text:
How To Remove A Splinter
by Prevention Published: Jun 22, 2014
In the well-known fable Androcles and the Lion, a tiny splinter brought the mighty lion to his knees. And if you've ever had a spiky splinter puncture your tender skin, you can relate. Getting a splinter in the hand or foot—or worse, someplace even more sensitive—pains even the strongest of us. Unfortunately, removing those little suckers can often be just as troublesome. "Splinters are small pieces of wood, glass, metal, or other matter that gets caught under your skin," says Dee Anna Glaser, MD. "Even though they're often small, they tend to hurt. Whether they are buried deep or not, you need to remove them as soon as possible so they don't cause infection." Here's what some of the pros recommend for how to painlessly—or at least somewhat painlessly—remove a splinter, and keep it from becoming a real thorn in your side.
Enlist Some Outside Help
First, get a loved one to sterilize a tweezers by cleaning it with rubbing alcohol, or heating it with a lighter or hot match. "It's hard to cause yourself pain, so ask a husband, wife, or someone else who cares about you to lend you a gentle, helping hand," says Glaser. Grab the protruding end of the splinter with the sterilized tweezers and gently pull it out in the direction it entered, says Glaser. If the splinter is embedded in the skin, sterilize a needle with rubbing alcohol, a lighter, or a match, then make a small hole in the skin over the end of the splinter. Then, lift the skin to expose the splinter, put the needle under the splinter until it can be grasped with the tweezers, and pull it out. Last, have your helper check to make sure that the entire splinter is gone. If not, repeat the previous steps. "For really small splinters or if you don't get the entire splinter on the first try, use a magnifying glass for a closer look," says Glaser.
MORE: 10 Brilliant Uses For A Banana Peel
After the whole splinter is out, clean the wound with hydrogen peroxide, allowing the bubbles to work on the area, says Joseph Bark, MD. Then apply a bandage, if needed, to keep the wound clean; otherwise, leave it open to the air, says Glaser. Either way, be on the lookout for signs of infection (redness, pus, increased pain, swelling, and even red streaking in that area). "Applying an antibiotic ointment once the area is cleaned will help the healing process," she says.
Take Preventative Measures
Glaser says that some splinters can be avoided with an ounce or two of prevention. She recommends the following:
Wear shoes outdoors at all times and whenever you walk on unfinished wood floors, wooden decks, or boardwalks.
Clean up all broken glass and metal shavings around the house immediately.
Be careful when handling broken glass, and wear hard-soled shoes to protect your feet.
Wear work gloves when handling plants with thorns, sharp tips, and spines.
Be careful when applying friction to an object while performing tasks such as woodworking. "If you're not careful, a small portion of that wood can dislodge into your skin," says Glaser. "So again, wear gloves."
MORE: 20 Natural Home Remedies That Work
When To Visit The Doctor
Call a health professional if the splinter is very large or deeply embedded and can't be pulled out easily, says Glaser. Deep splinters may require a doctor to make a small incision to extract it. "But, unless the splinter is removed, it will almost always become infected," she says. Speaking of infection, those with diabetes or compromised immunity should see a physician if they have a deeply embedded splinter, suggests Glaser. "These people are at a greater risk for more serious infection." Yet another instance to make a doctor's visit is if the splinter is a piece of metal, not wood, and your last tetanus shot was more than 5 years ago, says Glaser.
Joseph Bark, MD, is a dermatologist in Lexington, Kentucky, and director of Skin Secrets, a comprehensive skin-care facility.
Dee Anna Glaser, MD, is a professor in the department of dermatology at St. Louis University School of Medicine in Missouri.
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Prince Rajcomar Mathilda, también conocido como Prince Mathilda (nacido el 25 de abril de 1985) es un futbolista internacional de Curazao que juega actualmente para el Kozakken Boys de la Tweede Divisie de los Países Bajos; se desempeña en el terreno de juego como delantero.

Clubes

Carrera internacional
Prince Rajcomar formó parte de la selección sub-20 de los Países Bajos participando en la Copa Mundial de Fútbol Juvenil de 2005 celebrada en los Países Bajos.

En septiembre de 2014 Prince es llamado a la selección absoluta de Curazao para disputar las eliminatorias rumbo a la Copa Oro. Convierte su primer gol contra la selección de Puerto Rico al minuto 3 del segundo tiempo partido que terminó en empate con marcador de 2-2.

Palmarés
 Supercopa de los Países Bajos: 2004

Referencias
 Worldfootball.net.

 Ronald Zwiers Profile

 "Prince Rajcomar í Breiðablik (Staðfest) (Prince signs for Breiðablik)"

Futbolistas de Curazao
Futbolistas del VVV-Venlo
Futbolistas del FC Den Bosch
Futbolistas del KR Reykjavík
Futbolistas del BEC Tero Sasana Football Club
Futbolistas del Zalaegerszegi TE
Futbolistas del Fortuna Sittard
Futbolistas del Maastrichtse Voetbal Vereninging
Futbolistas de la selección de fútbol de Curazao
Nacidos en Maastricht

redpajama_set_name: RedPajamaWikipedia


The GSMA's Peter Montgomery explained how the combination of mobile data, big data, and information from clinics could help stop the spread of disease and illness in India.
One of the things the GSMA tried to highlight at Mobile World Congress 2018 was the place where tech legitimately can do good—for example in helping drive efficiencies in public health so that resources can stretch to have a bigger impact.
Montgomery said, "The GSMA is doing a lot a work around sustainable development and humanitarian work. And one of the projects we've been looking at is using the mobile networks to understand how that can better treat things like the outbreak of tuberculosis. So in India, approximately 1,400 people die every day from tuberculosis. And we've been looking at how we can use mobile data, big data, and information from clinics on the ground to better treat and eradicate the disease.
"We obviously have information on the ground that comes in from the various clinics. And we can look at that data to see where the outbreaks and the hotspots of tuberculosis are. So we know where the red zone is, where there's a large density population of TB, and the green zones, where there's less. And then if you look at the mobile data—the information from the mobile networks—you can understand anonymously the commuter patterns. Are people moving from a low-risk area into a high-risk area? And then looking at that even further, you can understand. But how do we address that? Do we need to educate people on that route? Do we need to put more clinics into our region? And then you begin to treat the cause and the root cause of it."
Montgomery added, "Obviously this was run as a proof-of-concept with a view to be rolling out. You're looking at how do you better spend the resource, so instead of blanket-treating the entire country, you can begin to work where there are target areas that we need to really address. What are the hotspots, and how do we then best [treat] those? Is it through education? Is it through a different approach on the medical side of things? And you can make informed decisions about what needs to be done on a more granular basis. Rather than spreading a lot a resources very thinly, you can target specific areas that need treatments really badly."


Thirroul, in the northern suburbs of Wollongong, NSW.
I did a Communication and Media Studies/Arts double degree at the University of Wollongong, majoring in French and journalism, and minoring in Spanish and international studies.
I initially applied out of an interest in working in the broader APS, but this Department soon stood out as the most invested in its graduate program, with a highly professional recruitment process. This is reflected in the Department's ranking at the top of APS graduate programs in 2014 and 2015. The portfolio area is a very diverse one, with a range of projects and programs affecting large numbers of Australians. Finally, there is also a healthy organisational culture that is reflected in the Department's good reputation as a workplace.
The range of opportunities offered by the graduate program, including the Industry Tour, Graduate Certificate in Public Administration, training and development and more, is second to none. The Department leaves no stone unturned in preparing its graduates for the APS5 level.
The amount and variety of training and development offered by the Infrastructure graduate program is remarkable. We've learnt the full range of skills required to work to a high level within the APS, from working for ministers, stakeholder engagement and career development to presentation skills, legislation and regulation training, and of course the Graduate Certificate in Public Administration.
I work in the Media and Communications section of the Communications branch in the Corporate Services division. This position is directly relevant to my academic background and I am finding it highly rewarding. My main tasks include coordinating event briefs and communications materials (media releases, talking points, etc.) for ministers and other Australian Government representatives attending milestone events for projects funded by the Department's infrastructure investment and regional development programs. I also coordinate media enquiries, provide information to members of the public and liaise closely with counterparts in state governments, local councils and community organisations to increase the media profile of the Government and Department.
My career objectives are closely aligned with my sporting goals as an elite football (soccer) referee. While the flexibility and balance of the APS is an important part of my decision to work in the public service, I am also focused on developing my skills in the media and communications field and being the most versatile, capable and dependable officer I can be. Always taking opportunities to be exposed to the diverse range of experiences the APS offers is another important goal.


The Cuban dissident journalist has been always an uncomfortable traveling companion for the Cuban Revolution. He's like an open wound that does not heal, a threat although his only weapon was a typewriter in the past or a computer now.
I remember midnight of March 15th 1993, when I was very nervous. It was my first mission as a member of a dissident press agency in Villaclara, and a colleague asked me to take to Havana the press-release and chronicles written by local journalists. I was advised that if the police should arrest me and find those papers, I would spend six months in prison for it.
At 3:30 a.m. the "special train" that crosses the island from East to West arrived; it's a journey of about 24 hours, in old wagons where humble people usually travel.
When I entered the wagon everything was dark and there were passengers everywhere; there were no seats available so I sat on the floor and tried to sleep, but the cold air coming in through the broken windows prevented the dream... when suddenly a flashlight broke the darkness and lit up my face.
A couple of cops asked me for ID. While the officer checked my documents his companion began rummaging in my luggage bag . I thought all was lost, but when the officer noticed from my identity card I was an employee of the Catholic Church, he returned it and moved on.
Thank God they did not check my coat full of dissidents' chronicles!
After 10 hours of travel, many stops and waits to make way for commercial freight trains, we finally arrived at the Central Station of Havana. There were cops on the platforms, checking the baggage of passengers and requesting personal identification again. However police methods in Havana are not exempt from racial prejudice, and mistaking me for a foreign tourist they let me go uneventfully.
Once outside the station, as I could not pay $5 for a taxi and the public buses spent full of people, I had no choice but to walk about seven miles to the house I was to visit.
Upon arrival, a man with the poise and voice of a poet greeted me with a warm hug, offered me a cup of coffee - the only thing that had landed in my stomach since the night before - and I then handed him the notes from my friends.
One by one the man read those pages, smiling at intervals. I've never seen someone so eagerly read those news, reports and allegations of human rights violations and abuses by the Cuban authorities. When he finished reading it he took the phone, dialed an international number and began to reread the pages.
Across the line, someone was in charge of recording and later would transcribe the texts to the pages of a newspaper. Thanks to that simple but effective method international public opinion was reported in the Special Period (1993-1998) of the Cuban reality, those difficult days; a reality that despite the passage of time is not yet published in Cuba's official newspapers, TV, radio or digital.
Never forget that words sing, they hurt, they glorify. They freed us from our ignorance. Do not forget that we live by the words: Love, Truth, God. And we fight for the words: Freedom, Glory, Honor. Resentment never gives birth to good works.


text:
Q: Continuous Probability Density Function: question on definition I have seen this with multiple definitions.  It also appears the Fundamental Theorem of Calculus applies.  Does anyone know the correct definition?  I think my confusion is between $\le$ and $=$. I list some definitions below to show my confusion.
$f_X(z) = \frac {d F_{\le X}(z)} {dz}$, $f_X : \mathbb{R} \rightarrow [0,1]$ is the PDF and $F_{\le X} : \mathbb{R} \rightarrow [0,1]$ is the CDF.
Wiki definition:
a)
$$
P(a \le X \le b) = \int_a^b f_X(z) \, dz = F_{\le X}(b) - F_{\le X}(a)
$$
I think this should really be two different CDFs, $\le$ and $\lt$ as follows.  Otherwise the value at $a$ is excluded.:
b)
$$
P(a \le X \le b) = \int_a^b f_X(z) \, dz = F_{\le X}(b) - F_{\lt X}(a)
$$
this definition is from my notes (which I think is the correct one):
c)
$$
P(a \lt X \le b) = \int_a^b f_X(z) \, dz = F_{\le X}(b) - F_{\le X}(a)
$$
I think whats confusing me is application of the Fundamental Theorem of Calculus.  It doesn't agree with equality in form (c) above:
d)
$$
\int_a^b f(t) \, dt = F(b) - F(a)
$$

A: They are all equivalent. I will illustrate by showing that a) gives c): If $f_X$ is an integrable function and, $$P(a \leq X \leq b)=\int_a^b f_X(x)\,dx$$ then we can put $a=b$ to see that for all $a$,$$P(X=a)=\int_a^a f_X(x) \, dx=0$$Therefore, $$P(a<x\leq b)=P(a\leq X \leq b)-P(X=a)=\int_a^b f_X(x) \, dx$$

redpajama_set_name: RedPajamaStackExchange


text:
Q: why am i getting a syntax error in extjs tree editor? why am i getting this error
"SyntaxError: function statement requires a name"
for the code in this
How to use ExtJS TreeEditor

A: These lines, out of context, are not valid Javascript (they should be enclosed in curly brackets to form an object literal). That's the source of the parse error in your question:
onTreeNodeDblClick: function(n) {
    treeEditor.editNode = n;
    treeEditor.startEdit(n.ui.textNode);
}

onTreeEditComplete: function(treeEditor, o, n) {
    //o - oldValue
    //n - newValue
}

Here's how you can fix it. Please, take notice of my comment... You're likely to run into further errors once you fix the first ones:
var tree = new Ext.tree.TreePanel({
    // The following function won't be defined, you'll have to configure
    // a working root to make this example work... And possibly more!
    root: this.getChildren(),
    height: 300,
    loader: new Ext.tree.TreeLoader(),
    useArrows: true,
    autoScroll: true,
    listeners: {
        dblclick: onTreeNodeDblClick
    }
});

var treeEditor = new Ext.tree.TreeEditor(tree, {}, {
    cancelOnEsc: true,
    completeOnEnter: true,
    selectOnFocus: true,
    allowBlank: false,
    listeners: {
        complete: onTreeEditComplete
    }
});

// It seems that the intention of the author was to declare independent
// functions (as opposed to object methods).
function onTreeNodeDblClick(n) {
    treeEditor.editNode = n;
    treeEditor.startEdit(n.ui.textNode);
}

function onTreeEditComplete(treeEditor, o, n) {
    //o - oldValue
    //n - newValue
}


DA17 Erith Marshes Expert Dry & Steam Carpet Cleaners| RDA Cleaning & Maintenance Ltd.
Looking for an affordable professional carpet cleaning company in Erith Marshes DA17? RDA Cleaning & Maintenance Ltd. now offers you high quality cleaning that can guarantee great results - full stain removal and disinfection. Our top-class carpet cleaning service is available also during weekends and bank holidays at no extra cost. Enjoy your home or office carpet clean & fresh today with a phone call to 020 3404 1646. Our customer support team will help you set up an appointment at a convenient time. We do key pick-ups and safe drop offs. Hire the best carpet cleaners Erith Marshes DA17 today!
To ensure your safety and the best results possible, we keep a high standard of home & business carpet cleaning Erith Marshes DA17 and a good work ethic for all of our cleaners. It is mandatory that they pass 3 months training by Prochem on steam & dry carpet cleaning methods, machine and detergent use. During their first 6 months with us they are accompanied with a senior cleaner who would show them all it needs to become a cleaning expert. Once the job is carried out, they would give you free maintenance advice.
Every service that you will see in this list has its quality ensured by a team of professional carpet cleaners Erith Marshes DA17 with years of experience. Note that RDA Cleaning & Maintenance Ltd. uses only the best of machinery and detergents available on the market.
In order to receive the best carpet cleaning service in Erith Marshes DA17, all you need to do is dial this number 020 3404 1646 right away. RDA Cleaning & Maintenance Ltd. will respond as soon as possible. Same day and bank holiday appointments. No deposit or concealed fees.


25.40mm x 63.50mm Wire & Cable Markers Self Laminating Printable, labels for LABXPERT & IDXPERT printers.
Brady XSL-21-427-YL-BK Self laminating cable and wire markers for Brady IDXpert printers. Extensively used for data and telecomms cables but can be used for pipe marking. They provide a quick: simple and secure method of identification.


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Phyllochaetopterus anglica är en ringmaskart som beskrevs av Thomas Henry Potts 1914. Phyllochaetopterus anglica ingår i släktet Phyllochaetopterus och familjen Chaetopteridae. Inga underarter finns listade i Catalogue of Life.

Källor

Havsborstmaskar
anglica


9.00 am Bonalbo Post Office (PO), 10.45 Old Bonalbo PO, 10.30 Urbenville PO, 11.30 Legume Hall, 1.30 Woodenbong PO, 2.30 Grevillia PO and 3.30pm Wiangaree PO.
Appointments can be obtained by contacting my office on 6621 3624.
I had the pleasure of attending the Lismore, Casino and Mallanganee Australia Day festivities and I thoroughly enjoyed the day with the local community, as this is the biggest single event held each year. I believe Australia Day provides an opportunity to highlight and recognise achievements of individuals and organisations. It is also an occasion to reflect and appreciate our wonderful country; Australia.
The electorate office has a Canon L900FX facsimile machine, in good working order to donate to a registered charitable organisation or to a locally based organisation. In order to give equal opportunity to all, I am calling for expressions of interest to put organisations' names into a lucky draw. Please telephone my office to notify your details before 5pm Tuesday 12 February, as the draw will be undertaken on Wednesday 13 February 2002 and I will notify the successful organisation on this day.
The Minister for the Environment has informed me that he has recommended the Governor proclaim The Island Creek Wildlife Refuge. This is located 20km north-east of Kyogle and contains Riparian rainforest in very good condition. It is habitat for a wide range of fauna including platypus, echidnas, antechinus and many different species of birds. I trust this is to the approval of constituents in this area. I would welcome any comments or feedback regarding this refuge.
The Coalition is supporting the Government's proposed changes to the Bail Act whereby repeat offenders will find it more difficult to obtain bail. Police say repeat offenders are responsible for some 90 per cent of crime. This leads to police frustration if the Courts are pre-disposed to grant bail. The result is that the prison system will be asked to cope with increased numbers being remanded in custody and some new prisons may have to be built. The Government has said that as a result of these bail changes, offences involving particularly car theft, burglary and violent street crime should decrease noticeably.
Whilst I always encourage people to make their own initial approaches, if any property owner or resident is still having problems with any insurance claim relating to last years storm damage or recent bushfire losses please contact me for assistance. I can inquire of the insurer where appropriate, or the Insurance Council, or the Insurance Enquiries and Complaints Commission on your behalf and possibly assist in speeding up the resolution of any issues.


The diagnosis of cancer in Fido or Fluffy can be devastating and overwhelming. As with people, cancer is a common diagnosis in cats and dogs. There are a variety of treatment modalities available, depending on the specific type of disease present. Some cancers in animals can be treated with surgery and/or radiation, some can be treated with chemotherapy and, unfortunately, some cannot effectively be treated at all, even with aggressive measures.
It is always difficult to tell a client that Fido has cancer. Truly, that is one of the worst parts of my job. Veterinary oncology has made huge advances in treating cancer in pets, however, and there are a variety of options available to treat cats and dogs. When I first mention to clients that chemotherapy might be the best choice to treat a pet's disease, I am often met with surprise, alarm, concern, questions and sometimes, relief. Many pet owners are not even aware that chemotherapy is an option in the first place, let alone that there is an entire veterinary subspecialty devoted to oncology.
Clients are often initially opposed to chemotherapy for Fluffy or Fido, because the idea of putting a pet through a grueling regimen of medications that may cause significant side effects seems cruel. However, there is an absolute, essential difference between human and veterinary chemotherapy which dramatically alters the way we treat veterinary patients. In veterinary oncology, the intent of chemotherapy is to palliate symptoms of the disease, and put it into remission for as long as possible. The intent is not to cure cancers with chemo, but to achieve long durations of remission. Thus, the doses used to treat animals with cancer are significantly reduced from those used in humans, so less side effects are seen. The advantage to this approach is that chemo is tolerated incredibly well by our patients. They do not lose their fur, they do not become lethargic and they do not typically suffer from severe nausea, vomiting, or weight loss.
This is not to minimize the potential for adverse effects with veterinary chemotherapy. We utilize many of the same drugs used in human cancer treatment, most of which are quite potent. Some of our patients do have gastrointestinal upset, some may have severe bone marrow suppression and some can have life-threatening complications such as cardiotoxicity. Overall, when the right patients are given the right doses of the right drugs, they can do quite well for a long time. Our goal, as always, is to ensure our patients have an excellent quality of life during their treatment. "First, do no harm" is the credo of human and veterinary doctors alike.
In Malvern, we are fortunate to have an excellent facility nearby that offer complete oncologic services: HOPE Veterinary Specialists, which offers surgical and medical oncology as well as radiation therapy and a veterinary cyberknife. We refer frequently to HOPE, and have close relationships with these excellent oncologists.
There are many considerations when contemplating the best course of action once Fluffy or Fido has been diagnosed with cancer. Chemotherapy is certainly not the best choice for every client or every pet diagnosed with cancer; it can be financially and emotionally challenging, and ultimately may not cure your pet's illness. Please don't hesitate to address any questions or concerns with your veterinarian; our goal is to help you make the best decisions for your family, and your pet!


Four teams will be seeking their first win on Friday as Tarxien Rainbows take on Sliema Wanderers and Floriana face Hamrun Spartans in the two matches in programme at the Hibernians Stadium.
In the first game, Tarxien Rainbows will be hoping to collect their first points in the game against Sliema Wanderers.
Under the guidance of former Sliema coach Danilo Doncic, the Rainbows suffered a narrow defeat to champions Valletta and are therefore eager to collect their first points as they take on the Wanderers who shared the spoils in a 1-1 draw with Floriana.
In the second game, Floriana and Hamrun Spartans cross swords in a clash between two teams who earned a point in their first outing.
The Greens drew 1-1 with Sliema Wanderers while Hamrun collected a point in a 2-2 draw with Balzan.


text:
ASA Seeks Claims for Fraud & Punitive Damages Against Lippert Components
Based on new evidence uncovered in its ongoing legal battle with Lippert Components, ASA Electronics now seeks to pursue claims for fraud and punitive damages against Lippert and Vince Smith. This past October, the Elkhart Circuit Court issued a 28-page preliminary injunction order against Lippert Components and Smith after Smith misappropriated ASA's trade secrets and used those trade secrets while working for Lippert. After the preliminary injunction order, the parties attempted to settle their disputes at mediation on March 26. That mediation, however, was unsuccessful.
"We were hopeful to resolve our disputes with Lippert and Smith at mediation on March 26, but unfortunately that didn't happen," said ASA Electronics' CEO, Tom Irions. "Now it's time for us to get our case ready for trial in order to make ASA whole. We believe that what Lippert did isn't right, it isn't fair, and it goes against the principles that Lippert claims to value. We encourage everyone to read our proposed amended complaint, filed April 3, 2020, which outlines in detail how Lippert harmed us."
Elkhart Court Gives ASA Green Light to Pursue Fraud, Punitive Damages Against Lippert
POLK ULTRAMARINE
KLIPSCH MARINE
iN COMMAND
© Copyright 2021 ASA Electronics. All Rights Reserved.

redpajama_set_name: RedPajamaCommonCrawl


text:
Wall St. ends up sharply as investors buy beaten-down shares
U.S. stocks bounced back sharply as investors snapped up beaten-down shares in the healthcare and technology sectors, and as data gave further signs of a pickup in the economy.
Stockbrokers on floor of New York Exchange
Apple (AAPL.O) provided the biggest boost to the major indexes, jumping three percent to $128.95 in its biggest daily percentage gain since January.
The Nasdaq snapped a four-day losing streak while the S&P tech sector .SPLRCT gained 1.5 percent, among the day's best-performing sectors. Biotech shares also rebounded, ending a five-day losing streak.
The Nasdaq Biotech Index .NBI was up 2.9 percent for the day, but lost 5.5 percent for the week, its worst such decline since March 2014. Shares of Gilead (GILD.O) rose 4.5 percent to $105.01, helping to lift both the Nasdaq and S&P 500, after its quarterly profit nearly doubled. The S&P healthcare index .SPXHC was up 1.3 percent.
Investors were also buoyed by an encouraging batch of data for April that suggested the U.S. economy was pulling out of a first-quarter soft patch.
"Yesterday people were thinking the market was going to fall off of a cliff, and today we're seeing a lot of institutional buying coming in," said Adam Sarhan, chief executive of Sarhan Capital in New York.
"So, the buy-the-dippers show up and defend the market. That leads me to believe there's more upside." Indexes posted losses for the week, however, with social media shares among the weakest performers following disappointing outlooks and results this week from several key players including Twitter (TWTR.N). The Dow Jones industrial average
.DJI rose 183.54 points, or 1.03 percent, to 18,024.06, the S&P 500 .SPX gained 22.78 points, or 1.09 percent, to 2,108.29 and the Nasdaq Composite .IXIC added 63.97 points, or 1.29 percent, to 5,005.39. LinkedIn (LNKD.N), Twitter and Yelp (YELP.N) all notched their biggest weekly percentage declines since their debuts.
LinkedIn, which reported results late Thursday, dropped 18.6 percent to $205.21. For the week, the Dow was down 0.3 percent, the S&P 500 was down 0.5 percent and the Nasdaq was down 1.7 percent.
Consumer sentiment jumped and vehicle sales for GM (GM.N) and Ford (F.N) were stronger-than-expected in April, while manufacturing expansion in the month held steady at near a two-year low.
Railcar makers gained after tougher oil-train safety standards, including rules to phase out older tank cars in three years, were announced.
Greenbrier (GBX.N) gained 8 percent to $62.33, while Trinity Industries (TRN.N) rose 6.9 percent to $28.96. American Railcar (ARII.O) was up 5.9 percent at $56.18.
Advancing issues outnumbered declining ones on the NYSE by 1,850 to 1,201, for a 1.54-to-1 ratio on the upside; on the Nasdaq, 1,664 issues rose and 1,118 fell for a 1.49-to-1 ratio favoring advancers.
The benchmark S&P 500 index posted 10 new 52-week highs and no new lows; the Nasdaq Composite recorded 35 new highs and 69 new lows.
About 6.3 billion shares changed hands on U.S. exchanges, compared with the 7.2 billion daily average for the last five sessions, according to data from BATS Global Markets.
How to dent the universe - Part 2
First, it was the Graduate Institute of International Affairs, University of Geneva, Switzerland, that didn't open the doors. Finally, it was the study of economic analysis, public diplomacy and project management from the London School of Economics, that I thought would do the trick. Though progress has been slow it very satisfactory. The struggle continues.…
Eze's final possession of his Dispossessed poetry: A biocritical reflection
I have had a chequered relationship with journalism—not just in my active engagement with the profession for one and a half decades, but in the sheer number of media houses in which I worked.
China asks US to 'meet halfway' after Trump threatens to cut ties
China on Friday urged the United States to meet it halfway and strengthen cooperation in the fight against the coronavirus pandemic after President Donald Trump threatened to sever bilateral ties.
East African Business Week News (EABW News) partners with APO Group
East Africa's Leading online business news website East African Business Week News (EABW NEWS) (www.Busiweek.com) has today entered a media partnership agreement with the APO Group to publish its business-related news as it happens. The APO Group provides Africa-related news content to Bloomberg, Reuters, CNBC Africa, Africanews.com, Africa.com and many more leading international news agencies.…
Business Insider Africa now a standalone site with expanded coverage across Africa
Business Insider Africa is re-launching as a freestanding news outlet in partnership with Pulse (https://Pulse.Africa/), the innovative African media group. As a standalone site with its own brand identity, Business Insider Africa now has extended business news coverage from across the continent as well as from around the globe. The site previously operated as a…
Business Insider Africa is co-hosting the BI Global Trends Festival 2020 from 19th – 23rd October
Business Insider Africa will be co-hosting the first-ever online BI Global Trends Festival, bringing together business leaders from Asia, Africa, Europe and the United States - for a truly global perspective. African visitors will benefit from highly discounted participation rates to experience speakers such as Reed Hastings, the co-founder of Netflix; Sebastian Thrun, the founder…


text:
Canucks, Linden Need To End Estrangement
Trevor Linden returns to Rogers Arena and the Vancouver Canucks on Monday night, though it seems his relationship with the franchise remains very frosty.
Linden was let go as President of the franchise in 2018, after a weak four year run on the job. Though owner Francesco Aquillini suggested the split was amicable, it seems that is far from the truth. Linden, the fan's favorite son, remains estranged from the franchise.
Now Linden's removal as President is perfectly justifiable. Perhaps Linden should never have been placed in this position to begin with, as he seemed over his head as a raw rookie in such a demanding job. That's bad on Aquillini.
The bottom line is the results weren't there. The results have been much more favorable since he left? Coincidence? Maybe. Elias Pettersson and Quinn Hughes' arrivals have fast tracked the Canucks rebuild faster than anyone could have imagined.
There have been rumblings that Linden was perhaps too involved in the rebuild process, and wasn't letting GM Jim Benning do his job with enough autonomy. The President is supposed to be the great over-seer of everything, but can not be a micromanager. He is to set the vision and keep the ship on course. But he does not have to be involved in every player transaction.
Of course we will never know how the inner workings of the management structure really worked. Had Linden had the benefit of Petterson and Hughes' arrivals he'd probably still be there. But since he left Benning and the Canucks have seemingly touch a lot of right buttons.
But Linden is not there. And like Pavel Bure, the Canucks greatest superstar, Linden is estranged from the franchise. That's never good.
Part of this estrangement is on Linden himself. He needs to be that franchise icon. Not for Francesco Aquillini. Not for the Canucks. Not even for himself. He needs to be what the fans want him to be. He will always be the fan favorite, but he has to give the fans what they want for that to happen.
That starts on Monday night when the Canucks celebrate their greatest players this week with Markus Naslund, Stan Smyl, Linden, no Pavel Bure, and now the Sedin Twins. Bure is a different cat, but hopefully this is the beginning of the healing process for Linden and the Canucks.


text:
\section{Introduction}
\label{Introduction}

In recent years, self-supervised learning \cite{doersch2015unsupervised} has achieved remarkable success in various visual feature learning tasks, such as image classification \cite{noroozi2016unsupervised}, semantic segmentation \cite{long2015fully}, object detection \cite{girshick2014rich}. In the absence of manual annotations, this approach uses only unlabeled data to obtain pseudo labels automatically through a proposed pretext task, and captures semantic features via predicting the pseudo labels. The proposed pretext task is constructed by \emph{external} signals, and usually image- or video-based, such as image geometric transformation recognition \cite{gidaris2018unsupervised}, image colorization \cite{zhang2016colorful}, video prediction \cite{srivastava2015unsupervised}, etc. To solve these tasks, CNNs can learn visual feature representations of images, which are then further used as a pre-training model for downstream visual tasks.

Even though self-supervision was initially proposed for the lack of large-scale labeled datasets, self-supervised technique has recently been applied to other fields due to its simplicity and effectiveness. $S^4L$ uses self-supervision to improve the performance of semi-supervised learning \cite{zhai2019s4l}. SS-GAN trains Generative Adversarial Networks (GANs) with self-supervised methods \cite{chen2019self}. And SLA proposes a framework that combines self-supervised techniques with supervised learning \cite{lee2020self}. But this external image-based method, that could be viewed as expand the dataset several times, entails multiple times the training overhead of the baseline. This motivates us to exploit the possibility of internal pretext task.

Our work is based on the following assumption: different channels correspond to different aspects of semantic features (see Figure \ref{cam}). If the network can distinguish which channels are missing, it will have a deeper understanding of the whole feature map. It's just like the blind man touching the elephant: if he can know a part of the elephant through each touch, then with enough touches he not only knows the different parts of the elephant, but also knows more about the elephant as a whole. We term this kind of self-supervised task using internal network signals \emph{internal pretext task}.

Specifically, we propose a self-supervised task by recognizing feature transformations, and apply it for the enhancement of supervised learning, which only incurs negligible time cost and memory overhead. We first define the feature transformations: divide the channels of the feature map into several groups, and discard a group of channels each time to get a new feature map. Then we concatenate all the feature maps and transmit them into the rest of the network. Each feature map corresponds to a joint label, that is, the combination of the original label and self supervised label, and the model is trained to predict the joint labels. Moreover, in order to prevent the overhead increase caused by the generated feature maps being transferred to the next convolutional layer, we further introduce a two-classifier structure.

Our contributions are:
\begin{itemize}
	\item We first propose and prove that feature transformations within CNNs can be regarded as supervisory signals to construct the self-supervised task, and term this self-supervised task using internal network signals \emph{internal pretext task}.
	\item We propose a generic framework that combines our internal pretext task with supervised learning, which incurs negligible computational overhead and significantly improves the classification accuracy.
	\item We exhaustively evaluate our method in various networks(e.g., ResNet-18, ResNet-110, PyramidNet-110-270) and various datasets (e.g., CIFAR-100, tiny-ImageNet, CUB200), which demonstrated the wide applicability, versatility and compatibility of our method.
\end{itemize}

\section{Related Work}
\label{Related Work}

Self-supervision is a generic learning framework that was proposed to learn visual features from large-scale unlabeled images or videos without any human annotations. In self-supervised learning, CNNs generate self-supervised labels through a proposed pretext task, and improve the ability to extract features for downstream tasks. Due to its unique advantage of training model with only unlabeled data, Self-supervision has been widely applied in various computer vision tasks. In this paper, we apply self-supervised technique into a supervised learning framework to enhance the classification of fully labeled images. And in this part, we first make an incomplete summary of self-supervised learning, and then introduce several methods of applying self-supervised techniques to other domains including supervised learning.

\paragraph{Self-supervised learning}

\cite{doersch2015unsupervised} trains a CNN model by artificially constructing a pretext task which is defined as predicting the relative position between two random patches in the same image. Based on this idea, a jigsaw puzzle was designed in 2016 as a pretext task: training the model to put nine scrambled blocks back to their original positions \cite{noroozi2016unsupervised}. Follow-up papers \cite{ahsan2019video,kim2018learning,wei2019iterative} formulate different jigsaw puzzles to learn visual representations. In addition to the above patch-based approaches, \cite{gidaris2018unsupervised} designs a pretext task to predict the rotation angle of images. Furthermore, \cite{misra2016shuffle} is proposed as a temporal context-based method in which the pretext task is formulated to determine whether the frame sequence in the video is arranged in the correct temporal order. And \cite{lee2017unsupervised} trains a model to learn features by sorting frame sequences.

\paragraph{Self-supervised techniques in other domains}

SS-GAN proposes a self-supervised Generative Adversarial Network (GAN) \cite{chen2019self}. It uses a self-supervised method based on image rotations to generate labels, and then achieves the goal by an auxiliary loss. $S^4L$ is a framework of semi-supervised loss caused by self-supervised learning objectives \cite{zhai2019s4l}. The authors believe that self-supervised learning techniques can greatly benefit from a small number of labeled examples. And SLA applies self-supervised methods to supervised learning \cite{lee2020self}. In the framework of SLA, the original classification task and the self-supervised task are treated as a joint task and a CNN model is trained to learn the joint task. SLA combines the generated labels with the original labels, and then solves the joint task by predicting joint labels of images.

\section{Methods}
\label{Methods}

\paragraph{Motivation.} SLA enhances supervised learning by a pretext task of recognizing image transformations, e.g., image rotations. However, SLA demands to generate multiple transformed images for each image, which is equivalent to expanding the dataset several times, and thus inevitably incurs a great deal of time consumption and memory overhead. This inspires us to design a self-supervised task that uses the internal signals of the network. To this end, we consider transforming the feature maps by discarding different channels, and define a self-supervised task to recognize the discarded channels. Intuitively, different channels correspond to different aspects of semantic features. To prove this, we train four models discarding four different groups of channels in the last layer, which means that only part of the features can participate in the network training. We demonstrate their class activation maps (CAMs) \cite{Zhou2016Learning} in Figure \ref{cam}. The visualization results show that features which discard different channels focus on different semantic information, which indicates that this feature transformation can be regarded as a supervisory signal to construct the self-supervised task. And we argue that if the network can distinguish which channels are missing, it will have a deeper understanding of the whole feature map.

\begin{figure}[htbp]
	\centering
	\includegraphics[width=1.0\linewidth]{cam.png}
	\caption{Class activation maps (CAMs) \cite{Zhou2016Learning} for ResNet-18 model trained on Stanford Dogs. Each column represents the visualization results of the same network in different images. The second column shows the results of training the baseline model, and the third to sixth columns show the results of the models discarding 4 different groups of channels in the last layer, which means that only part of the features of the last layer can participate in the network training. The visualization results show that discarding different channels makes the network pay attention to different aspects of features.}
	\label{cam}
\end{figure}

\paragraph{Notation.} Let $ x \in \mathbb{R}^{W \times H \times C} $ with label $ y \in \{1,...,N\} $ be the input where $ N $ is the number of classes, $H$, $W$, and $C$ denote height, width, and the number of channels respectively. Let $\mathcal{L}_{\mbox{{\tiny CE}}}$ denote the cross-entropy loss function, $D_{\mbox{{\tiny KL}}}$ denote the KL-divergence loss function, $ F(\cdot|\,\theta)$ denote a CNN model for fully labeled datasets, where $ \theta $ are the learnable parameters of model $ F(\cdot) $. Specifically, we define a set of $ k $ transformations $ T=\{t_{j}\}^{k}_{j=1} $, where $ t_{j} $ is the operator that applies to the feature map $ f=F(x|\,\theta) $. And $ \tilde{f}_{j}=t_{j}(f) $ is a transformed sample by $ t_{j} $. Let $K=k+1$ represent the number of total feature maps (Including $k$ transformed feature maps and 1 original feature map).

\paragraph{Transformation.} To propose a new type of internal pretext task, we first define the transformations $T$ of features. The key idea of $T$ is to discard specific channels of the feature map $f$. For a given feature map of size $\mbox{height}\times\mbox{width}\times n_{\mbox{{\tiny channels}}}$, we randomly divide all the channels of the feature map into $ k $ groups. Each group contains $n_{\mbox{{\tiny channels}}}/k$ channels. And for each transformation $ t_{j} $, we generate a binary mask $M_{j}$ of size $\mbox{height}\times\mbox{width}\times n_{\mbox{{\tiny channels}}}$. If a channel is chosen to be discarded, the corresponding $\mbox{height}\times\mbox{width}$ values in $M_{j}$  are all set to 0, and other values in $M_{j}$ are set to 1. By multiplying the original feature map $f$ with $M_{j}$, we get a new feature map $\tilde{f}_{j}$. Namely, we define the transformation operation $t_{j}$ as:
\begin{equation}\label{Trans1}
	\tilde{f}_{j}= t_{j}(f)= f \odot M_{j}
\end{equation}
where each transformed feature map ${f}_{j}$ is an incomplete version of the original feature map $f$. It's worth noting that their missing parts do not cross each other, and the summation of the missing parts is just the original feature map, which is:
\begin{equation}\label{Trans2}
	f= \sum_{j=1}^{k}(f-\tilde{f}_{j})
\end{equation}
\paragraph{Self-supervised feature enhancement.} Now we move on to the whole scheme of our method, with further discussion about how we define internal pretext task and how we enforce it in enhancing the supervised learning. We take the images $x$ with only original labels $y$ as the input, and train a model to obtain the corresponding feature map $f$ of each image. Then, through the pre-defined transformations $T$, i.e., discarding different channels of the feature map, $k$ groups of feature maps are generated, and self-supervised labels $j$ are assigned for each feature map. Next, we concatenate all the feature maps (including a original one) and transmit them into the rest of the network, and finally into a joint classifier $\sigma(\cdot;\mu)$. Each feature map corresponds to a joint label ($y$,$j$), that is, the combination of the original label $y$ and the self supervised label $j$ , and the network is trained to predict the joint labels.

\begin{figure}[htbp]
	\centering
	\includegraphics[width=1.0\linewidth]{Architecture.png}
	\caption{An specific training pipeline of our proposed network architecture after second convolutional layer (taking ResNet as an example). After an input image is passed into the network and through several convolutional layers, the feature map $ f $ is obtained. Then the channels of $f$ are randomly divided into $ k $ groups, and one group of channels is discarded each time to get $ \tilde{f}_{1},\tilde{f}_{2},\cdots,\tilde{f}_{k} $ . Finally, All of them are concatenated together and transmitted to a joint classifier.}
	\label{architecture}
\end{figure}

Figure \ref{architecture} shows the specific training pipeline of introducing internal pretext task after the second convolutional layer. For example, when training on CIFAR-100 \cite{krizhevsky2009learning} (100 labels), if we divide the total channels into 4 groups, and discard a group of channels as a transformation each time, then we can get 5 groups of feature maps (including the original one), so the model learns the joint probability distribution on all possible combinations, i.e., 500 labels.

Since the model uses a joint classifier $\sigma(\cdot;\mu)$, the joint probability of the final output can be written as:
\begin{equation}\label{joint probability}
	P(i,j|\,\tilde{f})=\sigma_{ij}(\tilde{f};\mu)=\frac{\exp(\mu_{ij}^{\top}\,\tilde{f})}{\sum_{k,l}\exp(\mu_{kl}^{\top}\,\tilde{f})}
\end{equation}
Therefore, given a set of $ M $ training images $ D=\{x_{i}\}^{M}_{i=1} $, the training objective that the CNN model must learn to solve is:
\begin{equation}\label{training objective}
	\min_{\theta}\frac{1}{M}\sum_{i=1}^{M}loss(x_{i},\theta)
\end{equation}
where the loss function $ loss(\cdot) $ is defined as:
\begin{equation}\label{loss}
	loss(x,\theta)=\frac{1}{K}\sum_{j=1}^{K}\mathcal{L}_{\mbox{{\tiny CE}}}(\sigma(\tilde{f};\mu),(y,j))
\end{equation}

\begin{figure}[htbp]
	\centering
	\includegraphics[width=1.0\linewidth]{TwoClassifierStructure.png}
	\caption{An overview of our proposed two-classifier structure. The features of the last two layers are transformed, and transmitted to two classifiers respectively.we use the original feature of the last layer for single inference, and for aggregated inference, we aggregate the probability of all $ 2 \times K $ features. }
	\label{two-classifier structure}
\end{figure}

\paragraph{Two-classifier structure.} Since the newly generated feature maps will continue to be transferred into the rest convolutional layers, which increases the computational cost, we further propose a two-classifier structure in the hope of cost reduction. As shown in Figure \ref{two-classifier structure}, feature maps are transformed after the last two layers, and transmitted to two joint classifiers $\sigma(\cdot;\mu_{1})$ and $\sigma(\cdot;\mu_{2})$ respectively. We usually avoid adding a classifier after the first layer because the features of the first layer are not trained enough to obtain high-level semantic visual representations. And then we train the network by optimizing the summation of their losses. The total loss function is defined as:
\begin{equation}\label{two classifier loss}
	\begin{aligned}
		\mathcal{L}_{\mbox{{\tiny 2FC}}}&=\mathcal{L}_{\mbox{{\tiny penultimate}}}+\beta\cdot\mathcal{L}_{\mbox{{\tiny last}}}\\&=\frac{1}{K}\sum_{j=1}^{K}\mathcal{L}_{\mbox{{\tiny CE}}}(\sigma(\tilde{f};\mu_{1}),(y,j))+\beta\cdot\frac{1}{K}\sum_{j=1}^{K}\mathcal{L}_{\mbox{{\tiny CE}}}(\sigma(\tilde{f};\mu_{2}),(y,j))
	\end{aligned}
\end{equation}
where $\beta$ is a hyperparameter in the range of $[0,1]$  to balance the impact of the classifier after the penultimate layer on the network. By introducing this two-classifier structure, although we increase the network parameters (an additional classifier is introduced), we can prevent features from being transferred to the next layer, which can significantly reduce the overhead and further improve the performance. We use this two-classifier structure for all experiments unless otherwise noted.

\section{Experiments}
\label{Experiments}
In this section we conduct an extensive evaluation of our approach on the most commonly used image datasets, including three benchmark image classification datasets CIFAR-10/100 \cite{krizhevsky2009learning}, tiny-ImageNet\footnote{https://tiny-imagenet.herokuapp.com/} and three fine-grained image datasets CUB200 \cite{wah2011the}, Stanford Dogs \cite{KhoslaYaoJayadevaprakashFeiFei_FGVC2011}, Stanford Cars \cite{KrauseStarkDengFei-Fei_3DRR2013}, as well as on multiple models, such as ResNet \cite{he2016deep}, SeNet \cite{hu2018squeeze}, DenseNet \cite{huang2017densely}, Wide ResNet \cite{zagoruyko2016wide}, PyramidNet \cite{han2017deep}. Furthermore, we also compare our approach with SLA \cite{lee2020self}, a recent proposed self-supervised method which is used in supervised learning, and combine it with SLA and other regularization methods. In all experiments, we refer baselines which use only random cropping and flipping for data augmentation as "Baseline". We provide implementation details of our experiments in the supplementary material.

\subsection{Evaluation Method}
\label{Evaluation Method}
Although the final joint classifier predicts the probability of $ N \times K $ labels for all $ K $ feature maps, we only care about the softmax probability of the original feature $ f $ on the original $ N $ labels, and then compute the accuracy of single inference. So we use the single softmax probability $P(i|\,f,j=1)$ to predict its label, which is as follows:
\begin{equation}\label{single probability}
	P(i|f,j=1)=\frac{\exp(\mu_{i1}^{\top}f)}{\sum_{k=1}^{N}\exp(\mu_{k1}^{\top}f)}
\end{equation}

We also introduce aggregated inference in our framework. Given a feature map, we can only consider the original $N$ labels because we already know which transformation is applied. Therefore, we use the conditional probability $ P(i|\,\tilde{f}_{j},j)$ to predict its original label. Furthermore, we aggregate the probability of all $ K $ feature maps to obtain comprehensive performance. And if there are two joint classifiers in the model, we aggregate the probability of all $ 2 \times K $ feature maps (see Figure \ref{two-classifier structure}. The aggregated probability can be written as:
\begin{equation}\label{aggregated probability}
	P_{\mbox{{\tiny agg}}}(i|x)=\frac{\exp(z_{i})}{\sum_{k=1}^{N}\exp(z_{k})}
\end{equation}
where $ z_{i}=\frac{1}{K}\sum_{j=1}^{K}\mu_{ij}^{\top}\tilde{f}_{j} $. And in order to restore the basic network structure in the test phase, we introduce self-distillation \cite{hinton2015distilling} to transfer the knowledge of the joint classifier $\sigma(\cdot;\mu)$ to another single classifier $\sigma(\cdot;v)$. In this way, images can be classified using only a basic network model, and the accuracy obtained is the most standard. To this end, we add a single loss $ \mathcal{L}_{\mbox{{\tiny CE}}}(\sigma(f;v),y) $ to train the single classifier, and use a KL-loss $ D_{\mbox{{\tiny KL}}}(P_{\mbox{{\tiny agg}}}(\cdot|x)||\sigma(f;v)) $  to make it learn from the joint classifier. After training, we only use the single classifier for inference without aggregation. In our experiments, we denote our three inference schemes, i.e., the single inference, the aggregated inference, and the inference of self-distillation as SFE-SI, SFE-AG and SFE-SD.

\subsection{Image Classification}

\paragraph{Classification on CIFAR-100.}
We empirically verify the effectiveness of our proposed method on benchmark image dataset CIFAR-100. To this end, we adopt ResNet-110, ResNet-164, SE-ResNet-110, DenseNet-100-12, Wide ResNet-28-10 and PyramidNet-110-270 as the baselines to evaluate our framework. Experiments show that our framework has significant improvement on all six models compared to the baseline. In particular, our method improves the top-1 accuracy of the cross-entropy loss from 74.21\% to 76.84\% of ResNet-110 under the CIFAR-100 dataset (see Table \ref{cifar100 table}). The results show that our method has a strong generalization to networks, and it can have a good performance whether on ResNet or other large networks such as PyramidNet.

And we notice that in our experiments, the aggregated inference does not improve the classification accuracy significantly compared to the single inference, and sometimes even leads to performance degradation. The reason is that part of the semantic visual representations are discarded when we transform the features, and if the discarded semantic visual representations happen to be an important part of the target to be identified, it may cause the network to get an incorrect classification result, which will play a negative role in aggregation. Experiments also show that self-distillation can well transfer the knowledge of the joint classifier to the single classifier.

\begin{table}[b]
	\caption{Validation accuracy (\%) on CIFAR-100 and tiny-ImageNet using various CNN architectures. SFE\,-\,SI, SFE\,-\,AG and SFE\,-\,SD indicate the single inference, the aggregated inference and the inference of self-distillation, respectively. (The best accuracy is indicated as bold).}
	\label{cifar100 table}
	\centering
	\begin{tabular}{lllll}
		\toprule[1.3pt]
		\midrule
		Model  & Baseline &\quad SFE\,-\,SI         	 &\quad SFE\,-\,AG        &\quad SFE\,-\,SD  \\
		\midrule
		ResNet-110 & 74.21{\small \,$\pm$\,0.35} &\quad 76.80{\small \,$\pm$\,0.22} &\quad \textbf{76.84{\small \,$\pm$\,0.25}} & \quad 76.24{\small \,$\pm$\,0.05}\\
		ResNet-164 & 75.10{\small \,$\pm$\,0.15} &\quad 77.33{\small \,$\pm$\,0.12}&\quad 77.53{\small \,$\pm$\,0.13}&\quad \textbf{77.68{\small \,$\pm$\,0.13}} \\
		SE-ResNet-110 & 76.38{\small \,$\pm$\,0.12} &\quad 77.50{\small \,$\pm$\,0.01}&\quad 77.59{\small \,$\pm$\,0.07}&\quad \textbf{77.75{\small \,$\pm$\,0.11}}\\
		DenseNet  & 77.60{\small \,$\pm$\,0.13}&\quad \textbf{78.41{\small \,$\pm$\,0.01}}&\quad 78.30{\small \,$\pm$\,0.05} &\quad 78.34{\small \,$\pm$\,0.05} \\
		WRN-28-10  & 79.30{\small \,$\pm$\,0.21} &\quad \textbf{80.71{\small \,$\pm$\,0.10}}&\quad 80.66{\small \,$\pm$\,0.06}&\quad 80.70{\small \,$\pm$\,0.01}\\
		PyramidNet & 81.54{\small \,$\pm$\,0.31} &\quad \textbf{82.82{\small \,$\pm$\,0.04}}&\quad 82.63{\small \,$\pm$\,0.06}&\quad 81.98{\small \,$\pm$\,0.03}\\

		\bottomrule[1.3pt]
	\end{tabular}
\end{table}

\paragraph{Classification on CIFAR-10 and tiny-ImageNet.}
Furthermore, our method has also wide applicability, versatility, and compatibility for datasets. It can also achieve good results on other image datasets such as CIFAR-10 and tiny-ImageNet. As shown in Table \ref{cifar10 and tinyimagenet table}, our framework achieves general improvements on CIFAR-10 and tiny-ImageNet, whether using the single inference, the aggregated inference, or the inference of self-distillation. Especially, we achieve the accuracy of training tiny-ImageNet on ResNet-110 to 63.79\%.

\begin{table}
	\caption{Validation accuracy (\%) on CIFAR-10, tiny-ImageNet using ResNet-110. SFE\,-\,SI, SFE\,-\,AG and SFE\,-\,SD indicate the single inference, the aggregated inference and the inference of self-distillation, respectively.  The best accuracy is indicated as bold.}
	\label{cifar10 and tinyimagenet table}
	\centering
	\begin{tabular}{lllll}
		\toprule[1.3pt]
		\midrule
		Dataset  & Baseline & \quad SFE\,-\,SI         	 & \quad SFE\,-\,AG        & \quad SFE\,-\,SD  \\
		\midrule
		CIFAR-10 	   & 94.59{\small \,$\pm$\,0.05}&\quad \textbf{95.06{\small \,$\pm$\,0.06}}&\quad 94.99{\small \,$\pm$\,0.08}&\quad 94.95{\small \,$\pm$\,0.03}\\
		tiny-ImageNet & 61.94{\small \,$\pm$\,0.06}&\quad \textbf{63.79{\small \,$\pm$\,0.34}}&\quad 63.47{\small \,$\pm$\,0.28}&\quad 63.04{\small \,$\pm$\,0.02} \\
		\bottomrule[1.3pt]
	\end{tabular}
\end{table}

\paragraph{Comparison with SLA.}
We compare our method with SLA, a recent proposed self-supervised method which is used in supervised learning. SLA expands data from the image level, while our framework only increases the number of features after convolutional layers, so the incremental cost is far less than that of SLA. Table \ref{comparison with sla} compares the time consumption and memory overhead of our method with SLA. As can be seen from table \ref{comparison with sla}, compared with the basic network, our method only increases the time consumption by 24.1\% and the GPU memory by 21.3\%, while SLA increases the cost by almost 3 times. Moreover, our method is comparable to SLA in the ability of the single inference. We cost much less than SLA and the accuracy is only 0.45\% lower than SLA.

\begin{table}
	\caption{The cost and single-inference accuracy of our method compared to baseline and SLA for training with CIFAR-100 on ResNet-110. All experiments run on the same GPU.}
	\label{comparison with sla}
	\centering
	\begin{tabular}{llll}
		\toprule[1.3pt]
		\midrule
		Method & \makecell[c]{GPU memory\\(MiB)} & \makecell[c]{Training time\\(sec/iter)}&  \makecell[c]{Single Accuracy\\(\%)}  \\
		\midrule
		baseline &\qquad 2846 {\small($+$\,0.0\%)}              &\quad 0.178 {\small($+$\,0.0\%)}       &\quad  74.21{\small \,$\pm$\,0.35}          \\
		SFE     &\qquad 3455 {\small($+$\,21.3\%)}                 &\quad 0.221 {\small($+$\,24.1\%)}       &\quad  76.80{\small \,$\pm$\,0.22}          \\
		SLA      &\qquad 9718 {\small($+$\,241.3\%)}                 &\quad 0.693 {\small($+$\,289.3\%)}      &\quad  77.25{\small \,$\pm$\,0.03}          \\
		\bottomrule[1.3pt]
	\end{tabular}
\end{table}

\paragraph{Combination with SLA and regularization methods.}
Furthermore, our method can also be compatible with SLA and other regularization methods to get better results. As shown in Table \ref{combination with sla}, we apply our method with SLA and existing regularization techniques, Mixup \cite{zhang2017mixup},Cutout \cite{devries2017improved}. In order to be unified with SLA, we only use the output of the last classifier for aggregated inference in this experiment. The experimental results show that our method consistently reduces the classification errors. As a result, it achieves 19.03\% error rate when training CIFAR-100 on ResNet-110. These results also demonstrate the compatibility of our method.

\begin{table}
	\caption{Classification error rates (\%) of SLA and other regularization methods with our method on CIFAR-100. The minimum error rate is indicated as bold.}
	\label{combination with sla}
	\centering
	\begin{tabular}{ll}
		\toprule[1.3pt]
		\midrule
		Method   			&\qquad\qquad       Error Rate   \\
		\midrule
		ResNet110				& \qquad\qquad\quad      25.79  \\
		+ Cutout				& \qquad\qquad\quad      24.49  \\
		+ Cutout + SFE (ours)			& \qquad\qquad\quad      22.59  \\
		+ Mixup     			& \qquad\qquad\quad      22.30  \\
		+ Mixup + SFE (ours)      	& \qquad\qquad\quad      21.10  \\
		+ SLA					& \qquad\qquad\quad      19.71  \\
		+ SLA + SFE	(ours)		& \qquad\qquad\quad    	 19.53  \\
		+ Cutout + SLA			& \qquad\qquad\quad    	 19.43  \\
		+ Cutout + SLA + SFE (ours)	& \qquad\qquad\quad    	 19.03  \\
		\bottomrule[1.3pt]
	\end{tabular}
\end{table}

\subsection{Fine-grained Image Classification}

The fine-grained image classification aims to recognize similar subcategories of objects under the same basic-level category. The difference between fine-grained recognition and general category recognition is that fine-grained subcategories usually share the same parts, which can only be distinguished by the subtle differences in texture and color characteristics of these parts. We adopt ResNet-18 as the baseline, and use CUB200, Stanford Dogs, Stanford Cars, three commonly used fine-grained datasets to verify that our proposed method also achieves good results on fine-grained classification tasks. As shown in Table \ref{fine-grained datasets table}, our improvement on fine-grained datasets is remarkable, especially on Stanford Dogs, which improves by 6.62 percentage points (63.58\% vs 56.96\%).

\begin{table}
	\caption{Validation accuracy (\%) on three fine-grained datasets CUB-200, Stanford Dogs and Stanford Cars using ResNet-18. SFE\,-\,SI, SFE\,-\,AG and SFE\,-\,SD indicate the single inference, the aggregated inference and the inference of self-distillation, respectively.  The best accuracy is indicated as bold.}
	\label{fine-grained datasets table}
	\centering
	\begin{tabular}{lllll}
		\toprule[1.3pt]
		\midrule
		Dataset  & Baseline & \quad SFE\,-\,SI         	 & \quad SFE\,-\,AG        & \quad SFE\,-\,SD  \\
		\midrule
		CUB200  	   & 69.17{\small \,$\pm$\,0.32} & \quad \textbf{73.01{\small \,$\pm$\,0.36}} & \quad 72.26{\small \,$\pm$\,0.20} & \quad 71.54{\small \,$\pm$\,0.38}\\
		Stanford Dogs & 56.96{\small \,$\pm$\,0.31} & \quad 63.43{\small \,$\pm$\,0.01}  & \quad \textbf{63.58{\small \,$\pm$\,0.01}} &  \quad 63.55{\small \,$\pm$\,0.02}  \\
		Stanford Cars & 80.01{\small \,$\pm$\,0.56} & \quad 85.18{\small \,$\pm$\,0.24}  & \quad \textbf{85.33{\small \,$\pm$\,0.27}} & \quad 84.11{\small \,$\pm$\,0.01}\\
		\bottomrule[1.3pt]
	\end{tabular}
\end{table}

\subsection{Ablation Study}

\paragraph{Exploring the pattern of feature transformation.}

In Table \ref{discussion of K and layers} we explore how the performance of the network depends on the number of Transformations $ K $, and layers where the features are transformed. We use CIFAR-100 to experiment on ResNet-110. For that purpose we randomly divide all channels into 4, 8, 16, 32 groups, i.e., $ K = 5, 9, 17, 33 $. We do not consider the case of $ K > 33 $ because of the high computational cost. In the meantime the situation of introducing internal pretext task after the first layer, the second layer and the third layer is considered respectively. In this experiment if the transformation occurs after the first or the second layer, the generated features will be transferred to the rest of the network, and finally to the joint classifier.

We observe that the earlier internal pretext task is proposed, the better the learned features are, because they get more training. For the second and third layers, within a certain range, as the self-supervised task become more difficult, we achieve better image classification performance. While for the first layer, the best performance is achieved when $ K = 5 $. We speculate that the more difficult task can excavate deeper feature information after the second and third layers, but after the first layer, the original classification task has not been trained enough, so the difficult self-supervised task will interfere with the original task, resulting in performance degradation.

In general, we can get the best accuracy by using $ K = 5 $ transformations after the first layer, 3.06 percentage points higher than baseline (77.27\% vs 74.21\%). And we can get a balance between the cost increase and performance improvement with transformations $K = 9$ after the second layer. And to further reduce the computational overhead, we propose a multi-classifier structure (a two-classifier structure is shown in Figure \ref{two-classifier structure}). By the way, we have also considered the cases of artificial channel division, partial channel division and one channel divided into multiple groups. Finally we think that it is more reasonable to divide all channels randomly.

\begin{table}
	\caption{The effect of the number of Transformations $ K $ and layers where the features are transformed on network performance. SI and AG indicate the single inference and the aggregated inference of the single classifier structure respectively.}
	\label{discussion of K and layers}
	\centering
	\resizebox{\textwidth}{15mm}{
		\begin{tabular}{lllllll}
			\toprule[1.3pt]
			\midrule
			& \multicolumn{2}{c}{Layer1} & \multicolumn{2}{c}{Layer2} & \multicolumn{2}{c}{Layer3} \\ \cmidrule(r){2-3}\cmidrule(r){4-5}\cmidrule(r){6-7}
			&\qquad SI         	 & \qquad AG          &\qquad SI           &\qquad AG          &\qquad SI           &\qquad AG          \\
			\midrule
			K = 1 + 4  & \textbf{77.02{\small \,$\pm$\,0.13}} 		 & \textbf{77.27{\small \,$\pm$\,0.14} } 	   & 75.20{\small \,$\pm$\,0.02}  	  & 75.34{\small \,$\pm$\,0.03}      & 75.10{\small \,$\pm$\,0.06}       & 75.16{\small \,$\pm$\,0.07}       \\
			K = 1 + 8  & 76.79{\small \,$\pm$\,0.10}          & 76.90{\small \,$\pm$\,0.16}     & 75.85{\small \,$\pm$\,0.07}         & 76.23{\small \,$\pm$\,0.10}      & 75.56{\small \,$\pm$\,0.18}          & 75.58{\small \,$\pm$\,0.17}         \\
			K = 1 + 16 & 76.21{\small \,$\pm$\,0.11}       & 76.22{\small \,$\pm$\,0.17}      & 76.14{\small \,$\pm$\,0.04}           & 76.27{\small \,$\pm$\,0.07}         &  75.57{\small \,$\pm$\,0.07}         &  75.60{\small \,$\pm$\,0.24}        \\
			K = 1 + 32 & 76.87{\small \,$\pm$\,0.08}        & 76.89{\small \,$\pm$\,0.13}      & \textbf{76.42{\small \,$\pm$\,0.08}}        & \textbf{76.36{\small \,$\pm$\,0.07}}       & \textbf{75.79{\small \,$\pm$\,0.08}}   	   &  \textbf{75.83{\small \,$\pm$\,0.08}}     \\
			\bottomrule[1.3pt]
	\end{tabular}}
\end{table}

\paragraph{Evaluation of the multi-classifier structures.}\label{Evaluation of the multi-classifier structure}
We explore different design choices of multi-classifier structure, these choices are classified into two groups. The first group discusses the number of layers that use classifiers. ResNet-110 contains three layers, we use classifiers after the last layers, last two layers and whole three layers, respectively. As shown in Table \ref{discussion of classifiers}, we can get the best accuracy when we use classifiers after the last two layers. We speculate that the reason why the performance is degraded when we add the classifier of the first layer is that features of the first layer are not trained enough to encode high-level semantic visual representations.

The second group foucuses on whether the internal pretext task is proposed or not. To further determine the benefits of our proposed pretext task, we compare the results using only multiple classifiers with the results using multiple classifiers and the pretext task. As shown in Table \ref{discussion of classifiers}, our proposed self-supervised task works well no matter how many classifiers are used, and we get the best result when we use two classifiers. Therefore, in our framework, we use a two-classifier structure and combine it with our proposed internal pretext task.

\begin{table}
	\caption{The effect of different design choices of multi-classifier structure on network performance. SI and AG indicate the single inference and the aggregated inference of the single classifier structure respectively.}
	\label{discussion of classifiers}
	\centering
	\begin{tabular}{lllll}
		\toprule[1.3pt]
		\midrule
		& \multicolumn{2}{c}{ResNet110} & \multicolumn{2}{c}{ResNet110\,+\,SFE} \\ \cmidrule(r){2-3}\cmidrule(r){4-5}
		&\qquad SI         	 &\quad \qquad AG          &\quad \qquad SI           &\quad \qquad AG            \\
		\midrule
		1 Classifier & 74.21{\small \,$\pm$\,0.35} & \quad 74.21{\small \,$\pm$\,0.35} & \quad 75.79{\small \,$\pm$\,0.08} & \quad\textbf{75.83{\small \,$\pm$\,0.08}}               \\
		2 Classifiers & 75.34{\small \,$\pm$\,0.25} & \quad 75.99{\small \,$\pm$\,0.07} &\quad 76.80{\small \,$\pm$\,0.22} &\quad\textbf{76.84{\small \,$\pm$\,0.25}}          \\
		3 Classifiers & 74.88{\small \,$\pm$\,0.28} & \quad 74.75{\small \,$\pm$\,0.11} &\quad \textbf{75.98{\small \,$\pm$\,0.03}}&\quad 75.88{\small \,$\pm$\,0.09} \\
		\bottomrule[1.3pt]
	\end{tabular}
\end{table}

\paragraph{Discussion on the setting of the hyperparameter $\beta$.}

As is known to us, the quality of the learned features after the penultimate layer is not as good as that of the last layer. In order to balance the impact of the learned features after the penultimate layer and the last layer on the network, we introduce a hyperparameter $\beta$ to reduce the weight of the loss of the penultimate layer.  As shown in Figure \ref{beta-curve}, for example, if we train CIFAR-100 on Wide ResNet-28-10, the network performance fluctuates with $\beta$, and generally decline with the increase of $\beta$, so we need to set a small weight $\beta$ to suppress the impact of the output features after the penultimate layer. We suspect that the quality of the features after the penultimate layer may be much worse than that of the last layer. When $\beta$ increases too much, the features after the penultimate layer will have a negative impact on the training of the network.

\begin{figure}[htbp]
	\centering
	\includegraphics[width=0.7\linewidth]{beta-curve.png}
	\caption{Validation accuracy under different settings of $\beta$. We train CIFAR-100 on Wide ResNet-28-10, and report the accuracy of the single inference (blue line) and the aggregated inference (yellow line). Baseline represents the result of the basic model which is nothing to do with $\beta$ (red line).}
	\label{beta-curve}
\end{figure}

\section{Conclusion}
\label{Conclusion}
We have introduced a novel self-supervised pretext task that uses internal signals of the network, termed internal pretext task. Then we have proposed a generic framework that applies this internal pretext task for the enhancement of supervised learning. Extensive experiments show that our approach is effective on various models and datasets. More broadly, we hope it could stimulate discussion in the community regarding this very important, but largely neglected perspective: internal signals of the network can also be applied to construct self-supervised tasks.
\medskip
\small{
	\bibliographystyle{plain}

redpajama_set_name: RedPajamaArXiv


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text:
Oral histories on the topic 'golf'
Roger Martindale
Roger Martindale, born 1943 in England, was a police constable in the UK until 1973 when he migrated to Australia. He joined the Commonwealth Police (now AFP) VIP protection service in 1973 which included being part of Prime Minister Malcolm Fraser's security team in 1978 and thereafter that of t...
Topics: 'A Current Affair' (TV program), Aboriginal Tent Embassy, AFL Grand Final, Aircraft, Alresford, ASIO, Attendants, Australian Federal Police, Australian Protective Service, awards, Bahamas, Barrie Cassidy, Barry Brown, Beijing, Bill Hayden, Bob Hawke, Bob Hunter, Bomb scare, Bushfires, Cabinet anteroom, Cabinet Room, Camaraderie, Canberra, China, CHOGM, CHOGRM, Chris Conybeare, Commonwealth Police, Constitutional Convention 1998, Cook Islands, Craig Emerson, Demonstrations, Denis Richardson, Doug Anthony, Dubrovnik, Duck shooting, Election campaigns, Fairstar (ship), Family life, Firearms, Frank Leverett, Frank Lowy, Gambling, Gareth Evans, Geoffrey Yeend, George Davidson, George Shultz, Golf, Gorbachev, Government House, Governor-General, Graham Freudenberg, Graham Richardson, Graham Seidel, Hampshire, Hazel Hawke, Hilton bombing, Hilton hotel, Horse races, Hostels, Ian Sharp, Ian Sinclair, India, Indira Gandhi, Ireland, Irish Republican Army, Israel, Jill Saunders, Joe Feldman, John Bowen, John Button, John Hewson, John Kerr, KGB, Kim Beazley, Kirribilli House, Kremlin, Laurie Oakes, Lionel Bowen, Malcolm Fraser, Margaret Thatcher, Michelle Grattan, Mick Young, Migration, Monterey, Mullumbimby, Nanjing, Nareen, New Dehli, New Parliament House, Ninian Stephens, Osvaldo Meneghello, Pakistan, Party Room, Paul Keating, Peter Harvey, Phil Lynch, Photographs, Policing, Portsmouth, Press Gallery, Princess Anne, Provisional Parliament House, Queen's Visit 1992, Radio, Rajiv Gandhi, Red Square, Right to Life, Robert Ray, Royal Marines, Royalty Protection, Group, Sandy Hollway, Security, Security Intelligence and Diplomatic Liaison, Shandong Province, Singleton Army Base, South Africa, South Pacific, Forum, Tamie Fraser, The Lodge, Thomas Kelly, Tiananmen Square, Tipperary, Training, Turkey, US Secret Service, VIP Protection, Washington, Wombat trail, Yasser Arafat, Yasushiro Nakasone, Yugoslavia, Zambia, Zelman Cowen
Fred Johnson (1907-2001)
Fred Johnson worked on the Parliament House construction site in the mid-1920s. After the Parliament House was opened in 1927, he joined the parliamentary staff, working in the building until his retirement in 1967, when he was Deputy Principal Attendant and Keeper of the Mace. Born in Sydney in ...
Topics: Accommodation, Alan Tregear, Alf Backley, Archie Cameron, Billiard Room, Billy Hughes, Bob Menzies, Bruce Mathews, Builders, Building workers, Burt Fields, Cabinet Room, Canberra (1920s), Caterers, Causeway, Charles Hawker, Charlie Adaman, Choristers, Dame Nellie Melba, David Fairbairn, Doug Blake, Frank Packer, Earle Page, Empire Parliamentary Association, Golf, Gracie Fields, Griffith (NSW), Harry Johnson, Hoskins (steel company), Hotel Canberra, Italians, James 'Digger' Dunn, Joe Alexander, John Butters, Joint House Department, Journalists, Kings Hall, Kitchen, Leeton (NSW), Les Dwyer, Littleton Groom, Members Bar, Members Dining Room, Opening ceremony 1927, Opposition Party Room, Parliamentary Refreshment Rooms, Party Room, Press Gallery, Provisional Parliament House, Queanbeyan, Records Office, Sammy Sampson, Save Our Sons, Security, Serjeant-at-arms, Speaker's Chair, St Andrews Cathedral choir, The Mace, Tradesmen, Vietnam protests, Wilfred Johnson, Working conditions (1920s), World War One
Gail Tregear
Gail Tregear, born in 1940, talks about her late father, Allan Tregear, who joined the Parliamentary service in the Senate in 1920 and was Clerk of the House of Representatives from 1955 to 1958. She also recalls memories of childhood and teenage years in the Provisional Parliament House.
Topics: Air-raid shelters, Albert Hall, Allan Tregear, Ben Chifley, Brassey House, Canada, Canberra, Canberra University College, Childhood memories, Clerks (House of Representatives), Cricket, Depression (1930s), Diaries, England, Family life, Film industry, Frederick Shedden, Gary Cooper, Golf, Hollywood, Home movies, Hong Kong, Horse races, House of Commons (UK), Illustrated London News (newspaper), John Curtin, Joint House Department, Kensington, Liberal Party, Malcolm Moir, Marriage, Mugga Way, Munitions, Nellie Melba, Opening ceremony 1927, Parliament House (Melbourne), Parliamentary Service, Phillip Island, Politics, Port Melbourne, Punch (magazine), Queanbeyan, Railways, Red Hill, Religion, Robert Garran, Robert Menzies, Saturday Evening Post (newspaper), Tennis, United States of America, University of Melbourne
Lesley Hindley
Lesley Hindley (nee Hyde) speaks about her early associations with the provisional Parliament House, through working as a typist in the early 1950s to her husband Rob Chalmers' long involvement with the Press Gallery as a journalist. Mrs. Hindley's daughter, Susan Pitt (nee Chalmers) sat in on th...
Topics: Aborigines, Adam Ingram & Son (Manuka, ACT), Air raid shelters, Alan Curtis, American servicemen (in Sydney), Archie Cameron, Arnott's Biscuit factory, Australian Labor Party, Australian Press Services, Ballet Russes, Ballroom dancing, Barbara Brooks, Beauticians, Ben Chifley, Bill Dovey, Bill Lascott, Bill McMahon, Boarding houses, Bob Hawke, Bondi (Sydney), Brisbane Line (World War 2), Cold War, Communist Party of Australia, Daily Mirror (newspaper), David Fairbairn, Diplomats, Domestic Science, Don Whitington, Dot Moll [?], Dover Heights, High School (Sydney), Eda Hollitt, Election campaign (Labor, 1972), Family background, Faye Brooks, Federal Golf Club, Fred Coleman, George Kerr, Golf, Greg Hyde, Hairdressing college (Sydney), Harold Holt, Hastings Deering (Sydney), Havelock House, Hostels, Hotel Civic, Hugh Burn, Jack Commins, Joan Commins, John Gorton, John McEwen, Journalists, Journalists Ball (Canberra), Kevin Power, King's Hall, Laddie Hindley, Lascott's School of Ballroom Dancing, Lawley House, Les Love, Little Pattie, Madam Gossamer, Marcia Cain, Margaret Whitlam, Marriage, Melbourne Cup, Mick Burn, Mike Willesee, Mining engineers, Mount Isa (Qld), Narrabundah (ACT), New Norfolk (mental hospital, Tasmania), New Zealand, Peter Whitney, Press Gallery, Prime Minister's Department, Primrose Coleman, Provisional Parliament House, Public Service Board, Ray Maley, Religion, Richard Carlton, Rob Chalmers, Robert Menzies, Ron McCawley, Rosa Hyde, Royal Canberra Golf Club, Rugby Union (North Island, New Zealand), Russia, Ruth Fairbairn, Sheila Phelan, Socialism, Susan Pitt, Sydney, Sydney High School, Tamie Fraser, Ted Cain, The Lodge, The Wellington (hotel), Wally Brooks, William Slim, World War Two, Zara Holt


text:
Started From The Bottom: See Gov. Fayose And Wife's Epic Throwback Photos After 29-Years Of Marriage
Lady Who Spent 23-years Growing Her Nails Crowned the 2018 Woman with Longest Fingernails (Photos)
Awesome! See the Super Brilliant 18-Year-Old Nigerian Girl Who Made History as 11 Universities Offer Her Scholarship
UNN, OAU, UNILAG, Unizik & More: 38 Nigerian Universities Increase Their Tuition Fees (See Full List)
Wife Of Late Burundi President Nkurunziza Fighting For Life After Contracting Coronavirus
Posted by Thandiubani on Thu 11th Jun, 2020 - tori.ng
It has been disclosed that Bucumi Nkurunziza, wife of late President of Burundi is fighting for her life after contracting coronavirus.
Pierre Nkurunziza and his wife Bucumi
It has been revealed that after Burundi President Pierre Nkurunziza succumbed to a heart attack in Bujumbura, his wife, Denise Bucumi Nkurunziza, is still receiving treatment at the Aga Khan University Hospital, in Kenya's capital Nairobi, TheNation reports.
Ms Nkurunziza had attended a volleyball match on Saturday afternoon and was taken to a hospital that evening after falling ill.
The former First Lady is receiving treatment for an underlying condition after contracting the coronavirus.
She received a message on Tuesday from President Uhuru Kenyatta, who sent a message of condolence and comfort to the people of the Republic of Burundi following the demise of President Nkurunziza.
In his message, President Kenyatta termed the outgoing Burundi leader as an outstanding regional counterpart who served his country with distinction as Head of State and Government and worked tirelessly for the peace and stability of his country and the Great Lakes Region.
The President said death had robbed East Africa of a prominent leader whose contribution to the integration and progress of the region will be sorely missed and prayed for God's calming comfort as the family, people of Burundi and East Africans mourn their departed leader.
Although he appeared to recover on Sunday and spoke to those around him, he suddenly deteriorated on Monday morning. He then suffered a heart attack and despite an immediate resuscitation attempt, doctors were unable to revive him.
Mr Nkurunziza died at a hospital in Karuzi, eastern Burundi. Ms Nkurunziza was airlifted to Nairobi on Thursday morning following a government-to-government agreement using Amref's acquired portable isolation chamber, which is an innovative medical unit and transportation system designed for optimal safety during patient loading and transport.
A source said the unit is a single patient isolator made of cleanable materials and is compatible with leading ambulance-stretcher systems as well as with most mechanical ventilator circuits.
"Due to our patient confidentiality policy, we cannot share any information with the public," the hospital's communication department had stated.
The government said there would be seven days for national mourning from Tuesday and that flags would be flown at half-mast.
Photo Of Kidnapped Oyo Farmer Who Was Killed By His Abductors After Collecting N2m Ransom
Handsome Nollywood Actor Dies After Battling Unknown Sickness (Photo)
BFF Goals: Viral Photos Of 4 Bestfriends Before And After Motherhood
Cheating Husband Begs Instagram to Delete Angry Girlfriend's Shocking Confession
See List of Approved Universities In Nigeria as Released by NUC
So Seductive: Curvy Female Blogger Breaks Internet with Her Hot Tattooed Waist (Photos)
14 Places Not To Park Your Car - FRSC Warns
Against Senate Resolution, Buhari Nominates Abubakar Fikpo As Acting DG NDE
Assistant Police Commissioner Bags Life Imprisonment for Buying Vehicle From Kidnappers
See the Photo of Nigerian Man Who Killed His Wife in Italy


In this powerful tale about the rise of Korea's global adoption program, four adult adoptees return to their country of birth and recover the personal histories that were lost when they were adopted. Raised in foreign families, each sets out on a journey to reconnect with their roots, mapping the geographies of kinship that bind them to a homeland they never knew. Along the way there are discoveries and dead ends, as well as mysteries that will never be unraveled.
In CROSSINGS, a determined group of internationally renowned women peacemakers attempt the impossible: crossing the demilitarized zone (DMZ) from North to South Korea, calling for peace on the Korean peninsula. The film follows key women leaders from twelve countries, including two Nobel Peace Laureates, on this historic journey as they face political and social obstacles, mixed public opinion, fear, and red-baiting. Through their journey, Crossings tells a story of global resonance about a divided nation and explores enduring questions about war's legacies and the role women play in resolving the world's most intractable conflicts.
Her passport said she was Cha Jung Hee. She knew she was not. So began a 40-year deception for a Korean adoptee who came to the U.S. in 1966. Told to keep her true identity secret from her new American family, this 8-year-old quickly forgot she was ever anyone else. But why had her identity been switched? And who was the real Cha Jung Hee? IN THE MATTER OF CHA JUNG HEE is the search to find the answers, as acclaimed filmmaker Deann Borshay Liem (FIRST PERSON PLURAL, POV 2000) returns to her native Korea to find her "double," the mysterious girl whose place she took in America.
LEGACIES OF THE KOREAN WAR is a web-based oral history project that gives voice to the memories of Korean Americans whose lives were shaped by the Korean War. The site includes short films, video and audio excerpts, a Korean War timeline, scholar essays and much more. Take a look at www.legaciesofthekoreanwar.org.
In 1966, Deann Borshay Liem was adopted by an American family and was sent from Korea to her new home. Growing up in California, the memory of her birth family was nearly obliterated until recurring dreams lead Deann to discover the truth: her Korean mother was very much alive. Bravely uniting her biological and adoptive families, Deann's heartfelt journey makes First Person Plural a poignant essay on family, loss, and the reconciling of two identities.
First Person Plural was broadcast nationally on PBS in December, 2000, through the award-winning documentary series, Point of View (POV). The film will have an encore presentation on PBS on August 10, 2010. We invite you to explore the film's official website for in-depth information about the program.
MEMORY OF FORGOTTEN WAR conveys the human costs of military conflict through deeply personal accounts of the Korean War (1950-1953) by four Korean-American survivors. Their stories take audiences through the trajectory of the war, from extensive bombing campaigns, to day-to-day struggle for survival, and separation from family members across the DMZ. Decades later, each person reunites with relatives in North Korea, conveying beyond words the meaning of family loss. These stories belie the notion that war ends when the guns are silenced and foreshadow the future of countless others displaced by ongoing military conflict today.
Arrested at 16 and tried as an adult for kidnapping and robbery, Eddy Zheng served over 20 years in California prisons and jails. Ben Wang's BREATHIN': THE EDDY ZHENG STORY paints an intimate portrait of Eddy—the prisoner, the immigrant, the son, the activist—on his journey to freedom, rehabilitation and redemption.
Burqa Boxers is a feature length documentary about Muslim women boxers in Kolkata, India, directed by Alka Raghuram and co-produced by Deann Borshay Liem and Premlatha Durham. It was developed with the support of Independent Television Service's (ITVS) Diversity Development Fund and produced with funding from CNC, France, and Vision Sud Est, Switzerland.
The Apology is a film that explores the forgotten history of the "comfort women," a term used to describe over 200,000 girls, some as young as nine years old, who were forced into sexual slavery during World War II in Asia. The film chronicles the horrific crimes these women endured over sixty years ago. But more importantly, the film chronicles their resilience and how today they work toward reconciliation and justice, while also struggling to make peace with the past.


According to Pablo Torre of ESPN, LeBron James is "hustling behind the scenes" to see if the Phoenix Suns would be willing to include No. 4 overall pick Josh Jackson in a trade for Kyrie Irving.
Torre was a co-host on First Take Thursday. Watch the video here.
Torre notes LeBron's close friendship with Suns Vice President of Basketball Operations James Jones plays a factor in LeBron's efforts to try to bring Jackson to Cleveland.
Suns point guard Eric Bledsoe has also been mentioned in trade rumors involving Irving and the Cavs could try to swing a deal for both Bledsoe and Jackson, which would likely involve other complementary pieces to balance out the trade and finances.
James, Bledsoe and Derrick Rose are working out together in Las Vegas this week.
So far, multiple reports have indicated Jackson is off limits in trade conversations, but LeBron is doing his part to try and change that.


A mold-breaking medical firm sees Tampa Bay as the country's most fertile ground for its outcome-based health care model. Will enough seniors buy into its disruptive vision?
Forget Medicare for All or any other political machinations of the health care system currently up for debate.
A Miami-based company is already disrupting health care as we know it — and it sees Tampa Bay as the ideal market for its rapidly growing brand, which, to succeed, will have to undertake a big challenge: to alter the way people think about getting health care, from reactive to preventive.
The company, ChenMed, backed by a socially conscious private equity firm called Turner Impact Capital, plans to invest $200 million in the Tampa Bay area in the coming years to build at least 10 concierge-style medical centers for underserved seniors whose health could be greatly improved with regular, affordable access to health care. That in and of itself is a shift, given concierge practices are traditionally designed for higher-income patients, who can afford to pay market rates, without insurance.
"Our incentives are aligned around keeping the patient healthy rather than a typical hospital or doctor's office, where the patient being unhealthy is what makes money." Dr. Gaurov Dayal, ChenMed's chief growth officer and president of new markets.
Doing business as Dedicated Senior Medical Center, privately held ChenMed opened its first health care facility in Tampa in summer 2017. Last fall, it unveiled another one, at 1901 Fletcher Ave.; it also has centers in Lakeland, Bradenton, Clearwater, Largo and St. Petersburg.
ChenMed, Dayal explains, caters to seniors who are part of the federal Medicare Advantage program, which incentivizes health care providers to keep patients out of emergency rooms and hospitals via an emphasis on managed preventative care. As a business practice, it's not as lucrative as traditional Medicare and its fee-for-service model. But the growing popularity of Medicare Advantage — nearly 16 million seniors are now enrolled in it nationwide — indicates ChenMed is wise to look to the future when formulating its growth strategy.
"Our incentives are aligned around keeping the patient healthy rather than a typical hospital or doctor's office, where the patient being unhealthy is what makes money," Dayal says.
Of course, that mindset shift in how people think about health care is a boulder-uphill moment for ChenMed. "We do spend a lot on marketing," Dayal says, "but honestly, our biggest sales tool is our members. We can spend as much as we want on marketing, but ultimately what matters most is how our patients feel when they walk out the door, and that's what we spend a lot of time and effort on."
Insurance companies provide a fixed budget per patient to ChenMed, so its Dedicated Senior Medical Centers can offer on-demand transportation and on-site specialist services like x-ray machines and a pharmacy. Doctors even give out their personal cell phone numbers to patients - much like standard concierge practices.
The company makes money by simply staying under its annual budget per patient. "It pushes us to focus on keeping patients healthy," Dayal says. For example, if a Medicare Advantage plan provides $10,000 for a person's medical expenses for the year and the services billed amount to only $8,000, ChenMed keeps $2,000 as profit. The company declines to disclose revenue, or profit margins.
Big private insurers that offer Medicare Advantage plans, like WellCare, Humana and Florida Blue, enjoy working with ChenMed because it boosts their patient satisfaction ratings. In turn, higher-rated plans receive larger budgets from the federal government, get more patients pushed to them and can provide a greater range of benefits.
Turner Impact Capital relishes a fruitful relationship with ChenMed, for whom it has financed and built six medical centers in Florida. And Turner Impact CEO Bobby Turner views ChenMed as an excellent investment because it's finding workable solutions to the health care system's flawed reimbursement model that's become a flashpoint of frustration for many sides — patients and doctors especially.


Supporting the Community is a long-term investment.
From our humble beginnings to the Corporation we are today, none of this would have been possible if it was not for you and for your continuing patronage. During our first 76 years, we have learned the value of community involvement. Supporting the community is a sound long-term investment.


text:
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Green Global Consulting (GGC) is a refreshingly different, honest, and transparent sustainability consultancy. Unbound by loyalties to specific vendors, service providers, or out-dated thinking, we operate without limitation, allowing our combined knowledge to explore the limitless possibilities offered by the world's most innovative technologies to solve your biggest sustainability challenges. For GGC, no problem is too complex to overcome.


At Documentary Media, we craft publications of the highest editorial, design and production quality. From limited-edition, private releases to traditionally published books for the retail market, we use a hands-on process that we have refined over 42 years.
Our services include concept and budget development, oral history interviews, archival research, writing, book design, production, editing, and project management. We also excel in book fulfillment, sales, and distribution. Talk with us about capturing your story in a book that will make you proud.
A celebration of Seattle's history, identity, and sense of self, this new book features 100 selections from "Seattle Now & Then," a column in the Seattle Times' Sunday magazine that's been pairing historic photos with present-day shots for over 36 years, started by Paul Dorpat. Seattle Now & Then contains fascinating glimpses of history and reflects on how our city has and hasn't changed. What looks different, for instance, between this 1907 view of Pike Place Market and the market we know today?