Water/cement-ratio (w/c-ratio) is an important factor affecting quality of the concrete, which has motivated engineers to do research on determining the w/c-ratio. The traditional way to estimate the w/c-ratio by weighing the cement and water from mix design. The estimated results are usually not accurate as the other factors affect the accuracy such as moisture in the aggregates and water added in the construction site. In this thesis other methods are investigated to determine the w/c-ratio of fresh and hardened concrete.
The research was carried out in the literature review which consisted of an effect of hydration process, a source of water in the concrete, workability of the concrete as well as w/c-ratio effect on the strength of the concrete. The laboratory work consisted of the preparation of nine concrete with three different w/c-ratio and each w/c-ratio is divided into three categories of air content ranging from 2% to 10% by volume. Different measurement methods are explained in the experimental part to determine the w/c-ratio of fresh and hardened concrete. Three methods are exploited to determine w/c-ratio of fresh concrete and one method for hardened concrete. The methods utilized in the fresh concrete are hydro-mix, SONO-WZ and microwave oven. The capillary suction method is used for the hardened concrete.
Based on the result and calculation, Hydro-mix shows moisture content in the real-mixing time and it shows good results with higher air content. SONO-WZ is a portable measuring device it shows good results with higher w/c-ratio and concrete with the air content in the range of 2% to 7%. SONO-WZ is not a suitable device for the 9–10% air content concrete that is because the air gaps in the high air content concrete which affect the flow of dielectric current. Microwave shows reliable results, however, the results are less than the actual w/c-ratio because there is still some entrapped air left in the sample.
Digitalization measurement system plays an important role in modernizing the quality control of concrete. It is beneficial to measure the workability, moisture content and air content of concrete in all phases of concrete production. These measurement technologies measure the properties of concrete in real-time with minimal effort and less supervision.


text:
Round 2, Game 7 Recap: Is This Real Life?
Can we drop the "same old Blues" gimmick now?
By Justin42 May 12, 2016, 1:11am CDT
Share All sharing options for: Round 2, Game 7 Recap: Is This Real Life?
Jerome Miron-USA TODAY Sports
Blues Lineup:
Forwards- Jaden Schwartz/Alexander Steen/Vladimir Tarasenko...Robby Fabbri/Paul Stastny/Troy Brouwer...Patrik Berglund/Jori Lehtera/David Backes...Scottie Upshall/Kyle Brodziak/Steve Ott
Defensemen- Jay Bouwmeester/Alex Pietrangelo...Joel Edmundson/Kevin Shattenkirk...Carl Gunnarsson/Colton Parayko
Goalie- Brian Elliott
Game Highlights:
After all of twenty seconds burned the Blues in game 6, game 7 needed to be an all out effort from them. Not that the effort wasn't there, but the result wasn't what the Blues had hoped. It was time for the Blues to put up or shutup for the second series in a row. How would they respond? In the form of one of the best games, if not THE best game of the entire year. The Stars would come out firing though as Brian Elliott would have to be on his game early in this one. Jason Demers would have the first good chance of the game just two minutes in.
The Blues would strike the score sheet first however. On the powerplay, less than 6 minutes into the period, Fabbri would jam home goal number one for the 1-0 Blues lead.
Elliott clearly was the right choice to start this game. (Not even sure why there was a debate other than Hitch's sleep on it comment) 5 minutes left in the period, and he would once again keep the Stars from finding the back of the net. Radek Faska would get the opportunity to be denied by Elliott on this shot.
A few moments later, it would appear that Tarasenko had made it 2-0. Not so fast. The Stars would challenge if the play was offsides. Unfortunately, it was determined that Tarasenko himself was offside on the play. Here's the explanation from NHL.com:
"After reviewing all available replays and consulting with NHL Hockey Operations staff, the Linesman determined that Tarasenko was off-side prior to the goal. According to Rule 78.7, The standard for overturning the call in the event of a 'GOAL' call on the ice is that the Linesman, after reviewing any and all available replays and consulting with the Toronto Video Room, determines that one or more Players on the attacking team preceded the puck into the attacking zone prior to the goal being scored and that, as a result, the play should have been stopped for an "Off-side infraction; where this standard is met, the goal will be disallowed."
Tarasenko's skate was in the air while the puck was crossing the blue line so it was ruled that he was offside. Here's the thing on that...It didn't matter because Stastny would score less than a minute later anyway to actually make it 2-0.
With a minute thirtyish left at that point, you would think that the Blues probably were done scoring for the period...Mr. Berglund would like to have some words with you on that as he would score with just 4 seconds left. The Blues were up 3-0 after one period. With three minutes gone in the second, Brian Elliott would come up big again. This time he would make a spectacular kick save on Colton Sceviour and give the middle finger to anyone that doubted him starting tonight.
With what's happened in the past, were the Blues of the second periods past going to show up tonight? David Backes sure didn't think so as he rings one off of the post and in for the 4-0 Blues lead. Different goalie/same result as Kari Lehtonen was chased for Antti Niemi. Also going to through out the great pass from Berglund to get Backes in that position in the first place.
The Stars would have their chances in the period though. The best one was on the powerplay midway through the period. Patrick Eaves and Jason Spezza would both get chances on the play, and Jamie Benn would even try to come in and finish the play but he ended up just breaking his stick on Elliott's glove.
The Blues would net ANOTHER goal. Still in the second period, Shatty would feed it up to Stastny, who would find Fabbri, who would hit Brouwer with the pass to finish of the tic-tac-goal. This would give the Blues a 5-0 lead and just maybe give fans hope that they were going to win their second game 7 of the 2016 postseason.
The Stars finally scored in the third period. A for effort. Even this was deflected in.
They almost scored twice in the third, but luckily Elliott was able to make the stop.
The Blues would score again in the third though. Tarasenko would finally get his goal, that he thought he had way back in the first. The Stars would pull Niemi, and with a little under 5 minutes to go, Tarasenko put the nail in the coffin.
AND FOR THE FIRST TIME SINCE 2001, THE BLUES ARE GOING TO THE WESTERN CONFERENCE FINALS.
The Blues come out and completely dominate the Stars and win by the final score of 6-1. All kidding aside, it was a great series that could have gone either way. Congrats to them on a great year. Game one of the Western Conference Final will begin Sunday night at Scottrade. Puck drop around 7. That also means another round of your favorite Gametime paper! Get out and buy one!
From The Twitter Machine:
Just in case you thought tonight might have been a dream...
In case you didn't believe 6-1 was the final score, here's proof ... #stlblues pic.twitter.com/BA6EX1tuc5
— Lou Korac (@lkorac10) May 12, 2016
Our favorite organist was at it again...this time Deep In The Heart Of Texas got the funeral treatment
#STLBlues eliminate the #DallasStars and @JABsMusic once again pays tribute to the fallen! https://t.co/HFXzgRPxgq
— Frozen Faceoff (@FrozenFaceoff) May 12, 2016
Blues fans at work tomorrow...#stlblues pic.twitter.com/b9ppkxErBa
— Justin Dubya (@justin03) May 12, 2016
And finally, us too Hitch, us too...
Hitchcock: "I'm happiest for the guys who were here before I got here. Those are the guys that did all the bleeding to get us here."
— St. Louis Blues (@StLouisBlues) May 12, 2016
Three Stars:
Third - Paul Stastny (1 goal, 2 assists. Swear someone in the lounge wanted him benched. Huh.)
Second - Robby Fabbri (1 goal, 2 assists...to think he might not have been with the big club this year)
First - Brian Elliott (31 saves. Hopefully can get some back treatment before the next round)
Blues At Stars Game Seven Storystream: Unnecessary
Beyond Checkerdome Quick Shot Round 2 Game 7
Video Highlights: Blues Defeat Dallas Stars, 6-1

redpajama_set_name: RedPajamaCommonCrawl


Discussion in 'CycleChat Cafe' started by Cab, 6 Jul 2007.
From my new toy (ATC2000). I've got some footage that I'd like to trim down to size. What software should I use? Freebie would be best, of course, but if a purchase is necessary then so be it.
I mainly use Windows Movie Maker to capture and edit video. This should be a freebie with most (recent) systems. There's also a load of extras for credits and transitions available from MSN.com. I've tried a few others but keep coming back to Movie Maker.
Once you''ve got the clip/s ready you'll need the usual software (Nero/Roxio) to burn the disc if you want it on DVD.
I'll give it a shot after filming a bit of the TdF (hopefully).
Cab, Virtualdub is a free video/movie editor depending on the filetype of your video's.
Avid free DV is very good for the money. You may suffer with hardware compatibility using avid free DV though. I've never yet got it to work with a camcoder. I used to work with and support avid editing suites for broadcasting so I'm not a numpty with this stuff (I hope). The good news is you can drag and drop the video as a file from the camera or SD card. You then import it into freedv if it'll run. Once you know your way around it it's good software. Makes me feel like I still get to play on media composer.
If not use windows movie maker. It's easy to use but a bit pants. Even basic rendering like a cross fade between shots is appalling with bad pixelation. It's not as important with an ATC2K as the piccies aren't high res to start with. Even using my cheap camcorder movie maker suffers on the most basic of edits. The free stuff you get with a mac is better but if you wanted to do lots of editing and already have a PC I'd spend the money on premiere elements.


text:
Home > Products > Joanne Kyger
Joanne Kyger
One of the major poets of the San Francisco Renaissance, Joanne Kyger was born in 1934 in Vallejo, CA. After studying at UC Santa Barbara, she moved to San Francisco in 1957, where she became a member of the circle of poets centered around Jack Spicer and Robert Duncan. In 1960, she joined Gary Snyder in Japan and soon traveled to India where, along with Allen Ginsberg and Peter Orlovsky, they met the Dalai Lama—all experiences she has written extensively about. She returned to California in 1964 and published her first book, The Tapestry and the Web, in 1965. In 1969, she settled on the coast north of San Francisco. She has published over 30 books of poetry and prose, including The Japan and India Journals: 1960-1964 (2015), On Time: Poems 2005-2014 (2015), As Ever: Selected Poems (2002), and About Now: Collected Poems (2007), which won the 2008 Josephine Miles Award from PEN Oakland. She taught at Naropa University, The New College of California, and Mills College. In 2006 she was awarded a grant from the Foundation for Contemporary Arts. She passed away in March 2017.
Kyger's aesthetics have been strongly influenced by her affiliation with poets of the Beat generation. How does the Beat tradition play out in the new century? Not through nostalgia or stylistic memorabilia, but an adherence to ethics of resistance, set in motion by Kyger and the poets whose circle she was part of.
Kyger never lacks for source material—"'Everything' is poetry, animated, kicking its heels."
Kyger beautifully observes her life and times. She's realistic, yet graceful and good-willed, annotating herself, her friends and acquaintances, while definitely saying no to Sartre's dictum, 'hell is other people.'
—Ed Sanders
Reviews of books by Joanne Kyger
There You Are: Interviews, Journals, and Ephemera
- Reading from On Time and discussion with Steve Heilig at The New School at Commonweal
To Rearrange the World: A discussion of Philip Whalen's "Life at Bolinas. The Last of California." PoemTalk #110 in Jacket2:


text:
Constanta lui Loschmidt sau Numărul lui Loschmidt (simbol: n0) este numărul de particule (atomi ori molecule) ale unui gaz ideal dintr-un volum dat (cunoscut și ca densitate numerică). Este cel mai adesea utilizat pentru condiții standard, sau normale, de temperatură și presiune. Conform regulilor din 2014 ale CODATA (Comitetul de Date pentru Știință și Tehnologie),  valuarea recomandată este de  per metru cub la 0 °C (sau 273,16 K) și 1 atm, comparativ cu valoarea recomandată în 2006 de aceeași CODATA, care era  de 2.686 7774(47) per metru cub la 0 °C și 1 atm.

Constanta este numită după fizicianul austriac Johann Josef Loschmidt, care a fost primul om de știință, care, în 1865, a fost capabil de a estima corect dimensiunile reale ale moleculelor. Termenul de "Constanta lui Loschmidt" este adesea folosit cu referire la Numărul lui Avogadro, mai ales în textele de limbă germană.

Constanta lui Loschmidt este dată de relația matematică:

unde p0 este presiune atmosferică standard la nivelul mării, kB este constanta lui Boltzmann iar T0 este temperatura termodinamică absolută. Relația sa cu constanta/numărul lui Avogadro, NA, este dată de relația:

unde R reprezintă constanta universală a gazului ideal.

Fiind o măsură a densității numerice volumice, constanta lui Loschmidt este utilizată pentru a defini  amagat-ul, o unitate practică de utilizare a densității numerice pentru gaze, dar și pentru alte substanțe.
1 amagat = n0 = ,
astfel încât constanta lui Loschmidt este exact '''1 amagat.' Determinări moderne
În setul de recomandări al CODATA pentru constante fizice, constanta lui Loschmidt este calculată utilizând constanta universală a gazului ideal și numărul lui Avogadro, conform relației de mai jos:

unde A(e) este masa atomică relativă a electronului, M este  masa molară constantă, c este viteza luminii în vid, α is the constanta structurii fine a materiei, R este constanta lui Rydberg și h este constanta lui Planck. Presiunea și temperatura pot fi ales (relativ) "la întâmplare", dar este recomandabil să fie cele standard, adică să fie cotate ca fiind cele folosite în calcularea constantei lui Loschmidt. Precizia cu care constanta lui Loschmidt este astăzi cunoscută este limitată strict la incertitudinea determinată de valorea constantei gazului ideal, comparativ cu gazele reale.

 Primele determinări
Prima determinare estimativă a lui Loschmidt dădea o valoare de aproximativ n'' = 1.81 m. Opt ani mai târziu Maxwell cita o valoare de "aproximativ 19 milioane de milioane de milioane" per cm, ori 1.9 m.

Vezi și
 Legea lui Avogadro

Referințe

Legături externe
  ThermoPedia
  Chemistry God
  ScienceWorld at Wolfram.com
  Definitions.net

Cantitate de substanță
Constante fizice

redpajama_set_name: RedPajamaWikipedia


As Hilda struggled with the spread of typhoid in France she also faced problems from a different direction – Britain. There are hints in her letters that the tension had been building for a while and as the year went on she found herself in open conflict with the people administering the Friends War Victims Relief Committee at the Quaker central offices in London.
She wrote to her friend Edith in a letter that gives a sense of just how many problems she was dealing with and how little time she had for all her work.
A combination of too much work and too little certainty makes one disinclined for writing letters, but I had so much to do and I have had to leave so much undone that I really can't say I have been any busier than usual.
Things have got so wrong between us and the London Committee that if I could be sure of getting out again I should try to get back to see them as soon as the other doctors come.
We hoped our difficulties with the authorities were over but I am rather afraid they are not, and we only have a very few of the long waited-for workers back at their jobs. The London Office appears to have forgotten the existence of the two doctors for whom I was waiting so anxiously – one of whom I know was ready to start last Wednesday.
I went to Paris last Saturday to meet Dr Kerr on his way back from Samoëns and to make arrangements for the new workers coming out. On Monday I went back to see to things at Châlons and Sermaize, finally reaching Sermaize at 9.30 in the most utter darkness of black pelting rain, and found my way to La Source by a combination of good luck and country-bred eyes, which did not save me from getting very wet in the deep pools of water of which that strip of road – always one of the worst round here - is full. One could steer by the tops of the trees but it was good luck not to meet anyone coming the opposite way. I am now on my way back to Paris to see about various things.
Mr Shewell is now doing the Paris work. I also have to see the representative of the Rockefeller Foundation, Professor Sabine, about the tuberculosis question, and M. Verde Delisle, who helped us when we first came out, about our present difficulties. We are going to have a Committee, and Friday afternoon I have to see patients for Samoëns. If Dr Macphail has not come by then I shall have to go to Samoëns myself until she does. Otherwise I shall come back to Châlons and Sermaize on Saturday and go to Troyes on Sunday.
Our workers in Troyes - who are starting a scheme of selling furniture to the refugees so as to enable them to save the exorbitant prices they are now paying for furnished rooms, or for hire for furniture – find the health has suffered very much more there than anywhere else we have been, and we are arranging for a district nurse to work there as soon as she can get permission to go there. I expect to find suitable cases for Samoëns there.
Although we have only Hilda's side of the story, she seems to have been frustrated with what she perceived as inefficiency and bureaucracy. In chaotic situations it can be hard to see the bigger picture. Inadequate communication, especially in wartime, can lead to misunderstandings and internal conflict. Poor information from the field can lead to unwise decisions by distant bodies. Can you think of a modern day example of such dissonance?
Quakers were divided in various ways during World War I. Some who were sympathetic to the government and the war were critical of the behaviour of more radical Quakers. This letter appeared in The Times on 19 February 1916.


I'll show you to my brethren.
I'm like this American Boy.
I'm likin this American Boy.
Reluctantly, 'cause most of this press don't f**k wit me.
Estelle once said to me, cool down down don't act a fool now now.
Ribena I know what you're drinkin.
Holler at the next chick soon as you're blinkin.
Look at this Peacock tail, means he's broke.
'Cause a lot WAGs want to hear it.
The pimps at they gladdest.


Ready for any task, the 11-inch tall Installer Soft Toe workboot is both durable and comfortable. The golden brown leather exterior is waterproof and features a vibrant yellow, orange, and navy stitching along the upper giving this boot a retro feel. The saddle perforated foot sports a round toe and the J-Flex Flexible Comfort System® insole guarantees cushion and support throughout the day. The rubber outsole provides maximum traction on various surfaces and its single stitch welt offers durability to everyday wear and tear giving this boot extended wear no matter the conditions.
A similar boot last time lasted for three years in a harsh work environment, and I was worried when I bought this new style that it would not be as good. Boy was I worried for nothing! This is nothing short of amazing.
I bought this boot a couple of weeks ago and they have been my favorite pair of boots ever since. They have been comfortable from the first time I put them on. Thanks Justin Boots for making Great Quality Boots. And now I have a job to where i can sell them.
I have a pair of of these and they are great, the rounded toe really gives you room for your toes. If y'all want to have a boot that feels great all day long then this is what you want. Justin Boots the best all around Boots get a pair today you will not be sorry.


Thank you for taking time to find out more about KLT Charting's Layaway scheme - the perfect way of spreading your payments if you can't afford them all in one go.
Please note that the minimum order amount to be eligible for the layaway scheme is £15. Any orders less than this amount must be paid in full immediately.
You can now order your layaway items via the website. Simply add your chosen items to your basket as normal, and proceed to the checkout. At the payment details screen, simply select 'Layaway' as your chosen payment method.
I will then generate an invoice via PayPal, and enable the partial payments option. This will allow you to pay as much as you want from your invoice, broken down into manageable chunks during the payment time period.
Please note, your order will not be dispatched until the invoice is paid in full, and for orders including hand dyed fabric, your fabric will not be processed and dyed until the invoice is paid, and it will then be subject to the usual waiting times.
All layaway invoices are required to be paid off within FOUR months. As such, the minimum payment required each month is the total invoice amount divided between the number of months outstanding.
If there is more than one month since your last payment, you will be sent a reminder email.
Any invoices that have had no payments or customer contact for three months will be cancelled. In this event, any partial payments made will be forfeited and NOT refunded.
By selecting the Layaway payment option at the checkout, you are agreeing to these terms and conditions.
We regret that the Layaway option cannot be used for Club orders.
Layaway terms updated and are correct as of 15/2/18. Any layaway orders placed before this time are unaffected by the changes.


text:
mo4ch:>Disgruntled Miami airport mechanic arrested for sabotaging plane with 150 passengers | Mo4ch News
Mo4ch News September 06, 2019 world
An American Airlines mechanic has been arrested and charged with sabotaging a plane in Miami in order to get the 150-passenger flight canceled so he could get paid overtime. His alleged plan worked – but was it worth prison time?
Abdul-Majeed Marouf Ahmed Alani glued a hard foam substance inside a tube connecting the plane's air data module, which computes crucial data about the aircraft's speed, angle, and other metrics, with an outside sensor in order to get the flight grounded and secure overtime pay for himself, according to charges filed against him after his arrest for "willfully damaging, destroying, or disabling an aircraft" on Thursday.
Also on rt.com 400+ 737 MAX pilots sue Boeing over 'unprecedented cover-up' that led to crashes & grounding
Fortunately for the 150 passengers on board the July 17 flight to Nassau, Bahamas, when the pilots started up the plane, the sabotaged instrument displayed an error message, and the takeoff was aborted. The plane was taken out of service for maintenance, and the foam-filled tube was discovered by another mechanic.
Alani insisted he was not trying to harm the aircraft or its passengers – he had only messed with the module "in order to cause a delay or have the flight canceled in anticipation of obtaining overtime work," according to the arrest affidavit. The disgruntled mechanic was "upset" over contract negotiations between American Airlines and the mechanics' union, a struggle that has lasted for years and which he said "affected him financially."
Federal air marshals were able to identify the mechanic from his limping walk on surveillance footage, which showed him accessing a compartment on the aircraft where the navigational system was located and fiddling around with it for about seven minutes. If convicted of the sabotage, Alani faces 20 years in prison.
Also on rt.com Airbus attempts to sell the world on pilotless planes - or would you rather fly Boeing?
But according to American Airlines, Alani isn't the only employee breaking the law. The airline sued his 30,000-strong union in May, accusing them of organizing an illegal work slowdown, and the court ruled in favor of the company. The union, which had vowed to wage a "bloody" battle over the summer, was slightly mollified, encouraging employees to heed the court's ruling, but continued to deny there had been an organized slowdown and announced on Wednesday that it would resume negotiations with American later this month.
Disgruntled Miami airport mechanic arrested for sabotaging plane with 150 passengers
By Mo4ch News at September 06, 2019


GNU Midnight Commander is a file manager for the shell, which is licensed under GPL and is therefore Free Software.
As Midnight Commander is a text mode application. It can be used locally and/or remotely, on the console or under the X Window System.
This is the new site for the Midnight Commander, due to some restructurements in the development team the repository was moved from savannah into this git repository.
You are interested in mc and would like to improve it? Cool! Feel free to post your patches to the tickets and review patches there. If you do this on a regular basis we'll ask you if you maybe want to become a dev. However when you work with the ticket system please have a look on our WorkingGuideLines, so that your work is noticed by us.
If you are unfamiliar with git, you can also have a short view on our GitGuideLines where the workflow with git is described quite practical.


Alfriston is a heavy cropping apple tree widely grown and admired by the Victorians. It supposedly originated in the village of Alfriston in Sussex, but it was previously known under various other synonyms relating to a Mr. Shepherd of Uckfield, also in Sussex . Alfriston's large and attractive bright green oblong apples keep well and are best thinned in June.
Like all our apple trees, we offer Alfriston bare root. At the height of the lifting season there may be up to a month's delay between placing the order and dispatching due to pressure of orders, which are dealt with in date sequence, and the weather. Orders for apple trees taken from March to September are confirmed in September ready for dispatch from November. Please note there is an overall minimum order size of £50.


text:
Keteka >>All destinations >>Products >>Ecuador >>Galapagos Islands >>Ecuador and theGalapagos Islands: 9-Day Encounter
Galapagos Islands, Quito, The Andes Region, EcuadorEcuador and theGalapagos Islands: 9-Day Encounter
Ecuador and theGalapagos Islands: 9-Day Encounter
Get to know Ecuador's heart and soul in this amazing nine day adventure. Hike along active volcanoes, snorkel with sea lions, and swim in bubbling volcanic lagoons. In the Galapagos Islands, see species found nowhere else on earth — including the great Galapagos tortoise. Explore the UNESCO World Heritage City of Quito, and small mountain villages. Visit the Otavalo artesanal market, and more!
Explore Ecuador's Unsurpassed Natural Beauty and Outstanding Cultural Heritage on this 9-Day Encounter
On this outstanding and unforgettable 9-day tour of Ecuador and the Galapagos Islands, hike along active volcanoes, snorkel with sea lions, and swim in bubbling volcanic lagoons. Visit the Charles Darwin Research Center to see endemic species found nowhere else on earth — including the great Galapagos tortoise.
Fall in love with Ecuador's history and living cultural heritage, too. Explore small mountain villages and visit native artesanal markets where you'll find textiles, native paintings, ceramics, jewelry, tapestries, and more. Explore the UNESCO World Heritage City of Quito and learn why it's considered the "Cultural Capital of South America."
You'll hike and swim along volcanoes, islands, and mangrove forests. You'll explore bustling cities and small mountain communities. Discover a small country's larger-than-life natural and cultural heritage on this unforgettable 9-day Ecuador tour.
Hike the Cuicocha Volcano and swim in the volcanic waters
Hike the Sierra Negra Volcano for panoramic views of the Galapagos Islands
Observe Galapagos tortoises at El Chato Reserve
Explore the Otavalo market, South America's most famous native artesanal market
Snorkel in the Galapagos with giant turtles, rays, penguins, and sea lions
Explore Quito a UNESCO World Heritage Site for its historic city center
Visit the birthplace of the "Baroque School of Quito" — a fusion of indigenous and European art
Start/End Location: Quito
Group size: 2 — 12 people
Travel style: Active
Additional Notes: Guides are certified Wilderness First Aid Responders, and carry a complete first aid kit.
Day 1: Arrive in Quinto and Explore the City
Welcome to Quito! Founded by the Spaniards over the ruins of an Inca city, Quito is considered "The Cultural Capital of South America" and has been named a UNESCO World Heritage Site for its outstanding fusion of indigenous and European architecture and for its historic city center — among the oldest and best maintained in all of South America.
A trip leader will meet you at your hotel to discuss details and answer any questions you may have about your trip, but otherwise, the day is yours to adventure or unwind as you'd like.
Visit the stunning city center and look for "Baroque school of Quito" art and architecture — an iconic blend of indigenous and European that once caused the king of Spain to announce he had no need for a Spanish Michelangelo.
Accommodation: Nu House Hotel or similar
Day 2: Stroll through South America's Most Famous Artesanal Market
Today, you'll explore Otavalo market, South America's most famous native artesanal market. You will leave early from Quito and enjoy the dynamic mountain scenery as you head north to Otavalo.
You will have plenty of time to explore the narrow cobblestone streets, admire the traditional blue ponchos, white trousers, and long braided hair of the native Otavalos, and of course explore the handicraft market with its wonderful textiles, native paintings, ceramics, jewelry, tapestries, and more.
You will continue to Cotacachi for lunch, an area famous for leather goods. Then you will head to Peguche, a tiny village known for waterfalls, wind instruments, and woven fabrics.
Accommodation: Pinsaqui Hacienda
Day 3: Hike to Cuicocha Crater in Cotacachi Cayapas Ecological Reserve
After breakfast, set out for breathtaking full-day hike around Cuicocha Crater Lake, an active volcano located amidst the stunning scenery of Cotacachi Cayapas Ecological Reserve.
You will hike 14.4 kilometers (9 miles) around the crater then take a boat ride to the center of the lagoon to watch bubbles rising from the caldera. Here you'll have the thrilling opportunity to jump into the volcanic waters for a refreshing swim!
Meals included: Breakfast and lunch
Day 4: Encounter the Galapagos and Isabela Island
Today you will explore the amazing archipelago on the Pacific Coast and the birth place of Charles Darwin's theory of evolution. Galapagos is a truly magnificent natural wonder, where you can swim with sea lions, turtles, penguins, sharks, and countless tropical fish.
You will take a morning flight from Quito to the island of Baltra, then head to Puerto Ayora, the biggest town on Galapagos. You will travel by speed boat to Isabela, where you'll hopefully spot dolphins and whales as you cross (July-October).
Isabela is the largest island in the archipelago. Isabela Island is formed by six shield volcanoes called Alcedo, Cerra Azul, Darwin, Ecuador, Sierra Negra, and Wolf. Five of them are active, thus making the island one of the most volcanically active places on earth!
Accommodation: Isabela Beach House or similar
Day 5: Hike along Sierra Negra Volcano and Snorkel in Tintoreras
After breakfast you set out on a full-day hike to the rim of Sierra Negra Crater — an active volcano! With a local guide, you'll hike about 9.6 kilometers (6 miles) along the crater's widest point and learn about native flora and fauna that thrive in the nutrient-rich, volcanic ash-filled soil.
Once at the summit you will have stunning views of the massive, lava filled crater. Depending on the timing or weather conditions, you may have the opportunity to continue onward to Volcan Chico.
Afterwards, you will visit the Tintoreras near to Puerto Villamil, home to many different types of wildlife. You can snorkel in the crystal clear water and spot white-tipped reef sharks, Galapagos penguins, marine turtles, and sea lions. One of the beaches is surrounded by mangroves, and is one of the few places where marine iguanas can successfully reproduce!
Day 6: Explore the Isabela Island Lava Tunnels
Today you will explore the stunning lava formations created by the volcanic activity on the island. This magical place has been used by BBC and National Geographic for documentaries and is a labyrinth of lava arches and tunnels, above and below the water.
The crystal clear water is full of large sea turtles, eagle rays, sea lions, fish, white-tipped reef sharks, and more. Through 'Los Tuneles,' turtles navigate their way from the mangroves to the sea giving you a great opportunity to snorkel with all these beautiful creatures.
You will kayak in the afternoon, paddling along the shoreline of Las Tintoreras, on the lookout for wildlife. Then watch a breathtaking sunset over the beach of Puerto Villamil.
Day 7: Visit the Charles Darwin Research Station and Spot the Famous Galapagos Tortoises
Today, you will take a speed boat from Isabela to Santa Cruz Island. Be on the lookout for dolphins and whales (July to October). After you check in at the hotel, you will visit the Charles Darwin Research Station where you can learn about the endemic species around Galapagos Islands, and the ongoing conservation and management efforts.
Then it's off to El Chato Reserve in the highlands of Santa Cruz, home to the famous Galapagos tortoises. Hiking through El Chato's stunning scenery with these lumbering giants is an unforgettable experience. You will also see other highland wildlife such as Pintail ducks, Egrets, Flycatchers, Finches, and Frigate birds. In Puerto Ayora you will have free time in the evening.
Accommodation: Galapagos Inn or similar
Day 8: Leave the Galapagos and Return to Quinto
After one final breakfast on the Galapagos Islands, you will transfer to Baltra's airport for a flight back to the mainland. You'll have the evening to enjoy the city.
Accommodation: Nu House or Similar
Day 9: Say Goodbye to Quinto
After breakfast, you'll say goodbye Quito and your guide will transfer you to the airport for your onward travel.
Entry fees to Cuicococha, Sierra Negra, Tintoreras, Lava Tunnels, Isabela island, Charles Darwin Research Station, and El Chato Reserves
Nightly accommodation (based on double occupancy)
Comfortable land and sea transportation
Meals as noted in above itinerary
Outdoor gear: hiking sticks, life jackets, kayaks, and paddles.
Expert English-speaking tour leader specialized in adventure expeditions
Airfare from Quito to Galapagos.
Single room supplement (Optional, $630.00 USD)
Travel insurance (Mandatory)
Meals not described in the itinerary (Consider food budget of $190.00 USD)
Galapagos National Park fee ($100.00 USD)
Pier Use Fee at Puerto Villamil ($5.00 USD)
Gratuities (Optional)=
Credit or debit card (For personal spending money)
Fleece top
4 shirts/t-shirts
1 pair of shorts
2 pairs of long trousers
1 pair hiking pants/track pants
Watch or alarm clock
Quick dry clothing
Waterproof camera if possible
Waterproof back pack
Dry bag for your belongings
Itinerary disclaimer: The intention is to adhere to the itinerary described, however, on occasions it may be necessary or desirable to make small changes. Weather conditions and unpredictable local circumstances may lead to route or travel time alterations. Hotels and flight schedules might change according to availability. Not all wildlife specified in the itinerary is guaranteed to be seen during the trip.
Water temperatures: The unique waters of the Galapagos Islands range between 65ºF and 85ºF.
Difficulty level: Moderate to challenging. The Cuicocha trek can be challenging because of the altitude. Hiking in Sierra Negra is moderate but long and muddy during the wet season (July – November). No experience is needed for snorkeling and kayaking.
Meals: Your guide will identify typical food and suggest the best places to eat. Some meals are not included giving you flexibility about where, what, and with whom you eat. If you have allergies or food restrictions, please inform the company prior to departure.
Accommodation: Prices are based on double occupancy. If you are travelling solo, you will be matched with someone of the same sex. If you prefer a single room at an extra cost, please contact the company prior to departure.
Sustainable eco and cultural tourism: You will receive a conservationist interpretation of the ecology of the Andes, Amazon Rainforest, and Galapagos Islands with the factors that threaten it. The company aims to promote long term conservation of the local people, culture, and their environment. Your visit will support responsible ecotourism. The company transports out and recycles all non organic garbage.
Quito, Ecuador -0.180653, -78.467838
Be the first to review "Ecuador and theGalapagos Islands: 9-Day Encounter" Cancel reply
Otavalo Indigenous Market & More - Full Day Tour
USD $80.00 Per Person
Baños, Ecuador
El Puyo Ecuador: Full-Day Trip Out of Baños
Amazon Region Ecuador, Ecuador
Kallari Chocolate Day Tour
Kichwa Kallari Chocolate Experience (3 Days)


50 participants took part, so 25 teams in the running to be one of the 3 teams to represent Renault in the race. Now there are only 6 participants left! Meet the women who'll represent Renault, behind the wheel of Dacia Duster, during the Rallye Aïcha des Gazelles 2013, that will take place in Morocco, from March 16 to 30.
The jury of the Rallye Aicha des Gazelles met on December 13, 2012 in the presence of Claire Martin, Jury President and Director of Corporate Social Responsibility, Thierry Bolloré, a member of the Executive Committee of Renault, Manufacturing and Supply Chain, and representatives of the Communication and Human Resources departments.
Laetitia RODIER (Direction de l'Ingénierie Véhicule / DIESC) and Sylvie QUITTOT (Direction de l'Ingénierie Véhicule / DIESC) as the Women@Renault team.
Anne-Sophie CALLY (World Supply Chain / ALE) and Paulina HACHOUD (World Supply Chain / DPPL) for the DFSCG team.
Saida BENHAMIDA (Tangier plant / Quality) and Safae CHALLAL (Tangier plant / HR) for the Renault Morocco team.
Natacha ROSENBERG (Direction Après Ventes / DCPA) and Aurélie TOURNAN (Direction Commerciale Europe / Renault Austria) who will also represent the Women@Renault network.
They represent all positions within Renault. Congrats to all our winners!


text:
\section{Introduction}

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

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

Per a passed 2018 PAMI-TC motion, reviewers should refrain from requesting significant additional experiments for the rebuttal or penalize for lack of additional experiments.
Authors should refrain from including new experimental results in the rebuttal, especially when not specifically requested to do so by the reviewers.
Authors may include figures with illustrations or comparison tables of results reported in the submission/supplemental material or in other papers.

Just like the original submission, the rebuttal must maintain anonymity and cannot include external links that reveal the author identity or circumvent the length restriction.
The rebuttal must comply with this template (the use of sections is not required, though it is recommended to structure the rebuttal for ease of reading).

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

\section{Formatting your Response}

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

All text must be in a two-column format.
The total allowable size of the text area is $6\frac78$ inches (17.46 cm) wide by $8\frac78$ inches (22.54 cm) high.
Columns are to be $3\frac14$ inches (8.25 cm) wide, with a $\frac{5}{16}$ inch (0.8 cm) space between them.
The top margin should begin 1 inch (2.54 cm) from the top edge of the page.
The bottom margin should be $1\frac{1}{8}$ inches (2.86 cm) from the bottom edge of the page for $8.5 \times 11$-inch paper;
for A4 paper, approximately $1\frac{5}{8}$ inches (4.13 cm) from the bottom edge of the page.

Please number any displayed equations.
It is important for readers to be able to refer to any particular equation.

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

List and number all bibliographical references in 9-point Times, single-spaced,
at the end of your response.
When referenced in the text, enclose the citation number in square brackets, for example~\cite{Alpher05}.
Where appropriate, include the name(s) of editors of referenced books.

\begin{figure}[t]
  \centering
  \fbox{\rule{0pt}{0.5in} \rule{0.9\linewidth}{0pt}}

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

To avoid ambiguities, it is best if the numbering for equations, figures, tables, and references in the author response does not overlap with that in the main paper (the reviewer may wonder if you talk about \cref{fig:onecol} in the author response or in the paper).
See \LaTeX\ template for a workaround.

\subsection{Illustrations, graphs, and photographs}

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

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

{\small
\bibliographystyle{ieee_fullname}

\section{Introduction}
``AI-generated content" has attracted increasingly more public awareness thanks to the significant progress in recent research of high-fidelity text-aligned image synthetic tasks.~\cite{ramesh2021zero,rombach2021high,ramesh2022hierarchical,saharia2022photorealistic,yu2022scaling}. Particularly, models trained on web-scale datasets have demonstrated their impressive ability to generate out-of-distribution images from arbitrary text inputs that describe unseen combinations of visual concepts.

Starting from DALL-E~\cite{ramesh2021zero}, researchers have proposed a variety of approaches to further
advance the state-of-the-art (SOTA) of text-to-image generation in terms of both generation quality and efficiency.
Latent Diffusion Model~\cite{rombach2021high} trains a diffusion model in the latent space of auto-encoder instead of pixel space, leading to better generation efficiency. GLIDE~\cite{nichol2021glide} adopts a hierarchical architecture, which consists of diffusion models at different resolutions. Such a model design strategy has shown to be effective and has been adopted by many follow-up works.
DALL-E 2~\cite{ramesh2022hierarchical} further introduces an extra image embedding input.
Such an image embedding not only improves the model performance in text-to-image generation but also enables applications, including image-to-image generation and generation under multi-modal conditions.
Imagen~\cite{saharia2022photorealistic} makes use of a rich pre-trained text encoder~\cite{raffel2020exploringt5}, demonstrating that a frozen text encoder pre-trained on the large-scale text-only dataset can help text-to-image generation models in understanding the semantics of text descriptions.
Parti~\cite{yu2022scaling} shows a further successful scale-up of the generative model, leading to impressive improvement in text-to-image consistency with transformer structure.

The aforementioned approaches focus on improving text-to-image generation by either scaling up trainable/frozen modules or designing better model architectures. In this work, we explore an orthogonal direction, where we propose novel techniques to improve the diffusion process itself and make it more suitable and effective for text-to-image generation.

\begin{figure*}[ht!]
    \centering
    \begin{subfigure}[t]{0.32\linewidth}
        \includegraphics[width=1.\linewidth]{figures/Corgi/Corgi_blackboard.png}
        \caption{A corgi in front of a blackboard with ``Bridge!" on it.}
    \end{subfigure}
    \begin{subfigure}[t]{0.32\linewidth}
        \includegraphics[width=1.\linewidth]{figures/Corgi/Corgi_neon_bridge.png}
        \caption{A corgi with a neon light reads ``Bridge!".}
    \end{subfigure}
    \begin{subfigure}[t]{0.32\linewidth}
        \includegraphics[width=1.\linewidth]{figures/Corgi/Corgi_real_photo.png}
        \caption{A realistic photo about corgi with word ``Bridge!".}
    \end{subfigure}
    \begin{subfigure}[t]{0.32\linewidth}
        \includegraphics[width=1.\linewidth]{figures/Corgi/Corgi_on_magazine.png}
        \caption{A magazine about Corgi, which has ``Bridge" on it.}
    \end{subfigure}

    \begin{subfigure}[t]{0.32\linewidth}
        \includegraphics[width=1.\linewidth]{figures/Corgi/corgi_whiteboard.png}
        \caption{A corgi is smiling, in front of a whiteboard with ``Bridge the Gap" on it.}
    \end{subfigure}

    \begin{subfigure}[t]{0.32\linewidth}
        \includegraphics[width=1.\linewidth]{figures/Corgi/corgi_speech.png}
        \caption{A corgi is giving a speech about ``Bridge the Gap" in cartoon style.}
    \end{subfigure}
    \vspace{-0.15in}
    \caption{We propose Corgi, a novel diffusion model designed for flexible text-to-image generation which can ``bridge the gap".
    }
    \label{fig:corgi}
    \vspace{-0.2in}
\end{figure*}

Specifically, we propose Corgi (\textbf{C}lip-based shifted diffusi\textbf{O}n model b\textbf{R}id\textbf{GI}ng the gap), a novel diffusion model designed for a flexible text-to-image generation. Our model can perform text-to-image generation under all the supervised, semi-supervised, and language-free settings.
By ``bridging the gap," we emphasize two key novelties in our method: (1) our model bridges the image-text modality gap~\cite{Liang2022MindTG} so as to train a better generative model. Modality gap is a critical concept discovered in pre-trained models such as CLIP~\cite{radford2021learning}, which captures the phenomenon
that multi-modality representations do not align in the joint embedding space. By bridging the gap with Corgi, we can better utilize CLIP in text-to-image generation, which is shown in the experiment to achieve better generation; (2) our model bridges the gap of data availability for different researchers/communities. In our design, Corgi can naturally enable semi-supervised and language-free text-to-image generation, where only a small portion or even none of the images in the training dataset are captioned. This is important because the cost of constructing a high-quality large image-text-paired dataset could be prohibitive, especially in the case where hundreds of millions of images need to be captioned.
We show that by merely using an image-only dataset and the public CC15M dataset~\cite{Sharma2018ConceptualCA,changpinyo2021conceptualcc12m}, Corgi achieves promising results that are comparable to the SOTA models on open-domain text-to-image generation tasks.
To summarize, our contributions are:
\begin{itemize}[nosep]
    \item We propose Corgi, a novel diffusion model that seamlessly incorporates prior knowledge from the pre-trained model ({\it e.g.,}CLIP) into its diffusion process;
    \item Our method is general and can

    be applied in
    different settings of text-to-image generation, {\it e.g.}, it naturally enables semi-supervised and language-free text-to-image generation;
    \item Extensive and large-scale experiments are conducted. Both quantitative and qualitative results illustrate the effectiveness of the proposed method. We especially demonstrate that, with only $1.7\%$ captioned images in the training dataset, it is possible to achieve results comparable to the SOTA method. In addition, our model achieves SOTA results under language-free setting across different downstream datasets.
\end{itemize}

\begin{figure}[t!]
    \centering
    \begin{subfigure}[t]{0.99\linewidth}
        \includegraphics[width=0.95\linewidth]{figures/model/train_gen.png}
        \caption{Train a decoder which generates images from image embeddings.}
    \end{subfigure}
    \begin{subfigure}[t]{0.99\linewidth}
        \includegraphics[width=0.95\linewidth]{figures/model/train_sft.png}
        \caption{Train a shifted diffusion model which generates embeddings from text.}
    \end{subfigure}
    \begin{subfigure}[t]{0.99\linewidth}
        \includegraphics[width=0.95\linewidth]{figures/model/inference.png}
        \caption{Generate image with text input at inference.}
    \end{subfigure}
    \vspace{-0.1in}
    \caption{Illustration of our framework. The trainable modules are colored in green, and the frozen modules are colored in blue.}
    \label{fig:model}
    \vspace{-0.3in}
\end{figure}
\section{Methodology}
We start by illustrating the proposed general framework for text-to-image generation. Our framework is shown in Figure \ref{fig:model}, which consists of three key components: (1) a pre-trained image encoder that maps images to their embeddings; (2) a decoder that generates images from the corresponding embeddings; and (3) a prior model that generates image embeddings from the corresponding text captions. In our implementation, we use the pre-trained CLIP image encoder because its output space is a multi-modal embedding space that has been demonstrated to benefit the text-to-image generation task~\cite{zhou2021lafite}. The decoder can be either a diffusion model or a generative adversarial network (GAN). Note that if one chooses the decoder as a hierarchical diffusion model and makes it conditioned on both image embedding and text, our final
structure will be similar to DALL-E 2~\cite{ramesh2022hierarchical}.

We adopt this framework because of its flexibility: it can perform different types of generation tasks such as text-to-image generation, image-to-image generation, and generation conditioned on both image and text. In addition, our framework naturally enables semi-supervised training, {\it i.e.}, the training dataset can be a mixture of image-text pairs and pure images that are not captioned. In this setting, the image-text pairs will be used to train the prior model, and all the pure images will be used to train the decoder. Such a semi-supervised training setting is important in practice, especially when training a text-to-image generation model on new domains with a limited budget.
As discussed in \cite{zhou2021lafite}, constructing high-quality image-text pairs could be very expensive and requires a heavy human workload. Our framework provides the community with the flexibility of choosing the number of images to be captioned based on their budget. As it will be shown in the experiments, semi-supervised training can obtain impressive results, which are even comparable to those of supervised training.

\begin{figure}[t!]
    \centering
    \includegraphics[width=0.5\linewidth]{figures/clip_space.png}
    \caption{Illustration of diffusions inside the multi-modal joint space of CLIP. We use the cone in red dotted lines to represent the effective output space of CLIP image encoder. The target embedding is represented by the black star. The green dashed arrow represents the sampling process of the baseline diffusion model, whose starting point is random noise. The blue solid arrow represents the sampling process of our shifted diffusion, whose starting point is an image embedding inside the red cone.}
    \label{fig:clip_space}
    \vspace{-0.25in}
\end{figure}

In this paper, we focus on improving the prior model, which is less exploited in previous works.
As shown in DALL-E 2~\cite{ramesh2022hierarchical}, a diffusion-based prior model is introduced to generate the target CLIP image embedding $\zb_0$ via the following sequential sampling process: $\zb_{t-1} \sim p_{{\bm{\theta}}}(\zb_{t-1} \vert \zb_t, \yb)$ for $t=T, ..., 1$, where $\zb_T \sim \mathcal{N}(\mathbf{0}, \mathbf{I})$, $t$ denotes timestep, $\yb$ denotes text caption, and $p_{{\bm{\theta}}}(\cdot|\cdot)$ is the inverse transition distribution induced from the diffusion model.
Although it is shown in \cite{ramesh2022hierarchical} that this prior model can benefit generation in terms of both image-text alignment and image fidelity,
we suspect that this vanilla sampling process may not be appropriate for generating high-quality CLIP image embeddings (which will be the inputs of decoder thus greatly influence the generation quality).
The reason is that, as revealed in \cite{Liang2022MindTG}, the effective output space of the CLIP image encoder is actually a very small region of the whole embedding space, as shown in Figure \ref{fig:clip_space}.
Consequently,  $\zb_T \sim \mathcal{N}(\mathbf{0}, \mathbf{I})$, which is the starting point of sampling, might be far away from the target embedding.
Intuitively, if $\zb_T$ is closer to the target $\zb_0$, we may be able to well approximate $\zb_0$ within fewer sampling steps. Likewise, we may better approximate the target within the same number of steps, if the initialization is closer to the target.

Based on this motivation, we propose shifted diffusion, a novel diffusion model that considers prior knowledge contained in the pre-trained CLIP image encoder. Specifically, the noise distribution $p(\zb_T)$ of shifted diffusion is a parametric distribution rather than standard Gaussian $\mathcal{N}(\mathbf{0}, \mathbf{I})$.
We present the details of our method in the following.

\subsection{Shifted Diffusion}
Let $q(\zb_0)$ be the distribution of ground-truth image embeddings in the joint latent space of the CLIP model.
Because of the fact that valid image embeddings only occupy a small region of the whole embedding space~\cite{Liang2022MindTG}, in order to achieve better generation in a diffusion process, $\zb_T \sim q(\zb_0)$ is expected to be closer to the target $\zb_0$, compared to the standard sampling of $\zb_T \sim \mathcal{N}(\mathbf{0}, \mathbf{I})$.
However, $q(\zb_0)$ is an intractable distribution, making it challenging to train a diffusion model based on it.

To tackle the aforementioned problem, we consider the initial distribution $p(\zb_T)$ to be a parametric Gaussian distribution as $\mathcal{N}(\zb_T; {\bm{\mu}}, \mathbf{\Sigma})$, which approximates $q(\zb_0)$, and can be obtained by simply analyzing the training dataset\footnote{In practice, we set $\mathbf{\Sigma}$ to be a diagonal matrix with its element $\mathbf{\Sigma}_{i,i}= \kappa \sigma_i$. $\sigma_i$ is the standard deviation of $i^{th}$ element of all image embeddings from the training dataset, $\kappa>0$ is a constant for scaling.}. We design the transition $q(\zb_t \vert \zb_{t-1})$ to be a Gaussian as
\begin{align}\label{eq:transition}
    q(\zb_t \vert \zb_{t-1}) \coloneqq \mathcal{N}(\zb_t; \sqrt{1 - \beta_t} \zb_{t-1} + \sbb_t, \beta_t \mathbf{\Sigma}),
\end{align}
where $\beta_t$ is a constant following \cite{ho2020denoising}.
Compared to the vanilla diffusion with
\[
q(\zb_t \vert \zb_{t-1}) \coloneqq \mathcal{N}(\zb_t; \sqrt{1-\beta_t} \zb_{t-1}, \beta_t \mathbf{I}),
\]
our diffusion process introduces a shift term $\sbb_t$ at every timestep $t$, thus termed {\em shifted diffusion}.

One can show that $q(\zb_t \vert \zb_0)$ has a closed-form expression (all proofs are provided in the Appendix):
\begin{align*}
    q(\zb_t \vert \zb_0) = \mathcal{N}(\zb_t; \sqrt{\bar{\alpha}_{t}} \zb_0 +  \sum_{i=1}^{t} \sbb_i\sqrt{\bar{\alpha}_{t}/\bar{\alpha}_i}, (1 - \bar{\alpha}_{t})\mathbf{\Sigma}),
\end{align*}
where $\bar{\alpha}_{t} = \prod_{i=1}^t (1-\beta_i)$.
Specifically, we choose $\sbb_t = (1- \sqrt{1-\beta_t}){\bm{\mu}}$, leading to
\begin{align}\label{eq:q_t}
    \hspace{-0.2cm} q(\zb_t \vert \zb_0) = \mathcal{N}(\zb_t; \sqrt{\bar{\alpha}_{t}} \zb_0 + (1 - \sqrt{\bar{\alpha}_{t}}){\bm{\mu}}, (1 - \bar{\alpha}_{t})\mathbf{\Sigma}).
\end{align}

\begin{remark}
As we can see, by selecting $\{\beta_t\}_{t=1}^T$ such that $\alpha_T \approx 0$, $q(\zb_T \vert \zb_0) $ can approximate $\mathcal{N}(\zb_T; {\bm{\mu}}, \mathbf{\Sigma})$, the distribution of the image embeddings. In other words, shifted diffusion process is a process that transfers a ground-truth image embedding into a \textit{random image embedding}; whereas the vanilla diffusion process is a process that transfers an image embedding into a random \textit{Gaussian noise}, which is certainly not what we expect.
\end{remark}
From Equation \eqref{eq:transition} and \eqref{eq:q_t}, we can get the closed-form expression of $q(\zb_t \vert \zb_{t-1}), q(\zb_t \vert \zb_0), q(\zb_{t-1} \vert \zb_0)$.
By the property of Gaussian distribution~\cite{bishop2006PRML} and some simple derivations, we can get the posterior distribution
\begin{align}\label{eq:q_posterior}
    & q(\zb_{t-1} \vert \zb_t, \zb_0) = \mathcal{N}(\zb_{t-1}; {\bm{\nu}}, \mathbf{\Lambda}), \\
    & {\bm{\nu}} = \gamma (\zb_t - \sbb_t) + \eta \zb_0 + \tau  (1 - \sqrt{\bar{\alpha}_{t-1}}){\bm{\mu}}, \nonumber \\
    &\mathbf{\Lambda} = (1 - \bar{\alpha}_{t-1})\beta_t \mathbf{\Sigma}/(1-\bar{\alpha}_{t}), \nonumber
\end{align}
where
\vspace{-0.5cm}
\begin{align*}
    &\gamma = (1 - \bar{\alpha}_{t-1})\sqrt{\alpha_t}/(1-\bar{\alpha}_{t}), \\
    & \eta = \beta_t\sqrt{\bar{\alpha}_{t-1}}/(1 - \bar{\alpha}_{t}), \\
    & \tau = \beta_t/(1 - \bar{\alpha}_{t}).
\end{align*}

With Equations \eqref{eq:q_t} and \eqref{eq:q_posterior}, we can easily optimize the loss function of the proposed shifted diffusion
\begin{align}\label{eq:diffusion_loss}
    \mathbf{L}_{{\bm{\theta}}} = \mathbb{E}_{q} \{ &\text{D}_{\text{KL}}(q(\zb_T \vert \zb_0) \Vert p(\zb_T)) - \log p_{{\bm{\theta}}} (\zb_0 \vert \zb_1) \\
    & + \sum_{t>1} \text{D}_{\text{KL}}(q(\zb_{t-1} \vert \zb_t, \zb_0) \Vert p_{{\bm{\theta}}}(\zb_{t-1} \vert \zb_t))\}~,\nonumber
\end{align}
where  $\text{D}_{\text{KL}}$ denotes KL-divergence.
Since both $q(\zb_t \vert \zb_0)$ and $q(\zb_{t-1} \vert \zb_t, \zb_0) $ are Gaussian distributions by our design, the KL-divergence terms have closed-form solutions, enabling easy stochastic optimization.

\begin{algorithm}[t!]
    \caption{Shifted Diffusion}\label{algo:sft}
    \begin{algorithmic}[1]
        \STATE {\color{blue} // Training}
        \STATE {\bfseries Require: $\{p_i(\zb_T)=\mathcal{N}(\zb_T; {\bm{\mu}}_i, \mathbf{\Sigma}_i)\}_{i=1}^k$, a diffusion model parameterized by ${\bm{\theta}}$, CLIP image encoder $f_{\text{img}}$ and text encoder $f_{\text{txt}}$.}
        \WHILE { {\em not converge} }{
            \STATE{Sample image-text data pair $(\xb_0, \yb)$, $\zb_0 = f_{\text{img}}(\xb_0)$}
            \STATE{Select corresponding $p_{c_{\yb}}$ by \eqref{eq:assign}}
            \STATE{Update ${\bm{\theta}}$ by gradient descent w.r.t $\text{L}_{{\bm{\theta}}}$}
            \IF{${\bm{\mu}}_i, \mathbf{\Sigma}_i$ are learn-able}
                \STATE{
                Update ${\bm{\mu}}_i, \mathbf{\Sigma}_i$ by gradient descent w.r.t $\text{L}_{p}$}
            \ENDIF
        }
        \ENDWHILE
    \STATE{\color{blue} // Inference}
    \STATE{Given a text caption $\yb$, select its corresponding distribution $p_{c_{\yb}}$, sample corresponding $\zb_T$}
    \FOR{t=T,...,1}
    \STATE{Sample $\zb_{t-1} \sim p_{{\bm{\theta}}}(\zb_{t-1} \vert \zb_t)$}
    \ENDFOR
    \STATE{Return $\zb_0$}
    \end{algorithmic}
\end{algorithm}

Although setting $p(\zb_T)$ to be a Gaussian distribution leads to a simplified loss that can be conveniently optimized, it also introduces some drawbacks as the ground-truth distribution of image embedding is not a single-mode Gaussian.
To tackle this problem, we propose to use a collection of Gaussian distributions, denoted as $\{p_i(\zb_T)\}_{i=1}^k$ with $p_i(\zb_T) \coloneqq \mathcal{N}(\zb_T; {\bm{\mu}}_i, \mathbf{\Sigma}_i)$. Let $\zb_0$ and $\yb$ be the ground-truth image embedding and its associated text caption, respectively. For each pair of $(\zb_0, \yb)$, we select its corresponding Gaussian $p_{c_{\yb}}$ by the top-1 cosine similarity as
\begin{align}\label{eq:assign}
    c_{\yb} = \mathop{\rm argmax}_{1\leq i \leq k} \text{Sim}({\bm{\mu}}_i, f_{\text{txt}}(\yb)),
\end{align}
where $\vect{f}_{\text{txt}}$ is the pre-trained CLIP text encoder,  and $c_{\yb}$ denotes the index of the selected Gaussian. One may also use
\begin{align}\label{eq:assign_noise}
   c_{\yb} = \mathop{\rm argmax}_{1\leq i \leq k} \mathbb{E}_{{\bm{\epsilon}}_i \sim p_{i}} \left[\text{Sim}({\bm{\epsilon}}_i, f_{\text{txt}}(\yb))\right]~,
\end{align}
which requires more computation because of the expectation.
After selecting $p_{c_{\yb}}(\zb_T)$, its parameters ${\bm{\mu}}_{c_{\yb}}$ and $\mathbf{\Sigma}_{c_{\yb}}$ will be used in Equation \eqref{eq:q_t} and \eqref{eq:q_posterior} for optimization. An extra positional embedding representing $c_{\yb}$ is also injected into the diffusion model, with a similar implementation as the time embedding for $t$. Compared to a single-mode Gaussian distributions, $\{p_i(\zb_T)\}_{i=1}^k$ is supposed to have better expressive ability, and $p_{c_{\yb}}$ is expected to better initialize $\zb_T$ to make it closer to the target $\zb_0$.

In our implementation, $\{p_i(\zb_T)\}_{i=1}^k$ is estimated by performing clustering on the training dataset. Similar to existing quantization methods~\cite{van2017neural}, we can also learn $({\bm{\mu}}_i, \mathbf{\Sigma}_i)$ by optimization. Specifically, we propose to update ${\bm{\mu}}_i, \mathbf{\Sigma}_i$ during training with the following loss function
\begin{align}\label{eq:loss_gaussians}
    \mathbf{L}_p =&  - \mathbb{E}_{\zb} \{\mathbb{E}_{\zb_T \sim p_{c_{\yb}}(\zb_T)} \{\text{Sim}(\zb_T, \zb_0)\}\} \\
    &+ \xi \dfrac{\sum_{i\neq j}}{k(k-1)}   \mathbb{E}_{\zb_{T} \sim p_i(\zb_T)} \{\mathbb{E}_{\zb^\prime_T \sim p_j(\zb_T)} \{\text{Sim}(\zb_T, \zb_T^\prime)\}\}, \nonumber
\end{align}
where $\xi$ is a hyper-parameter. The first term of $\mathbf{L}_p$ forces the sample $\zb_T$ from Gaussian $p_{c_{\yb}}$ to be close to the corresponding ground-truth $\zb_0$, and the second term ensures that $p_i(\zb_T)$ does not overlap each other too much. Note that $\{p_i(\zb_T)\}_{i=1}^k$ is only optimized with $\mathbf{L}_p$, {\it i.e.}, we manually stop the gradient back-propagation from $\text{L}_{{\bm{\theta}}}$ to $\{{\bm{\mu}}_i, \mathbf{\Sigma}_i\}_{i=1}^k$. Our algorithm is summarized in Algorithm \ref{algo:sft}, with more implementation details provided in the experiments section.

\begin{figure*}[ht!]
    \centering
    \begin{subfigure}[t]{0.22\linewidth}
        \includegraphics[width=1.\linewidth]{figures/examples/sailing.png}
        \caption{A photo of a light bulb in outer space traveling the galaxy with a sailing boat inside the light bulb.}
    \end{subfigure}       ~
    \begin{subfigure}[t]{0.22\linewidth}
        \includegraphics[width=1.\linewidth]{figures/examples/workout.png}
        \caption{A high-resolution photo of a chicken working out in a gym.}
    \end{subfigure}      ~
    \begin{subfigure}[t]{0.22\linewidth}
        \includegraphics[width=1.\linewidth]{figures/examples/hello_world.png}
        \caption{New York Skyline with 'Hello World' written with fireworks on the sky.}
    \end{subfigure}        ~
    \begin{subfigure}[t]{0.22\linewidth}
        \includegraphics[width=1.\linewidth]{figures/examples/water_tv.png}
        \caption{A television made of water that displays an image of a cityscape at night.}
    \end{subfigure}
    \begin{subfigure}[t]{0.22\linewidth}
        \includegraphics[width=1.\linewidth]{figures/examples/llama.png}
        \caption{A photo of llama wearing sunglasses standing on the deck of a spaceship with the Earth in the background.}
    \end{subfigure}       ~
    \begin{subfigure}[t]{0.22\linewidth}
        \includegraphics[width=1.\linewidth]{figures/examples/lets_paint.png}
        \caption{A high contrast portrait photo of a fluffy hamster wearing an orange beanie and sunglasses holding a sign that says ``Let's PAINT!".}
    \end{subfigure}      ~
    \begin{subfigure}[t]{0.22\linewidth}
        \includegraphics[width=1.\linewidth]{figures/examples/riding.png}
        \caption{A photo of an astronaut riding a horse in the forest. There is a river in front of them with water lilies.}
    \end{subfigure}  ~
    \begin{subfigure}[t]{0.22\linewidth}
        \includegraphics[width=1.\linewidth]{figures/examples/welcome_friend.png}
        \caption{A portrait photo of a kangaroo wearing an orange hoodie and blue sunglasses standing on the grass in front of the Sydney Opera House holding a sign on the chest that says Welcome Friends!}
    \end{subfigure}
    \vspace{-0.15in}
    \caption{Some generated examples on DrawBench and PartiPrompts.}
    \label{fig:generated_examples}
    \vspace{-0.2in}
\end{figure*}
\section{Experiments}

\subsection{Zero-shot Text-to-image Generation}
We first test the zero-shot text-to-image generation ability of Corgi. We prepare a dataset containing 900 million image-text pairs, which is composed of some commonly used datasets such as Conceptual Captions (CC3M)~\cite{Sharma2018ConceptualCA}, Conceptual Captions 12M (CC12M)~\cite{changpinyo2021conceptualcc12m}, filtered LAION-5B~\cite{schuhmann2021laion} and some image-text pairs collected by ourselves. Note that we make sure our dataset does not overlap with MS-COCO~\cite{lin2014microsoft}, CUB~\cite{WahCUB_200_2011}, Localized Narratives~\cite{pont2020connecting} and Multi-modal CelebA-HQ (MM-CelebA-HQ)~\cite{xia2021tedigan}, because we will test zero-shot or language-free performance on these downstream datasets.
\paragraph{Decoder} Following \cite{nichol2021glide, ramesh2022hierarchical, saharia2022photorealistic}, our decoder adopts a model architecture consisting of a hierarchy of diffusion models, which has shown impressive ability in text-to-image generation. Specifically, three diffusion models are trained on 64, 256, and 1024 resolutions, respectively. All the 900M images are used to train the decoder. During training, each image is processed by three different pre-trained CLIP models: ViT-B/16, ViT-B/32, and RN-101. The outputs from these three models will be concatenated into a single 1536-dimensional embedding, which is then projected into eight vectors and fed into the decoder (in the same manner as DALL-E 2).

\paragraph{Prior model} To handle the image-text alignment, we train a shifted diffusion model to generate image embeddings from captions. Our shifted diffusion model is a decoder-only transformer whose input is a sequence consisting of encoded text from T5 \cite{2020t5}, CLIP text embedding, an embedding representing diffusion timestep, an embedding representing the index of corresponding Gaussian, a noised CLIP image embedding and a final embedding
which will be used to predict the target CLIP image embedding.
We train two variants on datasets with different scales: one prior is trained on the full 900M image-text pairs; the other one is trained on CC15M\footnote{collection of CC3M and CC12M}, which is a subset of our full dataset.

\paragraph{Final model} Our two different prior models correspond to two final models. The decoders for the prior models (which are respectively trained on 900M and 15M image-text pairs) are trained on the 900M images. In other words, the two final models can be regarded as being trained in supervised and semi-supervised manners, where the training dataset of the first one consists of 900M image-text pairs, while the other one consists of 900M images but with only $1.7\%=15/900$ of them associating with captions. We denote these two variants as Corgi and Corgi-Semi, respectively. More implementation details are provided in the Appendix.

\begin{figure}[t!]
    \centering
    \begin{subfigure}[t]{0.45\linewidth}
        \includegraphics[width=1.\linewidth]{figures/compare_1.png}
        \caption{A panda making latte art.}
    \end{subfigure}
    \begin{subfigure}[t]{0.45\linewidth}
        \includegraphics[width=1.\linewidth]{figures/compare_3.png}
        \caption{Illustration of a mouse using a mushroom as an umbrella.}
    \end{subfigure}
    \vspace{-0.15in}
    \caption{Comparison of our final models trained in supervised (left) and semi-supervised manner (right).}
    \label{fig:dataset_comparison}
    \vspace{-0.1in}
\end{figure}

\begin{table}[t!]
\centering
\scalebox{0.75}{
    \begin{tabular}{lcc}
        \toprule
         Methods & Supervised FID $\downarrow$  & Zero-shot FID $\downarrow$ \\
         \midrule
         AttnGAN~\cite{xu2018attngan} & $33.10$ & \\
         DF-GAN~\cite{tao2021dfgan} & $21.42$ & \\
         XMC-GAN~\cite{zhang2021crossmodal} & $9.33$ & \\
         Lafite~\cite{zhou2021lafite} & $8.12$ & $26.94$ \\
        \midrule
        DALL-E~\cite{ramesh2021zero} & & $27.50$ \\
        CogView~\cite{ding2021cogview} & & $27.10$ \\
        GLIDE~\cite{nichol2021glide} & & $12.24$ \\
        LDM~\cite{rombach2021high} & & $12.63$\\
        Make-A-Scene~\cite{gafni2022make}& & $11.84$\\
        DALL-E 2~\cite{ramesh2022hierarchical} & & $10.39$\\
        CogView 2~\cite{ding2022cogview2} & & $24.00$ \\
        Imagen~\cite{saharia2022photorealistic} & & $7.27$ \\
        Parti~\cite{yu2022scaling} & & $7.23$ \\
        Re-Imagen~\cite{chen2022re} & & $6.88$ \\
        \midrule
        Corgi (Ours) & & $10.88$ \\
        Corgi-Semi (Ours) & & $10.60$ \\
         \bottomrule
    \end{tabular}
    }
    \caption{Text-to-image generation results on MS-COCO.}
    \label{tab:zero-shot}
    \vspace{-0.2in}
\end{table}

In Table \ref{tab:zero-shot}, we report zero-shot Fr\'echet Inception Distance (FID) evaluated on MS-COCO. Following previous works, the FID is calculated using 30,000 generated images, which corresponds to 30,000 randomly sampled captions from the validation set of MS-COCO. The results illustrate that our models obtain strong results, even when it is trained in a semi-supervised manner. Interestingly, our semi-supervised model obtains better FID than the supervised one. However, as we can see from Figure~\ref{fig:dataset_comparison}, the model trained with more image-text pairs leads to better image-text alignment and better image quality, as expected. One reason that it obtains relatively worse FID could be due to the dataset bias: CC15M might contain many samples that are similar to samples in MS-COCO, while this bias faded when they were merged to construct our full 900M dataset.

We note that most large-scale text-to-image generation models are trained on different datasets, but all tested on MS-COCO,
ignoring potential dataset bias. Comparing these models simply by FID might not be appropriate. Thus, comparing models by actual generation quality is necessary.
We follow previous works and choose to evaluate our model on DrawBench~\cite{saharia2022photorealistic} and PartiPrompts~\cite{yu2022scaling}. Some generated examples with complicated scenes are shown in Figure \ref{fig:generated_examples}.
We compare our model with DALL-E 2 and Stable Diffusion, as these are the only models allowing public access. For a fair comparison, we fine-tune our model for an extra 300,000 iterations so that the decoder can take both image embeddings and text as inputs, which is similar to DALL-E 2.
Some visual comparisons are provided in Figure \ref{fig:comparison_1}. More results are provided in the Appendix. As can be seen, our model leads to better generation in most cases in terms of image-text alignment and image fidelity. Furthermore, we conduct a human evaluation on DrawBench and PartiPrompts.
Specifically, we first generate four images for each prompt, then ten random human laborers are asked to judge which model is better (or comparable) in terms of image-text alignment and fidelity. The results are shown in Figure \ref{fig:human_evaluation}, where our model is shown to perform better in the two evaluation metrics consistently.

\begin{figure}[t!]
    \centering
    \includegraphics[width=0.95\linewidth]{figures/human_evaluation.png}
    \caption{Human evaluation on DrawBench and PartiPrompts.}
    \label{fig:human_evaluation}
    \vspace{-0.1in}
\end{figure}

\begin{table}[t!]
\scalebox{0.85}{
    \begin{tabular}{lcccccc}
        \toprule
         & \multicolumn{2}{c}{MS-COCO}  & \multicolumn{2}{c}{CUB} &  \multicolumn{2}{c}{LN-COCO}\\
         Methods & IS $\uparrow$ & FID $\downarrow$ & IS $\uparrow$ & FID $\downarrow$ & IS $\uparrow$ & FID $\downarrow$ \\
         \midrule
         Lafite \cite{zhou2021lafite} & $27.20$ & $18.04$ & $4.32$ & $27.53$ & $18.49$ & $39.85$\\
         Lafite-2\cite{Zhou2022Lafite2FT} & $31.16$ &  $10.26$ & $4.93$ & $16.87$ & $23.18$ & $25.51$\\
         Corgi (Ours) & $\mathbf{34.14}$ & $10.33$ & $\mathbf{5.08}$ & $\mathbf{15.80}$ & $\mathbf{28.71}$ & $\mathbf{16.16}$\\
         \bottomrule
    \end{tabular}
    }
    \caption{Language-free results on different datasets.}
    \label{tab:language_free}
    \vspace{-0.25in}
\end{table}

\begin{figure*}[t!]
    \centering
    ~~~~~~~~~
    \begin{subfigure}{0.78\linewidth}
        \includegraphics[width=1.\linewidth]{figures/Comparison/title.png}
    \end{subfigure}
    \begin{subfigure}{0.96\linewidth}
        \includegraphics[width=1.\linewidth]{figures/Comparison/c_infinity.jpg}
        \caption{\textcolor{red}{Infinity}.}
    \end{subfigure}
    \begin{subfigure}{0.96\linewidth}
        \includegraphics[width=1.\linewidth]{figures/Comparison/c_a_hamster_dragon.jpg}
        \caption{A \textcolor{red}{hamster dragon}.}
    \end{subfigure}
    \begin{subfigure}{0.96\linewidth}
        \includegraphics[width=1.\linewidth]{figures/Comparison/c_A_map_of_the_United_States_made_out_sushi_It_is_on_a_table_next_to_a_glass_of_red_wine.jpg}
        \caption{A  \textcolor{red}{map of the United States} made out \textcolor{red}{sushi}. It is on a table next to a \textcolor{red}{glass of red wine}.}
    \end{subfigure}

    \begin{subfigure}{0.96\linewidth}
        \includegraphics[width=1.\linewidth]{figures/Comparison/c_a_portrait_of_a_statue_of_the_Egyptian_god_Anubis_wearing_av.jpg}
        \caption{A portrait of a statue of the \textcolor{red}{Egyptian god Anubis} wearing \textcolor{red}{aviator goggles}, \textcolor{red}{white t-shirt} and \textcolor{red}{leather jacket}. A full \textcolor{red}{moon over the city} of Los Angeles is in the background at night.}
    \end{subfigure}
    \begin{subfigure}{0.96\linewidth}
        \includegraphics[width=1.\linewidth]{figures/Comparison/c_a_statue_of_Abraham_Lincoln_wearing_an_opaque_and_shiny_astronauts_helmet_The_statue_sits_on_the_moon_with_the_planet_Earth_in_the_sky.jpg}
        \caption{A statue of \textcolor{red}{Abraham Lincoln} wearing an opaque and shiny astronaut's \textcolor{red}{helmet}. The statue sits \textcolor{red}{on the moon}, with the planet \textcolor{red}{Earth in the sky}.}
    \end{subfigure}
    \vspace{-0.1in}
    \caption{Comparison with DALL-E 2 and Stable Diffusion. More results are provided in the Appendix.}
    \label{fig:comparison_1}
    \vspace{-0.1in}
\end{figure*}
\subsection{Language-free Text-to-image Generation}
\cite{zhou2021lafite} is the pioneering work to train text-to-image generation models on image-only datasets, termed language-free training as no associated captions are provided.
With a pre-trained shifted diffusion model, we can also perform language-free training on any downstream dataset. Given an image-only dataset, we can train or fine-tune a generative model that generates images from image embeddings. Consequently, we can directly use the pre-trained shifted diffusion model at inference time to perform text-to-image generation.
Because our shifted diffusion model is pre-trained on large-scale image-text pairs, it is expected to generalize well on any downstream domain.

We compare our method with \cite{zhou2021lafite, Zhou2022Lafite2FT}. For a fair comparison, our decoder uses the same network architecture as \cite{zhou2021lafite}, which is a StyleGAN2-based model instead of a diffusion model.
Some quantitative results are provided in Table \ref{tab:language_free}, from which we can see that our method leads to better results in general.
More results are provided in the Appendix.
Although Lafite-2 \cite{Zhou2022Lafite2FT} achieves a competitive FID on the MS-COCO dataset, it was trained on pseudo captions that require extra human workload. Specifically, to train Lafite-2, domain-specific vocabularies and prompts are needed, which requires human prior knowledge for each downstream domain. On the other hand, our shifted diffusion model is pre-trained and can be directly plugged into any domain without further training or fine-tuning.

\subsection{Ablation Study}

\paragraph{Baseline vs. shifted diffusion}
Although we have shown that our Corgi model obtains better generation quality than DALL-E 2 in the previous experiments, there is no direct clue suggesting that the improvement is due to the advantages of shifted diffusion over the baseline diffusion. This is because, as we pointed out, other important factors, such as the potential dataset bias and different implementation details, may also affect model performance.
Therefore, to better compare our shifted diffusion model with the baseline diffusion model, we conduct an ablation study where we train these two diffusion models with the same implementation and training dataset. Specifically, CC15M dataset is used for both models.
We choose $k=1$ for shifted diffusion, {\it i.e.}, only one Gaussian distribution is used in the shifted diffusion model for a fair comparison (a larger $k$ leads to better improvements). More implementation details are provided in the Appendix.

Firstly, we compare the cosine similarity of the generated image embeddings to the ground-truth image embeddings. We randomly sample 10,000 image-text pairs from the validation set of MS-COCO to prevent overlapping between training and testing datasets. The sampled captions are fed into different diffusion models to generate the corresponding image embeddings. Intuitively, we expect higher cosine similarity, as this implies that the model can better approximate the ground truth. The results are shown in Figure \ref{fig:ablation_1}. We can see that our shifted diffusion model leads to higher similarity scores than the baseline, implying a better image embedding generation.

Secondly, we compare the similarity scores of the generated embeddings to ground truth at different timesteps during the sampling process. We use 64 strided steps for both the baseline and the shifted diffusion model. The results are shown in Figure \ref{fig:baseline_sft_steps}, from which we can see that our method leads to higher similarity scores, especially at initialization. This indicates that our starting point is much closer to the target, consistent with our design intention.

\begin{figure}[t!]
    \centering
    \includegraphics[width=0.7\linewidth]{figures/sft_vs_baseline_steps.png}
    \caption{The evolution of similarity of generated embedding to ground-truth during sampling.}
    \label{fig:baseline_sft_steps}
\end{figure}

\begin{figure}[t!]
    \centering

    \begin{subfigure}[t]{0.45\linewidth}
        \includegraphics[width=1.\linewidth]{figures/ablation/ablation_a_person_holdinga_cup_and_a_doughnut.png}
        \centering
        \caption{A close-up photo of a person holding a cup and a doughnut}
    \end{subfigure}   ~
    \begin{subfigure}[t]{0.45\linewidth}
        \includegraphics[width=1.\linewidth]{figures/ablation/ablation_A_lady_sitting_on_a_park_bench_reading_a_book.png}
        \centering
        \caption{A lady sitting on a park bench reading a book.}
    \end{subfigure}
    \caption{Examples generated with baseline diffusion (left) and shifted diffusion (right).}
    \vspace{-0.2in}
    \label{fig:ablation_2}
\end{figure}

Thirdly, we compare the two models by their text-to-image generation results evaluated on the MS-COCO dataset. Similar to previous experiments, 30,000 captions are randomly sampled from the validation set of MS-COCO. We then use the baseline and the shifted diffusion models to generate 30,000 embeddings, respectively. The embeddings will be fed into our pre-trained diffusion-based decoder to generate 30,000 images for calculating the zero-shot FID score on MS-COCO. The FID results are shown in Figure \ref{fig:ablation_1}, from which we can see that shifted diffusion model indeed leads to better quantitative results. We also provide some generated examples in Figure \ref{fig:ablation_2}.
From
the generated images, we can see that the baseline model can fail to capture some details described in the text.

\begin{figure}[t!]
    \centering
    \includegraphics[width=0.9\linewidth]{figures/sft_similarities.png}
    \caption{Results of shifted diffusion with different settings.}
    \label{fig:sft_similarities}
\end{figure}

\begin{figure}[t!]
    \centering
    \begin{subfigure}[b]{0.380\linewidth}
        \includegraphics[width=1.\linewidth]{figures/similarity.png}

    \end{subfigure}
    \begin{subfigure}[b]{0.60\linewidth}
        \includegraphics[width=1.\linewidth]{figures/fid.png}

    \end{subfigure}

    \caption{Comparison between baseline and shifted diffusion. Shifted diffusion leads to image embedding more similar to ground truth, which results in a better downstream generation.}
    \label{fig:ablation_1}
    \vspace{-0.1in}
\end{figure}
\paragraph{Different settings for shifted diffusion}
Recall that our shifted diffusion model adopts one or more Gaussian distributions as its initialization for the sampling process. We investigate the influence of the number of distributions in this experiment. We train our shifted diffusion model with 1, 16, 128, and 1024 Gaussians, respectively. Furthermore, we train a model with 1024 learnable mean vectors and covariance matrices. All the models are trained on CC15M. We calculate the similarity of generated image embeddings to the ground-truth embeddings on the validation set of MS-COCO. The results are shown in Figure \ref{fig:sft_similarities}, from which we can see that using more Gaussian distributions leads to better results; furthermore, making the parameters learnable gains further performance improvement. Some more discussions are provided in the Appendix.

\section{Conclusion}
We propose Corgi, a novel and general diffusion model that benefits text-to-image generation under different settings. Extensive large-scale experiments are conducted. Strong quantitative and qualitative results are obtained, illustrating the effectiveness of the proposed method.

\clearpage
{\small
\bibliographystyle{ieee_fullname}

redpajama_set_name: RedPajamaArXiv


text:
7 days discovering Scotland's craggy southwest coast on a luxury yacht
Oban to Mull
Lying in the southwest of Scotland is Argyll and its islands. With a coastline longer than that of France, it is an interesting area for cruising or for a Scottish superyacht charter. This beautiful land has been inhabited for thousands of years and this is evident from the ancient standing stones and castles to be found here.
A sail from Oban to Glasgow, around Scotland's craggy southwest coast is without doubt, a memorable and exhilarating experience.
Day 1: Oban to Mull
The town of Oban, located to the west of the mainland has the best practical facilities in the area, and, nicknamed the seafood capital of Scotland, you can enjoy an array of superb seafood eateries. There are good landward travel connections and the marina has seaplane docking.
Mull, the largest island in this group, lies just across the dramatic sound that separates it from the mainland. From the picturesque harbour in Tobermory, surrounded by quaint multi-coloured houses, it's just a short journey by road to local whisky distilleries. On Mull's west coast you can tuck into sheltered anchorages in order to explore natural wonders such as Fingal's Cave on the tiny island of Staffa. Here, solidified lava forms perfect hexagonal columns that tower high into the cliffs.
Picture courtesy of TT Photo/Shutterstock.com
Jura and Islay
Yet more distilleries beckon as you cruise further south towards Jura and Islay – both will be familiar names to whisky connoisseurs. It's easy to go ashore and sample the famously peaty liquor as many of the coastal distilleries have moorings. It might be sensible not to imbibe too much if you plan on taking on the notorious tidal races that sweep these islands, such as the infamous whirlpool at Corryvreckan. Between Jura and Scarba a rock pinnacle rises from 200m to less than 30m from the surface at the point where the current is most constricted. With the right conditions and local knowledge, it makes for a very exciting, albeit potentially dangerous, ride in a RIB.
Picture courtesy of Scoot Photo
Crinan Canal/Mull of Kintyre
After the exhilaration of Corryvreckan, change down several gears and take in the Crinan canal, known locally as 'the most beautiful short cut on earth.' You'll have to abandon your yacht for the tender if you want to take this shortcut yourself, as only small craft can take this route to Loch Fyne. Larger vessels have to take the long route south round the Mull of Kintyre. On board your tender, this is a very pleasant 9-mile inland route; otherwise enjoy the 'wild west' as your yacht takes the 100-mile windward route. No matter how you get there, you'll soon be entering the Firth of Clyde, one of the largest areas of sheltered deep water in the entire British Isles.
PIcture courtesy of Dmitry Naumov/Shutterstock.com
Loch Fyne is a name now synonymous with excellent seafood. As you reach the head of the Firth, close to the Crinan canal, the Loch Fyne Oyster bar provides moorings and is the perfect place to sample the freshest local delicacies. At the right time of year, you can venture inland to explore the ancient buildings and hills, or visit to the Highland Games at Inverary Castle.
Picture courtesy of Antony Mc Aulay/Shutterstock.com
Right in the centre of the Firth, between Kintyre and the mainland, lies the Isle of Arran. It may not be the largest of islands, but it has an incredible variety of landscapes. Rocky mountains contrast with soft rolling hills, home to seven golf courses and 50 or so, whisky distilleries.
Picture courtesy of veroxdale/Shutterstock.com
Yet more treats are in store for golf lovers, in this its birthplace. The Troon Yacht Marina is a great place to berth and gain access to the nearby courses at Troon, Prestwick and Turnberry. It's also just 10 minutes from Glasgow Prestwick airport.
Picture courtesy of Dutourdumonde Photography/Shutterstock.com
But if you don't have to leave straightaway, why not continue up the Clyde. Historically, this area was famous for commercial shipbuilding; but now it's the pleasure craft industry that is booming as more and more yachts arrive to cruise the calm waters and take in the stunning views. The City of Glasgow lies on the Clyde itself. Recent investment in regeneration has elevated this old industrial town into a truly European city. Culture lovers can also take the one-hour trip by road to Edinburgh to enjoy fine dining, or take in the best of the arts at the Edinburgh Festival in August.
Picture courtesy of Targn Pleiades/Shutterstock.com
Rest of the World yacht destinations
Experience Five Star Luxury in Oman at The Kempinski Hotel Muscat
The Best Luxury Hotels in Remote Destinations
Sea Spa: Hareer Spa by L'Occitane at The Shangri-La Al Husn Hotel, Oman
Island-hopping through Thailand on board 68m Lady E
Yacht safari: cruising the Raja Ampat archipelago
Far Eastern Fantasy: Why Vietnam Should be Your Next Superyacht Stop-off
GC-FORCE 66
Admiral - The Italian Sea Group | 65.9 m | 12 guests | POA
Ateliers et Forges de l'Ouest | 36.9 m | 10 guests | POA
Princess 88MY
Princess | 26.82 m | 8 guests | £4,590,000


It is that time of the year again for the amazing vintage fashion and accessories show, The Manhattan Vintage Show. Starting today, and runs till tomorrow till 6pm, it is a must stop if you have never visited. I do need to get to one soon, as it's been a while since I stopped in. It is such a wonderful collection of vintage items all under one roof. From expensive to not expensive, there is something for everyone.
We're very excited about this season's show, as it just so happens to coincide with the Rock & Roll Hall Of Fame induction ceremony, and in honor of that, we'll be featuring a special exhibit entitled, "The Rock & Roll T-Shirt Hall of Fame." Digital influencer, Rachel Lynch, has come on board to help style the looks for the exhibit, which will include 30+ rare rock t-shirts ranging from Rod Stewart, to Def Leppard, to Tupac Shakur, all of which will be available for sale immediately following the show on the Manhattan Vintage website.
In addition to the show's featured exhibit, 70+ vendors will be on hand, spanning the decades with their one-of-a-kind pieces. This season, Vintage by Rosemaine will join the show from Paris with her incredible vintage couture finds including Ungaro, Laroche, and Miyake. Bai Water will also be on hand with a digital photo booth, Mani or Pedi will be providing complimentary rock & roll inspired nail art, and Deutsch Family Wine & Spirits will be serving some very rock inspired signature cocktails.
Who didn't love Boy George!
I admit I still listen to Def Leppard once in a while!


text:
Q: React invalid hook Error: Invalid hook call. Hooks can only be called inside of the body of a function component I have an error in React. It's a newly initiated project with just a few components.
The error is referenced to both the useState() and to useEffect().
Error message:
Uncaught Error: Invalid hook call. Hooks can only be called inside of the body of a function component. This could happen for one of the following reasons:
1. You might have mismatching versions of React and the renderer (such as React DOM)
2. You might be breaking the Rules of Hooks
3. You might have more than one copy of React in the same app
See https://reactjs.org/link/invalid-hook-call for tips about how to debug and fix this problem.
    at Object.throwInvalidHookError (react-dom.development.js:14906)
    at useState (react.development.js:1508)
    at IndexPage (IndexPage.js:9)
    at Route.js:55
    at updateContextConsumer (react-dom.development.js:18747)
    at beginWork (react-dom.development.js:19114)
    at HTMLUnknownElement.callCallback (react-dom.development.js:3945)
    at Object.invokeGuardedCallbackDev (react-dom.development.js:3994)
    at invokeGuardedCallback (react-dom.development.js:4056)
    at beginWork$1 (react-dom.development.js:23964)
    at performUnitOfWork (react-dom.development.js:22776)
    at workLoopSync (react-dom.development.js:22707)
    at renderRootSync (react-dom.development.js:22670)
    at performSyncWorkOnRoot (react-dom.development.js:22293)
    at scheduleUpdateOnFiber (react-dom.development.js:21881)
    at updateContainer (react-dom.development.js:25482)
    at react-dom.development.js:26021
    at unbatchedUpdates (react-dom.development.js:22431)
    at legacyRenderSubtreeIntoContainer (react-dom.development.js:26020)
    at Object.render (react-dom.development.js:26103)
    at Module.<anonymous> (index.js:8)
    at Module../src/index.js (index.js:19)
    at __webpack_require__ (bootstrap:856)
    at fn (bootstrap:150)
    at Object.1 (const.js:10)
    at __webpack_require__ (bootstrap:856)
    at checkDeferredModules (bootstrap:45)
    at Array.webpackJsonpCallback [as push] (bootstrap:32)
    at main.chunk.js:1

And
The above error occurred in the <Router.Consumer> component:

    at Route (http://localhost:3000/static/js/0.chunk.js:37878:29)
    at Switch (http://localhost:3000/static/js/0.chunk.js:38080:29)
    at div
    at Router (http://localhost:3000/static/js/0.chunk.js:37513:30)
    at BrowserRouter (http://localhost:3000/static/js/0.chunk.js:37133:35)
    at App

Both react and react-dom is the same version (^17.0.1)

The component code:
import React, { useState, useEffect } from 'react'
import General from '../generalInfo/General'
import Axios from 'axios'

import {MAIN_URL} from '../../utils/const.js'

const IndexPage = () => {

    const [generalInfo, setGeneralInfo] = useState();

    useEffect(() => {
        getGeneral();
    }, [])

    const getGeneral = () => {
        console.log("Getting general");
        Axios.get(MAIN_URL + "/general", {
            headers: {'Authorization': 'authorized', 'Content-type': 'application/json; charset=UTF-8'}
        }).then(response => {
            if(response.status === 404){
                console.log("Not Found");
                //Implement error handling
            }else{
                setGeneralInfo(response.data);
            }
        })
    }

    return (

                    /*Start of left column*/

                        <General general={generalInfo} />

                    /*End of left column*/

    )
}

export default IndexPage;

And I do only have one copy of both react and react-dom.
The package.json also includes axios and react-router-dom as dependencies.
The IndexPage.js is used within the React Router in the App.js file.
<Router>

      <Switch>
        <Route exact path="/" render={IndexPage} />
      </Switch>

  </Router>

Have anyone encountered this before?

redpajama_set_name: RedPajamaStackExchange


Conor McGregor is close to signing a deal for his next fight, and while he wasn't ready to share a potential date or opponent, he did tease a location for the match: Las Vegas. The former UFC featherweight and lightweight champion shared that detail with TMZ Sports while out on a run in New York, where he's been hanging out since pleading to one count of misdemeanor disorderly conduct last week.
Negotiations between the UFC and McGregor had been stalled out ever since the Irish superstar and a group of his gym teammates stormed the Barclays Center leading up to UFC 223 in April, smashing the windows of a bus bringing UFC fighters back to their hotel. McGregor was attempting to get to Dagestani fighter Khabib Nurmagomedov, who had slapped Conor's close friend Artem Lobov in a confrontation earlier that week. Now that the UFC knows McGregor isn't about to be barred from entering America, a deal can be made for him to fight Nurmagomedov in a proper, legal fashion.