This was literally for me. My horrifying chronicles of eye infection continue today. I had so many plans for today. I was supposed to head to workout class and then meet the bestie for lunch. I woke up feeling like I had a big ball stuck in my eyes. I checked the mirror and it was swollen, like I have been crying all night. But of course I haven't. My right eye kept tearing non stop the entire morning and it was still reddish and my vision was blurred so how on earth was I going to make it to class.
In the end I sent a text message to Karen telling her I could not make it. Texted the bestie to ask if we can meet earlier instead and she could so yay!
Dear kids, one thing I learnt today is, when its scorching hot and you look like you are crying while waiting for a cab, no one else will dare to cut your queue. I had problems opening my eyes because the sun was too glaring and my right eye was tearing continuously too so I had to keep wiping it away with a piece of tissue. Probably to bystanders, I looked like I was crying while standing there waiting for a cab under the scorching hot sun, carrying an umbrella. A guy behind me automatically stood behind me and let me have the cab first. Gosh, I think he really thought I was crying lol.
Oh well, thank you anyway young gentleman. That was a sweet gesture.
That is the thing about Changi Village, there so many halal places to eat here, its a hidden gem.
Made it to Peloton Coffee and Juice Bar. It my first time here. I am glad the air con is cooling inside because nothing irks me more than cafes that try to save electricity by turning up the temperature or worse, switching off the air conditioning altogether.
There were not too many choices for savoury so the bestie and me ordered the beef and salmon sandwiches.
Ah, it was good. I really like the fact that the cafe was not too crowded and they were prompt with their orders. It was a really a nice place to chill and chit chat leisurely.
They also provided free wifi, this place would be perfect for me to do some writing in, if only its nearer to home and my work place.
Trying to hide the swollen eye by wearing my nerdy glasses. Did it work?
Playing around with the new camera functions and the bestie agreed to use my Nikon camera for June trip! Yay! We are so worn out with work, we talk about our trip in June almost everyday lol. Are we crazy?
No one should come to Peloton without trying their waffles for dessert!
Actually all the waffles looked so good, we were spoilt for choice. Perhaps we can come back next time and try the other flavours. I have been craving for waffles for agesss.
And so I turned into a waffle monster and ate this greedily. Lol.
I hope it will get better tomorrow.


text:
Rick Porcello Another Mets Fan
As we discovered, Rick Porcello turned down more money from the Toronto Blue Jays and Chicago White Sox (a recurring theme) to sign with the New York Mets. Apparently, part of the reason was his growing up a Mets fan. Really, Porcello is one of us:
Rick Porcello said he "cried his eyes out" when the Mets lost the Subway Series to the Yankees in 2000.
— Adam Rubin (@AdamRubinMedia) March 8, 2015
His being drawn to tears after Mike Piazza flew out to Bernie Williams is going to resonate with this fanbase because those of us who experience it felt the same way even if we did not have a Wilmer Flores moment.
Porcello is one of us which means he gets it. That makes him one of another player on this team who lived and died with this team.
When he signed with the team, Brad Brach talked about how he bought a Mets jersey and went to the 2015 World Series.
Marcus Stroman wore a Darryl Strawberry jersey to the ballpark before his Citi Field debut. He has also spoken about how he wants the team to bring back the black jerseys tweeting out a picture of Pedro Martinez wearing one.
That's a refrain we've heard from Pete Alonso, who while he did not particularly grow up a Mets fan, was a big Piazza fan.
Finally, there is Steven Matz who was a Long Islander who grew up a Mets fan. He'd have his whole family at his debut leading to everyone falling in love with his grandfather.
Mostly, we all love this Mets team, and really, we love these players. It's a very likable team who just gets it. Porcello seems to be more of the same which is great. Hopefully, Porcello will have us crying "tears of joy" after the season.
No matter what, we know how much this team means to him, and we know he's going to give it everything he has. If for no other reason, this is going to make it even easier for us all to root for him.
Brad Brach , Darryl Strawberry , Marcus Stroman , MIke Piazza , Pedro Martinez , Peter Alonso , Rick Porcello , Steven Matz , Wilmer Flores
Mets Proposed 2020 Uniform Plan
Since the black jerseys were first introduced in 1998, they have garnered much debate amongst fans. For some, they are like the racing stripe jerseys. It is the jersey they associate the Mets wearing when they were on top of the world. Think back to it. The Mets wore the black jerseys for some really monumental occasions:
Robin Ventura's Grand Slam Single
Bobby Jones' one hitter
Mike Hampton clinching the 2000 pennant
David Wright celebrating the 2006 NL East clincher
There were many more moments as well. With the Mets soon to embark on the 20th anniversary of that 2000 pennant, there seems to be a recent push to bring back those jerseys. Certainly, it is something we saw Pete Alonso, Marcus Stroman, and some other brilliant writer bring up over the last few months.
Now there, were a few problems with the black jerseys. To a certain portion of the fanbase, it was an abandonment of the Mets true glory years. These were not the jerseys of Tom Seaver and Jerry Koosman. It looked nothing like the jerseys of Gary Carter, Dwight Gooden, Keith Hernandez, and Darryl Strawberry. Really, it was a complete abandonment of the Mets roots which was supposed to be a partial homage to the Brooklyn Dodgers and New York Giants.
On more than one occasion, we heard Howie Rose lament about the infrequency in which we all saw the pinstripes. That was truly bizarre as they remained the designated home jersey. The black jerseys were only supposed to be an alternate, but they were treated as anything but that. Throw in the awful cap, and you see things did need to be eventually changed.
Artist's rendering of @STR0 in a @Mets black uni #LFGM pic.twitter.com/76VoMBZc1r
— Athlete Logos (@athletelogos) November 24, 2019
Since 2011, the black jerseys have been retired. In that time, the Mets have had a welcome return of the pinstripes, and they finally added the blue alternates fans had wanted to see for years. Still, with the anniversary, it being the jersey many grew up seeing, and people liking the look of it, people also want to see the black jerseys. With Carlos Beltran's return, it does seem like the right time to do it.
What many don't want to see is the black jerseys overdone. They also want to see the pinstripes and the blue alternates. To that end, as previously proposed, the black jerseys should become the Friday night jerseys. If nothing else, it would be a call back to this epic Mike Piazza homer, which not so coincidentally, was on a Friday night:
As for the blue alternates, the Mets should put Mr. Met back as a sleeve patch, and the team should wear them as part of the Family Sundays.That ensures the blue alternates don't get lost in the shuffle, and as noted, the Mr. Met patch is a nice touch for the days when the team has the Mr. Met dash.
For the weekday games, the Mets should wear the pinstripes.
Aside from Friday and Sunday, this need not be a hard and fast rule. The team could catch fire in one jersey leading to the team wearing them more as a good luck charm. The team could opt to feature one as part of a national showcase game for Fox or ESPN. The one caveat being it makes little to no sense to wear the black jerseys during hot summer days. But overall, this is the framework which really works well for the team.
Planning it all out this way, allows the Mets to do some advertising around it, and it seems to satisfy all fans. More than that, it gives the team an opportunity to really boost jersey sales. Overall, when this keeps everyone happy, and it leads to more money for the team, it is difficult to imagine why the team would not proceed with this plan.
Bobby Jones , Carlos Beltran , Darryl Strawberry , David Wright , Dwight Gooden , Gary Carter , Jerry Koosman , Keith Hernandez , Marcus Stroman , Mike Hampton , MIke Piazza , Pete Alonso , Robin Ventura , Tom Seaver
It's Okay Pete Alonso Isn't A Unanimous Rookie of the Year
Pete Alonso officially joined Tom Seaver, Jon Matlack, Darryl Strawberry, Dwight Gooden, and Jacob deGrom as Mets players who have won the National League Rookie of the Year. With Alonso surpassing Aaron Judge's rookie record for homers, the honor was not only well deserved, but it made the announcement more of a coronation than anything.
Before Alonso was announced Yordan Alvarez was named the American League Rookie of the Year, and it was unanimous. When the announcement was made, we were all expecting to have two unanimous selections, but that didn't happen as Andrew Baggarly of The Athletic was the only voter who voted for Mike Soroka.
Now, it should be noted Baggarly had a very sound basis for his decision. In addition to throw away narratives like Soroka being a real contender for the Cy Young Award, he also noted Soroka had a better WPA and WAR. He then explained how he was more impressed with Soroka keeping the ball in the ballpark than he was with Alonso hitting it out in an era of the lively baseball.
Even though Baggarly made the article more about himself and his concerns about Mets fans coming after him noting he had been warned by other writers "Mets Twitter is a special flavor of Twitter." Still, even with the article being a bit too much "Look at me!" his justification for voting for Soroka wasn't.
While 29 other voters and every single Mets fan alive disagrees, Baggarly made the determination Alonso was merely the second best rookie in the National League last year. At the end of the day, when decisions like this are made all you can ask from the voter is for due diligence and for their making a good faith argument.
That is exactly what Baggarly did here. Upon reading his article, you can disagree with his conclusions and the evidence upon which he relies. What you cannot disagree with is he didn't undertake the analysis.
When all is said and done, the real question here isn't about why Baggarly thought differently than almost everyone. No, the real question is who cares?
In annals of baseball history, there aren't two separate lists for Rookies of the Year and unanimous Rookies of the Year. In fact, the voting is something which typically fades from memory.
For example, did you know Seaver wasn't a unanimous choice? Neither was Strawberry or Gooden. The Gooden decision was all the more wrong than Alonso not winning the award unanimously.
In the end, what matters is the honor. Seaver, who was inducted into the Hall of Fame with a higher percentage of the vote than any starting pitcher, is every bit the Hall of Famer Gary Carter (sixth ballot) and Mike Piazza (fourth ballot) are.
The same holds true for Alonso. He is every bit the Rookie of the Year Alvarez is and all those who preceded both players. In the end, all we should care about is he won, and better yet, all the things that are to come.
Darryl Strawberry , Dwight Gooden , Gary Carter , Jacob deGrom , Jon Matlack , MIke Piazza , Peter Alonso , Tom Seaver
Rookie of the Year Today, World Series Tomorrow?
Tonight, Pete Alonso is going to be named the National League Rookie of the Year. The only question right now is whether or not it will be unanimous. Considering Jacob deGrom wasn't a unanimous choice for Cy Young last year, we should not discount anyone making a dumb decision by not voting for Alonso.
When Alonso is named as the Rookie of the Year, he will join deGrom in a list of Mets who have won the Rookie of the Year award. Those other Mets are Tom Seaver, Jon Matlack, Darryl Strawberry, and Dwight Gooden. If you'll notice, Alonso will join Strawberry as the only other Mets position player to win the Rookie of the Year. Alonso will also be the first infielder.
If you dig deeper, there is something else you'll notice. Each and every one of the Mets players who have won a Rookie of the Year award have been part of a Mets pennant winning team. Of the group, Strawberry had to wait the longest with his appearing in the 1986 World Series three years after he won the award.
Alonso is a middle of the order type of bat who can power the Mets to a pennant in the near future. He broke all of Strawberry's rookie records. He surpassed the Mets single season mark shared by Todd Hundley and Alonso' new manager Carlos Beltran. He also passed the rookie first base mark held by Mark McGwire and the overall rookie record held by Aaron Judge.
With Alonso's drive and determination, we can see him being the type of player who can help lead the Mets to a World Series. He has shown that ability. The question is whether the team is going to help him and his teammates get there. But that is a question for another day.
For today, it is about Alonso. He is going to receive an award he has well earned, and with that award comes promise for the future. No matter what happens, Alonso will always carry that promise with his bat. For that reason alone, today is a day to rejoice and to appreciate the slugger whose ability on the field may only be surpassed by his ability as a teammate.
Carlos Beltran , Darryl Strawberry , Dwight Gooden , Jacob deGrom , Jon Matlack , Peter Alonso , Todd Hundley , Tom Seaver
If Giants and Jets Can Trade, Why Not Mets and Yankees?
Yesterday, the New York Jets traded Defensive Lineman Leonard Williams to the New York Giants for a 2020 third round draft pick and a conditional 2021 fifth round draft pick. This is a shocking trade between teams who don't just share a city but a building.
It was a gamble for the Giants in taking on an enigmatic player who is a pending free agent. For the Jets, this was seen as a coup to get a good return for a player they were not re-signing. However, if the Giants are able to get Williams to play like someone who was once the third overall pick in the draft, the Jets will constantly be reminded of their failure.
At the end of the day, who cares? Both the Giants and Jets did what they thought was best for their franchises. They put the fears aside, and they made a football trade just like they would've done with any other team. Somehow, this concept eludes the Mets.
Back in 2017, the New York Yankees were rumored to have interest in Lucas Duda. However, rather than trading Duda to the Yankees, the Mets opted to trade him to the Tampa Bay Rays for Drew Smith. There were rumors the Yankees could've bested the offer of what was just one relief prospect, but there was no real confirmation of what that return would be.
The Yankees were also to have been interested in Neil Walker. The Mets eventually wound up trading him to the Milwaukee Brewers for Eric Hanhold, a player the Mets recently designated for assignment so they could keep pitchers like Drew Gagnon, Donnie Hart, and Chris Mazza. In terms of the Yankees, we are not sure what they would offer, and there are some rumors the Yankees backed out of their deal because of Walker's medicals.
Finally, there was Jay Bruce. The Yankees were reported to offer multiple prospects for him. Instead, the Mets moved him to the Cleveland Indians for Ryder Ryan, a converted reliever.
Over the past few years, the Yankees have been rumored interested in a number of Mets players like Jacob deGrom, Noah Syndergaard, and Zack Wheeler. Those trades never materialized, but then again, no trade ever materialized between the Mets and another team with those players.
Still, the point remains there has long been a hesitation between the Mets and Yankees to make a trade. While it does seem to mostly come from the Mets side, there is assuredly some hesitation from the Yankees as well. That may be in no small part due to their Pedro Feliciano experience, or inexperience as it proved to be, and they may also harbor the same issues which are imputed on the Mets.
Whomever is to blame, they need to get over themselves, and they need to make smart trades between themselves to benefit both teams.
The Yankees have seen former Mets like Carlos Beltran, David Cone, and Darryl Strawberry play well for them. The Mets have seen former Yankees like Curtis Granderson, Orlando Hernandez, and Al Leiter play well for them. This is of little surprise as good players who can handle New York can play well for either team.
Given how that is the case, perhaps it is time both teams benefit from these players switching teams rather than seeing other franchises serve as the beneficiaries of being the ones who get these players in-between stops.
Carlos Beltran , Chris Mazza , Curtis Granderson , Darryl Strawberry , David Cone , Donnie Hart , Drew Gagnon , Drew Smith , Eric Hanhold , Jacob deGrom , Jay Bruce , Lucas Duda , Neil Walker , Noah Syndergaard , Orlando Hernandez , Pedro Feliciano , R.A. Dickey , Ryder Ryan , Zack Wheeler
Jacob deGrom And Seth Lugo Were Unstoppable
If you want to win a game, the best possible formula is Jacob deGrom and Seth Lugo. Tonight, that's what the Mets had, and it led to a separately needed win against a Diamondbacks team ahead of the Mets in the Wild Card standings.
For his part, deGrom was great furthering cementing his Cy Young case. His final line was 7.0 innings, three hits, one run, one earned, one walk, and 11 strikeouts. That one? Well, that was courtesy of our old "Friend" Wilmer Flores.
But that wasn't as damaging as a homer as a run against deGrom would normally be as deGrom actually received run support tonight. In fact, two of the three runs scored for him came off the bat of Pete Alonso who hit his 46th and 47th homers of the year.
Your major league HR leader.
4⃣6⃣ and counting… pic.twitter.com/qJWAPd1eSG
— New York Mets (@Mets) September 9, 2019
Is this guy good? Asking for a friend. pic.twitter.com/5r71hysahc
— New York Mets (@Mets) September 10, 2019
The other run came courtesy of an Amed Rosario RBI single in the fourth scoring Wilson Ramos. What made that interesting is Ramos had reached on a double against the shift. That shift was an unorthodox two outfielder shift. Presumably, this was implemented in response to Ramos' ground ball rates, and yet, his hitting the ball up the middle was an automatic double.
Going back to deGrom, what makes him so impressive is how he is dominant no matter what part of the game. He rips through the order the first, second, and third time through. The lineup turns over a fourth time just briefly. He gets the bad and good hitters. For example, he struck out Ketel Marte three times.
Another note here is deGrom controls all parts of the plate. He gets batters out north, east, south, and west. While we've seen his recent rise to dominance correlate to pitching up in the zone, he did it working the lower half tonight.
If a team thought they'd get a respite when he leaves a game, they'd be wrong as Lugo followed him to pitch the final two innings. The fresh Lugo was economical striking out four of the six batters he faced needing just 21 pitches to earn his fifth save of the year.
With the win, the Mets gained a game on both the Diamondbacks and Phillies. It's still an uphill climb, but the Mets are still alive.
Game Notes: Alonso surpassed Darryl Strawberry and some other guy by reaching base safely for a 34th consecutive game.
Amed Rosario , Darryl Strawberry , Jacob deGrom , Jose Reyes , Mickey Callaway , Peter Alonso , Seth Lugo , Wilmer Flores , Wilson Ramos
20/20 Hindsight: Mets Win A Series In Kansas City
The Mets went to Kansas City looking to sweep, and they wound up having to settle for less than that. Ultimately, they got the job done even if they did not perform well at all:
1. Alex Gordon may be the only Royals player remaining, but it was still good to see the Mets win a series in Kaufman Stadium, and it felt even better seeing Jeurys Familia get the win in a deciding game.
2. It's also great to see Michael Conforto homer in a game against the Royals and not watch the Mets not blow the game. Seeing where Conforto hit that homer, we should have called that a Blue Moon Shot.
3. Congratulations to Pete Alonso for breaking Cody Bellinger's National League rookie home run record. He now joins Mark McGwire and Aaron Judge as the only rookies to hit 40 homers in a season. This has been a great ride, and he's now in line to join Darryl Strawberry as the only other Mets position player to win Rookie of the Year.
4. It is criminal that when Alonso broke the record the call was made by Wayne Randazzo on the radio side and Gary Apple on the TV side. The Mets have all-time great broadcasters, and somehow that's what we were left with for this great moment. Mets needs to do better when there are vacations.
5. There were two different times Alonso looked like he was going to break that record. The first ball was called foul, and to his credit, Alonso shook it off and delivered with a huge go-ahead two RBI single. The next time the ball actually hit the foul line towards the top of the right field wall. Many times we see people struggle or slump as they near a milestone; Alonso powered onward.
6. Jacob deGrom had his 12th start of the season pitching 7.0+ innings allowed two earned or fewer. That mark ties him with Hyun-Jin Ryu for the most in the Majors. This should only highlight how great deGrom has been this year and how deserving he is of another Cy Young.
7. Yes, Ryu is having a great year, but deGrom's year is arguably better. For starters, deGrom has more innings pitched and strikeouts. Moreover, he has a higher K/9, K%, K-BB%, FIP, xFIP, fWAR, and bWAR while leading in other other categories as well.
8. One of the reasons the Mets took this series was Joe Panik playing great. Since joining the Mets, Panik is hitting .333/.379/.444 with a double, triple, and two RBI with two walks. On a side note, he was the second baseman when the Giants beat the Royals in the 2014 World Series.
9. With Jeff McNeil down, the Mets needed Panik to step up, and he has. The same goes for Juan Lagares, who has been the 2015 postseason version of Lagares who has hit .458/.458/.583 since August 13.
10. With the way Panik and Lagares are playing, it appears Todd Frazier is the guy who has to go to the bench. Since the All Star Break, he is hitting .192/.239/.377. If he's hitting this way, he cannot be in the lineup.
11. Going forward, Frazier has hit .283/.359/.543 off left-handed pitching. To that end, he should work out a de facto platoon with Panik, and given his glove, he should be the third baseman when Marcus Stroman is on the mound. Short of that, he should be a power bat off the bench and late inning defensive replacement.
12. These two were needed all the more with J.D. Davis twice going down with a calf injury in this series. With how hot he's been hitting, the Mets need his bat in the lineup, and they were without it in a series against the Royals. One side point here, good for Mickey Callaway for being cautious in taking him out rather than leaving him to run 90 feet.
13. Davis coming out of Sunday's game forced Amed Rosario to play left field. It didn't take long for the ball to find him, and the played the ball like he's been out there all year. He also doubled in his only at-bat as an outfielder. Maybe this shouldn't be a surprise because Rosario has been legitimately great lately.
14. Since the All Star Break, Rosario has hit .368/.403/.544. He's a 3 DRS at shortstop. When McNeil went down, he took over the leadoff spot, and he's been hitting .333/.383/.535 in the leadoff spot. He is literally doing all that is being asked of him, and he is emerging as a legitimately great player. This has been a real joy to watch.
15. Rosario having to play left field only highlights the stupidity of the Mets going with Ruben Tejada over Dilson Herrera. What makes the move all the more hilariously stupid was the Mets justification for going with Tejada over Herrera was versatility. Between the two, Herrera is the only one with outfield experience. Since Tejada rejoined the Mets, he is 0-for-8 at the plate with two strikeouts and someone already a -0.3 WAR. Herrera is hitting .294/.368/.706 while playing second base and left field. Again, this decision made zero sense.
16. On the topic of baffling decisions, when Robert Gsellman landed on the Injured List, the Mets called up Walker Lockett over Chris Flexen. Between the two, Flexen has the better stuff, and he has experience pitching out of the bullpen.
17. One area where Callaway was criticized for making a baffling decision was using Edwin Diaz to get out of a bases loaded no out situation. While it was a near disaster with a grand slam overturned on replay, Diaz got out of the inning allowing just two runs. In his next appearance, he pitched a scoreless ninth with two strikeouts. Maybe, just maybe in the long run, this was a great decision by Callaway.
18. Zack Wheeler's start against the Royals was disappointing. That's two straight disappointing five inning starts from him. This time, it was probably more bad luck than anything. However, this is his first real postseason race, so it will be interesting to see how he handles things in his next start.
19. The Mets would have been better off with a sweep, but they still won the series. They're also just two games back of the second Wild Card. Overall, when looking at this stretch of six games, many are discounting just how hot and grueling that stretch of road games are in Atlanta and Kansas City along with their losing one of their hottest hitters.
20. Good for the White Sox for having Bill Walton and Michael Schur do color commentary with Steve Stone out. As noted on Saturday, that is what the Mets should have been doing by using the multitude of great local broadcasters and fans in Gary Cohen's and Howie Rose's absence. On a final note there, John Sadak did a great job on the radio. Here's hoping there's a spot for him in 2020.
Amed Rosario , Chris Flexen , Darryl Strawberry , Dilson Herrera , Edwin Diaz , J.D. Davis , Jacob deGrom , Jeff McNeil , Jeurys Familia , Joe Panik , Juan Lagares , Marcus Stroman , Michael Conforto , Mickey Callaway , Peter Alonso , Robert Gsellman , Ruben Tejada , Todd Frazier , Walker Lockett , Zack Wheeler
Matz Can't Be Beaten At Citi Field
The Mets chances of the Wild Card may very well be tied to Steven Matz. He is the starter in whom you have the least amount of confidence, and he has the propensity to blow up at any time. We need not look any further than recent history with him shutting out the Pirates in one start followed by his getting knocked out in the fourth in his next start against the very same team.
The interesting thing with Matz is you usually get a good read on what type of day it is going to be from his first inning. Today, he allowed a leadoff single to Jon Berti, and two pitches later, he got Isan Diaz to ground into a double play. When he struck out Brian Anderson to end the inning, you got a good feeling about him on the day.
You had a better feeling about the game when Michael Conforto drew a two out walk in front of Pete Alonso who would then give the Mets a 2-0 lead with a homer off of Marlins starter Jordan Yamamoto.
People are going crazy for White Paw this summer. ❄🐻 pic.twitter.com/LZ8wiunUlT
— New York Mets (@Mets) August 7, 2019
One interesting development in Alonso's career is how he has a good month followed by a bad month. Well, Alonso had the worst month of his young career in July. He has turned things around in August homering in three straight games.
While this was a big development in this game, perhaps a bigger one was Matz navigating his way out of trouble in the second. He got himself into trouble with a leadoff walk to Garrett Cooper, and Cooper would score after consecutive singles from Starlin Castro and Lewis Brinson. Matz would catch a break with a Brinson TOOBLAN as he would get caught in a rundown when Alonso cutoff Jeff McNeil's throw. That was a gift out.
Todd Frazier would then cut down Castro at the plate on a Harold Ramirez ground ball, and Matz would work his way out of trouble to leave the game at 2-1.
From there, the game was basically all Mets as the Mets batters would put on a power show. Michael Conforto hit the first of his two run homers in the third giving the Mets a 4-1 lead. With that homer, he became the 14th Mets player to hit 100 homers, and he joined Darryl Strawberry and David Wright as the only Mets players younger than 27 to hit that mark.
In addition to Conforto, new puppy owner McNeil would keep is end of his bargain with his wife by hitting another homer.
Power surge in the Citi. 💪 pic.twitter.com/ECJwkDUPBv
Going back to Matz, he pitched 6.2 innings allowing just two earned on seven hits with two walks and seven strikeouts. Since the All Star Break, he's 2-1 with a 3.38 ERA. Better yet, he's 5-0 at Citi Field. When he is going at least six innings like he has in three of these five starts, you seem him as a very good fifth starter. Really, that's what the Mets need from him.
They also need this continued good work out of the bullpen. Matz would depart with two outs in the seventh leaving Bryan Holaday at second after a two out double. Justin Wilson came on, and despite allowing a single to Martin Prado, he would get the Mets out of the inning preserving what was then a 4-2 lead.
As noted, Conforto and McNeil would each homer in the seventh expanding that lead to 7-2. That was a safe enough lead for Jeurys Familia, who pitched a scoreless inning even with him allowing a walk. Luis Avilan recorded the final three outs in the ninth with the rain looming to get the Mets the four game sweep of the Marlins.
This win completed a stretch of 16 games against teams with a losing record. The Mets took care of business in that stretch going from nine games under .500 to three games over. They have gone from seven games back in the Wild Card standings to just one game out. Now the hard part begins.
Game Notes: The last time the Mets swept the Marlins in a four game series at Citi Field was April 2015. Once again, the Mets did not trust Chris Mazza or Donnie Hart to preserve a five run ninth inning lead.
Chris Mazza , Darryl Strawberry , David Wright , Donnie Hart , Jeff McNeil , Jeurys Familia , Justin Wilson , Luis Avilan , Michael Conforto , Peter Alonso , Steven Matz , Todd Frazier
Trivia Friday: Won Home Run Derby And Led League in Homers
On Monday, Pete Alonso joined Darryl Strawberry as the only Mets to win the Home Run Derby. When Strawberry won in 1986, he would not lead the league in homers. He would finish that season tied for fifth in homers 10 behind Mike Schmidt.
This year, Alonso is tied with Cody Bellinger for second in homers in the league. Both players are just one behind Christian Yelich. If Alonso could overcome Yelich, Bellinger, and sluggers just behind him, he would become the eighth player to win the Home Run Derby in the same year he led the league in homers. Can you name the seven who have done it? Good luck!
Andre Dawson Ryne Sandberg Juan Gonzalez Ken Griffey Jr. Sammy Sosa Ryan Howard Aaron Judge
Darryl Strawberry , Pete Alonso
Pete Alonso Wins The Home Run Derby
It wasn't easy with his cousin Derek Morgan throwing wild pitches and buzzing him in a few times, but Pete Alonso won the 2019 Home Run Derby. Alonso joins Darryl Strawberry (1986) as the only other Met to win a Home Run Derby.
TAKE. IT. HOME. #HomeRunDerby 🏆 pic.twitter.com/Sr5WenLWG7
Alonso needed surges to beat Carlos Santana and Ronald Acuna, Jr. in the first two rounds setting the stage for a final against Vladimir Guerrero, Jr. Guerrero was the story for much of the night after his breaking single round records and needing a third swing-off to finally beat Joc Pederson. Still, it would be Alonso who won with the Guerrero's handling it with pure class.
What a show❗
Congratulations Pete, great performance.
— Vladimir Guerrero (@VladGuerrero27) July 9, 2019
This Home Run Derby was great for baseball. It featured an epic Guerrero/Peterson matchup and some of the best young talent in the game. In the end, Alonso, who never needed his bonus time in any of the three rounds was the winner.
Tonight was the night Guerrero and Alonso were launched to stardom across baseball. With a big All Star Game tomorrow, Alonso could be considering a burgeoning superstar. That's what happens to players who kill the ball and donate a portion of their winnings to charity.
With Alonso winning, the joke was this was the Mets World Series. For what it's worth, when Strawberry won it, the Mets won the World Series. For that matter, when David Wright went to the finals in 2006, the Mets went to the NLCS.
On a night like tonight, Mets fans had reason to cheer and to believe anything is possible. For tonight, we get to celebrate and dream. Mostly, we get to appreciate the fact Alonso is a Met.
Darryl Strawberry , David Wright , Peter Alonso

redpajama_set_name: RedPajamaCommonCrawl


text:
The Best of Dark Terrors [Jones, Stephen and Sutton, David A ed]
Out of stock Default Title - sold out
Hardcovers,
new-releases-october-2021,
small-press,
subject-collections-anthologies,
subject-horror
Between 1995 and 2002, Dark Terrors: The Gollancz Book of Horror was Britain's premier non-themed anthology of original horror stories. Over six volumes, it published some of the biggest names in the field as well as many newcomers who have gone on to forge impressive careers in the genre. Edited by the World Fantasy Award-winning team of Stephen Jones and David A. Sutton, Dark Terrors established itself as a cutting-edge market for some of the most literary and disturbing fiction being produced on both sides of the Atlantic, winning the British Fantasy Award and the International Horror Guild Award in the process. Now The Best of Dark Terrors collects together twenty of the most memorable stories from the original books in a new volume, which also includes reminiscences by both the editors and their original publisher, Jo Fletcher, along with an Index detailing the authors and their work that were included in the legendary anthology series. For fans of superior horror fiction, the Terrors just got very Dark indeed...


A friend and former colleague at NBCC, Cindy Mitchell, asked me if I could do a small panel (8″x 12″) of her and her husband, Eric, on their motorbike. She has a photo in her office from the time they drove their Honda Goldwing over the famous "Tail of the Dragon" route in the Blue Ridge Parkway of North Carolina.
It was a great project with lots of very small pieces. I tried a mirrored finish glass for the underbelly parts of the bike. Very difficult stuff to work with because of grinder chipping and subsequent light shine through after soldering. Had to use paint to seal the light spots. Also used a silver paint on the gray glass to simulate chrome. That was OK… not quite chrome, though. Also there are several drilled holes in this piece which added to the delicacy of work. All in all, I was pleased with the results.
Here is the original photo and the finished panel.


text:
Pellucere Technologies To Begin Full-Scale Deployment of MoreSun product
Manufacturing operations in Oregon, following investment from Energy Innovation Capital
Anti-Reflective and anti-soiling technology improves solar panel efficiency
PV Magazine reported that Pellucere Technologies (formerly known as CSD Nano, Inc.) announced that it has secured a major investment from Energy Innovation Capital. The investment enables the beginning of "full-scale commercial deployment" of MoreSun, an anti-reflective and anti-soiling coating. The company says their process adds a 140 nm thick silica structure (above image) to the glass. The layer can be added both in the field and in the factory. The company says the fundamental benefit is their creation of a gradual transition from air to glass which reduces angles of incidence, allowing about 8% of formerly lost light to have a chance of being captured. Pellucere posits most anti-reflective coatings work in the same manner, however, MoreSun is just plain better.
In addition, John DeVos has taken the position of COO, Paul Ahrens will fill the CTO role at the company. All R&D and manufacturing operations will remain in Corvallis, Oregon. MoreSun was developed at Oregon State University, with support from the National Science Foundation, the Oregon Nanoscience and Microtechnologies Institute (ONAMI) and VertueLab.
Read the full article in PV Magazine here.
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text:
3FM Serious Request: The Lifeline 2019 was de zestiende editie van Serious Request. Net als in 2018 was er geen glazen huis, maar gingen dj's van de radiozender op de voet door het land om acties te bezoeken. De wandeltocht begon in Goes en eindigde in Groningen en duurde een week. Het evenement was in samenwerking met het Rode Kruis. De opbrengst kwam uiteindelijk neer op € 1.418.513.

Geschiedenis
Op 3 mei 2019 maakte 3FM bekend dat jaar opnieuw een editie van Serious Request te gaan organiseren. Twee maanden later werd er meer duidelijk over de invulling van deze editie. In plaats van drie routes, zoals in 2018, zal er langs één route tussen Goes en Groningen gewandeld worden. Dj's van de zender gaan in teams van 2 elkaar afwisselen om een stuk te lopen en uitzendingen te maken. Tijdens de laatste avond was in Groningen op het plein de Ossenmarkt een eindshow waar de opbrengst bekendgemaakt werd.

Op 8 november 2019 kondigde Sander Hoogendoorn in zijn ochtendprogramma aan dat muzikant Blanks het themalied voor deze editie ging produceren. Het nummer werd eind november uitgebracht onder de naam Everything's Changing.

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Doel
Tijdens Serious Request 2019 zal er geld ingezameld worden voor slachtoffers van mensenhandel. De opbrengst gaat gebruikt worden om voorlichting te geven en slachtoffers psychologische steun te bieden. In tegenstelling tot de editie in 2018 is er maar één doel. Ophalen van geld zal gebeuren via het verzamelen van directe donaties, opbrengsten uit acties, het organiseren van een veiling en het tegen betaling aanvragen van nummers.

Invulling

Dj-teams

Zes dj's van de zender vormden drie teams die tijdens Serious Request 2019 elkaar afwisselden langs de wandelroute. De drie teams bestonden uit:
 Sander Hoogendoorn en Herman Hofman
 Mark van der Molen en Jorien Renkema
 Eva Koreman en Frank van der Lende

Aanvankelijk zou radio-dj Rámon Verkoeijen ook meedoen, maar begin december 2019 werd duidelijk dat hij voor bepaalde tijd stopt met radio maken. Jorien Renkema verving zijn positie in de dj-teams die de route gingen afleggen.

Looproute

De looproute was verdeeld in 25 etappes die gemiddeld 17 tot 20 kilometer lang zijn. De totale route had een lengte van 488 kilometer. Elke etappe eindigde bij zogenaamde Chequepoints waar activiteiten georganiseerd werden en actievoerders en bezoekers hun ingezamelde geld konden inleveren. Op deze locaties wisselden ook de dj-teams.

De onderstaande tabel geeft de trajecten weer.

Eindfeest
Als afsluiting vond er in Groningen een eindfeest plaats op het plein Ossenmarkt. Voorafgaand aan de bekendmaking van het eindbedrag vonden er optredens plaats van artiesten. De Ossenmarkt was tevens het eindpunt van de laatste wandeletappe.

Verslaggeving

Tussenstanden
Elke dag werd er rond 18.00 de tussenstand bekendgemaakt. Dat gebeurde via het onthullen van een grote cheque bij de dj's die op dat moment aan het wandelen waren.

Externe link
 Website Serious Request

3FM Serious Request
Evenement in 2019

redpajama_set_name: RedPajamaWikipedia


Both signed and dated 1807. English scenes derived from a set of engravings called Views in Kent and Surrey (1770) after John Cleveley.
Oil and etched gold and silver leaf on glass, 11½" x 16½" (29cm x 42cm).
Over frames, 14½" x 19½" (37cm x 49.5cm).
Jonas Zeuner was born in Kassel and moved to Amsterdam in 1750, where he lived for the rest of his life. About 170 paintings by him are known, in this distinctive technique of a combination of oil paint and engraved gold and silver leaf on glass, and examples are in various museum collections, including those of the Rijksmuseum, Amsterdam and the Corning Museum of Glass, Corning, New York.
These present pictures are very similar to another pair of pictures by Zeuner, of exactly the same size, in the Victoria & Albert Museum, of Bradfield Mill, Berkshire and a Cottage scene near Colchester, Essex (1). Zeuner is said to have been in England at least between 1802 and 1806, and is known to have produced a number of pictures of English landscapes and city scenes, though whether from direct observation or from engravings is not clear.
1. See J. Sprenkels-ten Horn and A. Sprenkels, Jonas Zeuner (1727-1814), Zijn wereld weerspiegeld in zilver en goud, the catalogue of an exhibition at the Amsterdam Historical Museum, 1994, nos. 157 & 158, p. 73.
2. Sotheby's, Amsterdam, 27th March 2001, lot 85 (€82,000).
3. See for example, a picture of an Amsterdam house on Keizersgracht, sold Christie's Amsterdam, 27th June 2001, lot 226 (€61,307).
4. For example, Christie's London, English Furniture, 19th November 1987, lot 12 (£16,500).
5. International Art Treasures Exhibition, Bath, BADA & CINOA, 1973, pl. 246. Exhibited by Ayer & Co. (Antiques) Ltd., London.
6. Christie's London, English Furniture, 24th April 1980, lot 90 (£5,500); and again, Christie's London, European Furniture, Sculpture, Tapestries & Carpets, 12th June 2003 (£26,290).
67 Jermyn Street, London SW1Y 6NY, UK.


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

\input{s2_related_works}
\input{s3_data.tex}
\input{s4_model_development.tex}
\input{s5_model_assessment.tex}
\input{s6_results.tex}
\input{s7_disc-rev.tex}
\input{s8_conclusion.tex}

\input{s_acknowledge}

\appendices
\input{s9_appendix_a_Variables.tex}
\setcounter{table}{0}
\vspace{0.5cm}

\twocolumn
\ifCLASSOPTIONcompsoc

\else

\fi

\ifCLASSOPTIONcaptionsoff
  \newpage
\fi

\bibliographystyle{IEEEtran}

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

\subsection{The Problem}

Critical care environments require rapid decision making. To be meaningful, predictive models in these settings must immediately recognize precipitous changes in a patient's state when presented with data reflecting such changes  \cite{chan2008hospital,jones2011rapid}. Newly acquired information must be integrated quickly with the existing understanding of the patient's state to inform clinical decisions.

Long Short-Term Memory (LSTM) models \cite{hochreiter1997long} are a type of Recurrent Neural Network (RNN) architecture and have become increasingly popular for modeling tasks in clinical settings, including Intensive Care Units (ICUs) \cite{rajkomar2018scalable,esteva2019guide,oh2018automated,faust2018deep,laksana2019impact,miotto2017deep,aczon2017dynamic,carlin2018predicting}. It is occasionally noted that RNNs, including LSTM models, exhibit some lag after acquiring new information \cite{flovik_2018}. This characteristic is frequently observed in stock prediction tasks where the apparent predictive capability of a model is comparable to a model that predicts the last observed stock price \cite{kim2018forecasting,xiong2015deep}. However, the intersection of deep learning and healthcare, though ever widening, has a paucity of literature on this phenomenon. Commonly reported metrics such as Area Under the Receiver Operating Characteristic Curve, sensitivity, specificity, and mean absolute errors mask certain behaviors of RNN based models.

RNN models require computational cycles to incorporate new data into their memory cell \cite{olah2015understanding}. There are two primary observable effects of this, summarized briefly here. First, what we refer to as a \textit{pipe-up} period, requires the initial input to propagate through the memory cell to overcome the initialized cell state before that input significantly influences the predictions. Second, during all subsequent times, new information only slightly changes the overall prediction of a model at the time of input and requires propagation through the memory cell before fully augmenting the model's prediction. The first phenomenon is a special case of the second. These two effects are further illustrated in Sections \ref{sec:assess} and \ref{sec:results}.

\subsection{Proposed Solution}
This work aims to enable more pronounced changes in RNN predictions when the model is presented with data indicating acute clinical events by \emph{perseverating} the input. Instead of giving newly available input data to an RNN model only once, the input is replicated and given to the model multiple times, during both training and deployment, with only the prediction of the final perseveration maintained and made visible to the end-user. This approach provides additional computational cycles for new data to be incorporated into the model's internal states before a prediction is broadcast. We hypothesized that the resultant model would be able to react to acute events more quickly than traditionally trained LSTMs and still maintain overall model performance.
\section{Related Works}
\label{sec:related}

Since the seminal paper by R.E. Kalman in 1960 describing what is now called the Kalman Filter \cite{kalman1960new}, engineers have been aware of the trade off between integrating historical data and responding to new data. Most training techniques used in modern deep learning affect the balance between relying on historical trends and responding to new information; such techniques include dropout, optimizers, and activation functions  \cite{srivastava2014dropout,tieleman2012lecture,kingma2014adam,nair2010rectified}. Generating an appropriate target vector (e.g. using changes in values between consecutive time points instead of predicting raw values) is another training technique that can prevent generation of an auto-correlation model \cite{flovik_2018}.

Attention mechanisms \cite{luong2015effective} are sometimes used to manage the balance between historical and new data by appending the entire input sequence to the final hidden layer of an RNN. Doing so affords the model another opportunity to learn weights which expose the moments in time series data most relevant to the predictions. Attention networks were originally developed for post-hoc sequence-to-sequence modeling tasks such as image captioning and neural machine translation which permit access to the entire input sequence when making predictions \cite{xu2015show,luong2015effective}. An attention mechanism was applied by Zhang, et al. to predict risk of future hospitalization using medical records from a fixed observation window.\cite{zhang2018patient2vec}.
\section{Materials and Methods}
\section{Data}
\subsection{Clinical Data Sources}
Data for this work were extracted from de-identified observational clinical data collected in Electronic Medical Records (EMR, Cerner) in the Pediatric Intensive Care Unit (PICU) of Children's Hospital Los Angeles (CHLA) between January 2009 and February 2019. The CHLA Institutional Review Board (IRB) reviewed the study protocol and waived the need for IRB approval.  A patient record included static information such as gender, race, and discharge disposition at the end of an ICU \emph{episode},  defined as a contiguous admission in the PICU. A patient may have multiple episodes. The EMR for an episode also contained irregularly, sparsely and asynchronously \textit{charted} measurements of physiologic observations (e.g. heart rate, blood pressure), laboratory results (e.g. creatinine, glucose level), drugs (e.g. epinephrine, furosemide) and interventions (e.g. intubation, oxygen level).  Episodes without discharge disposition were excluded, leaving 12,826 episodes (9,250 patients) in the final dataset.

Prior to any of the computational experiments described here, the episodes were randomly partitioned into three sets: a training set for deriving model weights, a validation set for optimizing hyper-parameters, and a holdout test set for measuring performance. To minimize bias in the performance evaluation metrics, partitioning was done at the patient level, i.e. all episodes from a single patient belonged to only one of these sets: 60\% in the training set, 20\% in the validation set, and 20\% in the test set. No other stratification was applied. Table \ref{tab:demo} displays basic characteristics of the resulting data partitions.

Preprocessing steps described in previous work \cite{ho2017dependence} converted each episode data to a matrix structure amenable to machine learning model development. A row of values in this matrix represented measurements (recorded or imputed) of different variables at one particular time, while a column contained values of a single variable at different times. A complete list of variables used as model inputs can be found in Appendix \ref{appendix:EMR_vars}. Note that diagnoses, while available in the EMR, were not used as input features.

\begin{table}[!t]
\caption{Basic demographics of data used in this study for each data partition.}
\label{tab:demo}
\centering
\begin{tabular}{|l|lll|l|} \hline
    \textbf{}          & \textbf{training} & \textbf{validation} & \textbf{test} & \textbf{overall} \\ \hline
    Number of episodes & 7663           & 2541           & 26221           & 12826 \\
    Number of patients & 5551           & 1850           & 18501           & 9251 \\
    Mortality rate     & 0.035          & 0.037         & 0.043            & 0.037 \\
    Fraction male      & 0.56           & 0.55           & 0.58           & 0.56          \\
    Age mean (years) & 7.99 & 8.23 & 8.09 & 8.06   \\
    \hspace{0.5cm} std dev & 6.45 & 6.51 & 6.35 & 6.44 \\\hline
    \end{tabular}
\end{table}

\subsection{Target Outcome}

ICU mortality was chosen as the target outcome because it is a simple, unambiguous outcome. Importantly,
risk of mortality is used commonly as a proxy for severity of illness in critical care
\cite{pollack1996prism,pollack2016pediatric, slater2003pim2, leteurtre2015daily}.
The overall mortality rate of the data was $3.7\%$ (Table \ref{tab:demo}).

\section{RNN Models}

Many-to-many recurrent neural network models, consisting of stacked Long Short-Term Memory (LSTM) layers followed by a dense layer, were trained to predict ICU mortality of each patient episode. All the models output a probability of survival at each distinct time point where an observation or measurement of the patient was made, generating a trajectory of scores that reflect the changing status of a patient during their ICU episode.

\begin{figure}[!t]
\centering
    \includegraphics[width=3.4in]{Figures/prnn_and_baseline.png}
    \caption{The perseverating recurrent neural network (PRNN) is an RNN with repeating inputs.  Each input vector associated with a particular time is replicated $k$ times, and only the output from the last replication is considered as the prediction for that time.  The diagram above illustrates the process for $k=3$.}
    \label{fig:data}
\end{figure}

\begin{figure}[!t]
\centering
    \includegraphics[width=1.8in]{Figures/RNN_with_Attention_v3.png}
    \caption{Overview of a standard RNN model with a causal attention layer.}
    \label{fig:attention_model}
\end{figure}

The \textit{baseline} RNN model was trained in the traditional manner: when the model acquires new data $x(t_i)$, it generates and immediately broadcasts a prediction $y(t_i)$.
Other models which share the architecture of the baseline RNN model were trained using input \textit{perseveration}:
the same $x(t_i)$ vector was repeatedly given as input to the RNN model $k$ times, where $k$ is a controllable parameter.
While all $k$ outputs -- corresponding to the $k$ times that $x(t_i)$ was given to the model -- are used for optimization during training, only the last one is maintained and broadcast as the prediction at time $t_i$ during performance assessment and deployment. We call these models perseverating recurrent neural networks (PRNN). Figure \ref{fig:data} illustrates a PRNN model with $k=3$. Note that the baseline RNN can be considered as a PRNN with $k=1$.
Input perseveration provides the internal cell state memory of the RNN additional computational cycles to incorporate the current state of the patient into the final prediction.

An attention network using the same hyperparameters of the baseline RNN model was also implemented. Most attention networks have access to the entire sequence of inputs -- both past and future relative to the current one -- when making a prediction at any point in time.
However, continuous monitoring of patient status precludes access to future information, available in a retrospective study but not in a real deployment scenario. Therefore, the
attention layer between the last hidden layer and output layer used a \emph{causal mask} that exposed only the inputs up to the time when a prediction is being made \cite{vaswani2017attention}. This ensured that the network did not use future information to make its predictions while affording it another opportunity to consider the totality of information up to the current time.
See Figure \ref{fig:attention_model} for a diagram of the model.

All six models -- baseline RNN ($k=1$), PRNN for $k=2,3,4,5$, and attention RNN -- were implemented and trained using Keras 2.0.7 with the Theano 1.0.2 backend \cite{keras2018, 2016arXiv160502688short}. For each model, weights were optimized on the training and validation sets; performance was computed on the validation set after every epoch (i.e. a full cycle through the training set), and the best performing weights were saved as the final model. Table \ref{tab:hyperparam} displays the hyperparameters that were used for all six models. The final models were assessed for performance metrics on the test set.

\begin{table}[!t]
\caption{Hyperparameters for all permutations of the PRNN.}
\label{tab:hyperparam}
\centering
    \begin{tabular}{|l|l|}
    \hline
    \textbf{Hyperparameter}     & \textbf{Value}       \\
    \hline
    Number of LSTM Layers       & 3                    \\
    Hidden Units in LSTM Layers & {[}128, 256, 128{]}  \\
    Batch Size                  & 32                   \\
    Initial Learning Rate               & 1e-5                 \\
    Loss                        & binary cross-entropy \\
    Optimizer                   & rmsprop              \\
    Dropout                     & 0.2                  \\
    Recurrent Dropout           & 0.2                  \\
    Regularizer                 & 0.0001               \\
    Output Activation           & sigmoid              \\
    \hline
    \end{tabular}
\end{table}

\section{Model Assessments}
\label{sec:assess}

Standard metrics such as the Area Under the Receiver Operating Characteristic Curve (AUROC), precision, and recall scores for a binary classification task can capture a model's overall predictive performance. However, they can mask the lag phenomenon displayed by LSTMs that, although known, is rarely commented on in the literature.
The trajectory of probability of survival predictions should reflect the evolving state of a patient which can have instantaneous changes. Metrics were designed to quantify a model's pipe-up behavior and its ability to capture rapid changes resulting from clinically adverse events.
AUROC was used to compare overall predictive performance for the main task -- ICU mortality prediction.

\subsection{Pipe-Up Behavior}

A model's prediction at the first time step is a function of its weights, initial state of its memory cells and the first input. Of these, only the first input varies across different patient episodes; therefore, the distribution of a model's prediction at the first time point of all episodes indicates the model's level of reliance on the first input data. We refer to this period as the \textit{pipe-up} period.

The mean and standard deviation of the distribution of all $\hat{y}_p(t_0)$ predictions, where $\hat{y}_p(t_0)$ represents the first prediction for patient episode $p$, were computed for all survivors and non-survivors in the test set. These metrics were computed for all six RNN models described in the previous section and used to compare their responsiveness to the first available information about a patient episode.

\subsection{Responsiveness to Acute Events}\label{sec:variation_metric}

The models were also assessed for their instantaneous responses to clinically adverse events using an average variation metric computed from the predictions during such periods.
In consultation with clinicians, the times when any of the following occurred were defined as acute time points or intervals:
\begin{enumerate}
    \item \textit{substantial} increases in creatinine levels or inotrope score between two consecutive recordings;
    \item \textit{substantial} decreases in heart rate, mean arterial pressure, Glasgow Coma Score, blood oxygen level (SpO2), arterial blood gas (ABG) pH, and venous blood gas (VBG) pH between two consecutive recordings;

    \item a patient's heart rate goes to 0.
\end{enumerate}

We quantified \textit{substantial} changes as those that were in the top or bottom $X$ percentile ($X \in \{5.0, 1.0, 0.5\}$) of inter-measurement changes.
For example, a time point $t_i$ was considered acute in terms of creatinine when the \textit{increase} in creatinine level from $t_{i-1}$ to $t_i$ was in the top $X$ percentile of all creatinine level percent changes between any two consecutive time points in the test dataset.
Similarly, acute time points for heart rate were those times when the \textit{decrease} in heart rate was in the bottom $X$\% of all heart rate changes between any two consecutive time points of all test set episodes. Table \ref{tab:thresholds} shows the specific percent change thresholds indicating \emph{acute} changes in a patient. Thus, a 32\% drop in heart rate between two consecutive measurements would be in the top 1 percentile, while a 70\% increase in inotrope score would be in the top 5 percentile.

Applying three percentile-based thresholds to define acute patient state changes meant a total of 25 acuity definitions (2 * 3 increases in measured variables, 6 * 3 decreases in measured variables, and zero heart rate) used to assess models' responses when such events occur. It is important to note that we are not proposing these as general definitions of clinical acuity. They were designed to capture events that indicate precipitous changes in a patient's state with high specificity to assess the magnitude of the instantaneous change in predicted probability of survival.

Each previously defined acute event, denoted by $E$, identified a set of \emph{acute time points}, denoted by $S_E(p)$, for each patient episode $p$. Changes in a model's prediction at these time points were evaluated through an average temporal variation metric given by:
\begin{eqnarray}
    V_E(p) &=& \frac{100}{|S_E(p)| }\sum_{t_i \in S_E(p)} |\hat{y}_p(t_i) - \hat{y}_p(t_{i-1})|,\label{eq:var_metric}
\end{eqnarray}
where $\hat{y}_p(t_i)$ is the prediction for patient episode $p$ at time point $t_i$, and $|S_E(p)|$ is the number of time points in $S_E(p)$.
This metric measures how much the predicted probability of survival (scaled to $[0,100]$) changed, on average, at the defined acute time points of an episode. Model $A$ having a higher $AV(p)$ than model $B$ means that when both models were presented with data reflecting a precipitous change in a patient's state, model $A$'s prediction underwent a greater change than model $B$'s prediction, indicating that model $A$ had a more pronounced response to the acute event. The average of this metric across $N_p$ episodes gives a measure of the model's overall responsiveness to acute event $E$ of those episodes:
\begin{equation}
    \bar{V}_E = \frac{1}{N_p}\sum_{p} V_E(p). \label{eq:var_metric_all}
\end{equation}

\begin{table}[!t]
\caption{Thresholds of percent change used to define acute changes in patient state. Negative thresholds correspond to the bottom $p\%$ of changes and positive thresholds correspond to the top $p\%$  of changes, for $p\in[5, 1, 0.5]$.}
\label{tab:thresholds}
\centering
\begin{tabular}{|l|lll|} \hline
                           & 5\%     & 1\%     & 0.5\%  \\ \hline
    Heart Rate            & -18.8 & -31.6 & -37.1 \\
    Mean Arterial Pressure & -23.5 & -38.1 & -44.2 \\
    Glascow Coma Score     & -50.0  & -72.7 & -72.7 \\
    Pulse Oximetry         & -5.6  & -20.2 & -35.1 \\
    ABG pH                 & -1.5  & -2.9  & -3.5  \\
    VBG pH                 & -1.4  & -2.6  & -3.1  \\
    Inotrope Score        & 66.7   & 150.0  & 250.0  \\
    Creatinine             & 33.3  & 60.5  & 81.8  \\ \hline
\end{tabular}
\end{table}

\subsection{Overall Predictive Performance}

Model predictions at ICU admission and at 1, 3, 6, 12, and 24 hours after ICU admission were evaluated for predictive performance via the AUROC. Performance was only measured for episodes lasting at least $24$ hours to maintain a consistent cohort across time slices ($n=2130$, mortality rate = 0.046).

\section{Results}
\label{sec:results}

Figure \ref{fig:examples} illustrates trajectories of probability of survival predictions from two models, the baseline RNN ($k=1$) and a PRNN with $k=5$, for two non-surviving episodes. In the first episode (top), the patient had a rapid increase of creatinine level, which indicates kidney dysfunction, about 500 hours after ICU admission. When presented with this defined acute event, the baseline model's prediction dropped from 0.89 to 0.88, while the PRNN model dropped from 0.75 to 0.71.
In the second episode (bottom), the patient's Glasgow Coma Score decreased from 7 to 4. When presented with this defined acute event, the baseline model's probability of survival prediction dropped from 0.76 to 0.70, while the PRNN model dropped from 0.69 to 0.50.
In either case, the PRNN model's immediate response to an acute event was more pronounced than that of the baseline RNN model. Further, the trajectory of the baseline RNN's predictions after either acute event appears to lag behind that of the PRNN by about 1-2 hours.

\begin{figure}[ht]
    \centering
    \includegraphics[width=\linewidth]{Figures/examples.png}
    \caption{Two examples of predictions following acute events in two individual patient episodes. The blue curves correspond to the baseline ($k=1$) model, while the orange curves correspond to the PRNN model with $k=5$. The dotted vertical lines denote onset of an acute event for each patient: (A) a patient experiencing kidney failure and the moment of increased creatinine and blood urea nitrogen (BUN); (B) a patient's Glasgow Coma Score decreasing from 7 to 4, indicating significantly reduced levels of consciousness.\vspace{0.6cm}
    \label{fig:examples}}
\end{figure}

The remainder of this section describes the results from aggregating the assessment metrics described in Section \ref{sec:assess} across episodes in the test dataset.

\begin{figure*}
    \centering
    \includegraphics[width=\linewidth]{Figures/t0_surv_vs_non_surv_density_attn.png}
    \caption{Distribution of model predictions at the first available observation for survivors and non-survivors} \vspace{0.6cm}
    \label{fig:t0_dists}
\end{figure*}

\subsection{Pipe-Up Behavior}

Figure \ref{fig:t0_dists} shows the distribution of predictions at the first observation. The mean prediction for survivors increased with the perseveration parameter $k$: from $0.70$ ($k=1$) to $0.90$ ($k=5$). For both survivors and non-survivors, the standard deviation increased with $k$. The increase of standard deviation was more pronounced in the non-survivors, going from from $0.04$ at $k=1$ to $0.13$ at $k=5$.

\subsection{Responsiveness to Acute Events}
Figure \ref{fig:variation} displays the average variation as a function of $k$ and acuity definitions.  Two trends are apparent. First is the behavior of average variation as a function of percentile change in a given physiologic observation or intervention. For each model, the resulting average variation increased as the change in a physiologic or intervention variable became more severe (ie. from 95.0 to 99.5 percentile). Second, a clear positive correlation between $k$ and average variation is evident across all definitions of acuity.  The attention layer generally demonstrated lower average variation than the $k=1$ model.

\begin{figure*}
    \centering
    \includegraphics[width=\linewidth]{Figures/avg_variation.png}

    \caption{Comparison of the average variation metric $\bar{V}_E$ of different models as defined by Equation \ref{eq:var_metric_all} across all test set episodes, where the events are defined by: A) a 95th percentile change; B) a 99th percentile change; C) a 99.5th percentile change; D) a cardiac arrest.}
    \vspace{0.6cm}
    \label{fig:variation}
\end{figure*}

\subsection{Overall Predictive Performance}
Table \ref{tab:aucs} summarizes the AUROC of model predictions at different times after ICU admission of all episodes in the test set that lasted at least 24 hours. The AUROC gains due to input perseveration were larger at the earlier times of prediction ($t \leq 6$ hours), increasing at the first hour from $0.77$ when $k=1$ to $0.83$ when $k=5$. By the 12th hour, the AUROC did not significantly change with the perseveration parameter $k$. Performance increases for all models as additional hours of observation are available. Adding an attention layer displayed similar performance as the baseline model ($k=1$) at all evaluation times.

\begin{table}[]
\caption{Test set AUROCs for the mortality prediction at admission and after $1$, $3$, $6$, $12$, and $24$ hours of observation. Only the patient episodes lasting at least $24$ hours of ICU time were evaluated.}
\label{tab:aucs}
\centering
\begin{tabular}{|l|llllll|}
\hline
\textbf{k} & \textbf{0hr} & \textbf{1hr} & \textbf{3hr} & \textbf{6hr} & \textbf{12hr} & \textbf{24hr} \\ \hline
1          & 0.716 & 0.766       & 0.859       & 0.895       & 0.914        & 0.952        \\
2          & 0.729 & 0.804       & 0.871       & 0.902       & 0.916        & \textbf{0.956}        \\
3          & 0.731 & 0.819       & 0.875       & 0.902       & 0.916        & 0.955        \\
4          & 0.735 & 0.821       & 0.876       & 0.900       & 0.915        & 0.954        \\
5          & 0.736 & \textbf{0.832}       & \textbf{0.883}       & \textbf{0.905}       & \textbf{0.919}        & 0.955 \\  \hline \hline
attention  & 0.723 & 0.734             & 0.853             & 0.892             & 0.910              & 0.949  \\ \hline
\end{tabular}

\end{table}
\section{Discussion}
Real time predictions for ICU mortality are proxies for severity of illness \cite{leteurtre2015daily, badawi2018evaluation, hug2009icu} and should reflect acute changes in a patient. The examples in Figure 3 show that the mortality model trained with input perseveration (PRNN with $k=5$) responded more pronouncedly and immediately than the traditionally trained model ($k=1$) when both were presented with data reflecting acute changes. Subsequent to the acute changes, the responses of the traditional RNN appeared to lag behind the PRNN's. Standard metrics such as AUROCs for classification or mean absolute errors for continuous regression often mask this predictive lag and other deleterious behaviors that can be detrimental in critical or intensive care settings where rapid recognition and response are crucial.

Input perseveration provides the RNN's internal cells additional computational cycles to incorporate the current state of the patient into the final prediction.
The effect of this method was measured by metrics designed to capture a model's immediate responses to newly acquired data.

The variation metric described in Section \ref{sec:variation_metric} compares the models' immediate responses to data indicating precipitous changes in a patient's status. These changes require quick reaction time from the care team, therefore capturing them in the predictions is important. The comparison of variation metrics from the different models (Figure \ref{fig:variation}) shows that the LSTM became more responsive to acute clinical events as the level of perseveration, $k$, increased. When $k$ increased from 1 to 5, the variation metric corresponding to the defined precipitous events increased by a factor of 2-3 times. They also show that a given model's responsiveness increased with more acute events (i.e. those belonging to higher percentile changes for a given physiologic or intervention variable). This result is consistent with expectations about the variation metric. For example, one would expect the change in predictions of those with larger drops in blood gas level to be greater than those of less severe drops.

Figure \ref{fig:t0_dists} compares the distributions of initial predictions generated by the different models. Since the first prediction is a function of both the initial input (which varies across episodes) and the initial cell state memory (which is fixed), a wider distribution of these predictions across episodes indicates a higher reliance on the initial input. This is important because children admitted to the PICU have different severities of illness \cite{pollack1996prism, slater2003pim2}. Increasing $k$ resulted in a wider distribution as measured by the standard deviation. The increase was greater for the non-surviving population ($\sigma=0.04$ when $k=1$ to $\sigma=0.13$ when $k=5$) relative to the surviving population. This is again consistent with clinical expectations.

Increasing the first prediction's reliance on the initial measurement -- as achieved by the PRNN models with higher $k$ -- also resulted in higher AUROC at ICU admission (Table \ref{tab:aucs}). As the models observed the patient longer, their AUROCs increased as expected. Importantly, the increase of AUROC due to perseveration was greater at the early hours ($t\leq3$) when information is most scarce, with the 1-hour AUROC increasing from 0.77 when $k=1$ to 0.83 when $k=5$. This means that predictive clinical models relying on scarce data for early detection could benefit from the perseveration approach.

Adding a causal attention layer to the baseline ($k=1$) model had no apparent performance improvement in any of the metrics. The attention network theoretically can put more weight to the most recent state than to the previous ones. The results indicate that this mechanism did not improve on what the baseline LSTM's gates were already doing, but persistently giving the same input to the model -- i.e. perseverating the input  -- did.

There are limitations to the perseverated input approach. Although LSTMs are theoretically good at understanding temporal trends through their memory cells, there remain practical limitations to how long prior information can be maintained to inform future predictions \cite{zhang2016highway,yu2017long}. The PRNN has the potential to exacerbate these algorithmic deficiencies because of the memory cell's prolonged exposure to the same data. A comprehensive assessment of the impact of a potential reduction in temporal memory was not performed. However, the basic data perseveration technique can easily be generalized to any sequential data.

Additionally, perseveration increases the number of sequences requiring computation. Perseveration is used during both training and inference, and the compute time scales linearly for both, commensurate with the level of perseveration. However, the time required for inference is on the order of $30ms$ for $k=1$ (baseline) and $150ms$ for $k=5$ on an NVIDIA Titan RTX. Since the recording frequency is approximately every 15 minutes, these computational burdens do not hinder deployment.

Finally, this proof-of-concept study used only a single clinical outcome (ICU mortality) and data from a single center. Future work will examine the effect of perseverating the data input on other important clinical tasks such as risk of desaturation, sepsis, and renal failure.

\section{Conclusion}

This work demonstrates that perseverated data input increases the responsiveness of LSTM models to a variety of acute changes to patient state and also significantly increases predictive performance in the early hours following admission. The PRNN is a simple solution to the predictive lag exhibited by standard LSTMs when encountering an acute event and may enable more rapid responses to critical conditions.

\label{sec:conclusion}

\section{Attention RNN}

\newpage
\clearpage

\onecolumn
\section{EMR Variables}

\label{appendix:EMR_vars}
\vspace{0.5cm}
\begin{table}[h]
\caption{EMR variables (demographics, vitals and labs) in patient episode matrix. Demographics such as gender and race/ethnicity were encoded as binary variables.

\centering
\resizebox{\textwidth}{!}{
\begin{tabular}{lllll}\hline
\multicolumn{4}{c} {Demographics and Vitals}\\ \hline
Age & Sex\_F & Sex\_M & race\_African American \\
race\_Asian/Indian/Pacific Islander  & race\_Caucasian/European Non-Hispanic & race\_Hispanic & race\_unknown \\
Abdominal Girth & FLACC Pain Face & Left Pupillary Response Level & Respiratory Effort Level \\
Activity Level & FLACC Pain Intensity & Level of Consciousness & Respiratory Rate \\
Bladder pressure & FLACC Pain Legs & Lip Moisture Level & Right Pupil Size After Light \\
Capillary Refill Rate & Foley Catheter Volume & Mean Arterial Pressure & Right Pupil Size Before Light \\
Central Venous Pressure & Gastrostomy Tube Volume & Motor Response Level & Right Pupillary Response Level \\
Cerebral Perfusion Pressure & Glascow Coma Score & Nasal Flaring Level & Sedation Scale Level \\
Diastolic Blood Pressure & Head Circumference & Near-Infrared Spectroscopy SO2 & Skin Turgor\_edema \\
EtCO2 & Heart Rate & Nutrition Level & Skin Turgor\_turgor \\
Extremity Temperature Level & Height & Oxygenation Index & Systolic Blood Pressure \\
Eye Response Level & Hemofiltration Fluid Output & PaO2 to FiO2 & Temperature \\
FLACC Pain Activity & Intracranial Pressure & Patient Mood Level & Verbal Response Level \\
FLACC Pain Consolability & Left Pupil Size After Light & Pulse Oximetry & WAT1 Total \\
FLACC Pain Cry & Left Pupil Size Before Light & Quality of Pain Level & Weight \\ \\ \hline
\multicolumn{4}{c} {Labs}\\ \hline
ABG Base excess & CBG PCO2 & GGT & Neutrophils \% \\
ABG FiO2 & CBG PO2 & Glucose & PT \\
ABG HCO3 & CBG TCO2 & Haptoglobin & PTT \\
ABG O2 sat & CBG pH & Hematocrit & Phosphorus level \\
ABG PCO2 & CSF Bands\% & Hemoglobin & Platelet Count \\
ABG PO2 & CSF Glucose & INR & Potassium \\
ABG TCO2 & CSF Lymphs \% & Influenza Lab & Protein Total \\
ABG pH & CSF Protein & Lactate & RBC Blood \\
ALT & CSF RBC & Lactate Dehydrogenase Blood & RDW \\
AST & CSF Segs \% & Lactic Acid Blood & Reticulocyte Count \\
Albumin Level & CSF WBC & Lipase & Schistocytes \\
Alkaline phosphatase & Calcium Ionized & Lymphocyte \% & Sodium \\
Amylase & Calcium Total & MCH & Spherocytes \\
Anti-Xa Heparin & Chloride & MCHC & T4 Free \\
B-type Natriuretic Peptide & Complement C3 Serum & MCV & TSH \\
BUN & Complement C4 Serum & MVBG Base Excess & Triglycerides \\
Bands \% & Creatinine & MVBG FiO2 & VBG Base excess \\
Basophils \% & Culture Blood & MVBG HCO3 & VBG FiO2 \\
Bicarbonate Serum & Culture CSF & MVBG O2 Sat & VBG HCO3 \\
Bilirubin Conjugated & Culture Fungus Blood & MVBG PCO2 & VBG O2 sat \\
Bilirubin Total & Culture Respiratory & MVBG PO2 & VBG PCO2 \\
Bilirubin Unconjugated & Culture Urine & MVBG TCO2 & VBG PO2 \\
Blasts \% & Culture Wound & MVBG pH & VBG TCO2 \\
C-Reactive Protein & D-dimer & Macrocytes & VBG pH \\
CBG Base excess & ESR & Magnesium Level & White Blood Cell Count \\
CBG FiO2 & Eosinophils \% & Metamyelocytes \% & \\
CBG HCO3 & Ferritin Level & Monocytes \% &  \\
CBG O2 sat & Fibrinogen & Myelocytes \% &  \\ \hline
\end{tabular}
}

\label{rnn-inputs_5col}
\end{table
\newpage
\begin{table*}
\caption{EMR Variables (drugs and interventions) in baseline patient episode matrix.}
\centering
\resizebox{\textwidth}{!}{
\begin{tabular}{lllll}\hline
\multicolumn{4}{c} {Drugs}\\ \hline
Acetaminophen/Codeine\_inter & Clonazepam\_inter & Ipratropium Bromide\_inter & Oseltamivir\_inter \\
Acetaminophen/Hydrocodone\_inter & Clonidine HCl\_inter & Isoniazid\_inter & Oxacillin\_inter \\
Acetaminophen\_inter & Cyclophosphamide\_inter & Isradipine\_inter & Oxcarbazepine\_inter \\
Acetazolamide\_inter & Desmopressin\_inter & Ketamine\_cont & Oxycodone\_inter \\
Acyclovir\_inter & Dexamethasone\_inter & Ketamine\_inter & Pantoprazole\_inter \\
Albumin\_inter & Dexmedetomidine\_cont & Ketorolac\_inter & Penicillin G Sodium\_inter
\\
Albuterol\_inter & Diazepam\_inter & Labetalol\_inter & Pentobarbital\_inter \\
Allopurinol\_inter & Digoxin\_inter & Lactobacillus\_inter & Phenobarbital\_inter \\
Alteplase\_inter & Diphenhydramine HCl\_inter & Lansoprazole\_inter & Phenytoin\_inter
\\
Amikacin\_inter & Dobutamine\_cont & Levalbuterol\_inter & Piperacillin/Tazobactam\_inter \\
Aminophylline\_cont & Dopamine\_cont & Levetiracetam\_inter & Potassium Chloride\_inter
 \\
Aminophylline\_inter & Dornase Alfa\_inter & Levocarnitine\_inter & Potassium Phosphat
e\_inter \\
Amlodipine\_inter & Enalapril\_inter & Levofloxacin\_inter & Prednisolone\_inter \\
Amoxicillin/clavulanic acid\_inter & Enoxaparin\_inter & Levothyroxine Sodium\_inter &
 Prednisone\_inter \\
Amoxicillin\_inter & Epinephrine\_cont & Lidocaine\_inter & Propofol\_cont \\
Amphotericin B Lipid Complex\_inter & Epinephrine\_inter & Linezolid\_inter & Propofol
\_inter \\
Ampicillin/Sulbactam\_inter & Epoetin\_inter & Lisinopril\_inter & Propranolol HCl\_inter \\
Ampicillin\_inter & Erythromycin\_inter & Lorazepam\_inter & Racemic Epi\_inter \\
Aspirin\_inter & Factor VII\_inter & Magnesium Sulfate\_inter & Ranitidine\_inter \\
Atropine\_inter & Famotidine\_inter & Meropenem\_inter & Rifampin\_inter \\
Azathioprine\_inter & Fentanyl\_cont & Methadone\_inter & Risperidone\_inter \\
Azithromycin\_inter & Fentanyl\_inter & Methylprednisolone\_inter & Rocuronium\_inter \\
Baclofen\_inter & Ferrous Sulfate\_inter & Metoclopramide\_inter & Sildenafil\_inter \\
Basiliximab\_inter & Filgrastim\_inter & Metronidazole\_inter & Sodium Bicarbonate\_inter \\
Budesonide\_inter & Fluconazole\_inter & Micafungin\_inter & Sodium Chloride\_inter \\
Bumetanide\_inter & Fluticasone\_inter & Midazolam HCl\_cont & Sodium Phosphate\_inter \\
Calcium Chloride\_cont & Fosphenytoin\_inter & Midazolam HCl\_inter & Spironolactone\_inter \\
Calcium Chloride\_inter & Furosemide\_cont & Milrinone\_cont & Sucralfate\_inter \\
Calcium Gluconate\_inter & Furosemide\_inter & Montelukast Sodium\_inter & Tacrolimus\_inter \\
Carbamazepine\_inter & Gabapentin\_inter & Morphine\_cont & Terbutaline\_cont \\
Cefazolin\_inter & Ganciclovir Sodium\_inter & Morphine\_inter & Tobramycin\_inter \\
Cefepime\_inter & Gentamicin\_inter & Mycophenolate Mofetl\_inter & Topiramate\_inter \\
Cefotaxime\_inter & Glycopyrrolate\_inter & Naloxone HCL\_cont & Trimethoprim/Sulfamethoxazole\_inter \\
Cefoxitin\_inter & Heparin\_cont & Naloxone HCL\_inter & Ursodiol\_inter \\
Ceftazidime\_inter & Heparin\_inter & Nifedipine\_inter & Valganciclovir\_inter \\
Ceftriaxone\_inter & Hydrocortisone\_inter & Nitrofurantoin\_inter & Valproic Acid\_inter \\
Cephalexin\_inter & Hydromorphone\_cont & Nitroprusside\_cont & Vancomycin\_inter \\
Chloral Hydrate\_inter & Hydromorphone\_inter & Norepinephrine\_cont & Vasopressin\_cont \\
Chlorothiazide\_inter & Ibuprofen\_inter & Nystatin\_inter & Vecuronium\_inter \\
Ciprofloxacin HCL\_inter & Immune Globulin\_inter & Octreotide Acetate\_cont & Vitamin K\_inter \\
Cisatracurium\_cont & Insulin\_cont & Olanzapine\_inter & Voriconazole\_inter \\
Clindamycin\_inter & Insulin\_inter & Ondansetron\_inter & \\ \\ \hline
\multicolumn{4}{c} {Interventions}\\ \hline
Abdominal X Ray & Diversional Activity\_tv & NIV Mode & Range of Motion Assistance Type \\
Arterial Line Site & ECMO Hours & NIV Set Rate & Sedation Intervention Level \\
CT Abdomen Pelvis & EPAP & Nitric Oxide & Sedation Response Level \\
CT Brain & FiO2 & Nurse Activity Level Completed & Tidal Volume Delivered \\
CT Chest & Gastrostomy Tube Location & O2 Flow Rate & Tidal Volume Expiratory \\
Central Venous Line Site & HFOV Amplitude & Oxygen Mode Level & Tidal Volume Inspiratory \\
Chest Tube Site & HFOV Frequency & Oxygen Therapy & Tidal Volume Set \\
Chest X Ray & Hemofiltration Therapy Mode & PEEP & Tracheostomy Tube Size \\
Comfort Response Level & IPAP & Peak Inspiratory Pressure & Ventilator Rate \\
Continuous EEG Present & Inspiratory Time & Peritoneal Dialysis Type & Ventriculostomy Site \\
Diversional Activity\_books & MRI Brain & Pharmacological Comfort Measures Given & Visitor Mood Level \\
Diversional Activity\_music & Mean Airway Pressure & Position Support Given & Visitor Present \\
Diversional Activity\_play & Mechanical Ventilation Mode & Position Tolerance Level & Volume Tidal \\
Diversional Activity\_toys & MultiDisciplinaryTeam Present & Pressure Support &
 \\ \hline
\end{tabular}
}
\label{rnn-inputs_5col_b}
\end{table*}
\section*{Acknowledgments}

\section*{Acknowledgements}

This work was supported by the L. K. Whittier Foundation. The authors would like to thank Alysia Flynn for help aggregating the data and Mose Wintner for reviewing the manuscript and providing valuable feedback.

redpajama_set_name: RedPajamaArXiv


For most people a visit to Latin America is primarily about experiencing the awe inspiring landscapes and the warm, friendly people, but we also know that where you stay is important. We have included a number of different boats here for you to choose from; some rather decadent and others that are more affordable.
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It seems like we're always talking about Supreme Court quotes these days — whether it's Roberts speaking to students or Alito mouthing things at the State of the Union.Frankly it's just fun for all of us when the justices get out in the real world and loosen their tongues a bit.
An AP report (broken down here by Ashby Jones at the WSJ Law Blog) provides choice quotes from Justices Stephen Breyer, Antonin Scalia and Ruth Bader Ginsburg.
The exchange of the night, from the AP: "I never heard that before and I certainly don't agree with it," Scalia said in response to one point from Breyer.
"If I did make an argument you hadn't thought of before, I wish you'd think about it," Breyer replied a few minutes later.
The same article discusses a jab Ginsburg took at Republican Senator Jim Bunning. After Ginsburg has surgery for pancreatic cancer in February 2009, Bunning said she probably would not live for more than nine months.
It was not all joking around, however — Ginsburg said she was "unsettled, indeed alarmed" by the criticism of Justice Department lawyers who represented terror suspects.


text:
Intentional dreamer, culture speaker, mentor & designer, Julien Mainguy arrived in Canada in 2014 and he got involved with people and communities to make a difference. He thrives on building better societies, by using awareness on cultural management and by understanding yourself better.
Blog Canadian Culture How to Share Feedback in Canada?
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Giving feedback is both an enriching and challenging activity. When carried out correctly and respectfully, feedback can improve an individual's performance by providing guidance and direction. However, not everyone responds to feedback in the same way, and sometimes criticism can lead to hostility and other unproductive behaviours. The tips below are designed to provide you with the tools for giving constructive feedback that is respectful of everyone's needs.
Giving Feedback in Canada
Canada is a multicultural and egalitarian society which means that there's a lot of cultural diversity in the workplace. Therefore, it's essential to consider that not everyone will want feedback in the same way; more on this later.
Generally speaking, Canada favours indirect feedback where managers provide both positive and negative feedback simultaneously. For instance, you may critique the wordiness of your peer's presentation while also complimenting them on their inclusion of valuable graphics. This approach favours the well-being of the workplace environment as a whole over efficient delivery. It also means you should listen mindfully when given feedback to reflect on the entire message rather than your peer's first comment. There is an unspoken expectation in the Canadian workplace that you always think critically about the feedback you receive.
For someone new to this feedback style, a cultural mentor may be an essential resource for you. They can help model culturally appropriate feedback and develop a style that feels more authentic to you. While the indirect approach is favoured generally, it's incredibly flexible and often compliments other feedback styles more often than you'd expect.
When to Give Feedback?
Receiving feedback can be stressful for all parties involved, but it doesn't have to be. A common mistake is only to give feedback monthly or even annually. Not only does this create a disconnect with your staff or peers, but it can often mean that problems that should have been addressed earlier continue to snowball into more significant issues.
Instead, it would be best to give feedback as soon as you see something that may have significant consequences. It isn't always a problem that needs addressing. Often positive feedback can ensure that employees double down on an area of strength that will significantly enhance their productivity. In addition, giving feedback frequently will normalize the process, which can decrease fear and uncertainty surrounding feedback. Many find that no feedback is more stressful than a constructive comment.
Before giving feedback, be sure to ask for permission. It's critical to recognize that not everyone will be in the right headspace to receive feedback at all times properly. Rather than risking your personal or professional relationship with an individual, try to wait for the right opportunity when they can be fully receptive to you. Asking the question allows the individual to choose if the time is right for them and makes them feel more comfortable while you provide your comments. Before asking, it may be helpful to give context to your intended feedback so it can overcome any preconceived biases. For instance, they may be expecting a scathing review when you want to compliment a positive behaviour in reality.
When giving constructive feedback, try to find a private space for discussion. This way, the individual receiving feedback is more receptive and less self-conscious.
How to Give Feedback?
Feedback doesn't have to be a prerecorded monologue. Some of the best feedback is initiated by inviting the individual to evaluate their performance. You can do it through open-ended questions like "what areas are you hoping to improve on?" or "what would you do differently next time?" It will quickly reveal their level of self-awareness and allows the individual to take responsibility for their performance. It also ensures that you aren't providing redundant feedback nor making incorrect assumptions about the employee.
When giving feedback, you don't want it to feel too personal. Focus on an individual's skills or process instead of criticizing their character. Try to provide more objective feedback that avoids using "you" statements like "you need to work on how you communicate." Instead, address their actions or your experience. For the previous example, you might say something like: "I found the latest report had too many errors."
On the other hand, you don't want to give feedback that is so objective it feels impersonal. Instead, you want your comments to address the individual you're critiquing directly. It commonly occurs when trying to provide a critique at a group level. For example, you might say to a group, "Some of you need to improve your negotiation skills." While this does avoid singling out individuals, which is something to avoid, it's entirely irrelevant for a portion of the group. Individual performance improves overall group performance, so addressing those who need improvement directly and privately would be more effective.
Another tip is to try to be clear and specific when giving feedback. It means including detailed examples of the problem and the resulting consequences. If your feedback is vague and nonspecific, then a person will have difficulty implementing it, or their interpretation may lead to further issues. Instead, try to focus on measurable qualities, often known as KPIs or Key Performance Indicators. KPIs provide visible targets that team members can work towards.
Sometimes it may be helpful to write your feedback out ahead of time. This way, you can assess if your message is clear and practical. While an in-person meeting is generally ideal, written feedback is still a precious tool.
As with any necessary communication, it's vital to assess whether your message has been understood. Be sure to ask confirming questions and even get the individual to repeat what they heard to ensure that there hasn't been any misunderstanding. Again, it is imperative when giving indirect feedback as the message may get lost in the nuance.
Sandwich Method & Plussing
The compliment sandwich method is often utilized when giving corrective feedback. This method consists of complimenting an individual, then corrective feedback, followed by a final compliment. This way, your feedback is sandwiched between two compliments. It eases the individual into the feedback process and helps to reinforce the idea that constructive criticism is a positive process.
A common mistake is to view the sandwich method to diffuse the sting of negative criticism. While in reality, the process is designed to force you to carefully consider both the positive and negative aspects of a team member's performance. The positive qualities surrounding the corrective feedback should be as equally helpful and supportive as your criticism so that the process doesn't feel condescending. With this approach, employees feel more appreciated, and they can use this positive work environment for efficiency improvement.
An alternative method for providing feedback that Pixar Studios created is called "plussing." In plussing, feedback must also offer a possible solution to correct any issues rather than simply criticizing. This method was inspired by the "Yes and…" mantra of improvisation, where actors only add to each other's ideas and never take them away. But, again, the emphasis is on using feedback to encourage others to grow and not shut them down.
The Sandwich and Plussing methods are many approaches to feedback that all work together to successfully address the Canadian workplace's diverse needs. However, you'll find that some methods are more effective than others, so be sure to experiment with different approaches to discover what is best for each individual.
How to get ready?
While giving or receiving feedback can be very helpful, it's also very challenging. However, if you carefully consider the unique Canadian working environment and ask plenty of questions to ensure both parties are ready for feedback, you'll find the process is much easier. Remember to avoid using "you" statements and even practice new methods of criticism like the sandwich or plussing methods. Always remember that feedback isn't a reductive process. It's about strengthening each other. Set clear expectations, and don't forget to express how much you appreciate what they're doing right.
Want to practice giving feedback straightaway? Schedule a 30-minute session with one of our experts.


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