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Grable Betty: more books (101)
America I Love You (from Tin Pan Alley) by Edgar Leslie, Arthur Gottler, et all 1970
BETTY GRABLE THE HOUSE WITH THE IRON SHUTTERS by Heisenfelt Kathryn, 1943
PHOTOPLAY August 43 featuring Janet Blair on the cover. Articles & photos on Ingrid Bergman, Cary Grant, Gary Cooper, Rita Hayworth, Victor Mature, Betty GRable & Harry James, Alice Faye, Ann Sothern, Larraine Day, Joan Fontaine & Orson Welles in Jane Eyre, Spencer Tracy, Deanna Durbin, Bette Davis. Full page ad for Humphrey Bogart in Action In The North Atlantic and Gene Tierney in Heaven Can Wait. by Fred Sammus, 1943-01-01
Famous Stars Favorite Foods ( with two hundred thirty-six Famous stars Photographs ) & two Hundred eighty Personal Recipes includes Clark Gable, Judy Garland, Betty Grable, Oliver Hardy, Bob Hope, Tyrone Power, Tex Ritter, Jane Withers, Loretta Young , by Editior , with foreword Fannie Sniff, 1938
Vintage Betty Grable Autographed Publicity Photograph (Movie Memorabilia) by Betty Grable, 1948
Photoplay Magazine April 1949 Issue with Betty Grable Front Cover. by various or unknown, 1949
PHOTOPLAY Dec. 1944 featuring Betty Grable on the cover. Articles & photos on June Allyson, Judy Garland, Rita Hayworth, Bing Crosby, Joan Fontaine, Jeanne Crain, Gary Cooper, Bob Hope, Maria Montez. Full page ad for ABBOTT AND COSTELLO in Lost in A Harem and Gregory Peck in Keys Of The Kingdom. by Fred Sammus, 1944-01-01
PUT YOUR ARMS AROUND ME, HONEY From the 20th Century-Fox Picture Coney Island Starring Betty Grable, George Montgomery and Cesar Romero by Albert Von Tilzer (Music) Junie McCree (Words) , 1937
WHAT DID I DO? (From "When My Baby Smiles At Me"), Betty Grable & Dan Dailey on Cover, Sheet Music by Lyrics by Mack Gordon, Music by Josef Myrow, 1948
Betty Grable Bathing Suit Pinup: An entry from Gale's <i>American Decades: Primary Sources</i>
Hit Parader April 1951, Betty Grable cover, Vol IX, No. 5
What Did I Do ("When My Baby Smiles At Me"; Betty Grable & Dan Dailey on Cover) by Lyrics-Mack Gordo, Music-Josef Myrow, 1948
I Wish I Knew (Sheet Music) BETTY GRABLE & DICK HAYMES COVER from Billy Rose's Diamond Horseshoe by Mack; Harry Warren Gordon, 1945
lists with details
BETTY GRABLE PHOTO TRIBUTE
Galleries include photos taken to raise troop morale during World War II, famous pinup shots, magazine covers and various promotional images.
http://vintagemoviefan.freehomepage.com/
Extractions: BETTY GRABLE - SWEET ANGEL BETTY GRABLE PHOTO TRIBUTE BETTY GRABLE PHOTO GALLERY BETTY GRABLE AND THE WAR EFFORT BETTY GRABLE VIDEO CLIPS ... BETTY GRABLE BOOKS AND MEMORABILA Welcome to the Betty Grable photo gallery. This website is dedicated to celebrating the career of the wonderful Betty Grable in pictures. Ruth Elizabeth Grable was born in St. Louis on 18th December, 1916 to John Conn (always known as Conn) and Lillian. Betty was the third child to Conn and Lillian. Her sister Marjorie was born in 1909 and her bother John Karl was born in 1914, but died before he was 2 years old. Lillian was determined to see a daughter in show-business. After trying and failing with her first daughter Marjorie, Lillian set her sights on Betty to fulfill her mother's ambitions. Fortunately Betty was enthusiastic, and had the talent, so her early years were devoted to dancing lessons, learning to play the saxophone and singing. After a family vacation in 1929 to California, Lillian and Betty decided to stay, and try to get Betty into movies and eventually to the studios. Betty attended the Hollywood Professional School, took dancing lessons at the Ernest Blecher Academy and was coached at the Albertina Rosch School . Soon after her thirteenth birthday she answered a call and was accepted from Fox films to appear in the chorus of Let's Go Places . This number was a black face number, although it was finally a start in the movies. The legal minimum age for chorus work at the time was 15, but Betty had false papers stating her age was 15. Lillian convinced that her daughter was definitely worth more, hounded the execs at
Betty Grable Photo Gallery
Famous photograph of betty grable s legs, and other parts too.
http://www.suspense-movies.com/stars/betty-grable/
Betty Grable — Infoplease.com
December 18 Birthdays Steven Spielberg December 18 birthdays Steven Spielberg, Ty Cobb, Paul Klee, Brad Pitt, betty grable, Keith Richards, Ossie Davis,
http://www.infoplease.com/ipea/A0154758.html
Extractions: Arts and Entertainment Entertainment Biographies G Betty Grable actress Born: Birthplace: St. Louis, Missouri Vivacious leading lady of Fox movie musicals, who began as a chorus girl and became famous for her legs, which helped make her the top pinup girl of World War II. Her films include The Gay Divorcee Tin Pan Alley (1940), and Pin-Up Girl Betty Grable Archive Photos Died: Robert Goulet G Katharine Graham Lana Turner Biography (Actor) - Biography of Lana Turner, The actress discovered at Schwab's Drugstore Victor Mature - Victor Mature actor Born: 1/29/1913 Birthplace: Louisville, Kentucky This hunky Hollywood leading ... Lew Wasserman 2002 Deaths - Lew Wasserman Age: 89 legendary Hollywood mogul who served as chairman and CEO of MCA for four ...
Betty Grable - Dating, Gossip, News, Photos
betty grable pictures and biography. Who s Dated Who feature on betty grable including relationships, couples, pictures, biography, photos, pics, news,
http://www.whosdatedwho.com/celebrities/people/dating/betty-grable.htm
Extractions: var currentUser = new User('0'); logger.level = LoggerLevels.FATAL; Please select Dick Haymes George Stevens John Kennedy Oleg Cassini Ronald Reagan Rory Calhoun Jackie Coogan Artie Shaw Buster Crabbe Desi Arnaz Mickey Rooney Red Barry Tyrone Power Victor Mature George Raft Harry James Post a Comment View All Comments (1) posted by Buddy Beck I think Betty Grable was the second most popular fem star after Shirley Temple and just ahead of Doris Day posted 52 days ago Latest News Top Stories Sammy Davis Jr. widow sues over legacy...
Pat Kirkwood: 'Britain's Betty Grable' - Obituaries, News - Independent.co.uk
It led to her being described as Britain s betty grable but she hated references to her millionpound legs, It did make me cross. They are simply things to
http://news.independent.co.uk/people/obituaries/article3286797.ece
Extractions: var contextSectionId = '1164'; var contextName = 'independent_www_news_obituaries'; Click here... Search Search Go Independent.co.uk Web What are these? Thursday, 27 December 2007 Patricia Kirkwood was born, the daughter of a shipping clerk, in Pendleton, about three miles from Manchester's city centre, in 1921. Whilst on holiday with her parents in the Isle of Man, she took part in a talent contest and as a result, was asked to sing on the BBC's Children's Hour. In 1936, she played variety at the Hippodrome, Salford where she was billed as "The Schoolgirl Songstress". The following year, she played Dandini in Cinderella in a West End pantomime. Kirkwood's potential was obvious to all: she could act, dance and sing; she spoke well; and she had a gorgeous figure. She appeared with success in the films Save A Little Sunshine (1937) and Me And My Pal (1938) and made her first record, "Hurry Home".
Betty Grable - Rotten Tomatoes Celebrity Profile
Check out Rotten Tomatoes betty grable celebrity profile, celebrity gossip, movies, photos, biography, pictures, news, blogs, forums and more!
http://www.rottentomatoes.com/celebrity/betty_grable/
Betty Grable Finally Dances
by Martha P. Nochimson Analysis of the film That Lady in Ermine and betty grable.
http://www.sensesofcinema.com/contents/00/5/grable.html
Extractions: Sacher-Masoch By Martha P. Nochimson This paper was presented at the Society for Cinema Studies Conference 2000 at the Congress Plaza Hotel, Chicago on March 11, 2000, as part of the panel I2: Giving Hollywood the Slip(page): The Carnivalesque in Musical Movies Martha P. Nochimson has written on soap opera and on David Lynch and is working on a third book about the rhetoric of the onscreen couple. There's something carnivalesque about the very phenomenon of American movie stardom, with its odd fusions of contradictory character elements: and certainly there is something contradictory about Betty Grable's comparatively long World War II reign as queen of Hollywood musicals. Grable's stardom was built on a discourse of the domestic, the cherubic, but Grable could become threatening when her abundant physical energy took on a life of its own. Between sugar and threat, Grable paradoxically amalgamated a dangerous, raw energy with an image of the safe mainstream blonde. Her career charts an interesting course between the disparate poles of her attraction. Grable's famous World War II pinup, her most widely circulated image
100 Photographs That Changed The World By Life - The Digital Journalist
betty grable 1942 World War II took American boys to farflung places and some betty grable and her million-dollar legs were the perfect balm for what
http://digitaljournalist.org/issue0309/lm13.html
Betty Grable - Films As Actress:
betty grable was truly a potent force in 1940s Hollywood. For 11 consecutive years (1941–51), she ranked among the film industry s top stars.
http://www.filmreference.com/Actors-and-Actresses-Ga-Ha/Grable-Betty.html
Extractions: Film Reference Actors and Actresses Ga-Ha Nationality: American. Born: Ruth Elizabeth Grable in St. Louis, Missouri, 18 December 1916. Education: Attended Mary Institute, St. Louis; Hollywood Professional School; Ernest Belcher Academy; Albertina Rasch School. Family: Married 1) the actor Jackie Coogan, 1938 (divorced 1941); 2) the musician Harry James, 1943 (divorced 1965), daughters: Victoria, Jessica. Career: Du Barry Was a Lady Twentieth Century Hello, Dolly! Died: 2 July 1973. Betty Grable
Betty Grable Posters And Prints At Art.com
betty grable Posters and Prints Find betty grable Posters and Prints at Art.com or select a print or poster from Art.com s Galleries.
http://www.art.com/asp/display-asp/_/id--5422/Betty_Grable.htm
Extractions: search Subjects Artists Collections Best Sellers var SaveCartOnExistSessionID='C0986F5A2D354DA6B9F88483B88140E5'; home subjects people celebrity ... actresses betty grable gallery (8) Actresses Most Popular All Actresses All Actresses A Aaliyah Abbie Cornish Adriana Lima Aida Turturro ... Ava Gardner B Barbara Bel Geddes Barbara Carrera Barbara Feldon Barbara Hershey ... Brooke Shields C Calista Flockhart Cameron Diaz Candice Bergen Carey Lowell ... Cynthia Nixon D Dakota Fanning Dale Evans Dana Plato Daniela Bianchi ... Drew Barrymore E Eartha Kitt Eden Riegel Edie Falco Edith Piaf ... Evelyn Ankers F Fairuza Balk Faith Domergue Faith Ford Famke Janssen ... Franka Potente G Gabrielle Union Geena Davis Gena Rowlands Gene Tierney ... Gwyneth Paltrow H Halle Berry Hannah Spearritt Hayley Mills Haylie Duff ... Hunter Tylo I Illeana Douglas Ingrid Bergman Ingrid Pitt Ione Skye ... Izabella Scorupco J Jaci Velasquez Jaclyn Smith Jacqueline Bisset Jada Pinkett Smith ... Justine Waddell K Kaley Cuoco Karen Allen Karen Grassle Kate Beckinsale ... Kristin Scott Thomas...
Internet Broadway Database: Betty Grable Credits On Broadway
Official Broadway credits for betty grable, biographical information and other related facts.
http://www.ibdb.com/person.asp?id=42727
Betty Grable Quotes
betty grable quotes,betty, grable, author, authors, writer, writers, people, famous people.
http://thinkexist.com/quotes/betty_grable/
Extractions: Betty Grable quote Similar Quotes . About: Relationships quotes Being a Woman quotes Women quotes Add to Chapter... Betty Grable quote Similar Quotes Add to Chapter... Betty Grable quote Similar Quotes Add to Chapter... Betty Grable quote Add to Chapter... show_bar(362498,'it-s-loud-it-s-cheap-it-s-gaudy-it-s-like') Betty Grable quote Add to Chapter... show_bar(369509,'you-re-better-off-betting-on-a-horse-than-betting') Betty Grable quote Add to Chapter... show_bar(362132,'i-m-a-song-and-dance-girl-i-can-act-enough-to-get') Betty Grable quote Add to Chapter... show_bar(534900,'i-have-got-two-reasons-for-success-and-i-m') Submit a New Betty Grable quote Betty Gearen quotes Betty Gray quotes
Denny Jackson's Betty Grable Page
In reality, she cared more for her family and the family life more than stardom. In that way, she was a true star. betty grable PICS ARE HERE!! betty Pic 1
http://www.geocities.com/Hollywood/Hills/2440/index-g.html
Extractions: Ruth Elizabeth Grable was born on December 18, 1916, in St. Louis, Missouri. Her mother was a stubborn and materialistic woman who was determined to make her daughter a star. Ruth, who later became Betty, was enrolled in a renown dancing school at the age of 3. With her mother's guidance, Betty studied ballet and tap-dancing. At 13, Betty and her mother set out for Hollywood with the hopes of stardom. Lillian lied about her daughter's age, and Ruth landed several minor parts in films such as such as WHOPPEE!, NEW MOVIETONE FOLLIES, HAPPY DAYS and LET'S GO PLACES, all in 1930. In 1932, she signed with RKO Studio. The bit parts continued for the next three years. Betty finally landed a substantial part in 1934's BY YOUR LEAVE. One of her big roles was in COLLEGE SWING, released in 1936. Unfortunately, the public didn't seem to take notice. She was beginning to think she was a failure. The next year, she married former child star Jackie Coogan. His success boosted hers but they divorced in 1940. When she landed the role of Glenda Crawford in DOWN ARGENTINE WAY (1940), the public took notice of this shining bright star. Stardom came through comedies such as CONEY ISLAND and SWEET ROSIE O'GRADY, both in 1943.
Solid! -- Betty Grable
Best remembered for her famous legs, betty grable was one of Hollywood s top stars during the 1940s, appearing in many of the era s most popular musicals,
http://www.parabrisas.com/d_grableb.php
Extractions: Search Best remembered for her famous legs, Betty Grable was one of Hollywood's top stars during the 1940s, appearing in many of the era's most popular musicals, including Springtime in the Rockies Moon over Miami , and Down Argentine Way . Sadly, Grable's vocal talents were never exploited to their fullest potential. Though she worked as a big band vocalist in the 1930s she was never recorded, and during the height of her popularity she was contracted to 20th Century Fox, which did not allow its stars to pursue recording careers. Raised in the Mid-West, Grable was groomed to be a star by her mother. As a child she learned to sing, dance and play the saxophone. After a family vacation to California in 1929 Grable and her mother decided to move to the West Coast to try their luck in Hollywood, leaving her father and sister behind. Grable's first professional work came as a chorus girl at Fox studios. Though only thirteen at the time she lied about her age. When the studio discovered the truth she was fired. Undaunted, she continued auditioning, finding bit player work in other films, including a stint as a Goldwyn Girl, in the company of Lucille Ball and Paulette Goddard.
Betty Grable Relationships
Find out how betty grable handled her relationships and test what you and betty grable have going in love, marriage, friendship, partnership,
http://famous-relationships.topsynergy.com/Betty_Grable/
Extractions: /*********************************************** * Rich HTML Balloon Tooltip- © Dynamic Drive DHTML code library (www.dynamicdrive.com) * This notice MUST stay intact for legal use * Visit Dynamic Drive at http://www.dynamicdrive.com/ for full source code ***********************************************/ Group: Top Synergy Relationships Relationships with The Famous Top Synergy Connections CONTACT US ... CREDIT Relationships Desi Arnaz P Mickey Rooney P ... Friends Betty Grable in Relationships Her desire for harmonious relationships and surroundings is so strong that she avoids personal confrontation or any expression of intense, unpleasant emotions. Betty Grable is an idealist who would like to paint the world in pastel colors, and live in peace and harmony with others at all times. She is able to see points of similarity and unity with people who are vastly different from her, and befriend many different types of people. She is a considerate and thoughtful friend, and has a real flair for making others feel accepted and appreciated. Inwardly, Betty Grable is often torn with indecision and is much more at odds with herself than others would ever guess based on her flawless disposition. Betty Grable may have done extensive traveling in her early years, or in some way had a background which enables her to understand and identify with many different types of people or cultures.
Betty Grable TV Listings - Find Betty Grable On TV | TV-Now.com
Find betty grable TV Listings, Bio and more from TVNow.com.
http://www.tv-now.com/stars/grable.html
Extractions: Celebrities A-Z Channel Title Buy FMC Thu, Feb 7, 7:30 AM Four Jills in a Jeep Buy FMC Fri, Feb 8, 12:00 PM My Blue Heaven Buy Betty Grable Biography, TV Listings and More from TVGuide.com var strAct = document.forms[0].action; var iOC = strAct.indexOf("/OneCeleb.aspx"); document.forms[0].action = "/stars/" + strAct.substring(iOC); Advertisement
The Most Famous Pinups Of WW II
betty grable (12 K) Not just any picture of betty, but this picture (above), was probably . In November of 1937 betty grable and Jackie Coogan married.
http://www.skylighters.org/famouspinups/index.html
Extractions: Few GIs in World War II were ever very far from one of these photos. All three images — enduring to this day — exuded a glorious, youthful sexuality that enthralled lonely soldiers from Fort Dix to the Siegfried Line, and formed the basis for the Hollywood "sex goddess" image that was to dominate the 1940s and 1950s. The origin of the Hollywood "sex goddess" can be traced back in many respects to the great pinups of the war years, which culminated in its ultimate visualization in the post-war (1946) film Gilda , starring none other than wartime bombshell Rita Hayworth. Gilda has been called "superlative trash," but for all its artistic shortcomings it is the film that made Rita Hayworth a superstar before the word superstar was popularized. At the time she was one of the most popular — possibly THE most popular — of the World War II pinup queens. (Even a test version of the atomic bomb was named Rita and had a picture of her emblazoned on it.) It is not surprising that Gilda was written from the beginning as a "sex goddess" exploitation film — specifically for Miss Hayworth. Shooting started on the film even before the male star had been selected! After the war, thousands of returning servicemen took their wives and girlfriends to see the film, no doubt remembering her stunning images from countless barracks walls. The success of this fantasy and of the Gilda character itself was revealed in probably the most famous quote from the film:
The Betty Grable Collection, Vol. 1 By Irving Cummings On DVD Starring J. Carrol
Fourdisc set includes Down Argentine Way, Moon Over Miami, The Dolly Sisters, and My Blue Heaven.
http://www.moviesunlimited.com/musite/product.asp?sku=D63950
Dr. X's Free Associations: Photo Of The Day: Betty Grable
http//www.typepad.com/t/trackback/1069530/25075588. Listed below are links to weblogs that reference Photo of the Day betty grable
http://drx.typepad.com/psychotherapyblog/2008/01/photo-of-the-11.html
Betty Grable - Overview - MSN Movies
betty grable overview biography, filmography, interviews clips, photos, news, awards worked with, message board and more.
http://movies.msn.com/celebs/celeb.aspx?c=15952


The assignment will focus on a number of islands (targeting a total of 10-12) based on the following criteria (a) the size of PLN grid (5-150MW); (b) high average generation cost; and (c) electrification rate below 90%. The selected islands will be communicated to the Consultant before the start of the assignment. They are all located in the Eastern Islands and were divided into three groups, (i) islands with a small installed generation capacity and limited electrification, (ii) islands with an installed generation capacity of 50-150 MW with only diesel generation and very high average generation cost and (iii) islands with an installed generation capacity of 50-150 MW with diesel and coal generation and high average generation cost.
ESMAP seeks to build on the initial success of ENERGYDATA (a data publishing platform that hosts energy data provided by the WBG and other development partners) to develop new apps to respond to existing and upcoming needs from our clients, the private sector, and key development partners, and also consolidate those apps already developed and provide a more standardized user experience. ESMAP proposes to issue a new Master Agreement contract covering the development of new apps, consolidation of existing apps, curation of new and existing datasets, user interface improvements and routine maintenance of the ENERGYDATA site and all its current and future subdomains.
Assignment Description: The objective is to conduct a Climate Risk Assessment for the Mpatamanga Hydropower Project in Malawi, using the BEta version of the World Bank's Hydropower Sector Climate Resilience Guidelines.
The proposed study will (a) determine the cost of electricity service in Ethiopia and the revenue requirement of the electric utilities during 2018-2030; and (b) analyze the current electricity tariff structure and propose options for tariff optimization.


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redpajama_set_name: RedPajamaGithub


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Mariah Carey: The real story
Behind the scenes of her busted marriage with Sony Music chief Tommy Mottola and her controversial new album ''Butterfly''
September 26, 1997 at 04:00 AM EDT
Welcome to the newly single life of Mariah Carey. It is free and entangled, exhilarating and embattled. And it never stops moving.
At a Manhattan photo studio in late August, Carey is torn between posing for shots and dealing with the prelaunch mania surrounding her fifth album, Butterfly. Cell phones ring incessantly. Carey's new manager, Hollywood power broker Sandy Gallin, swings by to nail down details of her appearance at the MTV Video Music Awards. A stylist and hair and makeup artists commandeer the pop star's free minutes. At one point, Carey — who, these days, oscillates between moments of infectious playfulness and emotional rawness — shoves a tape into the VCR. It's the video for a remix of Butterfly's first single, "Honey." In it, she pinches the cheeks of rappers Sean "Puffy" Combs and Mase — a tender gesture, but one that also conjures The Godfather. "That freaked them out," she says, laughing. "They take that [gangster] stuff seriously."
At other moments, Carey disappears to her dressing room with a cell phone. One wireless conversation with a Butterfly producer erupts into a dispute. To the chagrin of her makeup artist, tears soon streak Carey's face. "Being able to handle things on my own is good," she explains later. The singer is in the midst of a complicated breakup from husband Tommy Mottola, the president and COO of Sony Music Entertainment and the man who, until this year, has overseen every aspect of her career. And behind the scenes, the split has sparked angry accusations of infidelity, abusive behavior, and artistic suppression. Carey's tears, she says, were inevitable: "It's so easy to become overwhelmed during the state I'm in right now, I just couldn't help it."
Around midnight, the 15-hour shoot is finally over, but the 27-year-old Carey's not ready to call it quits. Just before 1 a.m., she takes off for the Palladium nightclub with an entourage of friends. Carey throws off the day's stress by throwing in a tape by the Jerky Boys, whose antic juvenile humor proves the perfect release. "I love these guys," she says, mimicking along with the pranksters' mischievous phone voices.
At the club, Carey and party slip through a private entrance into near chaos. So many people fill every corridor that bodyguards are exercising crowd control backstage. Carey, however, makes her way through the multitude like an habitue. She kisses the night's headliner, the manically dreadlocked Busta Rhymes. Heads swivel as she sidles on, greeting a hot record producer here, an up-and-coming hip-hopper there, before climbing upstairs to a private office, where a bucket of Cristal awaits.
Carey hangs out in this cramped hideaway for most of the night. Her two bodyguards block the door, but a few VIP rappers, like Combs and Missy "Misdemeanor" Elliott, cameo in to say hi. "Mariah, she listens to rap. She's straight up just cool," says Elliott, a friend since the two cowrote a song for Butterfly. Contrary to tabloid innuendo, it's hardly the wild gangsta-rap atmosphere in which Carey has supposedly immersed herself. In fact, the only threatening thing is the guy blowing chunks just outside the office door. Carey — the pop diva who, in the past, has seemed so inaccessible — is experiencing it all. And as 3 a.m. approaches, it's still four hours before the insomniac will hit the sheets. She promises to make a 3 p.m. interview scheduled for the next day. "That's bright and early for me," she warns.
''Closer to Free''
Free and Naked
Colour Me Free
Free Wired
Live Your Life Be Free
Free Tunes Bad Vibes
No Free Will
Home of the Free and the Broke
Mariah Carey pushing new, 'interactive' 'melodic beverage' called Butterfly
Free and Equal Blues
Clive Owen to fly back to Broadway in revival of M. Butterfly
Free (Wheeling) Willie
Free Launch
The 5 best animated episodes of 2018 (that aren't the Adventure Time finale)

redpajama_set_name: RedPajamaCommonCrawl


noun oh·rina \ˌo-ree-naa\ the faculty or phenomenon of finding things initially anticipated to be agreeable to contain an element of surprise in contrast with one's initial excitement.
The exact origins of the phrase "ohrina" are not know with absolute certainty. However, it is understood that in the early part of the second decade of the second millennia (in accordance with the Gregorian calendar) a young betty named Rina returned to the United States of America having spent several years in East Asia. She would tell tales of teaching English as a second language, but her exact whereabouts and activities have been long subject to intellectual discourse. Once settled in the north western region of the village of Brooklyn (see "Breuckelen"), "Our Rina" would bring hand gestures and click consonants wherever she travelled. What is known with some certainty is that she was twice early for a date and once claimed to accidentally insist that a man with years of dedicated vegetarianism consume what was once a beautiful living thing, probably called Daisy.
Get a Ohrina mug for your mate Larisa.


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Ytterbium compounds are chemical compounds that contain the element ytterbium (Yb). The chemical behavior of ytterbium is similar to that of the rest of the lanthanides. Most ytterbium compounds are found in the +3 oxidation state, and its salts in this oxidation state are nearly colorless. Like europium, samarium, and thulium, the trihalides of ytterbium can be reduced to the dihalides by hydrogen, zinc dust, or by the addition of metallic ytterbium. The +2 oxidation state occurs only in solid compounds and reacts in some ways similarly to the alkaline earth metal compounds; for example, ytterbium(II) oxide (YbO) shows the same structure as calcium oxide (CaO).

Halides

Ytterbium forms both dihalides and trihalides with the halogens fluorine, chlorine, bromine, and iodine. The dihalides are susceptible to oxidation to the trihalides at room temperature and disproportionate to the trihalides and metallic ytterbium at high temperature:

3 YbX2 → 2 YbX3 + Yb (X = F, Cl, Br, I)

Some ytterbium halides are used as reagents in organic synthesis. For example, ytterbium(III) chloride (YbCl3) is a Lewis acid and can be used as a catalyst in the Aldol and Diels–Alder reactions. Ytterbium(II) iodide (YbI2) may be used, like samarium(II) iodide, as a reducing agent for coupling reactions. Ytterbium(III) fluoride (YbF3) is used as an inert and non-toxic tooth filling as it continuously releases fluoride ions, which are good for dental health, and is also a good X-ray contrast agent.

Oxides

Ytterbium reacts with oxygen to form ytterbium(III) oxide (Yb2O3), which crystallizes in the "rare-earth C-type sesquioxide" structure which is related to the fluorite structure with one quarter of the anions removed, leading to ytterbium atoms in two different six coordinate (non-octahedral) environments. Ytterbium(III) oxide can be reduced to ytterbium(II) oxide (YbO) with elemental ytterbium, which crystallizes in the same structure as sodium chloride.

Borides

Ytterbium dodecaboride (YbB12) is a crystalline material that has been studied to understand various electronic and structural properties of many chemically related substances. It is a Kondo insulator. It is a quantum material; under normal conditions, the interior of the bulk crystal is an insulator whereas the surface is highly conductive. Among the rare earth elements, ytterbium is one of the few that can form a stable dodecaboride, a property attributed to its comparatively small atomic radius.

Other inorganic compounds

Ytterbium(III) nitrate is the nitrate of ytterbium in the +3 oxidation state. The compound forms colorless crystals, dissolves in water, and also forms crystalline hydrates. It can be obtained by reacting ytterbium and nitric oxide in ethyl acetate:

 Yb + 3 N2O4 → Yb(NO3)3 + 3 H2O

Ytterbium phosphide is the phosphide of ytterbium in the +3 oxidation state. It can be obtained by reacting ytterbium and phosphine in liquid ammonia to form Yb(PH2)2·5NH3, which can be decomposed to obtain ytterbium phosphide:

Yb(PH2)2•5NH3 → Yb(PH2)2 + 5NH3

2Yb(PH2)2 → YbP + 2PH3 + H2

The compound forms black crystals of a cubic system, space group Fm3m.

Ytterbium(III) acetate is an the acetate of ytterbium in the +3 oxidation state. It has colorless crystals that are soluble in water and can form hydrates. Ytterbium acetate can be used as a raw material for the synthesis of upconversion luminescent materials;it can also be used as a catalyst for some specific organic reactions.

Alloys

Ytterbium dirhodium disilicide

Ytterbium dirhodium disilicide (YbRh2Si2), also abbreviated YRS, is a heavy fermion solid state compound of ytterbium, rhodium and silicon. It becomes superconducting when cooled to 2 mK. Just above this temperature the heat capacity is extremely high, and the electrons behave as if they were 1,000,000 times heavier than they really are.

Ytterbium-bismuth-platinum

Ytterbium-bismuth-platinum is an intermetallic material which at low temperatures exhibits an extremely high value of specific heat, which is a characteristic of heavy-fermion behavior.
Ytterbium-bismuth-platinum has a noncentrosymmetric cubic crystal structure; in particular it belongs to the ternary half-Heusler compounds.

Ytterbium-gallium-germanium

Ytterbium-gallium-germanium (also called YbGaGe) is an alloy of ytterbium, gallium and germanium. It sparked interest because one group of researchers reported that it exhibits zero thermal expansion, while being conductive. Such materials have applications in space and other environments where low thermal expansion materials are required. However, subsequent measurements by other groups were not able to reproduce those results, but rather found expansion coefficients similar to copper.

See also

 Thulium compounds
 Lutetium compounds

References

Ytterbium compounds
Ytterbium
Chemical compounds by element

redpajama_set_name: RedPajamaWikipedia


text:
Circuit Upholds Picketing Ordinance In Raleigh, N.C.
Courthouse News Staff / April 18, 2008
RICHMOND, Va. (CN) - The 4th Circuit upheld the constitutionality of a picketing ordinance in Raleigh, N.C., that requires demonstrators to notify city officials before staging public protests.
The ruling rejects the First Amendment claims of Dennis Green, who picketed outside a Promise Keepers event at the RBC Center with 10 other protesters in 2003.
A security officer booted the group from a public sidewalk because its members had not notified the city about the protest, in violation of Raleigh's picketing ordinance.
Green claimed the ordinance violated his right to free speech and peaceful assembly.
But the notice requirement imposes a minimal burden on protesters, the court ruled, because city officials have no discretion to deny any protest request - they must grant a receipt of notice immediately. Demonstrators may notify city officials in person, by phone or via fax, without giving advance notice, completing a lengthy application or paying fees.
"Given these minimal impositions, we cannot hold that the notice requirement burdens substantially more speech than necessary to further the city's interest in protecting law enforcement officers, motorists, pedestrians, the picketers themselves and those accessing businesses near the site of a demonstration," Judge Diana Gribbon Motz wrote for the unanimous panel.
The court rejected his other arguments, saying he failed to show that the picketing ordinances are unconstitutional.
Sixth Circuit skeptical of buffer zone at Louisville abortion clinic December 8, 2022
After independence, Ukraine turned into a 'sick man of Europe' as democracy faltered October 30, 2022
11th Circuit strikes down Florida town's portable sign ban for violating First Amendment June 28, 2022
Judge refuses to block San Diego ban on ghost guns October 20, 2021


This original watercolour is called Blue Belle. Should you wish be interested inpurchasing, please contact me. .
Limited Edition Watercolour Prints may be purchased unmounted and unframed direct from this website. Simply click on the drop down arrow.


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text:
Only holding # 13 Billboard for 1 week, BLACKPINK and Selena Gomez drop from the chart in the second week , will Ice Cream definitely be out of the chart next week?
Shop, no sidebar
Snax
Something weird has happened
You've just found the Loch Ness Monster
KBIZOOM
"On top of the music world"; BTS is becoming the center point of the U.S media
U.S. media such as Billboard, MTV, TIME, and VOGUE are dragging their attention to BTS's new album.
On August 24th (local time), Billboard published an article, titled, "BTS Takes Pride In Their Sense of Self in Vibrant Video for 'IDOL'". When Billboard introduced BTS's new album and new song "IDOL", they claimed, "In 2018, BTS is undeniably on top of the music world". They also added, "The hybrid styles are apparent not only in the video but also the song itself".
MTV also reported that "BTS Defies Expectations With Anthemic New Single 'IDOL'". In their article, they claimed that BTS had started a global festival based on Korean culture, and "BTS, driven by their undeniable creativity, passion, and ARMY of fans, is a movement, and "IDOL" is a thundering call to action — to get up, dance, and to love yourself".
In addition, many local media including Time, Vogue, and Hollywood Life also showed their interest in the collaboration of the global phenomenal group with Nicki Minaj. There were many feedbacks on BTS's title song, such as "Superstar BTS has taken a huge leap into the U.S main stream", "IDOL's music video is fantastic from beginning to end, and it shows a whole new dimension of sensation, from visual to performance" and "BTS has created a global sensation but they yet to forget their own identity".
Meanwhile, BTS has just released their repackage album "LOVE YOUSELF: Answer" on August 24th. It has topped iTunes' "Top Album Chart" in 65 countries, including the U.S., Canada and Japan, while the title song 'IDOL' topped the "Top Song Chart" in 66 countries and regions.
Source: Naver
Related Items:BTS
Knet has found Jennie's luxurious house in the first Vlog: The house's expensive value makes everyone jealous!
Korean netizens are angry that the reputations of Suga and V (BTS) are taken advantage of by their homeland
Korean actresses have Instagram with more than 10 million followers
BigFlo's Sungmin under fire for disrespecting Red Velvet on their Music Bank encore stage
[PANN] iKON's concert ticket sales seem to be a lie?
4 big questions for Kpop fans in 2021: will BTS win the Grammy? GOT7 and the "7 years" curse?
The way people in the West perceive Kpop has changed when BTS and Black Pink achieved success in the US in 2020....
Knet has a new concept for the top 5 K-pop girl groups "TeuReBleMaYeo" and worried about their future when the groups' contracts end?
Contracts of Kpop girl groups usually have a term of 7 years, do you realize that the contracts of the following groups...
A fanpage of 500 thousand likes of BLACKPINK came out to protect Nancy (MOMOLAND)
BLACKPINK's fan page has left a good impression for protecting Nancy after her sensitive images were leaked, even though before that the...
2020 – An excellent year for BLACKPINK
After 2019 with only one music product, Kill This Love, BlackPink had a remarkable comeback in 2020.
Jennie revealed the most difficult part about touring. The result was out of all predictions.
For Jennie, perhaps performing for hours on stage is not as difficult as this thing!
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Many people admire when Jennie (BLACKPINK) is truly living the life of a princess.
The recent actions of the Daegu Metropolitan Government against Suga and V are making...
In Korea, there are 8 actresses with 10 million followers on Instagram, of which...
The way people in the West perceive Kpop has changed when BTS and Black...
Seungri was accused of assaulting a JYP staff
Recently, Seungri is currently facing charges of assault. Recently, the Burning Sun scandal became...
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Breastfeeding, as mothers all know, is the source for a baby's development. However, your baby wants your milk at very odd hours.
While you sleep, eat, relax or work, there are different positions for you to breastfeed your baby. You may even find the position that works best for you and your baby. It could be your 'signature position'.
Your baby is on your lap while you hold him/her across your lap. You use both arms to support your baby's weight.
This breastfeeding position is similar to the previous one, but you use the opposite arm from where the baby feeds to support your breast.
While you relax, you can always place your baby on your stomach and breastfeed.
In this position, you hold your baby while the head is on a pillow to support the neck.
With this position, you lay down with your body and your baby's parallel. Ensure that you're comfortable breastfeeding while you lay down so that your baby will also be comfortable.
Also, don't rest on your arm, but on your pillow.
You can use a pillow or cushion to support your babies' heads while you feed them with the two breasts at the same time. Your hands should also support their upper backs or their heads.
It may seem challenging to breastfeed twins, but it's definitely possible!
In this position, you put your baby on your knees in an upright position, and you support your baby with one arm.


text:
\section{Introduction}
	\label{sec:intro}
	Object detection~\cite{lin2017feature,song2020fine,guo2020augfpn} is a fundamental task in the computer vision field, which attracts growing attention in recent years. A practical method to detect objects precisely can be useful in modern applications, such as surveillance video and robot navigation. Recent progress in object detection largely stems from the exploitation of deep convolutional neural network (CNN). Devising an effective CNN-based architecture is the mainstream approach for detecting objects across a wide range of scales. The modern detection framework can be categorized as one-stage approach and two-stage approach. The one-stage approaches such as YOLO~\cite{redmon2016you}, SSD~\cite{liu2016ssd}, FCOS~\cite{tian2019fcos} and CenterNet~\cite{duan2019centernet}, directly extract features to predict object classes and locations. In contrast, the two-stage approaches, \emph{e.g}\bmvaOneDot Faster R-CNN~\cite{ren2015faster}, and Cascade R-CNN~\cite{cai2018cascade}, firstly obtain the region of interests (ROI) by region proposal network and further generate refined bounding boxes and classes based on ROI. Both approaches make great progress in recent years.

	Currently, many methods~\cite{li2019scale,dai2017deformable,peng2019pod} expands the receptive fields for multi-scale feature learning and achieve encouraging results. For example, RFB~\cite{liu2018receptive} replaces the later convolution layers of SSD~\cite{liu2016ssd} with a multibranch convolutional block to enhance the features in multiple scales.
	Although these methods have made tremendous efforts for expanding the receptive fields of their detectors, how different kinds of convolutional kernels could affect FPN has not been fully investigated. To this end, we conduct extensive experiments to discuss the benefits and deficiencies brought by different combinations of convolutions.

	To explore the representation ability of features generated by different convolutions, we first embed the inception blocks into the conventional FPN. As shown in Figure~\ref{Inception FPN}, each inception block in the investigated inception FPN contains convolution filters with different kernel sizes (\emph{e.g}\bmvaOneDot, $1\times 1$, $3\times 3$, $ 5\times 5$). Better features can be effectively generated by a combination of different convolutions. Compared with the conventional FPN, the inception FPN enriches the spatial information by fully expanding the receptive fields, which significantly improves the detection accuracy.

	%

	However, the combination of filters with different kernel sizes obviously increases the overall computational costs. In addition, the difficulties for detecting objects in different natural images are exactly variant, which implies that not all objects need such a complicated computation for correct prediction. These observations further motivate us to introduce the dynamic mechanism~\cite{li2020learning,wang2018skipnet} into the inception FPN to balance the performance and the overall computation burden. As shown in Figure~\ref{Dynamic FPN structure b}, a learnable dynamic gate with negligible computational costs is inserted before an inception block in each lateral connection. The dynamic gate adaptively determines whether to execute the whole inception block based on the input. We perform experiments to show that the introduced dynamic gate can largely reduce the computational cost of the baseline model (\emph{i.e.}, the inception FPN) while maintaining similar high detection accuracy.

	In this paper, we first perform extensive experiments to investigate the impact of applying combinations of different convolutions on FPN. Although utilizing these convolutions improves the detection accuracy by enriching the spatial information, it also leads to a significant computational burden. Thus, we further propose DyFPN to overcome this problem. DyFPN adaptively determines whether to conduct the multiple convolutions based on the input images. The effectiveness of DyFPN is well evaluated on various backbone architectures. Experimental results on the MS-COCO benchmark show that replacing the baseline inception FPN with DyFPN consistently saves significant computational costs while preserving similar high accuracy.

	\begin{figure*}[tb]
		\centering
		\subfigure[inception FPN]{\label{Inception FPN}
			\includegraphics[height=3.2cm]{figure/inception_fpn.pdf}}
		\quad
		\subfigure[DyFPN]{\label{Dynamic FPN structure b}
			\includegraphics[height=3.2cm]{figure/dynamic_fpn_b.pdf}}
		\quad
		\caption{The comparison among the inception FPN and the DyFPN. The content in the dashed box represents the details of the inception block and dynamic block. The "G" denotes our proposed dynamic gate.}
		\label{fig:Inception and Dynamic network}
	\end{figure*}

	We organize the rest of the paper as follows. In Section~\ref{Related Work}, we investigate the related works on object detection and dynamic neural networks. Section~\ref{Methods} demonstrates the details of the investigated baseline inception FPN and our proposed DyFPN. Section~\ref{experiments} evaluates the inception FPN and DyFPN on benchmark datasets and various backbone architectures and Section~\ref{Conclusion} concludes this paper.

	\section{Related Work}
	\label{Related Work}
	\paragraph{Object Detection.} The objection Detection task aims at recognizing what the object is and where the object locates in an image. Benefit from the deep neural network, many methods~\cite{girshick2014rich,redmon2016you,lin2017focal} on object detection tasks have achieved impressive improvements in recent years. Faster R-CNN~\cite{ren2015faster} proposes an end-to-end detection approach by replacing Selective Search with a novel region proposal network. SSD~\cite{liu2016ssd} predicts a series of bounding boxes with different scales and aspect ratios from several feature layers. Cascade R-CNN~\cite{cai2018cascade} trains the model in a cascade manner with gradually increased IoU threshold. Exploiting features from different scales have been verified effective in detection task~\cite{liu2016ssd,pang2019libra,wang2019carafe,guo2020augfpn}. FPN~\cite{lin2017feature} constructs a bottom-up pathway, a top-down pathway, and lateral connections to fuse the features with different resolutions and scales in an efficient way. CARAFE~\cite{wang2019carafe} proposes an effective feature upsampling operator and integrates it into FPN to boost the performance. AugFPN~\cite{guo2020augfpn} refines the FPN using Consistent Supervision, Residual Feature Augmentation, and Soft RoI Selection, simultaneously. Libra R-CNN~\cite{pang2019libra} strengthens the multi-level features in the feature pyramid by using the integrated balanced semantic features.
	\paragraph{Dynamic Neural Networks.}
	Previous works on dynamic neural networks mainly focus on adjusting the architecture of models according to the input images~\cite{li2020learning,wang2018skipnet,huang2017multi,yuan2019s2dnas,wu2018blockdrop}. MSDNet~\cite{huang2017multi} adopts dynamic evaluation, which tackles easy examples at early classifiers while handles hard examples with the whole network. S2DNAS~\cite{yuan2019s2dnas} proposes a method to transform various CNN models into dynamic models without manually re-designing. For image classification, ConvNet-AIG~\cite{Veit2018} designs gates to determine whether to execute or skip the specific layers, which enables the dynamic adjustment of inference graphs conditioned on the input features. Dynamic routing~\cite{li2020learning} searches scale transform paths on the fly for semantic segmentation. Different from the previous dynamic networks, our DyFPN aims at alleviating the computational costs caused by the combinations of different convolutions while still benefiting from enriched spatial information.

	%

	%

	%

	\vspace{1cm}

	\section{Methods}
	\label{Methods}

	\subsection{Inception FPN}
	\label{The Basic Building Block}
	By effectively leveraging features from different layers in the backbone model, the feature fusion method is widely used to improve the network performance~\cite{wang2019learning,kim2018parallel,lin2017feature}. An efficient method to fuse features is building a feature pyramid. Typically, for a list of input features with different scales, the FPN takes a series of features $\{F_2, F_3, F_4, F_5\}$ as input and outputs the aggregated features $\{P_2, P_3, P_4, P_5\}$ as follows:
	\begin{align}
		P_{5} = & f_5(F_5),\\
		P_l = & f_l(F_l) + R(P_{l+1}), \ l=2,3,4,
	\end{align}
	where $l$ denotes the level of the pyramid. $R$ denotes the resizing operation to generate the features that are respectively of the same spatial sizes. The lateral connection $f_l(\cdot)$ is typically a 1$\times$1 convolutional layer which lacks sufficient spatial information for recognizing objects. To this end, we investigate an intuitive model called inception FPN, which enriches the spatial information of the feature pyramid by expanding the receptive fields. The inception FPN utilizes the inception block in the lateral connection and achieves significant advances in detection accuracy. As shown in Figure~\ref{Inception FPN}, the inception block consists of a set of convolutions with kernel sizes of 1$\times$1, 3$\times$3, and 5$\times$5 and sums the features from different convolutions as follows:
	\begin{equation}
		f_l(F_l) = \textit{Conv}_{1\times1}(F_l) + \textit{Conv}_{3\times3}(F_l) + \textit{Conv}_{5\times5}(F_l),
	\end{equation}
	where $l=2,3,4,5$. With convolutions of different kernel sizes in the lateral connection, the extracted features at each level benefit from different receptive fields. In the section~\ref{experiments}, we conduct extensive experiments to demonstrate that gradually replacing the 1$\times$1 convolutions with the more convolutions (\emph{i.e.}, 1$\times$1, 3$\times$3, 5$\times$5, and their dilated variants) can obtain richer spatial information and achieve better performance.

	\subsection{Dynamic Feature Pyramid Network}
	\label{Dynamic FPN}
	The inception FPN can largely improve the detection accuracy but brings heavy computational burdens. To this end, we propose DyFPN, which aims at tackling the problems of inception FPN by introducing a novel dynamic block. Basically, the dynamic block consists of three components: dynamic gate, inception block, and skip-connection. The experiments in section~\ref{experiments} demonstrate that the combination of these components achieves a better trade-off between accuracy and computational cost when compared to the inception FPN.

	\paragraph{Dynamic Block}
	In inception FPN, the lateral connections are static, which means it executes the same convolutions in the inference stage. However, it is intuitive that the detection difficulties of various input images are different, which implies that some ''easy'' images can be detected correctly without the enriched spatial information. Thus, the inception FPN contains computation redundancy. In contrast, our proposed dynamic block in DyFPN adaptively determines the operations in lateral connections based on the input image. The dynamic architecture for a specific image can largely reduce the computational cost and retain high detection accuracy. Figure~\ref{Dynamic FPN structure b} shows the details of the dynamic block. The motivation of the dynamic gate aims at predicting a one-hot vector, which denotes whether to execute or skip the branch of the inception block. We sum the features from a set of convolutional layers in the inception block. We always conduct a $1 \times 1$ convolutional layer at each level and consider it as skip-connection. The features from the branch of the inception block and the skip-connection are fused by summation. We insert the dynamic blocks to all the lateral connections of the feature pyramid. In training, the prediction of the gate is multiplied by the aggregated features from the dynamic block. In testing, the dynamic block does not need to be executed if the gate predicts 0 (\emph{i.e.}, the decision of skipping the inception block).

	In the dynamic gate, we first apply a non-linear function $g_l(\cdot)$ on $F_l$ and produces the logits of the gate signals:
	\begin{equation}
		\alpha_l = g_l(F_l),
	\end{equation}
	where $a_l\in\mathbb{R}$ which determines the sampling probability of inception block at level-$l$. Then the one-hot gate vector $\beta_l$ is obtained by Gumbel Softmax function as follows:
	\begin{align}
		\begin{split}
			\beta_{l}^i &= \textit{GumbelSoftmax}(\alpha_l^i|\alpha_{l})\\
			&= \frac{exp[\alpha_{l}^{i}+n_{l}^{i}/\tau]}{\sum_{i}exp[(\alpha_{l}^{i}+n_{l}^{i}/\tau)]}
		\end{split}
		\label{eq:gumbelsoftmax}
	\end{align}
	where $\beta_l^i\in\{0,1\}$, and $n_{l}^{i} \sim \textit{Gumbel}(0,1)$ is a random noise sampled from the Gumbel distribution. $\tau$ is a temperature parameter that influences the Gumbel Softmax function. Specifically, inspired by squeeze-and-excitation (SE) module~\cite{hu2018senet}, our dynamic gate is composed of a global average pooling layer, two fully-connected layers, and a ReLU layer:

	\begin{equation}
		\label{FCGate}
		\alpha_l = W_{2}(\delta(W_{1}P(F_l)+b_1))+b_2,
	\end{equation}
	where $P$ denotes the global average pooling layer. $\delta$ means the ReLU activation function. The input feature with a shape of $(C, H, W)$ from the selected layer in the backbone model is firstly squeezed by an average pooling operation in each channel to produce the feature of size $C$. Then we reduce the feature dimension by 4 in the first fully-connected layer. The second fully-connected layer, a non-linear activation function, and a Gumbel Softmax function~\cite{Wu2019FBNetHE} are further leveraged to generate the one-hot vector for the dynamic block. The introduced pooling operation largely reduces the computational costs of fully-connected layer applied on the input features, which makes the computational burden of the dynamic gate can be negligible.

	\paragraph{Resource Constraint.}In real-world scenarios, physical devices often impose different computing resource constraints on the model. Thus, DyFPN should be developed with the consideration of different computational expenses. However, if we use the detection loss only, the dynamic gate will tend to provide a sub-optimal solution, which conducts as many inception blocks as possible because features with the most enriched spatial information correspond to relatively lower detection loss generally. In order to achieve a satisfactory efficiency-accuracy trade-off, we propose a new loss $\mathcal{L}_{C}$ to guide the training. Here, we denote $C$ as the computational cost of the model, \emph{i.e.}, FLOPs. The maximum and minimum costs of the dynamic block can be calculated before training the model and we denote them as $C_{max}$ and $C_{min}$. For DyFPN, the decision of executing or skipping the inception block in all lateral connections of the feature pyramid leads to maximum or minimum computational cost, respectively. Here we introduce the losses for an end-to-end optimization:
	\begin{align}
		\begin{split}
			\mathcal{L}_{C} &= ((C_R - C_{target})/(C_{max} - C_{min}))^2\\
		\end{split}
	\end{align}
	where $C_R$ denotes the real computational cost of the dynamic block. $C_{target}$ represents the target resource cost. We can control the target cost by setting the hyper-parameter $\alpha\in(0, 1)$. The target cost can be formulated as follows:
	\begin{align}
		\begin{split}
			C_{target} &= C_{min} + \alpha * (C_{max} - C_{min})\\
		\end{split}
	\end{align}
	The total loss function can be optimized as follows:
	\begin{align}
		\begin{split}
			\mathcal{L} &= \mathcal{L}_{Det} + \lambda\mathcal{L}_{C}\\
		\end{split}
	\end{align}
	where $\mathcal{L}_{Det}$ and $\mathcal{L}_{C}$ represent the loss of detection and computational cost, respectively. We leverage $\lambda$ to balance the detection accuracy expectation and computational cost constraints, respectively.

	\section{Experiments}
	\label{experiments}
	\subsection{Dataset and Evaluation Metrics}
	All experiments are conducted on the MS-COCO 2017 detection dataset~\cite{Lin2014MicrosoftCC}, which contains 80 object categories. Following the protocol in MS-COCO, 118k images are used for training. We report the results of ablation studies for \textit{minival} with 5k images. Following common practice, MS-COCO Average Precision(AP) with different IoU thresholds is leveraged as the evaluation metric~\cite{guo2020hit,zhao2019m2det}.

	\subsection{Implementation Details}
	For all experiments, our detectors are trained end-to-end on a machine with 4 NVIDIA RTX 2080Ti GPUs. We utilize the SGD optimizer to train the model for 12 epochs, known as $1 \times$ schedule. Linear warm-up strategy for 500 iterations is leveraged at the beginning of training. We initialize the learning rate as 0.01 and decrease it to 0.001 and 0.0001 at 8th-epoch and 11th-epoch. The momentum is set as 0.9 and the weight decay is 0.0001. The batch size is set to 2 per GPU. The input size is $1333 \times 800$. The factor $\tau$ in Eq.~\ref{eq:gumbelsoftmax} is set to 1.0. Our implementation is developed by PyTorch framework~\cite{paszke2019pytorch} and mmdetection toolbox~\cite{mmdetection}.

	\subsection{Main Results}

	\paragraph{Effectiveness and Efficiency of DyFPN.}
	We compare DyFPN with model variants of FPN and inception FPN in Figure~\ref{fig:FPN_Inception_DYFPN_FLOPS_mAP}. We can see that the DyFPN achieves the best efficiency-accuracy trade-off among the model variants. In each lateral connection of the FPN, we replace $1 \times 1$ convolutional layer with $3 \times 3$, $5 \times 5$ and $7 \times 7$ convolutional layer, respectively. The FPN with a $7 \times 7$ convolution filter achieves the best performance while consumes the most computational costs. The model with the lowest accuracy follows the architecture of the conventional FPN. It can be observed that enlarging the size of the convolution filter in the FPN improves the accuracy while consumes more computational costs, which is not efficient. We combine different kinds of convolution filters and their dilated variants in the inception FPN, which accounts for different computational costs and accuracies. Although simply adding more convolution filters benefits the accuracy, the consequent computational costs drastically increase. To this end, the inception FPN is inefficient. Our DyFPN always outperforms the FPN and the inception FPN with similar computational costs. From the comparisons, we can conclude that our proposed method can largely improve the performance of the FPN in an efficient manner.

	\paragraph{Extension to Different Backbones.}
	We compare the DyFPN with the inception FPN on different backbones to demonstrate the effectiveness of DyFPN. In Table~\ref{tab:comparison_inceptionFPN_DyFPN}, we can see that the DyFPN largely saves the computational costs of the baseline with negligible performance variation on all backbones. Based on Faster R-CNN, the DyFPN (537.5G) reduces up to 40\% computational costs of the inception FPN (896.2G) with Resnet-50 as the CNN backbone, and the accuracy only drops 0.2 AP. The accuracy of DyFPN on Cascade R-CNN with ResNet-101 is even better than that of the inception FPN and the FLOPs also decreases 37.1\%. Basically, the methods based on Cascade R-CNN~\cite{cai2018cascade} detects more accurately than that based on Faster R-CNN, but Faster R-CNN saves more computational costs. The DyFPN achieves the best results with Cascade R-CNN and Resnet-101 as the backbone. Compared with the inception FPN, the computational costs of DyFPN are basically reduced by 34\%-40\% while the accuracy only drops 0.2-0.3 AP on most backbones.

	%
	%

	\begin{table}[tp]

		\begin{minipage}[b]{0.45\linewidth}

			\includegraphics[width=1.0\linewidth]{figure/FPN_Inception_DYFPN_FLOPS_mAP.pdf}
			\hspace{-0.9cm}
			\figcaption{Accuracy \textit{v.s.} FLOPs. Faster R-CNN with ResNet-50 is utilized as the backbone.}
			\label{fig:FPN_Inception_DYFPN_FLOPS_mAP}
		\end{minipage}

		\hspace{1mm}

		\begin{minipage}[b]{0.50\linewidth}

			\footnotesize

			\begin{tabular}{c|c|c|c}
				\hline
				Backbone                                                                            & Model         & AP   & \#FLOPs(G) \\ \hline
				\multirow{2}{*}{\begin{tabular}[c]{@{}c@{}}Faster R-CNN\\ ResNet-50\end{tabular}}   & Baseline & 39.0 & 896.2      \\
				& DyFPN         & 38.8 & 537.5 ($\downarrow40.0\%$)     \\ \hline
				\multirow{2}{*}{\begin{tabular}[c]{@{}c@{}}Faster R-CNN\\ ResNet-101\end{tabular}}  & Baseline & 40.5 & 963.6      \\
				& DyFPN         & 40.4 & 592.4 ($\downarrow38.5\%$)     \\ \hline
				\multirow{2}{*}{\begin{tabular}[c]{@{}c@{}}Cascade R-CNN\\ ResNet-50\end{tabular}}  & Baseline & 41.3 & 923.9      \\
				& DyFPN         & 40.9 & 561.0 ($\downarrow39.3\%$)     \\ \hline
				\multirow{2}{*}{\begin{tabular}[c]{@{}c@{}}Cascade R-CNN\\ ResNet-101\end{tabular}} & Baseline & 42.6 & 991.2      \\
				& DyFPN         & 42.7 & 623.1 ($\downarrow37.1\%$)     \\ \hline
				\multirow{2}{*}{\begin{tabular}[c]{@{}c@{}}Mask R-CNN\\ ResNet-50\end{tabular}}     & Baseline & 39.5 & 915.8      \\
				& DyFPN         & 39.2 & 587.0 ($\downarrow36.9\%$)     \\ \hline
				\multirow{2}{*}{\begin{tabular}[c]{@{}c@{}}Mask R-CNN\\ ResNet-101\end{tabular}}    & Baseline & 41.0 & 981.9      \\
				& DyFPN         & 40.9 & 644.5 ($\downarrow34.4\%$)     \\ \hline
			\end{tabular}
			\tabcaption{The comparison of the baseline and DyFPN on different backbones. The baseline denotes the inception FPN.}
			\label{tab:comparison_inceptionFPN_DyFPN}
		\end{minipage}
	\end{table}

	\subsection{Ablation Study}

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	\paragraph{Inception FPN.}
	In Table~\ref{tab:different convolution layers}, We conduct experiments to demonstrate that the enriched spatial information and expanded receptive fields significantly improve detection performance. We can see that gradually aggregating more features from different convolutions leads to accuracy improvement. We first follow the architecture of FPN and demonstrate its performance in the setting of \textit{k}=1, \textit{d}=1, where \textit{k} denotes the kernel size and \textit{d} denotes the dilation rate. Based on FPN, we add ${3 \times 3}$ convolution in each lateral connection, which leads to 0.8 absolute gain in AP. We can see that the addition of ${3 \times 3}$ convolution brings additional improvements for all sizes of objects. We further add ${5 \times 5}$ convolution and find a 0.4 increase in AP. However, the addition of ${5 \times 5}$ convolution achieves improvement for middle and large objects but drops in performance for small objects. We conjecture that the receptive field of ${5 \times 5}$ convolution overemphasizes large objects, which drowns the information of the small objects in the aggregated features. We add some convolutions with different dilation rates to further increase the receptive field. Note that although the ${3 \times 3}$ convolution with dilation rate 2 has the same receptive field as the ${5 \times 5}$ convolution, their parameters and computations are not the same. Thus, employing \textit{k}=3, \textit{d}=2 or \textit{k}=5, \textit{d}=1 leads to different results in the inception block. It can be observed that the addition of convolutions with a dilation rate of 2 and 3 improves the detection accuracy in almost all scales. Aggregating features from convolutions of different kernel sizes and dilation rates enables the receptive fields of the detection model to be expanded and the spatial information can be enriched, which results in an accuracy improvement. The method in the last row benefits from all kinds of convolutions and achieves the best results. To this end, the convolutional layers in our inception block finally adopt the following configurations: kernel size = [1, 3, 3, 3, 5, 5, 5], dilation rate = [1, 1, 2, 3, 1, 2, 3], padding = [0, 1, 2, 3, 2, 4, 6].

	In Figure~\ref{fig:feature_visulization}, we visualize the features generated by convolutions in inception FPN. We can see that simply exploring the spatial information in the feature pyramid cannot obtain satisfactory results while utilizing the convolutions with different kernel sizes can generate enriched spatial features for detection.

	\begin{table*}[tb]
		\centering
		\small
		\begin{tabular}{cccccccccccccc}
			\hline
			\begin{tabular}[c]{@{}l@{}}k=1 \\ d=1\end{tabular} & \begin{tabular}[c]{@{}l@{}}k=3 \\ d=1\end{tabular} & \begin{tabular}[c]{@{}l@{}}k=5 \\ d=1\end{tabular} & \begin{tabular}[c]{@{}l@{}}k=3 \\ d=2\end{tabular} & \begin{tabular}[c]{@{}c@{}}k=3\\  d=3\end{tabular} & \begin{tabular}[c]{@{}c@{}}k=5\\  d=2\end{tabular} & \multicolumn{1}{c|}{\begin{tabular}[c]{@{}c@{}}k=5\\ d=3\end{tabular}} & AP & AP$_{50}$ & AP$_{75}$ & AP$_{S}$ & AP$_{M}$ & AP$_{L}$ \\ \hline
			\checkmark      &       &       &         &        &      & \multicolumn{1}{l|}{}   &{37.4} &{58.3} &{40.4} &{21.2} &{41.1} &{48.6}        \\

			\checkmark  &\checkmark       &       &       &      &          & \multicolumn{1}{l|}{}   &{38.2} &{59.2} &{41.1} &{22.1} &{41.8} &{49.4}   \\
			\checkmark      &\checkmark        &\checkmark        &       &   & & \multicolumn{1}{l|}{}    &{38.6} &{59.9} &{41.6} &{21.9} &{42.1} &{50.4}        \\
			\checkmark  &\checkmark &     &\checkmark     &\checkmark       &      & \multicolumn{1}{l|}{}     &{38.5} &{59.9} &{41.8} &{22.0} &{42.2} &{50.1}  \\
			\checkmark  &\checkmark  &\checkmark  &\checkmark  &\checkmark &\checkmark   &\multicolumn{1}{c|}{\checkmark}  &\textbf{39.0} &\textbf{60.6} &\textbf{42.4} &\textbf{23.3} &\textbf{42.7} &\textbf{50.5} \\
			\hline
		\end{tabular}
		\caption{Ablation study of inception FPN. The Faster R-CNN framework with ResNet-50 model is utilized as the backbone. The best scores are \textbf{bold}.}
		\label{tab:different convolution layers}
	\end{table*}
	%

	%

	%

	%

	%

	%
	%

	%

	%
	%

	%

	\begin{table}[tp]

		\begin{minipage}[b]{0.51\linewidth}
			\hspace{-2.5mm}

			\includegraphics[width=1.0\linewidth]{figure/feature_visulization.pdf}
			\figcaption{The visulization of the features.}
			\centering
			\label{fig:feature_visulization}
		\end{minipage}

		\begin{minipage}[b]{0.47\linewidth}
			\label{tab:Comparison between Dynamic FPN and FPN with Random Gate}

			\footnotesize
			\begin{tabular}{l|c|c|c|c}
				\hline
				{Model}  &$\alpha$ &$\lambda$ &AP &\#FLOPs(G)  \\
				\hline
				{Baseline}  &{-} &{-} &{39.0} &{896.2}\\
				{DyFPN-Raw}  &{0.0} &{0.0} &{38.8} &537.5\\
				{DyFPN-I}  &{0.5} &{0.1} &{38.7} &523.6\\

				{DyFPN-II}  &{0.3} &{0.1} &{38.6} &399.3\\

				{DyFPN-III}  &{0.2} &{0.1} &{38.4} &329.0\\

				{DyFPN-IV}  &{0.2} &{0.5} &{38.3} &328.6\\
				\hline
			\end{tabular}%

			\tabcaption{Ablation studies of different resource budgets.}
			\label{tab:resource budgets}
		\end{minipage}
	\end{table}

	%

	\paragraph{Computational Resource Budgets.} To obtain a satisfactory efficiency-accuracy trade-off, we propose a computational cost loss to restrict the computational cost of the model. By tuning the hyper-parameter $\alpha$ and $\lambda$, we train DyFPN variants with different resource constraints, as shown in Table~\ref{tab:resource budgets}. We compare the resource constraint models with DyFPN-Raw, which is trained without the constraint. By tuning $\alpha$, we can set different target computational costs for DyFPN. It can be seen that the computational costs of DyFPN descend to different levels with different targets. With the strongest constraint, the FLOPs of DyFPN-IV is decreased to 61.1\% of DyFPN-Raw. Besides, the computational loss enables DyFPN-I to consume fewer computational costs while preserving similar accuracy as DyFPN-Raw.

	\paragraph{Realistic Acceleration.}
	Based on the Faster R-CNN framework and ResNet-50 model, we compute the latency, FLOPs, and AP for inception FPN and DyFPN in Table~\ref{tab:latency}. Considering the data-dependent property of the DyFPN, we report the average latency and FLOPs in the MS-COCO benchmark. We can see that the DyFPN still largely reduces the forward time in the realistic calculation without any special software or hardware designs.

	\paragraph{Effectiveness of the Dynamic Gate.}

	To demonstrate the effectiveness of our dynamic gate, we compare its performance with a random number generator. In Table~\ref{tab:Comparison between Dynamic FPN and FPN with Random Gate}, we apply different decisions to DyFPN in the training and testing stage. Here, \textit{R} denotes the random decisions and \textit{G} denotes the decisions from our dynamic gate. The backbone is Faster R-CNN with ResNet-50. The random decisions are randomly generated one-hot vectors. we can see that replacing the gate decisions with the randomly generated decisions in the testing period always leads to a dramatic performance drop, which indicates that the gate decisions are significant. For a fair comparison, we test the model 10 times with random decisions and average their results. Considering that the inconsistency between train/test-time inference leads to performance degradation to the model, we also train the models with random decisions. The results show that training the DyFPN with random decisions makes the model robust to the disturbance of the stochastic factors in testing. However, our gate decisions still outperform the random decisions with a large margin.

	\begin{table}[tp]

		\begin{minipage}[h]{0.58\linewidth}
			\small
			\begin{tabular}{l|c|c|c}
				\hline
				{Model}  &Latency(ms) &\#FLOPs(G) &AP \\
				\hline

				{Baseline}  &{287.6} &{896.2} &{39.0}\\
				{DyFPN}  &{188.6} ($\downarrow34.4\%$) &{537.5} ($\downarrow40.0\%$) &{38.8} \\
				\hline
			\end{tabular}%
			\tabcaption{Comparison of the latency, the computational costs, and the accuracy of different models on a RTX 2080Ti GPU. }
			\label{tab:latency}
		\end{minipage}

		\hspace{0.5mm}

		\begin{minipage}[h]{0.40\linewidth}

			\small
			\begin{tabular}{c|c|c|c|c}
				\hline
				\centering
				{Train}  &Test &AP &AP$_{50}$  &AP$_{75}$ \\
				\hline

				{R}  &{R} &36.9   &57.7 &39.9 \\
				{G}  &{R} &18.8   &30.0 &19.8 \\
				{G}  &{G} &38.8   &60.5 &42.0 \\
				\hline
			\end{tabular}%
			\tabcaption{Ablation studies of different decision sources.}
			\label{tab:Comparison between Dynamic FPN and FPN with Random Gate}
		\end{minipage}
	\end{table}

	\begin{figure}[t]
		\centering
		\includegraphics[height=0.46\linewidth]{figure/gate_selection.pdf}
		\caption{Qualitative results of the DyFPN. Each value denotes the number of the executed inception blocks for these images.}
		\label{fig:different number of executed dynamic blocks}
	\end{figure}

	\paragraph{Number of the Executed Inception Blocks in DyFPN.}
	We demonstrate the decisions of the dynamic gates and their corresponding images in DyFPN as shown in Figure~\ref{fig:different number of executed dynamic blocks}. There are four lateral connections in DyFPN. At each connection, the gate determines whether to execute the inception block. For each input, we count the number of executed blocks in all connections of DyFPN. From the visualization results, the number of executed blocks generally increases with the complexity of the objects, which is compatible with the human perception system~\cite{walther2011simple}. For example, in the first row, the number of objects is small and the target occupies most of the image. Thus, two inception blocks in DyFPN are sufficient to give correct predictions. The middle five images which contain more objects are decided to select three inception blocks. In the last five images, there exist many objects with different scales and they are blended with each other, which makes them need four inception blocks.

	\section{Conclusion}
	\label{Conclusion}
	In this work, we demonstrate that the combination of multiple convolutions improves detection accuracy by expanding the receptive fields and enriching the spatial information in the feature pyramid. The large amounts of computational cost lead by multiple convolutions can also be saved for some easier images. Thus, we propose a novel DyFPN for object detection. Based on the input image, the dynamic gate in DyFPN adaptively determines whether to execute the multiple convolutions in the feature pyramid. DyFPN can largely reduce the computational cost while preserving high accuracy. Experiments on various backbones demonstrate the effectiveness of the proposed DyFPN.

	%

redpajama_set_name: RedPajamaArXiv


text:
Metabolomics: the Superglue of Omics
As the profile of metabolomics soars, we offer our thoughts on the future of the field.
Martin Giera, Mary E. Spilker, Gary Siuzdak | 03/23/2018
Our understanding of cellular and molecular processes has increased exponentially during the last century. Several key questions about the function of our cells and, to a broader extent, their interactions with entire organisms have been answered, with the most prominent findings being the elucidation of our DNA structure and the sequencing of the human genome (1). The key outcome of 20th century biomedical and biological research was the knowledge we gained about the coding, content and flow of information within our cells and body. The concept that our cells contain our hereditary information in the form of DNA, which is transcribed into RNA and translated into proteins that are ultimately involved in the modulation of our metabolome is the central dogma of biology, and a comprehensive understanding of this process represents the fundamental goal of omics technologies (2).
But beyond a better understanding of ourselves, what has been the impact of these findings and technologies on our lives? Genomics (DNA sequencing) has certainly enhanced diagnosis and treatment of inborn errors and hereditary diseases. Transcriptomics (the analysis of RNA transcripts) complements genomics by allowing us to decipher gene expression, again helping to identify undiagnosed cases of genetic diseases. Proteomics (the comprehensive analysis of an organism's proteins) would ideally complement genomics and transcriptomics; however, several ongoing analytical challenges are still hampering comprehensive protein analysis. Nevertheless, we believe advances in proteomics happening today will allow for a more efficient use of proteomics tomorrow, so that it may become a complementary diagnostic approach to other omics techniques (3).
Metabolomics rules
Ultimately, it is the metabolome – the underlying biochemical layer of the genome, transcriptome and proteome – that reflects all the information expressed and modulated by all other omic layers. The metabolome is the closest link to the phenotype and hence at the forefront of personalized diagnosis and therapy (4)(5). This concept is now widely accepted and has enhanced the application of metabolomics in biomarker research, but it has also blurred our view of its broader implications. A sea change is taking place, which defines the metabolome not only as an organism's phenotype, but also includes the biological activity of metabolites and their respective control functions (6)(7)(8).
When taking a closer look at the involvement of metabolites in gene expression, transcription and translation control, it becomes evident that the metabolome is not simply a cellular information sink. Rather, the metabolome is the omics superglue, providing biochemical feedback across all omic layers (see Figure 1). Metabolites are essential in the control of gene expression, with several transcription factors controlled by specific molecules, including the liver X receptors, estrogen receptors and thyroid hormone receptors. In addition, as metabolic control has become better recognized (9), numerous other cellular receptors activated by metabolites have been discovered, including G-protein coupled receptors that are involved in signal transduction. Beyond these noncovalent protein–metabolite interactions, metabolites are also active substrates and products of enzymatic reactions, as well as participants in protein post-translational modifications, such as acetylation and palmitoylation. This activity even extends to metabolites shaping the metabolic environment to influence protein function, as illustrated by membrane proteins (10).
Figure 1. Metabolic activity among the different omic layers.
The metabolome is the closest link to the phenotype and hence at the forefront of personalized diagnosis and therapy.
Clearly, the metabolome interacts with, and hence coalesces, all the omic layers. Therefore, it is not surprising that scientists in the field have started to emphasize the biological activity of metabolites as an integral part of metabolomic studies (6)(8)(11). When systems biology evolved as a biological discipline, with the express purpose of comprehensively describing all the biochemical reactions of an organism, metabolomics was rapidly recognized as an important component, allowing us to confirm computational predictions and shed light on biochemical pathways (12). However, it is only recently that the concept of metabolite activity screening (MAS) has started to integrate metabolomics, systems biology, and bioactivity (7). The field is in its infancy, yet the practical impact of identifying novel bioactive metabolites and characterizing their associated control, function, and mechanisms of action is exciting.
Critical MAS
Omega-3 poly-unsaturated fatty acids (n-3-PUFAs) and their corresponding metabolic products exemplify the power of directly connecting metabolites with health. Evolving from the observation that Inuit populations – with their n-3-PUFA rich diets – have a lower incidence of heart disease, today, n-3-PUFAs can be considered one of the most important and scientifically proven dietary supplements, with numerous beneficial health effects (13). Charles Serhan's group at Harvard University has subsequently elucidated how n-3-PUFAs and their downstream metabolites function as immunologically active molecules, founding the field of resolution physiology and opening up novel strategies for the treatment of (chronic) inflammation (14). While the n-3-PUFA field originally sprung from a 1970s epidemiological study and the comparison of dietary habits, it is now at the cutting edge of metabolomics.
The combination of systems biology and MAS (systems-MAS), will allow us to systematically elucidate novel pathways and key metabolites, which might subsequently be used as novel therapeutics. An example is the recent systems-MAS finding that taurine significantly enhances drug-induced oligodendrocyte precursor cell differentiation and facilitates the in vitro myelination of co-cultured axons (15). This is especially interesting because metabolites are generally safe, inexpensive, readily available and can rapidly impact a system, be it a cell or an entire organism.
It is not surprising that scientists in the field have started to emphasize the biological activity of metabolites as an integral part of metabolomic studies.
As with any new scientific endeavor, systems-MAS needs to overcome several significant challenges, including the need to develop effective approaches to identify active metabolites and generate libraries cataloging their bioactivity. This process is challenging because metabolic activity will likely vary depending on the organism and the phenotype of that organism. The hope is that when enough biochemical pathway and biological activity data have been correlated, we will be able to shape the metabolic landscape to our own ends. It's possible we could even harness metabolism by stimulating the other omic layers, allowing us to better understand and manipulate physiology, to develop new therapeutics or stimulate or inhibit bacteria (influencing the microbiome). We find this concept particularly intriguing as it transforms our view of the metabolome, which changes from a phenotypic descriptor into a phenotype modulator (7). Perhaps, following on from the success of therapeutic antibodies, metabolites may evolve as a new means to "fix biology with biology".
Martin Giera is Associate Professor and Head of the Metabolomics group at the Center for Proteomics and Metabolomics, Leiden University Medical Center, Leiden, The Netherlands.
Mary E. Spilker is Associate Research Fellow at Pfizer Worldwide Research and Development and is currently a visiting scientist at the Scripps Center for Metabolomics at the Scripps Research Institute, La Jolla, California, USA.
Gary Siuzdak is Professor and Director of the Scripps Center for Metabolomics at the Scripps Research Institute, La Jolla, California, USA.
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International Human Genome Sequencing Consortium, "Initial sequencing and analysis of the human genome", Nature, 409, 860 (2001).
F Crick, "Central dogma of molecular biology", Nature, 227, 561 (1970).
PE Geyer et al., "Revisiting biomarker discovery by plasma proteomics", Mol Syst Biol, 13, 942–957 (2017).
M Jacob et al., "Metabolomics toward personalized medicine", Mass Spec Rev, 2017, 1–18 (2017).
I Kohler et al., "Analytical pitfalls and challenges in clinical metabolomics", Bioanalysis, 8, 1509–1532 (2016).
T Huan et al., "Systems biology guided by XCMS Online metabolomics, Nature Methods, 14, 461 (2017).
C Guijas et al., "Metabolites that modulate phenotype can be identified by metabolomics activity screening", Nat Biotechnol (2018) (Accepted manuscript).
M Giera, F Branco dos Santos, G Siuzdak, "Metabolite-induced protein expression guided by metabolomics and systems biology", Cell Metab (2018) (Accepted manuscript).
AS Husted et al., "GPCR-mediated signaling of metabolites", Cell Metab, 25, 777–796 (2017).
DA Los, N Murata, "Regulation of enzymatic activity and gene expression by membrane fluidity", Science's STKE, 2000, pe1 (2000).
F Branco dos Santos et al., "Probing the genome-scale metabolic landscape of Bordetella pertussis, the causative agent of whooping cough", Appl Environ Microbiol, 83, e01528-17 (2017).
DB Kell, "Metabolomics and systems biology: making sense of the soup", Curr Opin Microbiol, 7, 296–307 (2004).
JH O'Keefe, Jr, WS Harris, "From Inuit to implementation: omega-3 fatty acids come of age", Mayo Clinic Proc, 75 607–614 (2000).
CN Serhan, "Pro-resolving lipid mediators are leads for resolution physiology", Nature, 510, 92 (2014).
BA Beyer et al., "Metabolomics-based discovery of a metabolite that enhances oligodendrocyte maturation", Nat Chem Biol, 14, 22–28 (2018).
Associate Professor and Head of the Metabolomics group at the Center for Proteomics and Metabolomics, Leiden University Medical Center, Leiden, The Netherlands.
Mary E. Spilker
Associate Research Fellow at Pfizer Worldwide Research and Development and is currently a visiting scientist at the Scripps Center for Metabolomics at the Scripps Research Institute, La Jolla, California, USA.
Gary Siuzdak
Professor and Director of the Scripps Center for Metabolomics at the Scripps Research Institute, La Jolla, California, USA.
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text:
Overcoming disruption in a distributed world: Insights from the Anatomy of Work Index 2021
By Erin Cheng & Molly Talbert	Jan 14, 2021
Read this article in French, German, Portuguese, Spanish, or Japanese.
Every knowledge worker—all 1.25 billion of them worldwide—has been impacted by the COVID-19 pandemic. And, for many, the abrupt shift to remote work triggered an increase in the adoption of collaboration tools and an acceleration of digital transformations.
In 2019, we released our first Anatomy of Work Index to analyze how people spend time at work and the factors shaping those habits. Last spring, we launched the Anatomy of Work: Remote Teams Survey to examine how knowledge workers were coping with the sudden switch to remote and distributed work.
Today, we're launching the Anatomy of Work Index 2021, building off our previous surveys and taking into account all that has changed—or stayed the same. In this report, we investigate how work has transformed in a world still grappling with the effects of the global pandemic and what steps must be taken for resilience moving forward.
To understand how the office has transformed, what's working and what's not, and to shine a light on better approaches, we analyzed the attitudes and behaviors of over 13,000 knowledge workers in Australia, France, Germany, Japan, Singapore, the U.K., and the U.S.
The Asana Anatomy of Work Index 2021 explores how individuals, teams, and entire organizations can bring clarity to the chaos of work, reset for resilience, and flourish as they continue to adapt. Here's a look at some of the key insights from around the world.
Growing workloads and longer days
In a world that has changed so much this past year, work about work continues to dominate. Even with the shift to remote work for so many, knowledge workers continue to spend 60% of their day on work about work—not the skilled job they were hired to do.
What else has stayed the same—or not—year-over-year? According to respondents, the top barrier to productivity in 2020 was their growing workload rather than too many emails and messages to respond to, which was the case in previous years.
Piling on top of growing workloads are longer days spent working. While we found that teams have fewer ad-hoc conversations, like hallway catch-ups, this hasn't resulted in shorter working days. Casual chats have instead been replaced with unnecessary meetings, costing individuals 157 hours in the past year.
Although knowledge workers around the world had to adapt to similar challenges regarding remote and distributed work, each country had several unique responses:
Employees in Australia and New Zealand are more likely than average to feel heard by their organization, and are more optimistic about the year ahead in work.
French workers are spending the most time on work about work each week (nearly 66% of their time), and are also experiencing more difficulties in switching off at the end of the day due to remote work.
Only 13% of German employees feel completely heard by their organization, with one in three (32%) wanting more flexible hours when working remotely.
Japanese workers rely on written communication more than any other country and, as a result, grapple with messages and emails as their number one distraction.
Knowledge workers in Singapore have the longest working week (over 42 hours) and are the country most likely to work beyond 5:30 p.m.
The U.K. misses the fewest deadlines out of all countries, but indexes high on likelihood to have experienced imposter syndrome in the past year.
The highest of all countries, nearly nine in ten (89%) knowledge workers in the U.S. experienced burnout in 2020, with employees missing 36% of all deadlines each week.
Looking ahead: How will we work in 2021?
The new year marks a chance for new beginnings and is also an opportunity to better equip individuals and teams by providing meaningful job engagement and opportunities to accelerate their careers.
So what will work look like in 2021?
On the individual level, employees want work that's personally and professionally fulfilling, no matter where they are working from. Respondents in the survey cite engaging and enjoyable work as the number one motivator to achieve their best work, followed by adding value to the business and progressing their career in the new year. On an organizational level, providing clarity—from the top to the bottom—and a consolidation of tools will be key for success.
"Before COVID-19, there was a rapidly rising business imperative for increased clarity and alignment. Clarity is really difficult for teams to achieve even when they're in the office, but it's particularly challenging when working remotely," said Dustin Moskovitz, CEO, Asana. "Going forward, some companies will continue working from home, some will return to the office, and some will do everything else in between. Across that entire spectrum, Asana has an important role to play in driving clarity for teams, no matter where they do their work."
How to become a resilient, agile organization in 2021
While the reality of where and when we work has transformed, 2020 has shown us how the way we manage work has failed to keep up. Teams and organizations have struggled to coordinate. Work about work has remained prevalent, creating chaos, confusion, and burnout. This misalignment, in turn, is making it difficult for everyone to do their best work and for organizations to succeed.
To build agility and resilience, organizations must confront these issues and adapt in the year ahead. Moving forward, work management is a critical tool for workplaces to flourish.
"With Asana, we've been able to cut meetings by 90% and improve productivity of the team by 40%."
Mark Fazio, General Manager, MATE
By empowering entire organizations with the core tenants of teamwork—clarity, transparency, and accountability—work management can help take us from surviving to thriving in the year ahead.
To learn more, download the entire Anatomy of Work Index 2021.
Forrester's Margo Visitacion on OKRs and organizational alignment in a distributed world
Diving into Asana's ROI of Work Management Report


Main Reason Behind Baahubali Collections!
Baahubali movie completed two weeks and is running successfully in the 3rd week also. The film has created many records and the collections crossed Rs 400 crore mark. The movie collected Rs 436.1 Cr Gross, including all the languages. Baahubali is the first movie of 2015 to enter the 400 crore club in worldwide collections. Salman Khan's Bajrangi Bhaijaan also enter the 400 crore club. Baahubali is still running with packed houses in most of the theaters.
Here is the another record for Baahubali, as per the sources Baahubali movie watched more than 3.5 crore people only in India, which is the highest record so far. Earlier Aamir Khan's PK holds that record and 3.4 crore people watched that movie. Now, Baahubali created another new record.
The main reason for huge collections to Baahubali is many people interested to see the film in theaters rather than watching piracy copy. Even almost all the people preferred to see the film in theaters even if they have the option to see the film through some other sources (Piracy). Many audiences thought that Baahubali should be seen only on big screens due to its graphics and visuals. So, many people watched the film in theaters. This is the main reason for getting the huge collections to Baahubali.
What do you say folks!!!