Washington, D.C., Mayor Muriel Bowser has today appointed Peter Newsham the Chief of Police. Newsham's conduct from the Pershing Park mass arrests of 2002 to the dragnet arrests at Donald Trump's inauguration just one month ago demonstrate that he is an unrepentant serial mass violator of civil rights and is not fit to be Chief of Police.
Newsham's history with mass arrests is a matter of public record.The Partnership for Civil Justice Fund was class counsel and litigated the Pershing Park mass arrest case, which resulted in more than $10 million in damages for nearly 400 persons and major change in the laws governing police handling of demonstrators ― laws Peter Newsham violated just one month ago.
And it is this law that Peter Newsham appears to have willfully violated just one month ago.
The PCJF is calling on the D.C. Council to reject the mayor's appointment of Newsham.


Besides, this is not the first time when he talks about the Apple car. In a recent interview, he avoided answering this question, but he describes his engineer as a curious person and discovering the technology. Apart from this evidence, Apple took the advantage of a hundred of engineers from other car manufacturers and other technologies, including Tesla, Ford, GM, Samsung, A123 Systems, and Nvidia to work on a big project, named the Project titan. It means that there remains more evidence to prove the project of Apple car is the truth.
Apple has been detected to registering 3 domains that relate to the car, consisting of apple.car, apple.cars, and apple.auto ("car", "auto" mean the car in English). The information about the domains showed that Apple registered from the December of the last year throughout a company with the MarkMonitor domain. However, until now, all these domains don't work.
It is probably that this registration relates to the car play entertainment system for the car of Apple. However, the field of the technology believes that these domains were registered to serve the purpose of developing a car of this brand in the future.


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New Indiana Parenting Time Guidelines in 2022
By Keffer Hirschauer LLP
The coronavirus pandemic that started in 2020 created significant upheaval across the globe. Temporary business and community closures, and quarantine and isolation orders, upended daily life. Nowhere is that more apparent than in family law, where two-household parents worried about how to keep their families safe without violating court ordered parenting time schedules. To help resolve this and other problems, the Indiana Supreme Court issued new Indiana Parenting Time Guidelines, which take effect January 1, 2022. Here, an Indianapolis family lawyer reviews some of the key changes to the Indiana Parenting Time Guidelines.
Why the Old and New Indiana Parenting Time Guidelines Are Important
The Indiana Parenting Time Guidelines provide a starting point for developing parenting time orders in family law cases. Based on the premise that children with parents in different households benefit from having regular contact, interaction, and communication with both parents, the Guidelines also recognize that disagreements and tension can be high in divided families. As a result, the Guidelines also establish a concrete framework against which to interpret visitation orders so that families might more fully understand these orders and, thereby, avoid disputes about parenting time.
The new Indiana Parenting Time Guidelines effective in 2022 are, in part, a response to disputes that have arisen because of changes in our world, such as the global pandemic, as well as ongoing reevaluation of what works and what doesn't in this blueprint to help divorced and separated families. Understanding what's new, what's the same, and when the changes apply to you, an experienced Indianapolis family lawyer from Keffer Hirschauer LLP summarizes the major changes and what you need to know about them.
What's Different about the New Indiana Parenting Time Guidelines
Much of the Indiana Parenting Time Guidelines remain unchanged. The core part of the Guidelines, the actual visitation schedule recommended, is still tiered based on the child's age, and there are no changes regarding normal (non-holiday) visitation. However, in many areas that remain unchanged, the supreme court added new comments to clarify or explain particular provisions. Thus, reading the Guidelines and their comments is essential to a complete understanding.
Although much remains the same, the supreme court also made significant changes in some areas. The biggest changes found in the new Indiana Parenting Time Guidelines are in new sections on parenting time and custody during a public health emergency and on a new "shared parenting" plan model. The court also clarified points in several different areas, all of which likely resulted from a review of the more litigious family law cases.
This article summarizes some of the key changes to the Indiana Parenting Time Guidelines, but it is not a substitute for working with experienced Indiana divorce attorneys.
What the New Indiana Parenting Time Guidelines Say about Parenting in a Public Health Emergency
The coronavirus pandemic created a perfect storm for discord between divorced and separated parents. Quarantine orders, parents who differ on how to best protect their children from the virus, and school and childcare facility closures were a hotbed for disputes about how and whether to adjust the parenting schedule to accommodate the situation.
The new Indiana Parenting Time Guidelines include a section offering guidance on how to handle unexpected circumstances like those created by the pandemic. This new guideline reaffirms that court orders on parenting time, child support, and custody remain enforceable, even during a pandemic, but they also acknowledge a public health emergency can create situations in which compliance with existing orders is problematic.
To head off future disputes arising from a public health emergency, the new guideline specifies how parties may remain compliant with existing orders in their cases when a public health emergency interferes with business as usual, such as court and government office closures. In particular, the new guideline allows parties to file a joint written agreement to modify custody and parenting time orders to accommodate changes brought on by the public health emergency, although that agreement is only enforceable upon approval by the court.
Refinements to the Guideline on Make-Up Parenting Time
Fallout from the pandemic and the pandemic itself created problems for many families with parenting time orders. School and daycare closures created childcare problems, and the virus itself caused families to quarantine or even isolate. Missed visitation and attempts to make up that time caused a backlog of contempt and modification requests. In response, the supreme court clarified these points about make-up parenting time:
Parenting time missed due to circumstances beyond a parent's control can be rescheduled—this does not include parenting time missed due to poor planning or disorganization
The parent who realizes the need to alter the parenting time schedule must give notice to the other parent as soon as possible
Make-up parenting time should occur as soon as possible, but it may not be used to deny the other parent special days or holidays or interfere with previously scheduled events
Examples of valid reasons for make-up parenting time include mandatory work, illness, or military obligations, but they do not include voluntary decisions such as vacations
Make-up parenting time shall be in blocks no greater than three days and shall not be exercised during holidays or on special days
The New Guidelines Ditch the Parallel Parenting Plan Model and Replace it with a Shared Parenting Model
Prior to the current amendment, the Guidelines included a model parenting plan order for parents with an ongoing pattern of litigation, ongoing distrust and anger, significant problems communicating about the children, or other behaviors creating an inability to parent cooperatively. This parallel parenting plan detailed each parent's responsibilities when the child was with them.
The new Guidelines have replaced the parallel parenting plan model with a shared parenting plan model. The shared parenting plan targets a different demographic—parents who co-parent cooperatively, communicate effectively regarding the children, and can agree on an allocation of parental responsibilities.
This new part of the Guidelines is merely a starting point for discussion, not an actual guideline. An Indianapolis family lawyer from Keffer Hirschauer LLP can help you determine whether this more cooperative type of parenting plan may be right for your family.
Detailed Factors to Aid in Determining Whether Parenting Time in Excess of the Guideline Amount is Appropriate
Among other things, the Indiana Parenting Time Guidelines establish a standard recommendation for the parent who is considered the non-custodial parent. However, parties are free to agree to—or the court may award—parenting time in excess of the standard recommendation when warranted by the facts in the case.
In the Guidelines that take effect January 1, 2022, the supreme court has added a non-exhaustive list of factors that the trial court and the parties may consider in determining whether the parenting time order should deviate from the standard recommendation. Specifically, the court listed relevant questions to consider under each of these areas:
Factors related to the child
Factors related to the parent
Factors related to the parent-child relationship
Factors related to the co-parenting relationship
Factors related to the child's environment in each family's home and extended family
The Deadline for Filing a Notice of Intent to Relocate
When a parent who has or is seeking a custody or parenting time order wishes to relocate, Indiana Code § 31-17-2.2-1 requires that parent to give notice of the plan to move to the court and to anyone else who has or is seeking a custody or parenting time order regarding the child. The new Indiana Parenting Time Guidelines change the timeline for filing such notice from 90 days to 30 days before the intended move. However, the new Guidelines also reiterate the exception under § 31-17-2.2-1 that no notice is required if the move would not require a change in the child's school and would bring the two households closer together or not result in a distance greater than 20 miles.
Clarifying Parenting Time Communications
The Guidelines specifically require a parent to relay to the child messages from the other parent left on an answering machine, voicemail, or pager. The new Indiana Parenting Time Guidelines updated the modes of communication, replacing pagers with text and email.
In addition, the new Guidelines specify that no one may record a child's communications with the other parent.
Comments Regarding Transportation Responsibilities
The new Guidelines add two comments regarding scenarios in which transportation and child exchanges between one parent and another can involve conflict. First, parents are reminded to be mindful of not bringing to these exchanges a third party who may cause or increase conflict with the other parent. Secondly, the setting of exchanges at police departments is discouraged unless there is a history of repeated intimidation or violent acts between the parents or there is an existing protective order between them.
Child exchanges can be difficult when conflict between the parents is common, even if it doesn't rise to the level described above. An Indianapolis family lawyer from Keffer Hirschauer LLP can help you find alternative solutions to keep everyone safe at parenting exchanges and decrease the likelihood of threats or violence.
Refinements to the Indiana Parenting Time Guidelines for Holidays
The Guideline default plan provides for an interruption to normal parenting time to allow each parent time with a child on and near major holidays and other special days, such as the child's birthday and school breaks. The new Indiana Parenting Time Guidelines add the following:
Recommended holiday parenting time schedule for children three years and older should be based on the following:
The academic calendar of the school district in which the child resides if the child is not yet enrolled in school, or
The academic calendar of the school district in which the child is enrolled
The academic calendar of the parent who pays the majority of controlled expenses in cases where parents have equal parenting time
The new Guidelines also add more specificity regarding parenting time on the night before and the day of each child's birthday and make slight changes to the starting and ending time of visitation over school winter break and other recognized holidays.
Do the New Indiana Parenting Time Guidelines Apply to You?
The new Indiana Parenting Time Guidelines become effective on January 1, 2022. All cases entering new or modifying existing parenting time orders on or after that date shall rely on the new Guidelines. Anyone with existing parenting time orders should rely on the Guidelines in effect at the time that order was entered for clarification of their parenting time orders.
Many parents are unaware that the Guidelines are revised from time to time. Using the correct version of Guidelines is critical to accurately interpreting your parenting time order just as relying on the wrong version can create a potential for being found in contempt of that order. If you're unsure which version of the Guidelines applies in your case or how to interpret your parenting time order, the Indiana divorce attorneys at Keffer Hirschauer LLP can help.
Where Can You Turn for Help with Your Parenting Time Issues and Questions?
The old and new Indiana Parenting Time Guidelines are in place to reinforce the goals of parenting time and to provide clear direction of each parent's responsibilities. But parenting time orders and the rules on which they're based can be complicated, and a parent's deep desire to have time parenting time can sometimes result in conflict—conflict that can be harmful to the child and the parent-child relationship and expose a parent to possible contempt proceedings. Where can you turn for help?
The Indiana divorce lawyers at Keffer Hirschauer LLP bring to your case years of experience helping parents navigate difficult parenting time, custody, child support, and other post-divorce issues. To learn more about whether the new Indiana Parenting Time Guidelines apply in your case—or how applying them may change your parenting time options—call us today at (317) 857-0160 or complete our online contact form.
December | Family Law

redpajama_set_name: RedPajamaCommonCrawl


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Famous Baltimore Irish Americans
When the Great Potato Famine brought hundreds of thousands of Irish immigrants to America in the mid-19th century, Baltimore rapidly became the third most common point of entry for European immigrants, behind New York and Boston, due to ample work opportunities on the railroads. With the sudden influx of Irish immigrants to the southwestern part of Baltimore, the city of Baltimore quickly became one of the great Irish American cities.
Not surprisingly, many famous Irish Americans originated from Baltimore or spent many important years in the city. The following is a list of famous Irish Americans from Baltimore.
Famous New York Irish Americans
Famous Boston Irish Americans
Famous Chicago Irish Americans
Famous Philadelphia Irish Americans
Art Donovan | Defensive tackle who played primarily for the Baltimore Colts of the National Football League and was inducted into the Pro Football Hall of Fame in 1968
Charles Carroll | Delegate to the Continental Congress and United States Senator from Maryland who was the only Catholic signatory of the Declaration of Independence
Francis Russell O'Hara | Writer, poet and art critic who was a member of the New York School of poetry
George G. Meade
George Gordon Meade | Union General during the American Civil War who defeated Confederate General Robert E Lee at the Battle of Gettysburg in 1863
James Gibbons | Archbishop of Baltimore who became the second American to be elected a cardinal
John Carroll | Founder of Georgetown University and the first Roman Catholic bishop and archbishop in the United States
Julie Bowen | Emmy Award-winning film and television actress who starred on Boston and Modern Family
Martin O'Malley | Politician who served as Mayor of Baltimore and 61st Governor of Maryland
Michael Phelps | Olympic champion swimmer who won a record eight gold medals at the Beijing Olympics in 2008
Reddy Mack | Professional baseball player in the late 19th century who played second base with the Baltimore Orioles
Tom Clancy | Novelist who wrote many bestselling novels, including The Hunt for Red October and Clear and Present Danger
Tori Amos | Grammy Award-winning pianist, singer-songwriter and composer of such hits as "Cornflake Girl" and "A Sorta Fairytale"
William Henry Keeler | Archbishop of Baltimore who became Cardinal of the Roman Catholic Church
The Remarkable Life of Kitty McInerney: How a Poor Irish Immigrant Raised 17 Children in Great Depression New York
Labels: Irish American Cities, Irish American History, Irish American People
Irish American Cities (6)
Irish American Culture (6)
Irish American History (9)
Irish American People (13)
Irish Ships to America: Famous Ships of Irish Immigrants
When we think of the great waves of Irish immigration to America and the million Irish immigrants who left their homeland for a new life ...
Irish Immigration to America: How America Became Irish
The Irish began sailing to America in significant numbers by the 18th century. Hamstrung by English trade restrictions, mostly Protest...
Famous Irish American Songs
When the Irish stepped off ships by the tens of thousands during the great waves of Irish immigration to America, they brought with them ...
Famous Irish American Gangsters
From the 19th century Irish American ghettos, to 1920s Prohibition, to the modern age of drug trafficking, Irish American mobsters ha...
Famous Irish Americans
The Irish who immigrated to America in the mid-19 th century could never have imagined the impact they would make on American society. ...
Famous Irish American Singers & Musicians
Irish Americans brought their gift of song, dance and musicianship from Ireland centuries ago and have profoundly altered the American mu...
Bronx Irish Americans: American Irish History in the Bronx
The Bronx is the only borough of New York City situated on the American mainland. Formerly a wilderness of forests, meadows, and stream...
Famous Irish American Actors
Irish Americans have a long, proud history of performing on the stage and screen. Many of the greatest films and TV shows of all tim...
Famous Irish American Writers
Following in the footsteps of Irish literary giants like James Joyce, William Butler Yeats, and Oscar Wilde, many Irish Americans have ad...
Famous Irish American Books
As hundreds of thousands of Irishmen flooded American cities in the mid-to-late 19 th century, the Irish American experience rapidly pen...
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These Are the Most Obedient Dog Breeds
By Kelly Wynne On 11/30/20 at 12:48 PM EST
Culture Dogs Pets Family Animals
Every dog has the potential to add meaning to your life, but don't let those adorable puppy dog eyes blind you. Some are better suited to being obedient pals, while others have the potential to cause repeating conflicts within the household.
While you may be drawn to a certain type of dog, their lifelong behavior comes down to science. Some dog breeds are biologically wired to misbehave. While there's nothing wrong with adopting a dog that has a mind of its own, doing your research before adopting a new pet assures you'll match with a dog breed you have space, time and energy for.
These Are the Least Obedient Dog Breeds
Luckily, for wannabe dog owners, a ranking by Stanley Coren has you covered with a quick analysis of the most obedient dog breeds. Coren, a neuropsychology researcher and professor of canine psychology at the University of British Columbia, published the book The Intelligence of Dogs in 1994. In it, each dog breed is ranked on a scale of obedience, given Coren's analysis that a dog's genes make up 51 percent of its behavior.
The other 49 percent is based on the dog's environment, meaning an owner and lifestyle do have an impact on how each dog will generally behave. But genetically, Coren's evaluation named some dogs to be truly intelligent, and willing to corporate with human commands, more so than others. Here are the top 26 most obedient dogs.
A Labrador Retriever rests in its pen on the final day of the Crufts dog show at the National Exhibition Centre in Birmingham, central England, on March 11, 2018. OLI SCARFF/AFP via Getty Images/Getty
Most obedient dogs
These pups are expected to listen to their owners' commands on the first try around 95 percent of the time. That's loyalty if we've ever seen it.
1. Border Collie
A Border Collie jumps over a fence in the Rescue Dog Agility show in the main arena on the first day of Crufts dog show at the NEC on March 6, 2014, in Birmingham, England. Matt Cardy/Getty Images/Getty
These pups rand 35 of 196 in popularity, according to the American Kennel Club (AKC), and it's not because of their intelligence. Border Collies are notoriously bright, but equally energetic. That means, they're not right for an owner who doesn't have the time or space to occupy the dog's athletic needs.
2. Poodle
Tater a standard poodle listens to her owner Pam Greene after she was groomed during the Pet-A-Palooza Expo at the Orange County Great Park on October 17, 2010, in Irvine, California. Kevork Djansezian/Getty Images/Getty
One of the most controversial dog breeds is actually one of the smartest. Though poodles appear to be arrogant to some non-poodle-havers, the breed is more than willing to work with their owners to reach true companionship.
3. German Shepherd
Rumor the German Shepherd poses for photos after winning Best In Show at the Westminster Kennel Club Dog Show at Madison Square Garden, February 14, 2017, in New York City. Drew Angerer/Getty Images/Getty
One of America's most popular dogs, the German Shepherd, is also one of the smartest. Ranked second by the AKC, they're widely beloved because of their "loyalty, courage, confidence, the ability to learn commands for many tasks, and the willingness to put their life on the line in defense of loved ones."
4. Golden Retriever
A Golden Retriever attends the American Kennel Club Presents The Nation's Most Popular Breeds Of 2015 at AKC Headquarters on February 22, 2016, in New York City. Jamie McCarthy/Getty Images/Getty
Golden Retrievers truly are the golden dog. Ranked third most popular by the AKC, Goldens are considered top-tier family dogs because of their loving personalities and want to please.
5. Doberman Pinscher
A Doberman Pinscher walks during the Working Group round of the Westminster Kennel Club dog show on February 17, 2015, in New York City. Andrew Burton/Getty Images/Getty
Obedient and loving, Doberman Pinschers are known for loyalty to their families. This makes them one of the best guard dogs out of the top 39 most obedient breeds.
6. Shetland Sheepdog
A dog owner stands with her Shetland Sheepdog as she awaits judges scores during the first day of the Crufts dog show on March 6, 2014, in Birmingham central England. ANDREW YATES/AFP via Getty Images/Getty
Lassie was loyal and beloved by many, and her real-life ancestors follow suit. Shetland Sheepdogs are notoriously happy and intelligent, so much so they're known to take on the same spirit as their human family. https://www.akc.org/dog-breeds/shetland-sheepdog/
7. Labrador Retriever
A Labrador Retriever, the number 1 most popular breed of 2016,is shown at The American Kennel Club Reveals The Most Popular Dog Breeds Of 2016 at AKC Canine Retreat on March 21, 2017, in New York City. Jamie McCarthy/Getty Images/Getty
America's favorite dog breed is indeed one of the most obedient. No. 1 on the AKC's popular breeds list, Labrador Retrievers are considered one of the most affectionate breeds of dogs, in addition to being highly obedient by nature.
8. Papillon
A Papillion in the Agility Ring during the first-ever Masters Agility Championship on February 7, 2014, in New York at the 138th Annual Westminster Kennel Club Dog Show. TIMOTHY A. CLARY/AFP via Getty Images/Getty
The first small dog on the top obedient breeds list is the loving, good-spirited Papillion. In addition to being willing to learn, Papillons are great dogs when it comes to agility, either in competition or daily life.
9. Rottweiler
A Rottweiler, the 8th most popular breed of 2016, is shown at The American Kennel Club Reveals The Most Popular Dog Breeds Of 2016 at AKC Canine Retreat on March 21, 2017, in New York City. Jamie McCarthy/Getty Images/Getty
Another legendary guard dog, the Rottweiler puts family first. The AKC describes the breed as a "guardian" and doesn't take for granted the big dog's loyalty to its loved ones.
10. Australian Cattle Dog
Rusty an Australian Cattle Dog sits on the back of Kinnon & Co's Cobb & Co Stage Coach on March 25, 2011, in Longreach, Australia. Mark Kolbe/Getty Images/Getty
These athletic dogs were bred to hunt, and their sports-inspiring spirit makes them genetically ready to obey. Australian Cattle Dogs are "true-blue loyal, famously smart, ever alert, and wary of strangers," according to the AKC.
Still Really Obedient
Coren's next grouping are dogs that should respond to their human's command on the first try around 85 percent of the time.
11. Pembroke Welsh Corgi
Corgi dog Superfly prepares to race during the heats of the Southern California "Corgi Nationals" championship at the Santa Anita Horse Racetrack in Arcadia, California on May 26, 2019. MARK RALSTON/AFP via Getty Images/Getty
While this widely-beloved breed has made its way into pop culture (thanks, Queen Elizabeth) it's not necessarily the best at listening. Corgis fall in the top ranks, but are considered independent and "sensitive" by the AKC. That doesn't mean they aren't smart, though.
12. Miniature Schnauzer
A Miniature Schnauzer gets a treat from its handler after competing at the 142nd Westminster Kennel Club Dog Show at The Piers on February 13, 2018, in New York City. Drew Angerer/Getty Images/Getty
These bearded good boys are "bright, friendly, trainable companion[s], small enough to adapt to apartment life but tireless enough to patrol acres of farmland," the AKC writes. That means no matter your pup's purpose, be it work or family, they're expected to listen wholeheartedly.
13. English Springer Spaniel
Twiglet the 2 year old English Springer Spaniel poses for portraits in a garden on April 23, 2018, in Sydney, Australia. James D. Morgan / Contributor/Getty
Another hunting dog ranks on the most-obedient list. English Springer Spaniels were originally bred for birding, but their outdoor smarts have evolved them into one of the most playful family dogs.
14. Belgian Shepherd Dog (Tervuren)
Patricia Murphy and her Belgian Tervuren in the benching area at Pier 92 and 94 in New York City for the first day of competition at the 138th Annual Westminster Kennel Club Dog Show February 10, 2014. TIMOTHY CLARY/AFP via Getty Images/Getty
One dog that's expected to be "possessive" of loved ones is the Belgian Tervuren. Their loyalty is unmatched, which makes for a friend and protector, according to the AKC.
15. Schipperke, Belgian Sheepdog
A Shipperke dog poses. H. Armstrong Roberts/ClassicStock/Getty Images/Getty
These small pups are known to be somewhat obedient but may be better described as troublemakers. They're willing to follow orders, but just as willing to investigate anything new with a natural sense of curiosity.
16. Collie, Keeshond
A Keeshond waits to compete in the 138th annual Westminster Dog Show at the Piers 92/94 on February 10, 2014 in New York City. Andrew Burton/Getty Images/Getty
There are many reasons to love the small Keeshond breed, as their considered friendly and energetic. But one of the most-known features of the dogs is their facial coloring, which can make the dogs look like they're wearing glasses.
17. German Shorthaired Pointer
Carlee, a German Shorthaired Pointer, is posed for photographers after winning the Westminster Kennel Club Dog Show's Best In Show award at Madison Square Garden February 15, 2005, in New York City. Mario Tama/Getty Images/Getty
Another athletic dog is "willing to please" according to the AKC. Pointers are great hunting companions, but just as eager to be friends with their masters as they are ready to chase fallen prey.
18. Flat-Coated Retriever, English Cocker Spaniel, Standard Schnauzer
Dougie the Cocker Spaniel sits beside a sign reading 'Pawing Station' outside a polling station on June 8, 2017, in Stalybridge, Greater Manchester, United Kingdom. Anthony Devlin/Getty Images/Getty
There's a tie for 18th place in Coren's opinion. These three breeds are known to be equally willing to follow commands, though they're very different in terms of looks. All are known as family dogs, though, which may group them similarly.
19. Brittany
Teddy the Brittany dog attends 7th Annual AKC Meet The Breeds at Pier 92 on February 13, 2016, in New York City. Roy Rochlin/FilmMagic/Getty
If there's a breed known for optimism and an upbeat personality, it's the Brittany. These dogs, bred for hunting, are athletic but want to be involved heavily in their owner's lives at home. That doesn't mean they're comfortable cooped up inside, though. Brittany owners should be ready for lots of outdoor adventures with their furry friend.
20. Cocker Spaniel
Cocker Spaniels at the American Kennel Club "Meet The Breeds" Event at Jacob Javitz Center on September 28, 2013, in New York City. Monica Schipper/FilmMagic/Getty
Spaniels are known, in general, for their sporting ability, and that doesn't change with the small Cocker Spaniel. In addition, though, the dogs are ultra-loving, making them great for households with children. https://www.akc.org/dog-breeds/cocker-spaniel/
21. Weimaraner
A Weimaraner looks out from its bench on the third day of Crufts Dog Show at the NEC Arena on March 11, 2017, in Birmingham, England. Matt Cardy/Getty Images/Getty
These steel grey dogs are known for boisterous energy as well as intense love for their family. The active breed makes for another great companion for children, as well as adults who are willing to give the dog as much exercise as its wild demeanor demands.
22. Belgian Malinois, Bernese Mountain Dog
Flora, a 14-week-old Bernese Mountain dog walks in the wind on the sea front at Clevedon on March 2, 2016, in Clevedon, England. Matt Cardy/Getty Images/Getty
Another tie sees the Malinois and Bernese Mountain Dog as equals. These big-breed dogs are eager to please their owners with a "hardworking" or "strong" attitude, this AKC pages quote.
23. Pomeranian
Cheerio, Pomeranian, attends the Westminster Dog Show pre-event press conference at the Hotel Pennsylvania's Skytop Ballroom on February 5, 2009, in New York City. Andrew H. Walker/Getty Images/Getty
Another small dog with an eagerness to learn is the Pomeranian. The versatile dogs are great for small spaces, like apartments, because they can be entertained in or outdoors, even with little space.
24. Irish Water Spaniel
An Irish Water Spaniel competes in the Sporting Group during the second day of competition at the 140th Annual Westminster Kennel Club Dog Show at Madison Square Garden on February 16, 2016, in New York City. Matthew Eisman / Contributor/Getty
These curly-haired cuties are primed with energy and a willingness to participate. While they're not necessarily a popular, American dog breed, ranking 159 out of 196 on the AKC website, these pups are known to be incredibly hardworking.
25. Vizsla
A Vizsla competes in the Sporting Group during the second day of competition at the 140th Annual Westminster Kennel Club Dog Show at Madison Square Garden on February 16, 2016, in New York City. Matthew Eisman / Contributor
Another hunting dog isn't only committed to persevering in outdoor sports. Vizslas long for human companionship and probably aren't the right fit if you plan on leaving your dog home alone all day.
26. Cardigan Welsh Corgi
Handler Jaclyn Smith with her Cardigan Welsh Corgi waits in the benching area at Pier 92 and 94 in New York City for the first day of competition at the 138th Annual Westminster Kennel Club Dog Show February 10, 2014. TIMOTHY A. CLARY/AFP via Getty Images/Getty
Not only are these Corgis ready to defend their families, but their sturdy, hardworking nature makes them great dogs for energetic families. "Well-socialized Cardis are especially fond of kids and agreeable with other pets," the AKC claims, which should make them a top-contender for well-established families with children, pets and active lifestyles.
For the least obedient dogs, Coren's list continues here.


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CNN spotlights MAD Magazine's Al Jaffee
Posted by Alan Gardner
CNN profiles the career of MAD Magazine cartoonist Al Jaffee.
Today Jaffee, who will be 91 in March, is known for his own contraptions, many designed during his long relationship with Mad magazine. There's the Mad Fold-in, the magazine's inside back page, which cleverly turns one Jaffee work into another by folding one portion over another. There's "Snappy Answers to Stupid Questions," in which a humdrum question leads to several unexpected sarcasms.
And then there are his sketches of "Mad inventions," including an airbag suit and an improved beverage can. Some of them, like a smokeless ashtray or a multiroll toilet paper dispenser, have come to pass — much to Jaffee's amusement. He's been a primary contributor to Mad for more than 50 years, one of the chief purveyors of the humor that has shaped everything from "Airplane!" to The Onion to "The Daily Show with Jon Stewart." (Mad is a unit of DC Comics, which — like CNN — is a part of Time Warner.)
Amazing career.
#1 mikecrachiolo@gmail.com
December/5/2011
HE IS A GENIUS
#2 Dave Stephens
One of the all time best creative geniuses from Mad, no doubt about it… Glad CNN is posting this!
#3 David Jones
I did not read MAD magazine as a kid… I actually STUDIED it. Al Jaffee is one of my heroes!!!
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The construction work of Sona Breweries Plc started in 1980 on the present site KM 40, Lagos-Abeokuta Exp Way, Sango-Otta, Ogun state.
Actual production started in 1982 specifically in February, with the rolling out of bottles of Gold Lager Beer. The brewery started with an initial capacity of 500,000 hectoliters per annum and this was later expanded to 850,000 hectoliters.
Our brands won several NIS awards in Bronze, silver and Gold categories. The company was forced to close down in 1994 due to unfavorable political and economic climate.
The company reopened again in January 1995 under new ownership and management.
The easy digestive Maltonic contains vitamins A,B1,B2,B6 & C. It is one of the popular refreshing drink.


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Training usually purports to give us answers. Good teaching leaves us with questions. Training is about control. Teaching is about liberating creativity and innovation.
The problem is that society demands answers and is increasingly unable to process outcomes that lead to questions. Higher education is not exempt from this requirement. Ironically, the economy of the 21st Century demands that we teach questioning, instead of training answering. This is at odds with the cut-and-dry outcomes that legislators, board members, and senior administrators often want. As institutions and education leaders we are in a quandary as we are driven toward more and more activities that train our students, faculty, and staff without teaching them. The real casualty of this is innovation.
Technology and our Race Against the Machines have only made this issue more acute. Machines are increasingly easier to train. It's called programming. It forms the core of their being. Humans, however, have an innate drive for creativity that machines lack, and that is our ace in the hole when it comes to competing with machines. When we emphasize the training aspect of education, we risk becoming automatons and machines make better automatons than humans can or should be. Our job is to empower lifelong learners, not enslave them.
The solution to this training quandary is therefore not more training. The solution needs to start with understanding what is driving the need for the chains that training fits us for. There is logic behind everything. The problem there is that many don't understand the logic, and even those who do understand the logic don't always want to discuss it for fear that someone will discover the flaws in their logic.
Answers are unitary. There is only one answer. Questions are open-ended. We fail to realize that there are many paths to a creative plethora of solutions to our societal problems and that often the overlooked paths offer the best solutions. Technology offers the hope that we will be able to see more clearly (think data visualization). However, training often perpetuates and institutionalizes illogical processes and technologies. Over the long haul, this is a self-defeating strategy as automation and those human organizations that understand this fundamental fact will exploit the illogic and inefficiency of those who don't.
Ironically, bad technology implementations amplify our tunnel vision by blinding us to the possibilities. A large segment of our faculty, staff, and students accord it a status akin to magic. Arthur C. Clarke once famously wrote, "Any sufficiently advanced technology is indistinguishable from magic." The point of magic is that it is enjoyable because you don't understand it. The point of technology is that it is something that can enable us to do magical things. It has the power to turn us into magicians. Moreover, to be successful today, we have to understand how technology augments what we can do. We can't do that if we never get past the magical curtain.
Technology "training" almost always masks the man pulling the strings. It suppresses creativity rather than augments it. Training teaches us the "right" way of doing things. If technology gives us access to new frontiers and abilities, how can there be a right answer? Because we almost never teach technology, it is always a challenge whenever something new comes around.
We are going through another LMS migration at my institution and the solution to everything is train, train, train. The irony of this is that it almost never seems to make much of a difference how much "training" is given, the same number of people come in for personalized attention. The difference between those who need personalized assistance and those who don't is their comfort level. These days, comfort level has less to do with age and more to do with whether they are creative problem-solvers.
Creative problem-solving can be trained, but at its root it is a skillset that is often suppressed by a lot of training. Those who understand what a technology should do and can think through the process can adapt with minimal training when the technology changes. The training offered does not necessarily impact those who can't work through the creative problem-solving required. That's because most training is about rules, not possibilities. Trainees will still need help to get someone else to work through these problems for them.
This problem is pervasive. We are now developing these kinds of thinkers from an early age. Since the advent of standardized testing, K-12 education increasingly resembles training more than teaching. There are numerous exceptions, but so much effort revolves around training kids to perform well on the tests. After 12 years of this experience, it takes a lot to reconnect with their creative sides.
That being said, there is a role for training. People have to know the basic buttons and have some idea where they go. Training should, however, be a launching point, not the final destination. These days, outside of highly specialized fields such as medicine or nuclear fission, there isn't a reason for training that lasts more than a few hours. Anything beyond that should be creative in nature — not confining.
In most cases, people need to explore technology so that they can find their own "right way" of achieving their goals. Training inhibits that by showing them the "right way" of doing things. Steve Wozniak was never satisfied with that, and neither was Jobs, Engelbart, Musk, or any of the other innovators of the last half-century. We need more of that kind of thinking. That will take courage and creative exploration. The good news is it will be lot more fun than sitting through yet another endless lecture on the virtues of an LMS gradebook.
In the next part of this series, I will look at some strategies for implementing teaching over training and how we can start shifting our internal and external cultures toward the creative economy that technology is bringing to the fore.


What Is the Psychological Makeup for Human Factors?
One component of human factors (or ergonomics, the scientific study of interactions between mankind) is a human's psychological makeup. Human factors practitioners' primary concern is evaluating a human's behavior, especially if it is predictable. Therefore, they break human psychological makeup into two main psychological elements of interests: the physical and the behavioral.
The psychology of physical sensing and perceiving deals with how the brain interprets signals from the body's sensory inputs found on the skin, nose, ears, tongue, and eyes.
Feeling. Humans have cells that can pick up on pressure differences along their skin — this is how they feel — via two types of touch sensors. One sensor type picks up a general touch over a large area, such as those on the heel of a hand, while the other is more concentrated and refined and picks up minute changes in edges, such as sensors in the fingertips.
Hearing. Humans have a complicated series of devices in the ear that can take changes in the pressure of the air and send it to the brain as a signal that it interprets as sound. Several areas of the brain handle this processing.
Smelling. The human nose is amazingly sensitive and not only can detect scents but can also signal if there are dangerous — or attractive things — around.
Tasting. The human tongue is a wondrous muscle studded with receptors that can pick up different chemical elements and translate them into distinct taste elements, typically categorized as salty, sweet, bitter, sour, or umami (savory).
Seeing. The functionality of the human eye is almost magical. Specialized cells pick up three distinct colors, light intensity, and edge definitions and interpret those signals into the images perceived by mankind, providing a rainbow of colors and depth.
The one commonality between all these sensory perceptions that is of vital importance to human factors is that they are all stimulated by physical means. These physical means make up part of the human-machine interface and even the human-environment interface. Understanding what role they play and how they can affect both human performance and behavior are important when analyzing those human factors.
The behavioral aspect of a person's or population's psychological makeup relates to the elements that motivate actions or cause reactions. Therefore, how a human acts and why is an important data point. Human behavior underlies almost everything from economics to politics. In fact, economics is really about studying how people react to incentives and politics is about how people react to campaign speeches.
In ergonomics, scientists try to make things as efficient — or oftentimes comfortable and easy to use — as possible so that the human behavior data can be utilized to design a device or system for human consumption wherein the subject is motivated to use it for the desired outcome.
This often begs the question, "What about making sure the human doesn't get hurt through the work?" which falls under the category of motivational and reactive behaviors, studied by ergonomists. If it causes stress or injury, repetitive or otherwise, predictable human behavior tells ergonomists that people won't want to do it, and if they do, they will not be operating at their maximum human performance level and will not be efficient. Therefore, any proposal made by an ergonomist typically will preclude any harmful suggestions (as humans naturally select to avoid these).
The cultural aspect of the psychological makeup of a group of people can be part of the behavioral aspect, but it can also affect a person's cognitive capability. From a behavioral position, culture plays an important part in understanding what motivates an individual and how they react to certain stimuli.
Simple things such as language can cause extremely different reactions. For instance, the differences between Mexican and American cultures can greatly affect their levels of interest in a certain issue or item. Take the case of the Chevy Nova, a popular car in America that tried to sell internationally to the population of Mexico. When Chevy attempted to market the car, they failed to realize that "No Va" was Spanish for "No Go." The car did not sell well.
Another such example is that in America, curling your pointer finger towards you is a common hand signal for "come here." In some Middle Eastern and African cultures, however, that gesture is exclusively reserved to call a dog and is seen as insulting when used towards a person. Conversely, in some European cultures biting a thumb is seen as a vulgar insult while in America it has no known meaning.
On the cognitive side of these aspects, ergonomists deal with differences in the cultural lexicon. As humans grow up, they learn things they may not realize, inherently, from culture — certain things mean certain things. These become part of their instinctual understanding of the world. But not everything is universal. Color psychology is a prime example of something that can possess different meaning across cultures. Even though color theory has some universal elements as to how color is interpreted, what those interpretations are defined as may differ. So where green may represent good fortune in one culture, blue may imply that in another.
Shapes, patterns and how things are organized (to name a few) can imply greatly different meanings across cultures. Some cultures even affect a person's body mechanics dictating that a certain posture or walking style is preferred.


text:
Q: Unexpected button padding on ICS I have a custom button with a state list drawable that has a tight layout with very little padding. This button looks great on MDPI and HDPI devices, but padding is all wrong on my Motorola Xoom (ICS, MDPI device). Modifying the 'padding' and 'margin' attributes does nothing.
Anyone have any suggestions on what may be causing the unnecessary padding? I'm fairly confident that both the state list drawable and the 9patch drawables are fine (they render perfectly on other MDPI devices).
Attached is an example of what the final product looks like - green border denotes its ideal appearance (and the way the button looks on other devices); magenta border denotes the appearance on the Xoom.

A: I encountered some situation when developing a appwidget. I added a button and try to trim all paddings of it to make it small but failed, there are always a top padding and a bottom padding. I believe it's a constraint of Android system, so that all buttons in appwidget are large enough for end users to tap. However sometimes we insist small buttons due to boss... As the workaround Melllvar indicated, I used a focusable, clickable TextView instead of a Button, it works as expected.

redpajama_set_name: RedPajamaStackExchange


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text:
<!-- Do not edit this file. It is automatically generated by API Documenter. -->

[Home](./index.md) > [puppeteer](./puppeteer.md) > [Protocol](./puppeteer.protocol.md) > [Input](./puppeteer.protocol.input.md) > [TouchPoint](./puppeteer.protocol.input.touchpoint.md) > [rotationAngle](./puppeteer.protocol.input.touchpoint.rotationangle.md)

## Protocol.Input.TouchPoint.rotationAngle property

Rotation angle (default: 0.0).

<b>Signature:</b>

```typescript
rotationAngle?: number;
```

redpajama_set_name: RedPajamaGithub


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

\begin{figure}
\centering
\includegraphics[width=3.2in]{figures/result.pdf}
\caption{Performance comparison with other models (top-left is better). In terms of computation-accuracy and parameter-accuracy tradeoffs, the proposed CabViT-H models outperform other models by a clear margin. }
\label{fig:comparison}
\end{figure}

With the fast adoption of transformer structures in computer vision community, various types of attention patterns are proposed to improve the performance or the speed of transformer models. For example, the vanilla global multi-head self attention in ViT~\cite{dosovitskiy2020image}, the local windowed attention in swin transformers~\cite{liu2021swin}, the grid attention across interleaved tokens in MaxViT~\cite{tu2022maxvit}, and the attention on progressively pruned tokens in Dynamic ViT~\cite{rao2021dynamicvit}. These works seek to sparsify the attention patterns of the vanilla ViT to achieve better speed accuracy trade off.

In contrast, we propose a new model block as well as a family of models called CabViT, which improves the performance of vision transformers by further densifying the attention patterns with limited extra cost. Specifically, we propose to connect the input of standard multi-head attention (MHA) module with extra tokens transformed from previous blocks in the same stage, while still restrict the attention module to output the original amount of tokens. To further reduce the computational cost, we merge the tokens from previous blocks by using depthwise convolutions with large strides. These tokens are further calibrated by scaling them with learned parameters, before being taken into the attention units in subsequent blocks.

The new CaB connection has several advantages. 1) It helps transformers further exploit the interactions of tokens across different levels. 2) It reuses the previously generated tokens so that some of the information no longer needs to be preserved by the subsequent transformer operations, leading to potentially smaller models with similar accuracy.  3) Similar to the residual connections in ResNet, this extra cross layer connection encourages more information flows to the lower levels of the network, which at the same time further alleviates vanishing-gradient problems.

The newly densified connections bring in limited extra computational cost. As is detailed in Section \ref{sec:computational_complexity} 1) The number of output tokens are kept the same as in standard ViTs, which makes the extra computation cost increases linearly instead of quadratically. 2) In existing vision transformer architectures, majority of the computation cost lies in the feed forward network (FFN) part instead of the MHA part of transformer blocks. This linear growth of computational complexity does not significantly increase the overall computation cost. 3) We use depthwise convolutions with large kernels and long strides to aggregate the tokens from previous blocks to reduce the number of extra inputs.

We adopted the proposed CabViT design in two typical classes of transformer models, the plain ViT model as used in DeiT, and the ConvNet and transformer hybrid structures that are seen in many recent works \cite{mehta2021mobilevit,graham2021levit,li2022efficientformer}. Experiments show that the proposed CaB block can be seamlessly used to replace the corresponding transformer blocks in these architectures, and achieve significantly better results than their corresponding baselines. Specifically, CabViT achieves 80.3\% top-1 accuracy with about 6.2 million parameters and about 1.5G FLOPs, saving almost half parameters but gaining 1.2\% higher accuracy compared with the recently proposed  EfficientFormer. Fig~\ref{fig:comparison} displays the comparison result.

The contribution of this paper is summarized as follows.

\begin{enumerate}
    \item Opposite to many recent works that use sparse attentions to improve transformer models, we propose to design more efficient models by densifying the attention connection patterns.
    \item We propose the CabViT block with cross attention among blocks in the same stage. CabViT block reuses existing tokens and enhances the interactions of tokens from different levels.  In the CabViT block, we design a light-weight token merge and enhancement (TME) module to control its computational complexity.
    \item Based on the proposed CabViT block, we build several new models, which achieve better performance than state-of-the-arts.

\end{enumerate}

\begin{figure*}[!ht]
\centering
\includegraphics[width=6.6in]{figures/cabvit_stage.pdf}
\caption{A CabViT stage which consists of $L$ CabViT blocks. Compared with standard ViT blocks, we add token merge and enhancement (TME) part, which uses long stride large kernel convolutions to merge tokens spatially as digest tokens, and small kernel convolutions to further enhance tokens for channel mixing (FFN). The digest tokens are then used in later blocks as extra tokens for multi-head attention, after being calibrated by multiplying them with learned scaling factors ($\alpha$).}
\label{fig:CabViT}
\end{figure*}

\section{Related Works}\label{sec::review}

\subsection{Pure vision transformers} \label{sec::pure_vit}

Dosovitskiy {\it et al.} introduced transformer model into vision tasks and proposed the ViT~\cite{dosovitskiy2020image}. It cropped an image into $16\times 16$ patches as an input token sequence to the transformer and used positional encoding to model spatial relations among tokens. DeiT~\cite{touvron2021training} lowered the difficulty of ViT model training by knowledge distillation, and achieved competitive accuracy with less pretraining data. To Further improve the model architecture, researchers attempted to optimize the ViT toward improving its computational efficiency. Among them, the Swin transformer~\cite{liu2021swin} computes self attention among shifted local windows. The MaxViT ~\cite{tu2022maxvit} uses block attention and grid attention alternatively to keep spatially global information exchange while significantly reduce the number of tokens involved in the self attention computation. The DynamicViT~\cite{rao2021dynamicvit} prunes redundant tokens progressively and dynamically depending on the input features. Mohsen {\it et al.}~\cite{fayyaz2022adaptive} proposed a differentiable parameter-free adaptive token sampler and plugged it into ViTs to sample part tokens for attention computation.

Except for the DeiT, all methods above keep pure vision transformer architectures and seek to achieve better accuracy speed trade-off by using sparse attention via reducing the number of tokens in attention patterns. In contrast, we propose to achieve this goal by densifying the attention pattern, reusing existing tokens from previous blocks. Such interactions promote attentions across features of different scales and semantic levels, which is very common in ConvNets and many methods before the wide adoption of deep learning.

\subsection{Hybrid ConvNet and vision transformers}

Rather than simplifying ViTs, another popular line of research is to combine elements of ViTs and ConvNets to form new backbones. Two early attempts can be found in \cite{graham2021levit, xiao2021early}, where ConvNet blocks are employed to extract low level information in early stages and ViT blocks are adopted in deep stages. Such a hybrid structure improves optimization stability and model performance. Similarly, BoTNet~\cite{srinivas2021bottleneck} replaces the standard convolution with multi-head attention in the last few blocks of ResNet. In~\cite{mehta2021mobilevit}, the deeper stages of MobileNetv2~\cite{sandler2018mobilenetv2} are replaced with their proposed MobileViT block. There are other hybrid models which mix convolution operation with self attention and channel mixer operations. For example, ConViT\cite{d2021convit} incorporates soft convolutional inductive biases via a gated positional self-attention. CMT~\cite{guo2022cmt} and Next-ViT~\cite{li2022next} insert both convolution operataion and self attention module into a single block. PVT v1~\cite{wang2021pyramid}, PVT v2~\cite{wang2022pvt}, LIT~\cite{pan2022less} and LIT v2~\cite{pan2022fast} insert convolutional operations into each stage of ViT models to reduce the number of tokens, and build hierarchical multi-stage structures.

Hybrid models achieve better trade-off between model cost and accuracy compared with pure vision transformer models, while they usually have more complex architectures. Therefore, both pure ViTs and hybrid models have their own advantages for different applications. Our work is complementary to these hybrid design attempts, and can be used to replace the transformer part for most of these methods.

\subsection{Skip connections in ConvNets}
\label{sec:skip_connect}

In retrospect, our work is also related to several key improvements in ConvNets design that introduces extra connections to improve the information flows.

ResNet \cite{he2016deep} employs shortcut connections to overcome the degradation problem, where accuracy gets saturated and then degrades rapidly with the increasing convolutional network depth.
DenseNet \cite{huang2017densely} connects each layer to every other layer in a feedforward fashion. As with ResNet that builds the whole network by stacking several residual units, DenseNet consists of multiple dense blocks. Wang {\it et al.}~\cite{wang2018pelee} further improve DenseNet by using two-way dense layers to obtain different receptive fields. With limited extra computational cost, these model design choices solved bottlenecks existed in ConvNets and are still widely used in both academia and industry.

Our proposed cross attention is similar to the works above in that it reuses existing intermediate results and introduces extra connections to the overall network structure. As experiments show, our work introduces similar benefits to transformer models such as better model performance and faster model convergence.

\subsection{Cross attention transformers}

In transformers, cross attention is usually used to mix two different embedding sequences. In~\cite{vaswani2017attention, gheini2021strengths}, the output of encoder is fed to decoder via cross attention. CrossViT~\cite{chen2021crossvit} mixes small-patch and large-patch tokens with cross attention to extract multi-scale feature.

Our proposed CabViT shares the basic idea of integrating information with cross attention and uses it to enhances the interactions of tokens on potentially different semantic levels.

\section{The proposed CabViT}

\subsection{CabViT block and CabViT stage}
\label{sec: CabViT}

Fig.\ref{fig:CabViT} shows the major parts and connection relations of a CabViT stage which consists of $L$ CabViT blocks. Each CabViT block is composed of three major parts, which are named multi-head attention (MHA), token merge and enhancement (TME), and feed forward network (FFN) respectively.

\textbf{MHA}. Different from the standard ViT block, our proposed CabViT block receives two sets of tokens as input. The block in turn generates two sets of tokens as well, which are denoted as $x^l$ and $\bar{x}^l$ respectively. Taking the block at depth $l$ as an example, the inputs are the regular features from its previous block $x^{l-1}$ and a set of block digest tokens $(\bar{x}^{l-2}, \bar{x}^{l-3} ... \bar{x}^1)$ from earlier blocks in the stage. \textit{The MHA takes both $x^{l-1}$ and $(\bar{x}^{l-2}, \bar{x}^{l-3} ... \bar{x}^1)$ as input, but only generates $n$ tokens $\tilde{y^k}$ as its output}. Specifically, given the inputs above, the query, key and value for multi-head attention modules are constructed as follows:
\begin{eqnarray}
Q &=& W^Q x^{l-1} \\
K &=& W^K\left[x^{l-1}, (\mathbf{1} \alpha^{l})^T \otimes(\bar{x}^{l-2}, \cdots, \bar{x}^{1} ) \right] \\
V &=& W^V\left[x^{l-1},  (\mathbf{1} \alpha^{l})^T \otimes(\bar{x}^{l-2}, \cdots, \bar{x}^{1}) \right]
\end{eqnarray}
where $W^K,W^V,W^Q$ are the three weight matrices transforming input into key, value and query tokens respectively. Elements in  $\alpha \in \mathcal{R}^{d \times 1}$ are the learnable scale factors for digest tokens and $\otimes$ denotes the element-wise multiplication where each token in $\bar{x}$ is multiplied by a corresponding scaling scalar in $\alpha$. $s$ is the stride size in the depth wise convolutions that will be introduced shortly.

\begin{figure}
\centering
\includegraphics[width=3.0in]{figures/tme.pdf}
\caption{The detailed structure of the token merge and enhancement (TME) module.}
\label{fig:TME}
\end{figure}

The computed $Q,K,V$ are then connected to the standard dot product attention operators as in the original transformer \cite{vaswani2017attention}, which computes the globally mixed intermediate tokens $y^l$.

\begin{equation}\label{eqn::mha}
    y^l = x^{l-1}+W^P\left[\mathrm{softmax}\left(\frac{QK^T}{\sqrt{d}}+B\right)V\right]
\end{equation}
The $B$ in Eqn.\ref{eqn::mha} is a learnable matrix consisting of two parts, which are relative position bias for $x$ and relative depth bias for $\bar{x}$. The query has only $n$ tokens, so the output $y^l$ is of size $n \times d$.

\textbf{TME}. The intermediate tokens $y^l$ are then passed to the added token merge and enhancement (TME) part, which computes the digest tokens $\bar{x}^l$ and locally enhanced tokens $z^l$ using two separate depthwise convolutions. As shown in Fig~\ref{fig:TME}, our TME has two branches. In the token merge branch, we use a large kernel $(7\times7)$ large stride $(s=4)$ depthwise convolution to generate $\bar{x}^l$, resulting in a small number of tokens to be used by subsequent blocks. In token enhancement branch, a standard depth wise convolution ($3\times3, s = 1)$ is used to locally mix the tokens so as to enhance the 2d spatial relations in the token sequence.

\textbf{FFN}. The locally enhanced tokens $z^l$ are then used in the standard FFN part of ViT to compute the output token $x^l$, which together with $\bar{x}^l$ are the entire output of a CabViT block at depth $l$.

In summary, our CabViT are different from vanilla ViT in following ways:
\begin{itemize}
    \item \textbf{Asymmetric input and output}. In our CabViT, MHA part takes both regular tokens and digest tokens as input but keep the output sequence length fixed to $n$ as in regular transformers. This is the key point that why our proposed CabViT only introduce limited extra computational cost. More details are explained in section~\ref{sec:computational_complexity}.

    \item \textbf{Digest token scale}. It is worth noting that the digest tokens are scaled by a learned calibration factor $\alpha$ before they are used in the multi-head attention. We believe that the characteristics of tokens in different semantic levels (different blocks) are different. Without this calibration operation, digest tokens are hard to integrate to regular tokens in current blocks to work as we expected. A visual comparison shows this phenomenon in ablation study part.

    \item \textbf{Relative depth embedding}. In the MHA part, we choose to use relative positional encoding instead of absolute ones. Apart from its generally better performance, the choice also gives model the flexibility to encode relative depth of the digest tokens, which simplifies the model design.
\end{itemize}

\begin{figure}
\centering
\includegraphics[width=\linewidth]{figures/cabvit_models.pdf}
\caption{The overall structure of CabViT Mobels for image classification tasks. (a) Plain CabViT model (CabViT-P). This model is directly modified from the DeiT structure, where an extra classification query token is used to get classification features. (b) Hybrid CabViT model (CabViT-H). The ConvNet stages are composed of ConvNext blocks. The output tokens are global average pooled to get classification features. The detailed model scaling hyperparameters are specified in Sec.\ref{sec::model}}
\label{fig:CabViT_models}
\end{figure}

\subsection{CabViT Models}
\label{sec::model}

The CabViT block is a generic block that can be grouped as CabViT stages to construct models. To demonstrate the universal effectiveness of our new attention pattern, we build two types of CabViT based models, each falls into one of the major categories of transformer related computer vision models as described in Sec. \ref{sec::review}. The overall structure of these models for classification task are illustrated in Fig.\ref{fig:CabViT_models}.

\textbf{CabViT-P}. As shown in Fig~\ref{fig:CabViT_models} (a), following the style of vanilla ViT, we construct our plain CabViT model (denoted as CabViT-P). The model essentially uses one block of CabViT after the patch embedding layer that crops $16 \times 16$ patches and converts them into a token sequence.  The corresponding task related head is kept unchanged compared with the original ViT model.

\textbf{CabViT-H}. As illustrated in Fig~\ref{fig:CabViT_models} (b). Following the trend of combining ConvNet structures and transformer structures to build hybrid models, we also propose to build hybrid CabViT models, denoted as CabViT-H in the experiments. Following LeViT~\cite{graham2021levit} and ViT-C\cite{xiao2021early}, we adopt the most straightforward way to build our CabViT-H, where there are two conventional ConvNet stages followed by two CabViT stages. The ConvNet stage is composed of ConvNext blocks~\cite{liu2022convnet} plus a downsampling layer that uses pointwise and depthwise convolutions to reduce the feature map resolution by 4$\times$ and increases the feature map channels.

Based on the above overall structure, we build models of different sizes to compare with other works. The key scaling hyper-parameters on each stage of these models can be found in Sec \ref{sec::experiments} and is summarized below, where $D$ denotes the channel size:

\begin{itemize}
    \item CabViT-P-L0: D=192, L=12
    \item CabViT-H-L1: D=(64,128,192,320), L=(2,2,6,2)
    \item CabViT-H-L2: D=(96,192,320,480), L=(2,2,7,2)
\end{itemize}

\subsection{Computational Complexity of CabViT}
\label{sec:computational_complexity}

\begin{figure}
\includegraphics[width=3.2in]{figures/computation_cost.pdf}
\caption{Computational cost analysis. (a) Detailed analysis on the number of parameters and FLOPs for a transformer block; (b)-(c) Computation decompositions in blocks of stage 3 of DeiT-Tiny and stage 4 of EfficientFormer-L1 respectively.}
\label{fig::FLOPs}
\end{figure}

In terms of computational complexity, transformers are notorious for its $\mathcal{O}(n^2)$ scaling property, which is why so many prior works try to reduce the inputs to the MHA model. However, the extra computational cost of densifying the attention pattern is rather limited in our CabViT models, because of the following reasons:

1) The added extra computation is restricted to the MHA part, since we keep the output sequence length of MHA fixed to $n$ as in regular transformers. This leads to linear scaling cost in terms of the number of additional input tokens, which itself is a small ( $(l-1)*n/s^2$ ) compared with $n$. The computational cost of FFN part is kept unchanged compared with original ViT blocks.

2) In vision transformer model families, the FFN part actually constitutes the majority of computations. This is paradoxical to the fact that MHA scales at $\mathcal{O}(n^2)$  while FFN scales at $\mathcal{O}(n)$, largely because that the neglected constant token size $d$ in big O analysis is rather large and scales poorly.  Fig. \ref{fig::FLOPs} details the exact adds and multiplications (FLOPs) of a standard ViT block in terms of the number of tokens $n$ and the dimension of tokens $d$. Taking the transformer block DeiT-Tiny as an example, because $n=196$ and $d=192$ are at the same scale, the $2n^2d$ FLOPs in MHA is much less compared with that ($8nd^2$) of FFN (15\% vs 56\% respectively). For models in pyramid shapes such as swin transformers where $n < d$, the majority skews even more towards FFN because of the local windowing effect. Compared with the reference models, our CabViT variants only introduce about 13\% extra computational cost.

It looks like that the proposed CabViT is heavier than its reference model as it introduces extra computations. However, the counter-intuitive fact is that \textit{CabViT can be more light-weight than its reference model}. As is mentioned in introduction, CabViT reuses tokens from previous blocks, enhances interaction of tokens across blocks and improve information flow. Compared with the vanilla ViT, CabViT has better parameter efficiency. Therefore, CabViT can get comparable or even better performance with fewer layers and channels. Our experiments further verifies this advantage.

In addition to the detailed analysis above, we want to point out that the prior efforts to sparsify the attention patterns spatially in Sec.\ref{sec::pure_vit} and our proposal to densify the attention patterns across semantic levels are complementary to each other. While not the key emphasis of this paper, it is definitely worth exploring if the two types of designs can be combined to yield even more efficient and performant models.

\section{Experiments}
\label{sec::experiments}

In this section, we conduct image classification experiments on Imagenet-1K~\cite{deng2009imagenet}, and semantic segmentation experiments on ADE20K~\cite{zhou2017scene} to evaluate our proposed models. We first compare the proposed CabViTs with our baselines and the previous SOTA methods. Then, we conduct detailed study to show the effectiveness of our design choices.

\begin{table}
\centering
\begin{tabular}{lccc}
\hline
Models         &   \# params. &  FLOPs &  Top1 acc  \\
               &   (M)        &  (G)        &   (\%)      \\
\hline
DeiT-T         &    5.5        &         &  72.2           \\

CabViT-P-L0    &    5.9        &         &  74.3           \\
\hline
ConvNext-T ($\times$ 0.5)     &    7.4       &  1.2    &  77.5          \\
Swin-2G        &    12.8      &  2.0    &  79.2          \\

CabViT-H-L1    &    6.2       &  1.5    &  80.3          \\
\hline
ConvNext-T     &    29       &  4.5    &  82.1          \\
Swin-T         &    29       &  4.5    &  81.3          \\

CabViT-H-L2     &    16.3      &  3.9    &  83.0        \\
\hline
\end{tabular}
\caption{Comparison with baselines. The plain version CabViT-P is based on DeiT-T. The hybrid ViT CabViT-H models are based on ConvNext and Swin Transformers. }
\label{tab:baselines}
\end{table}

\begin{table}
\setlength\tabcolsep{3pt}
\centering
\begin{tabular}{lclccccc}
\hline
Models             & KD   &  Type    & Param. & FLOPs & Top1 \\
\hline
DeiT-Tiny          & Y    &  ViT     & 5.5         &  -        & 74.5        \\
DeiT-Tiny          & -    &  ViT     & 5.5         &  -        & 72.2        \\

CabViT-P-L0        & -    &  ViT     & 5.9         &  -        &  74.3       \\
\hline
LeViT-128          & Y    &  Hybrid  & 9.2         &  0.4      &  78.6       \\
EfficientFormer-L1 & Y    &  Hybrid  & 12          &  1.3      &  79.1       \\
ConvNext-T($\times$0.5)
                   & -    &  Conv    & 7.4         &  1.2      &  77.5       \\
ParCNet            & -    &  Conv    & 5.0         &  1.7      &  78.6       \\
RSB-ResNet-50      & -    &  Conv    & 26          &  4.1      &  79.8       \\
TNT-Tiny           & -     &  ViT    & 6.1         &  1.4      &  73.9     \\
Swin-1G            &  -    &  ViT    & 7.3         &  1.0      &  77.3      \\
Swin-2G            &  -    &  ViT    & 12.8        &  2.0      &  79.2      \\
EfficientFormer-L1* & -    &  Hybrid & 12          &  1.3      &
76.1       \\
MobileViT-V1       & -    &  Hybrid & 5.6         &  2.0      &  78.4       \\
EdgeViT-XS         & -    &  Hybrid  & 6.7         &  1.1      &  77.5       \\
MobileViTV2        & -    &  Hybrid  & 4.9         &  4.9      &  78.1       \\
CoaT-Tiny          & -    &  Hybrid  & 5.5         &  4.4      &  78.3       \\
PVT-V2             & -    &  Hybrid  & 14.0        &  2.1      &  78.7       \\
Mobile-Former      &  -    & Hybrid  & 14.0        &  0.5      &  79.3       \\
Edgenext           &  -    & Hybrid  & 5.6         &  1.3      &
79.4       \\
MobileOne          &  -    & Hybrid  & 14.8        &  3.0      & 79.4 \\

CabViT-H-L1        &  -    &  Hybrid &  6.2     &  1.5     &  \textbf{80.3}  \\
\hline
DeiT-S             &  Y    &  ViT    &  22       &  4.6     &  81.2  \\
LeViT-256          &  Y    &  Hybrid &  18.9     &  1.1     &  81.6  \\
EfficientFormer-L3 &  Y    &  Hybrid &  31.3     &  3.9     &  82.4  \\
ResNet50           &  -    &  Conv   &  25       &  4.1     &  78.5  \\
ResNet50*          &  -    &  Conv   &  25       &  4.1     &  79.1  \\
PoolFormer-S24     &  -    &  Conv   &  21.4     &  3.6     &  80.3  \\
ReGNetY-4G         &  -    &  Conv   &  21       &  4.0     &  81.3  \\
ParCNet-H          &  -    &  Conv   &  21       &  -       &  81.9
\\
ConvNext-T         &  -    &  Conv   &  29       &  4.5     &  82.1  \\
VAN-B2             &  -    &  Conv   &  30       &  5.0     &  82.8  \\
DeiT-S             &  -    &  ViT    &  22       &  4.6     &  79.9 \\
Swin-T             &  -    &  ViT    &  29       &  4.5     &  81.3  \\
T2T-ViT-14         &  -    &  ViT    &  22       &  4.8     &  81.5  \\
T2T-ViT-19         &  -    &  ViT    &  39       &  8.5     &  81.9  \\
MViTv2-T           &  -    &  ViT    &  24       &  4.7     &  82.3  \\
CSWin-T            &  -    &  ViT    &  23       &  4.3     &  82.7  \\
MobileViTV2        &  -    &  Hybrid &  18.5     &  7.5     &  81.2  \\
LITV1              &  -    &  Hybrid &  27       &  4.1     &  81.5  \\
LITV2              &  -    &  Hybrid &  28       &  3.7     &  82.0  \\
Next-ViT           &  -    &  Hybrid &  31.7     &  5.8     &  82.5  \\

CabViT-H-L2        &  -    &  Hybrid &  16.3     &  3.9     & \textbf{83.0} \\
\hline
\end{tabular}
\caption{Comparison with the state-of-the-art methods on ImageNet-1K validation set. KD means knowledge distillation is used during training. * denotes these models are trained following the training setting used in ConvNext. Accuracy and FLOPs are calculated on input image size 224$\times$ 224. }
\label{tab:comparison}
\end{table}

\begin{table}
\setlength\tabcolsep{3pt}
\centering
\begin{tabular}{llccccc}
\hline
Method    &   Backbone  & mIOU  & \# params. & FLOPs \\
\hline
DANet     &  ResNet-101 & 45.2  &  69  &  1119  \\
DpLab.v3+ &  ResNet-101 & 44.1  &  63  &  1021  \\
ACNet     &  ResNet-101 & 45.9  &  -   &  -     \\
DNL       &  ResNet-101 & 46.0  &  69  &  1249  \\
OCRNet    &  ResNet-101 & 45.3  &  56  &  923   \\
UperNet   &  ResNet-101 & 44.9  &  86  &  1029  \\
\hline
UperNet   &  Swin-T     & 46.1  &  60  & 945  \\
UperNet   &  ConvNext-T & 46.7  &  60  & 939  \\

UperNet   & CabViT-H-L2 & 46.7  &  46  & 730  \\

\hline
\end{tabular}
\caption{Semantic segmentation on the ADE20K dataset. We use UperNet as our segmentation method and compare our performance using the same method with other popular backbones.}
\label{tab:seg}
\end{table}

\subsection{ImageNet classification}

\textbf{Experiment settings}. The CabViT-P is implemented based on code of DeiT~\footnote{https://github.com/facebookresearch/deit}. The CabViT-H models are implemented based on code of ConvNext~\footnote{https://github.com/facebookresearch/ConvNeXt} and Swin~\footnote{https://github.com/microsoft/Swin-Transformer}.  We follow the training recipes in DeiT \cite{touvron2021training} to train our CabViT-P model, except that we did not use the knowledge distillation. To train the CabViT-H models, we use the same training hyper parameters and augmentations as used in ConvNext except that the batch size is restricted to 1024 and the initial learning rate is reduced to 2e-3. This change is because we don't have enough GPUs to support the default batch size of 4096 in ConvNext.

\textbf{Comparison with baselines}.
The results are summarized in Table~\ref{tab:baselines}, from which we can see that both types of our proposed CabViT models outperform their reference models by a significant margin. Compared with DeiT-T, CabViT-P-L0 does have 0.4 M more parameters, but it achieves 2.1\% higher accuracy. OurCabViT-H models achieve better performance in all three metrics: accuracy, model size and computational cost. CabViT-H-L1 achieves 80.3\% top-1 accuracy with about 6.2 million parameters and 1.5 billion FLOPs, saving about half parameters and 25\% computational cost but gaining 1.1\% higher accuracy compared with similarly sized Swin-2G. CabViT-H-L1 also has good scalability. The scaled up version CabViT-H-L2 outperforms the ConvNext by 0.9\% with 13\% less computational cost and 44\% fewer parameters. CabViT-H-L2 also surpasses Swin-T by 1.7\% while using fewer parameters and less computation.

In summary, our proposed CabViT design universally improves performances of both plain version and hybrid ViT models without increasing too much extra computation cost or even saving parameters and FLOPs.

\textbf{Comparison with other models}. In Table~\ref{tab:comparison}, we make a comparison with other models proposed in recent two years. Compared to the latest state-of-the-arts, including ConvNets, ViTs and Hybrid models, our proposed CabViT models achieve the best accuracy under the condition of having similar model sizes. In addition, the proposed models beat models strengthened by knowledge distillation in classification accuracy.

Specifically, CabViT-H-L1 outperforms MobileOne and EdgeNext by 0.9\% in classification accuracy, while keeping comparable or smaller model size and fewer computational cost. Among models having about 25 million parameters, CabViT-H-L2 achieves the highest classification accuracy with the fewest parameters. Compared with Next-ViT-S which achieves the second highest accuracy, CabViT-H-L2 saves about half parameters and 32\% computational cost, while gaining 0.5 percentage points higher accuracy. Also, note that both EfficientFormer-L3 and LeViT-256 are trained with knowledge distillation, which has been verified to improve accuracy significantly~\cite{touvron2021training}. Even so, CabViT-H-L2 still has better accuracy and smaller model size compared with these two models.

\subsection{Semantic segmentation}

\textbf{Training setting}. To evaluate the proposed CabViTs on downstream tasks. We apply them on the ADE20K semantic segmentation task.  Following Swin and ConvNext, we adopt UperNet~\cite{xiao2018unified} as our base framework and implement segmentation experiments on mmseg~\footnote{https://github.com/open-mmlab/mmsegmentation}. CabViT-H-L2 is trained for 160K iterations with a batch size of 16. Model pretrained on ImageNet-1K is used to initialize segmentation model.  More details are presented in supplementary materials.

\textbf{Comparison with baselines}. The results are summarized in Tab.\ref{tab:seg}, compared with Swin-T, CabViT-H-L2 achieves 0.6\% higher mIoU. Compared with ConvNext-T, CabViT-H-L2 has no advantage in mIoU but saves about a quarter parameters and computational cost. Compared with Swin-T and ConvNext-T, the last two stages of CabViT-H-L2 are narrower (384 vs 320, 768 vs 480). Correspondingly, the segmentation head for CabViT-H-L2 is also narrower, which saves parameters and computation, but may harm the final result. We suspect introducing a bridge such as point wise conv may solve this problem, and will investigate this direction in our future work.

\subsection{Ablation study}

\begin{figure}
\centering
\includegraphics[width=2.8in]{figures/result_vis.pdf}
\caption{Effects of digest token scale. Attention scores for digest tokens are marked with red boxes. From rows 1-4, attention maps are from blocks 2,4 and 6 of stage3, and block 2 of stage 4 respectively. }
\label{fig:learnable_sacle}
\end{figure}

\begin{figure}
\centering
\includegraphics[width=2.8in]{figures/loss.pdf}
\caption{Loss convergent curves of different models}
\label{fig:convergent_curves}
\end{figure}

\begin{table*}
\setlength\tabcolsep{3pt}
\centering
\begin{tabular}{cllccc}
\hline
Steps  & Models        & Model differences     & \# Param. (M)  & FLOPs (G)    & Top1 acc (\%)  \\
\hline
0   & ConvNext-T       &  Baseline.      & 29             & 4.5          &   82.1         \\
\hline
1   &  ConvNext-T $\times$0.5  &  Reduce channel width to half.
                               &  7.4       &  1.06     &  77.5       \\
2   &  ConvSwin      & Replace last two stages with swin
                               &  10.5      &  1.38     &  79.2       \\
3   &  ConvViT       & Replace window attention with global. attention
                               &  10.5      &  1.47     &  79.8       \\
4   &  ConvViT       & Cross attention among blocks.
                               &  10.5      &  1.52     &  79.9       \\
5   &  ConvViT       & Add learnable scale factors.
                               &  10.5      &  1.52     &  80.3       \\
6   &  CabViT-H-L1   & Reduce width and depth, and add TME.
                               &  6.2       &  1.46    &  80.3       \\
7   &  CabViT-H-L2   & Scale up.
                               &  16.3      &  3.9     &  83.0       \\
\hline

\end{tabular}
\caption{Ablation study on Imagenet-1K. Steps 1-7 shows the process of deriving our CabViT-H models from the ConvNext-T.}
\label{tab:ablation_components}
\end{table*}

In this section, we conduct ablation analysis on components proposed in our CabViT.

\iffalse
\textbf{Effects of the digest token scale factor}. As is mentioned in~\ref{sec: CabViT}, the characteristics of tokens in different semantic levels are different.  In ConvNets such as DensneNet~\cite{huang2017densely} and PeLeNet~\cite{wang2018pelee}, the feature from different layers are concatenated along channel dimension and then combined by conventional convolution operation. In ViT models, MHA is used to integrate different tokens according to their similarities. Tokens having different characteristics usually has low similarities, impeding the interaction of digest tokens and regular tokens in MHA.

Fig~\ref{fig:learnable_sacle} presents the visual results. From this figure we can find two problems:
1) Without using digest token scale factors, digest tokens present low similarity with regular tokens. Therefore, the information contained in digest tokens are hard to be integrated into regular tokens. 2) With the depth goes deeper, the difference between digest tokens and regular tokens become more obvious. This two problems are well solved by introducing learnable scale factor to adjust digest tokens.
\fi

\textbf{Effects of the digest token scale factor}. Fig~\ref{fig:learnable_sacle} shows the attention score maps of several CabViT blocks trained with and without digest token scale factors. It can be seen that without this factor, the attention between digest tokens and regular tokens are generally weak, and become weaker as the depth goes deepr ( down on the figure). This verifies our hypothesis that characteristics of tokens in different levels can be very different. Comparing the two columns of attention maps, it clearly shows that the scale factor helps increasing the correlation between regular tokens and the digest tokens, thus encourages the reuse of previously generated tokens.

\textbf{Faster convergence}. We show the loss curves of several models in Fig~\ref{fig:convergent_curves}, which verifies our claim that our model converges faster. Compared with ConvNext, ConvSwin loss drops slower in the first 25 epochs but has lower final loss. Similarly, ConvViT also does not drop fast in early stage. Benefiting from dense connection design, the loss of our CabViT-H is always the lowest.

\textbf{From baseline to CabViT-Hs}. The high performance of CabViT-H models are based on two key points, 1) combining strengths of ConvNets and ViTs; 2) dense but not heavy cross block attention design. Table~\ref{tab:ablation_components} displays the process of how we integrate the two key points in designing our models. Step 1 provides a baseline micro model to speed up experiments. Step 2 uses the simplest way to build a hybrid model, which inherits some advantages of ConvNets and ViTs, achieving promising performance. In step 3, based on computational complexity analysis in section~\ref{sec:computational_complexity}, window attention is replaced with global attention, which improves 0.6\% accuracy while introducing very limited extra cost. Step 4 attempts to add cross attention among blocks without adjusting digest tokens, which slightly improves performance. Step 5 adopts learnable scale  factors to adjust digest tokens and improves by 0.4\%. Up to now, the modified model outperforms our baseline by 2.8\%, at the cost of more parameters and higher computational cost compared with baseline. Step 6 introduces TME module to enhance models and also reduces width and depth to get a light weight model. Benefiting from TME module, the new light weight model has fewer parameters and acceptable computational cost, while keeping high accuracy. Finally, we scale up the light weight model and get CabViT-H-L2, which achieves the best trade-off between accuracy, model size and computational cost in its model size class.

\subsection{Inference speed comparison}
Here, we compare our model with other two recently proposed hybrid models to verify its inference efficiency. As shown in Fig~\ref{fig:infe_speed}, EfficientFormer-L3 and CabViT-H-L2 achieve faster speed compared with Next-ViT. EfficentFormer-L3 and our model have similar computational cost, but our CabViT-H-L2 has faster speed. Benefiting from the cross attention design has better parameter efficiency, CabViT-H-L2 is build with fewer layers and channels. We conjecture this is the main reason that why CabViT-H-L2 has faster speed than EfficientFormer-L3. From CabViT-H-L2 to EfficientFormer-L3, inference speed drops by 57.5\%, 11.6\% and 11.0\% on three devices.

\begin{figure}
\centering
\includegraphics[width=3.4in]{figures/infe.pdf}
\caption{ Inference speed comparison with other two typical hybrid Models. All three models are tested using $224\times224$ images and Pytorch-1.8.0 without batching or any other optimization. EfficientFormer-L3 and Next-ViT-S are tested using their source codes. FPS denotes frames per second.}
\label{fig:infe_speed}
\end{figure}

\textbf{}

\section{Discussions}

In this paper, we present a new type of attention patterns in transformer based models for computer vision.  The new pattern reuses existing tokens from previous blocks to create attentions across potentially different semantic levels, and is proved to be effective in different model scales and different vision tasks. For future directions, we propose to further mix the design with prior work that sparsify spatial attention patterns, which may produce even more efficient model backbones for various applications.

{\small
\bibliographystyle{ieee_fullname}

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Put some action to the plan! Raise 'em up!
The month of January is winding down already. It's time to make sure you are putting some shoe leather on your monetary plans for 2014. Get on the computer and increase the percentage that you are saving in your company retirement plan. Retirement age will get here faster than you realize, and cost of living expenses only go up. Get yourself ready. Raise your long term savings and investments.
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Have You Gotten Sick & Tired Yet?
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Try it. Make your list of Tolerations for 2014 and work it!
Wherever You Go – There You Are!
I was driving down the freeway and saw a billboard that read "Eliminate Your Debt. File Bankruptcy" along with an ad for a bankruptcy attorney that promised to make your debt problems go away.
As I read it at 70 miles an hour, it hit me how finances is another place that a misguided world is looking for a fast solution to a problem that was built with years of poor financial choices (overspending and undersaving one day at a time has put most of us in the situations that we are in today). And just like an over-eater who wants to lose weight, the solution cannot be found in a quick fix surgery or magic pill. Even those who undergo surgery can only have true success when there is a lifestyle change in eating and exercise habits. So too must you make a true lifestyle change to master your finances. Instant debt elimination without a change of better spending habits will not keep you from finding yourself in the exact same situation 5 years from now.
Commit to making better financial choices. Save more this year than you did last year. Get credit card debt out of your life one card at a time. Review your statements and bills to find those little fees and extra charges that are eating away at what you could be saving for tomorrow. Commit to paying no more late fees or overage fees, and commit to earning interest rather than paying it. You can do it. It all starts with a decision.


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