Diet coke isn't bad for you. But, water is always better.
Most likely you have read a blog post at some point saying aspartame causes cancer. The thing you should take into account is that 'toxicity is in the dosage'. The sweetener aspartame is one of the most thoroughly tested food additives in history. It is used in very small quantities in diet coke. There are many review papers about aspartame consolidating its safety. It seems to be one of those urban legends that got way out of hand. However consumer pressure always wins, this is why we're seeing the development of more stevia sweetened options by the soda giants.
By this stage you may already be satisfied. However things start to get interesting, and sound very scary, when you look even deeper into aspartame. The following is an overview of complex processes.
The metabolites of aspartame are the reason why aspartame is surrounded by so much controversy. Metabolites just means the things aspartame is broken down into next after you eat it. And they may sound like enough reason to quit diet coke for life when you look at them in isolation.
Aspartic acid, an amino acid which is also a neurotransmitter.
Phenylalanine, an amino acid which is toxic to people with a genetic disorder called Phenylketonuria. You might have you seen this label on diet coke and sugar free gum.
Methanol, this is a poisonous alcohol not to be confused with Ethanol from spirits, wine etc.
Ok, you may now be thinking how can this possibly be safe. Here's a few numbers to help put things in perspective. This is the dosage aspect.
The top 1% of the highest aspartame users in the world would consume the equal (pardon the pun) of 57 packets of sweetener or 10 cans of diet soda per day. This is about 2500mg per day. The average intake is closer to 2 diet soda cans per day, or 500mg per day. This more average amount of aspartame has a similar amount of aspartic acid and phenylalanine as found in 170g of milk or 84g of beef. And as much methanol as found in 230g of vegetable juice or 55g of gin. We're talking about less than 1 cup of milk, a palm size of steak and a double shot of gin. It turns out there are scary sounding compounds lurking in most everyday food items, but in minute quantities that don't harm us.
The other factor to consider is the rigorous approval process for food additives in the USA, and other counties. Animal and human models are used in scientific studies to figure out the safe levels in food. To give you a very rough idea, in these studies rats and mice may be fed up to 1000 x per day what would be consumed in an equivalent human diet. Once a safe level is determined, safety factors are added to account for interspecies differences. This is usually a factor of 10, which is quite big in toxicology terms. So roughly speaking, what is found to be safe is reduced by another 10 times as a precaution.
Without a doubt, diet soda is a much better choice than a full sugar soda. Sugar (specifically the fructose part) acts like a chronic toxin. Our livers struggle to break down fructose, it's one of the main reasons high sugar packaged foods are so unhealthy. However, artificially sweetened products may come with their own issues. Addiction to sweetness may be more than just our body's response. It could be that the taste factors into our cravings.
Having a diet coke occasionally might not be as bad for you as many scaremongers would lead you to believe. Just remember that nothing beats pure water in terms of a healthy beverage choice.
Authority Nutrition, Aspartame: Good or Bad?


From zero, the average consumption of HFCS in the U.S. has risen to over 60 pounds per person per year, on average. Starting in the early 1970s, there has been a dramatic rise in the U.S. in the rate of obesity and its related ailments including Type-2 diabetes and heart disease. This alarming development coincides almost exactly with the introduction and subsequent ramp-up of consumption of High Fructose Corn Syrup.
According to an August 23, 2007 report from the American Chemical Society, researchers have found new evidence that soft drinks sweetened with high-fructose corn syrup (HFCS) may contribute to the development of diabetes, particularly in children.
In the current study, Chi-Tang Ho, Ph.D., conducted chemical tests among 11 different carbonated soft drinks containing HFCS. He found "astonishingly high" levels of reactive carbonyls in those beverages. These undesirable and highly-reactive compounds associated with "unbound" fructose and glucose molecules are believed to cause tissue damage, says Ho, a professor of food science at Rutgers University in New Brunswick, N.J. By contrast, reactive carbonyls are not present in table sugar, whose fructose and glucose components are "bound" and chemically stable, the researcher notes.
HFCS is a sweetener found in many foods and beverages, including non-diet soda pop, baked goods, and condiments. It has become the sweetener of choice for many food manufacturers because it is considered more economical, sweeter and more easy to blend into beverages than table sugar. The long standing boycott of Cuban cane sugar combined with the annual corn subsidy to U.S. farmers of over $10 billion explains why sugar costs more than corn fructose.
The figures from the USDA for 2003 are that the average world price of refined sugar is 11 cents per pound compared to 28 cents in the U.S.—more than twice as much due to subsidies and import quotas. Meanwhile, the price of HFCS is 14 cents per pound. However, corn prices are expected to rise because of corn's increased use as a source of energy through ethanol production. This price rise plus new evidence of the dangers of HFCS may finally force the processing industry to cut back on its widespread use.
The process for making the sweetener high fructose corn syrup out of corn was developed in the 1970s. Use of HFCS grew rapidly, from less than three million short tons in 1980 to almost 8 million short tons in 1995. During the late 1990s, use of sugar actually declined as it was eclipsed by HFCS. Today Americans consume more HFCS than sugar.
From zero, the average consumption of HFCS in the U.S. has risen to over 60 pounds per person per year, on average. Starting in the early 1970s, there has been a dramatic rise in the U.S. in the rate of obesity and its related ailments including Type-2 diabetes and heart disease. This alarming development coincides almost exactly with the introduction and subsequent ramp-up of consumption of high fructose corn syrup. An estimated 16 million Americans have Type-2 diabetes, making it the sixth leading cause of death overall. Studies have linked a high intake of refined carbohydrates such as fructose with a high "glycemic index" to the development of diabetes.
The processing industry argues that fructose is just another form of sugar and does no more damage than sugar. However, High Fructose Corn Syrup is an extremely refined version of the fructose naturally occurring in nature. High-fructose corn syrup is produced by processing corn starch to yield glucose, and then processing the glucose to produce a high percentage of fructose. Three different enzymes, two of which have been genetically modified, are needed to break down cornstarch, which is composed of chains of glucose molecules of almost infinite length, into the simple sugars glucose and fructose.
A team of investigators at the USDA, led by Dr. Meira Field, compared the effects of sugar and fructose on laboratory rats according to the www.longlife.com article entitled "Should You Boycott High Fructose Corn Syrup?" This article points out that sucrose is composed of glucose and fructose. When sugar is given to rats in high amounts, the rats develop multiple health problems, especially when the rats were deficient in certain nutrients, such as copper.
The researchers wanted to know whether it was the fructose or the glucose moiety that was causing the problems. So they repeated their studies with two groups of rats, one given high amounts of glucose and one given high amounts of fructose. The glucose group was unaffected but the fructose group had disastrous results. The male rats did not reach adulthood. They had anemia, high cholesterol and heart hypertrophy--that means that their hearts enlarged until they exploded. They also had delayed testicular development. Dr. Field explains that fructose in combination with copper deficiency in the growing animal interferes with collagen production. In a nutshell, the little bodies of the rats just fell apart. The females were not so affected, but they were unable to produce live young.
Going back to the research done by Chi-Tang Ho, his group is also probing the mechanisms by which carbonation increases the amount of reactive carbonyls formed in sodas containing HFCS. They note that non-carbonated fruit juices containing HFCS have one-third the amount of reactive carbonyl species found in carbonated sodas with HFCS, while non-carbonated tea beverages containing high-fructose corn syrup have only about one-sixth the levels of carbonyls found in regular soda.


text:
Die Reste des Passage Tombs von Shrough (auch Galtymore genannt) liegen in einem Forst östlich der R664 (Straße) auf dem Gipfel des grenznahen Shrough Hill im Townland Shrough () im County Tipperary in der Nähe von Galbally im County Limerick in Irland. Das klassische Passage Tomb ist eine in 219 Exemplaren für Nordirland und den Norden der Republik Irland charakteristische Form der Megalithanlage, die zudem in Leinster, Munster und in wenigen Exemplaren auch in Großbritannien vorkommt.

Der große Cairn ist bis zu einer Höhe von 1,0 Meter erhalten geblieben und schließt am östlichen Rand einen trigonometrischen Punkt ein. Die Überreste der kleinen rechteckigen Kammer liegen außermittig und bestehen aus zwei Orthostaten auf jeder Seite, mit einem Halbstein am Westende. Das Ostende und die Decke des einzigen Passage Tombs im County Tipperary mit seiner ovalen Kammer, die innen 1,5 m lang und 1,2 m breit ist, sind offen.

Siehe auch
 Megalithanlagen in Irland

Literatur
 Peter Harbison: Guide to National and Historic Monuments of Ireland. Gill and Macmillan, Dublin 1992, ISBN 0-7171-1956-4.

Weblinks
 Kurzbeschreibung (engl.) und Bild

Shrough
Shrough
Kultplatz
Shrough
National Monument (Irland)

redpajama_set_name: RedPajamaWikipedia


text:
Kids These Days: Zoning Issues Arise for Goat Yoga in Manchester
By Kristen Johnson	• Published August 8, 2017 • Updated on August 8, 2017 at 11:21 pm
As people do the downward dog and sun salutation at a farm in Manchester, interspersed with their "oms" are the peeps of baby goats and the squeals of piglets.
Why goat yoga? Aussakita Acres's owner, Tracy Longoria, said, 'why not?'.
"Goat yoga is just a fun wacky way to get people to the farm to interact with the animals," Longoria said.
But Manchester Mayor Jay Moran said the concept had gotten someone's goat.
An anonymous complaint led a town zoning official to send the farm a cease and desist letter.
"I had to read it twice because I couldn't understand it," Longoria recalled.
Moran said that goat yoga is a health and recreation activity but the location was only zoned for farming, according to his zoning officer's opinion.
However, Longoria argues what they're doing isn't unlike any other Connecticut farm, where customers can pick their own fruit, run through a corn maze or a take a tour.
"People have been coming and doing activities on the farm long before we decided to adopt goat yoga," she pointed out.
That argument seemed to stick with Moran.
"I think what the owners tried to sell to me last night and educate me on is this is an opportunity to be on a farm and interact with the animals," Moran said.
Longoria, who is a member of Manchester's chamber of commerce, said that's good news not only for her farm but also other local businesses this kind of agri-tourism supports.
"We've had people as far as Pennsylvania come. When they leave here they're asking us, 'Where's a good place in town to eat?'," Longoria said.
Longoria said she plans to appeal to the local zoning board which takes its direction from state statute, not the mayor or town council.
However, the appeal buys her more time and she'll be able to keep holding classes with not only with the goats that call this farm home, but also the sheep and even a piglet or two until a decision is made.

redpajama_set_name: RedPajamaCommonCrawl


text:
Incredible 1960 time capsule apartment
Andrea James 7:00 am Wed Nov 30, 2016
Miles McDermott is a modern-day millennial hepcat from Phoenix who has set up the most impressive 1960-vintage pad I've seen outside of a soundstage.
Via a local news report:
"Grandma had some sweet taste," said McDermott, whose love of 1960s design started when he was younger. His mother collected antiques, and in design school, he got his first taste of the era with a hutch.
"It was a feeling that I can relate to NASA, finding a planet with life forms and its own language. It was just an entirely different world," he said. "And I didn't understand why we didn't make it anymore. It just didn't make any sense."
Over the years, the collecting continued: clocks, chairs, electronics, chandeliers — all of them now fill his central Phoenix home. Inside, it feels like a time capsule.
"I have people coming in here and they say I grew up with all of this stuff,' said McDermott. "Everybody slows down. Like they are stuck. They go in slow motion to try and observe everything because there is nowhere else where you can see this."
If you'd like to get on the Save The Sixties bandwagon, head over to his site.
• Save The Sixties (via Curbed)
Conversation Pits, once a popular feature of interior design, back in vogue
Here are some great photos of conversation Pits, a popular feature of interior design from the midcentury period to the 1970s. A conversation pit is typically a lower-than-ground-level seating area… READ THE REST
An architect breaks down the architecture and design of 5 sci-fi films
Dami Lee might be an architect, but she's also an astute student of film. In this video, she combines these interests as she looks at the buildings and other design… READ THE REST
Stunning new book of Scott Albrecht's deconstructed typographical art plus a gallery show in San Francisco
Brooklyn-based artist/designer Scott Albrecht has released a gorgeous new monograph of his deconstructed typography, sculptures, murals, and puzzle-like assemblages. I've been transfixed by Scott's work for a decade and I was honored… READ THE REST
Stay warm and toasty while unpredictable winter weather rages with this Personal Desk Heater
We thank our sponsor for making this content possible; it is not written by the editorial staff nor does it necessarily reflect its views. Once winter rolls around and we… READ THE REST
This ridiculously cool light will instantly change the vibe in any room.
We thank our sponsor for making this content possible; it is not written by the editorial staff nor does it necessarily reflect its views. When you realize you need a… READ THE REST


text:
'Angel' mentor turns efforts toward Carreño Festival's young dancers
By Carrie Seidman , Herald-Tribune
/ Friday, November 30, 2012
Leonard Kesten had seen that look in his wife's eyes before. It meant sleep would likely give way to late-night brainstorming and days to nurturing children that weren't biologically their own.
He also knew it was pointless to protest.
So last summer, when Suzanne Kesten decided to create a mentorship program for students of the Carreño Dance Festival, Leonard Kesten didn't consider how it might interfere with his imagined retirement in the Florida sunshine.
After all, he's been married for 57 years. He just accepted it as the next chapter.
The first chapter dates back to 1993, when Suzanne Kesten read that students at the Bronx elementary school she had attended ranked at the bottom of the list in reading and math scores. Immediately, the New York City native became a woman on a mission.
Suzanne and Leonard Kesten, newly relocated in Sarasota from New York City, have established the Suzanne Kesten Mentorship Program at the Carreno Dance Festival. / Herald Tribune photo by Mike Lang
She zeroed in on high-risk students at the school who showed intelligence and promise and started with weekly tutoring sessions, enlisting other alums to help. But she soon realized that in order to want a better future, the children needed to be exposed to things that existed beyond their own neighborhood.
So even as the other volunteers fell by the wayside, the Kestens took the students — often with siblings and parents in tow — to restaurants, concerts, Broadway shows and museums. At Christmas time they hosted a holiday party at their home in Bedford, N.Y., hand-selecting a book for each child. Later, they made it possible for children to participate in an international living program that sent them overseas to experience new countries, languages and cultures.
"It almost became a full-time job," says Kesten, a psychiatrist in practice for more than 40 years.
Two decades later, thanks to the Kestens' mentorship and financial support, more than 30 of those students, affectionately dubbed the "Loveables," have graduated from college and created careers and lives they once never could have imagined.
"It was a revolutionary program and an amazing chance for those children," acknowledges Robert de Warren, president and co-founder of the Carreño Dance Festival. "It really opened doors."
That, rather than any personal glory, says Kesten, was always her motivation.
"As much as those kids profited from the experience of the program, my family profited more," says Kesten, the mother of three (a son died at age 30) and grandmother of four. "I wanted to give them a foundation and move them forward. When someone outside of their parents shows an interest in a child, they think, 'This is someone who doesn't have to love me, but is loving me anyway. I must be special.'"
A relocation,
a renewal
Last year the Kestens — with Leonard urging a reluctant Suzanne, who prefers the pace and energy of the big city — relocated to Sarasota. Soon thereafter, they joined a tour group to Cuba sponsored by the Carreño Dance Festival. Longtime ballet aficionados and patrons of American Ballet Theatre in New York, where they had seen José Carreño both begin and end his career, they embraced the dance emphasis and the name behind the tour. But they were unaware of the venture Carreño had started with de Warren in Sarasota after his retirement from ABT in 2010.
"We knew nothing about the festival," says Suzanne Kesten. "We went on the trip to Cuba because we wanted to go to Cuba."
But all it took was one look at the island's talented ballet students, dancing among the crumbled ruins of a dilapidated Havana studio, for the trip to become more than just a geographical diversion for Kesten.
"I was impossible," the feisty Kesten admits, tossing an unapologetic smile toward her husband. "I'm probably one of the most responsive people when I see a child with talent. I immediately said, 'We've got to help them. How do we take them to the States?'"
You can't, her husband protested. It's just not possible.
You can't, seconded de Warren, whom Kesten pestered with questions over a week of breakfast tables filled with fresh fruit and Cuban pastries. The government won't allow it.
But there is something you could do, de Warren suggested — contribute to the fund that provides scholarships for the Carreño festival's summer intensive workshop for pre-professional ballet students in Sarasota.
"Are there kids who can't afford to go?" Kesten asked. De Warren confirmed the organization offered some $50,000 in scholarship aid. "OK then, that's what I'm doing," she decided.
Arcadian Broad, currently interning with the Orlando Ballet, is one of the first two students selected for the new mentorship program. / Photo courtesy Carreno Dance Festival
Kesten provided scholarships for a half dozen dancers to attend last August's workshop. But "writing a check has no significance to me," she insists. As with the Loveables, she was more interested in becoming involved for the long run.
She and her husband sat in the Sarasota Opera House orchestra seats during class and rehearsal every day of the three-week workshop. She picked out her own scholarship students and zeroed in on others who seemed to have exceptional talent. And as she watched "this metamorphosis — it was like 50 caterpillars emerging as butterflies" — ideas began to percolate.
"I became addicted," she admits. "We were there every day and I questioned Robert to death, as is my wont. And as he will tell you" — she nods toward her husband — "I was constantly asking, 'What do you think we could do?' That's how it all began."
"It" is the Dr. Suzanne Kesten Mentorship Program, a still-evolving mixture of financial aid, specialized coaching and personalized counseling for select students of the festival that will be offered year-round.
"We discussed what she had done in New York and how we could apply that to what we are doing here," says de Warren. "How could we keep in touch with these exceptional talents and, whether they have the resources or not, give them the guidance they will need to be successful?"
By the time of the performance that concluded the workshop, Kesten and de Warren had selected the first two mentees — Arcadian Broad, a 16-year-old phenom currently interning with the Orlando Ballet, and Gabriella Stilo, a then 12-year-old from Tampa whose skills and poise far exceeded her tender age. (Several additional mentees will be announced at the "Chance to Dance" CDF fundraiser in Sarasota on Tuesday.)
DeWarren says the new program is "a work in progress."
"It's becoming more defined, little by little," he says. "We want to help them reach their goal and be accepted into a good company. But the wonderful thing is that we will really get to know them."
Mary Lou Stilo says her daughter's selection was "a complete surprise," one she didn't realize until she saw the announcement in the performance program at the end of the workshop.
Gabriella Stilo, recently turned 13, earned a scholarship to the festival' summer intensive workship last August in Sarasota, then joined Broad as one of the first two students selected to be mentored. / Photo courtesy American Dance Competition
Gabriella, who just turned 13, already spends 12 hours a week in class and sometimes more in rehearsal (she is currently dancing both the roles of Clara and the Sugar Plum in the "Nutcracker" production of her school, America's Ballet School in Tampa).
A single pair of pointe shoes, which her daughter can go through in a week, is $80. So though Stilo doesn't yet have a clear concept of what the mentorship program will provide, she is well aware of the demands of pursuing a career in ballet and welcomes any support, be it moral or financial.
Her impression of Kesten, whom she speaks with frequently, is that "she's a lot like my daughter, very social."
Gabriella, who first met Kesten at a pool party during the summer intensive, says she already speaks to her mentor every few weeks and emails with her frequently.
"She makes me feel really good about myself," says Stilo. "I didn't know why she'd pick me because there were so many good dancers. But she's very nice and it just makes me feel more confident that she believes in me."
An indolent retirement on the best beaches in America is not as appealing to Kesten as the possibility of shaping a few more young lives. She'd like nothing better than if her mission became contagious.
Most recently, Kesten supplied funding for Tampa dancer William Dugan to compete at the Prix de Lausanne in Switzerland.
"To be able to give children an idea of the possibilities, of how to navigate the system, that's very important," she says. "I'm determined, because I think for the most part we are failing our youth."
As far as the personal payback, that has already begun. "Let's face it," she says, "there's a part of this that is selfish. You have the opportunity to see talent grow and develop. Who wouldn't want to be a part of that?"
DANCE PREVIEW
A CHANCE TO DANCE, performance by Carreño Dance Festival scholarship winners. Dec. 4, Hyatt Regency, 1000 Blvd. of the Arts, Sarasota. 5:45 cocktails, 6:30 performance, 7:30 dinner. $150; $250 patrons (includes champagne reception with Jose Carreño and Julie Kent of American Ballet Theatre at 5 p.m.). Reservations, 923-6775 or hsull@comcast.net. www.carrenodancefestival.com.
Carrie Seidman
Dance/arts critic, Sarasota Herald Tribune
Last modified: November 30, 2012
REVIEW: Pushing choreographic traditions in new directions
Now Playing Nov. 29-Dec. 6: Dance
They're baaack....
For the young and restless, an abbreviated "Nut"


Every book reviewed at Good Kindle Reviews is automatically entered into our Book of the Month award program. Books are rated on plot, originality, storytelling, characters and of course how well the overall writing captures the imagination of the reader.
Each month, one book is chosen to be the Top of the Class and awarded a Certificate of Achievement. Also, the book chosen is awarded a special marketing boost that includes an Amazon Giveaway that guarantees book sales, Author follows, and new readers.
And, every monthly winner is entered into the Book of the Year contest with even more prizes for Runner-up and First Place!


text:
Q: How to center this popup text box
This is a website that I and some friends of mine are building for their youtube channel.
I got this popup code from W3schools
I have the popups working but I want the popups to be 320px wide instead of the 160px and aligned with the image. The problem is when I widen the popup to 320px and try to center the popup onto the image, it never works out. All of my solutions have either moved the popups further to the right OR have messed with the actual layout itself.
What I've tried - after setting the width to 320px, in the CSS I've tried deleting the margin-left property and/or setting it to 0px, I've tried deleting the left property and/or setting it to auto, I've deleted the padding, I've messed with the position property in both the .popup and .popup .popuptext but alas, nothing has worked. They have either messed with the layout itself or just moved the popups further right. I've also tried researching online for the solutions but I haven't found a solution because I think that this problem is too specific, since I got the original code from W3schools that nobody has addressed it before and posted it online. Either that, or I'm still just too new to be able to research the answer specifically enough. Also, I'm still trying to wrap my head around the position property. I think the problem is in the position property because when I inspect the elements, I see how much positioning there is other than margin or padding, and I want to say that's what is throwing things off.
Here is the relevant CSS code-
.popup {
    position: relative;
    display: inline-block;
    cursor: pointer;
    -webkit-user-select: none;
    -moz-user-select: none;
    -ms-user-select: none;
    user-select: none;
}

/* The actual popup */

.popup .popuptext {
    visibility: hidden;
    width: 160px;
    background-color: #555;
    color: #fff;
    text-align: center;
    border-radius: 6px;
    padding: 8px 0;
    position: absolute;
    z-index: 1;
    bottom: 125%;
    left: 50%;
    margin-left: -80px;
}

/* Popup arrow */
.popup .popuptext::after {

}

/* Toggle this class - hide and show the popup */
.popup .show {
    visibility: visible;
    -webkit-animation: fadeIn 1s;
    animation: fadeIn 1s;
}

/* Add animation (fade in the popup) */
@-webkit-keyframes fadeIn {
    from {opacity: 0;}
    to {opacity: 1;}
}

@keyframes fadeIn {
    from {opacity: 0;}
    to {opacity:1 ;}
}

And here is a link to the actual site so you guys can see what's going on and check out the source code behind it.
The Site

Thank you so much. Any and all help is greatly appreciated. To see the popup, click the episode title.

A: It seems that if you remove the left: 50% and margin-left and then add width: 320px your problem is gone. Try not to just copy-paste from W3Schools as it's very outdated. A more reliable source is MDN.

A: You need to remove the margin-left.
After you do that, set your desired width and define a property called transformwith a value of translateX(-50%);
Leaving you with:
.popup .popuptext {
    visibility: hidden;
    width: 320px;
    background-color: #555;
    color: #fff;
    text-align: center;
    border-radius: 6px;
    padding: 8px 0;
    position: absolute;
    z-index: 1;
    bottom: 125%;
    left: 50%;
    transform: translateX(-50%);
}

I don't know if you are aware but Bootstrap already has its own popover.
On a side note, you should not be looking at W3Schools, it is not a good source.
Here are some better resources:
Plain Javascript
CSS Reference Codrops

A: you could always use bootstrap class text-center
http://getbootstrap.com/css/

redpajama_set_name: RedPajamaStackExchange


text:
package org.apache.shardingsphere.proxy.frontend.mysql.command.query.builder;

import org.apache.shardingsphere.db.protocol.mysql.packet.command.query.MySQLColumnDefinition41Packet;
import org.apache.shardingsphere.db.protocol.mysql.packet.command.query.MySQLFieldCountPacket;
import org.apache.shardingsphere.db.protocol.mysql.packet.generic.MySQLEofPacket;
import org.apache.shardingsphere.db.protocol.mysql.packet.generic.MySQLOKPacket;
import org.apache.shardingsphere.db.protocol.packet.DatabasePacket;
import org.apache.shardingsphere.proxy.backend.response.header.query.impl.QueryHeader;
import org.apache.shardingsphere.proxy.backend.response.header.query.QueryResponseHeader;
import org.apache.shardingsphere.proxy.backend.response.header.update.UpdateResponseHeader;
import org.junit.Test;

import java.util.Arrays;
import java.util.Collection;
import java.util.List;

import static org.hamcrest.CoreMatchers.anyOf;
import static org.hamcrest.CoreMatchers.instanceOf;
import static org.hamcrest.CoreMatchers.is;
import static org.junit.Assert.assertThat;
import static org.junit.Assert.assertTrue;
import static org.mockito.Mockito.mock;
import static org.mockito.Mockito.when;

public final class ResponsePacketBuilderTest {

    @Test
    public void assertBuildQueryResponsePackets() {
        QueryHeader queryHeader1 = new QueryHeader("schema1", "table1", "columnLabel1", "columnName1", 5, "VARCHAR", 4, 6, false, true, false, true);
        QueryHeader queryHeader2 = new QueryHeader("schema2", "table2", "columnLabel2", "columnName2", 8, "VARCHAR", 7, 9, false, true, true, true);
        List<QueryHeader> queryHeaders = Arrays.asList(queryHeader1, queryHeader2);
        QueryResponseHeader queryResponseHeader = new QueryResponseHeader(queryHeaders);
        Collection<DatabasePacket<?>> actual = ResponsePacketBuilder.buildQueryResponsePackets(queryResponseHeader, 255);
        assertTrue(actual.stream().findAny().isPresent());
        assertThat(actual.stream().findAny().get(), anyOf(instanceOf(MySQLFieldCountPacket.class), instanceOf(MySQLColumnDefinition41Packet.class), instanceOf(MySQLEofPacket.class)));
    }

    @Test
    public void assertBuildUpdateResponsePackets() {
        UpdateResponseHeader updateResponseHeader = mock(UpdateResponseHeader.class);
        when(updateResponseHeader.getUpdateCount()).thenReturn(10L);
        when(updateResponseHeader.getLastInsertId()).thenReturn(100L);
        Collection<DatabasePacket<?>> actual = ResponsePacketBuilder.buildUpdateResponsePackets(updateResponseHeader);
        assertTrue(actual.stream().findAny().isPresent());
        MySQLOKPacket actualItem = (MySQLOKPacket) actual.stream().findAny().get();
        assertThat(actualItem, instanceOf(MySQLOKPacket.class));
        assertThat(actualItem.getAffectedRows(), is(10L));
        assertThat(actualItem.getLastInsertId(), is(100L));
    }
}

redpajama_set_name: RedPajamaGithub


text:
Victorian state schools shortchanged under 'escape clause'
By Henrietta Cook
July 8, 2019 — 6.01pm
Victorian public schools are set to miss out on up to $5 billion due to an "escape clause" in the recent funding deal inked between the state and the Commonwealth.
That's the finding of a new analysis by former Productivity Commission economist and education researcher Trevor Cobbold, who has accused politicians of using accounting tricks to reduce funding for public schools.
There are concerns public school students are being shortchanged under the Gonski 2.0 agreement Credit:Arsineh Houspian
Last month, Victoria became the last Australian state to sign up to the Gonski 2.0 funding deal.
And for the first time, it has been allowed to include some of its spending on regional and rural student transport, the running of curriculum and school watchdogs and the depreciation of capital assets as part of its share of the schooling resource standard.
Under the new agreement, Australian states can claim previously excluded costs of up to 4 per cent as part of their contribution towards this standard.
The standard is the bedrock of the Gonski review and measures how much funding each school needs to support its students.
According to Mr Cobbold, these arrangements are at odds with the nationally agreed definition of the schooling resourcing standard, which excludes depreciation, transport, payroll tax and umbrella services because they do not directly contribute to teaching.
Mr Cobbold, who is the convenor of public education lobby group Save our Schools, said the situation inflated Victoria's contribution towards the funding benchmark. He said this would lead to public schools being shortchanged by $5 billion over 10 years.
"It's an escape clause to reduce the funding of public schools," he said.
"The whole strategy of improving results for disadvantaged students is undermined."
Under the deal, Victoria must lift its contribution towards the standard from 67.8 per cent, one of the lowest shares in Australia, to 75 per cent over 10 years.
This will cost Victorian coffers an extra $7 billion over the term of the bilateral agreement, which runs from 2019 to 2023.
While state schools will only reach 95 per cent of the standard over the next decade, non-government schools will reach 100 per cent of the benchmark in five years.
This disparity between the sectors was a key sticking point between the Commonwealth and Victoria during negotiations.
A Victorian government spokesman said the items included in the standard were consistent with other states.
"Our massive record investment in school infrastructure providing $5.6 billion to build and upgrade schools is not captured by the standard," he said.
"We were the last state to sign up to the federal government's school funding deal because we didn't think it was fair – and we still don't."
Federal Education Minister Dan Tehan it was "up to the Victorian government to explain why it's the lowest funder of public schools across the nation".
Justin Mullaly, the deputy president of the Victorian branch of the Australian Education Union, said including spending on other education costs "missed the point" of needs-based funding.
"What schools care about is the actual support they have to support children in classrooms every day," he said.
"Some of the key goals of the Education State, not least breaking the link between disadvantage and academic achievement, become difficult when you have funding deals that don't deliver support to the school level to help students."
He said there was an "astounding lack of transparency" around the deal. It is not yet known how much money will flow to schools across each year of the agreement.
"We are calling on the Victorian government to come clean on what the deal looks like over the five years," he said.
Henrietta Cook
Senior Reporter at The Age


text:
MILLER, George
Collection of the U.S. House of Representatives
Congress(es)
94th (1975–1977), 95th (1977–1979), 96th (1979–1981), 97th (1981–1983), 98th (1983–1985), 99th (1985–1987), 100th (1987–1989), 101st (1989–1991), 102nd (1991–1993), 103rd (1993–1995), 104th (1995–1997), 105th (1997–1999), 106th (1999–2001), 107th (2001–2003), 108th (2003–2005), 109th (2005–2007), 110th (2007–2009), 111th (2009–2011), 112th (2011–2013), 113th (2013–2015)
External Research Collections
Bibliography/Further Reading
MILLER, George, a Representative from California; born in Richmond, Contra Costa County, Calif., May 17, 1945; A.A., Diablo Valley College, Pleasant Hill, Calif., 1965; A.B., San Francisco State University, San Francisco, Calif., 1968; J.D., University of California Law School, Davis, Calif., 1972; lawyer, private practice; staff, California state senate majority leader, 1969-1974; elected as a Democrat to the Ninety-fourth and to the nineteen succeeding Congresses (January 3, 1975-January 3, 2015); was not a candidate for reelection to the One Hundred Fourteenth Congress in 2014; chair, Select Committee on Children, Youth, and Families (Ninety-eighth through One Hundred First Congresses); chair, Committee on Natural Resources (One Hundred Second and One Hundred Third Congresses); chair, Committee on Education and Labor (One Hundred Tenth and One Hundred Eleventh Congresses).
View Record in the Biographical Directory of the U.S. Congress
The Bancroft Library
Papers: 1975-2014, 268 linear feet. The papers of George Miller include correspondence, voting records, subject and issue files, press releases and newspaper clippings, a small amount of photographs, and audio and visual materials.
Oral History: In the Rosie the Riveter World War II Home Front Oral History Project, 2010, 15 pages. An oral history interview was conducted with Congressman George Miller by Javier Arbona on August 13, 2010. In the interview, George Miller discusses the following: the Port Chicago explosion and of rediscovering the incident; Navy mutiny trial; trajectory through Congress of Port Chicago memorial and Freddie Meeks' pardon; discussion of mutiny trial review; partial recollections of dealing with the Clinton White House; on meeting Freddie Meeks and giving visibility to the African American story; discussion of Port Chicago town condemnation; open-ended discussion of long-term plans for Port Chicago area; reflections on the role of race in American memory and World War II lessons; and framing Port Chicago in a personal perspective.
Institute of Governmental Studies
Papers: ca. 1980-1986, 6 pieces. Campaign ephemera for U.S. House re-election campaigns of George Miller from 1980, 1982 and 1986
University of California, Chico
Special Collections, Meriam Library
Videotape: 1990, 1 videocassette. Child care from the perspective of a Congressman [videorecording]: interview with George Miller by Gayle Kimball on August 1, 1990.
Bibliography / Further Reading
Miller, George, ed. Giving Children a Chance: The Case for More Effective National Policies. Washington: Center for National Policy Press, c1989.
History, Art & Archives, U.S. House of Representatives, "MILLER, George," https://history.house.gov/People/Listing/M/MILLER,-George-(M000725)/ (July 17, 2019)


If you have gone through some tough times in your life, you probably have a few financial scars left to show for it, including big hits to your credit. Now that those tough times are over, it's time to get back on track. Here are a few tips on how to rebuild your credit.
To make the most progress, it is best to know where you are starting from. You are entitled to a free credit reports from all three credit agencies once a year. Take a look at all three. Look for any errors that need correcting and possibly any late accounts that you can easily pay off.
For any accounts that you currently have, make sure you pay each one on time. Late payments can be the biggest detriment to your credit. Conversely, on-time payments can have the biggest positive impact.
Credit utilization is another big portion of your credit score. This refers to how much of a balance you carry, compared to your credit limit. The ideal percentage is less than 30%. If you are above that, pay as much as you can afford each month to bring those balances down.
If you have a lot of closed accounts on your history, consider opening a secured credit card account. With this type of account, you deposit money as the collateral, and then it works just like any other credit card. Be sure the account is reported to all three credit agencies.
If you are having a hard time getting the credit you need, you can try asking a family member to co-sign with you. This is a huge favor since they are equally responsible for the balance of the loan. If you stop paying, it will hurt their credit as well, so make sure you can fully afford the loan you want.
Learn more about your financing options by visiting us at Larry Jay Mitsubishi.


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https://www.berghahnjournals.com/browse?access=user&level=all&pageSize=10&sort=datedescending&t=History&t_0=Covid19&t_1=CulturalStudies
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Museums and the Pandemic, One Year On
Some Reflections on Academic Resilience
Joanna Cobley
Written as notes from the field, this article explores the overlaps between researcher development and the idea of academic resilience within the museum and heritage studies community. During a climate of uncertainty and rapid change, it argues that alongside the academic literature, positive psychology methods transfer well into the researcher development space. Methods involved informal email conversations with museum and heritage practitioners united by how COVID-19 and border lockdown presented new opportunities to connect, share ideas, and rethink. Presented as short narratives, these findings show how researchers and practitioners in northern Europe, the United Kingdom and Canada share similar concerns to those in the southern hemisphere about climate change, equity, well-being, resilience, and sustainability. These narratives highlight the importance of encouraging critical engagement, finding ways to traverse time zones that build international networks and provide leadership opportunities for researchers at any level.
In Museum Worlds
Art Gallery Education in New Zealand during COVID-19
The Emergence of a Community of Practice
Esther Helen McNaughton
This article describes the unprecedented coming together of New Zealand art gallery educators to respond to the challenges of the 2020 COVID-19 pandemic. This newly formed community of practice met virtually three times at critical points. At each stage, new concerns were discussed and understandings evolved. The gallery educators were able to approach shared issues cooperatively, enabling mutual support to a degree that had hitherto not been possible. By the end of these meetings, gallery educators were reestablishing their regular teaching practice with the integration of many of the innovations of the period. Additionally, the meetings fulfilled a preexisting desire for closer contact and professional support, and thus proved to be the foundation of an ongoing national professional group for New Zealand art gallery educators.
Meeting Report
135–148 (14 total)
Museums in the Pandemic
A Survey of Responses on the Current Crisis
Joanna Cobley, David Gaimster, Stephanie So, Ken Gorbey, Ken Arnold, Dominique Poulot, Bruno Brulon Soares, Nuala Morse, Laura Osorio Sunnucks, María de las Mercedes Martínez Milantchí, Alberto Serrano, Erica Lehrer, Shelley Ruth Butler, Nicky Levell, Anthony Shelton, Da (Linda) Kong, and Mingyuan Jiang
Throughout human history, the spread of disease has closed borders, restricted civic movement, and fueled fear of the unknown; yet at the same time, it has helped build cultural resilience. On 11 March 2020 the World Health Organization (WHO) classified COVID-19 as a pandemic. The novel zoonotic disease, first reported to the WHO in December 2019, was no longer restricted to Wuhan or to China, as the highly contagious coronavirus had spread to more than 60 countries. The public health message to citizens everywhere was to save lives by staying home; the economic fallout stemming from this sudden rupture of services and the impact on people's well-being was mindboggling. Around the globe museums, galleries, and popular world heritage sites closed (). The Smithsonian Magazine reported that all 19 institutes, including the National Zoo and the National Museum of the American Indian (NMAI), would be closed to the public on 14 March (). On the same day, New Zealand's borders closed, and the tourism industry, so reliant on international visitors, choked. Museums previously deemed safe havens of society and culture became petri dishes to avoid; local museums first removed toys from their cafés and children's spaces, then the museum doors closed and staff worked from home. In some cases, front-of-the-house staff were redeployed to support back-of-the-house staff with cataloguing and digitization projects. You could smell fear everywhere.
Online Exhibitions during the COVID-19 Pandemic
Sheila K. Hoffman
In the mid-1990s, when many museums were beginning to take their first hesitant steps toward building online personae, the worry still holding many back was that if a collection or experience were available online, in-person visitation would invariably decline (; ; ). In the 25 years since, that fear has largely been dispelled even as our technical ability to digitally capture and disseminate cultural collections has improved exponentially, even to the point that the online experience in some ways exceeds the in-person experience. Indeed, museums have moved far beyond the ability to show a few images of the major works in a collection, adding opportunities that mirror almost all the offerings of the in-person experience. But even this "Mona Lisa" effect has not driven in-person visitation down. Rather the opposite. Anyone who has elbowed through the crowds at many of the world's best-known museums can attest to that. Indeed, having been among this ubiquitous press of people, I could not help but think on such occasions that it would take an act of God to reduce the numbers and improve the quality of viewing.
210–215 (6 total)


text:
\section{Conclusion}

We present a simple yet effective loss function to enforce orthogonality on the output feature space and establish its performance improvements for a wide range of classification tasks. Our loss function operates in conjunction with the softmax CE loss, and can be easily integrated with any DNN. We also explore a variety of characteristics of the features learned with OPL illustrating its benefit for few-shot learning, domain-generalization and robustness against adversarial attacks and label noise. In future, we hope to explore other variants of OPL including its adaptation to unsupervised representation learning.

\section{Related Work}

\noindent\textbf{Loss Functions.}
Loss functions play a central role in all deep learning based computer vision tasks. Recent works include reformulating the generic softmax calculation to limit the variance and range of logits during training \cite{rbf_loss}, constraining the feature space to follow specific distributions \cite{LGM_loss}, and heuristically altering margins targeting specific tasks like few-shot learning \cite{negative_margin, aml_paper},  class imbalance \cite{khan2019striking,khan2017cost,hayat2019gaussian,focal_loss} and zero-shot learning \cite{rahman2018polarity}. OPL optimizes a different objective of inter-class separation and intra-class clustering through orthogonalization in the feature space.

\noindent\textbf{Generalizable Representations.}
Recent works explore the transferability of features learned via supervised training \cite{how_transferable_paper}, \eg FSL \cite{rfs_paper, meta_baseline_paper, unravelling_meta} and DG \cite{RSC_paper, dg_class_margins} tasks. Tian \etal \cite{rfs_paper} establish a strong FSL baseline using only standard (non-episodic) supervised pre-training. Adaptation of supervised pre-trained models to the episodic evaluation setting of FSL tasks is explored in \cite{feat_paper, meta_baseline_paper}. Goldblum \etal \cite{unravelling_meta} show the importance of margin-based regularization methods for FSL. Our work differs by building on orthogonality constraints to learn more transferable features, and is more compatible with CE as opposed to \cite{unravelling_meta}. Multiple DG methods also explore constraints on feature spaces \cite{RSC_paper, dg_class_margins} to boost cross-domain performance. In particular, \cite{dg_class_margins} explores inter-class separation and intra-class clustering through contrastive and triplet loss functions. OPL improves on these while eliminating the need for compute expensive and complex sample mining procedures.

\noindent\textbf{Orthogonality.}
Orthogonality of kernels in DNNs is well explored with an aim to diversify the learned weight vectors \cite{deep_hyperspherical, orthogonal_cnn}. The idea of orthogonality is also used for disentangled representations such as in \cite{orthogonal_face_age} and to stabilize network training since orthogonalization ensures energy preservation \cite{desjardins2015natural,rodriguez2016regularizing}. Orthogonal weight initializations have also shown their promise towards improving learning behaviours \cite{mishkin2015all,xie2017all}.  However, all of these works operate in the parameter space. Remarkably, the previous formulations to achieve orthogonality in the feature space generally depend on computing singular value decomposition \cite{lezama2018ole,sun2017svdnet}, which can be numerically unstable, difficult to estimate for rectangular matrices, and undergoes an iterative process \cite{bansal2018can}. In contrast, our orthogonal constraints are enforced in a novel manner, realized via decomposition on the sample-to-sample relationships within a mini-batch, while simultaneously avoiding tedious pair/triplet computations.

\section{Introduction}

\global\csname @topnum\endcsname 0
\input{figures/opl_explanation}

Recent years have witnessed great success across a range of computer vision tasks owing to progress in deep neural networks (DNNs) \cite{lecun2015deep}. Effective loss functions for DNNs training have been a crucial component of these advancements \cite{goodfellow2016deep}. In particular, the softmax cross entropy (CE) loss, commonly used for tackling classification problems, has been pivotal for stable and efficient training of DNNs.

Multiple variants of CE have been explored to enhance discriminativity and generalizability of feature representations learned during training.
Contrastive \cite{contrastive_loss} and triplet \cite{triplet_loss} loss functions are a common class of methods that have gained popularity on tasks requiring more discriminative features. At the same time, methods like centre loss \cite{centre_loss} and contrastive centre loss \cite{contrastive_centre_loss} have attempted to explicitly enforce inter-class separation and intra-class clustering through Euclidean margins between class prototypes. Angular margin based losses \cite{large_margin_softmax, sphere_face, arc_Face, cos_face, norm_face} compose another class of objective functions that increase inter-class margins through altering the logits prior to the CE loss.

While these methods have proven successful at promoting better inter-class separation and intra-class compactness, they do possess certain drawbacks. Contrastive and triplet loss functions \cite{contrastive_loss, triplet_loss} are dependent on carefully designed negative mining procedures, which are both time-consuming and performance-sensitive. Methods based on centre loss \cite{centre_loss, contrastive_centre_loss}, that work together with CE loss, promote margins in Euclidean space which is counter-intuitive to the intrinsic angular separation enforced through CE loss \cite{sphere_face}. Further, these methods introduce additional learnable parameters in the form of new class centres. Angular margin based loss functions \cite{sphere_face, large_margin_softmax} which are highly successful for face recognition tasks, make strong assumptions for face embeddings to lie on the hypersphere manifold, which does not hold universally for all computer vision tasks \cite{negative_cam}. Some loss designs are also specific to certain architecture classes \eg, \cite{negative_cam} can only work with DNNs which output Class Activation Maps \cite{zhou2015cnnlocalization}.

In this work, we explore a novel direction of simultaneously enforcing inter-class separation and intra-class clustering through orthogonality constraints on feature representations learned in the penultimate layer (Fig.~\ref{fig:opl_explanation}). We propose Orthogonal Projection Loss (OPL), which can be applied on the feature space of any DNN as a plug-and-play module. We are motivated by how image classification inherently assumes independent output classes and how orthogonality constraints in feature space go hand in hand with the one-hot encoded (orthogonal) label space used with CE. Furthermore, orthogonality constraints provide a definitive geometric structure in comparison to arbitrarily increasing margins which are prone to change depending on the selected batch, thus reducing sensitivity to batch composition. Finally, simply maximizing the margins can cause negative correlation between classes and thereby unnecessarily focus on well-separated classes while we tend to ensure independence between different class features to successfully disentangle the class-specific characteristics.

Compared with contrastive loss functions \cite{contrastive_loss,triplet_loss}, OPL operates directly on mini-batches, eliminating the requirement of complex negative sample mining procedures. By enforcing orthogonality through computing dot-products between feature vectors, OPL provides a natural augmentation to the intrinsic angular property of CE, as opposed to methods \cite{centre_loss,contrastive_centre_loss,hayat2019gaussian} that enforce an Euclidean margin in feature space. Furthermore, OPL introduces no additional learnable parameters unlike \cite{centre_loss,LGM_loss,contrastive_centre_loss}, operates independent of model architecture unlike \cite{negative_cam}, and in contrast to losses operating on the hypersphere manifold \cite{sphere_face,large_margin_softmax,arc_Face,cos_face}, performs well on a wide range of tasks. Our main contributions are:

\begin{itemize}[topsep=1pt,itemsep=0pt,partopsep=1ex,parsep=1ex,leftmargin=*]
    \item We propose a novel loss, OPL, that directly enforces inter-class separation and intra-class clustering via orthogonality constraints with no learnable parameters.
    \item Our orthogonality constraints are efficiently formulated compared to existing methods \cite{lezama2018ole,sun2017svdnet}, allowing mini-batch processing without the need to explicitly obtain singular values. This leads to a simple vectorized implementation of OPL directly integrating with CE.
    \item We extensively evaluate on a diverse range of image classification tasks highlighting the discriminative ability of OPL. Further, our results on few-shot learning (FSL) and domain generalization (DG) datasets establish the transferability and generalizability of features learned with OPL. Finally, we establish the improved robustness of learned features to adversarial attacks and label noise.
\end{itemize}

\section{Proposed Method}

Maximizing inter-class separation while enhancing intra-class compactness is highly desirable for classification. While the commonly used cross entropy (CE) loss encourages logits of the same class to be closer together, it does not enforce any margin amongst different classes. There have been multiple efforts to integrate max-margin learning with CE. For example, Large-margin softmax \cite{large_margin_softmax} enforces inter-class separability directly on the
dot-product similarity while SphereFace \cite{sphere_face} and ArcFace
\cite{arc_Face} enforce multiplicative and additive angular margins on
the hypersphere manifold, respectively. Directly enforcing max-margin constraints to enhance discriminability in the angular domain is
ill-posed and requires approximations \cite{large_margin_softmax}. Some works turn to the Euclidean space to enhance feature space discrimination. For example, centre loss \cite{centre_loss} clusters penultimate layer features using Euclidean distance. Similarly, Affinity Loss \cite{hayat2019gaussian} forms uniformly shaped equi-distant class-wise clusters based upon Gaussian distances in the Euclid space. Margin-maximizing objective functions in the Euclid space are not ideally suited to work along-side CE loss, since CE seeks to separate output logits in the angular domain. By enforcing orthogonality constraints, our proposed OPL loss maximally separates intermediate features in the angular domain, thus complementing cross-entropy loss which enhances angular discriminability in the output space. In the following discussion, we revisit CE loss in the context of max-margin learning, and argue why OPL loss is ideally suited to supplement CE.

\subsection{Revisiting Softmax Cross Entropy Loss}

Consider a deep neural network $\mathcal{H}$, which can be decomposed into $\mathcal{H} = \mathcal{H}_{\phi} \cdot \mathcal{H}_{\theta}$, where $\mathcal{H}_{\phi}$ is the feature extraction module and $\mathcal{H}_{\theta}$ is the classification module. Given an input-output pair $\{\mathbf{x}, y\}$, let $\mathbf{f}=\mathcal{H}_{\phi}(\mathbf{x}), \mathbf{f} \in \mathbb{R}^d$ be the intermediate features and $\hat{y} = \mathcal{H}_{\theta}(\mathbf{f}), \hat{y} \in \mathbb{R}^k$ be the output predictions. For brevity, let us define the classification module as a linear layer $\mathcal{H}_{\theta}=\mathbf{W}=[\mathbf{w}_1,\cdots\mathbf{w}_c]$ with no unit-biases, where $\mathbf{w}_i \in \mathbb{R}^d, i=1\cdots c$ are class-wise learnable projection vectors for $c$ classes.
The traditional CE loss can then be defined in terms of discrepancy between the predicted $\hat y$ and ground-truth label $y$, by projecting the features $\mathbf{f} \in \mathbb{R}^d$ onto the weight matrix $\mathbf{W} \in \mathbb{R}^{d \times c}$.

\begin{align}
	\mathcal{L}_{\texttt{CE}}(\hat y, y)  &= - \log \ \frac{\exp{(\mathbf{f}^T \mathbf{w}_y)}}{\sum\limits_{j} \exp{(\mathbf{f}^T \mathbf{w}_j)} } \\
	\propto \sum\limits_{j \ne y} & \exp{(\mathbf{f}^T \mathbf{w}_j - \mathbf{f}^T\mathbf{w}_y}) \notag\\
	\propto  \sum\limits_{j \ne y} & \exp{(\left \|  \mathbf{f} \right \|_2 \left \| \mathbf{w}_j \right \|_2 \cos (\theta_{j}) } - \left \| \mathbf{f} \right \|_2 \left \| \mathbf{w}_y \right \|_2 \cos (\theta_{y})) \notag
\end{align}

\input{figures/ce_vs_opl_vis}

Since CE does not explicitly enforce any margin between each class pair, previous works have shown that the learned class regions for some class samples tend to be bigger compared to others \cite{sphere_face, arc_Face,hayat2019gaussian}. To counter this effect and ensure all classes are equally separated, efforts have been made to introduce a margin $m$ between different classes by modifying the term $\cos (\theta_y)$ as $\cos (m+\theta_y)$ for additive angular margin \cite{arc_Face}, $\cos (m\theta_y)$ for multiplicative angular margin \cite{sphere_face} and $m+\cos (\theta_y)$ as additive cosine margin \cite{cos_face}. The gradient propagation for such margin-based softmax loss formulations is hard, and previous works rely on approximations. Instead of introducing any margins to ensure uniform seperation between different classes, our proposed loss function simply enforces all classes to be orthogonal to each other with simultaneous clustering of within-class samples, using an efficient vectorized implementation with straightforward gradient computation and propagation.

By considering the $\mathbf{w}_y \in \mathbf{W}$ vectors as individual class prototypes, the CE loss can be viewed as aligning the feature vectors $\mathbf{f}$ along its relevant class prototype. The cosine similarity in the form of the dot product ($\mathbf{f}^T \mathbf{w}_y$) gives CE an intrinsic angular property, which is observed in Fig.~\ref{fig:ce_vs_opl_vis} where features naturally separate in the polar coordinates with CE only. Moreover, during the standard SGD based optimization, the CE loss is applied on mini-batches. We note that there is no explicit enforcement of feature separation or clustering across multiple samples within the mini-batch. Given the opportunity to enforce such constraints since supervised training is commonly conducted adopting random mini-batch based iterations, we explore the possibility of within mini-batch constraints aimed at augmenting the intrinsic discriminative characteristics of CE loss.

\subsection{Orthogonal Projection Loss}
The CE loss with one-hot-encoded ground-truth vectors seeks to implicitly achieve orthogonality between different classes in the output space. Our proposed OPL loss, ameliorates CE loss, by enforcing class-wise orthogonality in the intermediate feature space. Given an input-output pair $\{\mathbf{x}_i, y_i\}$ in the dataset $\mathcal{D}$, let $\mathbf{f}_i = \mathcal{H}_{\phi}(\mathbf{x}_i)$ be the features output by an intermediate layer of the network. Our objective is to enforce constraints to cluster the features $\mathbf{f}_i \ \forall \mathbf{x}_i \in \mathcal{D}$ such that the features for different classes are orthogonal to each other and the features for the same class are similar. To this end, we define a unified loss function that simultaneously ensures intra-class clustering and inter-class orthogonality within a mini-batch as follows:
\begin{align}
    s &= \sum_{\substack{i,j \in B \\ {y_i} = {y_j} }} \langle  \mathbf{f}_i, \mathbf{f}_j  \rangle \label{eq:loss_1} \\
    d &= \sum_{\substack{i,k \in B \\ {y_i} \ne {y_k} }} \langle  \mathbf{f}_i, \mathbf{f}_k  \rangle \label{eq:loss_2} \\
	\mathcal{L}_{\texttt{OPL}} &= (1-s) + |d|  \label{eq:loss_opl}
\end{align}
where $ \langle  \cdot \ , \cdot  \rangle$ is the cosine similarity operator applied on two vectors, $| \cdot |$ is the absolute value operator, and $B$ denotes mini-batch size. Note that the cosine similarity operator used in Eq. \ref{eq:loss_1} and \ref{eq:loss_2} involves normalization of features (projection to a unit hyper-sphere) as follows:
\begin{align}
    \langle  \mathbf{x}_i, \mathbf{x}_j \rangle = \frac{\mathbf{x}_i \cdot \mathbf{x}_j}{\|\mathbf{x}_i\|_2 \cdot \|\mathbf{x}_j\|_2}
    \label{eq:cosine_sim}
\end{align}
where $|| \cdot ||_2$ refers to the $\ell_2$ norm operator. This normalization is key to aligning the outcome of OPL with the intrinsic angular property of CE loss.

In Eq. \ref{eq:loss_opl}, our objective is to push $s$ towards 1 and $d$ towards 0. Since $1-s > 0$ already, we take the absolute value of $d$ given $d \in (-1, 1)$.  This in turn restricts the overall loss such that $\mathcal{L}_{\texttt{OPL}} \in (0, 3)$. When minimizing this overall loss, the first term $(1-s)$ will ensure clustering of same class samples, while the second term $|d|$ will ensure the orthogonality of different class samples. The loss can be implemented efficiently in a vectorized manner on the mini-batch level, avoiding any loops (see Algorithm \ref{alg:opl}).

We further note that the ratio of contribution to the overall loss of each individual term in Eq. \ref{eq:loss_opl} can be controlled to re-prioritize between the two objectives of inter-class separation and intra-class compactness. While the unweighted combination of $s$ and $d$ alone performs well, specific use-cases could benefit from weighted combinations. We reformulate Eq. \ref{eq:loss_opl} as follows:
\begin{align}
    \mathcal{L}_{\texttt{OPL}} &= (1-s) + \gamma * |d|  \label{eq:loss_opl_gamma}
\end{align}
where $\gamma$ is the hyper-parameter controlling the weight for the two different constraints.

Since OPL acts only on intermediate features, we apply cross entropy loss over the outputs of the final classifier $\mathcal{H}_{\theta}$. The overall loss used  is a weighted combination of CE and OPL. We note that our proposed loss can also be used together with other common image classification losses, such as Guided Cross Entropy, Label Smoothing or even task specific loss functions in different computer vision tasks. The overall loss $\mathcal{L}$ can be defined as:
\begin{equation}
\mathcal{L} = \mathcal{L}_{\texttt{CE}} + \lambda \cdot \mathcal{L}_{\texttt{OPL}} \label{eq:loss_overall}
\end{equation}
where $\lambda$ is a hyper-parameter controlling the OPL weight.

\input{figures/opl_algorithm}
\input{figures/ablation_train_plots}

\input{figures/ablation_block_plots}

\subsection{Interpretation and Analysis}
\textbf{Overall Objective:} Consider $\mathbf{F}_c$ is a set of mini-batch samples comprising of normalized features from the same class $c$ in a given dataset $\mathcal{D}$. The overall  OPL constraints can be viewed as a minimization of the following objective to update the network $\mathcal{H}_{\phi}$ over the random variables $\mathbf{F}_c$:
\begin{align}
     \min_{\phi} \sum_{i=1}^{c} \sum_{j=1}^{c} \left| \mathop{\mathbb{E}}_{\scriptscriptstyle \substack{ (\mathbf{F}_i, \mathbf{F}_j) \sim \mathcal{D}}} \left[ \mathbf{F}_i \mathbf{F}_j^T \right] - \llbracket i=j \rrbracket \right|,
    \label{eq:opl_em}
\end{align}
where $| \cdot |$ is the absolute value operator and $\llbracket \cdot \rrbracket$ is the Iverson bracket operator. We refer to the term defined in Eq. \ref{eq:opl_em}  as the expected inter-class orthogonality. The behaviour of OPL in terms of minimizing this expectation is visualized in Fig. \ref{fig:ablation_block_plots} where average per-class feature vectors over the CIFAR-100 dataset are calculated for ResNet-56 models trained under `CE-only' and `CE+OPL' settings. Clear improvements over the CE baseline in terms of minimizing the expected inter-class orthogonality can be observed. Moreover, the stochastic mini-batch based application of OPL prevents naively pushing all non-diagonal values to zero as observed. This translates to allowing necessary inter-class relationships not encoded in one-hot labels of a dataset to be captured within the learned features.

\textbf{Decomposing OPL: }
Taking a step further, we decompose OPL into its sub-components, $s$ and $d$, as defined in Eq.~\ref{eq:loss_opl}. While $s$ computes the pair-wise cosine similarity between all same-class features within the mini-batch, $d$ calculates the similarity between different class features. These measures can directly be adopted to quantify the inter-class separation and intra-class compactness in any given features space. Moreover, the unweighted OPL formulation in Eq.~\ref{eq:loss_opl} can be considered a measure of the overall feature orthogonality
within any given embedding space. It will be interesting to compare the contribution of OPL towards inter-class separation and intra-class clustering of a feature space in contrast to the generic CE based training scenario. We present this comparison by training ResNet-56 on CIFAR-100 dataset in Fig.~\ref{fig:ablation_train_plots}. This separation of features achieved through OPL translates to performance improvements not only within the standard classification setting, but also in tasks requiring transferable or generalizable features. Goldblum \etal \cite{unravelling_meta} explore the significance of inter-class separation and intra-class clustering for better performance when transferring a feature embedding to a few-shot learning task. Similar notions regarding discriminative features are explored in \cite{dg_class_margins} for domain generalization. We explore the effects of OPL in few-shot learning settings, and visualize the novel class embeddings learned with OPL in \ref{sec: vis_class_embeddings} using LDA
\cite{lda_vis} to preserve the inter-class to intra-class variance ratio as suggested in \cite{unravelling_meta}.

\textbf{Why orthogonality constraints?:}
One may wonder what benefit orthogonality in the feature space can provide in comparison to simply maximizing margin between classes. Our reasoning is twofold: reducing sensitivity to batch composition and avoiding negative correlation constraints. Within the random mini-batch based training setting, the orthogonality objective provides a definitive geometric structure irrespective of the batch composition, while the optimal max-margin separation is dependent on the batch composition. Furthermore, in the common case where the output space feature dimension $d > c$ ($c$ number of classes), maximizing angular margin between normalized features on a unit hyper-sphere will lead to \emph{negative} correlation among the class prototypes (considering a maximal and equi-angular separation). We argue that this is an undesired constraint since the categorical classification task itself assumes non-existence of ordinal relationships between classes (\eg. use of orthogonal one-hot encoded labels). Moreover, extending the constraints to additionally cause negative correlation between classes unnecessarily focuses on already well-separated classes during training whereas our constraint which tends to ensure independence provides a more balanced objective to disentangle the class-specific characteristics of even more fine-grained classes.

\section{Experiments}
We extensively evaluate our proposed loss function on multiple tasks including \emph{image classification} (Tables \ref{table:comparison_cifar100} \& \ref{table:baseline_imagenet}), \emph{robustness against label noise} (Table \ref{table:noisy_cifar100}), \emph{robustness against adversarial attacks} (Table \ref{table:adversarial_robutness}) and \emph{generalization to domain shifts} (Table \ref{table:comparison_rsc}). We further observe the enhanced \emph{transferability} of orthogonal features \eg in the case of few shot learning (Tables \ref{table:comparison_rfs} \& \ref{table:comparison_metadataset}). Our approach shows consistent improvements and highlights the advantages of orthogonal features on this diverse set of tasks and datasets with various deep network backbones. Additionally, we demonstrate the plug-and-play nature of OPL by showing benefits of its use over CE, Truncated Loss (for noisy labels) \cite{truncated_loss}, RSC \cite{RSC_paper} and various adversarial learning baselines.

\subsection{Image Classification}
We evaluate the effectiveness of orthogonal features in the penultimate layer on image classification using our proposed training objective (Eq.~\ref{eq:loss_overall}).
Competitive results are achieved showing consistent improvements  (Tables~\ref{table:comparison_cifar100} \&~\ref{table:baseline_imagenet}) on two datasets: CIFAR-100 \cite{krizhevsky2009learning}, and ImageNet \cite{krizhevsky2012imagenet}.

\noindent\textbf{CIFAR-100} consists of 60,000 natural images spread across 100 classes, with 600 images per class. We apply OPL over a cross-entropy baseline for supervised classification on CIFAR-100 (following the experiment setup in \cite{negative_cam}), and compare our results against other loss functions which impose margin constraints \cite{sphere_face,large_margin_softmax,cos_face,arc_Face}, introduce regularization \cite{negative_cam,lezama2018ole,focal_loss,CB_focal_loss}, or promote clustering \cite{centre_loss,rbf_loss} to enhance separation among classes in Table \ref{table:comparison_cifar100}.  Despite its simplicity, our method performs well against state-of-the-art loss functions. Note that HNC \cite{negative_cam} is dependant on class activation maps, RBF \cite{rbf_loss} and LGM \cite{LGM_loss} involve learnable parameters, and CB Focal Loss \cite{CB_focal_loss} specifically solves class-imbalance. In contrast, OPL has a simple formulation easily integratable to any network architecture, involves no learnable parameters, and targets general classification. Additionally, we note how OPL has higher performance gains with respect to top-1 accuracy (in comparison to top-5 accuracy) which is the more challenging metric. We attribute this to the fact that increased separation through OPL mostly helps in classifying difficult samples. Further, we note top-5 is not a preferred measure for CIFAR-100 since most classes are different in nature as opposed to e.g., ImageNet with several closely related classes (where our gain is much pronounced for top-5 accuracy, as discussed next).
\input{figures/comparison_cifar_related_work}

\noindent\textbf{ImageNet} is a standard large-scale dataset used in visual recognition tasks, containing roughly 1.2 million training images and 50,000 validation images. We experiment with OPL by integrating it to common backbone architectures used in image classification tasks: ResNet18 and ResNet50. We train \footnote{https://github.com/pytorch/examples/tree/master/imagenet} the models for 90 epochs using SGD with momentum (initial learning rate 0.1 decayed by 10 every 30 epochs). Results for these experiments are presented in Table \ref{table:baseline_imagenet} and Fig.~\ref{fig:images_imnet}. We note that simply enforcing our orthogonality constraints increases the top-1 (\%) accuracy of ResNet50 from 76.15\% to 76.98\% without any additional bells and whistles. Moreover, given the large number of fine-grained classes among the 1000 categories of ImageNet (\eg, multiple dog species) which can be viewed as difficult cases, the better discriminative features learned by OPL obtains notable improvements in top-5 accuracy as well.
\input{figures/baseline_imagenet}
\input{figures/images_imnet}

\subsection{Robustness against Label Noise}
Given the rich representation capacity of deep neural networks, especially considering how most can even fit random labels or noise perfectly \cite{random_labels_fit}, errors in the sample labels pose a significant challenge for training. In most practical applications, label noise is almost impossible to avoid, in particular, when it comes to large-scale datasets requiring millions of human annotations. Multiple works \cite{Ghosh2017RobustLF, truncated_loss} explore modifications to common objective functions aimed at building robustness to label noise. Despite the explicit inter-class separation constraints on the feature space enforced by OPL, we argue that the random mini-batch based optimization exploited by OPL negates the effects of noisy labels. This hypothesis is supported by our experiments presented in Table \ref{table:noisy_cifar100} which show additional robustness of OPL against label noise. We simply integrate OPL over the approach followed in \cite{truncated_loss}, without any task-specific modifications.
\input{figures/noisy_cifar100}

\subsection{Robustness against Adversarial Attacks}
Adversarial attacks modify a given benign sample by adding adversarial noise such that the deep neural network is deceived \cite{szegedy2013intriguing}. Adversarial examples are out-of-distribution samples and remain a challenging problem to solve. Adversarial training \cite{madry2017towards} emerges as an effective defense where adversarial examples are generated and added into the training set. We enforced orthogonality on such adversarial examples in the feature space while optimizing the model weights and show our benefit on different adversarial training mechanisms \cite{madry2017towards, hendrycks2019using, Wang2020Improving}. Important to note that all the considered adversarial training schemes \cite{madry2017towards, hendrycks2019using, Wang2020Improving} are different in nature \eg  training in Madry \etal \cite{madry2017towards} is based on cross-entropy only, Hendrycks \etal \cite{hendrycks2019using} propose to exploit pre-training, while Wang \etal \cite{Wang2020Improving} introduce a surrogate loss along with cross-entropy. Our orthogonality constraint help maximizing adversarial robustness in all cases showing the generic and plug-and-play nature of our proposed loss. In order to have reliable evaluation,  we report robustness gains against Auto-Attack (AA) \cite{croce2020reliable} in Table \ref{table:adversarial_robutness}. On CIFAR10, our method increased robustness of \cite{madry2017towards} by $5.11\%$, \cite{hendrycks2019using} by $0.81\%$ and \cite{Wang2020Improving} by $2.2\%$.

\input{figures/adversarial}

\subsection{Domain Generalization (DG)}
The DG problem aims to train a model using multi-domain source data such that it can directly generalize to new domains without the need of retraining. We argue that the feature space constraints of OPL tend to capture more general semantic features in images which generalize better across domains. This is verified by the performance improvements for DG that we obtain by integrating OPL with the state-of-the-art approach in \cite{RSC_paper} and evaluating on the popular PACS dataset \cite{pacs_dataset}. The results presented in Table \ref{table:comparison_rsc} indicate that integrating OPL with \cite{RSC_paper} sets new state-of-the-art across all four domains as compared to \cite{RSC_paper}.

\input{figures/comparison_rsc}

\input{figures/comparison_rfs}
\input{figures/comparison_metadataset}

\input{figures/search_table}
\subsection{Few Shot Learning (FSL)}
In this section, we explore the transferability of features learned with our loss function in relation to FSL tasks. We evaluate OPL on three benchmark few-shot classification datasets: miniImageNet, tieredImageNet, and CIFAR-FS. We run additional experiments on Meta-Dataset \cite{meta_dataset} which is a large-scale benchmark for evaluating FSL methods in more diverse and challenging settings. Similar to \cite{cnaps_paper}, we expand Meta-Dataset by adding three additional datasets, MNIST, CIFAR10, and CIFAR100.
In light of work that shows promise of  learning strong features for FSL
\cite{rfs_paper}, we experiment with OPL using it as an auxiliary loss on the feature space during the supervised training. Quantitative results highlighting the performance improvements are presented in Table \ref{table:comparison_rfs}. Our results on Meta-Dataset are obtained using the \textit{train on all} setting presented in \cite{meta_dataset}. We integrate OPL over the method presented in \cite{SUR_paper}, train on the first 8 datasets of Meta-Dataset, and evaluate on the rest (including the three additional datasets from \cite{cnaps_paper}). Results are presented in Table \ref{table:comparison_metadataset}.  See \ref{sec: robustness_to_noise_fsl} for robustness of OPL features against \emph{sample noise} in FSL tasks.

\input{figures/ablation_batch_lambda}
\subsection{Ablative Study}
OPL in its full form (Eq.~\ref{eq:loss_opl_gamma}) contains two hyper-parameters, $\lambda$ and $\gamma$. We conduct a hyper-parameter search over a held-out validation set of CIFAR-100  (see Table~\ref{table:search_table}). The optimum values selected from these experiments are kept consistent across all other tasks and used when reporting test performance. Furthermore, we evaluate the performance of OPL on the test split of CIFAR-100 for varying $\lambda$ values keeping $\gamma$ fixed, to illustrate the minimal sensitivity of our method to different $\lambda$ values in Fig.~\ref{fig:ablation_lambda}.

Next, we consider how OPL operates on random mini-batches, and evaluate its performance against varying batch sizes (CIFAR-100 dataset). These results presented in Fig. \ref{fig:ablation_batch_size} exhibit how OPL consistently
\section*{\Large{Supplementary: Orthogonal Projection Loss}}
        \vspace{1.5em}
    \end{center}
  \end{@twocolumnfalse}
]

In this supplementary document we include:
\begin{itemize}\setlength{\itemsep}{0em}
    \item[--] Additional comparisons with the baseline on the MNIST dataset (\ref{sec:mnist}).
    \item[--] OPL performance with scalable neural architecture method \cite{Neural_Scale} (\ref{sec:scalable}).
    \item[--] Robustness of OPL against noise in the input images (\ref{sec: robustness_to_noise_fsl}).
    \item[--] Visualization of classification results (\ref{sec:visualization}).
\end{itemize}
\section{Experimentation}
Here, we present results of experiments conducted using OPL on a set of additional settings.

\subsection{Digit Classification (MNIST)}\label{sec:mnist}
We conduct experiments on the MNIST dataset integrating OPL over a CE baseline. We use a 4-layer convolutional neural network with 32-dimensional feature embedding (after a global average pool operation) following the experimental setup in \cite{rbf_loss}. Our results are reported in Table \ref{table:mnist_results}. Additionally, we conduct experiments appending a fully-connected layer to reduce the feature dimensionality to 2 for generating better visualizations on behaviour of OPL in feature-space (presented in Fig. \ref{fig:opl_explanation} in main article).
\input{figures/mnist_comparison}

\subsection{Scalable Architectures}\label{sec:scalable}
We consider the recent Neural Architecture Scaling approach  proposed in \cite{Neural_Scale} and plug-in our OPL on top of it to study our scalability.  Refer Table~\ref{table:comparison_cifar100_small} for the results.
\input{figures/comparison_cifar100_small}

\subsection{Robustness to Noise: FSL}
\label{sec: robustness_to_noise_fsl}
We have already established through empirical evidence how OPL improves performance for few-shot learning tasks as well as robustness to adversarial examples present during evaluation. We now explore the more challenging task of exploring robustness to input sample noise in a FSL setting (similar to the one in \ref{sec: vis_class_embeddings}). The base training is conducted with no noise present in training data. During evaluation, the support and query set images are corrupted with random Gaussian noise of varying standard deviation (referred to as $\sigma$). This can be considered a domain shift on top of unseen novel classes during evaluation. The features learned with OPL during base training exhibit better robustness to such input corruptions in this FSL setting. We report these results in Table \ref{table:comparison_rfs_noise}. The experiments conducted followed the method in \cite{rfs_paper} integrated with OPL.

\input{figures/comparison_rfs_noise}
\input{figures/ce_vs_opl_vis_fsl}

\section{Visualization}\label{sec:visualization}
\input{figures/app_images_imnet2}

In this section, we present additional visualizations exploring various aspects of OPL and its performance.

\subsection{Class Embeddings}\label{sec: vis_class_embeddings}
Consider a few-shot learning setting, where a model trained in a fully-supervised manner (referred to as base model / base training) on a set of selected classes which contains training labels (referred to as base classes) is later evaluated on a set of unseen classes (referred to as novel classes). The sets of base and novel classes are disjoint. The evaluation protocol would involve episodic iterations, where in each step a small set of labelled samples from the novel classes (referred to as support set) is available during inference, and an another set of those same novel classes (referred to as query set) is available for calculating the accuracy metrics.

Given how our proposed loss is already able to explicitly enforce constraints on the feature space during base training, we want to examine if the additional discriminative nature endowed on the features by OPL is aware of higher level semantics. To evaluate this, we explore the more challenging task of inter-class separation and intra-class clustering of novel classes which are unseen during the base training. We train a model following the approach in \cite{rfs_paper} integrating OPL, and visualize the separation of different class features for both base and novel classes in Fig. \ref{fig:ce_vs_opl_fsl}.

\subsection{ImageNet Examples}
\label{sec: imagenet_examples}
We further explore the performance of our model (CE+OPL) trained on ImageNet (model used for experiments presented in Table~\ref{table:baseline_imagenet} of main paper) by examining the failure cases of the baseline model that were improved upon when adding OPL. Visualizations for some randomly selected such cases are illustrated in Fig.~\ref{fig:app_imnet_01} and Fig.~\ref{fig:app_imnet_02}.

\input{figures/app_block_matrix}
\input{figures/app_images_imnet}

\subsection{Block Matrix}
\label{sec: block_matrix_analysis}
We defined the overall objective of OPL as a minimization of the expected inter-class orthogonality (refer Eq.~\ref{eq:opl_em} of main paper) and conducted empirical analysis using models training using our proposed loss function against a CE only baseline (illustrated in Fig.~\ref{fig:ablation_block_plots} of main paper). In this section, we conduct additional analysis on those block-matrices to further understand the outcomes of our orthogonality constraints on the learned feature space. It is interesting to note that while OPL enforces a higher degree of orthogonality between the average class vectors, it does not naively push everything to be orthogonal. We note that this allows any hidden knowledge learned during the training process (information not captured in the labels explicitly) to remain captured within the features. The results of the experiments conducted on this are illustrated in Fig.~\ref{fig:block_plots_explained}.

redpajama_set_name: RedPajamaArXiv


DAVOS, Switzerland (AP) — The Latest on the World Economic Forum in Davos, Switzerland (all times local): 11:35 p.m. France's economy minister says he expects the future chief of Renault to consolidate the French automaker's alliance with Japanese partner Nissan.
Singer and co-founder of RED Bono, 2nd right, gestures to Afsaneh Mashayekhi Beschloss, founder of RockCreek, Christine Lagarde, Managing Director of IMF and Rwanda President Paul Kagame, from left, as he arrives for the "Closing the Financing Gap" session at the annual meeting of the World Economic Forum in Davos, Switzerland, Wednesday, Jan. 23, 2019.
Chinese Vice President Wang Qisan applauds as he enters the stage with Klaus Schwab, founder and Executive Chairman of the World Economic Forum, at the annual meeting of the World Economic Forum in Davos, Switzerland, Wednesday, Jan. 23, 2019.
German Chancellor Angela Merkel addresses the annual meeting of the World Economic Forum in Davos, Switzerland, Wednesday, Jan. 23, 2019.
Singer and co-founder of RED Bono gestures while participating in the "Closing the Financing Gap" session at the annual meeting of the World Economic Forum in Davos, Switzerland, Wednesday, Jan. 23, 2019.
Spain Prime Minister Pedro Sanchez addresses the annual meeting of the World Economic Forum in Davos, Switzerland, Wednesday, Jan. 23, 2019.
The board is expected to name industrialist Thierry Bollore as CEO and Jean-Dominique Senard of Michelin as chairman. Le Maire told reporters at the World Economic Forum in Davos on Wednesday that choosing a president to consolidate the alliance was the "absolute priority."


text:
Step Up Orphan Opportunity Centre
"Football for all" in the Belskoye Ustye
Preparing and supporting university students
Subject-specific help
Views and Impressions
Distance learning is a new direction for the Centre and is currently in its developmental phase. In the modern era, education can be received from anywhere in the world. Thanks to this, Step Up's successful work with residents and graduates of orphanages and institutions can be applied from a distance to fulfil educational, developmental, and socialisation goals.
Our distance learning programme, which will be realised in cooperation with the Volunteers Helping Orphans charity, aims to give social and educational support to children in orphanages in the Moscow region.
The social and educational support on offer includes regular lessons divided up by school subject (algebra, geometry, physics, chemistry, biology, Russian or foreign languages) and taught via the Internet (Skype-lessons) by qualified teachers from Step Up.
We are always open to possibilities for cooperation and new suggestions. And we would be happy to hear from teachers who would like to take part in this programme.
new academic year
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Olga TikhomirovaDirector
I always worry when you guys throw lessons. This, unfortunately, happens. It is always hard to think that anyone of our guys joined standard for Russia orphan statistics. Inspire and delight me our guys. I never cease to admire their desire to move up. And we also have a terrific staff!
Orphan Opportunity Centre «Step Up». Unless indicated otherwise, all material on the website is available under Creative Commons СС-BY-SA-NC 3.0
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text:
The Grasshopper (1970) [American]
a.k.a.: The Passing of Evil
a.k.a.: Passions
a.k.a.: L'Amoureuse [France]
OK movie; Jacqueline has top billing, above the title ("Jacqueline Bisset in The Grasshopper"). She is in almost every scene, playing Christine Adams. The title refers to her character, who goes from a perky 19-year-old to a burned out 22-year-old, including a stint as a Las Vegas showgirl. Directed by Jerry Paris.
(14/3/2000) I recently re-watched this, and it's really not a bad picture. A somewhat exploitative B-movie, but it definitely has many good elements. I like the simple, straight-forward handling of the interracial marriage. And Jacqueline turns in a very good performance.
"The Grasshopper" is available on video; "Passions" is an alternate video title. "The Passing of Evil" is the name of the novel the screenplay is based on; it was also used for a video release. The video version I have, under the title "The Grasshopper", has to be one of the worst film-to-video transfers I've ever seen. [Update: I recently got a copy of the videotape under the name "The Passing of Evil"; it is just as bad quality, and it is recorded in SLP!]
I recently learned that it was titled "L'Amoureuse" in France.


With all of the controversy around genetically modified (GM) foods, many of us wonder just what doctors think about the technology. For a lot of people in Britain, we place a great deal of faith in our doctors and we are keen to understand their assessment of GM foods - whether or not these foods truly are safe or if they pose a harm to our health.
Unfortunately, the controversy extends in the medical field as much as it does to other professional areas. With some doctors believing that there is no risk to consuming these foods, others disagree and cite concerns about health risks from these products.
The British Medical Association (BMA) supports GM foods from the point-of-view that there is not yet any compelling evidence to prove that they pose a threat to health. However, they do note that much more research is encouraged and needed to show that GM foods are safe for both the people who consume them and the environment.
The BMA also acknowledges the benefits of GM foods in comparison with traditional crops and farming techniques. Still, they do further explain that long-term research is needed, primarily in the areas of human allergies. For now, short-term studies don't show any real harm, according to the BMA.
They also go on to report that research should further focus on groups of people who are considered vulnerable. This includes older individuals and infants as well as those with chronic health conditions, particularly immune-compromised people.
Another belief is that close monitoring should be performed of GM foods, which means research looking at its effects, especially over the long-term. While current short-term studies can be accepted and supported, the BMA does not feel it is enough to simply leave it at that and conduct no further research or monitoring of GM foods.
Despite the statement of the BMA, which does seemingly represent all of the doctors in the UK, there are still physicians and medical experts who do not believe that GM foods are safe. Instead, they are concerned about health effects from this technology. Their premise is that the evidence thus far is simply not enough to say that GM foods are safe.
The concern over a lack of long-term studies should mean that GM foods are banned until we can properly establish their safety for production and consumption. One group of doctors that is focused on bio-safety have called for a halt to GM trials in India. They are particularly worried about the use of antibiotic biomarkers, which they feel will have a strong effect on India because of the abundance of diseases in the country.
Their opinion is that all of the key, national health campaigns will be jeopardised. They are concerned about recent research that suggests an effect on reproduction in animals, which means there could be an issue with human health as well.
These doctors and medical experts also firmly believe that unbiased, independent research teams must conduct these studies. To date, there are many ties between researchers and biotechnology corporations as well as research teams hired specifically by some of these companies.
It's difficult for the public to decide if GM foods are safe when even doctors can't seem to agree on whether the technology is acceptable for human health. For now, it's best to critically examine all of the evidence and try to make informed, wise decisions about whether you want to purchase GM foods. You can read labels and be a savvy shopper or you can simply eat your food regardless of whether it is a GM food or not.


text:
wildness and womanhood
"With the deepest love in my heart for the animal kingdom, I will donate 25 percent of all proceeds from the sale of this book to
Earthfire Institute Wildlife Sanctuary."
~ Lucinda Bakken White
WINNER of a 2018
National Indie Excellence Award
Memoir – Finalist
5-star Review
San Francisco Book Review
2018 Beverly Hills Book Awards
Women's Issues – Winner
Spirituality – Winner
Living Now Book Award
Inspirational Memoir – Female – Bronze
Readers' Favorite Book Award
Non-Fiction – New Age – Finalist
San Francisco Book Festival Award
Autobiography – Runner Up
Sign up for the Authentic Wildness Newsletter™ and receive weekly inspiration in your email inbox.
Confessions of a Bone Woman:
Realizing Authentic Wildness in a Civilized World
Lucinda White is a happy-go-lucky nature child raised by a young single mother in the 1960s. In a dramatic turn of events, her wild nature is broken. She is shaped into a yuppie and becomes the wife of a prominent Silicon Valley CEO and a glamorous socialite. Successful by all accounts of external measure, she feels trapped by the shallow values of a dominant culture and ever more alienated from her true nature.
Something primal awakens in Lucinda when she unearths and touches an ancient buffalo bone and the experience impels her to secretly collect dead animal parts and learn the art of curing their decomposing flesh. As Lucinda challenges convention, her curiosity about the animal kingdom leads to wild and unlikely adventures rescuing road kill, tracking wolves, and encountering lions face to face.
Confessions of a Bone Woman is one woman's story of how she recognizes and learns to express her authentically wild nature in order to heal bone by bone and become her full self, redefining what it means to be a modern woman.
Available on Audible
Free Audiobook Sample
http://authenticwildness.com/wp-content/uploads/2019/03/retail-intro.mp3
Praise for Confessions of a Bone Woman
"…the trials and tribulations of this wise woman will help you on your own search for your true self."
-Awareness Magazine
"Intuitive and spiritually engaging, Confessions of a Bone Woman is one woman's authentic story of realizing her life's higher purpose in becoming a spiritual elder. This is a memoir like no other, leading the reader along a subjective path of wonder, opening their mind to where primal instinct and vivid dreams connect to the animal kingdom, symbolism, reincarnation, and all things metaphysical, helping the reader embrace the true spirit of healing and self-discovery."
-San Francisco Book Review
"Confessions of a Bone Woman is an uplifting and eye-opening take on the falseness of modern life, with an intriguing revelation about what really matters at the end of the day."
-Readers' Favorite
"A deftly presented autobiography of an interesting life lived in interesting times, Lucinda Bakken White writes with an impressive candor and exceptional perception. A consistently engaging and inherently fascinating read from beginning to end."
— Julie Summers, Midwest Book Review
"Confessions of a Bone Woman is a spellbinding memoir full of poetry and grit. Taking us on a heroine's journey, White's story is palpable as she answers call after call to wild adventures outside of her mainstream lifestyle and suffers through harrowing tests of loneliness, courage and determination to reclaim her authentic, untamed, feminine nature despite the challenges and pitfalls of the modern world."
— Amy Zerner and Monte Farber, authors of The Enchanted Tarot, Karma Cards and The Psychic Circle
"Confessions of a Bone Woman is a reckoning of an ancient soul making her way through the modern world."
— Pixie Lighthorse, author of Boundaries and Protection
"[An] important facet of this memoir and sets it apart from other similar life journeys is the sharing of her grounded perspective of Death as being an inescapable part of Existence. This book is a fascinating read and will inspire many on their journeys to wholeness."
— Bani Sodermark of bookpleasures.com
"Confessions of a Bone Woman shows that if you allow yourself to see beyond the conventions of society you can create the life that you truly want. I would recommend this book for those who are seeking to grow and change their lives."
— The Nerdy Girl Express
"Truly the confessional quality makes Confessions of a Bone Woman very strong. It will endure as a teaching manual for many who are still bound up. I applaud this wild, raw courage because so many of us are bound up by something."
— Gail Burkett PhD, author of Nine Passages for Women and Girls: Ceremonies and Stories of Transformation
"Confessions of a Bone Woman is a testament to wildness and womanhood. Lucinda Bakken White takes us on a journey that strips to the bone our defenses against our own nature, exposing the richness, fertility, and power in the marrow of our inner secret lives. White's book makes us remember the hunger and the howl we've spent all our lives trying to suppress, and proves that we can still be wild in a modern world. Open with caution: you may never be the same."
— Alison Nappi, creator of The Wildness Deck
"In Confessions of a Bone Woman, Lucinda Bakken White recounts her epic journey as a contemporary shaman, drawing us deep into her world where magic, nature, and symbolism meld with high-tech start-ups, haute couture, and homemaking. Her refreshingly open and honest book leads us through the dynamics, successes, and disappointments that arise as she commits herself to discovering how to live from her authentic wild self in our modern times."
— Madelyn Soldner Sullivan, creator of Made by Madelyn Sandals
© Copyright Lucinda Bakken White | All Rights Reserved
Realizing Authentic Wildness in a Civilized World and Authentic Wildness are trademarks of Lucinda Bakken White