in 9-point Times, single-spaced,
at the end of your response. When referenced in the text, enclose the citation
number in square brackets, for example~\cite{Authors14}.  Where appropriate,
include the name(s) of editors of referenced books.

\begin{figure}[t]
\begin{center}
\fbox{\rule{0pt}{1in} \rule{0.9\linewidth}{0pt}}

\end{center}
   \caption{Example of caption.  It is set in Roman so that mathematics
   (always set in Roman: $B \sin A = A \sin B$) may be included without an
   ugly clash.}
\label{fig:long}
\label{fig:onecol}
\end{figure}

\subsection{Illustrations, graphs, and photographs}

All graphics should be centered.  Please ensure that any point you wish to make is resolvable in a printed copy of the response.  Resize fonts in figures to match the font in the body text, and choose line widths which render effectively in print.  Many readers (and reviewers), even of an electronic copy, will choose to print your response in order to read it.  You cannot insist that they do otherwise, and therefore must not assume that they can zoom in to see tiny details on a graphic.

When placing figures in \LaTeX, it's almost always best to use \verb+\includegraphics+, and to specify the  figure width as a multiple of the line width as in the example below
{\small\begin{verbatim}
   \usepackage[dvips]{graphicx} ...
   \includegraphics[width=0.8\linewidth]
                   {myfile.eps}
\end{verbatim}
}

{\small
\bibliographystyle{ieee_fullname}

\section{Introduction}

Please follow the steps outlined below when submitting your manuscript to
the IEEE Computer Society Press.  This style guide now has several
important modifications (for example, you are no longer warned against the
use of sticky tape to attach your artwork to the paper), so all authors
should read this new version.

\subsection{Language}

All manuscripts must be in English.

\subsection{Dual submission}

Please refer to the author guidelines on the CVPR 2021~web page for a
discussion of the policy on dual submissions.

\subsection{Paper length}
Papers, excluding the references section,
must be no longer than eight pages in length. The references section
will not be included in the page count, and there is no limit on the
length of the references section. For example, a paper of eight pages
with two pages of references would have a total length of 10 pages.
{\bf There will be no extra page charges for CVPR 2021.}

Overlength papers will simply not be reviewed.  This includes papers
where the margins and formatting are deemed to have been significantly
altered from those laid down by this style guide.  Note that this
\LaTeX\ guide already sets figure captions and references in a smaller font.
The reason such papers will not be reviewed is that there is no provision for
supervised revisions of manuscripts.  The reviewing process cannot determine
the suitability of the paper for presentation in eight pages if it is
reviewed in eleven.

\subsection{The ruler}
The \LaTeX\ style defines a printed ruler which should be present in the
version submitted for review.  The ruler is provided in order that
reviewers may comment on particular lines in the paper without
circumlocution.  If you are preparing a document using a non-\LaTeX\
document preparation system, please arrange for an equivalent ruler to
appear on the final output pages.  The presence or absence of the ruler
should not change the appearance of any other content on the page.  The
camera ready copy should not contain a ruler.
(\LaTeX\ users may use options of cvpr.cls to switch between different
versions.)
Reviewers:
note that the ruler measurements do not align well with lines in the paper
--- this turns out to be very difficult to do well when the paper contains
many figures and equations, and, when done, looks ugly.  Just use fractional
references (e.g.\ this line is $095.5$), although in most cases one would
expect that the approximate location will be adequate.

\subsection{Mathematics}

Please number all of your sections and displayed equations.  It is
important for readers to be able to refer to any particular equation.  Just
because you didn't refer to it in the text doesn't mean some future reader
might not need to refer to it.  It is cumbersome to have to use
circumlocutions like ``the equation second from the top of page 3 column
1''.  (Note that the ruler will not be present in the final copy, so is not
an alternative to equation numbers).  All authors will benefit from reading
Mermin's description of how to write mathematics:
\url{http://www.pamitc.org/documents/mermin.pdf}.

\subsection{Blind review}

Many authors misunderstand the concept of anonymizing for blind
review.  Blind review does not mean that one must remove
citations to one's own work---in fact it is often impossible to
review a paper unless the previous citations are known and
available.

Blind review means that you do not use the words ``my'' or ``our''
when citing previous work.  That is all.  (But see below for
techreports.)

Saying ``this builds on the work of Lucy Smith [1]'' does not say
that you are Lucy Smith; it says that you are building on her
work.  If you are Smith and Jones, do not say ``as we show in
[7]'', say ``as Smith and Jones show in [7]'' and at the end of the
paper, include reference 7 as you would any other cited work.

An example of a bad paper just asking to be rejected:
\begin{quote}
\begin{center}
    An analysis of the frobnicatable foo filter.
\end{center}

   In this paper we present a performance analysis of our
   previous paper [1], and show it to be inferior to all
   previously known methods.  Why the previous paper was
   accepted without this analysis is beyond me.

   [1] Removed for blind review
\end{quote}

An example of an acceptable paper:

\begin{quote}
\begin{center}
     An analysis of the frobnicatable foo filter.
\end{center}

   In this paper we present a performance analysis of the
   paper of Smith \etal [1], and show it to be inferior to
   all previously known methods.  Why the previous paper
   was accepted without this analysis is beyond me.

   [1] Smith, L and Jones, C. ``The frobnicatable foo
   filter, a fundamental contribution to human knowledge''.
   Nature 381(12), 1-213.
\end{quote}

If you are making a submission to another conference at the same time,
which covers similar or overlapping material, you may need to refer to that
submission in order to explain the differences, just as you would if you
had previously published related work.  In such cases, include the
anonymized parallel submission~\cite{Authors14} as additional material and
cite it as
\begin{quote}
[1] Authors. ``The frobnicatable foo filter'', F\&G 2014 Submission ID 324,
Supplied as additional material {\tt fg324.pdf}.
\end{quote}

Finally, you may feel you need to tell the reader that more details can be
found elsewhere, and refer them to a technical report.  For conference
submissions, the paper must stand on its own, and not {\em require} the
reviewer to go to a techreport for further details.  Thus, you may say in
the body of the paper ``further details may be found
in~\cite{Authors14b}''.  Then submit the techreport as additional material.
Again, you may not assume the reviewers will read this material.

Sometimes your paper is about a problem which you tested using a tool which
is widely known to be restricted to a single institution.  For example,
let's say it's 1969, you have solved a key problem on the Apollo lander,
and you believe that the CVPR70 audience would like to hear about your
solution.  The work is a development of your celebrated 1968 paper entitled
``Zero-g frobnication: How being the only people in the world with access to
the Apollo lander source code makes us a wow at parties'', by Zeus \etal.

You can handle this paper like any other.  Don't write ``We show how to
improve our previous work [Anonymous, 1968].  This time we tested the
algorithm on a lunar lander [name of lander removed for blind review]''.
That would be silly, and would immediately identify the authors. Instead
write the following:
\begin{quotation}
\noindent
   We describe a system for zero-g frobnication.  This
   system is new because it handles the following cases:
   A, B.  Previous systems [Zeus et al. 1968] didn't
   handle case B properly.  Ours handles it by including
   a foo term in the bar integral.

   ...

   The proposed system was integrated with the Apollo
   lunar lander, and went all the way to the moon, don't
   you know.  It displayed the following behaviours
   which show how well we solved cases A and B: ...
\end{quotation}
As you can see, the above text follows standard scientific convention,
reads better than the first version, and does not explicitly name you as
the authors.  A reviewer might think it likely that the new paper was
written by Zeus \etal, but cannot make any decision based on that guess.
He or she would have to be sure that no other authors could have been
contracted to solve problem B.
\medskip

\noindent
FAQ\medskip\\
{\bf Q:} Are acknowledgements OK?\\
{\bf A:} No.  Leave them for the final copy.\medskip\\
{\bf Q:} How do I cite my results reported in open challenges?
{\bf A:} To conform with the double blind review policy, you can report results of other challenge participants together with your results in your paper. For your results, however, you should not identify yourself and should not mention your participation in the challenge. Instead present your results referring to the method proposed in your paper and draw conclusions based on the experimental comparison to other results.\medskip\\

\begin{figure}[t]
\begin{center}
\fbox{\rule{0pt}{2in} \rule{0.9\linewidth}{0pt}}

\end{center}
   \caption{Example of caption.  It is set in Roman so that mathematics
   (always set in Roman: $B \sin A = A \sin B$) may be included without an
   ugly clash.}
\label{fig:long}
\label{fig:onecol}
\end{figure}

\subsection{Miscellaneous}

\noindent
Compare the following:\\
\begin{tabular}{ll}
 \verb'$conf_a$' &  $conf_a$ \\
 \verb'$\mathit{conf}_a$' & $\mathit{conf}_a$
\end{tabular}\\
See The \TeX book, p165.

The space after \eg, meaning ``for example'', should not be a
sentence-ending space. So \eg is correct, {\em e.g.} is not.  The provided
\verb'\eg' macro takes care of this.

When citing a multi-author paper, you may save space by using ``et alia'',
shortened to ``\etal'' (not ``{\em et.\ al.}'' as ``{\em et}'' is a complete word.)
However, use it only when there are three or more authors.  Thus, the
following is correct: ``
   Frobnication has been trendy lately.
   It was introduced by Alpher~\cite{Alpher02}, and subsequently developed by
   Alpher and Fotheringham-Smythe~\cite{Alpher03}, and Alpher \etal~\cite{Alpher04}.''

This is incorrect: ``... subsequently developed by Alpher \etal~\cite{Alpher03} ...''
because reference~\cite{Alpher03} has just two authors.  If you use the
\verb'\etal' macro provided, then you need not worry about double periods
when used at the end of a sentence as in Alpher \etal.

For this citation style, keep multiple citations in numerical (not
chronological) order, so prefer \cite{Alpher03,Alpher02,Authors14} to
\cite{Alpher02,Alpher03,Authors14}.

\begin{figure*}
\begin{center}
\fbox{\rule{0pt}{2in} \rule{.9\linewidth}{0pt}}
\end{center}
   \caption{Example of a short caption, which should be centered.}
\label{fig:short}
\end{figure*}

\section{Formatting your paper}

All text must be in a two-column format. The total allowable width of the
text area is $6\frac78$ inches (17.5 cm) wide by $8\frac78$ inches (22.54
cm) high. Columns are to be $3\frac14$ inches (8.25 cm) wide, with a
$\frac{5}{16}$ inch (0.8 cm) space between them. The main title (on the
first page) should begin 1.0 inch (2.54 cm) from the top edge of the
page. The second and following pages should begin 1.0 inch (2.54 cm) from
the top edge. On all pages, the bottom margin should be 1-1/8 inches (2.86
cm) from the bottom edge of the page for $8.5 \times 11$-inch paper; for A4
paper, approximately 1-5/8 inches (4.13 cm) from the bottom edge of the
page.

\subsection{Margins and page numbering}

All printed material, including text, illustrations, and charts, must be kept
within a print area 6-7/8 inches (17.5 cm) wide by 8-7/8 inches (22.54 cm)
high.
Page numbers should be in footer with page numbers, centered and .75
inches from the bottom of the page and make it start at the correct page
number rather than the 4321 in the example.  To do this fine the line (around
line 20)
\begin{verbatim}
\setcounter{page}{4321}
\end{verbatim}
where the number 4321 is your assigned starting page.

\subsection{Type-style and fonts}

Wherever Times is specified, Times Roman may also be used. If neither is
available on your word processor, please use the font closest in
appearance to Times to which you have access.

MAIN TITLE. Center the title 1-3/8 inches (3.49 cm) from the top edge of
the first page. The title should be in Times 14-point, boldface type.
Capitalize the first letter of nouns, pronouns, verbs, adjectives, and
adverbs; do not capitalize articles, coordinate conjunctions, or
prepositions (unless the title begins with such a word). Leave two blank
lines after the title.

AUTHOR NAME(s) and AFFILIATION(s) are to be centered beneath the title
and printed in Times 12-point, non-boldface type. This information is to
be followed by two blank lines.

The ABSTRACT and MAIN TEXT are to be in a two-column format.

MAIN TEXT. Type main text in 10-point Times, single-spaced. Do NOT use
double-spacing. All paragraphs should be indented 1 pica (approx. 1/6
inch or 0.422 cm). Make sure your text is fully justified---that is,
flush left and flush right. Please do not place any additional blank
lines between paragraphs.

Figure and table captions should be 9-point Roman type as in
Figures~\ref{fig:onecol} and~\ref{fig:short}.  Short captions should be centred.

\noindent Callouts should be 9-point Helvetica, non-boldface type.
Initially capitalize only the first word of section titles and first-,
second-, and third-order headings.

FIRST-ORDER HEADINGS. (For example, {\large \bf 1. Introduction})
should be Times 12-point boldface, initially capitalized, flush left,
with one blank line before, and one blank line after.

SECOND-ORDER HEADINGS. (For example, { \bf 1.1. Database elements})
should be Times 11-point boldface, initially capitalized, flush left,
with one blank line before, and one after. If you require a third-order
heading (we discourage it), use 10-point Times, boldface, initially
capitalized, flush left, preceded by one blank line, followed by a period
and your text on the same line.

\subsection{Footnotes}

Please use footnotes\footnote {This is what a footnote looks like.  It
often distracts the reader from the main flow of the argument.} sparingly.
Indeed, try to avoid footnotes altogether and include necessary peripheral
observations in
the text (within parentheses, if you prefer, as in this sentence).  If you
wish to use a footnote, place it at the bottom of the column on the page on
which it is referenced. Use Times 8-point type, single-spaced.

\subsection{References}

List and number all bibliographical references in 9-point Times,
single-spaced, at the end of your paper. When referenced in the text,
enclose the citation number in square brackets, for
example~\cite{Authors14}.  Where appropriate, include the name(s) of
editors of referenced books.

\begin{table}
\begin{center}
\begin{tabular}{|l|c|}
\hline
Method & Frobnability \\
\hline\hline
Theirs & Frumpy \\
Yours & Frobbly \\
Ours & Makes one's heart Frob\\
\hline
\end{tabular}
\end{center}
\caption{Results.   Ours is better.}
\end{table}

\subsection{Illustrations, graphs, and photographs}

All graphics should be centered.  Please ensure that any point you wish to
make is resolvable in a printed copy of the paper.  Resize fonts in figures
to match the font in the body text, and choose line widths which render
effectively in print.  Many readers (and reviewers), even of an electronic
copy, will choose to print your paper in order to read it.  You cannot
insist that they do otherwise, and therefore must not assume that they can
zoom in to see tiny details on a graphic.

When placing figures in \LaTeX, it's almost always best to use
\verb+\includegraphics+, and to specify the  figure width as a multiple of
the line width as in the example below
{\small\begin{verbatim}
   \usepackage[dvips]{graphicx} ...
   \includegraphics[width=0.8\linewidth]
                   {myfile.eps}
\end{verbatim}
}

\subsection{Color}

Please refer to the author guidelines on the CVPR 2021~web page for a discussion
of the use of color in your document.

\section{Final copy}

You must include your signed IEEE copyright release form when you submit
your finished paper. We MUST have this form before your paper can be
published in the proceedings.

Please direct any questions to the production editor in charge of these
proceedings at the IEEE Computer Society Press:
\url{https://www.computer.org/about/contact}.

{\small
\bibliographystyle{ieee_fullname}

\section{Introduction}

Team membership classification (i.e. labelling each person on a playing surface as a member of team A, team B or a referee) is a critical task in sports video analytics:  most inferences and statistics depend upon knowing which player are on each team, including attempts on goal, offsides, and player configurations.   Accurate team affiliation labels can also improve player tracking. The problem can be challenging due to the extreme variations in player pose, occlusions, motion blur and uneven illumination.

Prior work  (e.g., \cite{lu2018lightweight,istasse2019associative}) has framed the problem as a supervised learning task in which labelled data (e.g., bounding boxes with team identifiers) are used to learn a classifier.  Early supervised methods employed hand-crafted colour-based features
\cite{lu2013learning,liu2014detecting}, while more recent approaches train convolutional neural networks (CNNs) on labelled datasets to perform player segmentation~\cite{istasse2019associative} and classification~\cite{lu2018lightweight}.

Unfortunately, the supervised player classification approach ~\cite{lu2018lightweight} has limited application, since it requires fine-tuning on every new game for optimal classifier performance . The team segmentation approach ~\cite{istasse2019associative} has been found to generalize better but does not provide player instance segmentation and requires expensive pixel-wise annotation to train the system. For all of these reasons, an unsupervised approach is preferred.

To date, unsupervised approaches \cite{mazzeo2010football,ivankovic2014automatic,d2009investigation,bialkowski2014representing,tong2011automatic} rely solely on
colour-based features such as colour histograms.  While these are simple and lightweight, typically many frames are needed from each new game in order to learn the colour distributions, and these methods fail when the two teams are wearing similar colours.

Our goal in this paper is to understand whether a more powerful representation, that may include both colour and configural information, can be learned in a fully unsupervised manner, and whether such a representation can reduce the number of frames needed for training and improve generallization to novel teams, jerseys, lighting and camera parameters.

To achieve this, we employ unsupervised contrastive learning to train a CNN to cluster players into two teams.     We demonstrate our system's performance on a new hockey dataset and compare it to previously proposed unsupervised team affiliation learning approaches.   Figure~\ref{fig:workflow} demonstrates overall system design.
The dataset and code are available at \href{https://github.com/mkoshkina/teamId}{https://github.com/mkoshkina/teamId}.

Our main contributions are:
\begin{enumerate}
\item We introduce what is, to our knowledge, the first unsupervised deep learning approach for team classification.  This novel contrastive learning approach allows us to generalize to novel games, teams and jerseys without labelled data.
\item We introduce a new annotated hockey dataset that can be used to evaluate player detection and team classification algorithms.
\item We show that our novel unsupervised algorithm outperforms prior unsupervised approaches by a large margin, especially when only a small number of frames are available for unsupervised learning before team assignments must be made.  This limits the burn-in time for real-time streaming applications and allows the system to adapt quickly to changes in lighting or camera parameters.
\item We show  how our system for team classification can be used to produce accurate team-conditioned heat maps of player positioning, useful for coaching and strategic analysis.
\end{enumerate}

\begin{figure*}
\begin{center}
\includegraphics[width=0.8\linewidth]{figures/workflow_overview.pdf}
\end{center}
   \caption{Overview of the proposed system.  Mask R-CNN is first used to detect and segment each person on the playing surface.  A pre-trained CNN is then used to classify referees, whlie remaining players are passed to our embedding network for clustering into teams.  This allows production of heat maps showing the distribution of the two teams over the playing surface.}
\label{fig:workflow}
\end{figure*}

\section{Related Work}

\subsection{Player Classification} Automatic labelling of players according to team is critical for sport video understanding, including player tracking \cite{lu2013learning,liu2014detecting,tong2011automatic,shtrit2011}, player configuration analysis, activity recognition \cite{bialkowski2014representing}  and detection of game events \cite{d2009investigation}.

Early work relied on colour histograms \cite{mazzeo2010football,shtrit2011,ivankovic2014automatic,d2009investigation,lu2013learning,liu2014detecting,bialkowski2014representing}  and `bag of words' representations of  colour features \cite{tong2011automatic}. These approaches are lightweight, however the exclusive reliance on colour features make them more sensitive to illumination changes and could lead to lower performance when teams are wearing similar colours.

In recent years, supervised deep learning based methods for player detection and player labelling have been proposed \cite{lu2018lightweight, istasse2019associative}. These methods perform well but require labelled data for training. In \cite{istasse2019associative}, a  CNN is trained to segment players and generate team pixel-wise descriptors, where pixels of players from the same team have descriptors that are close in embedding space. Pixels are then clustered to identify the players on the two teams. This method requires pixel-level team labelling to train the network and per-image pixel-level clustering at the inference stage.  Moreover, it does not provide instance-level segmentation so would not be suitable for use in player location heatmap generation.

Lu {et al.}~\cite{lu2018lightweight} also take a supervised approach, employing a cascaded CNN to learn team membership classification (team A, team B and others) from labelled data.  This method has good results but does not generalize well and thus requires fine-tuning on labelled samples from a new game in order to be used for that game.

Clearly, both simple colour-based unsupervised approaches and more sophisticated CNN-based supervised approaches have limitations.  Here we explore whether modern deep unsupervised learning methods can be used to overcome these limitations.

\subsection{Contrastive Learning and Deep Clustering}
Contrastive learning~\cite{hadsell2006dimensionality}  is a self-supervised representation learning approach that aims to map similar objects to be close in embedding space and dissimilar objects further apart, and has been shown to produce excellent results on a number of tasks \cite{he2019momentum,chen2020simple}.  In our work we use a simple CNN trained with triplet loss \cite{hoffer2015deep} to learn a feature space that best separates players into two teams.

Recent work in contrastive learning \cite{he2019momentum,chen2020simple}  shows excellent results in unsupervised representation learning on large datasets such as ImageNet\cite{ILSVRC15} or COCO\cite{lin2014microsoft}.  These methods are based on noise contrastive estimation and involve using an anchor (typically an augmented version of an original image), one positive (another augmented version of the same image) and a large number of negatives, randomly picked from the training set.  This setup works well for a dataset with a large number of categories, where randomly-picked images are unlikely to contain many positives. In our setting however, although we have a large number of images we have a relatively small number of categories (unique jersey designs).  More precisely, ImageNet contains 1000 categories and our training dataset has 10.   As a consequence, in our setting using random images as negatives results in a 10\% of false negatives.  This adversely affects training. For this reason, a simple triplet loss  works much better in our setting.

Our work is  inspired in part by deep clustering approaches  \cite{yang2017towards,yang2016joint} in which CNNs are used to jointly learn feature representations and cluster centres in an unsupervised fashion.  In our approach we use pseudo-labels from an initial k-means clustering as a supervision signal to train our contrastive learning CNN.  The main divergence from prior methods is that we are only interested in learning feature space that will lead to good data separation - cluster centres can be quite different in each new game.  Once trained, the network is only used to extract features from player images.

\section{Method}
\subsection{Overview}Our general goal is to develop automatic sports  analysis tools that provide valuable visualizations, statistics and analyses for coaches and players. Our current work is focused on hockey, but can easily be adapted to other team sports such as soccer, basketball and football.  In this paper we design and evaluate a system that automatically detects players, classifies them into teams and returns a heatmap of the distribution of players for each of the two teams.

We employ video from a stationary 4K camera that captures the whole playing surface, and use the Mask R-CNN network~\cite{he2017maskrcnn}) to detect and segment all people on the ice, including the players from the two teams and the referees.  Since the referee uniform is consistent across games, we first train a CNN to perform referee classification based upon labelled data (referee, non-referee).

In order to classify players we employ an embedding CNN trained with triplet loss to extract a learned feature vector for each player image.  We then use k-means to estimate cluster centres for the two teams from one or more initial frames of the video.  On all subsequent frames we assign each player to a team based on the closest cluster centre in feature space.  Using a learned homography, we geo-locate each detected player on the ice surface and use kernel density estimation (KDE) to construct a heatmap representing the distribution of players across the playing surface for each team.    Figure~\ref{fig:workflow} shows the pipeline for our system.

\subsection{Dataset}
\label{sec:dataset}
We introduce a new labelled hockey video dataset that will be made public on acceptance of this paper.  Despite the variety of available sport video datasets \cite{apidis,giancola2018}, to the best of our knowledge our new hockey dataset will be the only publicly-available sport video dataset that contains team affiliation labels.

The dataset is drawn from 15 different hockey games captured over two seasons.  Seven of the games (season 1) are captured with a wide-field stationary 4K (3840 $\times$ 2160 pixel) 30 fps camera that captures nearly the whole rink.  In order to better capture the whole rink, season 2 games are captured by two 4K cameras with 75 degree horizontal displacement, together capturing the whole rink with modest overlap.  We defined a virtual camera with intrinsic parameters matching the two real cameras and extrinsic parameters equal to the mean of the two real cameras.   Each of the two camera images was rectified to the virtual camera through a homography with the ice surface. The two virtual images were then smoothly blended. The resulting season 2 videos have resolution of 5930 $\times$ 1080 pixels.

We manually close-cropped the videos to the 3840$\times$900 (season 1 games) and 5680$\times$904 (season 2 games) rectangle bounding the rink (Fig. \ref{fig:workflow}).  From each game we randomly extracted a video clip of roughly 850 frames (28 sec).   Each game contains a unique combination of player uniforms, and since play is active in each clip there is considerable variation in player pose, motion blur and occlusions between players.  Players were automatically detected using Mask R-CNN (see below).  To eliminate coaches and bench players, we applied a heuristic to exclude detections close to the bottom of the frame that had bounding box height less than twice the width.

 {\em For evaluation only}, we manually annotated every 10th frame of each game clip, thus obtaining between 80-90 labelled frames per game.  Annotations consist of:
\begin{enumerate}
\item Mask R-CNN detections
\begin{itemize}
\item Class label (Team A, Team B, Referee)
\end{itemize}
\item Manual detections (including players not detected by Mask R-CNN)
\begin{itemize}
\item Class label (Team A, Team B)
\item Estimated image projections of points of contact with playing surface (skates on the ice)
\end{itemize}
\end{enumerate}

To label the Mask R-CNN detections, extracted player images with segmentation masks applied were manually inspected one-by-one. Only the images that could be identified by visual inspection to belong to a player (Team A, Team B ) or referee were labelled, the rest were marked as false positives.
If there were multiple players within one extracted image the player with the most pixels in the mask was selected.

We use the Mask-RCNN labels of detected players to evaluate the accuracy of team classification algorithms and use the manual detection annotations to evaluate the accuracy of our team positioning heatmaps.

The 15-game dataset was divided into training, validation and test sets with a 9-2-4 split.  Both training and test set contains a mix of season 1 and season 2 games. One limitation of the dataset is that even though each game has a unique combinations of teams playing, some teams appear multiple times through the dataset.  We have ensured that the test set includes a game with previously unseen teams.

\subsection{Player Detection and Segmentation}
\label{sec:detection}
We employ Mask R-CNN \cite{he2017maskrcnn} trained on MS COCO \cite{lin2014microsoft} to detect and segment all people on the playing surface.  To adapt to the different resolution, aspect ratio and expected size of people in our video relative to MS COCO, we partitioned each frame into left and right  images with a 40 pixel central overlap, running Mask R-CNN on each individually before merging results.  Bounding boxes detected in the left image that overlap boxes detected in the right image by 45\% or more are merged by selecting the larger of the two boxes.   We define the estimated image location of each player as the mid-point of the lower boundary of the R-CNN bounding box.

\subsection{Referee Classifier}
\label{sec:classifier}
Since referee uniforms are consistent across games, a supervised approach is appropriate.  We use the referee/non-referee labels from our Mask R-CNN detections to train and evaluate a simple CNN classifier. This is the only way that labelled data is used in our system, aside from evaluation.  Our CNN classifier takes as input R-CNN detection images with segmentation masks applied and classifies them as referee or non-referee.  We employ a small CNN with 3 convolutional layers (16, 32, and 64 output channels)  and 3x3 kernels followed by 2 fully connected layers.  We train the network with a binary cross entropy loss function, employing the Adam optimizer.

\subsection{Unsupervised Team Assignment: Feature Learning and Clustering}
\label{sec:features}
An ideal team labelling algorithm will be unsupervised, generalizing to new games without needing any labelled data, and will require minimal frames (burn-in time)  from the beginning of the game to determine accurate labels for each player on the team.

Previous unsupervised approaches used colour features such as histograms and bag-of-colours.  These approaches can be effective but since they do not consider spatial features, performance may suffer when teams are wearing jerseys with similar colour profiles, or when illumination variations render colour features unreliable.  Here we explore whether an embedding CNN trained by contrastive learning can produce a more powerful representation that, by incorporating both colour and spatial features, can learn a reliable feature representation from fewer frames, and thus have a shorter burn-in time.

We employ a CNN with 3 convolutional layers (16, 32, and 64 channels) and 3x3 kernels, each followed by a pooling layer, and two fully connected layers. The last layer returns a feature vector of length 1024. We train our network using the Adam optimizer on a training set of games using a triplet loss \cite{hoffer2015deep}. Input is a triplet of extracted images with the R-CNN mask applied: an anchor image, a positive image and a negative image.  The positive image is an image of a player believed to be from the same team as the anchor image, while the negative image is a player believed to be on the other team.  The triplet loss function, when back-propagated, drives the network to decrease the distance in the embedding space between the anchor and positive images, while increasing the distance between the anchor and negative images.  In order to ensure that the learned representation does not exclusively rely on colour, we randomly convert 50\% of training triplets to grayscale.

Unsupervised training of the embedding network requires a method for estimating whether two input images have the same or different labels.  We seed this process with a simple colour-based distance measure, representing each image as a normalized RGB histogram with 8 bins per colour channel  and then using k-means  to cluster players into two teams.

To form the triplets, we  first rank the player images $\mathbf{x}_i$ in terms of their team assignment confidence scores $p_{ij}$, using a standard
`soft k-means' measure:
\begin{eqnarray}
p_{i1} &=& \frac{\norm{\mathbf{x}_i-\mathbf{c}_2}}{\norm{\mathbf{x}_i-\mathbf{c}_1}+ \norm{\mathbf{x}_i-\mathbf{c}_2}}\\
p_{i2} &=& \frac{\norm{\mathbf{x}_i-\mathbf{c}_1}}{\norm{\mathbf{x}_i-\mathbf{c}_1}+ \norm{\mathbf{x}_i-\mathbf{c}_2}}
\end{eqnarray}
where $\mathbf{c}_j$ is the centre of cluster $j$ and $p_{ij}$ is the confidence with which image $i$ is assigned to cluster $j$.

 We consider only high-confidence samples ($p_{ij}>0.9$)) for training to limit the label noise.  We then randomly form triplets by sampling the anchor and positive images from one cluster (anchor and positive example) and the negative image from the other.

As the training proceeds we regenerate these pseudo-labels and training triplets, but replacing the histogram representation with the evolving embedded representation learned by the network.  We train until convergence (no improvement on the validation data for 3 epochs) or a maximum of 30 epochs on the initial colour histogram pseudo-labels and then generate new pseudo-labels from the evolving embedded representation every 10 epochs (or until convergence).  We find that the proportion of high-confidence samples  grows over time, indicating that the network is learning a representation that improves data separation. Figure~\ref{fig:network} illustrates this training process.

\begin{figure*}
\begin{center}
\includegraphics[width=0.8\linewidth]{figures/network_training-crop.pdf}
\end{center}
   \caption{Self-supervised training of embedding network.}
\label{fig:network}
\end{figure*}

Once unsupervised training of the embedding network on the training set is complete, we apply the network to novel games with unseen teams and uniforms.  We use the first $n_{burn}$ frames of the unseen game as input to k-means to determine the two cluster centres for this new game in the pre-learned embedding space.  Once the cluster centres are identified, we associate detected players in subsequent frames with the nearest cluster centre, and evaluate on annotated frames.    Figure~\ref{fig:data_usage} illustrates our use of data for training and evaluation purposes.

\begin{figure*}
\begin{center}
\includegraphics[width=0.7\linewidth]{figures/data_usage-crop.pdf}
\end{center}
   \caption{Data usage for training and evaluation.  a) Labelled frames are used to train the referee classifier, but the embedding network representation is learned in unsupervised fashion, without reference to labels.   b) To perform inference on a novel video, we use the first $n_{burn}$ frames to find cluster centres in the learned embedded representation.  Labelled frames are used only for  evaluation. }
\label{fig:data_usage}
\end{figure*}

\subsection{Team Positioning Heatmaps}
\label{sec:heatmaps}
One of the many useful applications of player detection and labelling is the generation of team positioning heatmaps that can help coaches and players understand how their players and the players on the opposing team tend to be distributed throughout a game or portion of the game.

To generate these heatmaps we use a learned homography to transfer the image coordinates of each detected player (midpoint of the bottom of each bounding box) to the corresponding point on a model of the playing surface.   The homography was computed from 19 corresponding pairs of points in one video frame and in a template model of the ice rink (Illustration showing keypoints and backprojected player positions is included in supplementary material).  We then used the standard least-squares reprojection method \cite{luong1996fundamental} to estimate the homography mapping image pixels to points on the ice surface.   Based on these player positions and the team affiliations estimated using our unsupervised contrastive learning algorithm over multiple frames, we compute a rectified map of player density (players per square metre per frame) using Gaussian kernel density estimation (KDE) \cite{rosenblatt1956, parzen1962}.   Figure~\ref{fig:heatmaps} shows examples of these automatically-generated maps.

\section{Evaluation}

\subsection{Implementation Details}
Our system is implemented in Python 3 with Pytorch and Sklearn.  We use the publicly-available Mask R-CNN network and weights \cite{matterport_maskrcnn_2017} with a confidence threshold of 0.6.   Both referee and embedding networks take as input player images with segmentation masks applied, resized to 62$\times$128 pixels, roughly the average size of a player image.  To reduce the impact of illumination variations we applied an affine transform $I^\prime _i(x,y) = aI(x,y)+b$ to the intensities $I_i$ of all three channels $i\in\{R,G,B\}$ such that $\min_{x,y,i}I^\prime_i = 0$ and $\max_{x,y,i}I^\prime_i = 255$.

K-means computation of cluster centres entails 10 random initializations:  The solution that minimizes the mean squared deviation from cluster centres is selected.

\subsection{Comparison with Other Unsupervised Approaches}
\label{sec:results}
We compare the performance of our unsupervised team affiliation algorithm against the two main previously proposed unsupervised team labelling approaches:   colour histograms~\cite{mazzeo2010football,ivankovic2014automatic,d2009investigation,lu2013learning,liu2014detecting,bialkowski2014representing}  and bag-of-words representations of  colour features~\cite{tong2011automatic}.  Since the code and datasets for these previous approaches are not available, we performed a hyperparameter search using k-fold cross-validation to determine the optimal parameters and use k-means clustering to determine cluster centres.  These optimal parameters were the number of bins per channel for the histogram algorithm and number of words for the bag of colours algorithm.  In addition, we evaluated whether to use the entire segmented player or just the upper half, since  the lower half of the uniform is fairly consistent across teams, and also experimented with multiple colour spaces (see below).

We also experiment with replacing features learned by our contrastive learning network with features learned with convolutional autoencoder (see Section~\ref{sec:ae}).

Comparison with previously used supervised approaches \cite{lu2018lightweight, istasse2019associative} is not feasible as the code and datasets are not available.

\subsubsection{Colour Histogram Algorithm}
Our colour histogram method simply histograms the colours within the segmented player, normalizing by the number of pixels.
We experimented with RGB, LAB and HSV colour spaces, and also tried eliminating the luma or value channel (i.e., two-dimensional AB and HV spaces) to reduce sensitivity to illumination, but found optimal performance with RGB coding.

Cluster centres are then found using k-means, using Euclidean distance in the colour histogram space.  The single hyperparameter is the number $n$ of bins per channel:  k-fold cross-validation revealed that $n=8$ produces best results for our dataset.  We also found that performance was slightly better if only the upper half of the segmented player was considered as the player jerseys are most distinct between teams.

\subsubsection{Bag-of-Colours Algorithm}
\label{sec:bag}
In our bag-of-colours method, we employ the expectation maximization algorithm to fit a Gaussian mixture model (GMM) with $n$ components to the normalized colours of the players in the initial training partition of the novel game.  These components then form the words of a dictionary with which to encode players in subsequent frames.  K-fold cross-validation reveals that $n=35$ components yields optimal results.  We  use k-means clustering to find each team's cluster centres in this 35-dimensional space and assign  players to the closest cluster.  We again find that considering only the top half of the segmented player yields superior results. We also consider a variation of this approach, pretrainied bag-of-colours,  where the dictionary of colours is learned on training set games.

\subsubsection{Autoencoder}
\label{sec:ae}
For additional comparison, we use small convolutional autoencoder \cite{masci2011stacked} trained on image reconstruction.  The encoder network architecture is kept the same as our embedding network and decoder mirrors the encoder.  After training on the images in our training set, we use encoder portion to extract 1024 feature vector for each test image.  We then use these features in the same setting as our embedding features to first learn cluster centres on the burn-in frames and then assign players to closest centre for the rest of the frames.

\subsection{Evaluation Methodology}
We evaluate team affiliation labelling on players detected by mask R-CNN.  These include false positives and imperfect segmentations.  In addition, since the referee classifier is also imperfect, some referees will be incorrect classified as players and will add noise to the contrastive learning process.   We test both our supervised referee classifier and our unsupervised embedding network team classifier on the test set consisting of 4 games.

Accuracy is evaluated over the 30 annotated frames immediately following the burn-in interval.  Since every tenth frame is annotated, this represents roughly 10 sec of video at 30 fps.

We assess effects of noise in initial pseudo-labels on embedding network performance by considering different team assignment confidence scores $p_{ij}$ thresholds.  Higher confidence threshold leads to better clustering performance. We include this evaluation in supplementary materials .

\subsection{Referee Classification}
For each game we have 80-90 frames annotated frames and there are 3-4 referees on the rink, so for 9 training games we have 2000 referees in our training set, augmenting these by left/right reflections yields a total of 4000 training vectors.  Employing a softmax threshold of 0.5, we achieve a mean accuracy of 98\%  with 93\%, precision, 96\% recall.  Precision-recall curve for referee classifier is included in supplementary materials

\subsection{Team Classification}
Table~\ref{table:results} shows the mean accuracy of team classification for the all algorithms under evaluation.  Results depend upon the number $n_{burn}$ of frames  available for learning cluster centres prior to inference.  When $n_{burn}$ is large (512 in this case), two colour-based and our methods perform fairly well, with colour-based methods rivalling our CNN approach.  However, when $n_{burn}$ is small (1 in this case), performance of the colour-only methods drops dramatically, while our embedding CNN approach still performs very well.

\begin{table}
\begin{center}
\begin{tabular}{|l|c|c|}
\hline
\bf{Method} & $\mathbf{n_{burn}=1}$ & $\mathbf{n_{burn}=512}$ \\
\hline\hline
Colour Histogram & $0.87 \pm  0.031$ & $\boldsymbol{0.97 \pm 0.012}$\\
Bag-of-colours & $0.76 \pm 0.032$ & $\boldsymbol{0.97 \pm  0.018}$\\
Pretrained Bag-of-colours & $0.86 \pm 0.099$ & $0.89 \pm  0.189$\\
Autoencoder & $0.70 \pm 0.076$ & $0.92 \pm  0.099$\\
\bf{Embedding CNN} &  $\boldsymbol{0.94 \pm 0.009}$ & $\boldsymbol{0.97 \pm 0.011}$\\
\hline
\end{tabular}
\end{center}
\caption{Team classification accuracy as a function of the number $n_{burn}$ of frames available for learning cluster centres prior to inference. We show mean and standard error of the accuracy over 4 test games.}
\label{table:results}
\end{table}

This behaviour is shown in more detail in  Fig.~\ref{fig:frames_experiments}.  We see that the simpler colour-based approaches and autoencoder approach improve continuously as the number of training frames increases, while our embedding CNN approach performs well even with only one training frame, improving only modestly thereafter.  At least 512 burn-in frames are required before the pure colour approaches begin to rival our embedding CNN algorithm.  Autoencoder method is lagging behind even with 512 burn-in frames.

\begin{figure}[t]
\begin{center}
   \includegraphics[width=0.9\linewidth]{figures/error_graph.pdf}
\end{center}
   \caption{Error rate as a function of the number $n_{burn}$ of initial frames used to learn cluster centres.  We show mean error of the accuracy over 4 test games}
\label{fig:frames_experiments}
\end{figure}

We believe that the advantage of our embedding CNN approach derives from the ability of our unsupervised contrastive learning network to learn from the training games an embedding space that is more effective for discriminating teams than colour histograms.  This more discriminative space then allows well-separated cluster centres to be learned very quickly from the novel game.

\begin{figure*}[h]
\begin{center}
\includegraphics[width=0.8\linewidth]{figures/heatmaps-crop.pdf}
\end{center}
   \caption{Team positioning heatmaps for a test game.}
\label{fig:heatmaps}
\end{figure*}

\subsection{Team Position Heatmaps Results}
One useful application of player detection and team classification is to allow visualization of team positioning over the course of a game or a portion of a game.  We demonstrate this by generating heatmaps for each game based upon the 800-900 frames used for each game in our experiments.  A learned homography is employed to back-project the image location (midpoint of the bottom boundary of the bounding box) to the playing surface.  We also back-project our manual detections (Section \ref{sec:dataset}) to form a ground-truth heatmap.  Gaussian kernel density estimation~ \cite{rosenblatt1956, parzen1962} is then used to estimated the player density (players per metre squared per frame) for both estimated and ground-truth heatmaps.  The Gaussian bandwidth for KDE is calculated using Silverman's rule of thumb \cite{silverman1986density}, and is roughly 30 pixels for all images (template rink image size is 496x240 pixels).

Fig. \ref{fig:heatmaps} shows example results from one test game for the three team classification methods using $n_{burn}=1$ frames to learn cluster centres.  We see that our embedding CNN approach more consistently represents the true player densities than the pure-colour histogram or bag of colours approaches.

For quantitative evaluation, we scale the maps to integrate to one and then compute the KL-divergence between estimated and ground truth densities over our test set (Table~\ref{table:kl}).  While the bag-of-colours algorithm outperforms a simple colour histogram, our embedding CNN approach substantially outperforms both pure-colour methods.

\begin{table}
\begin{center}
\begin{tabular}{|l|c|}
\hline
{\bf Method} & {\bf Mean KL-divergence} \\
\hline\hline
Colour histogram &0.072 \\
Bag-of-colours & 0.069 \\
{\bf Embedding CNN (Ours)} & {\bf 0.047}\\
\hline
\end{tabular}
\end{center}
\caption{KL-divergence of automatically-generated player positioning heatmaps from ground truth.}
\label{table:kl}
\end{table}

\subsubsection{Runtime}
Our experiments are conducted on 3.6GHz Intel Core i9 CPU x 16 with 64 GB RAM and an Nvidia GeForce RTX 2080 GPU.  Our method runs in real time on segmented player images.  It takes 21 miliseconds to learn team appearances for the game from a single frame and 11 miliseconds per frame for inference on subsequent frames. For convenience, we employed the widely-available but non-real-time Mask R-CNN network~\cite{he2017maskrcnn} for player detection and segmentation, which runs at roughly 5fps.  If replaced with a real-time segmentation network, such as Yolact~\cite{bolya2019yolact}, our whole system will run in real-time.  We leave this for future work.

\section {Conclusions \& Future Work}
Our results demonstrate that a learned representation that can incorporate both colour and spatial features can produce superior results for team classification than a pure-colour approach.  We also demonstrate that such a representation can be learned in unsupervised fashion, using contrastive learning with a triplet loss.  A major benefit is that unsupervised pre-learning of the representation allows for ultra rapid learning of cluster centres from novel games, which limits the burn-in period, allowing online inference.  We also show how this approach to team classification can be used to produce accurate team-conditional player positioning maps that can be useful for coaching and game analysis.

Improvements could be made by integrating with player tracking:  While team classification will aid tracking,  the converse is also true:  Tracking can potentially
eliminate occasional errors in team classification.

\textbf{Acknowledgements.} We acknowledge the support of the York VISTA (vista.info.yorku.ca) and Research Chair programs. We thank Canlan Ice Sports (www.canlansports.com) and York University Athletics for providing the video data for this project.

{\small
\bibliographystyle{ieee_fullname}

\section{Effect of Noise in Pseudo-Labels }
To evaluate effects of noise in initial pseudo-labels on embedding network performance we consider different team assignment confidence scores $p_{ij}$ thresholds.  Setting higher threshold results in selecting fewer samples in the initial training set, while using lower threshold results in having a larger but noisier sample set.  Table~\ref{table:noise} shows that selecting higher confidence threshold leads to better clustering performance.

\begin{table}[h]
\begin{center}
\begin{tabular}{|l|c|c|}
\hline
\bf{Threshold} & \bf{Error Rate} & \bf{ Train Set Size} \\
\hline\hline
$p_{ij} > 0.5$  &0.134& 77\%\\
$p_{ij} > 0.7$  &0.037 & 72\%\\
$p_{ij} > 0.9$  &0.031 & 60\%\\
\hline
\end{tabular}
\end{center}
\caption{Team classification error as a function of initial pseudo-labels confidence threshold.  We indicate pecentage of training samples that satisfied the threshold and were included in the initial training set. }
\label{table:noise}
\end{table}

\section{Homography}
In order to generate team position heatmaps, we use a learned homography to transfer the image coordinates of each detected player (midpoint of the bottom of each bounding box) to the corresponding point on a model of the playing surface.   The homography was computed from 19 corresponding pairs of points in one video frame and in a template model of the ice rink Figure~\ref{fig:homography} shows keypoints and backprojected player positions.

\begin{figure*}[h]
\begin{center}
\includegraphics[width=0.9\linewidth]{figures/homography-crop.pdf}
\end{center}
  \caption{Homography mapping video pixels to ice coordinates. (a) Keypoint pairs.  (b) Backprojected player positions.}
\label{fig:homography}
\end{figure*}

\section{Referee Classifier}
Figure~\ref{fig:referee} shows precision-recall curve for referee classifier.

\begin{figure}[H]
\begin{center}
   \includegraphics[width=0.9\linewidth]{figures/referee.pdf}
\end{center}
   \caption{Precision-Recall curve of referee classifier.}
\label{fig:referee}
\end{figure}

\end{document}

redpajama_set_name: RedPajamaArXiv


The Endocrinology Department cares for patients with hormone conditions.
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text:
from subprocess import call
import timeit
import csv
import statistics
import math
import numpy as np
with open('read_bench.csv', 'w') as csvfile:
    bench_writer = csv.writer(csvfile)
    bench_writer.writerow(['NumberOfDataSets', 'AverageTime', 'StandardDeviation'])
    for i in np.round(np.linspace(100, 1e6, 10)):
        t = timeit.Timer("call(['./readRandomDataSets', 'test.h5', str(%d)])" % i, setup="from subprocess import call")
        data = t.repeat(repeat=5, number=1)
        bench_writer.writerow([i, statistics.mean(data), statistics.stdev(data)])

redpajama_set_name: RedPajamaGithub


text:
typedef	unsigned int	Bitboard;

//	not really the place to put these, but it serves the purpose - just need to then add the include for this header into Application.cpp as well.
const int	BUFFER_WIDTH = 1920;
const int	BUFFER_HEIGHT = 1080;

//	What do I need?  ...
//	Player 1/2 starting config
//	Array for piece coordinates
//	Bitmasks for "everything":
//		Player 1/2 King row
//		Starting board config

struct GameState
{
	int	m_iCurrentPlayer;	//	who's turn is it?
	//	and where are all the pieces?
	Bitboard	m_P1Pieces;
	Bitboard	m_P2Pieces;
	Bitboard	m_Kings;
};

struct Move
{
	Bitboard	StartPos;
	Bitboard	EndPos;
	bool	bJumper;
};

extern	Bitboard	abbSquareMasks[32];
extern	vec3 aPieceCoords[32];
extern	vec3 aKingCoords[32];
extern	Bitboard	bbP1StartPieces;
extern	Bitboard	bbP2StartPieces;
extern	Bitboard	bbStartKings;
extern	Bitboard	bbP1KingRow;
extern	Bitboard	bbP2KingRow;
extern	Bitboard	bbRow0;
extern	Bitboard	bbRow1;
extern	Bitboard	bbRow2;
extern	Bitboard	bbRow3;
extern	Bitboard	bbRow4;
extern	Bitboard	bbRow5;
extern	Bitboard	bbRow6;
extern	Bitboard	bbRow7;
extern	Bitboard	bbCentreSquares;
extern	Bitboard	bbAllEdgePlaces;

extern	void	InitPieceCoordsArray();
extern	Bitboard	GenerateBitMaskFromCoords(int iX, int iZ);
extern	int	GetBitIndexFromXY(int iX, int iZ);
extern	int	GetBitIndex(Bitboard a_bbBoard);
extern	int	GetBoardXCoord(Bitboard a_bbBoard);
extern	int	GetBoardYCoord(Bitboard a_bbBoard);
extern	int	GetXOffsetFromRow(int a_iRow);
extern	int	GetBoardXCoordFromIndex(int a_iIndex);
extern	int	GetBoardYCoordFromIndex(int a_iIndex);
extern	int	GetFirstBitNumber(Bitboard a_bbBoard);
extern	Bitboard	GetPlayerJumpers(int a_iPlayer, GameState a_oCurrentState);
extern	Bitboard	GetCurrentAvailableMovers(GameState a_oCurrentState, bool &a_bJumpers);
extern	Bitboard	CheckForCaptureMoves(GameState a_oCurrentState);
extern	Bitboard	GetEmptySquares(Bitboard a_bbP1Pieces, Bitboard a_bbP2Pieces);
//	May need to add a CheckForCaptureContinue function for the multi capture move cases.
extern	int		CountPieces(Bitboard a_bbBoard);
extern	bool	ValidMove(Move a_oTestMove, GameState a_oCurrentState);
extern	void	GenerateMovesForMover(int a_iMover, GameState a_oGameState, std::vector<Move> &a_aMoveList, bool a_bJumper);
extern	bool	IsCaptureMove(Move a_oMove);

#endif	//	_BITBOARD_H_