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'Have trust and large ideas will work'
Jayant Murty, Director, Strategy, Media & Integrated Marketing, Asia Pacific, Intel Corp says that generosity, experience, status, utility, transparency and eco-sensitivity are the trends to consider in 360-degree thinking
Building brands in an era of multiple degrees of freedom was the topic taken up by Jayant Murty, Director of Strategy, Media & Integrated Marketing, Asia Pacific Region, Intel Corp. at the Advertising Conclave of GoaFest on Thursday.
He pointed out that we create extraordinary content on the Net. Citing the example of the Audi-Lady Gaga connect that led to the largest search operation on the Net, the robots at the London Design Festival, where people sent SMSes that were projected on the sky, he explained that it all happened without planning any integration and became a huge hit.
People are looking for more real information about brands, and this is becoming crucial information, he said. Giving examples of Nike, Apple, Skittles and Zappos which changed the rules of the game, he said that the role of brands was to create platforms where everyone can have conversations. "The company that understood the role of content is Nike; it's about how you engage consumers," he said.
Looking at the role of 360-degree, he highlighted the fact that the trends to consider in 360-degree thinking are generosity, experience, status, utility, transparency and eco-sensitivity. He pointed out that brand experience and eco-sensitivity are becoming big. A point in case for eco-sensitivity he presented was that Amazon was sending books with seeds in the packaging, so that people can plant the seeds and watch them grow.
"Think 360-degree, don't act 360-degree," was his tip. "Don't think about 360-degree as TV, etc, but as screens on tablets or mobiles."
Referring to Bill Gates, who spoke about "business at the speed of thought", he pointed out that a lot of this is happening today. At the end, Murty outlined what one should follow on the road ahead. "Try to solve the problem, curate. Don't institutionalise; work on open collaborations. Technology unleashes creativity, master its use. Be useful and niche. Identify the believers and engage. Don't go for large numbers. Have trust and large ideas will work," he concluded.For more updates, be socially connected with us on
WhatsApp, Instagram, LinkedIn, Twitter, Facebook & Youtube


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Best Geeky T-Shirts To Make You Look Cool & Intelligent At The Same Time
Being nerdy and wearing t-shirts which makes you look hot doesn't go in the same direction. If you ask a normal person what a nerd/geek is, he prolly gonna say someone who wears round boring eye glass and weird unfashionable shirts with a overly smart brain. Don't worry that someone is also me.
When I was in school, friends used to tell me dude just go get some better shirts and a better hair cut. Right now I'm not sure about the hair cut thing. It's still messy. But I'm pretty sure that the shirt has improved a lot xD.
In Cleus, we try our best to design geeky clothing to make geeks/nerds look cool and intelligent at the same time.
So let's not waste anymore time with blah blah words and go straight to the list,
Also remember, click on the product heading to go to the product page link of any of those mentioned t-shirt…
#AlwaysCurious T-Shirt
You know what's common in a intelligent human being?
He always keep questioning about everything that happens in this universe and that's how he gets more and more intelligent everyday.
"Curiosity is the spark behind the spark of every great idea. The future belongs to the people who are curious."
Take an example of Newton, millions of people saw an apple fall from the tree, But guess what Newton asked why and that changed the whole world.
Ins(pi)re T-Shirt
If you are geek, then you probably know what a pi is and if you are human, then you probably know what is the value of word "inspire" is. So by combining both of them in one t-shirt you get a geeky t-shirt which can make look hot and nerdy at the same time xD
Only Today T-Shirt
I know what you are thinking, like dude how the heck is that geeky. I know it's not nerdy at all. But it shows a characteristic which is common in every nerd. We are so focused in our work. We forget everything about the outside world and keep staying busy with our creative work until we finish it. That's what this t-shirt shows.
Be Greater Than The Average T-Shirt
Here is a t-shirt which is more into nerdy side of the world with a inspirational touch. If you wear this, are people gonna understand what it says? Not really. But pretty sure 80% people gonna stare at your tee for a long time 🤣 trying to understand how that text related to that mathematical statement…
My Code Works I Have No Idea Why
I guess most of my site visitors are programmers. Because this t-shirt for coders is the no.1 best selling t-shirt on Cleus. It explains a normal issue in the day to day life of a coder.
While doing some hardcore coding, Sometimes we face some issues, we try different things without clearly knowing the reason of that issue and everything starts working. It can go vice versa tho. So this t-shirt is specially made for coders and no.1 selling tee on Cleus.
In Pursuit Of Happiness
Are you into Chemistry? If yes then you probably know what it means. If you don't know that molecule is the symbol of serotonin which is responsible for your happiness in your brain. So basically "In Pursuit Of Happiness"
Hide & Seek Champion
Another best selling t-shirt and another one made specially for coders. If you guys code, then you probably know the importance of the semi colon and this t-shirt explains about that.
Honestly, I didn't think this t-shirt will sell that well while designing it. But things never go according to the expectation sometimes and this is the 2nd best selling t-shirt on Cleus.
Energy = Milk * Coffee^2
Here is a funny one if you are someone who likes both physics and coffee. You probably know what E = mc^2 means. It's one of the world's most famous equation by most famous scientist Einstein. Let's say m = milk and c = coffee so you get it right?
It's actually true tho, coffee contains caffeine and which gives us lil bit juice in night and morning. If you are coder like me who stays awake all night. Then coffee and tea are the best night friends xD.
These are some cool geeky t-shirts you may like. We update this list regularly. Anyways if you want to check more geeky t-shirts like it. Please check cleus.co
Subscribe to stay updated with interesting contents made for geeks.

subset: commoncrawl


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- Patterns
- >
- Women's Sewing Patterns
- >
- Jackets/Outerwear
- >
- The Assembly Line - Hoodie Parker
The Assembly Line - Hoodie Parker
The Hoodie parka is designed with an A-line shape for a relaxed fit. The hood is high at front which gives good protection for cold days. There are hidden front pockets with large flaps and we have used snap buttons for closing. Features, large front pockets, hood and front opening.
This is a multi-size pattern, including size XS-XXL. The pattern comes carefully packaged in an envelope together with step-by-step sewing instructions.
Difficulty: Average
Seam allowances are included in the pattern.
Model is 170 cm tall and wearing a size S.
Suitable fabrics: mid-heavy weight woven and stretch woven fabrics such as linen, denim, cotton drill, oilcloth.
Fabric requirements:
140-150 cm fabric width:
XS 2.6 m / S 2.7 m / M 2.8 m / L 3.0 m / XL 3.1 m / XXL 3.3 m
This is a multi-size pattern, including size XS-XXL.
Photo credits: The Assembly Line


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Email Jacky Smith MSc BABCP Accred, EMDR Accred, BACP Accred, AREBT Accred
Jacky Smith MSc specialises in Post Traumatic Stress Disorder (PTSD) Complex Trauma, depression, anxiety disorders, generalised anxiety, OCD, phobias, and panic disorder. Call 07793198447 to discuss an appointment in Ryde Isle of Wight.
Please either enable it in your browser and try again, or return to the profile page for Jacky Smith and select a different method of contact.


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Doctrine Definition - MilitaryDictionary.org
Term Source:
1.) Fundamental principles by which the military forces or elements thereof guide their actions in support of national objectives. It is authoritative but requires judgment in application.
See Also:
- see also joint doctrine
- see also multinational doctrine
In the United States, military vocabulary is standardized by the Department of Defence. These terms are used by the United States Army, Navy, Air Force, and Marine Corps.
Department of Defence, Dictionary of Military and Associated Terms
This term is marked as active and was last updated in 2015
Random Military Term
Term of the Day
More Military Terms:
Or: View Dictionary of Military Acronyms


text: When news broke about a young whale found dead with nearly 90 pounds of plastic in its stomach, PPMG moved quickly to leverage the news cycle for client Oceanic Society. We developed a quick POV and secured interviews for Oceanic Society spokesperson Lindsay Mosher with the Washington Post and Live Science, among others, focusing on ways individuals could reduce their plastic consumption and embrace "blue habits" to help the plastic pollution epidemic. PPMG caught the wave of news and the results secured 73 articles with 911,784,270 impressions within 48 hours.


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Jacksonville Truck Accident Proves Fatal
A major Jacksonville highway was shut down because of a fiery, fatal crash between a tractor-trailer and an SUV. According to an ABC News report, the fatal Jacksonville truck accident occurred on the Buckman Bridge segment of Interstate 295 south of Jacksonville. Officials say a tractor-trailer slammed into the SUV from behind resulting in the death of two adults and two children inside the smaller vehicle. Another SUV was damaged in the crash as well resulting in injuries to three of the occupants. The driver of the truck survived the crash.
Continue reading


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Two hundred years ago, on March 25, 1807, King George III signed a Parliamentary law abolishing the British slave trade. Though slavery itself would not be abolished for another 27 years, it was a key moment in the struggle for freedom, and its anniversary is being loudly celebrated in England and Jamaica this year.
In Canada, however, the reaction so far has been muted. That is a pity, because blacks here played a pivotal role in the move toward abolition when, in 1792, 1,200 of them boarded a ship in Halifax to resettle in Sierra Leone – showing the world what lengths people will go to in order to be free.
After the anti-slave-trade law took effect, the British navy began to patrol African shores to intercept vessels that traded in humans, choking off the supply of men, women and children to Canada, the United States and the Caribbean nations. But in Canada and the rest of the British Empire, slavery itself was not abolished until August 1, 1834. South of our border, slavery officially ended some three decades later, in 1865, with the Thirteenth Amendment of the United States Constitution.
When Canadians think of the abolition of slavery, white heroes are often the first who come to mind.
In Canada, we think of John Graves Simcoe, the first lieutenant-governor of Upper Canada, who attempted in 1793 to introduce a law to end slavery outright. Slave-owning legislators opposed him, however, and Simcoe could obtain no better than compromise legislation that set slaves free at the age of 25 and forbade the further importation of slaves. Quakers too struggled valiantly and peacefully to abolish the slave trade in Britain and slavery in North America. Quakers were among the many who helped black fugitives from the United States flee slavery and come north to what would become Canada. Other whites who fought against slavery included: John Brown, the fiery zealot who rustled up a ragtag group of men to attack a U.S. weapons arsenal in Harpers Ferry, Virginia in 1859, with a view to freeing black men and women and bringing the institution of slavery to its knees; and the British Parliamentarian William Wilberforce, who laboured for decades to persuade his elected peers to oppose the trade in human beings.
But lost in many public discussions of this history are stories of the incredible efforts that blacks played in the push toward abolition.
One of the ways that blacks fought made abolition more palatable was to make slavery more dangerous for the white slave owners. As Toronto writer Afua Cooper has documented thoroughly in her book The Hanging of Angélique, in 1734, a Portuguese-born black slave named Marie Joseph Angélique burned down her owner's home in Montreal, taking out a quarter of the city in the ensuing blaze, only to face barbaric torture and execution. Five years later, 20 black slaves collaborating in the famous Stono Rebellion in South Carolina rose up against their owners, killing some 20 whites before they were finally put down. And in 1791, in the most successful uprising of its kind in the 18th century, the slaves in the French colony of Saint Domingue, now known as Haiti, overthrew their rulers after looting and burning more than a thousand plantations, attacking and murdering whites as they went. Later, these same black rebels resisted military attacks by the British, taking the lives of thousands of British soldiers. But it would be a mistake to think that black resistance to slavery, and black participation in the abolition of the slave trade, was limited to violence.
Like the whites who advanced the cause of abolition in political circles, blacks also used words – and penned their stories – to argue against the sin of slavery. Olaudah Equiano, an African-born man who was enslaved in the Americas for years before obtaining his freedom, became the most famous abolitionist of colour in England when he published his life story, The Interesting Narrative of the Life of Olaudah Equiano, or Gustavus Vassa, the African, in 1789.
One of the most fascinating stories of blacks who resisted slavery and mistreatment involves the black Loyalists of Nova Scotia.
On January 15, 1792, 1,200 Nova Scotian blacks sailed in a flotilla of 15 ships from Halifax to Sierra Leone in West Africa. By moving permanently to the continent from which some of them had been abducted and enslaved decades earlier, these individuals nudged the world one step closer to abolition of the British slave trade in 1807.
The Nova Scotians – described at the time as "adventurers" – sailed in the opposite direction of slaving vessels that were still bringing stolen Africans across the Atlantic Ocean to the Americas.
The Nova Scotians' exodus predated the "back to Africa" preachings of Jamaican Marcus Garvey by more than a century. It took place decades before ex-American slaves founded the west African colony of Liberia. And the Nova Scotians are still remembered in Sierra Leone as being among the modern founders of the country. To this day, there are people in Sierra Leone who trace their lineage to the Nova Scotian settlers.
The blacks had come to Nova Scotia some 10 years earlier as United Empire Loyalists. Many of them had escaped slavery in the Carolinas and Virginia, among other states.
During the American Revolutionary War, the British attempted to swell their ranks against the rebels by encouraging blacks to flee their slave owners and side with the British. If they did, the British promised, they would be protected when the war was over and allowed their freedom.
Thousands of blacks responded, but that promise would soon be broken. Although they had served England as soldiers, labourers, cooks and in other capacities, when the time came to negotiate peace, the British cast the blacks aside. In the treaty they signed with the Americans, the British promised not to make off with "any Negroes or other Property of the American Inhabitants." In Manhattan, the last British stronghold in the American Revolutionary War, the terms of the peace treaty caused widespread despair among blacks who had faithfully served the Loyalist cause. But finally, and miraculously, the British sided with the blacks, and began to remove them in a steady supply of ships from Manhattan in 1783. Some blacks were sent to England, Germany and Quebec, but most ended up going to Nova Scotia.
It wasn't easy to leave. In theory, any black who wished to leave on a ship for the British colonies had to establish that he or she had served behind British lines for at least one year. Those who did leave had their names registered in The Book of Negroes, a British military ledger that ran to some 150 pages and included the names of 3,000 blacks – some free, some indentured and some enslaved, leaving New York as the property of British officers.
The Book of Negroes is a fascinating document, copies of which can be found in the National Archives of Canada, the Nova Scotia Public Archives and the National Archives in the U.K., among other places. It became the first major record of people of African descent in North America. In listing the name, age, physical description and life circumstances of almost every traveller, the carefully handwritten Book of Negroes stands out as a testament to how blacks – by risking everything to try life in a foreign land – struck another blow at the heart of slavery.Disembarking in Nova Scotian towns such as Halifax, Shelburne and Annapolis Royal, they believed that they had come to a promised land of safety and freedom.
Sadly, for many of the early black Loyalists, Nova Scotia turned out to be just as oppressive as the American colonies. The blacks had been promised land, provisions and tools in exchange for having served the British in the Revolutionary War, but most of them received nothing. They thought they could live in equality and freedom in Nova Scotia, but many blacks remained slaves and others were kept in conditions of such poverty that they indentured themselves – becoming virtual slaves again – to ward off starvation and hypothermia.
In Nova Scotia, they also found a land of bitter justice and overt discrimination. Blacks were hanged for trifling offenses such as stealing potatoes. Others were sentenced to whippings and received their lashes in stages at several consecutive street corners, in order to amplify their public humiliation. Public ordinances banned "Negro frolicks" – basically any party at which blacks danced and drank. Slave catchers prowled Nova Scotia and sometimes succeeded in kidnapping black Loyalists and returning them to slavery in the United States or the Caribbean islands.
In the town of Shelburne, which at the time held the largest black community in Nova Scotia, Canada's first race riot erupted in 1784. Black workers were consistently paid less than whites for the same work, but disbanded white soldiers, who themselves faced economic hardship, argued that the blacks were undercutting their wages and rose up against them. Blacks were beaten and powerless to stop the burning of their homes. The rioting lasted for days.
In the midst of this oppressive social climate, it must have seemed a miracle when, in October 1791, a British naval lieutenant arrived in Halifax and advertised free passage for blacks who wanted to create a new colony in Sierra Leone.
Lieutenant John Clarkson was white, 27 years old, and had come at the behest of entrepreneurs and abolitionists in England who were hoping to create a British colony in Sierra Leone that would profit not from the slave trade but from agriculture and trading natural resources.
The British abolitionists had already sent a much smaller group of blacks from London to Sierra Leone five years earlier, but the colony quickly disintegrated. At the very moment they were looking for others to send to Sierra Leone, Thomas Peters, a black man from Annapolis Royal, showed up in England to publicize the plight of black Loyalists in Nova Scotia. .
Peters' timing could not have been better. Shortly after he returned to Nova Scotia, Clarkson followed with the resettlement scheme and about one-third of the entire black population of the area leapt at the opportunity. Clarkson then began the monumental job of bringing the black Loyalists to Halifax, feeding and sheltering them over the winter, while outfitting 15 ships to take them to Sierra Leone.
The 15 ships left Halifax harbour together on that cold January day, but quickly lost track of each other during the two-month ocean crossing. Fever took the lives of 67 travellers enroute, although the food and the sanitary conditions on board the ships were eminently better than the floating hell of slave vessels. Finally, all the ships arrived safely in St. George's Bay, just off the coast of Freetown. The long, arduous and sometimes fatal task of building a colony began.
Many of the same frustrations that had plagued the Nova Scotians in Canada surfaced again in Africa: they did not get the land they were promised, they bickered with authorities about taxation matters, and they felt dominated by white officials administering the Sierra Leone Company, which ran the new colony in Freetown. Still, they were free, and over the decades they and their descendents acquired more independence and autonomy.
The journey east across the Atlantic Ocean stands out as all the more astonishing when one considers that some of the blacks travelling to Africa had actually been born on that continent – abducted in their own homelands by fellow Africans, sold to British or other European slaving operations established in festering "factories" along the coast of Africa, and then shipped across the ocean only to spill their blood, sweat and tears to develop the lands that are now known as Canada, the United States and the Caribbean nations.
This back-to-Africa migration – the first of its kind in history involving more than a thousand people – was entirely voluntary. Funded by the British government, its price tag exceeded Nova Scotia's annual budget.
The aim of the venture's British supporters was to prove that Africa could serve the British Empire in other ways than as a slave-exporting continent. They wanted to demonstrate that the British could use Africa as a base to export natural resources. In the end, this led to its own forms of exploitation, but in the shorter term, it did help to persuade Britons to think of Africa in new terms.
This little-known migration, launched from Canadian shores, was pivotal to the cause of abolition. And at its heart were 1,200 blacks determined to assert their freedom and to ensure that nobody could threaten to enslave them again. Their devotion to liberty should be recognized and celebrated.


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Class and Religious Identity: The Rhenish Center Party Inwilhelmine Germany
Class and Religious Identity: The Rhenish Center Party Inwilhelmine Germany
A religious animosity divided the state created by Prussia's armies and its most brilliant statesman, Otto von Bismarck. Germany's Catholic minority was frequently viewed by the Protestant majority as backward; by the Protestant clergy as heretical; by court and government officials as seditious and tied to a foreign power. Catholics viewed Bismarck's creation with equal misgivings; they complained that the institutions which controlled the state—court, army and government—systematically discriminated against them. To defend the rights of their co-religionists, Catholic politicians created a political party, the Center. Ironically most post‐ Bismarckian governments could only achieve a majority in the Reichstag with Center support. While Catholics were engaged in their struggle against Protestant adversaries, they also had to find ways to adapt to the changes that industrialization had brought to their lives. This study examines how Catholics of the Rhineland dealt with these threats to their religious and political culture.
Chronologically, this study will range from the early 1890s to the outbreak of the First World War. The early 1890s are a suitable point of departure, for they mark a turning point in the history of German political Catholicism. The church-state confrontation of the 1870s, known as the Kulturkampf, had died down by that time allowing the Center Party to pay attention to pressing political, social and economic questions rather than to fighting the state. Wilhelm II ascended the throne in 1888, and Bismarck's chancellorship survived the young Kaiser's investiture by less than two years. Lastly, the Center itself underwent a generational change after the death of Ludwig Windthorst, the party's legendary leader and perhaps Bismarck's staunchest and most skilled opponent in the Reichstag. As younger men succeeded as leaders, the party gradually moved away from the traditional politics of notables to a modern organization that took account of the large middle-class following of the Center and the political demands of Catholic workers. The year 1914 seems to be a . . .


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Darling bikini with removable tie front. Wide shoulder straps and scooped back. Bottoms provide moderate coverage with the ability to show more if worn higher on the hips. Smocking adds comfort and texture. Made from a stretchy, body-hugging, seersucker-like lycra mix. One size fits most.
88% Nylon 12% Lycra Elastane.
Made in the UK.
Hand wash only.
Model is 5'10, size 2 wearing size O/S swimsuit.
Measurements coming soon.


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By Dawn Hammer Tabata
Back to school. Back to nature, I say.
It's that time of the year again. Children are back at school busy with classes, homework, sports, etc. Parents are making sure their children are getting what they need to become healthy, successful adults. Scholastics, clubs, and organized sports probably take top priority.
The benefits of exercise or physical activity of any kind has been fully researched and proven effective for maintaining a healthy lifestyle, but what about the benefits of nature? Does the experience of nature have a supplemental beneficial effect on young people beyond the benefits of physical activity?
Many of us remember playing outside until the street lights came on or your mom called you in for dinner. I remember hours exploring the farm fields and woods' edge near my home. Most of today's children probably won't have this experience. The average American boy or girl spends as few as thirty minutes of unstructured outdoor play each day, and more than seven hours a day in front of an electronic screen (National Wildlife Federation).
Scientific research shows that outdoor play, in addition to increasing fitness levels, also reduces stress, anxiety, and depression in children while at the same time increasing positive social interactions and critical thinking skills. Exposure to nature can reduce stress in children by as much as 28% while also improving mood and self-esteem (NEEF USA). Even a 20-minute walk in nature can help children with ADHD symptoms have better concentration as well as improved behavior. While exercise in general has beneficial effects on children, many studies indicate that exercise in a natural setting increases benefits mentally, psychologically, and even physically (Science Daily).
I was hiking along with one of our summer camp groups a few weeks ago when I overheard one of the participants comment, "boy, there sure is a lot of nature around here." Grange Insurance Audubon Center encourages you to experience "a lot of nature around here" as often as you can. We offer many classes, programs, and hikes for children of all ages (adults, too) throughout the year, and close to home.
Go back to school, but don't forget to come back to nature!


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Last night's Agents of S.H.I.E.L.D. seems a good chance to get to know the characters better. The plot, such as it was, was entertaining enough. But it wasn't complex and overly twisty.
Sometimes you need TV like that.
And to show just how uncomplicated the plot was, they started with the Agents' plane in the sky getting a hole blown in the side just after Leader announced that things were fine.
Really? You have to do that old trick? So often, I find that covers a lack of suspenseful story. It's a hook when nothing in the first half of the episode will work to get you into the story. As much as I loved Alias, I got so tired of that devise during season 3. I still haven't recovered. I didn't help that Covert Affairs pulled a 10 week teaser like that in the recent first half of their season. And they certainly didn't need it.
It's an overdone device. Let it go already!
I should point out that, yes, I know it can be done well. Babylon 5, my favorite TV show (still) did it well with a couple of characters' relationship. But it wasn't the main focus of the show.
Okay, rant over. Moving on.
We start with Sky coming on board the plane as they are about to go wheels up for a mission in
Peru. Something unknown has been found in an
ancient temple. The last time something
unknown was found was when they found Thor's hammer.
The crew lands, but as the scientists are looking at this device, two groups of locals show up trying to shoot our team. One of them includes Phil Coulson's former lover, so we team up with them and all get on the plane.
Once back there, we find out the device is a weapon. An unstable weapon, as the scientists tried to tell everyone before it was taken on a wild ride back to the plane.
But that's also when the former lover and her men turn on our heroes. She wants this weapon and she wants it all to herself.
Fortunately, everyone but Phil is imprisoned together, and they are able to work together to find a way to get free and turn the tables on the crew that took over their plane. Unfortunately, that involves blowing a hole in the side of the plane. But they land and save the day.
The episode ends with the weapon being shot up into space and the former lover in custody, although it's expected that
Peru will bargain
to get her back.
As our crew watches the rocket, Sky gets a text from someone in the Rising Tide movement. So I guess she isn't the only member. But what is it she agreed to? My guess is she'll turn on S.H.I.E.L.D. at some point in the second half of the season.
Meanwhile, we also learned that whatever Melinda May used to do, she got the nickname "The Calvary." It's a nickname she now hates. And, after working together so well to take their plane back, Melinda and Grant's initial objections to Sky are gone or at least lessened. Wait until she turns on them.
And I do hope you listened to ABC and stuck around for the final scene. Samuel L. Jackson made a funny cameo as Nick Fury chewing out Phil for already wrecking the plane.
Don't let the size of the recap fool you. I did enjoy the episode. I just don't feel like there is a lot of depth or meat to focus on here.
However, did I miss something? Let me know in the comments.


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Moving through Autumn and into the build up to Christmas, chai spice is my favourite flavour. Whether it's a chai smoothie, porridge, latte, cake or granola, I'm there! And to make it just that little bit better, add some chocolate in. You can probably tell by now, I'm obsessed with nut butter, I just can… Continue reading Choc Chai Nutbutter
Tag: nuts
'Deepest Roast' Nut Butter
This recipe actually came around by accident when I thought I had burnt hazelnuts whilst making my chocolate hazelnut butter, it turns out almost burnt nuts produce a decadent and delightful nut butter! You will need quite a powerful blender for this, sometimes half way through blending, your machine may need a rest to prevent… Continue reading 'Deepest Roast' Nut Butter
Homemade Chocolate Hazelnut Spread (Three ways)
As a Nutella lover from an early age, only recently have I realised that I can recreate my favourite childhood food (Yes I ate it by the spoonful) in a healthier way. No additives, nor added sugar, just sticky, nutty, chocolate-y galore. And yes, it's better than Nutella, if you like extra nuts that is! Not… Continue reading Homemade Chocolate Hazelnut Spread (Three ways)


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Projects per year
Personal profile
Research interests
I work at the intersection of sociology, politics and political economy to investigate how social and political order is imgained, produced and contested in a range of empirical contexts.
There are three strands to my current work. First, theorising governance and governance analysis as regimes of governing practices. Second, I have a new strand of research investigating the socio-political implications of emerging digital statehoods and the use of digital and algorithmic tools as governing practices. Third, I continue my long-standing work on how governance of migration shapes social relations and political economy in/beyond Europe. I have particular methodological expertise in qualitative comparative research.
I have supervised and co-supervised more than 10 PhD students, and have twice been nominated for a university prize for excellence in doctoral supervision. I am always keen to hear from committed students working in my broad research areas.
I am the Director of Studies for the MSc in Public Policy, our parti-time, distance-learning/resdiential programme for policy professionals.
My other roles include editorial board membership of the Italian Journal of Social Policy, and I have also been Editor and Co-editor of the Journal of European Social Policy.
Research interests
- Governance analysis: society, politics and policy
- Digital statehoods: AI and automation as governing practices and their implications
- Migration governance and the production of socio-political order(s) in the EU and beyond.
Recent projects
- Mobile Welfare in a Transnational Europe: An analysis of Portability Regimes of Social Security Rights (TRANSWEL)
- Fellowship supervisor to Dr Thanos Maroukis on Jobs-to-Rent.
Recent blog posts
Current research students
- Nadege Ade: Governing healthcare: introducing universal healthcare system in Burkina Faso
- Jorge Garcia Castro: Public sector reform programmes: translation of a transnational policy agenda in Mexico and Spain
- King Papież: Transnational belonging and social rights of Polish migrants in the UK
- Anna Kvittingen: 'vulnerability' and '(in)visibility of refugees in Jordan
- Bo Donoghue: The false equivalency: liberty and domination in gig economy work
- Pamela Bernales-Baksai: Policy architectures, social practices: comparing health systems in Chile, Bolivia and Uruguay
- Lydia Medland: Fair food? View from a global food system enclave
- Alfred Carr: Migrants' experience of mental health services: Ghanaians in Calgary
Recent research students
- Jenny Harlock: Governance of voluntary sector in social care
- Regine Paul: Political economy of border drawing in labour migration
- Thomais Massala: Financialisation and UK pension system governance
- Fiona Morgan: Governance of informal care as a social risk in England
- Berenice Scandone: British-born Bangladeshi women in higher education: intersectional experiences and identities
Education/Academic qualification
History, Bachelor of Arts, University of Oxford
Social Policy , Master of Arts, University of York
Social Policy , Doctor of Philosophy, University of York
Fingerprint Dive into the research topics where Emma Carmel is active. These topic labels come from the works of this person. Together they form a unique fingerprint.
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Recent external collaboration on country level. Dive into details by clicking on the dots.
- 2 Finished
Future Research Leaders Incubator Scheme - Berenice Scandone
1/03/16 → 30/06/16
Project: Research-related funding
Marie Curie IEF - Thanos Maroukis
Carmel, E. & Maroukis, T.
1/10/12 → 30/09/14
Project: EU Commission
Research Output
Research Output per year
Beyond the Rights-Bearing EU Mobile Citizen: Governing Inequality and Privilege in European Union Social SecurityCarmel, E., Sojka, B. & Papiez, K., 2019, Boundaries of European Social Citizenship. Amelina, A., Carmel, E., Runfors, A. & Scheibelhofer, E. (eds.). London: Routledge
Research output: Chapter in Book/Report/Conference proceeding › Chapter
Boundaries of European Social Citizenship: EU Citizens' Transnational Social Security in Regulations, Discourses and ExperiencesCarmel, E. (ed.), Amelina, A. (ed.), Runfors, A. (ed.) & Scheibelhofer, E. (ed.), 2019, London: Routledge. 230 p.
Research output: Book/Report › Scholarly edition
Epistemological orientations and analytical frameworkCarmel, E., 2019, Governance Analysis: critical enquiry at the intersection of politics, policy and society. Cheltenham: Edward Elgar Publishing Ltd
Research output: Chapter in Book/Report/Conference proceeding › Chapter
Governance Analysis: critical enquiry at the intersection of society, politics and public policyCarmel, E. (ed.), 2019, Edward Elgar Publishing Ltd. 224 p.
Research output: Book/Report › Scholarly edition
Governance analysis at the intersection of politics, policy and societyCarmel, E., 2019, Governance analysis at the intersection of politics, policy and society. Carmel, E. (ed.). Cheltenham: Edward Elgar Publishing Ltd
Research output: Chapter in Book/Report/Conference proceeding › Chapter
An Analysis Of The Treatment Of Informal Care As A Social Risk in EnglandAuthor: Morgan, F., 2 Jul 2015
Supervisor: Carmel, E. (Supervisor) & Johnson, M. (Supervisor)
Student thesis: Doctoral Thesis › PhD
British-born Bangladeshi Women in Higher Education: Intersectional Experiences and IdentitiesAuthor: Scandone, B., 13 Feb 2018
Supervisor: Carmel, E. (Supervisor) & Ingram, N. (External person) (Supervisor)
Student thesis: Doctoral Thesis › PhD
Development of Health Policymaking Governance Guidance Tool (HP-GGT)Author: Hamra, R., 6 Oct 2018
Supervisor: Carmel, E. (Supervisor) & Ammar, W. (External person) (Advisor)
Student thesis: Doctoral Thesis › Doctor of Health (DHealth)
Labour Migration Management as Multidimensional Border-drawing: A Comparative Interpretive Policy Analysis in the EUAuthor: Paul, R., 5 Jul 2012
Supervisor: Carmel, E. (Supervisor) & Milner, S. (Supervisor)
Student thesis: Doctoral Thesis › PhD
Processes of Governing in European NetworksAuthor: Kan, H., 1 Sep 2010
Supervisor: Papadopoulos, T. (Supervisor), Room, G. (Supervisor) & Carmel, E. (Supervisor)
Student thesis: Doctoral Thesis › PhD


text:
New BET reporter Shorty da Prince, and HOT 107.5's very own, hosted BET's Red Carpet coverage for the 54th Annual GRAMMY Awards at the Staples Center in California. With Shorty's career as an artist launching over a decade ago, hosting such an event was surreal. After Sway of MTV kicked some knowledge Shorty's way, he took to twitter to express his gratitude:
GOD is so great man!!! Today was…… I really have no words for it… Can't wait til I'm on the other side tho… We almost there!! #LOVE
The coverage is set to air on BET Monday following the Grammy Awards.
Check out Shorty's twit pics from the Red Carpet below! Looks like he had the time of his life!
Jennifer Hudson Tribute To Whitney Houston [VIDEO]
Chaka Khan Pulled Out of "Inappropriate" Whitney Tribute
VIDEO: Paigion And Shorty da Prince Co-Host BET's 106 And Park!
Check out Shorty's latest mixtape 'The Remixtape' hosted by HOT 107.5′s own DJBJ.


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Skeyos launches industry-first 'buy-or-repair' parts comparison
As part of our mission to offer better transparency, dependability and simplicity to the MRO industry when buying aircraft component services, we announced the launch of our ground-breaking new parts module on June 4th, 2019. Introducing an industry-first comparison functionality for repairs and parts, the module revolutionises the way 'buy or repair' decisions for aircraft components are made.
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Mount Columbia
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Mount Columbia Trip Report (#9794)
- Signed By: Ben of Vancouver, BC, Canada
- Date submitted: September 05, 2003
Cool and sunny day. Our descent was a little tricky as we didn't follow the ridge far enough and hit some tough spots.


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What does it mean to be "well?"
That definition can vary from student to student, and oftentimes extends beyond just physical fitness.
The National Wellness Institute promotes six dimensions of wellness, including "emotional, occupational, physical, social, intellectual and spiritual."
According to the organization, "addressing all six dimensions of wellness in our lives builds a holistic sense of wellness and fulfillment."
Utah Yoga & Wellness in Midway, Utah, has a similar view, recognizing that in order for its students to be healthy, they must be mentally and emotionally sound, as well as physically and nutritionally fit.
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For studios looking to offer nutrition solutions of their own, Jones suggested partnering with a certified nutritionist like Charles, who can offer tailored nutrition guidance versus a one-size-fits-all approach.
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"It's a unique opportunity for the community," said Jones. "It's not about business as much as about what people need. It really is a community-based thing."


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KRON apologizes for private Puppetry
The title Puppetry of the Penis already suggests that something other
than Senor Wences' thumb would be showcased, but KRON-TV staff didn't expect the
kind of brief, no-briefs shot that made it on the air.
The shot of the "genital origami" practitioners -- in a segment for KRON's
morning news -- was supposed to have been blocked to avoid the exposure of cast
member Doug Friend, but the choreography proved flawed. An on-air apology was
followed by an off-air apology.
"Even though the nature of live television lends itself to surprises and
unexpected activities," the station said, "we take full responsibility and
assure viewers that this will not happen again."
Clearly, station manager Craig Marrs said, more caution could have been
exercised, but it basically was a case of bad choreography, although station
officials would not confirm or deny local reports that some staffers had been
Following the exposure, the performers were chastised by news managers and
escorted out, sources said.


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Chatswood is one of the major towns in the lower North Shore of Sydney for the purpose of residence as well as business. The city is well known for its business associations and engagements. With entitlement, comes responsibilities and that is why the competition, professionalism and personnel resources are high in Chatswood. The city has exceptionally great properties which are not only beautiful and homely but convenient and cost-effective as well. Investment in land is completely worthy in this city. There could be numerous forms and types of dwellings. One of such major and important investments is a secondary dwelling.
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18 Jan
Posted January 18, 2013 by scoop2go in Chapter 27, Imperialism, Military. 2 Comments
Posted by Kilgore Trout on January 23, 2013 at 1:13 pm
That third link is so biased
Posted by Tom S. on January 23, 2013 at 11:11 pm
How so?
Comments are closed.
Almost exactly NINE MONTHS from the first day of school– just like a pregnancy! Except this one will absolutely end on time, no matter what.
"Omne solum forti patria est." (The brave find a home in every land.)–Ovid
"History will be kind to me for I intend to write it." — Winston Churchill
"Those who do not remember the past are condemned to repeat it."- George Santayana


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Welcome to my blog! I am Basil René, a professionally trained chef with thirty two years experience.
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I would also love to hear what you want to read about. Be sure to leave a comment below.
Subscribe to this blog and never miss a post as you join me on this journey to share my years of experience and new experiences.


text: GW2League Teamspeak Ready to Launch! Hello everyone! After over a year of working, blowing up Teamspeak server after Teamspeak server, and waiting on Teamspeak Systems GmbH to get off their butts, we've finally set up the GW2League teamspeak! Community Assembly - Saturday, February 29th, 16:00 CET! Before we get started with this announcement, - reminder that this announcement, and some other stuff will be discussed on the Community Assembly, happening on Saturday, February 29th, at 16:00 CET! We'll be di Lelling February 16 0


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Atoms of recognition in human and computer vision
Edited by Michael E. Goldberg, Columbia University College of Physicians, New York, NY, and approved January 11, 2016 (received for review July 8, 2015)
Discovering the visual features and representations used by the brain to recognize objects is a central problem in the study of vision. Recent successes in computational models of visual recognition naturally raise the question: Do computer systems and the human brain use similar or different computations? We show by combining a novel method (minimal images) and simulations that the human recognition system uses features and learning processes, which are critical for recognition, but are not used by current models. The study uses a "phase transition" phenomenon in minimal images, in which minor changes to the image have a drastic effect on its recognition. The results show fundamental limitations of current approaches and suggest directions to produce more realistic and better-performing models.
Discovering the visual features and representations used by the brain to recognize objects is a central problem in the study of vision. Recently, neural network models of visual object recognition, including biological and deep network models, have shown remarkable progress and have begun to rival human performance in some challenging tasks. These models are trained on image examples and learn to extract features and representations and to use them for categorization. It remains unclear, however, whether the representations and learning processes discovered by current models are similar to those used by the human visual system. Here we show, by introducing and using minimal recognizable images, that the human visual system uses features and processes that are not used by current models and that are critical for recognition. We found by psychophysical studies that at the level of minimal recognizable images a minute change in the image can have a drastic effect on recognition, thus identifying features that are critical for the task. Simulations then showed that current models cannot explain this sensitivity to precise feature configurations and, more generally, do not learn to recognize minimal images at a human level. The role of the features shown here is revealed uniquely at the minimal level, where the contribution of each feature is essential. A full understanding of the learning and use of such features will extend our understanding of visual recognition and its cortical mechanisms and will enhance the capacity of computational models to learn from visual experience and to deal with recognition and detailed image interpretation.
The human visual system makes highly effective use of limited information (1, 2). As shown below (Fig. 1 and Figs. S1 and S2), it can recognize consistently subconfigurations that are severely reduced in size or resolution. Effective recognition of reduced configurations is desirable for dealing with image variability: Images of a given category are highly variable, making recognition difficult, but this variability is reduced at the level of recognizable but minimal subconfigurations (Fig. 1B). Minimal recognizable configurations (MIRCs) are useful for effective recognition, but, as shown below, they also are computationally challenging because each MIRC is nonredundant and therefore requires the effective use of all available information. We use them here as sensitive tools to identify fundamental limitations of existing models of visual recognition and directions for essential extensions.
A MIRC is defined as an image patch that can be reliably recognized by human observers and which is minimal in that further reduction in either size or resolution makes the patch unrecognizable (below criterion) (Methods). To discover MIRCs, we conducted a large-scale psychophysical experiment for classification. We started from 10 greyscale images, each showing an object from a different class (Fig. S3), and tested a large hierarchy of patches at different positions and decreasing size and resolution. Each patch in this hierarchy has five descendants, obtained by either cropping the image or reducing its resolution (Fig. 2). If an image patch was recognizable, we continued to test the recognition of its descendants by additional observers. A recognizable patch in this hierarchy is identified as a MIRC if none of its five descendants reaches a recognition criterion (50% recognition; results are insensitive to criterion) (Methods and Fig. S4). Each human subject viewed a single patch from each image with unlimited viewing time and was not tested again. Testing was conducted online using the Amazon Mechanical Turk (MTurk) (3, 4) with about 14,000 subjects viewing 3,553 different patches combined with controls for consistency and presentation size (Methods). The size of the patches was measured in image samples, i.e., the number of samples required to represent the image without redundancy [twice the image frequency cutoff (5)]. For presentation to subjects, all patches were scaled to 100 × 100 pixels by standard interpolation; this scaling increases the size of the presented image smoothly without adding or losing information.
Discovered MIRCs.
Each of the 10 original images was covered by multiple MIRCs (15.1 ± 7.6 per image, excluding highly overlapping MIRCs) (Methods) at different positions and sizes (Fig. 3 and Figs. S1 and S2). The resolution (measured in image samples) was small (14.92 ± 5.2 samples) (Fig. 3A), with some correlation (0.46) between resolution and size (the fraction of the object covered). Because each MIRC is recognizable on its own, this coverage provides robustness to occlusion and distortions at the object level, because some MIRCs may be occluded and the overall object may distort and still be recognized by a subset of recognizable MIRCs.
The transition in recognition rate from a MIRC image to a nonrecognizable descendant (termed a "sub-MIRC") is typically sharp: A surprisingly small change at the MIRC level can make it unrecognizable (Fig. 4). The drop in recognition rate was quantified by measuring a recognition gradient, defined as the maximal difference in recognition rate between the MIRC and its five descendants. The average gradient was 0.57 ± 0.11, indicating that much of the drop from full to no recognition occurs for a small change at the MIRC level (the MIRC itself or one level above, where the gradient also was found to be high). The examples in Fig. 4 illustrate how small changes at the MIRC level can have a dramatic effect on recognition rates. These changes disrupt visual features to which the recognition system is sensitive (6⇓⇓–9); these features are present in the MIRCs but not in the sub-MIRCs. Crucially, the role of these features is revealed uniquely at the MIRC level, because information is more redundant in the full-object image, and a similar loss of features will have a small effect. By comparing recognition rates of models at the MIRC and sub-MIRC levels, we were able to test computationally whether current models of human and computer vision extract and use similar visual features and to test the ability of recognition models to recognize minimal images at a human level. The models in our testing included HMAX (10), a high-performing biological model of the primate ventral stream, along with four state-of-the-art computer vision models: (i) the Deformable Part Model (DPM) (11); (ii) support vector machines (SVM) applied to histograms of gradients (HOG) representations (12); (iii) extended Bag-of-Words (BOW) (13, 14); and (iv) deep convolutional networks (Methods) (15). All are among the top-performing schemes in standard evaluations (16).
Training Models on Full-Object Images.
We first tested the models after training with full-object images. Each of the classification schemes was trained by a set of class and nonclass images to produce a classifier that then could be applied to novel test images. For each of the 10 objects in the original images we used 60 class images and an average of 727,000 nonclass images (Methods). Results did not change by increasing the number of training class images to 472 (Methods and SI Methods). The class examples showed full-object images similar in shape and viewing direction to the stimuli in the psychophysical test (Fig. S5).
After training, all classifiers showed good classification results when applied to novel full-object images, as is consistent with the reported results for these classifiers [average precision (AP) = 0.84 ± 0.19 across classes]. The trained classifiers then were tested on MIRC and sub-MIRC images from the human testing, with the image patch shown in its original location and size and surrounded by an average gray image. The first objective was to test whether the sharp transition shown in human recognition between images at the MIRC level and their descendant sub-MIRCs is reproduced by any of the models (the accuracy of MIRC detection is discussed separately below). An average of 10 MIRC level patches per class and 16 of their similar sub-MIRCs were selected for testing, together with 246,000 nonclass patches. These MIRCs represent about 62% of the total number of MIRCs and were selected to have human recognition rates above 65% for MIRCs and below 20% for sub-MIRCs (Methods). To test the recognition gap, we set the acceptance threshold of the classifier to match the average human recognition rate for the class (e.g., 81% for the MIRC-level patches from the original image of an eye) (Methods and Fig. S6) and then compared the percentage of MIRCs vs. sub-MIRCs that exceeded the classifier's acceptance threshold (results were insensitive to threshold setting over the range of recognition rates 0.5–0.9).
We computed the gap between MIRC and sub-MIRC recognition rates for the 10 classes and the different models and compared the gaps in the models' and human recognition rates. None of the models came close to replicating the large drop shown in human recognition (average gap 0.14 ± 0.24 for models vs. 0.71 ± 0.05 for humans) (Fig. S7A). The difference between the models' and human gaps was highly significant for all computer-version models (P < 1.64 × 10−4 for all classifiers, n = 10 classes, df = 9, average 16 pairs per class, one-tailed paired t test). HMAX (10) showed similar results (gap 0.21 ± 0.23). The gap is small because, for the models, the representations of MIRCs and sub-MIRCs are closely similar, and consequently the recognition scores of MIRCs and sub-MIRCs are not well separated.
It should be noted that recognition rates by themselves do not directly reflect the accuracy of the learned classifier: A classifier can recognize a large fraction of MIRC and sub-MIRC examples by setting a low acceptance threshold, but doing so will result in the erroneous acceptance of nonclass images. In all models, the accuracy of MIRC recognition (AP 0.07 ± 0.10) (Fig. S7B) was low compared with the recognition of full objects (AP 0.84 ± 0.19) and was still lower for sub-MIRCs (0.02 ± 0.05). At these low MIRC recognition rates the system will be hampered by a large number of false detections.
A conceivable possibility is that the performance of model networks applied to minimal images could be improved to the human level by increasing the size of the model network or the number of explicitly or implicitly labeled training data. Our tests suggest that although these possibilities cannot be ruled out, they appear unlikely to be sufficient. In terms of network size, doubling the number of levels (see ref. 17 vs. ref. 18) did not improve MIRC recognition performance. Regarding training examples, our testing included two network models (17, 18) that were trained previously on 1.2 million examples from 1,000 categories, including 7 of our 10 classes, but the recognition gap and accuracy of these models applied to MIRC images were similar to those in the other models.
We considered the possibility that the models are trained for a binary decision, class vs. nonclass, whereas humans recognize multiple classes simultaneously, but we found that the gap is similar and somewhat smaller for multiclass recognition (Methods and SI Methods). We also examined responses of intermediate units in the network models and found that results for the best-performing intermediate layers were similar to the results of the network's standard top-level output (Methods).
Training Models on Image Patches.
In a further test we simplified the learning task by training the models directly with images at the MIRC level rather than with full-object images. Class examples were taken from the same class images used in full-object learning but using local regions at the true MIRC locations and approximate scale (average 46 examples per class) that had been verified empirically to be recognizable on their own (Methods and Fig. S8). After training, the models' accuracy in recognizing MIRC images was significantly higher than in learning from full-object images but still was low in absolute terms and in comparison with human recognition (AP 0.74 ± 0.21 for training on patches vs. 0.07 ± 0.10 for training on full-object images) (SI Methods, Training Object on Image Patches and SI Methods, Human Binary Classification Test). The gap in recognition between MIRC and sub-MIRC images remained low (0.20 ± 0.15 averaged over pairs and classifiers) and was significantly lower than the human gap for all classifiers (P < 1.87 × 10−4 for all classifiers, n = 10 classes, df = 9, one-tailed paired t test) (Methods and SI Methods).
Detailed Internal Interpretation.
An additional limitation of current modeling compared with human vision is the ability to perform a detailed internal interpretation of MIRC images. Although MIRCs are "atomic" in the sense that their partial images become unrecognizable, our tests showed that humans can consistently recognize multiple components internal to the MIRC (Methods and Fig. 3C). Such internal interpretation is beyond the capacities of current neural network models, and it can contribute to accurate recognition, because a false detection could be rejected if it does not have the expected internal interpretation.
The results indicate that the human visual system uses features and processes that current models do not. As a result, humans are better at recognizing minimal images, and they exhibit a sharp drop in recognition at the MIRC level, which is not replicated in models. The sharp drop at the MIRC level also suggests that different human observers share similar visual representations, because the transitions occur for the same images, regardless of individual visual experience. An interesting open question is whether the additional features and processes are used in the visual system as a part of the cortical feed-forward process (19) or by a top-down process (20⇓⇓–23), which currently is missing from the purely feed-forward computational models.
We hypothesize based on initial computational modeling that top-down processes are likely to be involved. The reason is that detailed interpretation appears to require features and interrelations that are relatively complex and are class-specific, in the sense that their presence depends on a specific class and location (24). This application of top-down processes naturally divides the recognition process into two main stages: The first leads to the initial activation of class candidates, which is incomplete and with limited accuracy. The activated representations then trigger the application of class-specific interpretation and validation processes, which recover richer and more accurate interpretation of the visible scene.
A further study of the extraction and use of such features by the brain, combining physiological recordings and modeling, will extend our understanding of visual recognition and improve the capacity of computational models to deal with recognition and detailed image interpretation.
Data for MIRC Discovery.
A set of 10 images was used to discover MIRCs in the psychophysics experiment. These images of objects and object parts (one image from each of 10 classes) were used to generate the stimuli for the human tests (Fig. S3). Each image was of size 50 × 50 image samples (cutoff frequency of 25 cycles per image).
Data for Training and Testing on Full-Object Images.
A set of 600 images was used for training models on full-object images. For each of the 10 images in the psychophysical experiment, 60 training class images were obtained (from Google images, Flickr) by selecting similar images as measured by their HOG (12) representations; examples are given in Fig. S5. The images were of full objects (e.g., the side view of a car rather than the door only). These images provided positive class examples on which the classifiers were trained, using 30–50 images for training; the rest of the images were used for testing. (Different training/testing splits yielded similar results.) We also tested the effect of increasing the number of positive examples to 472 (split into 342 training and 130 testing) on three classes (horse, bicycle, and airplane) for which large datasets are available in PASCAL (16) and ImageNet (25). For the convolutional neural network (CNN) multiclass model used (15), the number of training images was 1.2 million from 1,000 categories, including 7 of the 10 classes used in our experiment.
To introduce some size variations, two sizes differing by 20% were used for each image. The size of the full-object images was scaled so that the part used in the human experiment (e.g., the car door) was 50 × 50 image samples (with 20% variation). For use in the different classifiers, the images were interpolated to match the format used by the specific implementations [e.g., 227 × 227 for regions with CNN (RCNN)] (15). The negative images were taken from PASCAL VOC 2011 (host.robots.ox.ac.uk/pascal/VOC/voc2011/index.html), an average of 727,440 nonclass image regions per class extracted from 2,260 images used in training and 246,000 image regions extracted from a different set of 2,260 images used for testing. The number of nonclass images is larger than the class images used in training and testing, because this difference is also common under natural viewing conditions of class and nonclass images.
Data for Training and Testing on Image Patches.
The image patches used for training and testing were taken from the same 600 images used in full-object image training, but local regions at the true location and size of MIRCs and sub-MIRCs (called the "siblings dataset") (Fig. S8) were used. Patches were scaled to a common size for each of the classifiers. An average of 46 image patches from each class (23 MIRCs and 23 sub-MIRC siblings) were used as positive class examples, together with a pool of 1,734,000 random nonclass patches of similar sizes taken from 2,260 nonclass images. Negative nonclass images during testing were 225,000 random patches from another set of 2,260 images.
Model Versions and Parameters.
The versions and parameters of the four classification models used were as follows. The HOG (12) model used the implementation of VLFeat version 0.9.17 (www.vlfeat.org/), an open and portable library of computer vision algorithms, using cell size 8. For BOW we used the selective search method (26) using the implementation of VLFeat with an encoding of VLAD (vector of locally aggregated descriptors) (14, 27), a dictionary of size 20, a 3 × 3 grid division, and dense SIFT (28) descriptor. DPM (11) used latest version (release 5, www.cs.berkeley.edu/∼rbg/latent/) with a single mode. For RCNN we used a pretrained network (15), which uses the last feature layer of the deep network trained on ImageNet (17) as a descriptor. Additional deep-network models tested were a model developed for recognizing small (32 × 32) images (29), and Very Deep Convolutional Network (18), which was adapted for recognizing small images. HMAX (10) used the implementation of Cortical Network Simulator (CNS) (30) with six scales, a buffer size of 640 × 640, and a base size of 384 × 384.
MIRCs Discovery Experiment.
This psychophysics experiment identified MIRCs within the original 10 images at different sizes and resolutions (by steps of 20%). At each trial, a single image patch from each of the 10 images, starting with the full-object image, was presented to observers. If a patch was recognizable, five descendants were presented to additional observers; four of the descendants were obtained by cropping (by 20%) at one corner, and one was a reduced resolution of the full patch. For instance, the 50 × 50 original image produced four cropped images of size 40 × 40 samples, together with a 40 × 40 reduced-resolution copy of the original (Fig. 2). For presentation, all patches were rescaled to 100 × 100 pixels by image interpolation so that the size of the presented image was increased without the addition or loss of information. A search algorithm was used to accelerate the search, based on the following monotonicity assumption: If a patch P is recognizable, then larger patches or P at a higher resolution will also be recognized; similarly, if P is not recognized, then a cropped or reduced resolution version also will be unrecognized.
A recognizable patch was identified as a MIRC (Fig. 2 and Fig. S4) if none of its five descendants reached a recognition criterion of 50%. (The acceptance threshold has only a small effect on the final MIRCs because of the sharp gradient in recognition rate at the MIRC level.) Each subject viewed a single patch from each image and was not tested again. The full procedure required a large number of subjects (a total of 14,008 different subjects; average age 31.5 y; 52% males). Testing was conducted online using the Amazon MTurk platform (3, 4). Each subject viewed a single patch from each of the 10 original images (i.e., class images) and one "catch" image (a highly recognizable image for control purposes, as explained below). Subjects were given the following instructions: "Below are 11 images of objects and object parts. For each image type the name of the object or part in the image. If you do not recognize anything type 'none'." Presentation time was not limited, and the subject responded by typing the labels. All experiments and procedures were approved by the institutional review boards of the Weizmann Institute of Science, Rehovot, Israel. All participants gave informed consent before starting the experiments.
In comparative studies MTurk has been shown to produce reliable repeatable behavior data, and many classic findings in cognitive psychology have been replicated using data collected online (4). The testing was accompanied by the following controls. To verify comprehension (4), each test included a highly recognizable image; responses were rejected if this catch image was not correctly recognized (rejection rate <1%). We tested the consistency of the responses by dividing the responses of 30 subjects for each of 1,419 image patches into two groups of 15 workers per group and compared responses across groups. Correlation was 0.91, and the difference was not significant (n = 1,419, P = 0.29, two-tailed paired t test), showing that the procedure yields consistent recognition rates. A laboratory test under controlled conditions replicated the recognition results obtained in the online study: Recognition rates for 20 MIRCs/sub-MIRCs in the online and laboratory studies had correlation of 0.84, and all MIRC/sub-MIRC pairs that were statistically different in the online study were also statistically different in the laboratory study. Because viewing size cannot be accurately controlled in the online trials, we verified in a laboratory experiment that recognition rates do not change significantly over 1–4° of visual angle.
Subjects were excluded from the analysis if they failed to label all 10 class image patches or failed to label the catch image correctly (failure rate, 2.2%). The average number of valid responses was 23.7 per patch tested. A response was scored as 1 if it gave the correct object name and as 0 otherwise. Some answers required decisions regarding the use of related terms, e.g., whether "bee" instead of "fly" would be accepted. The decision was based on the WordNet hierarchy (31): We allowed sister terms that have the same direct parent (hypernym) or two levels up. For instance, "'cow" was accepted as a label for "horse," but "dog" or "bear" was not. Part-names were accepted if they correctly labeled the visible object in the partial image (e.g., "wheel" in bicycle, "tie" in suit image, "jet engine" for the airplane part); descriptions that did not name specific objects (e.g., "cloth," "an animal part," "wire") were not accepted.
Training Models on Full-Object Images.
Training was done for each of the classifiers using the training data, except for the multiclass CNN classifier (15), which was pretrained on 1,000 object categories based on ImageNet (27). Classifiers then were tested on novel full images using standard procedures, followed by testing on MIRC and sub-MIRC test images.
Detection of MIRCs and sub-MIRCs.
An average of 10 MIRC level patches and 16 of their sub-MIRCs were selected for testing per class. These MIRCs, which represent about 62% of the total number of MIRCs, were selected based on their recognition gap (human recognition rate above 65% for MIRC level patches and below 20% for their sub-MIRCs) and image similarity between MIRCs and sub-MIRCs (as measured by overlap of image contours); the same MIRC could have several sub-MIRCs. The tested patches were placed in their original size and location on a gray background; for example, an eye MIRC with a size of 20 × 20 samples (obtained in the human experiment) was placed on gray background image at the original eye location.
Computing the recognition gap.
To obtain the classification results of a model, the model's classification score was compared against an acceptance threshold (32), and scores above threshold were considered detections. After training a model classifier, we set its acceptance threshold to produce the same recognition rate of MIRC patches as the human recognition rate for the same class. For example, for the eye class, the average human recognition rate of MIRCs was 0.81; the model threshold was set so that the model's recognition rate of MIRCs was 0.8. We then found the recognition rate of the sub-MIRCs using this threshold. The difference between the recognition rates of MIRCs and sub-MIRCs is the classifier's recognition gap. (Fig. S6). In an additional test we tested the gap while varying the threshold to produce recognition rates in the range 0.5–0.9 and found that the results were insensitive to the setting of the models' threshold. For the computational models, the scores of sub-MIRCs were intermixed with the scores of MIRCs, limiting the recognition gap between the two, as compared with human vision.
Multiclass estimation.
The computational models are trained for a binary decision, class vs. nonclass, whereas humans recognize multiple classes simultaneously. This multiclass task can lead to cases in which classification results of the correct class may be overridden by a competing class. The multiclass effect was evaluated in two ways. The first was by simulations, using statistics from the human test, and the second was by direct multiclass classification, using the CNN multiclass classifier (15). The mean rate of giving a wrong-class response (rather than producing the 'none' label) in the human experiments ranged from 37% for the lowest recognition rates to 4% at highest recognition rates. The effect of multiclass decision on the binary classifier was simulated by allowing each tested MIRC or sub-MIRC to be overridden by a class other than the tested category, with a probability that varied linearly between 4% for the highest-scoring results and 37% for the lowest-scoring results in each class. The gap between MIRC and sub-MIRC recognition was computed as before, but with the additional misclassifications produced by the simulated multiclass effect. The average recognition gap between MIRCs and sub-MIRCS was 0.11 ± 0.16 for multiclass vs. 0.14 ± 0.24 for binary classification. The multiclass effect was expected to be small because the scores in the models for the MIRCs and sub-MIRCs were highly intermixed. Multiclass classification also was tested directly using the CNN model that was trained previously on 1,000 categories (15), including 7 of our 10 classes. Given a test image, the model produces the probability that this image belongs to each of the network categories. The score for each MIRC and sub-MIRC is the probability of the tested class given the test image (e.g., the probability of the airplane class given an airplane MIRC or sub-MIRC). The average gap for the seven classes was small (0.14 ± 0.35) with no significant difference between MIRCs and sub-MIRCs.
Classification accuracy was computed by the AP of the classifier, the standard evaluation measure for classifiers (16). To compare the AP in the full object, the MIRC, and the sub-MIRC classification tasks, we normalize the results to the same number of positive and negative examples across the three test sets.
Intermediate units.
In training and testing the HMAX model (10), we examined whether any intermediate units in the network developed a specific response to a MIRC image during training. Following full-object image training, we tested the responses of all units at all layers of the network to MIRC patches and nonclass patches. We identified the best-performing unit at each of the network's layers (denoted S1, C1, S2, C2, S3, and C3) in terms of its precision in recognizing a particular MIRC type. On this set, the AP at the network output was 94 ± 9% for full-object images and 19 ± 19% for MIRCs. For units with best AP across the network, results were low, but still were higher than the single C3 output unit: AP = 40 ± 24% at the S2 level, 44 ± 27% at the C2 level, and 39 ± 21% at the S3 level.
Training Models on Image Patches.
The classifiers used in the full-object image experiment were trained and tested for image patches. For the RCNN model (15), the test patch was either in its original size or was scaled up to the network size of 227 × 227. In addition, the deep network model (29) and Very Deep Convolutional Network model (18), adapted for recognizing small images, were tested also. Training and testing procedures were the same as for the full-object image test, repeating in five-folds, each using 37 patches for training and nine for testing. Before the computational testing, we measured in psychophysical testing the recognition rates of all the patches from all class images to compare human and model recognition rates directly on the same image (see examples in Fig. S8). After training, we compared the recognition rates of MIRCs and sub-MIRCs by the different models and their recognition accuracy, as in the full-object image test.
We also tested intermediate units in a deep convolutional network (18) by selecting a layer (the eighth of 19) in which units' receptive field sizes best approximated the size of MIRC patches. The activation levels of all units in this layer were used as an input layer to an SVM classifier, replacing the usual top-level layer. The gap and accuracy of MIRC classification based on the intermediate units were not significantly changed compared with the results of the networks' tested top-level output.
Internal Interpretation Labeling.
Subjects (n = 30) were presented with a MIRC image in which a red arrow pointed to a location in an image (e.g., the beak of the eagle) and were asked to name the indicated location. Alternatively, one side of a contour was colored red, and subjects produced two labels for the two sides of the contour (e.g., ship and sea). In both alternatives the subjects also were asked to name the object they saw in the image (without the markings).
SI Methods
Training Models on Full-Object Images.
The human average MIRC recall was 0.81, and the sub-MIRC recall was 0.10. The models' average MIRC and sub-MIRC recall were 0.84 and 0.70, respectively. The HMAX model showed similar results, with MIRC and sub-MIRC recognition rates of 0.84 and 0.63, respectively, and a recognition gap of 0.21 ± 0.23.
The differences between the human and model recognition gaps were highly significant for all the models tested (n = 10 classes, df = 9, one-tailed paired t test): DPM: P < 1.05 × 10−5; BOW: P < 1.64 × 10−4; HOG: P < 4.2 × 10−5; RCNN: P < 3.88 × 10−6; and HMAX: P < 6.89 × 10−5.
In terms of accuracy, we computed the equal error rate (EER) in the receiver operating characteristic (ROC) curve of the computational models. The error (average across models) was 0.02 for full-object images but was high for the MIRCs (0.23) and sub-MIRCs (0.23). Similarly for HMAX, the EER was 0.03, 0.33, and 0.39 for the full object, MIRC, and sub-MIRC images, respectively.
When training with more class images, the MIRC vs. sub-MIRC recognition gap remained small compared with human recognition (models gap of 0.01 ± 0.18 vs. human gap of 0.7 ± 0.06: BOW: P < 0.046; HOG: P < 0.002; RCNN: P < 0.006; n = 3 classes, df = 2, one-tailed paired t test). The CNN multiclass model has been trained on 1.2 million images from 1,000 categories (including the seven categories we use). For this model, too, the recognition gap was small (0.14 ± 0.35), and recognition accuracy was low. The AP was 0.36, 0.01, and 0.01 for a full-object image, MIRC, and sub-MIRC, respectively, and the EER in the ROC curve was 0.03, 0.31, and 0.35, respectively.
Training Models on Image Patches.
None of the models produced a recognition gap that was comparable to the human gap: The human gap was higher, and the differences between each of the models and human results were all highly significant (n = 10 classes, df = 9, one-tailed paired t test; DPM: P < 1.87 × 10−4; BOW: P < 3.75 × 10−5; HOG: P < 1.3 × 10−6; RCNN: P < 1.71 × 10−7; HMAX: P < 4.62 × 10−8). The AP of MIRC recognition across classifiers was 0.74 ± 0.21 and lower (0.38) for the HMAX model. All additional deep-network models we tested [Very Deep Convolutional Network (18) and the net model for small images (29)] gave similar results.
Human Binary Classification Test.
We noted that models often produced false MIRC detections that appear unacceptable to humans. We therefore compared the distribution of errors made by humans and the HMAX model in recognizing minimal images. Humans (n = 30) were tested in 12 trials, each using 60 image patches, 30 positive class examples, and 30 nonclass images. The positive set included MIRC patches from the siblings' dataset above (Fig. S8). These images were similar to one of the discovered MIRCs depicting the same object part (e.g., horse torso) at the same image resolution and were recognizable when tested on human subjects in a free classification task. The 30 negative image patches were automatically selected by the following procedure: A DPM classifier (11) was trained on separate positive examples together with a large number of randomly selected patches, as described above in the sections on training models on image patches. We then used the 30 top-scoring nonclass patches as hard negatives for testing.
All 60 image patches were presented on the screen in five randomly ordered rows (12 patches per row). Subjects were asked to tag each image patch as a positive or negative example of the object category (e.g., ship). The experiment consisted of 12 trials in total, one trial per each of the 10 object categories, except for the eye (two patches), the horse (three patches) with different object parts, and the car (not tested). Of the 360 subjects, we discarded responses that failed to label one or more images, leaving 275 complete responses.
To compare human results with a biological model applied to the same images, the HMAX model (10) was trained on image patches as described above and was applied to the same 60 image patches that were presented to the human subjects in each of the 12 trials.
We tested whether the HMAX model response vector to the 60 images was a likely response, given the distribution of human responses, or an outlier. We measured the Euclidean distance between the response vectors of human subjects to the ground truth and found that the distance of the response vector of the model to the ground truth is unlikely to come from the same distribution. The test was a two-sample, tailed t test with the null hypothesis that the distance between the HMAX response vector and the ground-truth vector in each class (X), and the distance between human response vectors and the ground-truth response in each class (Y) are independent random samples from normal distributions with equal means and unequal, unknown variances (Welch's t test using MathWorks MATLAB ttest2 function). The null hypothesis was rejected (P = 9.41 × 10−5, n1 = 12, n2 = 275, df = 12.19).
Humans also were significantly better than the model at MIRC recognition. We compared the classification accuracy of the test images by humans vs. the HMAX model. For humans, we calculated the classification score for each test image as the fraction of positive responses out of the total number of responses for the image. We computed the ROC graphs for humans and the HMAX model for each of the 12 classes and used the EER for the comparison. The average human EER was significantly lower (humans: 0.75% error ± 13.6 × 10−3, model: 15.9% error ± 8.27 × 10−2; P = 1.30 × 10−6, df = 22, one-tailed paired t test).
We thank Michal Wolf for help with data collection and Guy Ben-Yosef, Leyla Isik, Ellen Hildreth, Elias Issa, Gabriel Kreiman, and Tomaso Poggio for discussions and comments. This work was supported by European Research Council Advanced Grant "Digital Baby" (to S.U.) and in part by the Center for Brains, Minds and Machines, funded by National Science Foundation Science and Technology Centers Award CCF-1231216.
↵1S.U., L.A., and D.H. contributed equally to this work.
- ↵2To whom correspondence should be addressed. Email: shimon.ullman{at}weizmann.ac.il.
Author contributions: S.U., L.A., E.F., and D.H. designed research; L.A. and D.H. performed research; L.A. and D.H. analyzed data; and S.U., L.A., and D.H. wrote the paper.
The authors declare no conflict of interest.
This article is a PNAS Direct Submission.
This article contains supporting information online at www.pnas.org/lookup/suppl/doi:10.1073/pnas.1513198113/-/DCSupplemental.
Freely available online through the PNAS open access option.
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