Home » Blogs » Charles Gaba's blog » Claire McCaskill proposes interesting solution to "bare county" problem which I just don't see working.
Since participation in the exchanges (either individual or small business) is purely voluntary in most states, this has led to a real problem: What happens if none of the insurance carriers decide to participate in a particular county or an entire state? This scenario nearly came true in Pinal County, Arizona this year, but that crisis was averted when Blue Cross Blue Shield of AZ agreed to jump in at the last minute. It looked like it was gonna happen across 16 counties in Tennessee next year, but so far it appears that BCBSTN is riding to the rescue there as well.
Sooner or later, though, at least one or more counties is likely to find themselves without any carriers offering individual market policies on the ACA exchange...or potentially even off of the exchange in some cases.
Now, there are a few exchanges which have come up with policies to mitigate or eliminate this problem: Until recently, the state of Nevada tied participation in the individual ACA exchange to managed Medicaid contracts, which I think is a fantastic idea. Carriers who want a slice of that sweet, sweet MCO (Managed Care Organization) money also had to agree to offer at least a couple of policies on the ACA exchange as well. They abandoned that rule last year, but only partly: Instead, Nevada now gives "bonus points" to MCO applicants who also offer exchange policies, meaning they have a far better chance of winning the contract.
In Vermont, for the first couple of years of ACA exchange operation, state law required that any carrier offering either individual or small group policies do so exclusively via their exchange--no off-exchange sales were allowed at all. This rule was weakened for the small business market due to ongoing technical problems with the exchange, and was mostly abandoned for the individual market as well at some point or another; the vast majority (84%) of indy market enrollees still run through the exchange, but a few thousand are off-exchange at the moment.
Finally, there's the DC exchange, which has a couple of special circumstances. First of all, like Vermont, DC implemented a regulation requiring all individual and small group policies to be sold via their ACA exchange only. Unlike Vermont, however, DC has not abandoned this policy for either market to my knowledge.
Personally, I think Nevada should've stuck with their original policy...and, for that matter, I think DC's policy makes even more sense. Both of these are among the 20 policies I think should be made nationally to improve, stabilize and strengthen the ACA as a whole. However, for the moment, these are restricted to DC, VT (sort of) and NV (sort of).
The other unusual thing about the DC small business exchange (SHOP), of course, is that due to an odd amendment baked into the ACA (with a long and stupid history behind it), a huge chunk of DC's SHOP enrollees are actually members of Congress and Congressional staffers. The short version is that the only way for Congress/their staff to receive the generous financial assistance on their policies that other federal employees receive is if they enroll through the DC SHOP exchange.Since the Federal Employees Health Benefits (FEHB) program covers a substantial chunk of the premiums, this means that thousands of Congressional staffers (and their bosses) are enrolled via the DC SHOP exchange. Legally, under the ACA, "Congress" is designated as a "small business" for purposes of health insurance. It's kind of silly, but Republican Senator Chuck Grassley insisted on it, so there you go.
The second, and more practical problem with Sen. Alexander's idea is that one of the reasons some carriers drop ACA exchange participation while still offering pretty much the same policies off of the exchanges is that they don't want lower income enrollees, period. Off exchange enrollees have to pay full price, while 85% of exchange-based enrollees are subsidized...which by definition means their incomes fall between 100-400% of the Federal Poverty Line. Since lower incoome people tend to be higher risk as well, while higher income people tend to be lower risk, some insurance carriers have decided that they can reduce their claims by only offering policies off-exchange to full-price enrollees. Presto! Wealthier people can pay full price while lower-income people can't afford to do so and end up going without.
On a call w/ MO press today, Claire announced the bill, which would allow people in provider-less counties to access the DC health exchange.
First and most obvious: A DC policy doesn't do much good to someone living in Tennessee unless they want to drive hundreds of miles for a regular checkup. The answer to that is the same as that for Congressional enrollees: The DC SHOP exchange includes policies with nationwide networks of doctors/hospitals. That's how members of Congress and/or their staff from all 50 states are able to use those policies. I presume that McCaskill's proposal would allow anyone living in a county without any exchange participants to enroll in one of the same DC exchange policies with nationwide networks.
Second: How would APTC/CSR be handled? APTC/CSR is currently only available on the individual exchanges; the SHOP exchanges have a whole different setup and formula for financial help since it's designed for small businesses, not individuals buying for themselves...and even then, the assistance is a lot less generous than APTC/CSR is anyway. Is McCaskill proposing that individuals could use the DC Indy exchange? If so, does DC HealthLink even offer nationwide network plans on the individual exchange, or are they limited to SHOP plans?
Finally, assuming both of those issues are resolved, this still opens up the exact same problem as Senator Alexander's "off exchange subsidies" proposal.
This year, the DC exchange has exactly 4 insurance carriers offering policies on both the Individual and Small Business (SHOP) exchanges: Aetna, CareFirst BlueCross BlueShield, Kaiser Permanente and UnitedHealthcare. They have a total of 171 poliicies total...151 SHOP and 20 Individual policies are available.
Now, Aetna recently announced that they're dropping out of the entire individual market nationwide next year. They didn't specify DC, but I assume they're included, which would leave just 3 carriers on the DC exchange: CareFirst, Kaiser and United.
To the best of my knowledge, these three companies are doing just fine in both of the DC markets (indy and SHOP)...but again, that's based on their existing enrollment numbers and risk pool mix, which is currently limited to District of Columbia residents + about 11,000 members of Congress and their staff.
What would happen if they were suddently told that they have to also sell to, say, 40,000 Tennesseans from Nashville?
Would that improve their risk pool? Perhaps, but probably not. Certainly it would potentially double or triple the number of enrollees, which could be a good or bad thing depending on the numbers and the mix.
The point is that while DC may legally require carriers to use the ACA exchange if they want to sell individual or small business policies...there's nothing legally requiring them to offer those policies at all. CareFirst, Kaiser and United are all huge, multi-state companies; they could drop the DC market altogether the way that Aetna is apparently doing without losing more than a small chunk of revenue in the process.
I don't know the details of Senator McCaskill's plan yet, of course, but this is something I'd look out for. Saying "if there aren't any plans available in your state, use DCs" doesn't work if there aren't any available there either.
Worse yet, the DC carriers would have to set their preimum pricing early in the summer...but might not know which/how many bare counties would be joining their potential pool until late summer/early autumn every year.
In short, unless all of these issues are addressed in McCaskill's bill, I just don't see how this idea would work.


Marcus Meissner discovered a race condition in gdm's handling of the ~/.ICEauthority file permissions. A local attacker could exploit this to become the owner of an arbitrary file in the system. When getting control over automatically executed scripts (like cron jobs), the attacker could eventually leverage this flaw to execute arbitrary commands with root privileges.


Adriaan Gomez began her business, Beego Handmade, just over a year ago, offering hand-sewn pillows, framed art and her own handmade cards. Each piece is unique and can also be made to order. "The bulk of what I do lately has been custom orders," Gomez says.
To prevent customers from being overwhelmed with too many fabric choices, she curates a manageable selection based on their preferences. "It makes me happy to be able to sort through that chaos for them," she says.
Her latest offering, a zipper pouch, is a colorful and functional bag available in a variety of sizes. Gomez's items can be found at local stores including Orange, Nest and Magnolia Lane, and online at BeegoHandmade on Etsy. This summer, look for her wet bag, designed to hold a moist bathing suit.


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They are always sincere and communicate kindly with their many interns and farm residents, striving to develop the experiences and abilities appropriate for each person.
I know that most of the people who had experiences in Gingerhill Farm appreciated and respected Iris and Zach very well.
I hope that as many young people will be able to learn important knowledge and experience in living from Iris and Zach through Gingerhill Farm Retreat, it will lead them to great happiness.


text:
Q: How to check and then insert in sqlite? I have a area table in sqlite database. Everytime i am just performing insert operation onto the sqlite database. How can i check if any record exists or not. If not exist simply insert. If exist then update records.
Please help me.

A: you can do easily "insert or ignore into tbl_name"
here you can see the example
http://www.raywenderlich.com/913/sqlite-tutorial-for-ios-making-our-app
this would be usefull for you....
http://www.sqlite.org/lang_conflict.html

A: Yes, you can do that with a single query.
INSERT ON CONFLICT IGNORE should help you: http://www.sqlite.org/lang_conflict.html
Put a unique key on the name, this will create a conflict when you try inserting a record if the name already exists.
The default is ABORT, so without the IGNORE, the statement will return an error. If you don't want that, use IGNORE.

A: You can do INSERT OR REPLACE if you have a primary key on the table. For example:
sqlite3 *database = NULL;

NSString *documentsPath = NSSearchPathForDirectoriesInDomains(NSDocumentDirectory, NSUserDomainMask, YES)[0];
NSString *path = [documentsPath stringByAppendingPathComponent:@"test.sqlite"];

int rc = sqlite3_open([path UTF8String], &database);
NSAssert(rc == SQLITE_OK, @"Open failed");

// note, use PRIMARY KEY when creating table

rc = sqlite3_exec(database, "CREATE TABLE IF NOT EXISTS test (animal TEXT PRIMARY KEY, sound TEXT)", NULL, NULL, NULL);
NSAssert(rc == SQLITE_OK, @"Create failed: %s", sqlite3_errmsg(database));

// create a record that will be replaced by the subsequent `INSERT OR REPLACE`

rc = sqlite3_exec(database, "INSERT INTO test (animal, sound) VALUES ('dog', 'meow')", NULL, NULL, NULL);
NSAssert(rc == SQLITE_OK, @"INSERT failed: %s", sqlite3_errmsg(database));

// this will REPLACE entry if value with same PK found, otherwise it would INSERT

rc = sqlite3_exec(database, "INSERT OR REPLACE INTO test (animal, sound) VALUES ('dog', 'woof')", NULL, NULL, NULL);
NSAssert(rc == SQLITE_OK, @"INSERT failed: %s", sqlite3_errmsg(database));

// now retrieve values and make sure it worked like we thought it would

sqlite3_stmt *statement = NULL;
rc = sqlite3_prepare_v2(database, "SELECT animal, sound FROM test", -1, &statement, NULL);
NSAssert(rc == SQLITE_OK, @"prepare SELECT failed: %s", sqlite3_errmsg(database));

while ((rc = sqlite3_step(statement)) == SQLITE_ROW) {
    const unsigned char *animal = sqlite3_column_text(statement, 0);
    const unsigned char *sound  = sqlite3_column_text(statement, 1);
    NSLog(@"%s goes %s", animal, sound);
}
NSAssert(rc == SQLITE_DONE, @"step failed: %s", sqlite3_errmsg(database));

sqlite3_finalize(statement);

sqlite3_close(database);

And that will report that the INSERT OR REPLACE replaced the previous value rather than inserting second record:

2013-11-21 08:59:25.285 AnimalSounds[53549:70b] dog goes woof

If you don't have primary key, rather than this simple INSERT OR REPLACE, you'd have to break it into two steps, either:

*

*Look for record with SELECT: If found, do UPDATE; if not found, do INSERT.

*First DELETE any records that would match whatever criteria you want, and then do INSERT.
This first approach is a bit safer, but you could use the second approach if you had to (though you would probably use transactions a do a ROLLBACK if you had any problems). Needless to say, the INSERT OR REPLACE approach is even easier, but requires a primary key.

A: First call get record query in Database. Here I am add a example, I am checking that user login information available in database or not. So add below code. IF User record is available than i get record array otherwise nil.
 +(NSArray*)getTBL_LOGIN
{
    NSMutableArray *Favourite=[[NSMutableArray alloc]init];

 sqlite3 *database;

 TabBarAppDelegate *x=(TabBarAppDelegate*)[[UIApplication sharedApplication]delegate];
if(sqlite3_open([[x dataBasePath] UTF8String],&database) == SQLITE_OK) {
    NSString *str = [NSString stringWithFormat:@"select * from tbl_login"];
    const char *sqlStmt=[str UTF8String];
    sqlite3_stmt *compiledStmt;
    if(sqlite3_prepare_v2(database, sqlStmt, -1, &compiledStmt, NULL) == SQLITE_OK) {
    while(sqlite3_step(compiledStmt)==SQLITE_ROW)
            {
                NSString *uid=[NSString stringWithUTF8String:(char *)sqlite3_column_text(compiledStmt, 0)];

                NSString *username=[NSString stringWithUTF8String:(char *)sqlite3_column_text(compiledStmt, 1)];

                NSDictionary *d=[NSDictionary dictionaryWithObjectsAndKeys:uid,@"uid",username,@"username",nil];
        [Favourite addObject:d];
    }
    }
    sqlite3_finalize(compiledStmt);
}
sqlite3_close(database);

if([Favourite count]>0)
{
    NSArray *ar=[NSArray arrayWithArray:Favourite];
    return ar;
} else {
    return nil;
}
}

If you get the record count >=1 then record exist so you have to call update query if you get record count 0 than record is not available in database so you have to call insert query

A: In a situation where I imported all updates into another database table, I could use following:
-- Existing table: t(uc UNIQUE, v1, v2, v3);
-- Updates table: ut(uc UNIQUE, v2);
INSERT OR REPLACE INTO t
    SELECT ut.uc, et.v1, ut.v2, et.v3 FROM ut
    LEFT JOIN t AS et ON ut.uc=et.uc;

This statement will insert new rows from ut into t. Existing rows are replaced with a row containing new data from ut and existing data from t.
For this to work, you must have a UNIQUE column (which makes sense as you are looking for a row update or insert a new one), and have new data available so it can be queried (in same or another database).
This worked for me, hope it may help you.
Another solution, maybe with better performance is using two statements:
 UPDATE t SET v1='some value', v2=123 WHERE unique_col='some_id';
 INSERT OR IGNORE t(v1, v2, unique_col) VALUES('some value', 123, 'some_id');

UPDATE will become a null operation when 'some_id' is not found.
INSERT will ignore all existent 'some_id'.

redpajama_set_name: RedPajamaStackExchange


text:
Tebow stays put
By From Sun news services
Tim Tebow started to walk off stage, then called an audible. The Florida quarterback turned around, grabbed the microphone and told teammates, coaches and fans exactly what they wanted to hear.
"Oh, by the way, let's do it again," Tebow said. "I'm coming back."
Tebow thrilled more than 40,000 at the school's national championship celebration yesterday by announcing plans to return for his senior season, with hopes of leading the Gators to their first undefeated season and fourth national title.
"Overall, I just felt loyal to this place," Tebow said. "I feel like I'm a role model, and a lot of times people start things and don't finish them. I didn't want to be like that."
Tebow threw 32 touchdown passes in 2007, ran for 23 more scores and became the first sophomore to win the Heisman Trophy. He did less this season but accomplished more by leading Florida to its second national title in three years.
Tebow completed 64 percent of his passes for 2,746 yards and 30 touchdowns with four interceptions. He also led the team in rushing, gaining 673 yards and scoring 12 touchdowns.
The 6-foot-3, 240-pound left-hander was 18-for-30 passing for 231 yards and two touchdowns with two interceptions in Thursday's 24-14 victory over Oklahoma in the Bowl Championship Series title game. He also ran for 109 yards.
There was speculation he might head to the NFL, especially because he has talked about wanting a bigger platform to share his religious message and expand his community work, but Tebow said he never strongly considered leaving early.
Coach Urban Meyer set him up with several NFL coaches to talk about his pro prospects. Although Tebow said he got mixed feedback, nothing he heard swayed his decision. He told Meyer of his decision to stay Saturday.
Wide receiver Percy Harvin and linebacker Brandon Spikes, fellow juniors considering turning pro, didn't address their futures yesterday.
BCS: : Utah's attorney general is using his personal blog to answer criticism that he's wasting state resources by investigating whether the BCS violates federal antitrust laws.
Mark Shurtleff contends the BCS unfairly puts Utah, and schools in other conferences like the Mountain West that don't get an automatic bid to a BCS bowl, at a competitive and financial disadvantage. For the second time in five years, Utah finished the season undefeated and was shut out of playing in the BCS national title game.
Utah finished the season ranked No. 2 in the Associated Press poll.

redpajama_set_name: RedPajamaCommonCrawl


I have worked in the personal training industry for over 12 years. I have trained a diverse clientele with diverse fitness goals. I'm not your typical personal trainer. I am 62 in an industry where the average age is probably 30. Despite being from a generation that discouraged woman from being athletic, much less using weights to build strength, I realized around the age of 50 that a long, quality life depends on not only a strong heart, but also strong bones and muscles to allow me the freedom to participate in life as my life evolved. It was and still is important to me to be able to safely pick up and hold my grandchildren from a standing position among other life movements that become more challenging as we get older.
Although I have a long, successful background in nutrition, weight loss and functional strength training people of all ages, I currently specialize in nutrition, weight loss, and strength training people who are 50 plus. After working in the weight loss industry for 35 plus years, I've had the opportunity to be a part of the ever-changing world of diet and nutrition. It's a nutritional "Wild West" out there and a nutrition/exercise style that works for one does not necessarily work for another. Feeling/being fit, healthy and vital after 50, 60, 70 or 80 is not that daunting. I've been on the healthy, fit journey for many years now and I can assure you of 3 things.
+ I love to eat. Food is one of my primary sources of pleasure. And, although what we eat is an important component to good health and fitness that doesn't mean that we must give up all our indulgences.
+ I love exercise and moving even if I don't always want to do it. It not only keeps me in shape physically but it's a big part of my psychological well being. It definitely plays a part in keeping depression and poor stress management at bay.
+ I understand how connection with others supporting my pursuit in meeting my goals is crucial in maintaining my goals. I know in my 62 years what hormonal heaven and hormonal hell is. I also know that it does not necessarily dictate the outcome of my fitness program. Having another or others who have or who are living this lets us know anything is possible.


On his aboriginal drive as Alabama's starting quarterback, Tua Tagovailoa led the Crimson Tide into the red area and accomplished a comedy that underscored both his great abeyant and about inexperience.
Tagovailoa apish a handoff, spun 360 degrees abroad from a Louisville linebacker and aggregate himself to adept a brawl to the bend of the end area as addition Cardinals amateur affable at his legs.
The comedy was according genitalia improvisational ability and affected risk, as he lobbed the brawl to receiver Jerry Jeudy while the Louisville cornerback angry his arch – the apostle absolutely had no abstraction he'd be victimized all ceremony on highlight shows as the biting culprit on an animating touchdown.
The moment additionally about concluded the loudest positional agitation of the offseason – Tua or Jalen Hurts as Alabama's starter. Alike with Hurts acceptable 26 of 28 amateur as a starter, it's adamantine to anticipate him accepting starting snaps afresh unless Tagovailoa gets injured. We are entering the Tua Era at Alabama, and the aboriginal allotment actualization this could be the best accomplished quarterback of Nick Saban's tenure.
While Tagovailoa has about played aloof 1.5 amateur as the Tide's primary quarterback, it's not too aboriginal to get aflame about what he could become. In his 12 seasons with the Crimson Tide, the accomplished abstract aces Saban has had at quarterback is A.J. McCarron, who went in the fifth annular of the 2014 NFL draft. The alone added quarterback to get drafted was Greg McElroy, who went in the seventh round.
Tagovailoa has a connected way to go to bout the wins, command of the arrangement and administration of those Crimson Tide greats. But the flashes we saw of him on Saturday night – the arm talent, adroitness and abeyant – absolutely accomplish it reasonable to activity that he could accord Alabama some action at the position that it's lacked during Saban's tenure. Playing beneath new coordinator and comedy addition Mike Locksley, Tagovailoa showed blow on abysmal balls, calmly advance the brawl to playmakers and delivered some electricity to the Alabama casual game.
Hurts fabricated a adornment in the aboriginal bisected and accomplished the d 5-of-9 casual for 70 yards. Saban threw a atmosphere anger in his ESPN postgame interview aback Maria Taylor asked a actual fair and able catechism about what answers the quarterbacks had provided with their performances on Saturday.
The questions that buried Alabama's offseason will die bottomward afterwards Saturday night. This is Tua's job for the actual future, and the Tide division feels destined to afresh end with an actualization in the Academy Football Playoff.
FAU's aperture d at Oklahoma led to a beck of absolute publicity for Lane Kiffin, who for a decade has been one of the best alienated coaches in academy football. Fired by USC and the Raiders and removed at Alabama the ceremony afore the appellation d two seasons ago, Kiffin has consistently delivered adorable brawl and ambiguous maturity.
The flurry of creamy publicity came in allotment because of Kiffin's arresting admission division at FAU. He gambled on himself, took a paycut and active a historically accustomed affairs to a alliance title. It was impressive, as FAU went 11-3 and won Conference USA.
The board is still out on how abundant Kiffin has changed. One affair is certain: He decidedly aching his affairs at a bigger job afterwards this division with a 63-14 face-plant at Oklahoma.
Some pundits alike predicted that FAU could comedy with Oklahoma, which fabricated the tenor of the accident added embarrassing. FAU didn't accord in the aforementioned timezone with the Sooners. The final could accept been worse, as Oklahoma led 42-0 at the half.
Athletic admiral may be able to discount Kiffin's brashness. But annihilation hurts a ascent apprenticeship applicant like a accident on a big stage. (Troy's Neal Brown had one, too, as the Trojans fell to Boise State 56-20). Acceptable will ultimately actuate Kiffin's fate, and Saturday did little to advice him advance momentum.
Can Northwestern argue in the Big Ten with some of the acceptable admiral – Michigan, Penn State and Michigan State – attractive pedestrian? Well, they accept the longest win band of any Power Bristles team. They connected it to nine amateur with a abrasive win at Purdue on Thursday night.
The Wildcats accept a adept arresting advanced seven and tailback Jeremy Larkin looked adequate as the affection aback adjoin Purdue with 143 yards on 26 carries.
But the bigger capricious from the d will be how drillmaster Pat Fitzgerald uses Clayton Thorson the blow of the season. Thorson was on what academy admiral told the Chicago Tribune was a "pitch count" that was medically pre-determined afterwards an ACL tear. He aggregate snaps with walk-on TJ Green. (Green bankrupt the game, which Northwestern algid because of a absurd accidental acerbity amends on Purdue arresting accouterment Lorenzo Neal).
If Thorson can balance and c his angle count, don't be afraid to see the Wildcats accumulate surging.
There's still allowance on the McKenzie Milton for Heisman bandwagon. The activating UCF inferior quarterback accomplished eighth in the Heisman chase aftermost division and showed signs on Thursday night that he's on a academy aisle this season. Milton threw bristles touchdown passes in a 56-17 blast of Connecticut on Thursday, commutual 24-of-32 passes for 346 yards. UCF has won 14 afterwards games, the longest band in the nation.
Coming into Josh Heupel's debut, some UCF admirers scoffed at the angle that he'd run UCF's breach at a academy bounce than above drillmaster Scott Frost. Heupel's breach comes from the Baylor birth and Frost's the Oregon, the two standard-bearers for bounce in the spread-offense ancestry. The best absorbing accomplishment of the night may accept been that none of UCF's eight scoring drives lasted best than 2:39.
Look for that bounce to abide to rev up, which is bad account for both North Carolina and Pittsburgh, which arise on UCF's September schedule.
Few players authored added absorbing quarterback debuts than Deathwatch Forest accurate apprentice Sam Hartman. He becoming the starting job in the deathwatch of the abeyance of activating adept quarterback Kendall Hinton, who'll absence the aboriginal three games. Hartman led Deathwatch Forest to an overtime achievement at Tulane, commutual 31-of-51 passes for 378 yards. Tulane drillmaster Willie Fritz ample the box and dared Hartman to exhausted him, and he did aloof that.
A majority of the casual assembly went to an accustomed star, Greg Dortch (12 catches for 149), and a ascent one Sage Surratt (11 catches for 150 yards). Wake's escape sets them up for addition solid season, which will axis with a Thursday home d adjoin Boston Academy on Sept. 13.
Few coaches had a worse night than Louisville's Bobby Petrino. His new arresting coordinator gave up 51 points, as Brian VanGorder best up area we aftermost remembered seeing him as Notre Dame's arresting coordinator – at the circle of overmatched and inept.
Petrino's buyout is about $6 actor afterwards this year, which isn't a lot of money at a abode attractive to accomplish a change in culture. (And with bounded hero Jeff Brohm sitting out there.) Look for this awkward Louisville accomplishment to access that drumbeat.
As the altercation surrounding above Ohio State abettor Zach Smith above over in Columbus aftermost month, a quiet aplomb permeated the Ohio State football staff. No amateur becoming added constant acclaim than redshirt green quarterback Dwayne Haskins. Aback Haskins active with Ohio State, Urban Meyer declared him the best accomplished quarterback he'd anytime signed. That seemed abstract for a guy who recruited Tim Tebow and Cam Newton, but the aboriginal flashes actualization why there was so abundant praise.
Meyer will acknowledgment to Ohio State on Monday to resume his apprenticeship duties. While he won't drillmaster the aing two games, he'll be in the architecture and resume advancing the Buckeyes and arch the staff.Could this achievement and the acknowledgment of Meyer collapse some of the brawl that's amidst the affairs for the accomplished month?
What do Michigan's accident and the two a upsets beggarly in the big picture? Likely added skepticism adjoin the Big Ten East. Michigan's accident isn't a big agency in perception, as they were underdogs. Michigan State has a affluent history of afraid off aboriginal apathy for 10-win seasons, and this is accepted to be one of the bigger Utah State teams in years. The best apropos allotment may be Penn State abandonment a 14-point second-half advance to go to overtime. It's alarming to accomplish too abounding across-the-board generalizations off a baby sample size, but the declared vaunted abyss Big Ten East will be looked at a bit added cynically from this point forward.
There's a lot of brawl larboard to comedy afore that becomes relevant. In the abbreviate term, there's a atramentous billow over the Pac-12. Not alone did its top adversary lose, but UCLA absent to Cincinnati in Chip Kelly's debut, USC looked b adjoin UNLV and Arizona got pushed about by BYU, accident 28-23, and the league's top Heisman quarterback candidate, Khalil Tate, went 17-of-34 casual for 197 yards and a TD.
The added affecting moment came in Athens, Georgia, during halftime of Georgia's 45-0 blast of Austin Peay. Georgia drillmaster Kirby Smart's wife, Mary Beth Smart, offered a $5,000 ysis from the Kirby Smart Ancestors Foundation to the ancestors of Austin Peay abhorrent band drillmaster Joshua Eargle.
In the weeks arch up to the game, there'd been a GoFundMe folio started by Eargle's family. It was committed to adopting money for his daughter, 5-year-old Landrey Eargle, who was built-in with a attenuate abiogenetic alteration that has appropriate all-encompassing and big-ticket medical care. (She's the alone being in the United States and the fourth in the apple to accept the mutation.) With the ancestors accepting emptied their coffer accounts, 401Ks and accepting ashamed with acclaim agenda debt, they accomplished out to the accessible for help.


text:
Day Surgery Plan Gains Wide Acceptance
Jul 1, 1982Article, Day Surgery
Day Surgery Article
Publication Status 3b (Australian Surgeon July 1982)
Review Status SR
Copyright Copyright of this article is vested in the author. Permissions for reprints or republications must be obtained in writing from the copyright holder. This article has been republished here with permission from the copyright holder. (Scanned from The Australian Surgeon. If there is any discrepancy between this scanned reproduction and the original the original takes precedence.)
Lindsay Roberts, FRCS FRACS
Chairman, Australian Day Surgery Council, 1990 – 2000
President Elect, International Association For Ambulatory Surgery 1999
The campaign for the establishment of day surgery units is gaining momentum.
Hardly a week goes by without some mention in the public media of the need to establish a facility in which patients can receive minor surgery procedures without incurring the costs generated by even an overnight stay in an acute hospital bed.
The final report on day surgery prepared by the joint Working Party of the Royal Australasian College of Surgeons, the Australian Association of Surgeons, the Faculty of Anaesthetists of the Royal Australasian College of Surgeons and The Australian Society of Anaesthetists has now been presented to the Federal Minister for Health and State Minister for Health and all have accepted the recommendation of the report in principle.
The major health funds are also in favour and it would appear to be only a matter of time before a fund benefit rebate is agreed and day surgery units come into being. In the light of all these developments AAS members should not overlook the important, indeed vital role played by the Australian Association of Surgeons to bring matters to their present stage.
Indeed, the AAS was instrumental in both establishing the feasibility of such units and in persuading other interested parties of the need for a system which ensured a high standard of patient care, yet which still allowed costs to be contained.
In May 1980 Lindsay Roberts first presented a paper on day surgery to the NSW Committee of the AAS.
His paper was taken up by Federal Council and after considerable work the joint Working Party was established under the chairmanship of E. Durham Smith.
By now most surgeons are well aware of the advantages of day surgery units.
in summary, day surgery allows the treatment of large number of patients at less cost than in-patient surgical treatment for the same conditions.
As the report states, costs are reduced because staff and facilities are not needed at night at weekends or on public holidays; fewer staff are required for a day surgery centre than for in-patient surgery; acute beds in hospitals are not needlessly taken up.
A survey mentioned in the report suggests that up to 25 per cent of patients who occupy acute hospital beds could be treated in day surgery units.
Hospitals will benefit not only because of economic savings.
Recruiting problems will be eased because hours on duty in a day centre will suit part-time or married people; and fewer short stay patients will allow in-patient facilities to be run more efficiently.
The preparation of records, the examination and preparation of patients for operations and post-operative care, and discharge of short stay patients, also detract staff from the care of patients with major illnesses.
Advantages to patients and their relatives include less anxiety because an overnight stay in hospital is avoided, a quicker return to normal activities including work, and less stress for relatives who will not need to spend time and often money on travel and even accommodation to visit the in-patient.
The report states that the type of facility required for a day surgery already exists in many public and private hospitals and could be available in free standing facilities constructed specifically for this purpose.
But it stresses that the standards of patient care offered at administrative, clinical and technical levels must be as good as those provided in in-patient accommodation. (Recommended surgical standards and procedures are outlined later in this article.)
In order to ensure that standards of care are maintained, the report recommends that day centres be registered with State health authorities and the registration be on the basis of accreditation by an appropriate committee.
It envisages the need for a federal representative committee responsible for establishing standards.
This would be the Australian Council on Hospital Standards or a subcommittee of that body.
Each state or territory should have a licensing authority responsible for regulating, registering and accrediting individual centres.
And each centre should have its own executive committee to manage and monitor performance and ensure the complete observance of standards.
Of course, there are still some procedural matters to be finalised before day surgery centres come into being.
With the backing of government at all levels, the funds and the active support of all those involved in and responsible for patient care in Australia, these can be quickly solved.
There is no doubt that day surgery centres, if established and run along the lines recommended by the joint Working Party, will be successful.
And the money saved can be put to good use in correcting growing deficiencies in Australia's health services.
Surgical Standards and Procedures for day centres listed in Appendix B of the report are as follows;
The surgeon must be accredited and the requirement for accreditation is the possession of the diploma of FRACS or its equivalent.
It shall be the responsibility of the surgeon and/or his anaesthetist to select those patients suitable for treatment as day surgery patients with maximum regard for safety and the maintenance of the highest professional standards.
In the event that any patient is considered unfit for discharge following an operation, then the attending medical practitioner shall be responsible for the patient's transfer to an appropriate public or private hospital for further management.
It shall be the responsibility of the day surgery centre executive committee to maintain the physical plant and equipment and the appropriate staff to set standards.
A list of defined procedures suitable for day surgery is not recommended, but the suitability of the patients selected is the responsibility of the surgeon and/or anaesthetist. Craniotomy, formal laporatomy and thoracotomy would not be acceptable procedures for day surgery centres. The surgical and anaesthetic staff should themselves be subject to delineation of privileges as defined for that particular institution.
The final decision about fitness for anaesthesia rests with the anaesthetist who is to administer the anaesthetic.
Guidelines for the selection of patients include:
i. An assessment that post-operative pain can be controlled without parentral narcotics after discharge.
ii. An assessment that the post-operative course is predictable.
iii. A willingness on the part of the patient to be so treated.
iv. Provision of transport by a person other than the patient and availability of a suitable person at home to watch the patient.
v. No language difficulty for the communication of instructions.
vi. Exclusion of patients who do not satisfy the categories as defined above.
The pre-operative preparation by the surgeon would include an explanation of the procedure, the reasons for it and the expected result.
Clear mechanisms must be established for the notification of pre-admission arrangements by the day centre. In the case of children a pre-admission visit to the centre may be helpful to reduce anxiety.
After admission to the centre, the patient undresses, is supplied with an identity bracelet and gown and shaved if necessary; clothes are stored in the cubicle cupboard. Admission instructions should advise that no responsibility is taken for the care of valuables although a storage area should be provided.
After discharge from the recovery ward, a period of observation is required in the day centre ward.
During this period.
a. A minimum recovery period of three hours should be allowed from the completion of the last procedure of the day to that patient's discharge if general or major regional anaesthesia have been used. Adequate staff must be available during this time.
b. Parents should be encouraged to stay and participate in their child's care while the child is in the unit.
c. If appropriate to the medical condition staff should make available tea or coffee to patients and relatives.
d. Before discharge the patient should be fully awake and well orientated. There should be minimal nausea or vomiting. The circulation should be stable and there should be no apparent surgical complications.
e, There must be a post-operative clinical check by the surgeon or anaesthetist. If the nurse in charge has any doubt about the fitness of the patient for discharge, she must contact the surgeon or anaesthetist before the patient is discharged. However, the final responsibility must remain with the anaesthetist or surgeon.
At the discharge area:
a. Clerical staff prepare the necessary discharge documents.
b. The professional checking the discharge assesses the adequacy of the discharge instructions.
c. The Charge Nurse checks that the necessary discharge drugs and dressings are supplied.
d. The staff issue the compulsory warnings about transport, cessation of work and prohibition of alcohol, and issue the written warning instructions for patients recovery from any anaesthesia, general, local or regional block.
e. The anaesthetist should be available for consultation post-operatively should relevant complications develop; all patients who are discharged should have written instructions telling the name and appropriate medical practitioner and how to contact him if complications develop.


The first post … a sort of beginning.
As I mentioned on my About page, I'm a keen amateur cook with a leaning towards breakfast foods, and breakfast is a beginning, isn't it? So let me tell you what I had for breakfast (yes, it's going to take me a while to get into this writing groove, please have patience with me).
But now that I think about it, this really wasn't a great day to start my blog if I'm going to talk about my breakfast, because I had a bowl of Rice Krispies, a banana, and a yogurt! Hardly a gourmet start to the day, was it?
But I have a good excuse. I was out with a guy last night, and we got home really super-late from dinner, so by the time I woke up this morning I only had about half an hour to shower, eat breakfast, and hustle my buns over to work for my first coaching session. There, told you it was a good excuse.
And no, I don't want to share any details about the guy. Let's see where this goes before I start pouring my heart out to almost strangers (except Mom … yes Mom, I know you're reading … everyone say Hi Mom!) And really, this isn't the sort of thing you go talking about with your mom, is it?
Oh man, am I going to get a phone call from my Mom tomorrow! Hanging out those kind of hints oin the line without giving any details. I know, I'm bad.


All our volunteers are amazing and leave a huge legacy, a heritage, through their actions. Many young people benefit from this, as did many of the volunteers who were themselves in scouting or guiding.
The Scout Association has a series of awards that recognise over-and-above service. Below is our list of all those who have received the higher awards. All exceptional, all supported by a great team, all had much fun.
As a young person, the highest award that can be achieved is the Queen's Scout which is over and above the DofE Gold award. Many of these people are now volunteers in scouting.


As the European leg of the world tour concludes, emotions peak, and Mariah is relieved to find out her backup singer and best friend MaryAnne finally has forgiven Anthony, her co-creative director. Meanwhile, Mariah begins to recognize that her feelings for Tanaka transcend friendship. During a fun night out in Amsterdam, one of the dancers tries the local specialty for the first time and gets extremely high, and Mariah can't resist the urge to prank him with a big surprise when he wakes up in his bed.


Perhaps with the exception of Plumbing and Mechanical Magazine (which exists), it's not every day you see a shower featured prominently on the cover of a glossy. Until now. Enter V Magazine's Winter 2012 cover, featuring Scarlett Johansson mid-cleanse.


text:
\section{Introduction}
With the rise of large-scale knowledge graphs (KG) such as DBpedia \cite{auer2007dbpedia} and Freebase \cite{bollacker2008freebase}, question answering over knowledge graph (KGQA) has attracted massive attention recently, which aims to leverage the factual information in a KG to answer natural language question. Depending on the complexity of question, KGQA can be divided into two forms: simple and complex. Simple KGQA often requires only one hop of factual knowledge, while complex ones require reasoning over a multi-hop knowledge subgraph (KSG) and selecting the correct answer among several candidate answers. In this paper, we focus on the latter, i.e., complex KGQA, which will be more challenging.

Currently, most KGQA approaches resort to semantic parsing \cite{berant2013semantic, yih2015semantic, dong2018coarse} or retrieve-then-extract methods \cite{yao2014information, bordes2014question}.
Semantic parsing methods usually translate a natural language question to a KG query and then use it to query the KG directly. However, semantic parsing methods often rely on complex and specialised hand-crafted rules or schemas.
In contrast, retrieve-then-extract methods are easier to understand and more interpretable. They first retrieve the KG coarsely to obtain a knowledge subgraph (KSG) containing answer candidates.Then, the target answer is extracted from the retrieved KSG.
This paper follows the research idea of the retrieve-then-extract methods.

For most previous works \cite{bao2016constraint, sun2018open, lin2019kagnet, yasunaga2021qa}, they retrieve a knowledge subgraph from the original KG by choosing topic entities (e.g., KG entities mentioned in the given question) and their few-hop neighbors. However, since the KG is often of large volume and the initial retrieval process on it is coarse-grained and heuristic, the KSG retrieved by this method may still contain thousands of nodes and most of them are irrelevant to the given question, especially when the number of topic entities or hops significantly increases. The larger the KSG is, the more difficult it is to find the correct answer in it.
To reduce the size of the KSG, \citet{sun2018open} computed the similarity between the question and the relations around the topic entities and then used the personalized PageRank algorithm to select the most relevant relations. However, this method only considers the semantic similarity between the question and the relations while ignoring the structural information around each entity node. \citet{saxena2020improving} directly computes knowledge embeddings on the whole retrieved KSG, which is computationally intensive.

Unlike some previous work that concentrated on improving the models' ability to select answers on a large graph, we mainly focus on how to substantially reduce the size of the retrieved knowledge subgraph and ensure a high answer recall rate.
Therefore, a more refined learning to rank model is required. To this end, we propose to partition the KSG into several sub-KSGs and use a learning to rank model to select the most relevant sub-KSGs to the given question. In this way, traditional text matching models \cite{yang2016anmm, wang2017bilateral, talman2019sentence, devlin2018bert} can be used to compute the similarity score between a given question and a sub-KSG. However, these sequential based models often ignore the important structure information within the question and the sub-KSG.

To address the aforementioned issues, in this paper, we propose a new knowledge subgraph partition algorithm based on single source shortest path, which can partition a large-scale question-specific KSG to several sub-KSGs.
Furthermore, we propose a novel graph-augmented learning to rank model (G-G-E) to select top-ranked sub-KSGs, which combines a novel subgraph matching networks based on Gated Graph Sequence Neural Networks (GGNNs) \cite{li2015gated} to capture global interactions between question and subgraphs, and an enhanced Bilateral Multi-Perspective Matching (BiMPM) model \cite{wang2017bilateral} to capture local interactions within parts of question and subgraphs. Finally, we apply one of the state-of-the-art (SOTA) KGQA answer selection model to the original complete KSG and the combined top-ranked sub-KSGs separately, and further demonstrate that reducing the size of the answer candidate subgraphs clearly helps to select correct answer effectively and efficiently. To evaluate our approach, we conduct extensive experiments on two benchmark datasets. The experimental results on the datasets have shown that our proposed model can significantly improve subgraph ranking performance compared to existing state-of-the-art methods.

In summary, the contributions of this paper can be summarized as follows:
\begin{itemize}
    \item We propose a new knowledge subgraph partition algorithm based on single source shortest path.
    \item We propose a novel graph-augmented learning to rank model, which combines a novel subgraph matching networks based on GGNNs and an enhanced BiMPM model.
    \item Our proposed graph-augmented learning to rank model outperforms a set of SOTA ranking models.
    \item Further answer selection experiments on the original complete KSG and the combined top-ranked sub-KSGs demonstrate reducing the size of the answer candidate subgraphs can help improve the performance of answer selection.
\end{itemize}

\begin{figure}
\centering
\includegraphics[width=1.0 \textwidth]{partition.pdf}
\caption{An Example of Knowledge Subgraph Partition Algorithm. The areas surrounded by two dashed lines belong to two different sub-KSGs.}
\label{fig:partition}
\end{figure}

\section{Knowledge Subgraph Partition}
\label{Parition-Algorithm}
For better use of the ranking model, we need to partition the knowledge subgraph into several sub-KSGs. Intuitively, nodes and relationships within a sub-KSG should be more related to each other. As shown in Fig.\ref{fig:partition}, ``m.051cc'' is the topic entity of the given question and nodes on the same path from topic entity node ``m.051cc'' should be partitioned in the same sub-KSG. In particular, entity nodes in this example graph are denoted by Freebase IDs (starting with ``m.''). The first sub-KSG surrounded by the red dashed line is about the education information of topic entity ``m.051cc'', which contains the true answer node ``m.0gl5\_''. The second sub-KSG surrounded by the green dashed line is about the namesake entity ``m.076hxb3'' of topic entity ``m.051cc''. The second sub-KSG is also a confusing subgraph because it contains tokens like ``education'' and ``school'', which are consistent with the context of the question. Therefore, the learning to rank model is expected to distinguish not only irrelevant sub-KSGs, but also confusing ones.

In order to partition related nodes in the same sub-KSG, we propose a knowledge subgraph partition algorithm detailed in Algorithm \ref{alg:algorithm1}. Given a question $q$ and its answer entities $E_a^q$, we first use the retrieval method proposed by \cite{sun2018open} to obtain a question-specific KSG $S$, which may contain thousands of answer candidate entities and relationships. Then, our proposed algorithm is adopted to partition the retrieved KSG into several sub-KSGs serving as inputs to the graph-augmented learning to rank model to select the most relevant sub-KSGs.

\begin{algorithm}
\SetAlgoLined
\textbf{Input:} Question $q$ with its retrieved KSG $S$ and topic entity $n_t$ and answer entities $E_a^q$\\
Build KSG $S$ as a directed graph $G_S$;\\
Set $n_t$ as the root node; \\
Find the shortest paths $P$ to all nodes with $n_t$ as the source node; \\
Set an empty set $Set_S = \{\}$ to save all partitioned sub-KSGs; \\
Set an empty set $Set_l = \{\}$ to save the match labels of the partitioned sub-KSGs; \\
 \For{each path $p_i$ ($n_i$ as target node) in $P$}{
 \If{$n_i$ has child nodes \text{and} the child nodes of $n_i$ are all leaf nodes}{
 Partition the path from $n_t$ to $n_i$ as a sub-KSG $S_{n_i}$; \\
 Add the child nodes of $n_i$ to $S_{n_i}$; \\
 Set the match label $l_{n_i}$ of $S_{n_i}$ as 0;\\
 \For{$n_a$ in $E_a^q$}{\If{exists path from $n_t$ to $n_a$}{Set the match label $l_{n_i}$ as 1;\\
 break;}}
  Add $l_{n_i}$ to $Set_l$ ;\\
  Add $S_{n_i}$ to $Set_S$ ;\\
 }
 }
 \caption{KSG Partition}
 \label{alg:algorithm1}
\end{algorithm}

The above graph partition algorithm follows the intuition that the answer to the given question is usually found on a multi-hop path from the topic entity node. In order to keep the size of the sub-KSG moderate, we partition it starting from the node whose child nodes are all leaf nodes.

\section{Graph-augmented Learning to Rank}

Given a question $q$ and a set of sub-KSGs $S_q = \{S_{q,1}, ..., S_{q,n}\}$. We compute the ranking score $y$ representing the relevance of $q$ and $S_{q,i}$ for subgraph ranking. The overall model architecture is shown in Figure \ref{fig:model}. Our model consists of a graph construction module for the input question and the input triples, a BiGGNN encoder capturing global interactions and an Enhanced BiMPM encoder capturing local interactions.

\subsection{Graph Constructions}
\paragraph{Question Graph.} Question graph $G_q$ is a directed graph constructed by the dependency parser from Stanford CoreNLP \cite{manning2014stanford} \footnote{\url{https://stanfordnlp.github.io/CoreNLP}}.
The dependency parsing graph represents the grammatical structure of the input question. Nodes in the dependency parsing graph are the tokens in the question and an edge indicates a modified relationship between two token nodes. In particular, we only use the connection information for the edges, not the labels for the edges.

\paragraph{Sub-Knowledge Subgraph.} A sub-KSG consists of a set of triples $S_{q,i} = \{(s, r, o)| s, o \in \mathcal{E}, r \in \mathcal{R}\}$, where $\mathcal{E}$ and $\mathcal{R}$ denote the entity and relation set. Relation $r$ is regarded as an additional node. We assume there is a directed edge from subject node $s$ to $r$, and another directed edge from $r$ to subject node $o$. In the following sections, we will introduce how to calculate a relevant score between a question $q$ and a subgraph $S_{q,i}$ ($S$ for short).

\begin{figure}
\centering
\includegraphics[width=0.8\textwidth]{modelv3.pdf}
\caption{The Proposed G-G-E Model Architecture. The model contains two components: (1) A Subgraph Matching Networks component on the left (i.e., G-G in the figure); (2) An Enhanced BiMPM component on the right (i.e., EBiMPM in the figure).}
\label{fig:model}
\end{figure}

\subsection{Subgraph Matching Networks}
To better exploit the global contextual information and the structural information, we expand GGNNs from uni-directional embedding to bi-directional embedding. Given a question graph $G_q$ or a sub-KSG $S$, each node $v$ is initialized with its word embedding (e.g., average word embeddings for multi-token nodes).
To calculate the representation of each node $\mathbf{h}_v^{(l)}$ at layer $l$, the BiGGNN encoder first aggregate the information of neighbouring nodes to compute aggregation vectors:
\begin{align}
    \mathbf{m}_{v\vdash}^{(l)}=\sum_{{u} \in N_{\vdash} (v)} \mathbf{W}_{\vdash}^{(l-1)} \mathbf{h}_{u\vdash}^{(l-1)}\\
    \mathbf{m}_{v\dashv}^{(l)}=\sum_{{u} \in N_{\dashv} (v)} \mathbf{W}_{\dashv}^{(l-1)} \mathbf{h}_{u\dashv}^{(l-1)}
\end{align}
where $N_{\vdash}(v)$ and $N_{\dashv}(v)$ denote the neighbours of $v$ with outgoing and ingoing edges. $\mathbf{W}^{(l-1)}_{\vdash}$ and $\mathbf{W}^{(l-1)}_{\dashv}$ are trainable weight matrices. Then, a Gated Recurrent Unit (GRU) \cite{cho2014learning} is used to update the node representation at layer $l$ based on the aggregation vectors and the node representation at previous layer:
\begin{align}
\mathbf{h}_{v\vdash}^{(l)}=\text{GRU}(\mathbf{m}_{v\vdash}^{(l)}, \mathbf{h}_{v\vdash}^{(l-1)}) \\
\mathbf{h}_{v\dashv }^{(l)}=\text{GRU}(\mathbf{m}_{v\dashv}^{(l)}, \mathbf{h}_{v\dashv}^{(l-1)})
\end{align}
After obtaining all node representations of the question graph $G_q$, max pooling is applied to compute the graph embedding $\mathbf{r}$:
\begin{equation}
\label{equ:ge}
    \mathbf{r} = \text{max}(\{[ \mathbf{h}_{v\vdash}^{(L)}; \mathbf{h}_{v\dashv}^{(L)}], \forall v \in \mathcal{N}\})
\end{equation}
where $\mathcal{N}$ is the node set and $[\cdot;\cdot]$ is the concatenation operator. $L$ is the maximum number of layers. By stacking layers, BiGGNN encoder is able to consider non-immediate neighbours. The concatenation representation of node $v$ is $[ \mathbf{h}_{v\vdash}^{(L)}; \mathbf{h}_{v\dashv}^{(L)}] \in \mathbb{R}^{2D}$ and the set of node representations is in $\left | \mathcal{N} \right | \times 2D$ dimension. The max pooling operation is applied on the first dimension and the graph embedding is $\mathbf{r} \in \mathbb{R}^{2D}$.

\subsection{Enhanced BiMPM}
Bilateral Multi-Perspective Matching (BiMPM) is a strong text matching model due to its capacity of capturing the local interactions.
To better learn local interactions for sentence between the question and the sub-KSG, we propose to add an attention layer and an enhanced representation layer on the basis of the original BiMPM model.
Specifically, our proposed EBiMPM first uses a shared BiLSTM-based context representation layer to encode two input sequences to get two embeddings $\mathbf{q} \in \mathbb{R}^{l_1 \times d}$ and $\mathbf{S} \in \mathbb{R}^{l_2 \times d}$, where $l_1$ and $l_1$ are the lengths of the input texts.
Second, the newly-added attention layer applies a bi-directional attention mechanism between $\mathbf{q}$ and $\mathbf{S}$. The attentive embedding of the i-th question token $\mathbf{q}_i$ over $\mathbf{S}$ is computed as:
\begin{equation}
    \widetilde{\mathbf{q}_i} = \sum_{j=1}^{l_2} \frac{\text{exp}(\mathbf{q}_i^T\mathbf{S}_j)} {\sum_{k=1}^{l_2} \text{exp}(\mathbf{q}_i^T\mathbf{S}_k)} \mathbf{S}_j
\end{equation}
Similarly, we can compute the attentive embedding $\mathbf{\widetilde{S_i}}$ of the i-th sub-KSG token $\mathbf{S}_i$ over $\mathbf{q}$:
\begin{equation}
    \widetilde{\mathbf{S}_i} = \sum_{j=1}^{l_1} \frac{\text{exp}(\mathbf{S}_i^T\mathbf{q}_j)} {\sum_{k=1}^{l_1} \text{exp}(\mathbf{S}_i^T\mathbf{q}_k)} \mathbf{q}_j
\end{equation}
The attention layer outputs the attentive embeddings $\mathbf{\widetilde{q}}$ and $\mathbf{\widetilde{S}}$. Third, the enhanced representation layer fuses $\mathbf{q}$ and $\mathbf{\widetilde{q}}$ to get the improved question representation:
\begin{equation}
\widehat{\mathbf{q}} = f([\mathbf{q};\widetilde{\mathbf{q}};\mathbf{q}-\widetilde{\mathbf{q}};\mathbf{q}\odot  \widetilde{\mathbf{q}}])
\end{equation}
where $f(\cdot)$ is a one-layer perceptron and $\odot$ is the point-wise multiplication operation. Similarly, we can compute the enhanced subgraph representation $\widehat{\mathbf{S}}$:
\begin{equation}
\widehat{\mathbf{S}} = f([\mathbf{S};\widetilde{\mathbf{S}};\mathbf{S}-\widetilde{\mathbf{S}};\mathbf{S}\odot  \widetilde{\mathbf{S}}])
\end{equation}

Then, $\mathbf{q}$ and $\mathbf{S}$ are fed into the BiMPM matching layer \cite{wang2017bilateral} to get two sequences of matching vectors $\overline{\mathbf{q}} \in \mathbb{R}^{l_1 \times 8l}$ and $\overline{\mathbf{S}} \in \mathbb{R}^{l_2 \times 8l}$, where $l$ is the number of perspectives.
The matching layer defines four kinds of matching strategies to compare each time-step of one sequence against all time-steps of the other sequence from both forward and backward directions: (1) \textit{Full-Matching} Each forward or backward hidden state in one sentence is compared with the last hidden state in the other sentence; (2) \textit{Maxpooling-Matching} Each forward or backward hidden state in one sentence is compared with each hidden state in the other sentence and maxpooling is applied to obtain the maximum value of each dimension; (3) \textit{Attentive-Matching} The attentive vector of each token in one sentence is computed over the other sentence and each hidden state is compared with its corresponding attentive vector; and (4) \textit{Max-Attentive-Matching} The hidden state in the other sentence with the highest cosine similarity is selected as the attentive vector of each token in this sentence.

Finally, [$\overline{\mathbf{q}}$;$\widehat{\mathbf{q}}$] and [$\overline{\mathbf{S}}$;$\widehat{\mathbf{S}}$] are regarded as inputs to a shared BiLSTM-based aggregation layer to get the final representation:
\begin{align}
    \mathbf{r}_q' = \text{max}(g([ \overline{\mathbf{q}};\widehat{\mathbf{q}}])) \\

    \mathbf{r}_S' = \text{max}(g([\overline{\mathbf{S}};\widehat{\mathbf{S}}]))
\end{align}
where $\text{max}(\cdot)$ denotes max pooling and $g(\cdot)$ denotes a BiLSTM-based aggregation layer.

\subsection{Ranking Score Function}
The representations of the question and the sub-KSG learned by the subgraph matching networks and EBiMPM are concatenated separately and inputted to a cosine similarity ranking score function:
\begin{equation}
    \hat{y} = cos([\mathbf{r}_q;\mathbf{r}_q'], [\mathbf{r}_S;\mathbf{r}_S'])
\end{equation}
At last, we take Mean Square Error (MSE) as the loss function:
\begin{equation}
    L = \frac{1}{N_m}\sum_{m=1}^{N_m}(y_m-\widehat{y_m})^2
\end{equation}
where $N_m$ is the number of samples and $y_m$ is the label.

\subsection{Answer Selection Model}
After using the ranking model to obtain the top sub-KSGs, we merge them into a smaller graph compared to the original large KG graph and feed it into an answer selection model. In this paper, we use one of the state-of-the-art KGQA model GraftNet \cite{sun2018open} as our answer selection model, which is a heterogeneous graph neural network model. To improve the overall performance, GraftNet also incorporates external Wikipedia knowledge and computes a PageRank \cite{haveliwala2003topic} score for each entity node. However, we only use the basic model of GraftNet as our answer selection model to better validate the effectiveness of our proposed graph-augmented learning to rank model. GraftNet performs a binary classification to select the answer:
\begin{equation}
    Pr(v|q, S) = \sigma (\mathbf{W} \mathbf{h}_v^{(L)}+\mathbf{b})
\end{equation}
where $\mathbf{h}_v^{(L)}$ is the final nodes representation learned by GraftNet and $\sigma$ is the sigmoid function. The answer selection model is trained with binary cross-entropy loss, using the full KSG and the merged top-ranked sub-KSGs as input respectively.

\section{Experiments}

\begin{table}[]
\begin{tabular}{l|cccccc}
\hline
dataset & train & dev & test & \# entities in KSG & \# sub-KSGs & coverage rate \\ \hline
WebQSP  &  2848 & 250 & 1639 & 1429.8 & 1279.9 &  94.9\% \\
CWQ     & 18391 & 2299 & 2299 & 95.9 & 50 & 95.7\% \\ \hline
\end{tabular}
\caption{Statistics information of the WebQSP dataset and the CWQ dataset.}
\label{Statistics-table}
\end{table}

\subsection{Datasets}
\label{sec: Dataset}
We conducted experiments on two multi-hop question answering datasets, i.e., WebQuestionsSP (WebQSP) \cite{yih2015semantic} and ComplexWebQuestions (CWQ) \cite{talmor2018web}.
Table \ref{Statistics-table} shows the statistical information of the datasets. For WebQSP, we use the partition algorithm to construct the sub-KSGs based on the data processed by \cite{He-WSDM-2021}, which follows the retrieval method proposed by \cite{sun2018open}. Because the WebQSP dataset is small, the train and dev matching datasets used for training phase are constructed by selecting a sub-KSG containing true answers and random sampling 20 sub-KSGs for each example. For the test dataset, each example contains a natural language question and all partitioned sub-KSGs. The model computes a ranking score for each (question, sub-KSG) pair. As shown in Table \ref{Statistics-table}, the average number of entities in each KSG is 1429.9 and each KSG produces an average of 1279.9 sub-KSGs after the partition process. The coverage rate means that about 94.9\% of examples in the dataset can find answers in their corresponding KSGs.

For CWQ, we use the preprocessed datasets released by \citet{kumar2019difficulty}.
Each sample contains a question, a subgraph from which the question is derived and a set of answer entities.
The CWQ dataset contains 22989 matched (question, subgraph) pairs. The division ratio of train set, dev set and test set is 8:1:1.
For the train set and the dev set, we produce the same number of negative examples as the positive ones. For each question, we select a confusion-prone subgraph from the training subgraph set that is similar to the matched subgraph but contains no answer nodes as a negative sample. TF-IDF is used to compute the similarity of the text of two subgraphs.
For the test dataset used for ranking evaluation, it consists of a matched subgraph and 49 unmatched subgraphs which are similar to the matched one. Therefore, the average number of sub-KSG (subgraph) for the CWQ dataset is 50. We merge these 50 sub-KSGs (subgraphs) to form a pseudo KSG for each example. As shown in Table \ref{Statistics-table}, the average number of entities in a pseudo KSG is 95.9 and the coverage rate of the test dataset is 95.7\%.

\subsection{Models and Metrics}
In the next experiments, our proposed BiGGNN-BiGGNN-EBiMPM (G-G-E) model is compared with the following baselines:
\begin{itemize}
    \item {BiMPM \cite{wang2017bilateral}:} an LSTM-based model for text matching;
    \item {EBiMPM:} adding an attention layer and an enhanced representation layer on BiMPM.
    \item {BERT \cite{devlin2018bert}:} a shared BERT model to encode the question sequence and the subgraph triples sequence;
    \item {BiGGNN-BiGGNN (G-G):} both question graph and sub-KSG are encoded by a BiGGNN respectively;
\end{itemize}

To evaluate the graph-augmented learning to rank model, we use Recall@K (R@K) and Mean Reciprocal Rank (MRR) as the evaluation metrics. Recall@K is the proportion of examples that can find sub-KSGs containing answers in the top-K sub-KSGs. Mean reciprocal rank is the average of the reciprocal ranks of the sub-KSGs containing answers.
Furthermore, we use Hits, precision, recall and F1 to evaluate whether reducing the size of the KSG is beneficial to the subsequent answer selection model. Hits is the proportion of examples where GraftNet can select answer nodes in the subgraph merging the top-K sub-KSGs.

\subsection{Experimental Settings}
Our proposed model are implemented by MatchZoo-py \cite{Guo:2019:MLP:3331184.3331403} and Graph4NLP \cite{wu2021graph}. We use Adam \cite{kingma2014adam} optimization with an initial learning rate 0.0005. The batch size is 64 for CWQ and is 50 for WebQSP. Word embeddings are initialized with 300-dimensional pretrained GloVe \cite{pennington2014glove} embeddings\footnote{http://nlp.stanford.edu/data/glove.840B.300d.zip}. BiGGNN encoder is stacked to 2-layer.  Early stopping is introduced during the training phase and the validation set is evaluated every epoch.
All models use cosine similarity as ranking score function. All experiments are run on Tesla V100.

\begin{table*}[]
\centering
\small
\begin{tabular}{l|cccccc|cccc}
\toprule[1pt]
Dataset & \multicolumn{6}{c|}{WebQSP} & \multicolumn{4}{c}{CWQ} \\ \hline
Model &  MRR &  R@1 &  R@10 &  R@100 &  R@200 &  R@300   &  MRR  & R@1 & R@10 & R@20 \\ \hline
BiMPM & 0.6118 & 0.5308 & 0.7663 & 0.8816 & 0.9029 &	0.9121 & 0.6802 & 0.5698 & 0.9056 & 0.9652 \\
EBiMPM & 0.6612 &  0.5954 &  0.7803  & 0.8798 & 0.8993 &	0.9090 & 0.7074 & 0.6089 & 0.9064 & 0.9643 \\
BERT & 0.6817 & 0.6186 & 0.7895 & 0.8852 & 0.9048 & 0.9139 & 0.7361 & 0.6642 & 0.8838 & 0.9508 \\
G-G & 0.6867 & 0.6320 &	0.7895 & 0.8828 & 0.9048 & 0.9176 & 0.7123 & 0.6372 & 0.8708 & 0.9395 \\
\textbf{G-G-E} &  \textbf{0.6967} & \textbf{0.6430} &  \textbf{0.7968} & \textbf{0.8913} & \textbf{0.9133} & \textbf{0.9237} & \textbf{0.7540} & \textbf{0.6746} & \textbf{0.9234} & \textbf{0.9669}  \\
\toprule[1pt]
\end{tabular}
\caption{Ranking Experimental Results. Bold fonts indicates the best results.}
\label{results-table}
\end{table*}

\begin{table}[]
\begin{tabular}{c|cccc|c|cccc}
\toprule[1pt]
Dataset & \multicolumn{4}{c|}{WebQSP}   & \multicolumn{5}{c}{CWQ}   \\ \hline
Data    & Hits  & Precision                     & Recall & F1    & Data   & Hits  & Precision   & Recall & F1    \\ \hline
top 100 & 0.6043& 0.6041& 0.5824& 0.5128& top 10 & \textbf{0.4236} & 0.5296&  \textbf{0.4105} & \textbf{0.3274} \\
top 200 & 0.5975& \textbf{0.6561} & 0.5858& 0.5355& top 20 & 0.4000   & 0.5149& 0.3772& 0.2923\\
top 300 & \textbf{0.6049} & 0.62  & \textbf{0.6387} & \textbf{0.5495} & full   & 0.3956& \textbf{0.5673}& 0.3389& 0.2736\\
full    & 0.579 & 0.5738& 0.6245& 0.5219&        &       &       &       & \\
\toprule[1pt]
\end{tabular}
\caption{Answer selection results on WebQSP and CWQ.}
\label{answer-selection}
\end{table}

\subsection{Results Analysis}
Table \ref{results-table} shows the ranking performance on two datasets. In particular, the upper limit of Recall@K is 100\% rather than the coverage rate because we eliminate examples for which we can not find an answer.
It can be seen that our proposed full model G-G-E consistently outperforms other baselines on all datasets, including the BERT model. To guarantee a high answer recall for the merged subgraph, we are more concerned about Recall@K than Recall@1, especially when K is large. Our proposed G-G-E model is 0.6 to 1 percentage point higher than the best baseline models for metrics Recall@100, Recall@200 and Recall@300 in dataset WebQSP. In the dataset CWQ, the Recall@10 of the G-G-E model also improved by 1.7\% compared to the best baseline model.
Moreover, on the WebQSP dataset, G-G is significantly better than BiMPM, increasing by 0.07 on MRR and 0.1 on Recall@1 respectively, which indicates the graph structure information plays a more important role on this dataset.

To further validate that reducing the size of KSG help improve the performance of answer selection, we merge the top 100, 200 and 300 sub-KSGs of the WebQSP dataset and the top 10, 20 sub-KSGs of the CWQ dataset. The experimental results are shown in Table \ref{answer-selection}. For WebQSP, the answer selection model performs best on the top-300 merged subgraph, increasing by 0.026 on Hits and 0.027 on F1. The top-300 merged subgraph is almost a third of the size of the original full KSG, which contains an average of 1280 sub-KSGs. The improvements also verify the effectiveness of our proposed partition algorithm. For CWQ, the answer selection model performs best on the top-10 merged subgraph, increasing by 2.8\% on Hits and 5.4\% on F1. The top-10 merged subgraph is a fifth of the size of the full KSG. In general, by using our proposed partition algorithm and graph-augmented learning to rank model, we can further reduce the size of the KSG, while ensuring the answer recall rate.

\begin{table*}
\centering
\begin{tabular}{l}
\toprule[1pt]
\textbf{Question:} what artistic movement did m.0gct\_ belong to ? \\ \hline
\textbf{Mispredicted Subgraph:} (m.0gct\_ , influence\_influence\_node\_influenced\_by,  m.0160zv) \\ (m.0160zv,  visual\_art\_visual\_artist\_associated\_periods\_or\_movements , m.0160zb) \\
\textbf{Ground Truth Subgraph:} (m.0gct\_, visual\_art\_visual\_artist\_associated\_periods\_or\_ \\ movements, m.049xrv) \\ \hline
\textbf{Question:} who did m.01ps2h8 play in lord of the rings ? \\ \hline
\textbf{Mispredicted Subgraph:} (m.01ps2h8, film\_actor\_film, m.0k5s9k), (m.0k5s9k, film\_ \\ performance\_film, m.017gl1) \\
\textbf{Ground Truth Subgraph:} (m.01ps2h8, film\_actor\_film m.0k5sfk), (m.0k5sfk, film\_ \\ performance\_character, m.0gwlg) \\ \hline
\toprule[1pt]
\end{tabular}
\caption{Examples of mispredicted KG subgraphs by the G-G-E model on the WebQSP dataset.}
\label{case-table}
\end{table*}

\subsection{Ablation Study}
We conduct an ablation study to investigate the contribution of each component to the proposed model. As shown in Table \ref{results-table}, we evaluate models with only graph neural network encoder (G-G) and with only sequence encoder (EBiMPM), respectively. The performance gain of G-G-E model compared to G-G and EBiMPM can empirically demonstrate the effectiveness of combining the two encoders for capturing both global and local interactions between the question and the knowledge subgraph.

\subsection{Case Study and Error Analysis}
To investigate the limitations of the graph-augmented learning to rank model, we manually checked the sub-KSGs that were incorrectly considered as containing answers in Table \ref{case-table}. The topic entity in the question and the entities in the subgraph are replaced by their Freebase ID.
The first mispredicted subgraph contains a redundant hop ``influence\_influence\_node\_influenced\_by''. This may because our model ignores the number of hops of the question. The second example fails to map ``play'' in the question to the relation ``film\_performance\_character''.
It confuses the model because the mispredicted subgraph is very similar to the real one.

\section{Related Work}
\subsection{Knowledge Graph Question Answering}
With the rapid development of large-scale knowledge graphs (KG) such as DBpedia \cite{auer2007dbpedia} and Freebase \cite{bollacker2008freebase}, question answering over knowledge graph has attracted widespread attention from a growing number of researchers. However, due to the large volume of the knowledge graph, using the knowledge in it to answer questions is a challenging task. In general, Knowledge Graph Question Answering has two mainstream research methods, namely semantic parsing based methods and retrieve-then-extract methods.

\paragraph{Semantic parsing based methods} Semantic parsing based methods convert natural language questions to knowledge base readable queries. \citet{lan2021survey} summarises these methods in the following steps: (1) Using a \textit{Question Understanding} module to analyze questions semantically and syntactically. Common question analysis techniques include dependency parsing \cite{abujabal2017automated}, AMR parsing \cite{kapanipathi2020question} and skeleton parsing \cite{sun2020sparqa}. (2) Using a \textit{Logical Parsing} module to convert the question embedding into an uninstantiated logic form. This module creates a syntactic representation of the question such as template based queries \cite{bast2015more} and query graphs \cite{hu2018state}. (3) Using a \textit{KB Grounding} module to align the logic form to KB \cite{bhutani2019learning, chen2019uhop}. The logical query obtained from the above steps can be searched directly in KB to find the final answer. These approaches usually require the design of specific rules in the second step mentioned above and limit the domains where such approaches can be applied.

\paragraph{Retrieve-then-extract methods} Retrieve-then-extract methods are also known as information retrieval based methods. These methods first retrieve the KG coarsely to obtain a knowledge subgraph containing answer candidates and then extract answers from the retrieved subgraph. \citet{bordes2014question} first proposed a subgraph retreival method and a subgraph embedding model which can score every candidate answer. In the following work, \citet{miller2016key} adopted a memory table to store KB facts encoded into key-value pairs. \citet{sun2018open} proposed a graph neural network model to perform multi-hop reasoning on heterogeneous graphs. PullNet \cite{sun2019pullnet} improved the graph retrieval module by iteratively expanding the question-specific subgraph. BAMnet \cite{chen2019bidirectional} modeled the bidirectional flow of interactions between the questions and the KB using an attentive memory network. EmbedKGQA \cite{saxena2020improving} directly matched pretrained entity KG embeddings with question embedding, which is computationally intensive.

\subsection{Learning to Rank}
Modern search engines often leverages a multi-level cascade structure including a stack of ranking models with the aim of providing users with a ranked list of results efficiently. The multi-level structure begins with an efficient high-recall ranker, which aims at retrieving as many possible correct answers as possible. The goal of this ranker is consistent with the coarse-grained retrieval module in information retrieval based KGQA. However, the learning to rank model is often regarded as the last ranker of the multi-level structure, which re-ranks a small set of documents retrieved by the previous rankers.

Over the past few years, learning to rank models have already achieved great success in the information retrieval area. Traditional learning to rank models rely on hand-crafted features, which are often time-consuming to design. Recently, many ranking models based on neural networks have emerged. Deep Structured Semantic Model (DSSM) \cite{huang2013learning} in 2013 is the first neural network ranking model using fully connected neural networks. In this same year, DeepMatch \cite{NIPS2013_8a0e1141} was proposed to model the complex matching relations between responses to a tweet, or relevant answers to a given question. \cite{hu2014convolutional} proposed convolutional neural network models ARC-I and ARC-II for matching two sentences. Since 2016, researches on neural ranking models have surged, with more related work, deeper discussions and wider applications. \cite{wang2016machine} proposed a model based on match-LSTM and Pointer Net \cite{vinyals2015pointer}. ANMM \cite{yang2016anmm} is an attention based neural matching model combining different matching signals for ranking short answer text. \cite{wang2017bilateral} proposed the BiMPM model under the "matching-aggregation" framework to match the sentences from multiple perspectives. With the development of pretrained language models such as BERT \cite{devlin2018bert}, the performance of neural ranking models is taken to a next level. These neural ranking models have limitations when applied to information retrieval based KGQA because the inputs are considered as raw text sequences and the structural information in the KG is ignored.

\section{Conclusions}
In the information retrieval based KGQA, this paper focuses on a subgraph ranking task with the aim of reducing the size of the coarsely retrieved knowledge subgraph and capturing both local and global interactions between question and sub-KSGs. We propose a KSG partition algorithm and a graph-augmented learning to rank model to match-then-rank them. We further validate that reducing the size of knowledge subgraph is beneficial to the subsequent answer selection in an information retrieval based KGQA process. The experimental results on multiple datasets demonstrate the effectiveness of our model. In the future, we will further explore a more effective answer selection model over the small-scale knowledge subgraph selected by our learning to rank model.

\begin{acks}
The work is partially supported by the National Nature Science Foundation of China (No. 61976160), Technology research plan project of Ministry of Public and Security (Grant No. 2020JSYJD01), Thirteen Five-year Research Planning Project of National Language Committee (No. YB135-149) and the Self-determined Research Funds of CCNU from the Colleges' Basic Research and Operation of MOE (No. CCNU20ZT012, CCNU20TD001).
\end{acks}

\bibliographystyle{ACM-Reference-Format}

redpajama_set_name: RedPajamaArXiv


The Cloud and Older Hardware: Enforced Obsolescence?
One hidden effect of cloud computing being that it's no longer possible to do what we all used to do: Have an older PC running, say, Windows XP and Office 2000, which was perfectly adequate for our needs. Now the auto-refresh of every cloud app forces us to "upgrade" even if it's too much for our poky older hardware. Unless, that is, you pay attention during your QA process.
One of the touted advantages of cloud computing (especially Software as a Service) is that it removes the need to support older application versions. The IT department doesn't have to worry about keeping end-user software in compliance with security patches or upgrading the business' computers with commercial applications (such as Microsoft Office) or custom in-house software. Developers appreciate this, too, since with SaaS, nobody issues trouble tickets on the 3-year-old version of the application which users refuse to relinquish.
So far, so good. The user logs in and gets the latest version of the software. Developers and IT know that the version the user is touching is the most recent build, because that's all that the user can access. Oh boy! Simplicity! Predictability!
But while savvy developers test their apps on older browsers (even, God help them, IE6), I don't think very many test on older, memory-constrained hardware.
It used to be that you could stick with your old hardware and software because it did the job perfectly well. Many users' software needs remain constant. My old Dell Windows system (6 or 7 years old now?), sitting on the corner of the desk, runs Windows XP and uses Office circa 2000. I bet it can read most Word docs (though slowly). I replaced that laptop with a newer Windows laptop almost two years ago, but there's nothing to say that the old Dell doesn't do the job it was bought to do.
We computer geeks forget that most people are not motivated to buy a new computer. Most businesses try to amortize the equipment over three years, at least, and they hope all the keys will stay attached that long.
Both of those older computers work just fine, in the sense of no parts are falling off or battery giving up. (Though the really old computer's keyboard has a really strange smell. What's up with that?) They do everything I asked them to do when I bought them, including run the then-standard software I relied on.
I knew it was time to upgrade the last MacBook when I could no longer run the software I needed. Not just the latest versions of Microsoft Office or Pages (though they got really painful to use) but also the number of web-based things that are now opened-up within 5 minutes of me booting the system. The old hardware is not CPU-bound nearly as much as memory-bound. I'm in minimal mode at the moment on my current system right-this-second, for instance, and I have 8 tabs open in Firefox and 6 in Chrome. On top of Mail, Skype, iChat, a text editor, Preview, and Office. And that's the minimum. I've just gotten started today.
Software sloppiness is an undeniable fact, and it's one that's been around since I began programming on mainframes. Few developers work hard to tune performance for more than speed, and those who do compare application behavior on current browsers and current hardware. Some developers and QA staff test web apps on older browsers (including you poor slobs who must support IE6). But who among you tests applications on, say, 2006-era PCs? And optimizes the code to help that user get her work done?
Are you doing anything to support users of older equipment? At what point do you (or your organization) decide these people no longer matter? I'd like to hear from you.


In August of 2007, F1554 grade 36 replaced ASTM A307 grade C which also covered mild steel anchor bolts. Galvanizing. ... Headed anchor bolts can also have an anchor plate welded to the head of the bolt if the head itself does not provide enough pull-out resistance.
A36 steel plate is similar to Q235B steel plate, ... An anchor bolt, for instance made from ASTM A36 Steel is equivalent to one made from Q235B steel grade. 97 Views. Rushabh Doshi, Student. Answered May 3, 2016. ... Which steel manufacturer in India supplies Steel plates Grade 70 or equivalent?
A572 steel plate, a572 steel, a572 steel plate, a572 high strength low alloy, a572 steel bar, a572 carbon steel plate. ... A709 Grade 50. ... A709-50 ASTM A709 is fracture critical and covers the requirements for structural steel for bridges and other structures. Grade 50 steel of this specification requires a minimum yield strength of 50 ksi.
ASTM A 709 Regular Quality Mild Steel ... Expanded Metal Floor Plate Grating ASTM A 656 ASTM A 709 100 or ASTM A 588 100XF 50 or ASTM A 572 50 or ASTM A 572 ASTM A 516 Universal Mill. UNIVERSAL MILL ASTM A 36 ASME SA-36 ASTM A 709 Grade 36 Random Lengths 20 Ft. Identification Color: Plain Ends Size in Lbs. Est. Lbs.


Nobles and knights started using Coat-of-Arms during the Middle Ages. To identify himself during battle and military tournaments, a knight painted something on his shield. This became his personal identification. He also used this picture as his personal seal — almost like ones signature in modern times. A signature is only one persons identification and nobody else.
In time the oldest son started using his fathers shield (and the Coat-of-Arms on the shield). Even later this Coat-of-Arms were used as family Coat-of-Arms.
At present we are only aware of three Henning families — one in South Africa and two in Germany using specific Coats-of-Arms. Over the ages several Henning / Hennings / Hennig Coats-of-Arms were used. In the authoritative publication "Armrial Général" by J B Rietstap, which have been illustrated by Roland, more than 112 000 family Coats-of-Arms are shown. Of these eleven (11) were identified as Henning / Hennings / Hennig Coats-of Arms. These Coats-of-Arms are also shown below.
The Coat-of-Arms of the Peter Henrich Hennig Family League of South Africa. This Coat-of-Arms was officially registered with the State Herald of South Africa — Government notice nr 1353 in Government Gazette nr 9791 dated 21 June 1985. The simbolism of the Coat-of-Arms is described in the pages of Henning families of South Africa.
The Henning families who used this Coat-of-Arms since the Middle Ages, originally lived at Demmin, near Stralsund and Greiffswalt in Mecklenburg-Vorpommern, Germany. Later they moved to Karnin (also near Stralsund) Until the 30-years War (1618 — 1648) these areas were under control of Danmark. This is the area where the name Henning originated during 1290. Compare this Coat-of-Arms with the Coat-of-Arms, Henning, Allem(agne) further on.
The HENNING and VON HENNING descendants of Laurentuis Henning of Rudolstadt.
Laurentius Henning (* Rudolstadt 28-3-1602) was the first son of Caspar Henning, who came to Rudolstadt (Germany) from Frankenhausen towards the end of the sixteenth Century.
During 1660 Laurentius was allowed to the aristocracy in Wienna and obtained the right to use the name "VON HENNING AUF SCHÖNHOFF" and this Coat-of-Arms. He and his descendants were members of the Prussian military aristocracy. The family tree of this family can be found under the heading "Genealogy", "Germany(2)"
Compare this Coat-of-Arms with the coat-of-arms, Henning de Schönhoff, Prusse here-under.
The Henning family who used this Coat-of-Arms lived in the areas which is known today as Poland and Latvia.
The Henning family who used this Coat-of-Arms lived in the area which is known today as Latvia.
The Prussian family who used this Coat-of-Arms, lived in the area which is known today as Germany.
The family who used this Coat-of-Arms lived in the vicinity of Cologne, Germany.
This Coat-of-Arms correspond with the Coat-of-Arms used by the Henning family of Demmin/ Karnin (above).
Take note that this family used the name Hennings. Pom is probably the abreviation of Pommern (Germany).
Another Hennings family. Originally from Danmark.
This family lived in Germany. In al probability a variation of the Coat-of-Arms used by the Demmin / Karnin Henning family.
Two variations of the Coat-of-Arms used by a Hennig family of Bavaria, Germany.
The Coat-of-Arms of a Hennig family of Prusse (today part of Germany).


Designed to the exact specifications of Bassmaster Classic Champ, Kevin VanDam, the Strike King KVD Square Bill Silent Crankbaits are silent assassins perfect for KVD's style of shallow water power fishing. In addition to allowing the crankbait to bounce and deflect off cover, the square bill design also allows these KVD cranks to constantly wander with an erratic searching action while still running true. The KVD Square Bill Silent Crankbaits do not have internal rattles, making them absolutely perfect for skittish, pressured fish.


They need to trade McCollum. Portland's FO is worthless...can't even surround an elite scorer like Lillard with a contending team.
The guy kills GSW every time they play, imagine if he had a better supporting cast.
McCollum is the least of their problems.
Good effort, but sloppy end of quarter play. It was winnable, but our guys must be tired coming back home after the business trip. Rest up, recalibrate, and go out there with the same never give up effort playoff mentality that they need to cultivate for the coming years. Gotta hit those free throws. They're easy points... all confidence at the line. Great swarming defense, but sloppy game after the road games. Lessons learned game.
They were playing too lackadaisical when they had the 11 pt lead in the 3rd and werent valuing possessions.
This. They started playing school yard ball...thought they had it in the bag.
Same point stands for Noahp45. Best to take something positive out of this, and I agree with what he said.


FC PLATINUM reminded their rivals they are not prepared to surrender the title they have won for the past two seasons with a dominant performance that put Harare City to the sword in a Castle Lager Premiership match here yesterday.
The visitors' dominance was evident from the first whistle as they bullied their opponents in almost every department and goals in each half from Ali Sadiki and defender Elvis Moyo ensured the reigning champions began their 2019 campaign on a positive note.
While Harare City were playing their first competitive match of the season, it was a different case with the platinum miners whose fitness levels were superior due to their participation in the African Champions League, where they were eliminated in the group stages of the tournament.
City coach Mark Harrison admitted at the final whistle that their opponents were better equipped going into the season opener.
"It was always going to be very difficult for us to play this game. I keep saying it, I said it before the game and I'm saying it now," Harrison said.
"They were sharp and all over the field and quite rightly so because they have been playing competition football for the past three months in Caf.
FC Platinum coach Norman Mapeza lauded his charges for a job well done.
While FC Platinum were the better side in the opening 20 minutes there were, however, few chances of any note with Harare City well organised and defending in numbers.
Former Bulawayo Chiefs forward Perfect Chikwende could have put FC Platinum ahead only to shoot weakly at City keeper Maxwell Nyamupanedengu after finding space inside the box.
Sadiki, however, deservedly put the visitors ahead with a beautiful finish at the far post after defender Ray Uchena had failed to clear Chikwende's header from his path.
City defender Marshal Machazani had an opportunity to equalise for the home side on the stroke of halftime following a rare attack only to shoot tamely at goalkeeper Petros Mhari.
The Sunshine Boys came back a more charged side in the second half keeping the visitors on the back foot only to be let down by poor finishing from Martin Vengesai and Wilfred Muvirimi.
They were punished for those misses as Moyo secured the three points after a wonderful team move before finding space between Nyamupanedengu and his near post.


text:
Apple Watch Series 6 deals
Apple Watch SE deals
The best Apple Watch deals happening now: get $80 off the Series 6
Tercius Bufete , Sarah Saril , and Sarah Lord
Apple; Alyssa Powell/Insider
Good smartwatches keep you in the loop when you're away from your phone, and the Apple Watch is our best overall pick for its ease of use and robust health features. Apple sells it in three different versions, Apple Watch Series 6, SE, and the Series 3, with the oldest model starting at $200.
Apple Watches go on sale frequently and there's no need to ever pay full price for one. Check out the table of contents to the left to hop to the best deals for any specific model.
If you're on the fence about which one to buy, here's a quick breakdown:
Series 6 SE Series 3
Our review Best overall Best on a budget Best for the basics
Average price $350 $270 $170
Available sizes 40mm, 44mm 40mm, 44mm 38mm, 42mm
Introduction year 2020 2020 2017
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Still wondering which one is right for you? Or how the Apple Watch stacks up to the competition? Here's our guide for the best Apple Watches and our picks for the best smartwatches overall.
The best Apple Watch deals available right now
Deal icon An icon in the shape of a lightning bolt. Great Price
Apple Watch Series 6 (40mm, GPS)
The Apple Watch Series 6 has been replaced by the Series 7, but it still offers many of the same features. It's available for $100 off right now.
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Average price: $350 for 40mm, $409 for 44mm
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Extra features: Large always-on display, fast charging, blood oxygen app, ECG app, fall detection, heart rate data, fitness tracking
The Apple Watch Series 6 is the flagship model that comes loaded with all of Apple's most advanced health features, its latest processor, a brighter always-on display, and fast charging.
Shoppers should expect to pay 15% less than retail for this model on a regular basis. Discounts over 20% are a little rarer.
Apple Watch SE (40mm, GPS)
With a recent Apple processor and many of the same features as the Series 7, the Apple Watch SE is a great budget-friendly option. During Black Friday , you could also get a $50 Apple Gift Card when you bought an Apple Watch SE.
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Extra features: Large display, fall detection, heart rate data, fitness tracking
The Apple Watch SE comes with many of the features you'd want from the Series 6, but lacks the new blood oxygen sensor, brighter always-on display, and fast charging. However, for most people, the Apple Watch SE may be the best option, since it costs over $100 less but can do almost everything the Series 6 can.
Like the Series 6, shoppers shouldn't pay retail price for the SE. It sees a discount of 15% regularly.
The Apple Watch Series 3 in aluminum is the perfect blend of style and functionality for a decent price for its age. The Apple Watch Series 3 rarely leaves its discounted price of $169, but we've seen it go down as low as $109 in the recent past.
$169.00 from Best Buy
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If price is your main concern, the older Apple Watch Series 3 is worth consideration. It is the cheapest option sold by the Apple Store. Even though it lacks more advanced features like an always-on screen and ECG monitoring, it offers the core Apple Watch experience at a much more accessible price.
The Series 3, while listed as $200, rarely leaves its discounted price of $169. Suffice it to say, shoppers should not buy it for more than $169, but should take advantage of any price below $169.
Read more about how the Insider Reviews team evaluates deals and why you should trust us.
Tercius Bufete
Tercius Bufete is the Deals Editor for Insider's reviews team. Tercius joined the company in mid-2020. Previously, he was a commerce editor at Kinja Deals and an associate editor at Consumer Reports. He graduated from California State University, Chico with a degree in journalism and a minor in photographic studies. Say hello at tbufete@businessinsider.com, and tweet him @Tercius. Learn more about how our team of experts tests and reviews products at Insider here. Learn more about how we research deals.
Sarah Saril
As a Deals Reporter for Insider's Reviews team, Sarah sifts through the depths of the internet to surface only the best of deals for our readers. Prior to joining Insider Inc., she was a Deals Writer at Wirecutter, a New York Times company. After graduating from California State University, Fullerton with a degree in Marketing, Sarah started out as a Freelance Deals Writer at Wirecutter; here she focused on newsletter creation, social media promotion, and syndication in addition to deals writing before stepping into a full-time role. Her accomplishments from this position, in addition to the countless deals she's written, include implementing multiple social media marketing campaigns and launching a Deals Facebook group for the company. Say hi (or send a picture of your pet) to Sarah at ssaril@businessinsider.com, or reach her on Twitter at @LordPonty or on Instagram at @lord_ponty. Learn more about how our team of experts tests and reviews products at Insider here. Learn more about how we research deals.
Tech and Electronics Reviews Fellow
Sarah covers tech and electronics as a fellow on the reviews team. She is a recent graduate of Wellesley College. Learn more about how our team of experts tests and reviews products at Insider here.
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Alyssa Powell


I have a passion for teaching, for English and in particular, in KS1. I love helping teachers and children enjoy their time at school through collaborative teaching and learning, involvement of parents, problem solving and consequently raising learning levels.
I am currently a Year 2 teacher, KS1 Phase Leader and KS1 English Leader at Amesbury Archer Primary School. I am an experienced teacher and have loved teaching in both military and state schools in Europe and the UK.
I have led staff and teaching assistant training, supported students and NQTs, worked on research based on data and assessment as well as introduced new school initiatives to encourage enjoyment in English.
- Learning to Learn, Talk for Writing and methodologies which enhance children's learning.