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NOTE: We're looking for a knowledgeable Red Hot Chili Peppers nerd! A review for Stadium Arcadium hasn't been published — yet. We need someone who can write a full track-by-track review of this album (at least a couple paragraphs per song); if you know the music, you can submit a review. You'll be compensated when visitors make purchases through vendor links on their pages — for as long as your review remains on the site. Get more details in the FAQ.
Stadium Arcadium by Red Hot Chili Peppers
"Dani California"
"Snow (Hey Oh)"
"Hump De Bump"
"She's Only 18"
"Slow Cheetah"
"Especially In Michigan"
"C'mon Girl"
"Wet Sand"
"Desecration Smile"
"Tell Me Baby"
"Hard To Concentrate"
"She Looks To Me"
"Make You Feel Better"
"So Much I"
"Turn It Again"
"Death Of A Martian"
NOTE: We're looking for a knowledgeable Red Hot Chili Peppers nerd! A review for By The Way hasn't been published — yet. We need someone who can write a full track-by-track review of this album (at least a couple paragraphs per song); if you know the music, you can submit a review. You'll be compensated when visitors make purchases through vendor links on their pages — for as long as your review remains on the site. Get more details in the FAQ.
By The Way by Red Hot Chili Peppers
"Universally Speaking"
"This Is The Place"
"The Zephyr Song"
"Can't Stop"
"I Could Die For You"
"Throw Away Your Television"
"Cabron"
"On Mercury"
"Minor Thing"
"Warm Tape"
"Venice Queen"
All in all, this is one of the most successful albums the Red Hot Chili Peppers have been able cough up. Rap, rock and just about anything else you can name, they have it on Californication (and they do it wickedly, too).
After a reunion of sorts of the band's "classic" former members, every bit of energy has been pumped up for this album. Coming back from the dead, guitarist John Frusciante's power chord genius is solidified on this album, his melancholic, graceful notes and syncopated noises allowing bassist Flea to bounce rambunctiously back and forth.
Singer Anthony Kiedis, on the other hand, has changed his overall tone and adopted a new melodic sensibility, more soulful and often dwelling in a different, lower range. On tracks like "Californication" and "Scar Tissue", one can truly separate the old and new Kiedis by the way he drives the lyrics.
The band's familiar old funk-rap style is featured on songs like "Around the World", "I Like Dirt", "Get On Top" and "Right on Time". "Parallel Universe" and "Otherside", however, show a different approach to metal and rock, plus a little rapping. If it's gentle guitar strumming you want, you can turn to the gentle, evocative "Road Trippin'" for the final say.
Looking back on the Peppers' musical lineage, I personally feel that more engaging, evolving albums are on their way from this reformed group — and that they'll be using Californication as a blueprint of sorts. They are surely not your everyday funk group.
Red Hot Chili Peppers — Californication: Track-by-track review
This was one of the first Chili Peppers songs that really caught my ear. Nobody can deny the great interaction between each band member on this track.
The solidarity of the guitar, bass and drums is almost too good to be true, as is Frusciante's falsetto in the chorus. The introductory bass attack is very powerful; beware!
The band's playing is tight, and alternately noisily aggressive and slow and spare, but it is maintains a spirit of fun — the one thing that was largely missing from 1994's One Hot Minute. Kiedis' vocals, by the end, devolve into a flurry of nonsense "ching chang ling long ning nang" syllables, which both fit the music and keep the mood poppy and entertaining. It is pure RHCP — taking chances, revelling in goofiness, and producing music that holds up over repeated listenings.
2. "Parallel Universe"
"Parallel Universe" is a "kinda hard to swallow" song if you're a mere pop fan. Blared with everything they've got, this is one of the most metallic funk songs on this album. I'll give it an 8 out of 10 for aggressiveness.
The mood is more focused, and less goony, than the preceding song. Don't forget that being silly is only one ingredient in the Chili Peppers' stew; they have no problem with rolling up their sleeves and giving us real, heartfelt communication.
"Parallel Universe" contains one of the greatest, most soaring and devastating choruses in the entire Chili Peppers cannon. With a poignant yet exhausting melody, Kiedis cries "Christ, I'm a sidewinder, I'm a California king!" as the band pulses underneath with an exhilaration that manages to avoid sloppiness.
3. "Scar Tissue"
The first single from the album, this poppy song was chosen to show the world that yes, Frusiante was back and yes, the band was not resting on its laurels.
Frusciante's ending guitar solo here has been called one of his greatest solos of all time, with its slow meticulousness making it that much better. Kiedis's vocals and lyrics are presented with an unheard-of melodic sensibility. It can be draining — no wonder they called it "Scar Tissue".
4. "Otherside"
Back to guitars again!
Frusciante's pure, simple playing ability is proven again here, but this song is more abstract than others. Flea and drummer Chad Smith's great bridge in this song is downright intoxicating, while John lets the distorted harmonics ring. The vocal harmonies during the outro are brilliant.
And managing to rise above even his own band's instrumental and vocal prowess, Frusciante plays some of the album's most interesting guitar work on "Otherside's" bridge.
5. "Get On Top"
Unlike Flea's loose, grandiose bass-slapping on One Hot Minute or Mother's Milk, on this record it's very earthy and tight. The bass-and-guitar driven theme to this song is interesting, and Frusciante's playing is again very minimalistic. Despite this restraint, "Get On Top" is more of an aggressive, pounding song, thanks in large part to Kiedis' manic, mile-a-minute vocals.
The arching vocal harmonies in the chorus are executed by the band's singers very well, and long-time Peppers fans will give Flea a 10 for slapping on this one.
6. "Californication"
And the title track almost speaks for itself…
The lyrics, guitar and bass are in perfect interaction with one another, like they are conversing in a language of their own. The pre-chorus bass and guitar chords are flashy, but don't overshadow the clean, carefully-placed notes that are so common a feature of Frusciante's solos on this album.
Lyrically, the song deals with the glitz, promises, broken dreams, and cruelty of California's entertainment industry. The Chili Peppers have always been obsessed with the state, but Kiedis isn't afraid to tell it like it is, which makes his love for the place all the more affecting.
7. "Easily"
"Easily" is not your everyday rap/rock track. The verses don't stand out, but the outros do, as they contain at least 4-5 harmonized guitars, which sounds amazing. The guitar breaks between verses show how Frusciante can create a solo based on the vocal motif of the song, but transpose it to a different scale than Kiedis'.
8. "Porcelain"
An exceedingly mellow song about a teenage girl's pregnancy. This is easily Frusciante's simplest playing in his Peppers career. It's a very solemn tune, with Chad playing his simplest drums to date, just light brushes and snare.
The tone of the instruments is very reminiscent of early jazz of the 1920's, but stronger, owing to the sonic requirements of a modern day album, and so the song is almost, but not quite, a jazz tune.
9. "Emit Remmus"
No words can explain this song. You'll have to listen to it personally to judge it. I think everyone has a different take on this special track.
It comes at you fast, and is something of a rote RHCP-style song. Which is not to say it isn't good; it's fun, and it certainly brings the listener up from the quietness of the last song. Well-placed and well-played.
10. "I Like Dirt"
Frusciante uses his left-hand muting syncopation technique for a song not dissimilar to "Emit Remmus". The guitar solos are done with a very odd sounding distortion on the pedal.
Every album has a few black sheep, and it seems like this is the one here, although the expertly-executed guitar solos are more reminiscent of BloodSugarSexMagik-era Frusciante than many other songs on this album.
11. "This Velvet Glove"
The main guitar/bass motif on "This Velvet Glove" is catchy, as are Kiedis's decidedly un-melancholic vocals. Chad plays remarkably subtle and appropriate drums here.
12. "Savior"
After "Californication", this is another track on which Kiedis shows off his acclaimed "solemn" voice. The sound suffers here though, despite producer Rick Rubin's usually-steady hand. From start to finish, the whole track sounds like it was recorded at an indoor concert. Better luck next time, guys.
13. "Purple Stain"
This is a mixed track: jazz, rap, reggae, rock; you'll never quite know what it has in store for you until you get there. The sly, grinning lyrics are quite explicit — if you want them to be.
14. "Right On Time"
The guitar playing is syncopated and the tune is aggressive, but it's simple nonetheless. Generally, it's back to the old, funky Peppers style for this one, although the breezy chorus is not dissimilar to others found on this album.
15. "Road Trippin'"
Finally, a song on this intense album that allows you to genuinely relax. The guitar and bass are in a minor key, one that succeeds in evoking a very nice feeling of antiquity. This is most evident in the bridge.
This is something of a musical companion piece to "Porcelain", although lyrically this is about travelling and spending time with good friends. It sounds like the start of a journey, and as the last track on the album, acts as a gateway for the listener to go do some exploring of his own. All in all, an excellent way to end such an adventurous, careening album.
Californication by Red Hot Chili Peppers
"Get On Top"
"Emit Remmus"
"I Like Dirt"
"This Velvet Glove"
"Purple Stain"
"Right On Time"
One Hot Minute
NOTE: We're looking for a knowledgeable Red Hot Chili Peppers nerd! A review for One Hot Minute hasn't been published — yet. We need someone who can write a full track-by-track review of this album (at least a couple paragraphs per song); if you know the music, you can submit a review. You'll be compensated when visitors make purchases through vendor links on their pages — for as long as your review remains on the site. Get more details in the FAQ.
One Hot Minute by Red Hot Chili Peppers
"Deep Kick"
"One Big Mob"
"Falling Into Grace"
"Shallow Be Thy Game"
Blood Sugar Sex Magik
NOTE: We're looking for a knowledgeable Red Hot Chili Peppers nerd! A review for Blood Sugar Sex Magik hasn't been published — yet. We need someone who can write a full track-by-track review of this album (at least a couple paragraphs per song); if you know the music, you can submit a review. You'll be compensated when visitors make purchases through vendor links on their pages — for as long as your review remains on the site. Get more details in the FAQ.
Blood Sugar Sex Magik by Red Hot Chili Peppers
"The Power Of Equality"
"Breaking The Girl"
"Funky Monks"
"Suck My Kiss"
"I Could Have Lied"
"Mellowship Slinky In B Major"
"Righteous And The Wicked, The"
"Naked In The Rain"
"Apache Rose Peacock"
"The Greeting Song"
"Sir Psycho Sexy"
"They're Red Hot"
NOTE: We're looking for a knowledgeable Red Hot Chili Peppers nerd! A review for Mother's Milk hasn't been published — yet. We need someone who can write a full track-by-track review of this album (at least a couple paragraphs per song); if you know the music, you can submit a review. You'll be compensated when visitors make purchases through vendor links on their pages — for as long as your review remains on the site. Get more details in the FAQ.
Mother's Milk by Red Hot Chili Peppers
"Good Time Boys"
"Subway To Venus"
"Nobody Weird Like Me"
"Knock Me Down"
"Taste The Pain"
"Stone Cold Bush"
"Pretty Little Ditty"
"Punk Rock Classic"
"Sexy Mexican Maid"
"Johnny, Kick A Hole In The Sky"
"Song That Made Us What We Are Today (Demo)"
"Knock Me Down (Original Long Version)"
"Sexy Mexican Maid (Original Long Version)"
"Salute To Kareem (Demo)"
"Castles Made Of Sand (Live)"
"Crosstown Traffic (Live)"
The Uplift Mofo Party Plan
NOTE: We're looking for a knowledgeable Red Hot Chili Peppers nerd! A review for The Uplift Mofo Party Plan hasn't been published — yet. We need someone who can write a full track-by-track review of this album (at least a couple paragraphs per song); if you know the music, you can submit a review. You'll be compensated when visitors make purchases through vendor links on their pages — for as long as your review remains on the site. Get more details in the FAQ.
The Uplift Mofo Party Plan by Red Hot Chili Peppers
"Fight Like A Brave"
"Funky Crime"
"Me And My Friends"
"Skinny Sweaty Man"
"Party On Your Pussy"
"No Chump Love Sucker"
"Walkin' On Down The Road"
"Organic Anti-Beat Box Band"
"Behind The Sun (Instrumental Demo)"
"Me And My Friends (Instrumental Demo)"
Freaky Styley
NOTE: We're looking for a knowledgeable Red Hot Chili Peppers nerd! A review for Freaky Styley hasn't been published — yet. We need someone who can write a full track-by-track review of this album (at least a couple paragraphs per song); if you know the music, you can submit a review. You'll be compensated when visitors make purchases through vendor links on their pages — for as long as your review remains on the site. Get more details in the FAQ.
Freaky Styley by Red Hot Chili Peppers
"Hollywood (Africa)"
"American Ghost Dance"
"If You Want Me To Stay"
"Blackeyed Blonde"
"The Brothers Cup"
"Lovin' And Touchin'"
"Catholic School Girls Rule"
"Sex Rap"
"Thirty Dirty Birds"
"Yertle The Turtle"
"Nevermind (Demo)"
"Sex Rap (Demo)"
"Freaky Styley (Original Long Version)"
"Millionaires Against Hunger"
NOTE: We're looking for a knowledgeable Red Hot Chili Peppers nerd! A review for Red Hot Chili Peppers hasn't been published — yet. We need someone who can write a full track-by-track review of this album (at least a couple paragraphs per song); if you know the music, you can submit a review. You'll be compensated when visitors make purchases through vendor links on their pages — for as long as your review remains on the site. Get more details in the FAQ.
Red Hot Chili Peppers by Red Hot Chili Peppers
"True Men Don't Kill Coyotes"
"Baby Appeal"
"Get Up And Jump"
"Why Don't You Love Me"
"Green Heaven"
"Mommy Where's Daddy"
"Out In L.A."
"You Always Sing The Same"
"Grand Pappy Du Plenty"
"Get Up And Jump (Demo)"
"Police Helicopter (Demo)"
"Out In L.A. (Demo)"
"Green Heaven (Demo)"
"What It Is (AKA Nina's Song) (Demo)"


So lately I have been opening iTunes to get a Vista security message telling me that TuneUpUpdater.exe wants to run. My first thought was "What crap did Apple force onto my computer now?" I mean they make good stuff it's just that I want to install the software I want. I do not want QuickTime, or Bonjour, or the other thing I always uncheck when I update iTunes. I finally got around to googling it and found out that the internet doesn't have too much on this software.
TuneUp is a add-on for iTunes that claims to go through your iTunes library and fix all the metadata, thereby correcting the genres, the "track01", and of course the dreaded missing "The" before bands (such as The Beatles.) First thing's first; the reviews I was able to find said it had about an 85-90 percent correction rate. If you are normal your list of songs spans a minimum of thousands and more likely reaches tens or hundreds of thousands. If you only had 5,000 songs that's still up to 750 songs labeled incorrectly. That could mean they were changed from being correct to being wrong, or not corrected at all. The software uses the Gracenote database which in theory is a good idea but in practice it often does not work. Software such as this has been around a while and each one has failed in one key area: same exact songs on different albums. To test it I always used my Radiohead discography since they have the same song on a few albums. Each time GraceNote identified the song correctly but in each case it picked the wrong album (ie: everything was album x instead of x, y and z. One software had such poor implementation that it overwrote their studio albums with live recordings.
So in short, be wary of products that "help" your music library and make promises that, when mathematically analyzed, are far-fetched and have the possibility to do more harm than good. If you really have that many bad tracks 1: Stop power torrenting and 2: spend 30 minutes a day working through your current library. This is going to be one of the few times I thank Vista's annoying security feature.
This entry was posted in PC Fixes and tagged ad-ware, Gracenote, iTunes, music library, torrent, TuneUp. Bookmark the permalink.


We would love to hear from you. Vectored Air is a small business that grew out of a great idea. We love the RC hobby and as such are not always in the office. Therefore, the best way to get in touch with us is the contact form below.


78'5" by 6'1" by 6'1"
The Type A midget submarine was originally designed in 1933 and operated off Chitose and Chiyoda. Built in total secrecy at a special factory near Kure, the midget submarines were used for special operations, such as the raid on Pearl Harbor. They were also used against Sydney and against Diego Suarez in Madagascar. However, according to Japanese staff officers interviewed after the war, the midget submarines were not originally intended for use against heavily defended ports, but for the Great Decisive Battle. The admiral responsible for their development, Kishimoto Kaneharu, was deeply opposed to their use in the Pearl Harbor operation. Their designation came from their cover name ("Metal Fittings, Type A" or, later, "A Target").
Later in the war they became a coastal defense weapon. A detachment of six Type A midget submarines was sent to Kiska shortly after the Japanese occupation of the island, but were scuttled when the island was evacuated.
These craft were of all-welded construction and had no provisions for recharging their batteries. Not could their batteries be recharged by their mother submarines. The U.S. Navy concluded that those salvaged at Pearl Harbor carried net wire cutters to help them penetrate defended harbors.
About 46 of these craft were built by the end of 1942. Another 16 were apparently ordered but never completed. Four were lost at Pearl Harbor, three at Diego Suarez on 30 May 1942, 4 at Sydney on 31 May 1942, eight off Guadalcanal in 1942, and three in the Aleutians in 1942-43.


How To Spell a minute ago?
What are the common mistypes for a minute ago?
What are the common typos for a minute ago?
This graph shows how "a minute ago" have occurred between 1800 and 2008 in a corpus of English books.


Dieser Beitrag wurde unter Fashion abgelegt und mit Fashion, Hoodie, Sequin skirt, Sequins, Skirts verschlagwortet. Setze ein Lesezeichen auf den Permalink.
And I say why not? And those booties just make the look sing!!!! We need to have some fun with our clothes and find different ways to wear them. Sometimes this means thinking outside the box. Needless to say, I am ALL for this look.
thank you so much for your lovely feedback! I'm very happy, that you like my look. I love contrasts like this one.
I love those sparkles. The mix of fabrics is so cool – I love this look, including the booties. The whole look comes together really well!
Thank you so much for your nice feedback, dear! I'm happy that you like my look!


If you're in the Morgan area and looking for quality service to help you find the perfect new flooring for your Morgan home, come in to your local Carpet One. Morgan-area Carpet One stores near you are your Utah destination for top-of-the-line flooring options, flooring installation, and warranty protection that are the best in the industry. Our inventory has everything from lush carpeting to versatile vinyl flooring and so much more.
Working with Carpet One in the Morgan area will redefine the way you shop for flooring. At your Carpet One near Morgan, we proudly provide you with flooring for all lifestyles and all budgets. Our sales professionals will see you through the flooring selection process along with the Carpet One SelectAFloor™ system, which aids you in choosing the best floor for your home at the best price. Whether you're looking to add contemporary style with ceramic tiling or laminate flooring that is both durable and beautiful for the high-traffic rooms of your home, rest assured that you can find what you need by shopping at Carpet One near Morgan. Plus, there are a variety of SelectAFloor ™ warranties from which you can choose to protect the life of your floor long after it is installed.
Affordable, quality flooring has never been easier to come by than at your Morgan-area Carpet One. Rely on your Carpet One near Morgan to help you achieve your vision and renovate your floors with professional ease. Come in to your Carpet One near Morgan and let us help you start your flooring redesign journey today!


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Tulsa Sign Company | Where Can I Place My Sign
Hey guys, welcome back to the highway man science podcast. Uh, your top Tulsa sign company in the Tulsa Bartlesville sky took and surrounding areas here in Oklahoma. And, uh, we've been talking about just to kind of the different services that we provide, different types of signage and, uh, kind of defining what those are and educating you the listener in a, in what signage is available and what kind of signage we do and what we can help you with. And, uh, we've kind of just the last few times, um, you know, we've talked, it's been, we've gone over, uh, auto wraps, uh, channel letters, lighted signage led displays, and, um, those were all kind of the top end, um, you know, higher in more expensive options. Um, but also, you know, um, some of the, um, more professional options, the options that people really catch people's eye, um, I really seem to get the best, but there are a lot of, um, there are a lot of options that are, um, for signage, for your building that are non lighted. And a lot of these can look very, very classy as well. Um,
Yeah, depending if you're just wanting like a flat sign on your building, uh, just like a metal sign. Uh, there is a material that we use, uh, it's called ACM. It stands for aluminum composite material and, uh, this is being used for a lot of storefronts. Um, you see this material a lot on, um, um, car dealerships, um, even some banks, uh, they actually use this material a lot for, um, kind of a different style of siding. Um, but it's also used for signage and basically what it is a reason it's called aluminum composite material. Basically what it is. It's a, it's a three layer, um, piece, uh, material that has a sheet of aluminum, um, with a PVC core and then another sheet of aluminum sandwiching the PVC, uh, which makes it durable, rigid. Um, it can't rock can't rust. Um, it just hasn't really, it hasn't really long live.
It comes in a Ray of colors. Um, and also we can print, uh, your graphics, print your design and install it onto the, uh, onto the metal surface. What's great about these, these come in different size sheets, um, four by eight, four by 10, five by 10, um, and also cut them down. Um, and we, we do, we do a lot of just, um, you know, business type signs, a lot of four by eights that, you know, somebody wants a four by eight on their building with their graphic, and these can look really, really nice and fit into your budget very, very well. Um, now it doesn't have the pop of a lighted sign. Doesn't have the professional look of a, um, like a channel letter sign, like we've talked about before, but these look very, um, that they can look very, very nice, um, yeah, in a two dimensional type way.
Thanks For Joining the highway man signs podcast, your top Tulsa sign company in the Tulsa, Bartlesville, Skiatook, and surrounding areas. Speaker 1 (03:19): Um, they're also great. Uh, the material ICM is also great for, um, wayfinding signage. It's great for, um, just the metal signs around, uh, your property, um, directional signage. Um, so it has, has a lot of different uses and coming squared corners around corners, just depending on how, uh, you know, you want the DOD to be. And we can also cut it into, uh, custom shapes. Now we are a little bit limited on that. Um, but we can do some simple shapes if you want around one, if you wanted something to kind of follow the edges of your, um, of your logo, um, and, uh, and just kinda make it look a little bit more, um, custom.
So, yeah, so there's a lot of different, that's kind of the ACM, that's kind of one of the, you know, non-audit options. Another non-audit option is, um, letters. So we can order, uh, either plastic form letters, uh, laser cut, acrylic letters, um, metal letters, um, you know, either be a like a laminate type metal letter, a, which has like an acrylic base with a, like a brushed aluminum or copper or bronze color laminate on the top of it. And those like were very, very nice and are, are very affordable, um, all the way up to cast bronze cast, aluminum metal letters. Um, now those can get very, very pricing. Those they're also very, very classy. They're great for, um, really upscale areas. Um, and it kind of reminds me of, uh, we didn't really talk about this on the channel letter, uh, podcast, but, um, this kind of segues into that.
There's also ways to backlight them where, um, it's, it's, it's called backsliding. There, there are two different ways of lighting. I didn't really get this in the other one. So this is kind of going onto a tangent, but, um, basically where the, uh, a normal standard channel at, or where it's lit through the front of the letter a and lights up the color of the letter where it'd be red or blue or orange or whatever. Um, that's called front lighting. Well with like cast letters and Catholic cast and metal letters, or even, uh, what they call first channel letters, uh, it's called backlighting. And what this does is at night, it give the impression that it has a glow around the letters. So the letter looks dark, but then we have a colored or white glow around each letter, like very, very classy, um, a little bit limited on the installation options.

redpajama_set_name: RedPajamaCommonCrawl


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Oregon Health Authority: Fewer masks, more cases
By Rob Manning (OPB)
April 20, 2022 3:20 p.m. Updated: April 20, 2022 6:16 p.m.
State health officials gave their first COVID-19 briefing since early March.
COVID-19 is the disease caused by the new coronavirus. (Graphic)
In their first public briefing about the COVID-19 pandemic in more than a month, officials at the Oregon Health Authority acknowledged what the public has seen in state reports for the last few weeks: Case numbers are going up. Oregon has seen case numbers go up from a low of 200 or 300 cases on a single day to about 600 daily cases, according to official state numbers.
Watch the recording: Oregon Health Authority COVID-19 press briefing
But officials struck a reassuring tone, saying the key indicators that are of highest concern — hospitalizations and deaths — are not going up. Deputy state health officer Tom Jeanne said the rise in cases is consistent with models and expectations, and a result of not just the arrival of the new BA.2 subvariant of omicron, but also due to recent changes in people's behavior.
"It's been a little more than four weeks since the lifting of mask use in most settings, and some of the jump we're seeing in case counts is likely tied to less mask use, and more Oregonians gathering indoors and returning to pre-pandemic social activities," Jeanne said.
With the federal government eliminating mask requirements this week on public transportation. including on airplanes, buses and trains, state health officials anticipate a further rise in COVID-19 case counts. But Jeanne wasn't sure how much of a factor that policy change would be.
"It's hard to say, this is in the context of everything else that's changing," Jeanne said, noting the change in seasons and evolution of social behavior.
"What we know is that cases are already going up in Oregon, they're going up around the country right now."
Health officials have emphasized in recent weeks that what's more important than rising COVID-19 case numbers is the rate of hospitalizations. Jeanne noted there are far fewer people in the hospital either with COVID or directly because of it than in earlier stages of the pandemic. This month, the hospitalization average has been fewer than 100 patients, down from a high of more than 10 times that high during previous surges. However, Jeanne acknowledged that hospitalizations tend to be a lagging indicator.
"We're also going to see an impact on our hospitalizations, eventually here, and deaths, as those cases go up," Jeanne said. "But the projections look like any peak we're going to see in the near future here is going to be quite a bit lower than our large peaks last year and this past winter."
The general message from health officials didn't change much at the Wednesday briefing, with a focus on getting vaccinated, or a booster shot if possible. Officials noted the frequency of breakthrough cases, and the strong possibility of getting the virus even for people who've been vaccinated. But they reiterated that vaccines and boosters are also highly effective at limiting the severity of disease — and keeping people out of the hospital.
OHA acknowledged that its data picture continues to be incomplete. It's possibly even less complete than it's been at previous stages of the pandemic, as more people test themselves for COVID-19 without reporting positive results to local public health authorities.
"The proportion of cases reported to public health also has likely declined with more home tests available," Jeanne said.
State health officials said Oregon's undercount could mean the public is only learning of roughly one-tenth of COVID-19 in the community. At the same time, public health officials noted that an "undercount" of cases has been a challenge since the start of the pandemic, with symptomatic people not getting tested.
Many people who get COVID-19, especially if they've been vaccinated and are at low risk for severe infection, can recover from the disease at home without specialized medications for COVID-19. But public health physician Paul Cieslak said that there is a treatment available in Oregon that's considered 89% effective at preventing severe disease: paxlovid. He noted that it's intended for people at the highest risk for severe illness.
"This is the sort of thing where you need a diagnosis and the medication that requires a prescription," Cieslak said. "It should be one-stop shopping for people who think they have COVID-19 and are at risk for severe disease."
OHA said the Wednesday briefing is the first of monthly updates it will provide on the pandemic, going forward.
Oregon COVID-19 cases increase, but health authorities are not worried
While the number of COVID-19 cases increase, authorities say they expected a jump and are not concerned.
Oregon sues COVID-19 testing company that allegedly stored samples in garbage bags
Oregon has joined the list of states suing a pair of Illinois businesses that allegedly provided inaccurate COVID-19 tests while funneling money to the owners.
In Oregon, an optimistic forecast for BA.2, the subvariant of omicron
Experts say it is unlikely to fuel a large spike in new cases like the original omicron wave.
Tags: Health, COVID-19
BBC Newsday


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Work on Trump's border wall to begin near Tucson, 2 other locations
Customs and Border Protection has identified areas where the existing "fence or old brittle landing-mat fencing are no longer effective."
Work on Trump's border wall to begin near Tucson, 2 other locations Customs and Border Protection has identified areas where the existing "fence or old brittle landing-mat fencing are no longer effective." Check out this story on chillicothegazette.com: http://azc.cc/2mp0Oiq
Michael Squires and Rafael Carranza, The Republic | azcentral.com Published 1:16 p.m. ET Feb. 25, 2017 | Updated 12:39 p.m. ET Feb. 27, 2017
A general view a cross on the United States and Mexico border wall in Nogales on Jan. 25, 2017.(Photo: Cronkite News)
Work on the wall President Donald Trump has promised to erect along the nation's 2,000-mile southwestern border will begin near Tucson, as well as El Paso, Texas, and El Centro, Calif., according to the Department of Homeland Security.
Customs and Border Protection has identified areas where the existing "fence or old brittle landing-mat fencing are no longer effective," the federal agency revealed in documentation accompanying memos by Homeland Security Secretary John Kelly.
The document provided no further detail on the locations where work would begin on the structure that was a signature Trump campaign promise and is central to the president's hard-line stance on illegal immigration.
Kelly's memos dealt with the agency's implementation of Trump's executive order on immigration enforcement and border security.
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CBP said it is also evaluating "priority areas" where it can build a wall or "similar physical barrier on the border where it currently doesn't exist." That evaluation, which will be completed within 180 days, will include current security, geography and federal and state resources to achieve "operational control" of the border, the agency said.
Kelly toured the Arizona-Mexico border this month and discussed border security with law-enforcement officials, including Santa Cruz County Sheriff Tony Estrada and Pima County Sheriff Mark Napier.
Estrada, a Democrat, said at the time that he had questions about the utility of a wall.
RELATED: U.S. Customs and Border Protection plans to award Mexico border wall contracts by April
"I think that's still an open question, being that we're unique all along the border from San Diego to Brownsville," he said. "It requires different approaches. In some places, you may not even need a wall."
Napier, a Republican, said following the meeting that they had "talked about the value of technology in some areas and physical structures in others."
An internal Department of Homeland Secretary document that surfaced this month put the cost of the wall at about $21.6 billion. The document said construction would take up to three and a half years.
The cost estimate is far higher that the $10 billion-$12 billion Trump has said it would cost.
ROBERTS: Now we know who would pay for the wall: Us
That document included a three-phase construction plan for the 1,250 miles of border without physical barriers. The first phase of construction would begin in September, covering some 26 miles overall in San Diego, El Paso, and the Rio Grade Valley in south Texas.
The Arizona border is included in the second phase, covering 151 miles. That phase would also included additional parts of Texas, in Laredo and the Big Bend area.
The final phase would cover the remaining sections.
Read or Share this story: http://azc.cc/2mp0Oiq
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text:
La guerra di Coorg fu combattuta tra la Compagnia britannica delle Indie orientali e lo Stato di Coorg nel 1834. Nel febbraio 1834, una forza di 7000 uomini al comando del generale di brigata Lindsay iniziò le operazioni contro il raja di Coorg, Chikavira Rajendra, che aveva iniziato le ostilità contro i britannici. A causa del cattivo stato delle strade, le truppe britanniche furono divise in quattro colonne, che dovevano entrare a Coorg da direzioni diverse per poi convergere sulla capitale di Mercara (odierna Madikeri). L' la divisione nord al comando del colonnello Gilbert Waugh entrò nel territorio di Coorg e il  le sue avanguardie entrarono in contatto con il nemico. A mezzogiorno, le unità più avanzate arrivarono davanti alla posizione fortificata di Soamwar Pettah (odierna Somvarpet). La postazione fu attaccata, ma la colonna britannica dovette ritirarsi. Analogo esito ebbe l'attacco di un'altra colonna. A capo della resistenza di Coorg vi era "Madanta" (Mathanda) Appachu.

Il  Diwan Lakshminarayana e Mahomed Taker Khan, un amico del raja, si recarono al campo britannico con una bandiera di pace. Kulputty Karnikara Manoon, tenuto prigioniero dal raja, fu consegnato ai britannici, che chiesero la resa del raja. Il  Diwan Bopu si arrese al colonnello Fraser, che il  fu condotto alla fortezza di Mercara. Il  il raja, che si trovava nel palazzo di Nalknad, entrò nel forte di Mercara accompagnato le sue mogli e si arrese ai britannici.

Le perdite britanniche durante la campagna furono di 93 morti e 200 feriti.

Tra gli ufficiali britannici che parteciparono alla guerra di Coorg vi furono Colin Mackenzie e William Anson McCleverty.

Note

Bibliografia

Storia del Karnataka

redpajama_set_name: RedPajamaWikipedia


Verse is reading for both the print edition (portfolios in any genre, 20-40 pages) and the site (3-5 poems, mini-essays, or micro fictions) until 3/31/14. To submit, follow the link on the right. Contributors to the print edition receive $10/page, $250 minimum.


text:
\section{Introduction}

\subsection{Response-adaptive randomization}
Randomization remains a pivotal methodology for advancement in medical knowledge.
It removes any systematic bias and allows direct inference between the treatment group and outcome.
Traditionally, fixed randomization scheme (1:1 or 2:1)  is used to due to simplicity in design and execution of the trial.
However, response-adaptive randomization (RAR) design utilizes accrual information to tilt the randomization ratio to the better performing treatment group.
Patients enrolled in these trials are not only treated to obtain the effectiveness of treatment but also treated to the best way possible \citep{wei1978randomized}.
Response-adaptive randomization design primarily targets to solve both the issues mentioned above at once with a good balance.
The biasness in the trial can be greatly affected if one arm is very superior to the other arm(s) due to a drastic alteration in the randomization ratio.

Response-adaptive randomization has been highly advocated due to ethical reasons.
The primary use of adaptive designs in clinical trials is that it improves the benefit/risk for the patients enrolled in a trial \citep{hey2015outcome}.
For instance, in Zidovudine Treatment (AZT) trial conducted to test the reduction of maternal-infant transmission of HIV type 1, patients were randomized to 1:1 \citep{connor1994reduction}.
The equal randomization scheme used placed 239 women in the treatment group (AZT) and 238 patients in the placebo group.
Out of the 238 women in the AZT group, 60 of the infants were transmitted with the HIV virus while only 20 infants contracted the HIV virus for the AZT group \citep{connor1994reduction}.
The outcome of the trial confirmed that the new treatment works.
Given the seriousness of the outcome of this study, it is reasonable to argue that 50-50 allocation was unethical.
As the outcome of the trial becomes available, the randomization ratio should have tilted in favor of the AZT group.
Response-adaptive randomization design could have reduced the number of infants that contracted HIV disease from their maternal.

On the other hand, opponents of RAR have argued that adaptive randomization challenges the whole notion of equipoise \cite{begg2015ethical}.
Hey and Kimmelman has also argued that most new treatments offer small improvement standard treatment, thus they offer limited benefit and require a larger sample size \cite{hey2015outcome}.
Hey and Kimmelman also suggested that equal randomization helps reduce the trial size and length, thus it benefits future patients rather than current patients enrolled in the trial \cite{hey2015outcome}.
Korn and Friedlin measure the difference in non-responders under equal and adaptive randomization and found that adaptive randomization required a larger trial to achieve the same power and type I error \cite{korn2011outcome}.
The major drawback to response-adaptive randomization design is that the trial needs to be short to be able to obtain the outcome of the trial for future randomization \citep{karrison2003group}.

\subsection{Time-trend issues}

However, one of the main critics against response-adaptive randomization is the time-trend issue.
This contributes to the main factor of why RAR is infrequently used.
The type I error rate is usually not controlled at the nominal level under traditional Bayesian RAR design \citep{thall2015statistical}.
Besides affecting type I error, studies have shown that there is a large bias in the estimation of treatment difference under traditional RAR design\citep{thall2015statistical}.
Figure~\ref{plot:RARplot} shows that the increase in response in the treatment group tilts the randomization ratio in favor of the treatment.
However, this is directly confounded by time.

\begin{figure}
\centering
\includegraphics[width=0.45\textwidth]{RAR_plot.pdf}
\caption{Effect of time as a blocking factor on randomization ratio}
\label{plot:RARplot}
\end{figure}

Example of time trend issues in response-adaptive randomization (RAR) design includes \citep{chappell2007continuous, karrison2003group}:

\begin{itemize}
\item The disease itself can change, sometimes radically (e.g., AIDS in the early 1990s).
\item Our definition of the disease can change due to new scientific discoveries or diagnostic methods (e.g., stage migration in nasopharyngeal carcinoma due to the introduction of CAT scans to Hong Kong ~ 2005).
\item Inclusion criteria can change, either formally (in which case we can stratify analysis on before vs. after the change) or informally due to "recruiting zeal" or other issues (in which case we can't).
\item Centers can change, such as when VAs enter the trial earlier or later than academic institutions.
\item Patients within centers can change, especially but not only with chronic diseases, due to the phenomenon of "A queue of desperate patients lining up at the door".
\item In addition to these examples, an investigator who wants to game the system could cross his/her fingers that his favored treatment arm is ahead, then progressively enroll better prognosis patients over time
\end{itemize}

In long duration trials, time-trends more likely to occur.
Patient's characteristics might be completely different throughout the trial or even at the beginning and end of the trial (which is also known as ``patient drift'') \citep{karrison2003group}.
Since most of the RAR design have adaptiveness on the fly, there is one important assumption made. "The sequence of patients who arrive for entry into the trial represents samples drawn at random from two homogenous populations, with no drift in the probabilities of success" \citep{begg2015ethical, karrison2003group}.
However, this assumption is usually violated.
For example, there were more smokers enrolled in the latter part of the trial than the beginning of the trial in the Lung Cancer Elimination(BATTLE) \citep{liu2015overview}.

Given the serious flaw in RAR design, there is not much literature on this area to address the time-trend issue.
Randomization test for adjusting type I error inflation was proposed by Simon and Simon using different RAR rules for double-arm trials \citep{simon2011using}.
Jennison and Turnbull explored a group-sequential method for RAR with continuous outcome utilizing multi-stage
terminology \cite{jennison1999group}.
Karrison et al. introduced a stratified group-sequential method with a simple example of altering the randomization ratio to address this issue \citep{karrison2003group}.
Coad used a very similar stratified analysis to Karrison et al. \citep{coad1992comparative}.
Rosenberger et al. introduced a covariate-adjusted RAR procedure where time mechanism can be added as a covariate in the model \citep{rosenberger2001covariate}.
Thall et al. investigated type I error under a linear time-trend induced in the traditional response-adaptive randomization design and showed that the type I error is significantly above the nominal level \citep{thall2015statistical}.
Villar et al. explored the hypothesis testing procedure and adjusting for covariates for correcting type I error inflation and the effect on power in RAR design with time-trend effect added to the model for two-armed and multi-armed trials \citep{villar2018response}.

Time-trend can not only greatly affect the biasness in the difference in treatment effect but it can also wrongly reject a true null hypothesis.
We propose a block (group-sequential) design where the randomization ratio is altered in a block level instead of a patient by patient basis using both frequentist and Bayesian approach.
In each block, the randomization ratio is kept constant.
The block design is similar to the stratified group design introduced by Karrison et al. \citep{karrison2003group}.
Then, we further study the robustness in different block sizes using both frequentist and Bayesian approach.
We also compare these results with traditional RAR design and with fixed (1:1) randomization.

\section{Trial Design and Simulation}
\subsection{Block Design for RAR and Why?}
In binary outcomes, events (success/failure) are observed within a short period from the beginning of the treatment.
In a block design, patients are enrolled in a sequential manner.
For instance, in two-arm design (treatment A and B) patients are enrolled in block with sample size of $n_{Ak}$ and $n_{Bk}$, for $k = 1, 2, 3, ... , K$, where $n_{Ak}$ and $n_{Bk}$ represent the sample sizes in treatment group A and B in block $k$.
In this design, the randomization ratio is constant for patients within each block and the randomization ratio is altered at the block level.
Unlike traditional RAR which alters the randomization ratio by patient basis, this method speeds up the process of RAR trials since randomization ratio is modified for a group of patients in the block.
The initial randomization ratio is set to 1:1.
Bashir et al. have implemented two-block design where patients are randomized 1:1 in a group of 10 and then based on the outcome, they randomized the next ten patient to the superior treatment \citep{bashir2012randomized}.
However, at the second block, they randomized all the patient to the lower dosage because the probability of randomizing patient to the lower dosage was 0.9 compared to 0.1 to the higher dosage.
This design should be considered a randomized control trial for the first block and an observational study for the second block.
As asserted by Karrison et al., the block design will eliminate bias due to drift through stratification \citep{karrison2003group}.
However, the optimal block size remains unclear and it is highly dependent on the operating characteristics of a trial.
Blocks with a large number of patients also help reduce the large probability of imbalance in the wrong direction, where more patients are assigned to the inferior treatment compared to traditional RAR.
Blocks with fewer patients can reduce the power of the trial because if patients are not randomized to both treatments in a block, the block becomes uninformative.

\subsection{Simulation and Design}
We investigated the effect of a different number of blocks using simulations for both frequentist and Bayesian approach.
The rules for altering the randomization ratio in both Bayesian and frequentist design are described in the subsection below.
The target sample size of  $N$ = 200 subjects were considered with number of blocks (strata), $K$ = 1, 2, 4, 5, 10, 20, 100, 200.
When $K$ =  1, this follows the traditional equal allocation design and when $K$ = 200, this follows the traditional RAR design where stratification is ignored.
Upon completion of enrollment and collection of data of the subjects in each block, the interim analyses are done to revise the allocation rule.
The interim analysis also allows for early stopping and details of early stopping for both Bayesian and frequentist design is included below.
10, 000 independent simulations were performed for each design yielding a Monte-Carlo standard error of 0.25\%.

In each simulation, the success rate of treatment A (control group), $p_A$ is set to 0.25.
The alternatives (the success rate of treatment B, $p_B$) is set to 0.25 (null case), 0.35, 0.45.
On top of simulating both Bayesian and frequentist design for $p_A$ and $p_B$ specified above, we also simulated RAR with drift effect.
To examine the effects of drift, we increased both $p_A$ and $p_B$ linearly from their initial values to a final value of 0.25 larger.
The drift was added onto each block rather than patient by patient basis.
For instance, the success rate in strata k for treatment A is $p_A(k) = 0.25 + 0.25((k -1) / K)$, where $K$ is the total number of strata.
While both the $p_A$ and $p_B$, the treatment effect remains constant throughout the trial.
For both frequentist and Bayesian design, the final analysis is done using stratification for a number of blocks, K where $ 1< K < 200$.
For traditional RAR and traditional RAR with 1:1 allocation ratio, the standard analysis is used.
Details of the analysis are attached in the Bayesian and frequentist approach section.

\subsection{Frequentist Approach}

The allocation rule for the frequentist approach is altered using optimal allocation ratio for 2-armed trial specified by Rosenberger et al \cite{rosenberger2001optimal}.
The allocation probability for treatment A in stratum $j$ is defined as
$$\pi_{j, A} = \frac{\sqrt{\hat{p_A}}}{\sqrt{\hat{p_A}} + \sqrt{\hat{p_B}}},$$
where $\hat{p_A}$ is the estimated success rate of treatment A and $\hat{p_B}$ is the estimated success rate of treatment B up to block $j - 1$.
However, the allocation probability is only altered upon observed both event (success and no response) in both the arm.
Simulation for early stopping is also included, the alpha spending approach was incorporated to stop for early success or failure \cite{demets1994interim}.
For traditional RAR and fixed allocation, one-sided chi-square test was used to analyze the outcome.
However, for the block design, one-sided Cochran-Mantel-Haenszel test was utilized to deal with the stratification.
Yates's correction was not applied for both chi-square and Cochran-Mantel-Haenszel test due to the overly conservative nature of the correction \cite{haviland1990yates}.
Treatment B is proclaimed superior to treatment A if the one-sided p-value $< 0.05$.

The computation of the mean proportion of treatment difference is computed as below for stratified design.
In two treatment scenario (treatment A \& B), suppose there are $K$ strata, let $p_{Ak}$ and $p_{Bk}$ be the proportion of success in treatment A and stratum $k$.
Let $n_{Ak}$ and $n_{Bk}$ be the number of patients in treatment A and treatment B and stratum $k$.
Let
$$\hat{\delta_k} = \hat{p_{Bk}}- \hat{p_{Ak}}$$
be the observed difference in proportion between treatment A and B in stratum $k$.
The observed proportion of treatment difference ($\delta$) is computed as below,
$$\hat{\delta} = \sum^K_{k = 1} w_k \hat{\delta_k}$$
where $w_k = \frac{(n_{Ak}^{- 1} + n_{Bk}^{- 1})^ {-1}}{\sum_{k = 1}^ K(n_{Ak}^{- 1} + n_{Bk}^{- 1})^ {-1}} $ is the weight of the stratum k and $\sum_{k = 1}^ K w_k = 1$.
For non-stratified design, where the block size $K$ = 1, 200, the difference in proportion between treatment B and A ($\delta$) is obtained as follows,
$$\hat{\delta} = \hat{p_B} -  \hat{p_A}$$,
where $p_B$ is the estimated proportion of success in treatment B and $p_A$ is the estimated proportion of success in treatment A.

\subsection{Bayesian Approach}

In the Bayesian approach, the Bayesian adaptive randomization (BAR(1/2)) method introduced by Thall and Wathen is employed \cite{thall2007practical}.
The probability of randomizing subjects to treatment A in stratum j is defined as
$$\pi_{j, A} = \frac{(p_{A>B} (data))^ {1/2}}{(p_{A>B} (data))^ {1/2} + (p_{B>A} (data))^ {1/2}},$$
where $p_{A > B}(data)$ is the posterior probability that treatment A has a higher success rate than  treatment B and $p_{A>B} (data)) = 1 - p_{B>A} (data))$ .
The beta-Binomial conjugate prior is used for the estimation of the posterior probability.
The posterior probability that the treatment A has a higher event rate than treatment B is
\begin{equation*}
\begin{split}
p_{A > B}(data) \sim & beta(y_A + a_0, N_A - y_A + b_0) -  \\
& beta(y_B + a_0, N_B - y_B + b_0) > 0,
\end{split}
\end{equation*}
where $y_A$ and $y_B$ denote the number of events  in treatment A and B, $N_A$ and $N_B$ denote the number of subjects in treatment A and B, $a_0$ and $b_0$ denote the prior rate parameter of the beta distribution.
Similar to Thall and Wathen's approach, beta(0.5, 0.5) priors were assumed for both treatment A and B \cite{thall2007practical}.
Since there is no closed-form solution for the difference in beta distributed random variables, Monte Carlo simulations were performed to estimate the posterior of the treatment difference.

Similar to the frequentist design, simulation for the possibility of early stopping are included.
If treatment B is selected to be better than treatment A, then if $P_{B > A}(data) > .99$, the trial is stopped early for success and if $P_{B>A}(data) < .01$, the trial is stopped early for failure.
For non-stratified design, where $K$ = 1, 200, if the final posterior probability $P_{B > A}(data) > .95$, then treatment B is declared superior to treatment A.
The mean estimate of the proportion of treatment difference is estimated using Monte Carlo simulation.
The mean value of $P_{B>A}(data)$ is used to estimate the proportion of treatment difference.

For stratified design by block, Bayesian logistic regression was implemented to estimate the posterior probability of treatment difference.
The logistic regression model is defined as below
$$Logit(y_{ij}) \sim \beta_0 + \beta_{trt} x_{ij} + \sum_{j = 1}^K \beta_{j} x_{ij}, $$
where $y_{ij}$ is the outcome of treatment $i$ and stratum $j$, $x_{ij}$ is the indicator variable of treatment $i$ and stratum $j$, $\beta_0$ is the intercept term, $\beta_{trt}$ is the treatment effect, $\beta_j$ is the stratum effect of stratum $j$.
The uninformative prior applied in the model are as defined
$$\beta_0, \beta_{trt}, \beta_j \sim N(0, \sigma^2_j), \qquad \sigma_j^2 \sim Inv-\chi^2(1, 2.5).$$
The posterior value of $\beta_{trt}$ is used to estimate the proportion difference in treatment.
The R package \textit{arm} with \textit{quasi} family and $\mu(1-\mu)$ link was used to fit Bayesian logistic regression and obtain the posterior samples of $\beta_{trt}$ \cite{arm}.
The proportion difference in treatment is estimated using the mean posterior value of $\beta_{trt}$.
If difference of proportion between B and A is estimated using $\beta_{trt}$, then treatment B is declared superior if $E(\beta_{trt} > 0) > 0.95$.

\section{Results}
For all cases, the simulation was replicated for 10,000 times.
The simulation is reproduced for both Bayesian and frequentist design with and without time trends.
The number of blocks (K) choosen are as following, K = 1, 2, 4, 5, 10, 20, 100, 200.
For all the simulation, the power (treatment B is superior to treatment A), bias, probability of sample imbalance of more than 20 patients assigned to treatment A over treatment B, the mean, 2.5\% and 97.5\% percentile of difference between $N_B - N_A$ (sample size imbalance favoring treatment B over treatment A) are reported.
The power reflects the proportion of 10,00 trial that declares treatment B superior to treatment A.
The type I error (false-poitive) is simulated by setting both  $p_A, p_B = 0.25$.
The bias computes the estimated difference in proportion of treatment estimate, bias = $\hat{\delta} - \delta$.
$\pi_{20}$, the probability of assigning 20 or more subjects to treatment A over treatment B ($P(N_A -N_B > 20)$) is included in the simulation.
Due to nontrivial possibility of assigning more patients to the inferior arm by chance, it is vital to analyze $\pi_20$.
To highlight the main advantage of RAR compared to equal randomization, the difference in sample size between the superior arm and inferior arm is presented.
Since the difference between subjects assigned to treatment B and treatment A ($N_B - N_A$) is skewed and dispersed as illustrated in Figure~\ref{plot:ssdiff}, the mean, 2.5\% and 97.5\% of $N_B- N_A$ are reported.

\begin{figure}
\centering
\includegraphics[width=0.5\textwidth]{samplesizediff.pdf}
\caption{Distribution of difference in number of patients assigned to treatment B compared to treatment A ($N_B- N_A$) for a 200 patient trial with no early stopping using both Bayesian and frequentist stratified design with 10 number of blocks. The  sample size difference is collected for 100 different simulation with $p_A = 0.25$ and $p_B = 0.35$. The black dashed line included shows $\pi_{20}$. }
\label{plot:ssdiff}
\end{figure}

\subsection{Simulation with No Time-Trend}

Table~\ref{freqnoearlystopnodrift} and Table~\ref{bayesnoearlystopnodrift} displays the results of simulations for a number of blocks, K = 1, 2, 4, 5, 10, 20, 100, 200 using the frequentist and Bayesian approach with no drift applied and not stopping early for success or failure.
The frequentist approach (Table~\ref{freqnoearlystopnodrift}) manages to control the type I error unlike the Bayesian design in Table~\ref{bayesnoearlystopnodrift}.
Even though stratified RAR design has higher type I error, the type I error is still controlled under the nominal level (Table~\ref{freqnoearlystopnodrift}).
However, type I error is high for 4, 5, 10 and 20 number of blocks under the Bayesian design (Table~\ref{bayesnoearlystopnodrift}).
Block design with 2, 4, 5 and 10 number of blocks provides a higher power when $p_B = 0.35, 0.45$ (Table~\ref{freqnoearlystopnodrift}).
In the Bayesian design, the fixed randomization ratio provides the best power.
Although the type I error is close to the nominal level under K = 2, 4 and 5, with a slight increase in sample size it could be lowered to the nominal level.
A small increase in sample size might still be favorable if more subjects are treated with the best possible cure.
Block design with a small number of subjects in a block should not be considered due to low power in the design as shown in Table~\ref{freqnoearlystopnodrift} and \ref{bayesnoearlystopnodrift} with 100 blocks.
This poor performance is due to the reality that some of the blocks can be noninformative if subjects in the block are randomly assigned to the small treatment group.
Ethically, this design (2 subjects per block) places more subjects at risk without contributing to the advancement in science.

\begin{table}[ht]
\resizebox{\columnwidth}{!}{%
\begin{tabular}{|c|c|c|c|c|c|c|}
\hline
$p_B$ & Block & Power & Bias & $\pi_{20}$ & $N_B - N_A$ \\
\hline
0.25 & 1 & 0.03 & 0.00 & 0.06 & 0.00 (-28, 28) \\
   & 2 & 0.05 & 0.00 & 0.03 & 6.97 (-22.00,  36) \\
   & 4 & 0.05 & 0.00 & 0.02 & 11.80 (-18, 44) \\
   & 5 & 0.05 & 0.00 & 0.01 & 13.01 (-18, 46) \\
   & 10 & 0.05 & 0.00 & 0.01 & 14.63 (-16, 48) \\
   & 20 & 0.05 & 0.00 & 0.01 & 15.02 (-16, 48) \\
   & 100 & 0.05 & 0.00 & 0.01 & 15.24 (-16, 48) \\
   & 200 & 0.03 & 0.00 & 0.01 & 15.11 (-16, 48) \\
   \hline
 0.35 & 1 & 0.33 & 0.00 & 0.07 & 0.15 (-28,  28) \\
   & 2 & 0.45 & 0.00 & 0.02 & 9.94 (-20, 42) \\
   & 4 & 0.45 & 0.00 & 0.01 & 15.83 (-16, 52) \\
   & 5 & 0.46 & 0.00 & 0.01 & 16.83 (-16, 52) \\
   & 10 & 0.44 & 0.00 & 0.01 & 19.28 (-14, 58) \\
   & 20 & 0.42 & 0.00 & 0.01 & 19.97 (-14, 58) \\
   & 100 & 0.20 & 0.00 & 0.01 & 20.26 (-12, 58) \\
   & 200 & 0.35 & 0.00 & 0.01 & 20.61 (-14, 58) \\
   \hline
 0.45 & 1 & 0.85 & 0.00 & 0.07 & 0.04 (-28, 28) \\
   & 2 & 0.91 & 0.00 & 0.01 & 14.99 (-16, 48) \\
   & 4 & 0.90 & 0.00 & 0.00 & 23.26 (-10, 60) \\
   & 5 & 0.91 & 0.00 & 0.00 & 25.26 (-8, 62) \\
   & 10 & 0.90 & 0.00 & 0.00 & 28.62 (-6,  66) \\
   & 20 & 0.88 & 0.00 & 0.00 & 29.03 (-6, 68) \\
   & 100 & 0.47 & 0.00 & 0.00 & 29.75 (-4, 68) \\
   & 200 & 0.84 & 0.00 & 0.00 & 29.80 (-6, 70) \\
\hline
\end{tabular}
}
\caption{RAR using frequentist approach with no early stopping criteria and no drift applied. The $p_A$ is set to 0.25 for all cases. $Bias = \hat{\delta} - \delta$. $\pi_{20}$ denote the probability of assigning more than 20 patients in the inferior treatment group, $P(N_A -N_B > 20)$. $N_A$ and $N_B$ denote the number of patient assigned to treatment A and B. The mean (2.5\%, 97.5\%) of $N_B - N_A$ is reported in the last column. 10,000 simulation was done for each case. }
\label{freqnoearlystopnodrift}
\end{table}

The estimation of the actual treatment effect is unbiased under the frequentist design regardless of the number of blocks used.
However, the estimation of treatment effect is biased for most stratified RAR design and traditional RAR as shown in Table~\ref{bayesnoearlystopnodrift}.
Unlike the Bayesian approach (Table~\ref{bayesnoearlystopnodrift}), the variability in sample size assigned to treatment A and B is smaller in the frequentist approach (Table~\ref{freqnoearlystopnodrift}).
The mean difference in the subject's assignment of treatment is also smaller in the frequentist design compared to the Bayesian design.
Thus, the frequentist design is a little conservative of assigning patients to the better-performing treatment, unlike the Bayesian design.
At the largest difference in proportion $p_B = 0.45$, $p_A = 0.25$, there is still room for imbalance in the wrong direction in the frequentist design compared to the Bayesian design (Table~\ref{freqnoearlystopnodrift} and \ref{bayesnoearlystopnodrift} ).
The imbalance in sample size favoring the inferior treatment is small under all scenario as shown by $\pi_{20}$ in Table~\ref{freqnoearlystopnodrift}.
The difference in sample size ($N_B - N_A$) is relatively small and close to 0 for the frequentist design.
On the other hand, the imbalance is large for the Bayesian design as illustrated by $\pi_{20}$ in Table~\ref{bayesnoearlystopnodrift}.
The large difference in sample size (more than half the total sample size) is seen in the Bayesian design with the most extreme difference in the outcome of treatment A and B.

Table~\ref{freqearlystopwnodrift} and Table~\ref{bayesearlystopnodrift} displays the results of simulations for a number of blocks, K = 1, 2, 4, 5, 10, 20, 100, 200 using the frequentist and Bayesian approach and with early stopping criteria for success or failure implemented.
Parallel to the earlier results, the drift is higher in Table~\ref{freqearlystopwnodrift} and Table~\ref{bayesearlystopnodrift}.
Table~\ref{bayesearlystopwdrift} emphasizes that K = 10, 20, 100 and 200 have a inflated type I error of 0.12, 0.16 and 0.18  similar to Table~\ref{bayesnoearlystopwdrift}.
As seen in type I error and power in Table~\ref{freqearlystopwdrift} and \ref{bayesearlystopwdrift}, a large number of stratum with Bayesian design should not be considered for clinical studies.

\begin{table}[ht]
\resizebox{\columnwidth}{!}{%
\begin{tabular}{|c|c|c|c|c|c|}
\hline
$p_B$ & Block & Power & Bias & $\pi_{20}$ &  $N_B - N_A$ \\
\hline
0.25 & 1 & 0.05 & 0.00  & 0.07 & -0.09 (-28, 28) \\
   & 2 & 0.06 & 0.00 & 0.31 & -0.01 (-78,  76) \\
   & 4 & 0.08 & 0.00 & 0.36 & -1.13 (-106, 104) \\
   & 5 & 0.08 & 0.00 & 0.36 & -0.14 (-112, 110) \\
   & 10 & 0.13 & 0.00 & 0.37 & 0.63 (-124, 124) \\
   & 20 & 0.14 & 0.01 & 0.39 & -1.48 (-132, 128) \\
   & 100 & 0.01 & 0.00 & 0.38 & 0.58 (-132, 132) \\
   & 200 & 0.07 & 0.00 & 0.39 & -1.05 (-132, 132) \\
   \hline
 0.35  & 1 & 0.46 & 0.00 & 0.07 & -0.06 (-28,  28) \\
   & 2 & 0.46 & 0.01 & 0.06 & 39.73 (-38,  98) \\
   & 4 & 0.46 & 0.01 & 0.07 & 57.26 (-48, 136) \\
   & 5 & 0.46 & 0.01 & 0.07 & 60.77 (-50, 144) \\
   & 10 & 0.50 & 0.02 & 0.07 & 66.64 (-56, 156) \\
   & 20 & 0.58 & 0.01 & 0.07 & 69.57 (-62, 162) \\
   & 100 & 0.08 & -0.08 & 0.07 & 71.26 (-64, 164) \\
   & 200 & 0.44 & 0.01 & 0.07 & 72.48 (-60, 164) \\
   \hline
 0.45  & 1 & 0.91 & 0.00 & 0.07 & -0.03 (-28,  28) \\
   & 2& 0.89 & 0.01 & 0.01 & 69.77 (2, 110) \\
   & 4 & 0.87 & 0.03 & 0.01 & 99.58 (12, 150) \\
   & 5 & 0.86 & 0.02 & 0.01 & 103.36 (14, 158) \\
   & 10 & 0.86 & 0.03 & 0.00 & 113.34 (22, 170) \\
   & 20 & 0.88 & 0.02 & 0.01 & 117.73 (26, 176) \\
   & 100 & 0.29 & -0.12 & 0.01 & 119.93 (24, 178) \\
   & 200 & 0.86 & 0.02 & 0.00 & 120.38 (24, 176) \\
\hline
\end{tabular}
}
\caption{RAR using Bayesian approach with no early stopping criteria and with no drift applied. The $p_A$ is set to 0.25 for all cases. $Bias = \hat{\delta} - \delta$. $\pi_{20}$ denote the probability of assigning more than 20 patients in the inferior treatment group. $N_A$ and $N_B$ denote the number of patient assigned to treatment A and B. The mean (2.5\%, 97.5\%) of $N_B - N_A$ is reported in the last column. 10,000 simulation was done for each case. }
\label{bayesnoearlystopnodrift}
\end{table}

\subsection{Simulation with Time-Trend}

Table~\ref{freqnoearlystopwdrift} and Table~\ref{bayesnoearlystopwdrift} displays the results of simulation for number of blocks, K = 1, 2, 4, 5, 10, 20, 100, 200 using the frequentist and Bayesian approach with 0.25 drift applied and not stopping early for success or failure.
With time drift, the false positive rate is still under the nominal level for the frequentist design with all block sizes as seen in Table~\ref{freqnoearlystopwdrift}.
However, the type I error is inflated for traditional RAR, 10 and 20 blocks in the Bayesian design.
Having a 2, 4, 5, 10 and 20 blocks still remain the most powerful design with the most extreme difference in outcome of $p_A$ and $p_B$.
In the Bayesian design, block size with 50 subjects is still comparable to the traditional fixed randomization ratio design, since the type I error is controlled under 0.05 and it has the highest power of 0.91 under the maximum difference between $p_A$ and $p_B$.
Large number of blocks still remains as a poor design as illustrated earlier in Table~\ref{bayesnoearlystopnodrift}.
The estimatation of treatment difference remains the similar to the simulation with no time-trend except the bias is a little higher under traditional RAR design as presented in Table~\ref{bayesnoearlystopwdrift}.
The difference in sample size, the imbalance in the wrong direction and the variability between the arms ($N_B - N_A$) remains comparable to the simulation without time drift applied.

Table~\ref{freqearlystopwdrift} and Table~\ref{bayesearlystopwdrift} displays the results of simulation for number of blocks, K = 1, 2, 4, 5, 10, 20, 100, 200 using the frequentist and Bayesian approach and with early stopping criteria for success or failure implemented.
The output in Table~\ref{freqearlystopwdrift} is similar to Table~\ref{freqnoearlystopwdrift} except the treatment different is slightly biased.
However, it is shown that clinical trials with early stopping are slightly biased compared to trials without early stopping criteria  imposed.
Table~\ref{bayesearlystopwdrift} emphasizes that K = 10, 20, 100 and 200 have a inflated type I error of 0.14, 0.17 and 0.11  similar to Table~\ref{bayesnoearlystopwdrift}.
As seen in type I error and power in Table~\ref{bayesnoearlystopwdrift} and \ref{bayesearlystopwdrift}, large number of stratum design should not be considered for clinical studies.

\begin{table}[ht]
\resizebox{\columnwidth}{!}{%
\begin{tabular}{|c|c|c|c|c|c|}
\hline
$p_B$ & Block & Power & Bias & $\pi_{20}$ & $N_B - N_A$ \\
\hline
0.25 & 1 & 0.03 & 0.00 & 0.07 & 0.00 (-28, 28) \\
   & 2 & 0.05 & 0.00 & 0.04 & 5.28 (-24, 34.00)\\
   & 4 & 0.05 & 0.00 & 0.02 & 9.56 (-20, 40) \\
   & 5 & 0.05 & 0.00 & 0.02 & 10.81(-20, 42) \\
   & 10 & 0.05 & 0.00 & 0.01 & 12.90 (-16, 44) \\
   & 20 & 0.05 & 0.00 & 0.01 & 13.21 (-16, 46) \\
   & 100 & 0.05 & 0.00 & 0.01 & 13.28 (-16, 44) \\
   & 200 & 0.03 & 0.00 & 0.01 & 13.23 (-16, 46) \\
      \hline
0.35  & 1 & 0.30 & 0.00 & 0.07 & 0.06 (-28, 28) \\
   & 2 & 0.40 & 0.00 & 0.03 & 7.09 (-22, 38) \\
   & 4 & 0.41 & 0.00 & 0.02 & 12.16 (-18, 44) \\
   & 5 & 0.41 & 0.00 & 0.02 & 13.55 (-18, 48) \\
   & 10 & 0.40 & 0.00 & 0.01 & 16.36 (-14, 52) \\
   & 20 & 0.38 & 0.00 & 0.01 & 16.92 (-14, 50) \\
   & 100 & 0.18 & 0.00 & 0.01 & 17.30 (-14, 52) \\
   & 200 & 0.29 & 0.00 & 0.01 & 17.61 (-14, 52) \\
      \hline
 0.45 & 1 & 0.82 & 0.00 & 0.07 & -0.07 (-28, 28) \\
   & 2 & 0.89 & 0.00 & 0.02 & 11.02 (-20, 40) \\
   & 4 & 0.88 & 0.00 & 0.01 & 18.41 (-14, 52) \\
   & 5 & 0.88 & 0.00 & 0.01 & 20.40 (-12, 56) \\
   & 10 & 0.88 & 0.00 & 0.00 & 23.70 (-10, 58) \\
   & 20 & 0.85 & 0.00 & 0.00 & 24.32 (-8, 60) \\
   & 100 & 0.46 & 0.00 & 0.00 & 25.02 (-8, 60) \\
   & 200 & 0.80 & 0.00 & 0.00 & 25.31(-8, 62) \\
\hline
\end{tabular}
}
\caption{RAR using frequentist approach with no early stopping criteria and with 0.25 drift applied. The $p_A$ is set to 0.25 for all cases. $Bias = \hat{\delta} - \delta$. $\pi_{20}$ denote the probability of assigning more than 20 patients in the inferior treatment group. $N_A$ and $N_B$ denote the number of patient assigned to treatment A and B. The mean (2.5\%, 97.5\%) of $N_B - N_A$ is reported in the last column. 10,000 simulation was done for each case.}
\label{freqnoearlystopwdrift}
\end{table}

\begin{table}[ht]
\resizebox{\columnwidth}{!}{%
\begin{tabular}{|c|c|c|c|c|c|c|}
\hline
$p_B$ & Block & Power & Bias & $\pi_{20}$ & $N_B - N_A$ \\
\hline
0.25& 1 & 0.03 & -0.01 & 0.03 & 0.44 (-21, 28) \\
   & 2 & 0.07 & 0.00 & 0.29 & -0.30 (-80, 77) \\
   & 4 & 0.05 & -0.01 & 0.41 & -5.84 (-96, 86) \\
   & 5 & 0.06 & 0.00 & 0.43 & -1.66  (-117, 113) \\
   & 10 & 0.12 & 0.01 & 0.34 & 3.32  (-148, 148) \\
   & 20 & 0.20 & 0.02 & 0.33 & 12.40 (-137, 140) \\
   & 100 & 0.01 & 0.00 & 0.36 & 4.18 (-130, 141) \\
   & 200 & 0.17 & 0.02 & 0.32 & 12.74 (-106, 138) \\
  \hline
 0.35 & 1 & 0.51 & 0.00 & 0.10 & -1.74 (-32, 26) \\
   & 2 & 0.42 & 0.01 & 0.06 & 38.02 (-35, 97) \\
   & 4 & 0.35 & 0.01 & 0.07 & 52.78 (-87, 140) \\
   & 5 & 0.46 & 0.03 & 0.06 & 68.38 (-48, 148) \\
   & 10 & 0.45 & 0.03 & 0.03 & 72.88 (-34, 149) \\
   & 20 & 0.51 & 0.02 & 0.11 & 73.24 (-50, 162) \\
   & 100 & 0.11 & -0.07 & 0.11 & 74.40 (-80, 162) \\
   & 200 & 0.59 & 0.05 & 0.07 & 75.92 (-60, 162) \\
   \hline
 0.45 & 1 & 0.91 & 0.00 & 0.08 & -0.10 (-35, 28) \\
   & 2 & 0.93 & 0.02 & 0.00 & 68.28 (20, 111) \\
   & 4 & 0.91 & 0.04 & 0.01 & 105.34 (26, 148) \\
   & 5 & 0.79 & 0.03 & 0.01 & 105.96 (5, 160) \\
   & 10 & 0.82 & 0.04 & 0.00 & 119.52 (27, 173) \\
   & 20 & 0.75 & 0.03 & 0.01 & 119.74 (13, 177) \\
   & 100 & 0.22 & -0.13 & 0.00 & 123.12 (34, 175) \\
   & 200 & 0.89 & 0.06 & 0.00 & 125.48 (54, 179) \\
\hline
\end{tabular}
}
\caption{RAR using Bayesian approach with no early stopping criteria and with drift applied.The $p_A$ is set to 0.25 for all cases. $Bias = \hat{\delta} - \delta$. $\pi_{20}$ denote the probability of assigning more than 20 patients in the inferior treatment group. $N_A$ and $N_B$ denote the number of patient assigned to treatment A and B. The mean (2.5\%, 97.5\%) of $N_B - N_A$ is reported in the last column. 10,000 simulation was done for each case.}
\label{bayesnoearlystopwdrift}
\end{table}

\section{Conclusion}
RAR design has a lot of appealing properties mainly assigning more patients to better-performing treatment.
However, trialist needs to be careful with issues of time-trend and methods to alter the randomization ratio.
Time-trends can significantly impact the type I error rate which can affect the validity of clinical studies.
As a statistician, we cannot emphasize more on the importance of controlling the false-positive rate in a clinical setting.
Thall et al. have shown that methods that are able to control type-I error have failed to detect the true treatment difference \citep{thall2015statistical}.

Besides controlling for false positive rate, trialist needs to make sure the method of altering the randomization ratio is not too extreme which can greatly affect the bias and question the notion of randomization in clinical studies.
Thall et al. have emphasized on the difference in simulation between BAR(1) and BAR(1/2), with BAR(1) having a large imbalance in the wrong direction and larger false-positive rate.
Zelen's play-the-winner rule was implemented to an extracorporeal membrane oxygenation (ECMO) trial where the first patient was assigned to both control and treatment group \citep{zelen1969play, mike1993neonatal}.
Due to the failure in the control and success in the treatment group, all subsequent patient's were randomized to the treatment group \citep{mike1993neonatal}.
However, it was later discovered that the first patient assigned to the control group was much sicker than all the patients randomized to the treatment groups.

On the other hand, scientists need to be aware of the risk that RAR poses which includes having assigned more patients to the inferior treatment.
As highlight by Thall et al., ``\textit{The practical and ethical point is that AR may behave pathologically in that it carries a nontrivial risk of creating a large sample size imbalance in favor of the inferior treatment}'' \citep{thall2015statistical}.
Large imbalance in the wrong direction can also be controlled by methods that do not alter the randomization ratio rapidly.

It is shown that a small number of blocks (K = 2, 4 and 5) has a good tradeoff between efficiency and ethically treating patients to the best known superior treatment.
A large number of blocks should be clearly avoided for both ethical reason and poor design.
Traditional RAR does not only delay the trial but also affects the clinical conclusion achieved.
We have not considered the multiple treatment design with more than 2-arms.
The design would be much more complex and it should be examined further.

An R package (blockRAR), for the frequentist and Bayesian models, is implemented in R and released as open source software under the MIT license.
The blockRAR package is available at Comprehensive R Archive Network (CRAN) and at \href{https://thevaachandereng.github.io/blockRAR/}{https://thevaachandereng.github.io/blockRAR/}.
We used blockRAR version 1.0.0 for all analyses.

\begin{sm}
Supplementary material are available.
\end{sm}

\begin{dci}
None declared.
\end{dci}

\begin{funding}
None declared.
\end{funding}

\begin{acks}
We are grateful to Tom Cook for his helpful comments and suggestions.
\end{acks}

\bibliographystyle{SageV}

\section{Supplementary Table}

\begin{table}[H]
\centering
\resizebox{0.75\textwidth}{!}{%
\begin{tabular}{|c|c|c|c|c|c|c|}
\hline
$p_B$ & Block & Power & Bias & $E(N)$ & $\pi_{20}$ & $N_B - N_A$ \\
\hline
0.25 & 1 & 0.02 & 0.00 & 200.00 & 0.07 & -0.33 (-28, 26) \\
   & 2 & 0.05 & 0.00 & 198.87 & 0.03 & 6.98 (-22, 36) \\
   & 4 & 0.06 & 0.00 & 197.13 & 0.02 & 11.41 (-18, 42) \\
   & 5 & 0.06 & 0.00 & 196.80 & 0.02 & 12.10 (-18, 44) \\
   & 10 & 0.06 & 0.00 & 195.50 & 0.01 & 13.64 (-16, 46) \\
   & 20 & 0.06 & 0.00 & 194.94 & 0.01 & 14.26 (-16, 44) \\
   & 100 & 0.06 & 0.00 & 194.88 & 0.01 & 14.40 (-16, 46) \\
   & 200 & 0.05 & 0.00 & 194.75 & 0.01 & 14.36 (-16, 44) \\
 \hline
0.35 & 1 & 0.33 & 0.10 & 200.00 & 0.07 & 0.10 (-28, 28) \\
   & 2 & 0.46 & 0.01 & 193.04 & 0.03 & 8.04 (-22, 38) \\
   & 4 & 0.47 & 0.01 & 183.53 & 0.01 & 12.63 (-18, 42) \\
   & 5 & 0.46 & 0.01 & 182.57 & 0.01 & 13.28 (-16,  44) \\
   & 10 & 0.48 & 0.02 & 177.72 & 0.01 & 15.57 (-14, 46) \\
   & 20 & 0.46 & 0.02 & 176.53 & 0.01 & 16.20 (-14, 46) \\
   & 100 & 0.21 & 0.02 & 175.45 & 0.01 & 15.69 (-14, 44) \\
   & 200 & 0.43 & 0.02 & 174.71 & 0.01 & 15.90 (-12, 45) \\
\hline
0.45 & 1 & 0.84 & 0.00 & 200.00 & 0.07 & 0.14 (-28, 28) \\
   & 2 & 0.91 & 0.02 & 166.63 & 0.02 & 7.65 (-18, 38) \\
   & 4 & 0.91 & 0.02 & 147.84 & 0.01 & 12.78 (-14, 40) \\
   & 5 & 0.91 & 0.02 & 144.01 & 0.00 & 13.82 (-12, 40) \\
   & 10 & 0.91 & 0.03 & 135.66 & 0.00 & 15.86 (-10, 42) \\
   & 20 & 0.88 & 0.03 & 132.36 & 0.00 & 16.30 (-8, 42) \\
   & 100 & 0.40 & 0.03 & 129.44 & 0.00 & 16.30 (-8, 42) \\
   & 200 & 0.88 & 0.03 & 129.05 & 0.00 & 16.33 (-8, 42) \\
\hline
\end{tabular}
}
\caption{RAR using frequentist approach with early stopping criteria and no drift applied. The $p_A$ is set to 0.25 for all cases. $E(N)$ represents the mean sample size for 10,000 simulation. $Bias = \hat{\delta} - \delta$. $\pi_{20}$ denote the probability of assigning more than 20 patients in the inferior treatment group, $P(N_A -N_B > 20)$. $N_A$ and $N_B$ denote the number of patient assigned to treatment A and B. The mean (2.5\%, 97.5\%) of $N_B - N_A$ is reported in the last column. 10,000 simulation was done for each case.}
\label{freqearlystopwnodrift}
\end{table}

\begin{table}[H]
\centering
\resizebox{0.75\columnwidth}{!}{%
\begin{tabular}{|c|c|c|c|c|c|c|}
\hline
$p_B$ & Block & Power & Bias & $E(N)$ & $\pi_{20}$ & $N_B - N_A$ \\
\hline
0.25 & 1 & 0.02 & 0.00 & 200.00 & 0.07 & 0.20 (-28, 28) \\
   & 2 & 0.05 & 0.00 & 198.81 & 0.04 & 5.07 (-24, 34) \\
   & 4 & 0.06 & 0.00 & 197.06 & 0.02 & 9.37 (-20, 38) \\
   & 5 & 0.06 & 0.00 & 196.58 & 0.02 & 10.20 (-18, 40) \\
   & 10 & 0.06 & 0.00 & 195.63 & 0.01 & 12.04 (-18, 42) \\
   & 20 & 0.07 & 0.00 & 194.63 & 0.01 & 12.65 (-18, 42) \\
   & 100 & 0.05 & 0.00 & 194.73 & 0.01 & 12.72 (-16, 44) \\
   & 200 & 0.05 & 0.00 & 194.43 & 0.01 & 12.88 (-16, 42) \\
  \hline
0.35 & 1 & 0.30 & 0.00 & 200.00 & 0.07 & -0.34 (-28, 28) \\
   & 2 & 0.42 & 0.01 & 193.08 & 0.03 & 5.77 (-22, 34) \\
   & 4 & 0.43 & 0.01 & 184.22 & 0.02 & 10.03 (-18, 40) \\
   & 5 & 0.44 & 0.01 & 182.46 & 0.01 & 11.32 (-16, 40) \\
   & 10 & 0.44 & 0.02 & 179.00 & 0.01 & 13.23 (-16, 42) \\
   & 20 & 0.41 & 0.02 & 177.90 & 0.01 & 14.00 (-16, 42) \\
   & 100 & 0.19 & 0.02 & 176.47 & 0.01 & 13.82 (-14, 42) \\
   & 200 & 0.40 & 0.02 & 175.74 & 0.01 & 13.82 (-14, 42) \\
  \hline
 0.45 & 1 & 0.83 & 0.00 & 200.00 & 0.07 & -0.18 (-28, 26) \\
   & 2 & 0.89 & 0.02 & 170.19 & 0.02 & 5.86 (-20, 34) \\
   & 4 & 0.89 & 0.02 & 149.63 & 0.01 & 10.35 (-16, 36) \\
   & 5 & 0.89 & 0.02 & 144.74 & 0.01 & 11.99 (-14, 38) \\
   & 10 & 0.88 & 0.03 & 137.67 & 0.00 & 13.82 (-12, 40) \\
   & 20 & 0.86 & 0.03 & 134.38 & 0.00 & 14.52 (-10, 40) \\
   & 100 & 0.38 & 0.03 & 131.54 & 0.00 & 14.73 (-10, 40) \\
   & 200 & 0.86 & 0.03 & 130.83 & 0.00 & 14.71 (-10, 41) \\
\hline
\end{tabular}
}
\caption{RAR using frequentist approach with early stopping criteria and with 0.25 drift applied. The $p_A$ is set to 0.25 for all cases. $E(N)$ represents the mean sample size for 10,000 simulation. $Bias = \hat{\delta} - \delta$. $\pi_{20}$ denote the probability of assigning more than 20 patients in the inferior treatment group. $N_A$ and $N_B$ denote the number of patient assigned to treatment A and B. The mean (2.5\%, 97.5\%) of $N_B - N_A$ is reported in the last column. 10,000 simulation was done for each case.}
\label{freqearlystopwdrift}
\end{table}

\begin{table}[H]
\centering
\resizebox{0.75\columnwidth}{!}{%
\begin{tabular}{|c|c|c|c|c|c|c|}
\hline
$p_B$ & Block & Power & Bias & $E(N)$ & $\pi_{20}$ & $N_B - N_A$ \\
\hline
0.25 & 1 & 0.05 & 0.00 & 200.00 & 0.07 & -0.11 (-28, 28) \\
   & 2 & 0.07 & 0.00 & 197.63 & 0.30 & -0.13 (-72, 72) \\
   & 4 & 0.08 & 0.00 & 194.81 & 0.35 & -0.60 (-94, 92) \\
   & 5 & 0.09 & 0.00 & 193.40 & 0.34 & 0.39 (-96, 96) \\
   & 10 & 0.14 & 0.01 & 187.42 & 0.36 & -0.51 (-100, 100) \\
   & 20 & 0.17 & 0.02 & 183.41 & 0.35 & -0.03 (-102, 102) \\
   & 100 & 0.03 & 0.01 & 179.80 & 0.36 & -0.84 (-102, 100) \\
   & 200 & 0.11 & 0.00 & 178.16 & 0.35 & 0.78 (-100, 102) \\
\hline
0.35   & 1 & 0.46 & 0.00 & 200.00 & 0.07 & 0.01 (-28, 28) \\
   & 2 & 0.46 & 0.01 & 188.94 & 0.07 & 29.57 (-42, 86) \\
   & 4 & 0.47 & 0.02 & 181.31 & 0.06 & 40.88 (-46, 110) \\
   & 5 & 0.47 & 0.02 & 178.76 & 0.07 & 42.38 (-50, 112) \\
   & 10 & 0.53 & 0.04 & 171.69 & 0.06 & 45.63 (-50, 118) \\
   & 20 & 0.58 & 0.04 & 164.57 & 0.07 & 43.95 (-52, 118) \\
   & 100 & 0.14 & -0.04 & 158.05 & 0.07 & 43.38 (-52, 118) \\
   & 200 & 0.51 & 0.05 & 154.43 & 0.07 & 41.65 (-50, 116) \\
\hline
0.45 & 1 & 0.91 & 0.00 & 200.00 & 0.07 & -0.09 (-28, 28) \\
   & 2 & 0.89 & 0.01 & 158.55 & 0.01 & 31.54 (-18, 90) \\
   & 4 & 0.88 & 0.04 & 139.12 & 0.01 & 44.53 (-10, 112) \\
   & 5 & 0.88 & 0.04 & 136.40 & 0.00 & 47.81 (-8, 118) \\
   & 10 & 0.87 & 0.07 & 124.71 & 0.00 & 48.81 (-6, 120) \\
   & 20 & 0.89 & 0.07 & 117.98 & 0.00 & 48.68 (-4, 122) \\
   & 100 & 0.35 & -0.04 & 107.96 & 0.01 & 44.84 (-2, 118) \\
   & 200 & 0.90 & 0.07 & 106.20 & 0.01 & 43.46 (-2, 118) \\
\hline
\end{tabular}
}
\caption{RAR using Bayesian approach with early stopping criteria and with no drift applied. The $p_A$ is set to 0.25 for all cases. $E(N)$ represents the mean sample size for 10,000 simulation. $Bias = \hat{\delta} - \delta$. $\pi_{20}$ denote the probability of assigning more than 20 patients in the inferior treatment group. $N_A$ and $N_B$ denote the number of patient assigned to treatment A and B. The mean (2.5\%, 97.5\%) of $N_B - N_A$ is reported in the last column. 10,000 simulation was done for each case. }
\label{bayesearlystopnodrift}
\end{table}

\begin{table}[H]
\centering
\resizebox{0.75\columnwidth}{!}{%
\begin{tabular}{|c|c|c|c|c|c|c|c|}
\hline
$p_B$ & Block & Power & Bias & $E(N)$ & $\pi_{20}$ & $N_B - N_A$ \\
\hline
0.25 & 1 & 0.05 & 0.00 & 200.00 & 0.07 & 0.31 (-28, 28) \\
   & 2 & 0.06 & 0.00 & 197.99 & 0.30 & -0.01(-74, 72) \\
   & 4 & 0.08 & 0.00 & 191.95 & 0.34 & 0.34 (-96, 94) \\
   & 5 & 0.09 & 0.00 & 189.51 & 0.36 & -0.31 (-100, 98) \\
   & 10 & 0.12 & 0.01 & 182.17 & 0.35 & 1.12 (-98, 100) \\
   & 20 & 0.16 & 0.02 & 177.34 & 0.36 & -0.09 (-102, 100) \\
   & 100 & 0.04 & 0.01 & 172.12 & 0.35 & -0.01 (-98, 98) \\
   & 200 & 0.18 & 0.00 & 169.73 & 0.34 & -0.11 (-98, 96) \\
  \hline
0.35 & 1 & 0.41 & 0.00 & 200.00 & 0.06 & 0.36 (-26, 28) \\
   & 2 & 0.41 & 0.01 & 190.52 & 0.08 & 28.20 (-40, 86) \\
   & 4 & 0.43 & 0.02 & 175.93 & 0.08 & 37.48 (-58, 108) \\
   & 5 & 0.44 & 0.03 & 172.72 & 0.08 & 39.44 (-56, 110) \\
   & 10 & 0.46 & 0.04 & 163.12 & 0.07 & 40.31 (-58, 114) \\
   & 20 & 0.52 & 0.05 & 154.79 & 0.07 & 39.74 (-54, 114) \\
   & 100 & 0.16 & -0.03 & 148.25 & 0.07 & 37.99 (-50,110) \\
   & 200 & 0.60 & 0.06 & 144.84 & 0.07 & 36.12 (-56,110) \\
  \hline
0.45 & 1 & 0.90 & 0.00 & 200.00 & 0.07 & -0.02 (-28, 28) \\
   & 2 & 0.86 & 0.01 & 162.58 & 0.01 & 33.17 (-18, 90) \\
   & 4 & 0.84 & 0.03 & 135.21 & 0.01 & 41.46 (-10, 110) \\
   & 5 & 0.83 & 0.04 & 129.45 & 0.01 & 42.66 (-8, 112) \\
   & 10 & 0.82 & 0.06 & 117.76 & 0.01 & 44.40 (-6, 116) \\
   & 20 & 0.82 & 0.08 & 108.13 & 0.01 & 42.12 (-4, 114) \\
   & 100 & 0.35 & -0.04 & 100.34 & 0.01 & 39.46 (-4, 112) \\
   & 200 & 0.93 & 0.09 & 95.66 & 0.01 & 37.27 (-4, 110) \\
\hline
\end{tabular}
}
\caption{RAR using Bayesian approach with early stopping criteria and with drift applied.The $p_A$ is set to 0.25 for all cases. $E(N)$ represents the mean sample size for 10,000 simulation. $Bias = \hat{\delta} - \delta$. $\pi_{20}$ denote the probability of assigning more than 20 patients in the inferior treatment group. $N_A$ and $N_B$ denote the number of patient assigned to treatment A and B. The mean (2.5\%, 97.5\%) of $N_B - N_A$ is reported in the last column. 10,000 simulation was done for each case.}
\label{bayesearlystopwdrift}
\end{table}

\bibliographystyle{SageV}

redpajama_set_name: RedPajamaArXiv


text:
chantalpetit, de son vrai nom Chantal Petit, née en 1951 à Agadir est une artiste, peintre, sculptrice et vidéaste française.

Biographie
Née le 27 avril 1951 à Agadir au Maroc, Chantal Petit, qui utilise la graphie chantalpetit pour ses œuvres, vit et travaille à Malakoff.

Elle passe son enfance entre le Maroc et la France.  De 1970 à 1972 elle étudie aux Arts Décoratifs, ainsi qu'aux Beaux-arts de Paris où elle suit les cours d'Etienne Martin.

En 1987, elle épouse Roman Cieslewicz.

Elle est professeur d'art graphique à Penninghen Paris.

Carrière artistique
Entre 1973 et 1979, elle se consacre au théâtre, tout en poursuivant une œuvre graphique. Elle collabore en tant qu'illustratrice avec différentes revues et maisons d'édition. Durant ces années, elle est membre du groupe GEL (groupe d'expression libre), compagnie de théâtre expérimentale fondée et dirigée par Véronique Petit et Eduardo Manet. Elle crée par ailleurs plusieurs scénographies, notamment pour Roger Blin. Elle rencontre à la même époque Roland Topor et les membres du groupe panique qui l'invitent à participer à l'exposition Panique Universelle, à la maison de la culture de Rennes en 1980. Elle apparaît également dans la revue Le fou parle.

La première œuvre personnelle majeure de chantalpetit est son Festin des dieux.

En 2002 et 2003, elle peint une série de tableaux inspirés du Caravage intitulée Épiphanies-Caravage. Elle laisse souvent la toile à nu, sans peinture. Elle reprend quelques détails du Caravage, dont le Garçon avec un panier de fruits, mais l'inspiration lui vient surtout d'une biographie du Caravage offerte en 1993 par un ami.

En 2016, elle produit les Sculptures de peinture. Elle justifie ce titre en affirmant qu'elle utilise la toile non comme support, mais comme matériau. Elle se filme en train de sculpter à l'aveugle.

Style artistique
Chantalpetit utilise plusieurs genres, dont l'abstrait, le figuratif, le primitivisme, l'expressionnisme ou encore le style classique.

Expositions

Expositions personnelles (sélection)

Expositions collectives
{{colonnes|nombre=2|
 2016 : Lignes du visage, Musée Boucher-de-Perthes, Abbeville, FRAC Picardie
 2016 : Ceramix - De Rodin à Schütte, La Maison Rouge, Paris
 2015 : Ceramix - From Rodin to Schütte, Bonnefantenmuseum, Maastricht
 2015 : Les Cahiers dessinés, La Halle Saint Pierre, Paris
 2014 : Le Mur -  La Maison Rouge, Paris
 2014 : Amitiés parisiennes, galerie Area, Paris
 2014 : Avoir 10 + 1 pour les 10 ans du CAC , Lithos, Saint-Restitut 2013 : Acquisitions récentes 2009-2013 , Frac Picardie, Amiens
 2013 : Galerie Moisan, Paris
 2012 : Galerie Jacques Elbaz, Paris
 2011 : Tous cannibales, La Maison rouge, Paris
 2011 : My Paris, Me collector, Berlin 2011 : Galerie Polad Hardouin, Paris
 2010 : Voyage dans ma tête, La Maison Rouge, Paris
 2007 : Salon du dessin contemporain, Paris
 2004 : L'intime, le collectionneur derrière la porte, La Maison Rouge, Paris
 2004 : Cent Peintres pour l'Humanité, Paris
 2003 : Espace Accatone, Paris
 2003 : Galerie Suzanne Tarasiève, Barbizon
 2001 : FIAC, parcours privé, Paris
 2001 : Espace Poirel, Nancy
 2001 : Galerie Henri Bussière, Paris
 2000 : Fondation d'entreprise Ricard, Paris
 1994 : Centre Georges Pompidou, Paris
 1993 : Galerie Vallois, Paris
 1993 : Galerie de l'Uquam, Montréal
 1992 : ELAC, Lyon
 1991 : Objets trouvés d'artistes, Galerie du jour, Agnès B., Paris
 1989 : Les paysages de l'art contemporain, Ecole nationale des Beaux-Arts, Paris
}}

 Publications
 Chantal Petit, Collection Rencontres'' avec Lucas Djaou, Éditions Lucas Djaou, Paris, France, 2017

Notes et références

Notes

Références

Liens externes

Peintre française
Peintre français du XXe siècle
Peintre français du XXIe siècle
Sculptrice française
Élève de l'École nationale supérieure des beaux-arts au XXe siècle
Élève de l'École nationale supérieure des arts décoratifs
Naissance en avril 1951
Naissance à Agadir
Naissance dans le protectorat français au Maroc


Your non-profit website is a crucial anchor for all your organization's fundraising and marketing activities whether you're reaching out to people via print, email, social media or texting. And you already know that a very substantial portion of your donations happen in December. So, how much attention are you paying to how donors experience your mission and impact when giving to you online?
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text:
Cajuns hope to keep USM turning it over
Midway through the season, playing Southern Mississippi may not have seemed like the best matchup for the UL Ragin' Cajuns.
Cajuns hope to keep USM turning it over Midway through the season, playing Southern Mississippi may not have seemed like the best matchup for the UL Ragin' Cajuns. Check out this story on theadvertiser.com: https://www.theadvertiser.com/story/sports/college/ul/2016/12/12/cajuns-hope-keep-usm-turning-over/95342054/
Kevin Foote, kfoote@theadvertiser.com Published 8:26 p.m. CT Dec. 12, 2016 | Updated 10:35 p.m. CT Dec. 12, 2016
UL linebacker Otha Peters recovers a loose ball and returns it for a score with safety Tracy Walker leading the way during the Cajuns' win over ULM.(Photo: SCOTT CLAUSE / USATODAY Network)
The secret to beating the Golden Eagles was forcing turnovers. USM was doing so at an alarming pace.
By that time, though, the Cajuns' defense hadn't quite developed the propensity to force a bunch of turnovers.
And when UL did force one here and there, it never produced big game-changing scoring plays.
Almost weekly, UL coach Mark Hudspeth stressed the need for his stingy defense to add that extra element of thievery to its arsenal.
That all changed over the last two games.
With the Cajuns needing a strong finish to qualify for the New Orleans Bowl for the fifth time in the last six seasons, UL's defense scored in an interception return in the win over Arkansas State and then on two fumble returns in the blowout of ULM in the finale.
"Defensively, we decided to basically become ball hawks," UL senior safety Tracy Walker said. "We started running to the ball full speed, giving it everything you've got, everybody just risking it all. That's been helping us make plays. We're going to continue to do that. That's what's been helping our defense to continue to strive week in and week out."
In all, the Cajuns forced six turnovers over that two-game span to help get UL back to even in the turnover margin for the season.
Over Hudspeth's first four seasons, the worst the Cajuns ever finished was a minus-1. In 2015, the Cajuns dipped to a minus-9 and it showed with a 4-8 overall record.
Southern Mississippi certainly discovered that feeling this season.
Only three teams in the country have turned the ball over more times than the Golden Eagles this season. USM has thrown 15 interceptions and fumbled it 15 times for a total of 30 turnovers.
Moreover, USM ranked 125th out of 128 teams in turnover margin at a minus-16.
By comparison, the Cajuns have only turned it over 16 in 12 games to be ranked 35th nationally in that category, while forcing 16 turnovers to be even.
With turnover numbers that poor, it illustrates how productive USM has had to be in other areas to even earn a 6-6 record and bowl berth.
"We know the (Southern Miss) offense is very explosive," UL linebacker Otha Peters said. "The quarterback is very experienced, very good; he's just not gonna throw it to you, you've got to go get 'em, you know?
"So every chance we get – we see the running back with the ball out, or the quarterback with d-line getting pressure on him – we've got to go for the strip sack, we've got to get the ball on the ground and give our offense more opportunities, give them more chances to score."
The impact those turnovers have on the outcome of the game is pretty obvious. In USM's six wins, the Eagles are only minus-2 in the turnover margin. In its six losses, USM is a minus-14.
While the totals aren't nearly as high in UL's case, the point remains the same. The Cajuns are plus-4 in their six wins and a minus-4 in their six losses.
"Turnover battle always is a big part of the game," UL linebacker Tre'Maine Lightfoot said. "I just think, 'We win the turnover; we probably win the game.'
"When opportunity comes, we've got to catch the ball. Fumble on the ground, we've got to go get it. We've got to want it better than they do. … You can change the game just by getting turnovers."
Amazingly, the Golden Eagles didn't have a game this season in which they didn't turn it over at least once.
Conversely, the Cajuns have done that five times, ironically losing two of them.
On the other hand, the Cajuns have had three games where the defense didn't force a turnover and only forced more than one in four games.
"That can be the difference between winning and losing this game, is who can create the most turnovers," Peters said.
Southern Miss has committed multiple turnovers in eight of 12 games, while the games have only done that five times.
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text:
Daily online magazine on the global economy, politics and culture
Stephen Green
Former Minister for Trade and Investment in the United Kingdom.
The EU: Like Building a Cathedral
By Stephen Green, April 25, 2017 in Rethinking Europe
Will the "cathedral" – aka the EU – ever be completed? Is it worth the effort? Or might it fall down and bankrupt its builders?
The European Union: Like Building a Cathedral!
By Stephen Green, October 25, 2016 in Rethinking Europe
The United States of Europe: Inspiration from India?
By Stephen Green, July 9, 2015 in Rethinking Europe, Global HotSpots
If India could manage a union successfully, why couldn't Europe?
Europe: The United States of Greater Austria?
Franz Ferdinand envisaged a reconstruction of the Austro-Hungarian empire. Could it have worked?
Today's Asia and Europe's Identity
By Stephen Green, April 6, 2015 in Rethinking Europe, Globalist Bookshelf
What exactly is Europe's identity in this new, globalized world?
Europe's Path and the Great Global Convergence?
By Stephen Green, March 14, 2015 in Rethinking Europe, Globalist Bookshelf
A continent in decline.
Germany: The Reluctant Leader of the New Europe
By Stephen Green, February 7, 2015 in Rethinking Europe, Global HotSpots, Globalist Bookshelf
Germany's nuanced role as the new leader of Europe.


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