text:
"I've Written Enough Fiction"
Larry McMurtry says his career as a novelist is in its final chapters. But now that he's settled into his hometown of Archer City, his life as a bookseller is just getting interesting.
Kathryn Jones
https://www.texasmonthly.com/articles/ive-written-enough-fiction/
From the December 1997 Issue Subscribe
Larry McMurtry is walking the streets of Archer City, keys in hand, unlocking doors to his bookstores around the town square. It's a crisp morning in October, and McMurtry—wearing a navy wool sweater, jeans, running shoes, a wool scarf loosely draped around his neck, and black-rimmed glasses—looks every bit the resident bookworm, not the famous writer, millionaire, and Hollywood celebrity. Indeed, McMurtry of late spends more time selling books than writing them. The bookstore he and his sister Sue Deen started in a former Ford dealership here—first they called it the Blue Pig, then Booked Up—has spilled into three other buildings downtown. The dusty ranch town McMurtry lamented as utterly "bookless" when he was growing up is now one of the most bookish burgs in America.
Not all the folks in Archer City—about 25 miles from Wichita Falls—are happy to see McMurtry come back and buy up pieces of his hometown. Some still haven't forgotten or forgiven the gloomy portrayal of their town in the book and the movie The Last Picture Show, McMurtry's semiautobiographical story about small-town love and loss that was filmed here. Or they grumble that the downtown stores are full of old books instead of things they really need and that Archer City is turning into a tourist town—"McMurtryville." But many others have welcomed McMurtry back as a benefactor who has put Archer City on the map—once in the books he wrote and now in the books he sells. With more than 100,000 volumes, Booked Up is already one of the largest rare-and-used-book stores in Texas. And Archer City, a single—Dairy Queen community (population: 1,800) built around a 106-year-old sandstone courthouse, is becoming known as an international "book town." Out-of-state license plates are a common sight in the parking spaces in front of Booked Up's redbrick main store. Booklovers have come from as far away as Australia.
That McMurtry, 61, has returned to build a business and a life in a town he fled as a young man is ironic but not entirely surprising. He has long admitted to conflicting feelings about his hometown and state, critical of them yet attracted to them; an insider with an outsider's distance. And his priorities, his approach to work, and his life have changed markedly in the past ten years, a span that began in the wake of the Pulitzer prize—winning Lonesome Dove's remarkable success in 1985. The 1989 hit television miniseries of the book made McMurtry famous, and his two main characters, the crusty Texas Rangers Augustus McCrae and Captain Woodrow F. Call, pop-western icons. In 1989 he also bought a house in Archer City. After undergoing quadruple-bypass surgery in December 1991, McMurtry went into a depression and couldn't work for a while, even at his beloved bookstores. He took on a collaborator, Diana Ossana. And after years of bouncing between Washington, D.C., Hollywood, Archer City, and Tucson, Arizona, McMurtry settled, seemingly for good, in his hometown, where he's closer to his family—especially his seven-year-old grandson, Curtis, whom he adores—and his bookstores. Now he's talking about another transformation. "I have one more novel I'd like to write," he says. "I've written enough fiction." The last novel will probably be the final volume of the Archer City trilogy (following The Last Picture Show and Texasville), the story of the life of one man's hometown.
NOT ENOUGH WAS HAPPENING IN THIS TOWN—we were losing ground to Wichita Falls," McMurtry says as he gives me a tour of his book empire. "So as the little local merchants folded their tents, we started buying up buildings." In a rare interview that starts in his bookshop and continues at his house, McMurtry doesn't try to discourage me from writing about him, as he's been known to do in interviews in the past. But he would clearly rather talk about books and the shop than himself. He's a complex person of many contradictions: intense yet reserved, candid one minute and private the next, settled yet restless. He can be as "indifferent as a butter churn," as his childhood friend Ceil Cleveland once described him, and yet polite, caring, and generous. He has a wry sense of humor—"I call this the hell room where lesser art books go to die," he says, showing me a room crammed with books yet to be sorted and organized. A writer with a huge loyal following and unusual celebrity, he would rather spend an evening at home in his library than on the literary dinner-party circuit. His family says he has mellowed a lot since his heart surgery. "He's pretty calm now," his sister Sue Deen says. "He seems to like it here and seems more settled now than he has been for a long time."
McMurtry has actually been moving back to Archer City for years—a step at a time. In 1980, while living in Washington, he moved his prized book collection to his family's ranch house at Idiot Ridge, a windswept knoll outside town. Then he and Deen opened the Blue Pig in 1987. Two years later he bought the largest of Archer City's three mansions, a three-story Prairie-style yellow-brick house built in 1928 that was formerly a country club. (He grew up in a frame house just down the street.) Lined with floor-to-ceiling bookshelves stocked with McMurtry's personal collection of 15,000 to 20,000 books, the house is elegant but simple. The main house has large rooms, high ceilings, white walls, original hardwood floors, a magnificent oak stairway, and an unusual glass-top table built of bones. Recently McMurtry transformed the attic into a children's library for his grandson and his sisters' children. Out back is a carriage house that he turned into a two-level library, where he does much of his research.
That passion for books—collecting them, selling them, and writing them—took root early. While teaching English at Rice University in Houston in the mid-sixties, McMurtry managed a shop called the Bookman and scouted for books for dealers. After he moved to the Washington, D.C., area in 1969, he opened a serious rare-book shop, the first Booked Up, in a one-room, one-story building in affluent Georgetown. A couple of years later, he moved to a second Georgetown location, above which he kept a small apartment. During his time in Washington he also opened and closed bookstores in Dallas and Houston. Since opening the Blue Pig, he has been consolidating his holdings in Archer City and quietly building up stock. He sometimes handles the big purchases, and like the rodeo cowboy turned antique dealer in his novel Cadillac Jack, he loves the hunt for good buys. When he lived in Washington, he would sometimes take a bedroll and camp out all night to be the first in line at a book sale. McMurtry has whittled down the Washington shop—which he recently moved again to a location across the street—to four thousand or five thousand books, making it more of a specialty rare-book shop. Marcia Carter, McMurtry's longtime partner in the Georgetown store, says she sent about four hundred boxes of books from the shop to Archer City. "It's wonderful what he's doing there," Carter says. "He thinks on a Texas scale."
McMurtry also owns a bookstore in Tucson, but he says he'll likely sell that in a few years to focus on the bookshops in his hometown and in Georgetown. Most of the stock in Archer City came from books McMurtry bought from bookstores in urban areas that couldn't afford the expensive real estate and sold out. Fragments of fifteen to twenty bookshops—including several from New York City— now reside in Archer City. In January 30,000 more books that McMurtry bought from a bookseller in Hastings-on-Hudson, New York, are arriving, and he's got a lead on another big load of books from a shop in Arizona. "Buying is like breath to a bookseller," McMurtry says.
Archer City is not as unlikely a setting for a book town as it may first seem. Other international book towns are also out in the middle of nowhere—booklovers and dealers are an eccentric lot, and they will travel thousands of miles to find good buys. The most famous book town, Hay-on-Wye in Wales, a village of only 1,200 residents, has more than thirty bookstores and holds a prestigious annual literary festival. Montolieu, in southern France, was a dying industrial town until Michel Braibant, a master bookbinder, revitalized the town as a mecca for graphic arts and books. The best-known book town in the United States is Stillwater, Minnesota, which boasts several large antiquarian bookstores and historic inns. McMurtry says he would eventually like to turn one of his buildings downtown into a collective to bring other booksellers to town.
McMurtry's shop and his name have already attracted more visitors to Archer City. Unfortunately there's not much to make them stay after browsing for books. The Texasville Cafe, where tourists and townsfolk once gathered to drink coffee and eat fried catfish, is for sale. The old Royal Theater, immortalized in The Last Picture Show, is a crumbling pile of rocks anchoring one end of Main Street. McMurtry is helping with a drive to raise $2 million to transform the small theater into a 14,000-square-foot multipurpose complex for hosting productions by the town's Picture Show Players community theater group as well as drama workshops, film festivals, literary readings, and art and photography exhibits. McMurtry lets the theater troupe and the Royal renovation group operate out of one of his buildings.
Abby Abernathy, a 35-year-old actor and producer who heads the Royal Theater Fund, bought the theater in 1986 after it had been condemned. McMurtry didn't want to be involved with early plans to renovate the theater into a movie house. "He said it was a heinous idea," Abernathy remembers. "He said, 'Why spend your money trying to recreate something you can't recreate?'" McMurtry got interested, though, when the project broadened. Abernathy, who also renovated Archer City's historic hotel, the Spur, says that it has taken longer to get grant money than anticipated and that plans have been scaled back from the 20,000 square feet originally envisioned. But Abernathy, whose father was one of those opposed to the filming of The Last Picture Show in Archer City 27 years ago, vows the Royal will rise from the rubble, perhaps by the end of next year.
All of this is irrelevant to some people in Archer City, who point out that the town doesn't even have a dry goods store downtown anymore or a full-time physician or a hospital. Most of the big supporters of the Royal Theater renovation aren't from Archer City. But the town has felt the ups and downs of farming, ranching, and the oil patch for so long that many residents see the book town as their best shot at rejuvenation. "Larry is building us a mountain," says Tom O'Neil, a local banker and the head of the chamber of commerce, which sits in a small building that looks like a metal storage shed next to one of Booked Up's stores. "That in itself is going to attract more incremental business."
OF COURSE, MCMURTRY DIDN'T RETURN to revitalize Archer City. He came back to devote himself to his passion: books. Although this is where his people are from, the move must have been an unsettling one. In one of the essays in his 1968 collection In a Narrow Grave, McMurtry writes about his "deep ambivalence" about Texas and his "contradiction of attractions" about his heritage: "I am critical of the past, yet apparently attracted to it." Though McMurtry has said he was only "nominally a cowboy" and never liked cattle much himself, his grandfather, father, and uncles ranched thousands of acres in the area.
The modern McMurtrys have avoided the hard-core ranching life, though his sisters Deen (who got out of the book business four years ago) and Judy McLemore have in the past three years started a small cow-calf outfit. Judy also runs the town's title company. Larry's brother, Charlie, is a lecturer in the English department at Angelo State University, in San Angelo. His mother lives in Wichita Falls, and his son, singer-songwriter James McMurtry, lives in Austin with his wife, Elena (Larry and Jo Ballard, James's mother, divorced in 1966). McMurtry tries to visit James' son, Curtis, regularly. He had to dash out of town to buy Curtis a birthday present the day I visited. One of the staff suggested that he might want to look for something in the F.A.O. Schwarz catalog that had just arrived in the mail from the big New York toy store. No, McMurtry said, he had to drive to Wichita Falls because his grandson had to have a high-tech ray gun.
McMurtry's extended family includes Ossana, who has homes in Tucson and Archer City. The two met in 1985 and became friends. Ossana, who had done some writing and worked as a legal assistant, took McMurtry into her Tucson home several months after his heart surgery to help him recuperate. While recovering he wrote Streets of Laredo, which Ossana helped edit. He dedicated the book to her and her daughter Sara. They became collaborators, next working on a screenplay of Pretty Boy Floyd, which they sold to Warner Bros. and expanded into a book, published in 1994. This year they published Zeke and Ned together. Some people have wondered whether they have a romantic relationship, but both have insisted they're just close friends. They see each other frequently and both presided over the opening of the Texas Book Festival in Austin in early November.
"WORKING IN RARE BOOKS AND ANTIQUARIAN books is a progressive thing," McMurtry says. "You don't always get better as a writer—you get old, you get tired, you exhaust an original gift. But in books you're dealing in knowledge. The older you get, the better you get. It's almost the opposite of being a writer." McMurtry says he's winding down as a writer of fiction. Comanche Moon, his fourth and final novel about Lonesome Dove's Gus and Call (the book takes them through their prime adulthood), came out last month from his longtime publisher, Simon and Schuster. (His two other novels about Gus and Call were 1993's Streets of Laredo and 1995's Dead Man's Walk.) "I got very tired of it," he says, picking up a prepublication copy of Comanche Moon. "Four is really stretching it." But, he adds, "I think it works." McMurtry reportedly received a multiyear contract of at least $10 million from Simon and Schuster for his last four books. He won't comment on that, but says that Comanche Moon was the final one in that group and that he's not seeking another multiyear contract. It's also questionable whether Comanche Moon will be made into a television miniseries, as were the other three Gus-and-Call novels. "It's not encouraging at the moment," he says. "The miniseries is getting harder and harder to get going. The networks are getting more and more nervous about it." Whether he'll do much more work with Hollywood is "unpredictable," McMurtry says, adding that he continues to be interested in good projects.
McMurtry is more interested these days in writing nonfiction and essays, a literary form he seized early in his career with In a Narrow Grave. Its essay on Southwestern literature took the first hard look at the literary contributions of the then-holy trinity of Texas letters J. Frank Dobie, Walter Prescott Webb, and Roy Bedichek and stirred up a hornet's nest of debate about the state of Texas letters. Another collection of essays, Film Flam (1987), took an entertaining look at his experiences with Hollywood. He has continued to write essays and reviews for publications like the New Republic, the New York Times, and American Film. The New York Review of Books in October ran an essay he wrote about Southwestern historian Angie Debo. His next project will likely be a short biography about the Sioux warrior Crazy Horse for a new series to be published by Viking Penguin Books.
BACK AT THE MAIN BOOKSTORE ON SOUTH Center Street, McMurtry perches on a stepladder in a cavernous room—the former garage of the old Ford dealership—filled with rows and rows of bookcases. He scribbles prices in the books without consulting a price guide. "I've been doing this for thirty years," he says, "and there aren't many books that I haven't seen before." The workers share a clear camaraderie; McMurtry jokes with his three young staffers and chats with several out-of-state dealers who are buying many boxes of books. A newly adopted shop cat, a calico named Colette (after the French writer), darts about. In the front room glass cases hold rare first editions, books with ornate covers, and unusual, expensive items—such as a batch of letters from poet Ezra Pound ($2,500). Browsers can spend hours going through the buildings, looking at books on everything from art to Continental literature (many in their original languages) to poetry—one of Booked Up's buildings has a whole room of poetry, mostly acquired from one poetry bookstore in New York.
McMurtry isn't just indulging his passion for books—he's making a profit. He says that in recent years Booked Up's sales have doubled annually. McMurtry started advertising the store in international antiquarian book publications for the first time last December and expects sales to triple this year. The average book sells for $20 to $50, but some are less than $10. The staff put together a small bookcase behind the front desk filled with McMurtry's books, but he says he doesn't like to sell his own books himself. First editions of his books, especially early ones, have become collectible in recent years, fetching hundreds of dollars. "They're expensive," he says.
In another touch of irony, one of the most expensive books in the shop is by J. Frank Dobie, the Texas writer McMurtry whiplashed as a provincial folklorist many years ago. A signed, limited edition of Dobie's The Mustangs sells for $4,000. Texana is popular at the store, but McMurtry says "it doesn't excite me much unless it's really good."
McMurtry has a story for just about every book. He'll pick up a copy and talk about the author, where he bought the book and from whom, and why it's in his shop. He gets rid of a lot of books that aren't collectible, sending them to prop shops in Hollywood or secondhand bookstores like Half Price Books. I couldn't pass up a first edition of America and Alfred Stieglitz, a collection of essays about the photographer and champion of modern art that had a signed note inside from artist Georgia O'Keeffe, who was married to Stieglitz. O'Keeffe wrote the note on a bookplate belonging to one Miles Hart. Hart, McMurtry explains, was a Glen Rose lawyer who was adept at getting writers and others to correspond with him. When McMurtry bought Hart's library, he also acquired some letters from poet Robert Graves. "It's always entertaining because there are little surprises," he says. "You never know what you'll find."
McMurtry has been in the book business so long that he's seen books that he owned while going to school at Rice, with his signature inside, turn up in a load of books he's bought. Sometimes copies of his own books that he inscribed and gave to someone have come back to the shop. "Larry will say, 'Well so-and-so must have gotten divorced because one of them had to sell their books,'" Deen says with a laugh. "Larry has always said that books go the full circle."
And so, it seems, has he.
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All the kids stare at Donna Brown. From the moment the whistle falls from her mouth, their gazes are fixed on her, anxiously awaiting her words.
The fiery Brown barks out instructions, and the kids follow them without question.
To some, the charismatic coach could appear intense and ruthless, but then she follows it up with some horseplay and joking, putting any notion of seriousness aside. This two-faced combination has served Brown well through her years of coaching basketball.
Brown excelled on the court in her days at Vicksburg High and turned her basketball beginning into a career. After spending four years as a starter for Mississippi State, she took up an assistant coaching job at Belmont University in Nashville.
Her success there will lead her to a promotion next week to associate head coach, Belmont head coach Tony Cross said.


The elephant poaching crisis is going to be on the agenda of the US – China Strategic and Economic Dialogue, scheduled for this Wednesday June 24 in Washington DC.
In fact, since then, nearly 50 tonnes of ivory confiscated from illegal trade have been crushed or torched in many countries and territories around the world, France, Chad, Belgium, Hong Kong SAR of China, Kenya, Congo Brazzaville, and UAE.
While a rule change at the national level is progressing slowly, the states of New York and New Jersey have banned ivory trade at the local level, prompting a series of legislative initiatives in eleven other states across the US.
On the eve of the U.S.—China Strategic and Economic Dialogue, I couldn't help but hope for a race between the world's two largest super powers.
But a noble race to save the noble giant—elephants.


No one who has attempted to publish the Bible in a new way has been free from serious, life threatening, persecution. We are blessed to have thousands, even millions, of friends, throughout the world, many of whom have been praying for us. Groups of thousands of intercessors have convened at different times over the past four years and asked God to intervene on our behalf, to foil and expose all wickedness, and pray for those who have so spitefully used and abused us.
It was Mr. Hall that directed Nova Spivack to use my investment for other than what it was agreed upon, we have the e-mails. It was Mr. Hall that boasted in his deposition that you have never said 'no' to him.
Mr. Hall and Mr. Levinsohn, you make Nova Spivack seem positively angelic compared to you both.
Mr. Levinsohn, if you are going to play politics, you're never going to win playing with third string players, you knew that, right?
It's because I'm a woman, isn't it? It's an automatic reflex to put women in their 'place'. And that I was born into such an enviable position? I'm a chip off the old block fellas.
update 2: The Classic Shell Game As Illustrated by Vulcan Capital- ABOVE THE LAW?
So, is the message I am getting, that what an idiot I am to think the law is protection for the more vulnerable among us who are so often taken advantage of? That judges are to halt the unscrupulous is a fairy tale that everyone knows doesn't happen? That you have to cheat and bully and lie to to make your way in this world, that you must be callous and disregarding of anyone's rights or interests except your own? Is that the message these men are sending to others? I get only anonymous comments on the blog: people are scared of these guys.
I feel like I'm drilling through a concrete of corruption I had no idea was so wide and deep; apparently I still haven't come to the end of it. Continuing these machinations only provides even more evidence of how they consider themselves above the law, and a law unto themselves, answerable to no one.
The Word of God is always sown with blood.
Paul Allen, Steve Hall, Ross Levinsohn will do anything to avoid a public trial.
We are demanding a public trial. All their efforts are to bury this and not have their actions exposed.
you will have nothing for which it is worthwhile to live.
in the catalogue of my Dad, William S. Paley's, collection of paintings, about working with Dad.
Mr. Allen, 'substantial' would be the value of my Dad's paintings on view at the De Young in San Francisco, wouldn't you say? Just so we're clear on the terms. For a man worth $14 billion dollars who is not afraid to boast about it, and goes out of his way to show off his knickknacks, I'm sure you can agree that that would be a 'substantial' amount. In that case, we do have a deal.
The trial should be very interesting if it goes forward. All of you have demonstrated, as you have just kept trying to strong arm Kate Paley unrelentingly for going on 5 years, expressing your contempt, exceptionably notable in the 3 days of grueling deposition you put her through, that you go through life without a thought or care for anybody but yourselves, and that you truly believe no one has any worth or value except yourselves. Its been quite remarkable to witness this up close and personal as it were. To be the direct recipient, etc. Who would have thunk it?
there will I bestow all my fruits and my goods.
I'll just keep adding one painting a day until you say it's substantial enough, ok?
Shall we include the Picasso below, or stop with everything up to the Cezanne above?
Anyway, what 'substantial' means to you, and what it s to me, or, what you think I should be grateful to think it means for me, and what it actually does mean for me- well, that's a lot of supposition.
Thank you for this opportunity to illustrate.
Let's include the Lautrec, shall we?
All financials that as an investor we of course are entitled to see, were kept from us until after the trial was scheduled to begin, five months ago, when Mr. Hall and Mr. Levinsohn declared bankruptcy for the company they themselves own in order to stop the trial from going forward.


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Unknowns to Help You Win Your 2009 Fantasy Baseball League
by nautical2k
Baseball is weird.
What other sport allows you to expand your roster by 60% for the last month of the season? Then, you have to decrease your roster by 15 players in time for the playoffs!
These September call up players can play a pivotal role down the stretch. So, I'm always amazed that teams don't all call up 15 more players on September 1! I mean, why wait? You could have that speed merchant pinch runner from a Low A team that can steal that base and possibly earn a wild card spot for you!
Other guys, usually who play on less fortunate teams, get a chance to make an immediate impact. It's these players that you should be looking for for your fantasy baseball stretch run.
Josh Anderson, OF — Braves' center fielder Anderson led off for the Braves last night and had a 3 for 5 night. Since getting called up on August 28, after the Braves' dealt Mark Kotsay to the Red Sox, he has started all four games. Anderson hit .314 at AAA Richmond while stealing 42 bases and was an International League all star. He also had a 27 game hit streak. Clearly, Kotsay became expendable. He's a lefty with no power, but will help your stolen base totals down the stretch. Anderson was up earlier this year and is hitting .326 thus far with 4 steals. He may get a rest now and then to give Gregor Blanco a chance to play, but expect Anderson to be the main man in center for the Braves in September.
Jonothon Niese, SP, Mets — With John Maine likely out for the season, Niese, just 21, gets the start tonight for the Mets in Milwaukee in a packed house pressure situation. Niese has a 92 MPH fastball, nasty splitter, good curve, and nice changeup. He was 5-1, 3.40 at AAA New Orleans after spending the first half of the year at AA Binghamton. Niese definitely has the stuff to be effective at the major league level, but does he have the mental toughness to survive a New York September in a rugged pennant race?
Taylor Teagarden, C, Rangers — Teagarden, just back from winning the bronze medal in Beijing, was expected to get significant playing time in September, but he should get more now that Jarrod Saltalamacchia has an arm injury. Teagarden, who entered the game for the injured Saltalamacchia last night for Texas, pounded a three run homer. Thanks to his first homer against the Twins in July, Teagarden now has two homers in just 8 at bats. Don't expect these Ruthian numbers to continue as Teagarden only hit .225 with 7 HR's at Spokane before leaving for China, but he will get a chance and if you have a weak backup catcher, why not take a chance on Teagarden. Besides, he has a cool name.
Ryan Shealy, 1B, Royals — There's a lot of talk in Kansas City about a young Hawaiian named Kila Ka'aihue. And, why not, Ka'aihue cracked 38 homers and hit .314 in the minors this year in just 124 games. Plus, talk about a cool name. But, the belief here is that Ryan Shealy will get more playing time in September than Ka'aihue. Shealy, 29, has played for Colorado and Kansas City and has hit .267 with 12 homers in three years of part-time duty, although this is his first call up this year. The key to the Royals' crowded first base situation in September though is that Shealy is out of options and Ka'aihue, 24, is not. The Royals have to make a decision now or they lose him next season. One thing is for certain, however. Ross Gload, currently at the top of the Royals depth chart, does not have the pop required of a major league first baseman with only 3 dingers all season.
Travis Snider, OF, Jays — With Alex Rios and Vernon Wells signed through 2014, there's really not an opening for the 20-year-old Snider right now. He will see some action at DH in September, likely sharing that role with Adam Lind. Snider was the 14th overall pick in 2006 out of high school. The stocky corner outfielder has 225 RBI in 305 games in his minor league career.
These guys probably won't get a lot of action in September, but they are awesome prospects likely to be impact players in 2009. If you are in a keeper league and you are out of it for this season, now is the time to pick up these 2009 fantasy baseball sleepers on the cheap.
Nate Schierholtz, OF, Giants — He's the guy who ran over the Chinese catcher in the Olympic games. Schierholtz hit .304 in 100+ AB's last year for the Giants.
David Price, SP, Rays — Price is the 6′ 6″ first rounder out of Vanderbilt last year who has blistered through the minors this year. He won his first 11 starts!
Matt LaPorta, OF, Indians — The former Florida Gator was drafted 7th overall by the Brewers in 2007. This dude has power. 12 homers in his first 30 games in the minors and LaPorta also hit a homer in the bronze medal game for Team USA.
Matt Wieters, C, Orioles — The Sporting News claims Wieters could be "one of the top five backstops by this time next year." Wow, some claim. Has anybody told Ramon Hernandez? Wieters is a big time slugger. He is not, however, expected to get a September call up and will play in the Arizona Instructional League.
Scott Elbert, RP, Dodgers — A 2004 first rounder who has great stuff. He's 3-0, 1.46 in his last ten games with Jacksonville. He'll be limited to one inning per game with the Dodgers.
Dallas McPherson, 3B, Marlins — McPherson leads all minor leaguers with 42 homers this year at AAA Albuquerque. Unfortunately for McPherson, he would have been up earlier, but is blocked by Jorge Cantu who has been the Fish's best player this season.
Tony K. Gwynn Jr., OF, Brewers — He will likely steal some bases for the Brewers coming off the bench in September. His Dad was a pretty good player too.
Matt Gamel, 3B, Brewers — Gamel was hitting .384 in mid-June, but has slipped some since to .329 when he got the call up to the Brew Crew. Gamel will challenge Bill Hall for the Brewers third base job next year.
Anthony Wayne works in the marketing department of fantasy baseball tips site Fantasy Baseball Dugout in Pennsylvania. He is also editor of College Fantasy Coach, a great source of college fantasy football information.
Related Fantasy Baseball Articles
Tags: 2009, Baseball, Fantasy, Help, League, Unknowns
This entry was posted on February 22, 2011 at 3:24 pm and is filed under Fantasy Baseball. You can follow any responses to this entry through the RSS 2.0 feed.

redpajama_set_name: RedPajamaCommonCrawl


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

  Over the past decades, complex networks have been used as the model
  of choice for a truly broad class of physical problems, ranging from
  highway traffic \cite{joubert_large-scale_2010} to brain
  connectivity \cite{hermundstad_learning_2011}.
  When modeling such systems, dynamics are typically defined at a very
  detailed, ``fine'' scale, specifying individual node and edge
  interactions; explicit closed equations governing network
  macroscopic (collective) properties are often unavailable
  \cite{durrett_graph_2012,joubert_large-scale_2010,roche_agent-based_2011,swaminathan_modeling_1998}.
  The detailed interactions are often complicated functions dependent
  on multiple system parameters, which, when one also accounts for
  large network sizes inherent in many interesting problems, can make
  systematic numerical exploration computationally prohibitive.
  The lack of \textit{explicit} macroscopic equations prohibits the
  use of traditional numerical techniques such as fixed-point
  computation and stability analysis, that could offer valuable
  insight into the network's behavior, leaving little alternative but
  to work with full direct simulations of the entire network.
  Faced with these inherent limitations, investigators must either
  restrict their attention to a more modest parameter space
  \cite{hodgkin_quantitative_1952} or simplify the network model,
  potentially removing important features
  \cite{brown_variability_1999}. \par

  Equation-free modeling offers a promise to circumvent these
  challenges, allowing one to investigate the complex network at a
  macroscopic level while retaining the effects of full system details
  \cite{kevrekidis_equation-free:_2004,gear_equation-free_2003}.
  Underlying this method is the assumption that, although we cannot
  analytically derive equations governing network evolution, such
  closed equations do, in principle, exist.
  Furthermore, these unavailable equations are assumed to involve a
  small number of dominant collective (coarse-grained) variables.
  The important features of the complete network can be in fact, by
  this assumption, well represented by these select observable
  quantities.
  This may seem too restrictive, yet it is exactly the behavior
  witnessed across many network types: despite the initial complexity
  of the full system configuration, certain collective network
  properties appear to evolve smoothly in time, while the evolution of
  other, ``secondary'' properties, can be strongly correlated with
  that of the few ``primary'' variables
  \cite{bold_equation-free_2014,rajendran_coarse_2011,siettos_equation-free_2011}.
  Once these significant variables are uncovered, we can combine short
  intervals of full system simulation with operators that map the full
  system description to and from its representative coarse variable
  ``summary'', thus enabling previously-infeasible system-level
  analysis (see Section \ref{sec:ef} for further details). \par

  Here, we apply this framework to a dynamically evolving multigraph
  model.
  This offers a test of the methodology in the previously unexplored
  context of multigraphs.
  We demonstrate the acceleration of direct network simulations
  through Coarse Projective Integration (CPI), and the location of
  coarse stationary states through a matrix-free Newton-GMRES method.
  In addition, principal component analysis (PCA) and diffusion maps
  (DMAPS), two well established dimensionality-reduction techniques,
  are shown to enable an algorithm for characterization of the
  underlying low-dimensional behavior of the system. \par

  The paper is organized as follows: we begin in Section \ref{sec:m}
  with a description of the multigraph dynamic model.
  Sections \ref{sec:ef} and \ref{sec:ng} provide most details of the
  equation-free approach, specify how it was applied to our system,
  and present subsequent results.
  Section \ref{sec:dr} summarizes our use of PCA and DMAPS, and
  assesses their effectiveness in analyzing hidden, low-dimensional
  structure in this network system.

  \section{Model description}
  \label{sec:m}

  We study the edge-conservative preferential attachment model, a
  detailed mathematical analysis of which can be found in
  \cite{rath_time_2012} and \cite{rath_multigraph_2012}.
  The system evolves in discrete steps $t = 0,1,\ldots t_f$, and we
  denote the $n$-vertex graph at each point by $G_n(t)$.
  The initial state, $G_n(0)$, is an arbitrary distribution of $m$
  edges among the $n$ vertices; the total number of both edges and
  vertices is conserved.
  No restrictions are placed on this initial distribution: multiple
  edges and loops are permitted.
  The system is then advanced step-by-step based on the following
  procedure:

  \begin{enumerate}
  \item Choose an edge $e_{ij} \in E(G)$ uniformly at random, and flip
    a coin to label one of the ends as $v_{i}$
  \item Choose a vertex $v_{k}$ using linear preferential attachment:
    $P(v_{k} = v_{l}) = \frac{d_{l} + \kappa}{\sum\limits_{i=1}^{n}
      d_{i} + n \kappa}$
    \label{step:prob}
  \item Replace $e_{ij}$ with $e_{ik}$,
  \end{enumerate}

  \noindent where $d_i$ is the degree of vertex $v_i$, $E(G)$ is the
  set of edges in the graph, and $\kappa \in (0, \infty)$ is a model
  parameter affecting the influence degrees have on the probability
  distribution in Step \ref{step:prob}.
  That is, taking $\lim\limits{\kappa \rightarrow 0}$, we recover
  ``pure'' preferential attachment, and probability scales directly
  with degree,
  while
  $\lim\limits_{\kappa \rightarrow \infty} P(v_k = v_l) = \frac{1}{n}
  \; \forall \; l$, and individual degrees have no effect. A single
  step of this evolution process is illustrated in
  Fig. ({\ref{fig:step-illustration}}). Note that this can also be
  represented as a graph in which only one edge is permitted between
  each pair of nodes, but with edge weights signifying the number, or
  strength, of connections between nodes.
  \par

  Evolving the system in this manner, the degree sequence approaches a
  stationary distribution over $O(n^3)$ steps.
  As explained in \cite{rath_time_2012}, this distribution is
  dependent only on the system parameters $\rho = \frac{2*m}{n}$ and
  $\kappa$.
  Fig. (\ref{fig:dse}) illustrates the evolution of the degree
  sequence of two networks with different initial configurations but
  the same values of $\rho$ and $\kappa$ respectively; as expected, we
  observe they approach an identical stationary state.

  \begin{figure}[ht!]
    \centering
    \includegraphics[width=0.9\textwidth]{graph-evo}
    \caption{Schematic of the substeps of the evolution dynamics of the
      multigraph $G(t)$. \label{fig:step-illustration}}
  \end{figure}

  \begin{figure}[th!]
    \vspace{-5mm} \centering
    \begin{subfigure}[t]{0.49\textwidth}
      \centering
      \includegraphics[width=\textwidth]{lopsided-degs-a}
      \subcaption{\label{fig:lopsided-init}}
    \end{subfigure}
    \begin{subfigure}[t]{0.49\textwidth}
      \centering
      \includegraphics[width=\textwidth]{erdos-degs-a}
      \subcaption{\label{fig:erdos-init}}
    \end{subfigure}
    \caption{Evolution of the multigraph model's sorted degree
      sequence. Two distinct transients are shown, initialized with
      (a) an Erd\H{o}s-R\'{e}nyi random graph with $m = 5,050$ edges
      and (b) a graph in which half the vertices are isolated, and
      half uniformly share $m = 5,050$ edges. Both approach the same
      ultimate stationary sequence. To smoothen the evolution of this
      stochastic system, here we plot the instantaneous average of twenty
      simulations. \label{fig:dse}}
  \end{figure}

  \begin{figure}[th!]
    \centering
    \includegraphics[width=0.8\textwidth]{triangle-slaving-n2-n3}
    \caption{Evolution of higher-order network statistics (here, the
      total triangle count) for three different network
      initializations. They are quickly ($O(n^2)$ steps) drawn to a slow
      manifold on which they slowly evolve over ($O(n^3)$ steps) to a
      stationary state. \label{fig:sv}}
  \end{figure}

  \section{Equation-free modeling}
  \label{sec:ef}

  \subsection{Coarse-graining}

  A prerequisite to the implementation of equation-free algorithms is
  the determination of those few, select variables with which one can
  ``close'' a useful approximate description of the coarse-grained
  dynamics of the full, detailed network.

  This set of variables should clearly be (much) less than those of the
  full system state, and they should evolve smoothly (so, either our
  network would have a large number of nodes, or we would take
  expectations over several realizations of networks with the same
  collective features).

  Based on the profiles depicted in Fig. (\ref{fig:dse}), the graph's
  degree sequence, (equally informatively, its degree distribution) is a
  good candidate collective observable.

  It appears to evolve smoothly, while also providing significant
  savings in dimensionality, from an $O(n^2)$ adjacency matrix to a
  length-$n$ vector of degrees. \par

  It now becomes crucial to test whether the evolution of other
  properties of the graph can be correlated to the degree sequence.

  If not, our current description is incomplete (an equation cannot
  close with only the degree sequence) and there exist other important
  coarse variables that must be included in our macroscopic system
  description.

  To assess this, we constructed graphs with identical degree sequences
  but varying triangle counts and recorded the evolution of this
  observable under the dynamics prescribed in Section
  \ref{sec:m}. Fig. (\ref{fig:sv}) shows that, within a short time, the
  triangle counts are drawn to an apparent shared ``slow manifold'',
  despite the variation in initial conditions.

  This supports our selection of the degree sequence as an adequate
  coarse variable to model the system. \par

  Next we describe the other two key elements to equation-free modeling
  which map to and from our microscopic and macroscopic descriptions:
  restriction and lifting operators.

  \begin{figure}[ht!]
    \centering
    \includegraphics[width=\textwidth]{cpi-diagram}
    \caption{Schematic of our restriction ($\mathbf{R}$) and lifting
      ($\mathbf{L}$) procedures. The graph's sorted degree sequence is
      calculated ($R_1$) and then fit to a third-order polynomial
      ($R_2$). This maps graphs ($G$) to vectors of four coefficients
      ($\mathbf{c}$). To lift, the polynomial is used to generate a
      degree sequence ($L_1$), which is then used to construct a
      consistent graph ($L_2$). This process maps vectors of four
      coefficients ($\mathbf{c}$) to graphs ($G$).
      $\mathbf{R} = R_2 \circ R_1 $ is our restriction and
      $\mathbf{L} = L_2 \circ L_1$ is our
      lifting. \label{fig:cpi-diagram}}
  \end{figure}

  \subsection{Restriction}

  The restriction operator ``translates'' the full network description
  to its corresponding coarse variable values.

  Here, this involves mapping a multigraph to its sorted degree
  sequence, a simple calculation.

  However, we may be able to further reduce the dimensionality of our
  coarse system by keeping not the entire length-$n$ degree sequence,
  but rather a low-order polynomial fitting of it.

  To do so, we first sort the sequence to achieve a smooth,
  monotonically increasing dataset, then fit the result to a third-order
  polynomial, which had been observed to closely approximate the
  sequence evolution throughout time.

  Our coarse description thus consisted, at every moment in time, of the
  four polynomial coefficient values, specifying a particular sorted
  degree sequence, as shown in Fig. (\ref{fig:cpi-diagram}).

  The restriction operator therefore maps multigraphs to length-four
  coefficient vectors.

  This procedure is represented by the blue arrows of
  Fig. (\ref{fig:cpi-diagram}).

  \subsection{Lifting}

  The lifting operator performs the inverse role of restriction:
  translating coarse variables into full networks.

  This is clearly a one-to-many mapping.

  Specifically in the context of our model, we map vectors of polynomial
  coefficients to full networks in a two-stage process.

  First, the coefficient vector is used to recreate a sorted degree
  sequence by evaluating the full polynomial at integer values and
  rounding to the nearest degree, as depicted in
  Fig. (\ref{fig:cpi-diagram}).

  If the sum of the resulting degree sequence is odd, a single degree is
  added to the largest value to ensure the sequence is graphical.

  Second, this degree sequence is used as input to any algorithm that
  produces a network consistent with a graphical degree sequence (here
  we typically used a Havel-Hakimi algorithm, creating a graph whose
  degree sequence matches that of the input
  \cite{havel_remark_1955,hakimi_realizability_1962}).

  While the canonical Havel-Hakimi method produces simple graphs, it is
  not difficult to extend it to multigraphs by allowing the algorithm to
  wrap around the sequence, producing multiple edges and self-loops.
  The lifting procedure is represented by the red arrows of
  Fig. (\ref{fig:cpi-diagram}).

  \subsection{Coarse projective integration (CPI)}
  \label{sec:cpi}

  The components described above are combined to accelerate simulations
  through Coarse Projective Integration.

  Denoting the lifting operator by $\mathbf{L} \; : \; c \rightarrow G$
  where $c \in \mathbb{R}^4$ is the vector of polynomial coefficients,
  and the restriction operator as $\mathbf{R} \; : G \rightarrow c$, the
  algorithm progresses as follows:

  \begin{enumerate}
  \item Advance the full system for $t_h$ steps
    \label{cpi:heal}
  \item Continue for $t_c$ steps, restricting the network at intervals
    and recording the coarse variable values
  \item Using the coarse variables collected over the previous $t_c$
    steps, project each variable forward $t_p$ steps with, here, a
    forward-Euler method
    \label{cpi:proj}
  \item With the new, projected coarse variables, lift to one (or more
    copies of) full network
    \label{cpi:init}
  \item Repeat from Step (\ref{cpi:heal}) until the desired time has
    been reached.
  \end{enumerate}

  Note that Step (\ref{cpi:heal}) is intended to allow a singularly
  perturbed quantity to approach its slow manifold.

  Upon initializing a new full system (here, a new network) in Step
  (\ref{cpi:init}), higher order observables (for example, the triangle
  count) will be far from the values they would attain in a detailed
  simulation.

  If the coarse system model closes with our chosen variables, then
  either (a) these quantities do not affect the dynamics of the degree
  sequence; or (b) they do, but then by hypothesis these quantities
  quickly evolve to functions of the selected coarse variables (i.e.,
  they approach the slow manifold).

  In the second case, after a short interval of ``healing'' they will be
  drawn to the expected trajectory, after which we begin sampling.

  This is analogous to Fig. (\ref{fig:sv}).

  The computational gains arise from the projective step,
  (\ref{cpi:proj}).

  Here, we advance the system $t_p$ steps at the cost of one evaluation of forward Euler, instead of $t_p$ direct detailed steps. \\

  Results of the application of this general framework with the specific
  lifting and restriction operators previously outlined are shown in
  Fig. (\ref{fig:cpi-results}). We used an $n=100$-vertex graph with
  $m=50000$ edges and parameter value $\kappa=1$. We ran the model for a
  total of $10 \cdot n^3$ steps, with $t_h = 10 \cdot n^2$, $t_c = n^3$
  and $t_p = 50 \cdot n^2$.

  We see good agreement between the CPI-accelerated and normal systems,
  while reducing the number of detailed steps by one third.

  It is important to mention that this method was applied not to a
  single system instantiation,
  but to an ensemble of fifty such realizations.
  This ensured that when properties such as the fitted polynomial
  coefficients were averaged over the ensemble they evolved smoothly
  despite the stochastic nature of the system; in effect, we are
  computing the ``expected network evolution'' averaged over consistent
  realizations.

  \begin{figure}[h!]
    \vspace{-5mm} \centering
    \begin{subfigure}{0.59\textwidth}
      \centering
      \includegraphics[width=\textwidth]{cpi-3d-comp-stretch}
      \subcaption{\label{fig:cpi-error}}
    \end{subfigure}
    \begin{subfigure}{0.39\textwidth}
      \centering
      \includegraphics[width=\textwidth]{cpi-relative-error-n3}
      \subcaption{\label{fig:self-error}}
    \end{subfigure
    \caption{CPI results: (a) the evolution of the CPI-accelerated
      degree sequence (red) compared to direct simulation (blue) and (b)
      error in CPI-accelerated runs calculated by comparing
      CPI-accelerated degree sequences to those arising from an ensemble
      of direct simulations. \label{fig:cpi-results}}
  \end{figure}

  \section{Coarse Newton-GMRES}
  \label{sec:ng}
  Aside from the computational savings of model simulations offered by
  CPI, the equation free framework also permits the calculation of
  system steady states through fixed point (here, matrix-free
  Newton-GMRES) algorithms.

  Referring back to the CPI procedure outlined in Sec. (\ref{sec:cpi}),
  we can define an operator $\Theta: \mathbf{d} \rightarrow \mathbf{d}$
  projecting coarse variables at $t$ to their values at $t + \delta t$:
  $\mathbf{d}(t+\delta t) =\Theta(\mathbf{d}(t))$.

  Note that in this section, we take the sorted degree sequence as our
  coarse variable.

  A system fixed point could then be located by employing Newton's
  method to solve the equation

  \begin{align}
    \label{eq:f}
    \mathbf{F}(\mathbf{d}) \equiv \Theta(\mathbf{d}) - \mathbf{d} = 0
  \end{align}

  However, this requires the repeated solution of the system of linear
  equations
  $DF(\mathbf{d}^{(k)}) \cdot \delta \mathbf{d}^{(k+1)} =
  -\mathbf{F}(\mathbf{d}^{(j)})$,
  in which the system Jacobian, $DF$ is unavailable.

  Thankfully, we may circumvent this obstacle by estimating the
  directional derivatives $DF(\mathbf{d}) \cdot \delta \mathbf{d}$ via a
  difference approximation of the form

  \begin{align}
    DF(d) \cdot \delta \mathbf{d} \approx \frac{\| \delta \mathbf{d} \| \mathbf{F}(\mathbf{d} + h \| \mathbf{d} \| \frac{\delta \mathbf{d}}{\| \delta \mathbf{d} \|}) - \mathbf{F}(\mathbf{d})}{h \| \mathbf{d} \|}
  \end{align}

  \noindent for nonzero $\mathbf{d}$, which in turn is evaluated through
  calls to $\mathbf{F}$ as defined in Eq. (\ref{eq:f})

  This is precisely the approach of the Newton-GMRES method, in which
  the solution to a linear system is sought for in expanding Krylov
  subspaces \cite{kelley_solving_2003}.

  Applying this algorithm in conjunction with the $\Theta$ operator
  defined in Sec. (\ref{sec:cpi}) allowed us to locate the stationary
  distribution without simply running and observing the full system for
  long times, as would otherwise be required.

  Results are shown in Fig. (\ref{fig:newton-results}).

  We note that coarse stability results can also be obtained via an
  iterative Arnoldi eigensolver, again estimating matrix-vector products
  as above.

  \begin{figure}[h!]
    \vspace{-5mm} \centering
    \begin{subfigure}{0.49\textwidth}
      \centering
      \includegraphics[width=\textwidth]{newton-error}
      \subcaption{\label{fig:newton-error}}
    \end{subfigure}
    \begin{subfigure}{0.49\textwidth}
      \centering
      \includegraphics[width=\textwidth]{newton-comp}
      \subcaption{\label{fig:newton-comp}}
    \end{subfigure
    \caption{Coarse Newton-GMRES results: (a) evolution of the error in
      the coarse Newton-GMRES iteration scheme; and (b) visual
      comparison of the algorithm's solution to the stationary state
      obtained from direct simulation. \label{fig:newton-results}}
  \end{figure}

  \section{Algorithmic coarse-graining}
  \label{sec:dr}
  Crucial to the above analysis was the determination of suitable
  coarse, system variables: it is the starting point of any equation
  free method.

  However, the discovery of such a low-dimensional description is highly
  non-trivial.

  Currently, as in this paper, they are ``discovered through informed
  experience'': through careful investigation of direct simulations, and
  knowledge of previous analytical results.

  Clearly, any process which could algorithmically guide this search
  based on only simulation data would be of great benefit to the
  modeling practitioner.

  We now illustrate two such algorithms: principal component analysis
  (PCA) and diffusion maps (DMAPS).

  First, we briefly discuss some aspects of the important issue of
  defining distances between networks, a prerequisite for any
  dimensionality-reduction technique.

  \subsection{On network distances}

  When applying dimensionality reduction techniques to a dataset, it is
  necessary to define a distance (or similarity) between each pair of
  data points.

  If these points are a set of vectors in $\mathbb{R}^n$ one has a
  number of metrics to choose from, the Euclidean distance being a
  common first option.

  Unfortunately, when individual points are not vectors but networks,
  the definition of a useful and computationally easily quantified
  metric becomes far more challenging.

  Examples such as the maximal common subgraph and edit distances,
  defined in \cite{bunke_graph_1998} and \cite{gao_survey_2010} define
  metrics on the space of graphs, but their computation is
  $NP\textnormal{-}hard$.

  Other computationally feasible approaches include comparing
  distributions of random walks on each graph
  \cite{vishwanathan_graph_2010}, calculating the so-called $n$-tangle
  density \cite{gallos_revealing_2014}, or calculating the edit distance
  with an approximation algorithm \cite{riesen_approximate_2009,zeng_comparing_2009}.  \\

  The strategy used in the following computations, detailed in
  \cite{rajendran_analysis_2013} and \cite{xiao_structure-based_2008},
  enumerates the number of times a certain set of motifs (or subgraphs)
  appears in each network in the dataset.

  This maps each network to a vector in $\mathbb{R}^n$, and the
  Euclidean distance is subsequently used to quantify the similarity of
  two graphs.

  Due to computational restrictions, we chose here to only count the
  number of three- and four-vertex single-edge subgraphs contained in
  each network.

  As there are eight such motifs, shown in Fig. (\ref{fig:motifs}), this
  process $\gamma$ maps each graph to an eight-dimensional vector:
  $\gamma : G \rightarrow \mathbb{R}^8$.

  We applied this operation to a simplified version of each graph,
  wherein any multiple edges were reduced to a single edge.

  \begin{figure}[h!]
    \vspace{-5mm} \centering
    \includegraphics[width=\textwidth]{motifs}
    \caption{List of the single-edge subgraphs used to embed each
      network.  The number of times each appeared in the simplified
      input graph was calculated, mapping input graphs to
      $\mathbb{R}^8$. \label{fig:motifs}}
  \end{figure}

  \subsection{PCA}
  \label{sec:pca}

  PCA is used to embed data into linear subspaces that capture the
  directions along which the data varies most
  \cite{jolliffe_principal_2014}.

  Given some matrix $X \in \mathbb{R}^{n \times m}$ in which each of the
  $m$ column vectors $x_i$ represents a different collection of the $n$
  system variables (i.e. a different data point), PCA computes a reduced
  set of $k<n$, orthogonal ``principal components''
  $z_i \in \mathbb{R}^n$ that constitute an optimal basis for the data
  in the sense that, among linear, $k$-dimensional embeddings,
  $w_i = Z^Tx_i$ captures the maximum possible variance in the dataset,
  where
  $Z = \begin{bmatrix} | & | & & | \\ z_1 & z_2 & \hdots & z_k \\ | & |
    & & | \end{bmatrix}$.

  This approach has found wide application, but can suffer from its
  inability to uncover simple \textit{nonlinear} relationships among the
  input variables.

  Indeed, many datasets will not ``just lie along some hyperplane''
  embedded in $\mathbb{R}^n$, but will rather lie on a low-dimensional
  nonlinear manifold throughout the space. \\

  Theoretical results in \cite{rath_time_2012} state that, for a given
  network size $n$, the final stationary state depends only on the
  number of edges present $m$ and the model parameter $\kappa$.

  To assess both the validity of our graph embedding technique and the
  usefulness of PCA in the context of complex networks, we generated a
  dataset of stationary states over a range of $m \in [50, 5000]$ and
  $\log(\kappa) \in [0, 2]$ values by directly running the model over
  $2n^3$ steps ($N=30$ values of each parameter were taken, for a total
  of $900$ networks).

  We fixed the network size to $n=50$ vertices.

  Each resulting graph $G(m_i, \kappa_j) = G_{ij}$ was then embedded
  into $\mathbb{R}^8$ by counting the number of times each of the
  subgraphs shown in Fig. (\ref{fig:motifs}) appeared in the network.

  Thus $\gamma(G_{ij}) = v_{ij} \in \mathbb{R}^8$.

  We then proceeded to perform a principal component analysis on this
  collection of vectors $\{v_{ij}\}_{i,j=1}^N$.

  Interestingly, the first two principal components $z_1$ and $z_2$
  succeeded in uncovering a two-dimensional embedding of the dataset
  corresponding to the two underlying parameters $m$ and $\kappa$, as
  shown in Fig. (\ref{fig:pca}) in which the data is projected on the
  plane spanned by these two vectors.

  This suggests that, given some final network state $G(m, \kappa)$, by
  projecting its embedding onto these first two principal components one
  could obtain a reasonable approximation of the hidden parameter
  $\kappa$.

  \begin{figure}[h!]
    \vspace{-5mm} \centering
    \begin{subfigure}{0.49\textwidth}
      \centering
      \includegraphics[width=\textwidth]{pca-2d-rho-a}
      \subcaption{\label{fig:pca-rho}}
    \end{subfigure}
    \begin{subfigure}{0.49\textwidth}
      \centering
      \includegraphics[width=\textwidth]{pca-2d-kappa-a}
      \subcaption{\label{fig:pca-kappa}}
    \end{subfigure
    \caption{PCA of motif-based embeddings: (a) coloring the
      two-dimensional PCA embedding with $\rho$ and (b) coloring the
      two-dimensional PCA embedding with $\kappa$. \label{fig:pca}}
  \end{figure}

  \subsection{Diffusion Maps}

  Unlike PCA, DMAPS uncovers parameterizations of nonlinear manifolds
  hidden in the data.

  This is achieved by solving for the discrete equivalent of the
  eigenfunctions and eigenvalues of the Laplace-Beltrami operator over
  the manifold, which amounts to calculating leading
  eigenvector/eigenvalue pairs of a Markov matrix $A$ describing a
  diffusion process on the dataset.

  As the eigenfunctions of the Laplace-Beltrami operator provide
  parameterizations of the underlying domain, the output eigenvectors
  $\Phi_i$ from DMAPS similarly reveal and parameterize any hidden
  nonlinear structure in the input dataset. The $k$-dimensional DMAP of
  point $x_i$ is given by

  \begin{align*}
    \Psi(x_i; t) = \begin{bmatrix} \lambda_1^t \Phi_1(i) \\ \lambda_2^t
      \Phi_2(i) \\ \vdots \\
      \lambda_k^t \Phi_k(i) \end{bmatrix}
  \end{align*}

  \noindent where $\lambda_i$ is the $i^{th}$ eigenvalue of the Markov
  matrix $A$, $\Phi_i(j)$ the $j^{th}$ entry of eigenvector $i$, and the
  parameter $t$ allows one to probe multiscale features in the
  dataset. See \cite{coifman_diffusion_2006,nadler_diffusion_2006} for
  further details. \\

  First we applied DMAPS to the dataset described in
  Sec. (\ref{sec:pca}).

  Given the apparent success of PCA in this setting, one would expect
  DMAPS to also uncover the two-dimensional embedding corresponding to
  different values of $m$ and $\kappa$.

  Fig. (\ref{fig:dmaps-rk}) shows that this is indeed the case: using
  $\Phi_1$ and $\Phi_4$ to embed the graphs produces a two-dimensional
  surface along which both $\rho$ and $\kappa$ vary independently. \\

  Additionally, we were interested in embeddings of different model
  trajectories.

  This dataset was generated by sampling two different model simulations
  as they evolved ($N=2000$ points were sampled from each trajectory).

  The parameters $n$, $m$ and $\kappa$ were held constant at $200$,
  $20100$ and $1.0$ respectively, but one graph was initialized as an
  Erd\H{o}s-R\'{e}nyi random graph (Fig. (\ref{fig:erdos-init})), while
  the other was initialized as a ``lopsided'' graph
  (Fig. (\ref{fig:lopsided-init})).

  Every $500$ steps the graph would be recorded till $N$ snapshots were
  taken of each trajectory, for a total of $1000000$ steps.

  Letting $G_e(t)$ refer to the Erd\H{o}s-R\'{e}nyi-initialized system
  at step $t$, and $G_l(t)$ the lopsided-initialized system, the
  embedding $\gamma$ was used to create points
  $\gamma(G_e(t)) = v_e(t) \in \mathbb{R}^8$ and
  similarly $\gamma(G_l(t)) = v_l(t) \in \mathbb{R}^8$. \\

  DMAPS was then applied to this set of $2N$ points, and the
  three-dimensional embedding using $\Phi_1$, $\Phi_2$ and $\Phi_3$ is
  shown in Fig. (\ref{fig:dmaps-results}).

  At first, the two trajectories are mapped to distant regions in
  $\mathbb{R}^3$ due to their different initial conditions.

  They evolve along different ``regions'' of the embedding, taking two
  distinct trajectories on their approach to the stationary state.

  Eventually, their embeddings meet as they arrive at this final, shared
  state, each asymptotically along opposite sides of the slowest
  eigenvector of the linearization of the steady state.

  DMAPS thus proves useful in elucidating both geometric and dynamic
  features of the system as shown in Figs. (\ref{fig:dmaps-rk}) and (\ref{fig:dmaps-results}). \\

  We see that both PCA and DMAPS, when combined with a suitable
  embedding of each graph, can help uncover useful information
  pertaining to the underlying dimensionality of the problem dynamics.

  \begin{figure}[h!]
    \vspace{-5mm} \centering
    \begin{subfigure}{0.49\textwidth}
      \centering
      \includegraphics[width=\textwidth]{dmaps-2d-rho-a}
      \subcaption{\label{fig:dmaps-rho}}
    \end{subfigure}
    \begin{subfigure}{0.49\textwidth}
      \centering
      \includegraphics[width=\textwidth]{dmaps-2d-kappa-a}
      \subcaption{\label{fig:dmaps-kappa}}
    \end{subfigure
    \caption{DMAP of motif-based embeddings from a collection of
      simulations run at different parameter values: (a) coloring the
      two-dimensional DMAPS embedding with $\rho$ and (b) coloring the
      two-dimensional DMAPS embedding with $\kappa$. As with PCA, DMAPS
      uncovered the parameters governing the stationary state, known
      from \cite{rath_time_2012} \label{fig:dmaps-rk}}
  \end{figure}

  \begin{figure}[h!]
    \vspace{-5mm} \centering
    \begin{subfigure}{0.49\textwidth}
      \centering
      \includegraphics[width=\textwidth]{dmaps-3d-convergence-a}
      \subcaption{\label{fig:dmaps-results-regular}}
    \end{subfigure}
    \begin{subfigure}{0.49\textwidth}
      \centering
      \includegraphics[width=\textwidth]{dmaps-3d-convergence-zoom-a}
      \subcaption{\label{fig:dmaps-results-zoom}}
    \end{subfigure
    \caption{DMAP of motif-based embeddings from two separate simulation
      trajectories: (a) embedding of two trajectories starting from
      different initial conditions. While the trajectories are initially
      mapped to separate regions of ``embedding space'', they eventually
      evolve to the same coarse stationary state.  Final states are
      circled, and point size grows as more steps are taken in each
      trajectory.  (b) Enlarged view of the final points in the previous
      plot, revealing the approach to a similar final state along
      opposite sides of the slowest eigenvector of its linearization.
      Here, the average of the final fifty points is circled. Due to the
      stochastic nature of the system, the final embeddings mildly
      fluctuate randomly about the shared stationary
      state. \label{fig:dmaps-results}}
  \end{figure}

  \section{Conclusion}

  The equation free framework was successfully applied to the
  edge-conservative preferential attachment model, accelerating
  simulations through CPI and locating stationary states through coarse
  Newton-GMRES. This indicates potential avenues for improving
  simulation times of other complex network models, such as those
  found in epidemiology.

  Additionally, an underlying two-dimensional description was
  uncovered via PCA and DMAPS. These automated methods of
  dimensionality-reduction are quite general, and can in principle be
  applied in a wide range of network settings to probe hidden
  low-dimensional structure. For an application in the setting of
  labeled nodes, see ({\cite{kattis_modeling_2016}}). \\

  However, an open area of investigation is the interpretation of the output from
  the PCA and DMAPS techniques.

  As a linear map, PCA retains a clear relationship between its input
  and output, but is less effective when data lie on highly nonlinear
  manifolds.

  DMAPS may perform better the task of dimensionality reduction, but it
  is unclear how the embedding coordinates relate to physical features
  of the sampled networks.

  While this approach opens several promising avenues in coarse-graining
  complex network dynamics, it also reveals two ``bottlenecks'' for the
  process: (a) the selection of informative and practically computable
  graph distance metrics, necessary in the discovery of good coarse
  variables; and (b) the construction of network realizations consistent
  with (conditioned on) specific values of collective observables.

  Both items are, and we expect will continue being, the subject of
  intense investigation by many research groups, including ours.

  \begin{acknowledgement}
    The authors are grateful to Bal\'{a}zs R\'{a}th for helpful
    discussions and for several insightful suggestions that have greatly
    enhanced this work.
  \end{acknowledgement}

\end{onehalfspace}

\bibliographystyle{abbrv}

redpajama_set_name: RedPajamaArXiv


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The New World of Briggs
Halbert's Family Heritage, 1999. First edition. softcover wraps. Fat 4to, clean and tight. This is not just a genealogy of the Briggs family name, though it has many thousands of entries of Briggs and Briggs offshoots. It is also a history of immigration from Preindustrial Europe to the 1990s. Researchers..... More
The History of the Robinson Family
Robinson, F. Willard
Self Published, 1993. First edition. Hardcover. Clean, tight 4to, 224 pp. Very slight fading and shelf wear. "...provide(s) a family record that would have been lost in the annals of time. Within the contents of this record are materials upon which some future family writer and historian could produce an..... More


SMI is an acronym that can contain many meanings which are listed below.
Security Management Interface is not the only word formed from SMI. There are 4 more listed below.
SMI is also derived from Sun Microsystems Inc.
If you have more information or know of another definition for SMI, please let us know so that we can review it and add that information to our database.
Every attempt has been made to provide you with the correct acronym for SMI. If we missed the mark, we would greatly appreciate your help by entering the correct or alternate meaning in the box below.


I love that CCA is a kingdom-building ministry as well as a school with a phenomenal educational standard for my children. CCA is an investment in my children, their future, kingdom-building work, and the world. I can not thank God enough for the loving and talented teachers and staff! I love that my children will one day be CCA alumni!


The homage to the original Scrambler models from the 70's, can be considered as more than successful, if you take a look at the brandnew Ducati Scrambler models like Icon, Classic, Urban Enduro, Full Throttle and Desert Sled!
The optically very attracting dependance on the original from this retro bike is no surprise, because the designers from Borgo Panigale already created some milestones in motorcycle history!
Of course, we couldn't leave our fingers from this 800ccm 90 degrees V2 engine bike from Bologna and offer a variety of beautiful Carbon parts for the Scrambler series.


Light up the outdoors with these fantastic solar lights! Save money and energy with this eco-friendly garden light! Simply attach the lights to any flat surface such as fences, signs, doorways, decks, and railings with the easy-to-use lamp clamps and they will be charged during their daily exposure to the sun. Charged by the sun, these lights automatically switch on at night and stay on until switched off manually or the sun rises. Plus there's no messy, dangerous cords to get in the way.


Prime Concepts Group is the exclusive manufacturer and distributor of all Randy Gage products.
Call us at 1-800-946-7804 or (316) 942-1111 within 60 days of the invoice date to receive your Return Authorization Number.
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Please note that we reserve the right to charge a minimum 20% restocking fee if these return procedures are not followed. Also, the customer is liable for lost or damaged return items.
Visit Randy Gage's Prosperity Store.


text:
Wapakoneta-Central Library Cridersville Minster New Bremen New Knoxville Waynesfield
Library Catalog Site Ohio Web Library (?) Ohio Digital Library (?)
Wapakoneta
Cridersville
New Bremen
Waynesfield
Library Monthly Calendar of Events & Classes
Internet Acceptable Use Policy
Library Cards & Borrowing
Requesting Items and Placing Holds
SEO Interlibrary Loan (ILL)
eBooks - Ohio Digital Library - Libby/Overdrive
Hoopla - Digital Borrowing Service
Lynda.com Online Training
Books, Art & Literature
Careers, Practice Tests & Exams
Help in Tough Times
Ohio Libraries Share MORE
Ohio Web Library
Students & Homework
Game & Puzzle Collection
Obituary Database
Kitchen Collection
Mobile Hotspots Collection
PACKED Subscription Service
It's Story Time at your Library
LEGO Robotics Invade the Library
Services & Materials
Children's Books: A Guide to the Library
Storytimes @ the Library
Teen and YA Programs
Youth Services Staff
Classroom Collection Request
Library Card Applications for Students
Die Cut Machine
Community Read
Friends of the Cridersville Library
Friends of the FJ Stallo Memorial Library (Minster)
Friends of the New Bremen Public Library
Friends of the New Knoxville Community Library
Friends of the Wapakoneta Library
Friends of the White Memorial Library (Waynesfield)
Our collection has been divided as follows in each library of our system:
New Books are those most recently received and have a sticker in the upper right hand corner of the cover labeled NEW. New children's books are available for loan for 28 days and may be renewed once.
Picture Books are traditionally books with lots of pictures and a little text, though some now have more text for older readers and some have no text at all. Look for the big yellow stickers with the first letter of the author's last name on the spine. Picture books are available for loan for 28 days and may be renewed once.
Holiday Books can be fiction, non-fiction, picture or board books and they are labeled with a sticker corresponding to the holiday on the spine of the book. Holiday books are available for loan for 14 days and may be renewed once.
Board Books are smaller books with thicker pages and are especially intended for very small children who tend to be less gentle with books. Board books can be found in their own section in the library and are available for loan for 28 days and may be renewed once.
Easy Readers are for those just learning to read so they have large-sized print, few words per page, and lots of white space. Easy Readers have been marked with a red sticker with the white E on the spine. Within the Easy Reader category, books are divided by difficulty and labeled as such: the earliest readers with few words, big spaces, and many pictures are labeled with a pink dot. Slightly harder books with more difficult words have a yellow dot. Green dot books have longer words and may have chapter breaks in the story. The hardest level labeled with a blue dot are the early chapter books with more challenging themes and ideas.
Juvenile Fiction refers to those books written for kids from about grades 4th-6th. Look for the big blue stickers with the first letter of the author's last name on the spine. Young Adult Fiction, or those books written for those in grades 7th and up, can be found in the young adult are of the library and are marked with a YA blue sticker. Both Juvenile and Young Adult Fiction are available for loan for 28 days and may be renewed once.
Non-fiction books are those written for information. They can be found arranged in the non-fiction shelves by their Dewey Decimal number. (You can also find Easy Reader Non-fiction books in this collection. Look for the big red stickers with the white E on the spine on the Non-fiction shelves.) Young Adult Non-ficition can be found in the Adult Non-fiction area. Both Non-fiction Juvenile and Young Adult books are available for loan for 28 days and may be renewed once.
Audio/Visual Materials are books on tape, CD music, and DVDs. There are AV materials available in both Juvenile and Young Adult areas of the library. Books on tape or CD are available for loan, up to 5 items for 14 days and may be renewed once. Music CDs are available for loan, up to 5 items for 7 days and may be renewed once. DVDs are available to loan, up to 3 items for 3-7 days and may be renewed once.
Magazines are available to take home. We try to have a variety of magazines to suit a wide range of ages and interests. Both Juvenile and Young Adult magazines are available for loan for 7 days and may be renewed once.
Adaptive toys are available for all ages and abilities to loan. These are toys and learning devices that have been modified for children with special needs. The adaptive toys are available to loan. up to 3 toys for 7 days and can be renewed once.
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When I first heard about MOOCs, I thought they were the answers to my prayers. I could sign up for fun, interactive MOOCs for little or no money, and take them from home. I was in! What I discovered was mixed—some MOOCs were fun and some not. I began to ask myself the following questions about MOOCs.
Are MOOCs (Massive Open Online Courses) the educational wave of the future – inexpensive and available any time to anyone with a computer?
Or, as many students who drop out complain, are they boring, impersonal, and tedious?
But, first, what are MOOCs?
MOOCs are online courses involving large-scale interactive participation and open access via the web. Besides traditional course materials like videos, readings and problems, MOOCs provide interactive user forums that build a community for the students and professors. MOOCs are a recent development in distance education.
Stanford University offered some of the first MOOCs -- open courses with an enrollment of over 100,000. Several Stanford professors then launched the MOOC provider Coursera, leveraging the technology developed at Stanford to offer live courses. Other Stanford professors founded Udacity, first providing computer courses via MOOCs, and then branching out to other subjects. Harvard and MIT started edX, featuring MOOCs with courses from their universities and UC Berkeley, but recently added courses from Georgetown University and universities in Canada, Australia, Switzerland and the Netherlands. MOOC providers now partner with numerous universities worldwide.
I've discovered that MOOCs can be fun. If you are a student, see if you can try out a MOOC before signing up. You'll know soon enough if a MOOC works for you. If you are creating a MOOC, you can use the following five tips to develop interesting, engaging and personalized MOOCs.
ASSIGN STUDENTS TO PEER GROUPS -- Make the groups small (less than 10 members), and base them on location or similar interests and background. Ask the group members to rate their peers, creating a "Team Rank" score for each team member. The rating system holds students accountable to their peers, creates social pressure to keep their rankings up, and engages the students in the learning experience. Working in a group helps students improve their group learning skills, like virtual team management, negotiating and understanding their role in the team, leadership, and communication.
HAVE THE LECTURER TALK TO STUDENTS AS REAL PEOPLE – Build various video interactions using an interactivity builder. Harbinger Group's Raptivity has video interactions you can customize using its video turbopack, found at http://www.raptivity.com/elearning-product/raptivity-turbopack/videos-turbopack. Create short (10-minute or less) video segments featuring the lecturer discussing key topics, mixed with slides, live diagrams, practitioner interviews, video clips and thought questions posed to the students. Add discussion forums, social media exchanges, and real time "video office hours" with the lecturer. Throw in elements to make the course seem alive, like a challenge to find a hidden message in the video.
MAKE ADVISERS AND HELP DESK TECHNICIANS AVAILABLE -- Have support available at all times. Students located in different time zones worldwide need a real person to answer questions or troubleshoot a technical issue. Students need to be engaged both through technology and old-fashioned human contact.
MAKE EDUCATION SOCIAL -- Teach students how to approach the content they are being taught, how to synthesize it, and how to express it. Design the MOOC to maximize faculty-student and student-student interaction.
INCLUDE MESSAGE BOARDS -- Encourage students to communicate with the tutor and other students at any time.
With interactivity builders, communication and support, you too can turn MOOCs into interesting, engaging and involving classes for students. Please share any tips you have for developing interactive MOOCs, and tell us about any fun MOOCs you've found.
Templates - To Use or Not to Use?


text:
Q: How do you suppress Python DeprecationWarnings on Linux Terminal? I installed i18ndude (an internationalization utility to be used in Plone) using easy_install.
When I try to run the utility i18ndude on my terminal, I get:
/usr/local/lib/python2.6/dist-packages/i18ndude-3.1.2-py2.6.egg/i18ndude/odict.py:7: DeprecationWarning: object.__init__() takes no parameters
  dict.__init__(self, dict)

How do I suppress these warning messages when calling the utility from command line? Is it possible? I know in theory I should install other Python interpreter, and call i18ndude from that, but I would like a simpler approach (like a parameter or something like that).
BTW, I'm using a i18ndude script from Plone official site.

A: Redirection can be used, but it would suppress all the messages sent to that "stream"; e.g.
i178ndude 2>/dev/null
sends to the null device the stream 2 (normally the stderr of a program, but deprecation warnings could be sent to other streams). This is the "fix it even though you don't know how" fix. Indeed there's an option, -W, that can be used like this: -W ignore::DeprecationWarning or simply -W ignore that ignores all warnings. You can write a script that call the python interpreter on your program, or more logically modify the #! of the prog with something like #!/usr/bin/env python -W ignore::DeprecationWarning

A: If running as a script you could use:
#!/usr/bin/env python -W ignore::DeprecationWarning

A: You can temporarily suppress warnings:

If you are using code that you know will raise a warning, such as a deprecated function, but do not want to see the warning, then it is possible to suppress the warning using the catch_warnings  context manager:

import warnings

def fxn():
    warnings.warn("deprecated", DeprecationWarning)

with warnings.catch_warnings():
    warnings.simplefilter("ignore")
    fxn()

While within the context manager all warnings will simply be ignored. This allows you to use known-deprecated code without having to see the warning while not suppressing the warning for other code that might not be aware of its use of deprecated code. Note: this can only be guaranteed in a single-threaded application. If two or more threads use the catch_warnings context manager at the same time, the behavior is undefined.

A: See cmdoption-W:

-W arg
Warning control. Python's warning machinery by default prints warning messages to sys.stderr. A typical warning message has the following form:
file:line: category: message
By default, each warning is printed once for each source line where it occurs. This option controls how often warnings are printed.
Multiple -W options may be given; when a warning matches more than one option, the action for the last matching option is performed. Invalid -W options are ignored (though, a warning message is printed about invalid options when the first warning is issued).

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text:
Home » Babysitters » TX » San Antonio
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San Antonio babysitters profiles include videos, qualifications & parent reviews.
28 available babysitters in San Antonio, TX
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text:
Q: Get data from multiple select in javascript on submit I need help with getting the selected data from my multiselect into my javascript datastring when the submit button is pressed.
So I have this select:

<select multiple class="form-control" name="DivisionSelect[]">
  <option value="|TD">Traffic Division</option>
  <option value="|CP">Metro - C Platoon</option>
  <option value="|DP">Metro - D Platoon</option>
</select>

I want the selected data to be lined next to each other like this: "|TD|DP" if Traffic and D Platoon are selected. How can I do this?

A: You can use join() on select value and add it to string.

$('select').change(function() {
  var str = '&Divs="' + $(this).val().join('') + '"';
  console.log(str)
})
<script src="https://ajax.googleapis.com/ajax/libs/jquery/2.1.1/jquery.min.js"></script>
<select multiple class="form-control" name="DivisionSelect[]">
  <option value="|TD">Traffic Division</option>
  <option value="|CP">Metro - C Platoon</option>
  <option value="|DP">Metro - D Platoon</option>
</select>

To get this value on form submit you can use submit event on form and get  value of select using find

$('form').submit(function(e) {
  e.preventDefault();

  var value = $(this).find('select[name="DivisionSelect[]"]').val();
  var str = '&Divs="' + (value ? value.join('') : '') + '"';
  console.log(str)
})
<script src="https://ajax.googleapis.com/ajax/libs/jquery/2.1.1/jquery.min.js"></script>
<form action="">
  <select multiple class="form-control" name="DivisionSelect[]">
  <option value="|TD">Traffic Division</option>
  <option value="|CP">Metro - C Platoon</option>
  <option value="|DP">Metro - D Platoon</option>
</select>
  <br>
  <input type="submit" value="Submit">
</form>

A:

$("select").change(function() {

  var value = $("select option:selected").map(function() {

    return $(this).val()
  }).get().join("")

  console.log("&Divs="+value)
})
<script src="https://ajax.googleapis.com/ajax/libs/jquery/2.1.1/jquery.min.js"></script>
<select multiple class="form-control" name="DivisionSelect[]">
  <option value="|TD">Traffic Division</option>
  <option value="|CP">Metro - C Platoon</option>
  <option value="|DP">Metro - D Platoon</option>
</select>

Use .map()

Description: Translate all items in an array or object to new array of items.

A: Hev you tried, to read out the selected option, and then appending them to a String? You can append via concat() :
var str1 = "Hello ";
var str2 = "world!";
var res = str1.concat(str2);

I hope this helps :)

A: I Think below code will work for you.
var querystring = "&DivisionSelect=";
$('[name="DivisionSelect"] :selected').each(function(i, selected){
  querystring += $(selected).val()+querystring;
});
alert(querystring);