Melee!
Super Smash Bros. Melee for the Nintendo GameCube was released in 2001. Since then, it has become one of the most adored games of the last twenty years. The game features many characters from various Nintendo game franchises, which players choose between before entering a match. The objective of each match is relatively simple: fight your opponent(s) and hit them beyond the boundaries of the stage. In contrast to fighting games, each time a player is struck they gain percentage points. As these percentage points accumulate, each successive attack received sends the player’s character further away and closer to the stage boundaries. Each time a player goes past the stage boundary, they lose a “stock” (i.e. a life). Lose all your stocks and the game is over.
Today, Melee is a popular esport across North America and Europe, where players can watch or enter tournaments with cash prizes. Although the game is simple in premise, mechanically, the game is incredibly complicated and players require a tremendous amount of practice to become a highly skilled at the game.
The best Melee player in the world is Juan “Hungrybox” DeBiedma, currently ranked #1 by a panel of competive players and community figures. Hungrybox plays Jigglypuff, a character originally found in the Pokémon franchise, and he is sponsored by Team Liquid, a high profile esports organization.
His placings in the image below are from the first half of 2018.
In late 2017, Hungrybox won five consecutive high level tournaments, much to the chagrin of his opponents and critics. Displeasure over his dominance led to some online discussions of “What should top players do to beat Hungrybox?”.
For context, Fox from the StarFox series is considered to be the best character to use versus Jigglypuff, as he is the best, but also the most technically challenging, character in the game. Fans in turmoil doubted Fox and discussed which alternate characters or strategies might be effective against Hungrybox. These discussions among fans became the subject of ridicule by Leffen, currently ranked #4 in the world, in the following tweet:
i was thinking, what should we do to beat hungrybox? as if it were fate, i came upon this reddit thread.
— Leffen (@TSM_Leffen) November 7, 2017
Now I know exactly what not to do! pic.twitter.com/PyT3cPwnb1
Leffen’s tweet features screenshots of fan comments on Reddit that suggest absurd or simplistic strategies that disregard the logic behind high level play. His tweet should speak for itself, but there is a large knowledge gap between fans and top players when it comes to playing against the best player in the world.
The sentiment of Leffen’s tweet is later echoed by Tafo, another community member.
Imagine how many Young Link comments that would show up if I showed this yesterday (Hbox's 2017 char data) pic.twitter.com/ZjT8tDy1QO
— CLG Tafo (@tafokints) November 7, 2017
Although Tafo’s comments are also made at the expense of fans, he includes a dataset of Hungrybox’s character matchup data for 2017 (against players in the top 50). Tafo effectively highlights a specific misunderstanding that fans have about character matchups in Melee.
The essential sentiment is that fans tend to look past sample size when making inferences on matchup win rates or general performance.
Here is the same data reorganized:
## OpponentCharacter Wins Losses TotalGames WinPercent
## 1 Marth 7 1 8 87.50
## 2 Samus 12 2 14 85.71
## 3 Sheik 12 2 14 85.71
## 4 Falco 8 2 10 80.00
## 5 Captain Falcon 32 10 42 76.19
## 6 Yoshi 3 1 4 75.00
## 7 Ice Climbers 9 4 13 69.23
## 8 Pikachu 4 2 6 66.67
## 9 Fox 145 104 249 58.23
## 10 Young Link 2 2 4 50.00
Here is where the essential question of this blog post comes in.
Does Hungrybox win more versus Fox than he does versus Young Link? At face value, the answers seems obvious. Hungrybox wins 58.23% of his matches versus Fox and 50.00% of his matches versus Young Link, so clearly he wins less versus Young Link, right?
In order to make that conclusion, one would have to disregard the sample size or the amount of evidence behind each matchup. Up until 2017/11/07, Hungrybox had won 2/4 matches versus Young Link, whereas he had won 145/249 matches versus Fox. Knowing this, can we confidently say that Hungrybox’s win percentage versus Young Link is truly lower than his win percentage versus Fox?
Thankfully, there is a statistical technique called empirical Bayes estimation that will allow us to predict the proportion of wins in each of Hungrybox’s character matchups. The specific goal of this analysis will be to gain a better estimate of Hungrybox’s win percentage versus Young Link, given the data in Tafo’s tweet. This analysis will allow us to make an informed approximation of which is the best character to use against Hungrybox in high level play.
Before we dive in, I would like to credit David Robinson for his excellent series of posts on Bayesian data analysis. Learning about empirical Bayes estimation through his posts inspired me to use these methods on competitive Melee data.
Set-Up
#data tidying and wrangling
library(dplyr)
library(tidyr)
#visualization
library(ggplot2)
library(ggthemes)
library(RColorBrewer)
library(ggrepel)
#stats
library(MASS)
Import and Arrange Data
Here is the Hungrybox’s matchup data arranged by win percentage in descending order.
Hbox <- read.csv(file = "Hbox2017.csv")
Hbox %>%
arrange(desc(WinPercent))
## OpponentCharacter Wins Losses TotalGames WinPercent
## 1 Marth 7 1 8 87.50
## 2 Samus 12 2 14 85.71
## 3 Sheik 12 2 14 85.71
## 4 Falco 8 2 10 80.00
## 5 Captain Falcon 32 10 42 76.19
## 6 Yoshi 3 1 4 75.00
## 7 Ice Climbers 9 4 13 69.23
## 8 Pikachu 4 2 6 66.67
## 9 Fox 145 104 249 58.23
## 10 Young Link 2 2 4 50.00
And here is the win percentage data visualized.
Across the y-axis is the characters of Hungrybox’s opponents and the x-axis is Hungrybox’s win percentage in those matchups at the time of 2017/11/07. Again, the raw data suggest that Hungrybox wins less versus Young Link than versus Fox. My goal is to generate a better approximation of Hungrybox’s win averages across all matchups.
Win Averages
#Calculate win averages
Hbox <- Hbox %>%
mutate(WinAverage = Wins/TotalGames)
Alternatively, here is a the distribution of win averages across all matchups. This distribution leaves a lot to be desired because it has a small number of observations. This is due to the fact that there are not many characters (out of the 26 possible characters) that are played in high level Melee. Unfortunately, the small amount of data will not yield the strongest approximations of win percentages, but we will continue with what we have.
Prior
For this analysis, we will be relying on the beta distribution and its parameters, alpha and beta, to model the distribution of win averages. Secondly, we will use that distribution to inform new estimates for each of Hungrybox’s character matchups.
First, we must estimate the alpha and beta parameters for the beta distribution. For our purposes, we will use the “method of moments” to estimate the alpha and beta parameters from the mean and variance of our data using this custom function:
estBetaParams <- function(mu, var) {
alpha <- ((1 - mu) / var - 1 / mu) * mu ^ 2
beta <- alpha * (1 / mu - 1)
return(params = list(alpha = alpha, beta = beta))
}
Credit goes to this Cross Validated user for their insight and function.
So, let’s calculate the mean and variance of Hungrybox’s win averages across all matchups and plug those values into the estBetaParams
function.
H.mu <- mean(Hbox$WinAverage)
H.var <- var(Hbox$WinAverage)
Hbox.est <- estBetaParams(H.mu, H.var)
Hbox.est
## $alpha
## [1] 8.500409
##
## $beta
## [1] 3.076596
The resulting alpha and beta parameters will inform the shape of our beta distribution (shown in red).
Again, our distribution and number of observations leaves something to be desired. Nonetheless, we will use this distribution to inform our approximations of Hungrybox’s individual character matchups.
To do so, we add alpha and the number of wins in each matchup then divide that by the sum of the total number of games, alpha, and beta.
\[
\frac{Number of Wins + \alpha_0}{Total Number of Games + \alpha_0 + \beta_0}
\]
#How does empirical Bayes estimation change the matchup averages?
Hbox.eb <- Hbox %>%
mutate(eb_estimate =
(Wins + Hbox.est$alpha) / (TotalGames + Hbox.est$alpha + Hbox.est$beta))
Hbox.eb %>%
dplyr::select(-WinPercent) %>%
arrange(desc(abs(eb_estimate - WinAverage)))
## OpponentCharacter Wins Losses TotalGames WinAverage eb_estimate
## 1 Young Link 2 2 4 0.5000000 0.6740968
## 2 Marth 7 1 8 0.8750000 0.7917661
## 3 Samus 12 2 14 0.8571429 0.8015172
## 4 Sheik 12 2 14 0.8571429 0.8015172
## 5 Pikachu 4 2 6 0.6666667 0.7111797
## 6 Falco 8 2 10 0.8000000 0.7647219
## 7 Ice Climbers 9 4 13 0.6923077 0.7120644
## 8 Yoshi 3 1 4 0.7500000 0.7382940
## 9 Fox 145 104 249 0.5823293 0.5890789
## 10 Captain Falcon 32 10 42 0.7619048 0.7559290
The data above have been arranged in descending order by the absolute difference between their new win average estimates and their old win average estimates. Young Link shows the largest change in win percentage between the two estimates, which is interesting considering that we were hoping to improve our estimate of this specific matchup. Alteratively, Hungrybox’s win percentage in the Fox matchup shows one of the smallest differences between the two estimates. This makes sense considering that we have a large amount of data for that specific matchup.
Visualize the changes in win average estimates
Generally, we can see that as win averages increase, so do the empirical Bayes estimations of the win percentages. We also have two distinct red lines that represent different insights.
The y-intercept of the dashed red line indicates the estimate of Hungrybox’s win average in a matchup given no data. What does this value tell us? Well, what if we had no matchup evidence at all for a matchup? Luigi or Peach are characters that are not included in this dataset. So what would our best estimate of Hungrybox’s win averages versus those characters?
sprintf("%.3f", Hbox.est$alpha / (Hbox.est$alpha + Hbox.est$beta))
## [1] "0.734"
Given the data we have, we can estimate Hungrybox’s win average to be 0.734 against characters with no previous evidence. That’s an excellent win rate.
The solid red line indicates where Hungrybox’s win averages are equal to the Bayesian estimates. What this allows us to infer, is the change in estimates. The further away a point lies from the line, the greater change between the original average and the new average. For example, Young Link, Sheik, Marth and Samus show more change in their estimates compared to the other characters.
Let’s make a cleveland dot plot to visualize the differences between the new and old win average estimates.
As mentioned before, Hungrybox’s win average versus Young Link has increased substantially (0.500 to 0.674). The new estimate for Young Link is now more in line with the estimates of all of Hungrybox’s matchups. Conversely, there was only a very small change in Hungrybox’s win average estimate for the Fox matchup (0.5823 to 0.5890). The win average estimate for Fox is now lower than any other matchup in the dataset.
Conclusions
To reiterate, this empirical Bayesian estimation analysis used the distribution of Hungrybox’s win averages across all matchups to improve estimates of win averages in each of Hungrybox’s individual matchups.
So, what did we learn? First we learned that sample size and the number of observations is important. We should not make bold conclusions on percentages or proportions that have a small number of underlying observations. That principle should also be applied to this analysis. Due to the nature of high level competitive Melee, only a few matchups made up Hungrybox’s win average distribution. This affected the estimates of the alpha and beta parameters of the beta distribution, as well as the resulting estimates for each of Hungrybox’s matchups.
With these limitations in mind, this analysis suggests that Young Link is not an optimal character choice versus Hungrybox. In unsurprising fashion, Fox remains Hungrybox’s most difficult matchup, a sentiment believed by Hungrybox, himself. It is an almost natural assumption that the best character in the game would be the hardest to play against, even if you’re #1. Despite the somewhat underwhelming conclusion, the important lesson is that extreme or simplistic solutions should not be applied to complex problems. This statement holds especially true when there is little evidence behind the proposed solution. In order for Young Link to be considered as a legitimate character choice versus Hungrybox, we would require a substantial amount of evidence.
So, does this mean that players should only play Fox against Hungrybox’s Jigglypuff?
Recent tournament results suggest otherwise.
Zain’s astonishing rise through the Melee rankings led him to beat Hungrybox in two best-of-5 tournament matches at Shine 2018. One of those matches was a decisive 3-1 Grand Finals set versus Hungrybox. That feat is compounded by the fact that Zain beat Hungrybox using Marth, a character that historically performs poorly versus Hungrybox’s Jigglypuff. In fact, the last Marth player to beat Hungrybox was PewPewU in 2015. So, character matchups and their estimates are not everything in Melee and that’s just one reason why the competetitive scene can be so exciting to watch.
I am very glad to publish this first post on my blog, especially since I have been sitting on this analysis since Tafo posted the data on Twitter. Taking an analytical stance on one of the many nuances of Melee was very enjoyable for me and I hope you enjoyed reading it. Thanks again to David Robinson for inspiring this post.
If you have any questions or concerns about this blog post, feel free to message me on Twitter or leave a Disqus comment. If you’re interested in competitive Melee, you can watch “The Smash Brothers” documentary, search through competitive Melee VODs, or watch livestreams of tournaments on Twitch. You can use “When’s Melee?” to find out when those tournaments are scheduled.
All of the code for this post can be found here.
Bonus: Maximum Likelihood Estimation
In addition to estimating the alpha and beta parameters through the method of moments, we can also use maximum likelihood estimation. Here, I use the fitdistr()
function from the MASS
package to do that with one line of code.
Hbox.est2 <- fitdistr(Hbox$WinAverage,dbeta,list(shape1=1,shape2=1), lower = c(0,0))
Hbox.est2
## shape1 shape2
## 10.403528 3.780885
## ( 4.665568) ( 1.625258)
Our estimates of alpha and beta have not changed substantially, but let’s see how this method may have differed in terms of our conclusions.
Maximum Likelihood Estimation
## OpponentCharacter Wins Losses TotalGames WinAverage eb_estimate2
## 1 Young Link 2 2 4 0.5000000 0.6820967
## 2 Marth 7 1 8 0.8750000 0.7844935
## 3 Samus 12 2 14 0.8571429 0.7948907
## 4 Sheik 12 2 14 0.8571429 0.7948907
## 5 Pikachu 4 2 6 0.6666667 0.7135966
## 6 Falco 8 2 10 0.8000000 0.7609665
## 7 Ice Climbers 9 4 13 0.6923077 0.7137740
## 8 Yoshi 3 1 4 0.7500000 0.7370889
## 9 Fox 145 104 249 0.5823293 0.5904739
## 10 Captain Falcon 32 10 42 0.7619048 0.7547205
Previous Analysis: Method of Moments
## OpponentCharacter Wins Losses TotalGames WinAverage eb_estimate
## 1 Young Link 2 2 4 0.5000000 0.6740968
## 2 Marth 7 1 8 0.8750000 0.7917661
## 3 Samus 12 2 14 0.8571429 0.8015172
## 4 Sheik 12 2 14 0.8571429 0.8015172
## 5 Pikachu 4 2 6 0.6666667 0.7111797
## 6 Falco 8 2 10 0.8000000 0.7647219
## 7 Ice Climbers 9 4 13 0.6923077 0.7120644
## 8 Yoshi 3 1 4 0.7500000 0.7382940
## 9 Fox 145 104 249 0.5823293 0.5890789
## 10 Captain Falcon 32 10 42 0.7619048 0.7559290
Again, what if we had no evidence at all for a matchup? What would we predict Hbox’s win average to be in this situation?
Maximum Likelihood Estimation
sprintf("%.3f", Hbox.est2$estimate[1] / (Hbox.est2$estimate[1] + Hbox.est2$estimate[2]))
## [1] "0.733"
Previous Analysis: Method of Moments
sprintf("%.3f", Hbox.est$alpha / (Hbox.est$alpha + Hbox.est$beta))
## [1] "0.734"
And our estimates of win averages?
The data do not change very much! It’s nice that this method also reflects the same conclusions as the previous analysis.


check out github
this is a page, written in css and html, which explores the use of columns.
also written in css and html, this page features the "rolling stones" and includes a sidebar. we also played with some not too pretty color schemes.
this site is a collection of html and css illustrating the use of the "spacing" class in html. it also chronicles the daily happenings of a lovable scamp.
using, you guessed it, html and css we look a lecture dedicated to rocks. we played with text-alignment in css to achieve the desired effect.
this page is a little more involved and uses bootstrap, html, and css. it features columns, rows, a jumbotron, a responsive navigation bar, and a carousel. it also has pictures of beautiful machu picchu.
utilizing html and css, we linked to cupcake recipes and posted images of delicious looking cupcakes.
after making this site, my partner and i were on our way to learning how to properly maintain our files on github.
i recently moved to portland from seattle, where i worked for the downtown public library. my position at the library involved wearing many hats which included, but was not limited to:
i was propelled to portland by the job change of my gentlefriend, and figured this was my only real chance to develop some of the skills that many libraries lack i.e. web development abilities. which is why i'm here at epicodus. i have very little knowledge of coding and am greatful all the help i'm recieving. in my freetime i enjoy hiking, crosswords, photography, singing loudly in the car, dr who, and shaking my fist at squirrels.


I didn’t have any tears when I lost the Pumpkin vote. But I had some when I got to be back in the top 20. I’m a big softy. That said, this is my home and I spend the vast majority of every day 7 days a week doing things to support and grow this community and ecosystem. So, it hurt when someone that significant dropped my witness, but it’s a million times better to see people all over the world come out and support me! I’m so thankful!
I love the work I do and this let’s me do it.
I quit my job to work on Steem full time. Being a top 20 witness right now doesn’t provide a ton of income, but it provides enough safety net that I don’t have work a 40 hour a week fiat job I hate. Thanks for a boost back into the top 20 so I can continue to grow the Minnow Support Project, MSP-Waves, Steem Monsters and now Steem Engine too.
It came from all over!
USA, Europe, and Korea for the win!
Thanks to people all over this planet that came to help me get back to being a consensus witness. In some ways this made my witness stronger than ever. I’m the only top 20 that doesn’t have a vote from @blocktrades or @pumpkin. Makes me feel like “the people’s champion.” Many people have stated that the top 20 is completely controlled by the two biggest actively voting stakeholders on the chain. I’m proof for now that it isn’t entirely true. It’s kinda amazing to have achieved this level of support. All it took was working 7 days a week on this blockchain for close to 2 years! That’s all you gotta do :)
Back on the mission!
Spread the Values of Peace, Abundance, and Liberty
Train and retain new members on the platform
Bring to life new tools to grow the Steem ecosytem
With love,
@aggroed
Witness 18
Special thanks to: @byubat, @transisto, @delegate.lafona, @acidyo, and @drorion for coming through with some new extra large votes to put me back in.
I’m grateful to the smaller accounts for sure too. This was a team effort: @djynn, @sinner264, @jaydih, @karenb54, @tarazkp. @abh12345, @twinpapa, @coolbowser, @paulag, @caladan, @dune69, @ew-and-patterns, @ericet, @geekgirl, @whatsup, and @valth.
As always thanks to the 9000+ people that for for me and here’s just a few more that let me do my job:
This page is synchronized from the post: ‘Consensus Witness Again! Phew!’


if not template:
tex = codecs.open(path,"r","utf-8").read()
else:
tex = codecs.open(template,"r","utf-8").read()
// calculate pid score, if it is not passed as argument
if "pid_score" not in keywords:
msa = read_msa(infile)
//// load templates
tex = util.read_text_file(template or path)
// calculate pid score, if it is not passed as argument
if "pid_score" not in keywords:
pid_score = 0


| Technique | {{{autocompaste: 'AutoComPaste', shortcuts: 'Keyboard Shortcuts'}[currentBlock.technique]}} |
| Granularity | {{currentBlock.granularity | capitalize}} |
| Trial No. | {{currentBlockNum * 3 + currentTrialNum + 1}}/54 |
Copy the following text using the technique mentioned above.
Tip: Use Shift + Enter as a shortcut for "Next Trial".
This is a trial run. When you are ready, press the button below to start the real experiment.


I like this phrasing of the debate about App Engine’s role, from Doug Cutting: Cloud: commodity or proprietary? (via). (Well, I like it more than the sharecropping phrasing referenced in my last post.) I guess I’m excited about this because like Doug I do want a "cloud" of sorts, and this is a move towards that in a way that makes sense to me. Maybe to state my motivations more clearly: I hate computers. I really hate them a lot. I dream of some world of Platonic ideals where software just exists, and existence that state it works. App Engine feels like a strong move in the direction of computers-not-mattering. What does App Engine run on? I don’t care! Where is the server located? I don’t care! What is BigTable? I am comfortable thinking of it only in its abstract sense, an API that works, and I don’t know how, nor do I need to know how. I don’t need to know these things if they just work. Always. Totally reliably. I’m not shy about digging into code. I tend to be light on my reading of documentation, because I’d much rather open up source code and poke around. But when something can work so reliably that I can treat it as completely opaque then it’s a blessing, because I can start to forget about it and think about bigger goals.
There was a time when people were concerned about Big Endian vs. Little Endian in computers. You had to think about this sort of little detail when programming. People formed actual opinions on which way was best. To think! Similarly XML has removed a large number of fairly pointless format decisions people might make. There is progress. Commodity hosting (reliable, consistent hosting, better than what we have now) feels like similar progress.
Unlike Doug I’m optimistic that App Engine is a move in the direction of a commodity cloud. The APIs seem to lack the stench of proprietary APIs. They are based on Google services, but they reasonably abstract and reasonably minimal. This does not seem like some kind of "play" (and the developers’ seem to be reassuring about their intentions). There’s a tendency to be cynical about any company’s work, that it has underlying intentions that are at odds with any competitor (present or future), that anything good is just a loss leader meant to hook you in so they can squeeze you later. Some companies deserve such cynicism. I don’t know that this company, or this team, deserves that.
Mind you, I don’t say this from a Best-Tool-For-The-Job perspective. I believe in the moral foundations of Free Software, not just the technical advantages of its development process. But I’m a Utilitarian, and it doesn’t make me uncomfortable that not everything is Free if I think it’s a step forward for overall freedom. I think App Engine has the potential to be a very powerful tool for enabling people to create and use web applications. If it was great, but still a dead end, then maybe that wouldn’t be good enough. But I don’t think this is a dead end.
Update: Indeed people are reimplementing the interface: see the appdrop.com announcement
Automatically generated list of related posts:
- App Engine and Pylons So I promised some more technical discussion of App Engine...
- App Engine and Open Source This is about Google App Engine which probably everyone has...
- What Does A WebOb App Look Like? Lately I’ve been writing code using WebOb and just a...
Every publicly traded company has only one purpose in life: Maximize shareholder value. There is no other purpose for their existence anymore.
I don’t fault them for this. I would be happy if I could run a publicly traded company (that does well). There’s nothing wrong with it, it’s just something they have to do.
But I don’t believe for a second that Google does this out of the pure goodness of their hearts. Every last one of their management team is, after all, accountable to their share holders.
Sometimes they will do something that benefits many people. Maybe App Engine is one such thing. Maybe they won’t increase their hosting prices. Maybe they won’t make you buy an expensive SLA when your site has finally become successful and you need decent service levels the most. Maybe they won’t look into your traffic stats and know more about your visitors and your site traffic than you yourself do, before they approach you with an offer.
Maybe they won’t do any of this, in which case it would be wonderful.
But even then they would do this at least as a sort of PR thing, something that helps them with mind-share, brand awareness, brand ubiquity, etc.  They can’t do anything without shareholder value in mind.
Publicly traded companies are supposed to maximize shareholder value. Publicly traded companies don’t do anything. Individuals do things. Management is accountable to shareholders. Management is not subservient to shareholders. Employees are not subservient to management. Companies can provide incentives to get their employees to maximize shareholder value. If the company feels an employee isn’t doing enough, they can fire that employee.  Employees can volunteer for that role, but it’s not exactly a popular choice.
Some corporations actually seem to have a fuck-people-over culture. It seems to be cultivated in the executive class. It isn’t cultivated in the programmer class. In corporations where programmers are subservient to executives, I think bad things can happen. This usually requires the underlings to feel desperate or ignorant or disinterested.
 But it’s just bullshit, and it demeans the individual’s ability to make choices.
For some reason your post made me remember the SETI project, which probably was the oldest example of “cloud” on a global scale. When the Internet was young and everyone using it were a nerd (for better or worse), lots of people did devote computing time to SETI. For some reasons it never got beyond a mere curiosity. Perhaps the application itself was at fault – had we discovered alien life as a result of SETI, who knows what effect would it have.
We now have Amazon and Google offering cloud services. Microsoft is seemingly absent, it’s clear that don’t have any idea about what to do (besides buying Yahoo hoping that they will know what to do). But, I’m tempted to ask: is there anything that requires the cloud to run in a proprietary environment? Of course, there’s a huge investment, and management, but it should be possible to implement something like this to run in an open environment.
Maybe someone will come up with a open source implementation, or some company will launch a product that allows people to implement their own “mini-clouds”, in such a way that these mini-clouds can discover and connect to each other, forming a bigger cloud where computing resources can be allocated and shared between applications. I don’t know. I think that’s a matter of time before someone does something like that.
I believe that the idea is important not because this “open cloud” may kill Google or Amazon. I don’t believe it will get to this point. But the existence of an open implementation creates more competition, lower the entry cost, and raise awareness in the market. It also allows people to experiment and create new business model. It’s good for everyone, and it’s kind of a proof that the market is mature.
@Carlos: in some way I see the Apache Hadoop project http://hadoop.apache.org/ going in the direction of an open-source alternative. You may compare their HBase http://hadoop.apache.org/hbase/ to BigTable and HDFS http://hadoop.apache.org/core/docs/current/hdfs_design.html to GFS if you want.
Thanks for your insightful post on these issues. I myself hadn’t thought about the potential implications of being locked into a particular cloud.
The AppEngine solves a general problem of running Web applications. I often see Subversion or Trac mentioned in blogs and articles as applications that can now be run more conveniently using virtual machines or computing clouds. My company, ProjectLocker (http://www.projectlocker.com), and some of our competitors, have been running these specific applications for years in a straightforward ASP model. Clouds or virtual machines still leave you with installation and configuration headaches and costs, while ProjectLocker and companies like us take all the pain out of configuring, administering, and backing up your source code, bug, and Wiki data. We already support continuous integration and are working on more value-added tools as well. And since we’re based on open source tools with standard data formats, it’s easy to pick up and move to us or from us if you don’t like where you are.
You do acknowledge that reliable, consistent hosting is a step in the right direction. The good old ASP model, when implemented properly, is less sexy than clouds and virtual machines but still provides a lot of value.
Except with ProjectLocker, you are locked in. Unless you have a paid account, there’s no way to take your trac instance somewhere else, apparently.
In response to comment 3: Microsoft is not completely absent in this concept. They are just more focused on providing cloud [services](http://www.microsoft.com/sql/dataservices/default.mspx) to their corporate customers.
[The Semantic Web](http://www.w3.org/2001/sw/) is an idea whose time has come. AppEngine, EC2, et. al. will help to enable this framework of data sharing.
> What does App Engine run on? I don’t care!
Until something about how it runs randomly makes your code take longer than 3 seconds to run, at which point Google kills the request.
> What is BigTable? I am comfortable thinking of it only in its abstract sense, an API that works, and I don’t know how, nor do I need to know how.
Until the way you’re using it causes it to take more than a few seconds to run, at which point Google kills the request.
There’s always reasons to care about how your app is run. Since Google will kill requests that take too long, and so far have zero tools for profiling why it was killed, what part of it was taking too long, why did it take too long, etc. this could become rather important. I’d think a tool for gauging how long DataStore requests take would be important since many people will be unfamiliar with changes to how they should think about their data when its not in a RDBMS.
I’m sure they’ll provide some tools at some point, but the fact is, for awhile yet, yes, we will probably still need to care about those things.
I’m still waiting for a App Engine like thing that gives me the freedom to move to another provider when I either outgrow one (maybe Google’s way of scaling doesn’t meet my needs), or when the price of staying there gets too high (whether monetary, or privacy policy, etc.). Lock-in is no fun, no matter how awesome the service might be. At least Google’s lock-in is probably somewhat minimal as their API’s are rather abstract, and its just as trivial to run most of an App Engine app elsewhere.
In the end, would one be better off just running a VM with Apache/lighttpd/etc, a real Django and whatever else you need on Amazon’s EC2? There you get cloud computing with all its advantages, but you are determining your own software/language/server settings. And if you want to move to another provider, or start running your stuff on its own dedicated box… well, all you need is to find a RedHat host somewhere (I think Amazon EC2 uses RedHat for its server images, right?) and off you go. Moving to a different provider therefore should be quite straight forward in that case. Certainly more so than in the case where you develop your app specifically towards a Google API.
@Raphael: thanks for the pointers. I assume that these projects are focused on building “private clouds”. That’s great, but I wonder if it will be possible to do it in a much larger scale, a “peer to peer” global cloud. But that’s just a dream, I don’t know if it’s practical or feasible – only that it seems interesting.


Hi, I'm Chris and welcome to my website. I am an avid follower of technology, Japanese culture, and the Christian faith. I'm based in Sydney, Australia, but love to travel all over the world! Hopefully you'll discover some interesting facts, have a good laugh at my stories, and be inspired to walk down a similarly awesome path!


Picking:off
|
Camera position
The trial needs a product in order to calculate and display model option


I am senior at Smith College studying Economics, Statistical and Data Science, and Mathematics. Through my courses, I have gained experience in coding, research, and teaching. Outside of my courses, I have been able to create a start-up, assist in writing an introductory statistics textbook, and help support Pasifika students in the Five Colleges. Reach out to me through the links below and let's connect!
As a research assistant, I was able to get hands-on Economic research experience. Not only was I able to utilize STATA and R, but I was also taught how to use ArcGIS for the purpose of this research project. In addition to this, my research partner and I were able to study different approaches and techniques through several literature reviews and personal contact by the authors.
As an SDS tutor, I have been able to broaden my statistical and coding technique. Students ask very interesting questions which then contributes to my own understanding of the material. Through this experience, I have learned how to think on my feet, strengthen my coding and statistical understanding, but also help students broaden theirs as well.
When first introduced to statistics, I struggled with understanding the material. It made it even more difficult given that our course heavily relied on our textbook. Therefore, that experience helped me immensely in assisting the Sociology department in writing an Introduction to Statistics textbook. I not only created questions that had real-world applications, but I ensured that the material was also easily translated and understood. I also utilized STATA and R to create pictures of distributions.
In partnership with the Conway Center at Smith College, my co-founders and I created ReBike, an Eco-friendly bike start-up that connected bike owners and bike seekers. Through this experience, we learned how to think on our feet, how to pitch a product, and how to create a product from scratch. Overall, we raised $14,500 for our start-up through Smith College and the Draper Competition.
GPA: 3.5
GPA: 3.875
This was the final project of my summer at the American Economic Association Summer Program where I looked at the possible association between childhood neighborhood characteristics and number of inventors . Using a poison likelihood estimation (ran in STATA), I found an association between a higher share of non-white races within a neighborhood and a percentage decrease in the probability of having an inventor. Also, the cool part was that I was able to personally share my research to Ben Bernanke.
For GGSquad2's DataFest project, we studied the Canadian Women's Rugby 7’s team data and offered two actionable steps; the first being that the players synchronize their training schedules together and on the individual level, syncing fatigue and session load and maximizing their correlation. We won best multi-level actionable solution.
In our Machine Learning course, our final was to make a submission to House Prices: Advanced Regression Techniques on Kaggle using a LASSO regularized multiple regression model. In the code provided (although not all of the code utilized for this project), we scored the lowest RMSLE in our class thus winning Dr. Kim’s ML project final.
For our Multiple Regression final project, we looked at the possible association between ‘lifestyle predictors’ and the likelihood of experiencing domestic violence. Using IPUMS data from Tanzania and a logistic regression, we found that for certain wealth quintiles, there was an association with a higher odds ratio of experiencing domestic violence.
In my most recent SDS seminar studying Structural Equation Modeling, my in-class research project studied household migrant and non-migrant behavior in Tonga with the release of the New Zealand working visa in 2007. I found that for migrants households, ‘Feeling of Community’ contributed the most to the ‘Importance of migrating’, whereas health evaluations had a negative association holding all else constant.
For our Introduction to Data Science final, our project looked at the economic costs and profit from becoming an Uber driver; essentially adding more insight into the gig economy. By using Uber data on the number of rides given by census tract in Los Angeles, we inferred the economic costs outweighing the gains given the available data and time period of project.
Outside of my classes and research, I enjoy spending time with my Pasifika Student Association family. As president, I help create events like 'Design Night' or 'Anti-Colonial' potlucks; doing my best to have Pasifika folks from all over feel at home at Smith. In addition to this, I enjoy tutoring and helping peers with Statistics, Econometrics and code questions.
When not out and about, you can find me on the squash courts! This is my second year playing on the Smith Squash Club and it's been a blast ever since I joined. If not on the courts, I'm petting Shadow (my partner's cat)


This post is the summary of the paper: The accrual failure detector by N. Hayashibara, X. Défago, and T.Katayama. In Proc. 23rd IEEE Intl. Symp. on Reliable Distributed Systems (SRDS’04), pp. 66-78, Florianópolis, Brazil, October 2004. IEEE CS Press.
More at: D2S Laboratory
Problem definition
Detecting failure in a distributed system setting is a desirable task for many obvious reasons. This paper introduces an implementation of an adaptive (accrual) failure detector. In this accrual failure detector, the conditions of the network is accumulated and used to update the probabilistic model for failure suspicion. Compares to the existing models in 2004, which output of suspicion level is binary, this implementation has the advantage of returning a real-value suspicion level. The authors compared their implementation to Chen Fault Detection and Bertier Fault Detection. For the benchmark scheme, they set up two computers between Japan and Switzerland transferring “heart beat” signal from Japan. They then later analyzed the collected data over a week and reported the result.
- Input: A set of master processes and a set of worker processes . The master processes are in charge of monitoring the worker processes.
- Output: An indicator of failure for each worker processes.
- Assumption: In this paper, for the shake of simplicity, the authors assumed that the master processes will never crash. Furthermore, only one worker and one master scheme was discussed in this paper.
Notations
Note: The setting in this paper is sending heart beat signals from Japan to Switzerland.
| Notation | Explaination |
|---|---|
| Suspection value. Higher value means the higher chance the failure happened. | |
| Hyperparameter. Threshold for . | |
| “Heart beat” signal period. | |
| Timeout for transmission. | |
| Average transmission time experienced by the messages. | |
| Master process that monitors other process for failure dection. | |
| Worker process that sends “heart beat” signals. | |
| Time until q begins to suspect p permanently in case of failure happened. | |
| Average mistake rate at which a failure detector generates wrong suspicions. | |
| Eventually perfect failure detector class. | |
| Suspicion level of p at time t. | |
| Dynamic threshold upperbounds . | |
| Dynamic threshold lowerbounds . | |
| The time when thest most recent heart beat was received. | |
| The current time. | |
| The probability that the next heart beat will arrive after time unit since the last one. |
Method
As mentioned above, this paper proposed an abstract accrual failure detector and a simple implementation of the idea with only 2 processes. This figure illustrates the differences in the conventional failure detection architecture and the proposed accrual failure detection architecture.
The main different in the proproposed architecture is the ability to return many suspicion levels instead of just binary levels. This scheme enable the system to perform many action as well as adaptive action based on the suspicion input level. In the proposed architecture, the suspicion level is represented by a value called . The suspicion level is defined by a logarithmic scale:
This formula is intuitive in the sense that it penaltize the delay by a log scale of some pre-defined probabilistic model . In this paper, the authors defined a threshold for . Since this variable is computed in the log-scale, also has logarithmic meaning. Each of the unit step increase of will lead to ten times confident interval of failure detection. However, this fact only means that the confident about a failure dection is high, it doesn’t take into account the speed of the dectection.
The probabilistic model is given by the formula:
Note that the formula above is just the implementation of the abstract accrual failure detector in this paper. Theoretically speaking, we can choose any computable that is suitable to our need. The picture below demonstrate this probabilistic model.
Until this point, we have the suspicion level and the probabilistic model for computing . In order to adapt the network condition into the failure detection scheme, the authors created a sized window with size . When the heart beat signal is arrived, it time stamp is stored into the window. The mean and variance of the data in the window is maintained as the window received new data. In addtion, there are two variable keeping track of sum and sum of square of all element in the window are also maintained for computation convenient. The dataflow of the proposed implementation:
The dataflow figure above captured all the essential steps of the proposed algorithm. From the network, heartbest signal is collected and its time period is stored in the sampling window. To my understanding, the sampling window is a FIFO unit that removes oldest data when new data is added. The mean and variance for the probabilistic model is then computed using the data in the sampling window. At every time step, the value of is computed using the probabilistic model, the time of last arrival and the current time. This value will then be used in different application for various action. For example, in action 1, the threshold for is . Let’s say in this moment, , hence the machine will perform action 1 (e.g. warning, reallocate resources, etc.). On the other hand, action 2 has the threshold of , which is larger than at the moment, hence no action is performed. More interestingly, the multi-level suspicion enables the use of parametric action, which means the machine doesn’t have to behave in a binary manner (performs action or not), but it can perform actions to a certain degree adapting to the current situation.
Experiment
As mentioned above, the setting for experiment is a heartbeat signal transmission between Switzerland and Japan. Three failure detection schemes are compared: Chen FD, Bertier FD, and (this) FD. The window size of 1,000 is used for all failure detectors. In this paper, the authors conducted 4 experiments:
- Exp1: Average mistake rate . This experiment aims to provide some reasoning between the average mistake rate and the threshold .
- Exp2: Average detection time . In this experiment, the relation between average detection time and the threshold is studied. Consistent with what I mentioned above, while the average mistake rate decreased with high threshold (8-12), the detection time is increased significantly.
- Exp3: Effect of window size. The window size is plotted against the mistake rate. There are three lines representing 3 values of : 1,3,5. The result showed that larger widnow size leads to lower mistake rate. The result for different values of is also consistent with experiment 2.
- Exp4: Comparision with Chen FD and Bertier FD. The authors conducted two experiment in this category. First experiment is comparision in the internet setting and the second is in the LAN setting. In both experiment, FD outperformed the other two methods.
More detail is provided in the authors’ paper.
Conclusion
This post only provides very high level abstraction of the authors’ work. I left out many discussion on the propertiesof failure detector, time period or heartbeat signal and the effect of network delay. Nevertheless, the results provided in this paper showed that the new scheme doesn’t imply additional cost in term of performance while it yields much better deteciton results under the authors’ benchmark. On another note, the authors also stated that based on their experimental result, it was sufficient to use normal distribution for the probabilistic model.


Before Change
if raw_label_keys is None:
raise ValueError("raw_label_keys must be specified.")
if raw_feature_keys is None:
raw_feature_keys = list(
set(six.iterkeys(raw_metadata.schema.column_schemas))
- set(raw_label_keys))
def default_transforming_serving_input_fn():
Serving input_fn that applies transforms to raw data in Tensors.
raw_serving_features = {
After Change
exclude_raw_keys=raw_label_keys,
include_raw_keys=raw_feature_keys,
convert_scalars_to_vectors=convert_scalars_to_vectors)
return _legacy_serving_input_fn(receiver_fn)
def build_default_transforming_serving_input_receiver_fn(
raw_metadata,


[1]
when searching for the wrong way through a one-way system.
[2]
when searching for the jackhammers penetrates through a one-way system.


Draw the diagram with entities, trust borders, etc.
Embedded below there's diagram.html, exported from draw.io. Edit this space with a description.
Identify threats and address them in the editable table below, using indexes from the main diagram. Refer to the documentation for information about how to compile fields.
Edit this space afterwards to include notes on how to read during reporting. Print the page to keep a copy.


{% raw %}
Please sign-in
Menu
Introduction to Google Sign-In
Authentication with backends
Authorization on Client
Authorization on Server
Google Sign-In Sample
Sign In
Sign Out
Welcome back!
[[profile.name]]
{% endraw %} {% if mode == 'authorization_client' or mode == 'authorization_server' %}
Read files from Google Drive
Title
Last Modified
[[item.name]]
[[item.modifiedTime]]
{% endif %}


Overview
Analytics
Home
Sign Out
Reports
Reference Period
Line
MACHINE
ENERGY
MATERIAL
MANPOWER
MAINTAINCE
VOLUME
FINANCE
SETTINGS
Volume Needed
Total Contribution Margin
Reference and Vision Comparison
(H Mac)


Capacitor Simulation:This simulation illustrates the interaction of charged particles inside the two plates of a capacitor. Each plate contains twelve charges interacting via the Coulomb and Pauli forces, where one plate contains positive charges and the other contains negative charges. Because of their mutual repulsion, the particles in each plate are compelled to maximize the distance between one another, and thus spread themselves evenly around the outer edge of their enclosure. However, the particles in one plate are attracted to the particles in the other, so they attempt to minimize the distance between themselves and their oppositely charged correspondants. Thus, they distribute themselves along the surface of their bounding box closest to the other plate.
|


Setting the Host Edit Skin and Container
How to set the default skin and container which are applied to the editing pages for all sites within this DNN installation.
- Navigate to Host > Host Settings.
- Select the Basic Settings tab.
- Expand the Appearance section.
- At Edit Skin, select the skin for all Host pages from the drop down list.
- At Edit Container, select the container for all Host pages from the drop down list.
- Optional. Click the Preview Edit Skin and Container button to preview the selected skin and container in a new Web browser.
-
Click the Update button.


| 12V |
Screw terminal - 2 pins |
package THT; hole size 1.0mm,0.508mm; pins 2; pin spacing 0.1in (2.54mm) |
| 5V |
Screw terminal - 2 pins |
package THT; hole size 1.0mm,0.508mm; pins 2; pin spacing 0.1in (2.54mm) |
| ADC |
DIP - 16 pins |
package DIP (Dual Inline) [THT]; hole size 1.0mm,0.508mm; true; chip label IC; pins 16; pin spacing 300mil |
| Bypass IC6 |
0 Ω Resistor |
package 1206 [SMD]; tolerance ±2%; resistance 0Ω |
| C1 |
Ceramic Capacitor |
package 100 mil [THT, multilayer]; capacitance 100nF; voltage 6.3V |
| C2 |
Ceramic Capacitor |
package 100 mil [THT, multilayer]; capacitance 100nF; voltage 6.3V |
| C3 |
Ceramic Capacitor |
package 100 mil [THT, multilayer]; capacitance 100nF; voltage 6.3V |
| C4 |
Ceramic Capacitor |
package 100 mil [THT, multilayer]; capacitance 100nF; voltage 6.3V |
| C5 |
Ceramic Capacitor |
package 100 mil [THT, multilayer]; capacitance 100nF; voltage 6.3V |
| C6 |
Ceramic Capacitor |
package 100 mil [THT, multilayer]; capacitance 100nF; voltage 6.3V |
| C7 |
Ceramic Capacitor |
package 100 mil [THT, multilayer]; capacitance 100nF; voltage 6.3V |
| C8 |
Ceramic Capacitor |
package 100 mil [THT, multilayer]; capacitance 100nF; voltage 6.3V |
| C9 |
Ceramic Capacitor |
package 100 mil [THT, multilayer]; capacitance 10µF; voltage 6.3V |
| C10 |
Ceramic Capacitor |
package 100 mil [THT, multilayer]; capacitance 0.1µF; voltage 6.3V |
| C11 |
Ceramic Capacitor |
package 100 mil [THT, multilayer]; capacitance 1µF; voltage 6.3V |
| C12 |
Ceramic Capacitor |
package 100 mil [THT, multilayer]; capacitance 1µF; voltage 6.3V |
| CH0 |
Screw terminal - 2 pins |
package THT; hole size 1.0mm,0.508mm; pins 2; pin spacing 0.1in (2.54mm) |
| CH1 |
Screw terminal - 2 pins |
package THT; hole size 1.0mm,0.508mm; pins 2; pin spacing 0.1in (2.54mm) |
| CH2 |
Screw terminal - 2 pins |
package THT; hole size 1.0mm,0.508mm; pins 2; pin spacing 0.1in (2.54mm) |
| CH3 |
Screw terminal - 2 pins |
package THT; hole size 1.0mm,0.508mm; pins 2; pin spacing 0.1in (2.54mm) |
| CH4 |
Screw terminal - 2 pins |
package THT; hole size 1.0mm,0.508mm; pins 2; pin spacing 0.1in (2.54mm) |
| CH5 |
Screw terminal - 2 pins |
package THT; hole size 1.0mm,0.508mm; pins 2; pin spacing 0.1in (2.54mm) |
| CH6 |
Screw terminal - 2 pins |
package THT; hole size 1.0mm,0.508mm; pins 2; pin spacing 0.1in (2.54mm) |
| CH7 |
Screw terminal - 2 pins |
package THT; hole size 1.0mm,0.508mm; pins 2; pin spacing 0.1in (2.54mm) |
| DC/DC |
SIP - 3 pins |
package SIP (Single Inline) [THT]; hole size 1.0mm,0.508mm; true; chip label IC; pins 3; pin spacing 300mil |
| IC1 |
DIP - 8 pins |
package DIP (Dual Inline) [THT]; hole size 1.0mm,0.508mm; true; chip label IC; pins 8; pin spacing 300mil |
| IC2 |
DIP - 8 pins |
package DIP (Dual Inline) [THT]; hole size 1.0mm,0.508mm; true; chip label IC; pins 8; pin spacing 300mil |
| IC3 |
DIP - 8 pins |
package DIP (Dual Inline) [THT]; hole size 1.0mm,0.508mm; true; chip label IC; pins 8; pin spacing 300mil |
| IC4 |
DIP - 8 pins |
package DIP (Dual Inline) [THT]; hole size 1.0mm,0.508mm; true; chip label IC; pins 8; pin spacing 300mil |
| LED |
Red LED - 3mm |
package 3 mm [THT]; leg yes; color Red (633nm) |
| R1 |
33 Ω Resistor |
package THT; tolerance ±5%; bands 4; resistance 33Ω; pin spacing 400 mil |
| R2 |
33 Ω Resistor |
package THT; tolerance ±5%; bands 4; resistance 33Ω; pin spacing 400 mil |
| R3 |
33 Ω Resistor |
package THT; tolerance ±5%; bands 4; resistance 33Ω; pin spacing 400 mil |
| R4 |
33 Ω Resistor |
package THT; tolerance ±5%; bands 4; resistance 33Ω; pin spacing 400 mil |
| R5 |
33 Ω Resistor |
package THT; tolerance ±5%; bands 4; resistance 33Ω; pin spacing 400 mil |
| R6 |
33 Ω Resistor |
package THT; tolerance ±5%; bands 4; resistance 33Ω; pin spacing 400 mil |
| R7 |
33 Ω Resistor |
package THT; tolerance ±5%; bands 4; resistance 33Ω; pin spacing 400 mil |
| R8 |
33 Ω Resistor |
package THT; tolerance ±5%; bands 4; resistance 33Ω; pin spacing 400 mil |
| R9 |
220 Ω Resistor |
package THT; tolerance ±5%; bands 4; resistance 220Ω; pin spacing 400 mil |
| REF |
DIP - 8 pins |
package DIP (Dual Inline) [THT]; hole size 1.0mm,0.508mm; true; chip label IC; pins 8; pin spacing 300mil |
| SPI |
Screw terminal - 4 pins |
package THT; hole size 1.0mm,0.508mm; pins 4; pin spacing 0.1in (2.54mm) |
| TRIM |
SIP - 3 pins |
package SIP (Single Inline) [THT]; hole size 1.0mm,0.508mm; true; chip label IC; pins 3; pin spacing 300mil |


Demo
Simple
Top
Zoom
Zoom to Left
Bottom
Height Expand
From Right
From Left
Expand
Close Me
Another Modal


Your privacy is important to us. It is Vitris Wireless, LLC's policy to respect your privacy regarding any information we may collect from you across our website, https://tryvitris.com, and other sites we own and operate.
We only ask for personal information when we truly need it to provide a service to you. We collect it by fair and lawful means, with your knowledge and consent. We also let you know why we’re collecting it and how it will be used.
We only retain collected information for as long as necessary to provide you with your requested service. What data we store, we’ll protect within commercially acceptable means to prevent loss and theft, as well as unauthorised access, disclosure, copying, use or modification.
We don’t share any personally identifying information publicly or with third-parties, except when required to by law.
Our website may link to external sites that are not operated by us. Please be aware that we have no control over the content and practices of these sites, and cannot accept responsibility or liability for their respective privacy policies.
You are free to refuse our request for your personal information, with the understanding that we may be unable to provide you with some of your desired services.
Your continued use of our website will be regarded as acceptance of our practices around privacy and personal information. If you have any questions about how we handle user data and personal information, feel free to contact us.
This policy is effective as of 17 May 2018.


--- layout: page permalink: /contact.html title: Contact Andrew Eddie noindex: true comments: false reading_time: false --- Fill out my Wufoo form!


--- title: Lexus RX 2016 description: The Lexus RX is a vehicle outfitted for autonomous driving development. layout: content name: Lexus RX 2016 excerpt: A popular SUV vehicle model for autonomous driving development image_path: /images/vehicles/lexusrx2016-01.jpg ---
{{page.description}}
Author/Organization: LG Electronics
Supported LGSVL Simulator version: 2020.06


Angry Teacher
问题描述 :
Mr. O’Cruel is teaching Math to ninth grade students. Students of course are very lazy, so they do not like to do their homework. On the other side, Mr. O’Cruel doesn’t like lazy students.
Recently Andrew failed to do his homework again, so he was given a special task. If he doesn’t do it, he will be expelled from his school. The task seems very easy, but it is very technical, so it would take a lot of time. Andrew is given a polynomial p(x) = anxn + an-1xn-1 + . . . + a1x + a0 with integer coefficients.
He must calculate the value of the polynomial for k successive integer numbers starting from l. Of course writing all these numbers would require too much paper. So as a proof of completing the task, for each number x from l to l + k – 1 Andrew is asked to provide the sum of squares of m last digits in decimal notation of p(x).
Since Andrew is lazy, he doesn’t want to do the task by himself. So he asks you to write the program that calculates the values requested.
Recently Andrew failed to do his homework again, so he was given a special task. If he doesn’t do it, he will be expelled from his school. The task seems very easy, but it is very technical, so it would take a lot of time. Andrew is given a polynomial p(x) = anxn + an-1xn-1 + . . . + a1x + a0 with integer coefficients.
He must calculate the value of the polynomial for k successive integer numbers starting from l. Of course writing all these numbers would require too much paper. So as a proof of completing the task, for each number x from l to l + k – 1 Andrew is asked to provide the sum of squares of m last digits in decimal notation of p(x).
Since Andrew is lazy, he doesn’t want to do the task by himself. So he asks you to write the program that calculates the values requested.
输入:
The first line of the input file contains n, l, k, and m (0 <= n <= 10, 0 <= l <= 101000 , 1 <= k <= 1 000, 1 <= m <= 1 000).
The following n + 1 lines contain coefficients of the polynomial: an , an-1 , . . . , a1 , a0 (0 <= ai <= 101000).
The following n + 1 lines contain coefficients of the polynomial: an , an-1 , . . . , a1 , a0 (0 <= ai <= 101000).
输出:
Output k lines — for x from l to l + k – 1 output the sum of squares of last m digits of p(x).
样例输入:
3 0 10 2 1 0 2 1
样例输出:
1 16 10 25 58 45 85 89 85 80
解题代码:
//* @author: import java.math.*; import java.util.*; public class Main { static BigInteger l, t; static int m, n, k; static BigInteger[] a = new BigInteger[15]; static BigInteger[][] q = new BigInteger[15][15]; static char[] c; static char[] cc; public static void main(String[] args) { Scanner in = new Scanner(System.in); n = in.nextInt(); l = new BigInteger(in.next()); k = in.nextInt(); m = in.nextInt(); BigInteger w = BigInteger.TEN.pow(m); for (int i = 0; i <= n; ++i) a[i] = new BigInteger(in.next()); for (int i = 0; i < Math.min(k, n + 1); ++i) { t = a[0]; for (int j = 1; j <= n; ++j) { t = t.multiply(l); t = t.add(a[j]); } t=t.mod(w); q[n][i] = t.mod(w); int ret = 0; c = t.toString().toCharArray(); int ll = c.length; for (int j = 0; j < Math.min(m, ll); ++j) { ret += (c[ll - 1 - j] - '0') * (c[ll - 1 - j] - '0'); } System.out.println(ret); l = l.add(BigInteger.ONE); } if (k > n) { for (int i = n - 1; i >= 0; --i) { for (int j = 0; j <= i; j++) q[i][j] = q[i + 1][j + 1].subtract(q[i + 1][j]).mod(w); } for(int i=1;i<=n;++i) q[0][i]=q[0][0]; int po = 1; for (int i = n + 1; i < k; ++i) { for (int j = 1; j <= n; ++j) q[j][(po + j) % (n + 1)] = q[j - 1][(po + j - 1) % (n + 1)] .add(q[j][(po + j - 1) % (n + 1)]).mod(w); int ret = 0; c = q[n][(po + n) % (n + 1)].mod(w).toString().toCharArray(); int ll = c.length; for (int j = 0; j < Math.min(m, ll); ++j) { ret += (c[ll - 1 - j] - '0') * (c[ll - 1 - j] - '0'); } System.out.println(ret); l = l.add(BigInteger.ONE); ++po; } } } }
(adsbygoogle = window.adsbygoogle || []).push({});
哎，path[0]应该是从零开始吧，为什么是赋值1呢？
“可以发现,树将是满二叉树,”这句话不对吧，构造的树应该是“完全二叉树”，而非“满二叉树”。
因为是要把从字符串s的start位到当前位在hash中重置，修改提交后能accept，但是不修改居然也能accept
算法是程序的灵魂，算法分简单和复杂，如果不搞大数据类，程序员了解一下简单点的算法也是可以的，但是会算法的一定要会编程才行，程序员不一定要会算法，利于自己项目需要的可以简单了解。
[…] 如果此题没有要求 相对位置不变，可以参考文章：http://www.acmerblog.com/interview-9-2427/ […]
如何解决overload的问题呢？
约瑟夫也用说这么长……很成熟的一个问题了，分治的方法解起来o(n)就可以了，有兴趣可以看看具体数学的第一章，关于约瑟夫问题推导出了一系列的结论，很漂亮
simple, however efficient. A lot of instances it is difficult to get that a??perfect balancea?? among usability and appearance. I must say that youa??ve done a exceptional task with this. Also, the blog masses quite fast for me on Web explore.
漏了一个"不"，已修正:)
必须试过啊
妈呀！这个模板好长OwQ！
图像处理那题解题报告在哪儿呢？


{% extends "pano/base.html" %}
{% load puppetdb_extras %}
{% load common %}
{% block head %}
{% load static %}
{% endblock %}
{% block content %}
Classes
{% with summary.classes_failure|length as len_failure_classes %}
| Failed |
{{ len_failure_classes }} |
|
{% endwith %}
{% with summary.classes_skipped|length as len_skipped_classes %}
| Skipped |
{{ len_skipped_classes }} |
|
{% endwith %}
{% with summary.classes_success|length as len_success_classes %}
| Changed |
{{ len_success_classes }} |
|
{% endwith %}
{% with summary.classes_noop|length as len_noop_classes %}
| Noop |
{{ len_noop_classes }} |
|
{% endwith %}
Nodes:
{% with summary.nodes_failure|length as len_failure_nodes %}
| Failed |
{{ len_failure_nodes }} |
|
{% endwith %}
{% with summary.nodes_skipped|length as len_skipped_nodes %}
| Skipped |
{{ len_skipped_nodes }} |
|
{% endwith %}
{% with summary.nodes_success|length as len_success_nodes %}
| Changed |
{{ len_success_nodes }} |
|
{% endwith %}
{% with summary.nodes_noop|length as len_noop_nodes %}
| Noop |
{{ len_noop_nodes }} |
|
{% endwith %}
Resources:
{% with summary.resources_failure|length as len_failure_resources %}
| Failed |
{{ len_failure_resources }} |
|
{% endwith %}
{% with summary.resources_skipped|length as len_skipped_resources %}
| Skipped |
{{ len_skipped_resources }} |
|
{% endwith %}
{% with summary.resources_success|length as len_success_resources %}
| Changed |
{{ len_success_resources }} |
|
{% endwith %}
{% with summary.resources_noop|length as len_noop_resources %}
| Noop |
{{ len_noop_resources }} |
|
{% endwith %}
Types:
{% with summary.types_failure|length as len_failure_types %}
| Failed |
{{ len_failure_types }} |
|
{% endwith %}
{% with summary.types_skipped|length as len_skipped_types %}
| Skipped |
{{ len_skipped_types }} |
|
{% endwith %}
{% with summary.types_success|length as len_success_types %}
| Changed |
{{ len_success_types }} |
|
{% endwith %}
{% with summary.types_noop|length as len_noop_types %}
| Noop |
{{ len_noop_types }} |
|
{% endwith %}
{% block details %}
{% endblock %}
{% endblock %}


Void QuantConnect.Algorithm.QCAlgorithm.SetCash (
Double
startingCash
)
Set initial cash for the strategy while backtesting. During live mode this value is ignored and replaced with the actual cash of your brokerage account.
Void QuantConnect.Algorithm.QCAlgorithm.SetCash (
Int32
startingCash
)
Set initial cash for the strategy while backtesting. During live mode this value is ignored and replaced with the actual cash of your brokerage account.
Void QuantConnect.Algorithm.QCAlgorithm.SetCash (
Decimal
startingCash
)
Set initial cash for the strategy while backtesting. During live mode this value is ignored and replaced with the actual cash of your brokerage account.
Void QuantConnect.Algorithm.QCAlgorithm.SetCash (String
symbol,Decimal
startingCash,*Decimal
conversionRate )
Set the cash for the specified symbol.


GETDIRENTRIES
Section: Linux Programmer's Manual (3)
Updated: 2014-06-13
Index
Return to Main Contents
NAME
getdirentries - get directory entries in a filesystem-independent format
SYNOPSIS
#include <dirent.h>
ssize_t getdirentries(int fd, char *buf, size_t nbytes
, off_t *basep);
Feature Test Macro Requirements for glibc (see
feature_test_macros(7)):
getdirentries():
_BSD_SOURCE || _SVID_SOURCE
DESCRIPTION
Read directory entries from the directory specified by
fd
into
buf.
At most
nbytes
are read.
Reading starts at offset
*basep,
and
*basep
is updated with the new position after reading.
RETURN VALUE
getdirentries()
returns the number of bytes read or zero when at the end of the directory.
If an error occurs, -1 is returned, and
errno
is set appropriately.
ERRORS
See the Linux library source code for details.
ATTRIBUTES
Multithreading (see pthreads(7))
The
getdirentries()
function is thread-safe.
CONFORMING TO
Not in POSIX.1-2001.
Present on the BSDs, and a few other systems.
Use
opendir(3)
and
readdir(3)
instead.
SEE ALSO
lseek(2),
open(2)
Index
- NAME
-
- SYNOPSIS
-
- DESCRIPTION
-
- RETURN VALUE
-
- ERRORS
-
- ATTRIBUTES
-
- Multithreading (see pthreads(7))
-
- CONFORMING TO
-
- SEE ALSO
-
This document was created by
man2html,
using the manual pages.
Time: 02:55:10 GMT, September 18, 2014


MAGNOLIA A film review by David N. Butterworth Copyright 2000 David N. Butterworth
***1/2 (out of ****)
Anyone unconvinced of Paul Thomas Anderson's talent following the critical success of "Boogie Nights" (the film which put Mark Wahlberg's "talent" on the map) need look no further than his latest film, "Magnolia."
"Magnolia" stars Julianne Moore, William H. Macy, John C. Reilly, Melora Walters, Philip Seymour Hoffman, and Philip Baker Hall, all of whom appeared in "Boogie Nights." Utilizing a stock company of repertory regulars is an approach employed by the likes of Robert Altman and Alan Rudolph, and it works well for Anderson too; every single member of his ensemble delights in profound and significant ways. "Magnolia" also stars Jason Robards and Tom Cruise, and they complement Anderson's veterans extremely nicely.
In a story of intersecting lives and emotions, Robards is bed ridden and dying of cancer, Moore is his emotional wreck of a trophy wife who's finally learned to love him, and Hoffman is his sensitive and beleaguered nurse. Cruise, in a dynamic performance, plays a misogynistic motivational speaker with turbulent ties to the dying man. In parallel story threads, Reilly is a San Fernando Valley cop who falls for the coke-addicted daughter (Walters) of a TV game show host (Hall), and Macy appears as a former game show prodigy with love in his heart for a tinsel-toothed barkeep.
As you can probably tell from these brief character descriptions, "Magnolia" is structured more as a series of vignettes than a traditional central narrative yet many of these disparate storylines come together very untraditionally in the film's breathtaking final act. The film is a little over three hours long but you'd never know it; the minutes simply fly by.
Anderson, some say an upstart of a filmmaker at 29, wrote as well as directed "Magnolia," yet the film plays out with the sophistication and maturity of someone beyond his years. Since "Magnolia" was supposedly inspired by a lyric from an Aimee Mann song, it makes perfect sense that Anderson would employ the talents of the former 'Til Tuesday lead singer, whose melancholy refrains are featured predominantly on the film's soundtrack. That soundtrack also features impressive music by Jon Brion (Anderson's "Hard Eight"). His insistent score is especially effective in its ability to heighten passions, pulling the characters together with its urgent layering across scenes.
In addition to drawing superlative performances from his cast, Anderson takes two major risks in the film, sequences so against the grain you'd expect some conventional bigwig to have exorcised them from the final cut. The fact that they meld perfectly with the escalating human drama only further substantiates Anderson's skill as a writer and director of unique and remarkable vision.
Unfortunately, "Magnolia" was released in Philadelphia one week too late to be included on my list of outstanding films of 1999, but even this early in the new year I can guarantee it will make it onto my best films of 2000 list. As a candidate for the best film of the year, it's gonna be a tough act to beat.
-- David N. Butterworth<EMAIL_ADDRESS>Got beef? Visit "La Movie Boeuf" online at http://members.dca.net/dnb
The review above was posted to the
rec.arts.movies.reviews newsgroup (de.rec.film.kritiken for German reviews).
The Internet Movie Database accepts no responsibility for the contents of the
review and has no editorial control. Unless stated otherwise, the copyright
belongs to the author.
Please direct comments/criticisms of the review to relevant newsgroups.
Broken URLs inthe reviews are the responsibility of the author.
The formatting of the review is likely to differ from the original due
to ASCII to HTML conversion.
Related links: index of all rec.arts.movies.reviews reviews


Pricing table ver.1
Starter
$
9
/mo
- Unlimited minutes
- 1 Call-Us button
- 1 Operator
- 5 GB Storage
- Call waiting
- Customizable design
- Business hours
- Call history
- Customer groups
- Country control
- Call recording
- Mobile apps (iOS, Android)
Basic
$
49
/mo
- Unlimited minutes
- 5 Call-Us buttons
- 5 Operators
- 50 GB Storage
- Call waiting
- Customizable design
- Business hours
- Call history
- Customer groups
- Country control
- Call recording
- Mobile apps (iOS, Android)
Best
value
value
Standard
$
99
/mo
- Unlimited minutes
- 10 Call-Us buttons
- 10 Operators
- 100 GB Storage
- Call waiting
- Customizable design
- Business hours
- Call history
- Customer groups
- Country control
- Call recording
- Mobile apps (iOS, Android)
Ultimate
$
299
/mo
- Unlimited minutes
- 50 Call-Us button
- 50 Operator
- 1 TB Storage
- Call waiting
- Customizable design
- Business hours
- Call history
- Customer groups
- Country control
- Call recording
- Mobile apps (iOS, Android)


--- layout: post authors: ["Rob Beagrie"] title: "Software Carpentry at Cambridge University" date: 2014-08-29 time: "19:00:00" tags: ["Bootcamps", "Cambridge University"] ---
At the beginning of this week Thomas Kluyver and I taught a Software Carpentry bootcamp at the University of Cambridge. We had around 35 learners of very different experience and took them through Bash, Git and Python over the course of two days.
First of all I want to thank everyone who helped organize and run the bootcamp this week, especially Thomas for sharing the teaching duties and our excellent helpers Jelena Aleksic and David Molnar. We also had a lot of help from our host Gabriella Rustici along with Paul Judge and David Judge. We held the bootcamp in a university teaching room, so we used a suite of desktop computers and had them all loaded with the Software Carpentry virtual machine, which eliminated much of the extra time spent solving setup issues on the first morning.
Since we also wanted people to go away with a working setup that they could use on their own, I sent round the standard bootcamp installation instructions to all the learners the week before the bootcamp and asked them to have a go installing all the software on their own machines. The idea was that we could then help people with individual installation issues during the coffee/lunch breaks if necessary. In the end, only two people came to us having had problems with installation, which probably reflects a mixture of improvements to the SWC windows installer, a relatively high number of more experienced learners and a number of people not attempting to install things before the bootcamp.
The pre-bootcamp questionnaire revealed a very wide spread of previous experience. We had a number of absolute beginners, but nearly one third of the group reported that they currently programmed once a week or more. Almost all of the respondants also indicated that they had already had some experience with the command line. In order not to frustrate those who had more advanced knowledge, we decided to add links to some of the intermediate Software Carpentry lessons on the bootcamp website. At the beginning of the first day we let everyone know that we were aware of the wide range in experience but we would be working at a pace suitable for novices, so anyone who felt comfortable with the material should feel free to work at their own pace and move on to the intermediate material if they finished early. Walking around during the challenges I had the impression that the vast majority of the class was working at the instructor's pace, but perhaps knowing that they weren't being forced to slow down helped the more advanced people not to get annoyed. Even so, we had a few pink stickies at the end of the day from people who felt that the Bash lessons were too slow for more experienced users, and that the course website had not been clear enough about the expected entry level.
Before the bootcamp I invested some time in organizing the files for the shell lessons. Since we were working from virtual machines, it made sense to have all the example files in a consistent file tree. I think this helped a lot with the lesson flow, and we had a few learners comment that the unix session was especially easy to follow. One person did say that we could have been clearer about which directory everyone was supposed to be in at the beginning of each new section. Thomas took the afternoon's Git session which seemed to go really well. Instead of having the learners collaborate from two repositories on their local machines, we had them pair up and work on each other's repositories. This approach seemed to motivate people really well and we got a lot of positive comments about that session at the end of the day.
Our biggest technical hurdle came on Wednesday morning, when it transpired that I had forgotten to check that the iPython notebook was installed on the virtual machines. After some copying and pasting of "sudo apt" commands, we got the notebook working only to find that numpy and matplotlib were not installed either. In the end it only took around 15 minutes to get everyone set up and people seemed quite quick to forgive me my oversight! Our audience were keen to learn about debugging and defensive programming, so Thomas and I decided to skip SQL in order to give ourselves enough time to cover everything. Despite having a whole day for python, we had still only gotten through the first three lessons by the afternoon coffee break and decided to skip over if/else to get to the material on assertions and test driven development. We got quite a few positive comments about this material at the end, but there was also a little frustration as some of the challenges in lesson five require if/else statements to solve.
One final thing I'd recommend for anyone planning to run a bootcamp in the future - I took a small notebook with me and wrote down lots of small issues that cropped up as we were going through the material. If I hadn't written these all down immediately I would have forgotten them by the end of the day, but I now have a little stockpile of material for issues and pull requests I can send back upstream, hopefully making things easier for myself (and everyone else) next time around!


PyHEADTAIL.rfq package¶
Submodules¶
PyHEADTAIL.rfq.rfq module¶
This module contains the Python implementation of a pillbox-cavity RF quadrupole - referred to as the RFQ - as it was proposed by Alexej Grudiev in ‘Radio frequency quadrupole for Landau damping in accelerators’, Phys. Rev. Special Topics - Accelerators and Beams 17, 011001 (2014) [1]. Similar to a ‘Landau’ octupole magnet, this device is intended to introduce an incoherent tune spread such that Landau damping can prevent the growth of transverse collective instabilities.
The formulae that are used are based on [1] and make use of the thin- lens approximation. On the one hand, the RFQ introduces a longitudinal spread of the betatron frequency and on the other hand, a transverse spread of the synchrotron frequency.
The effect in the transverse plane is modelled in two different ways
RFQ as a detuner acting directly on each particles’ betatron tunes,
RFQ as a localized kick acting on each particles’ momenta xp and yp.
The effect in the longitudinal plane is always modelled as a localized kick, i.e. a change in a particle’s normalized momentum dp. For model (II), the incoherent betatron detuning is not applied directly, but is a consequence of the change in momenta xp and yp.
@author Michael Schenk, Adrian Oeftiger @date July, 10th 2014 @brief Python implementation of a pillbox cavity RF quadrupole for
Landau damping.
@copyright CERN
-
class
PyHEADTAIL.rfq.rfq.
RFQKick
¶ Python base class to describe the RFQ element in the localized kick model for both the transverse and the longitudinal coordinates.
-
__abstractmethods__
= frozenset({'track'})¶
-
__dict__
= mappingproxy({'__module__': 'PyHEADTAIL.rfq.rfq', '__doc__': 'Python base class to describe the RFQ element in the\n localized kick model for both the transverse and the\n longitudinal coordinates.\n ', 'track': <function RFQKick.track>, '__dict__': <attribute '__dict__' of 'RFQKick' objects>, '__weakref__': <attribute '__weakref__' of 'RFQKick' objects>, '__abstractmethods__': frozenset({'track'}), '_abc_impl': <_abc_data object>})¶
-
__module__
= 'PyHEADTAIL.rfq.rfq'¶
-
__weakref__
¶ list of weak references to the object (if defined)
-
_abc_impl
= <_abc_data object>¶
-
abstract
track
(beam)¶
-
-
class
PyHEADTAIL.rfq.rfq.
RFQLongitudinalKick
(v_2, omega, phi_0)¶ Python implementation of the RFQ element acting on the particles’ longitudinal coordinate dp.
-
__abstractmethods__
= frozenset({})¶
-
__init__
(v_2, omega, phi_0)¶ An RFQ element is fully characterized by the parameters v_2: quadrupolar expansion coefficient of the
accelerating voltage (~strength of the RFQ), in [V/m^2].
omega: Angular frequency of the RF wave, in [rad/s]. phi_0: Constant phase offset wrt. bunch center (z=0), in
[rad].
-
__module__
= 'PyHEADTAIL.rfq.rfq'¶
-
_abc_impl
= <_abc_data object>¶
-
track
(beam)¶ The formula used to describe the longitudinal kick is given by
- Delta p_z = -(x^2 - y^2) (e v_2 / (beta c)) *
sin(omega z / (beta c) + phi_0).
-
-
class
PyHEADTAIL.rfq.rfq.
RFQTransverseDetuner
(v_2, omega, phi_0, beta_x_RFQ, beta_y_RFQ)¶ Collection class to contain/manage the segment-wise defined RFQ elements RFQTransverseDetunerSegment acting on the betatron tunes (detuner model of the RFQ). This is a pure Python class and it derives from the DetunerCollection class defined in the module PyHEADTAIL.trackers.detuners.
-
__abstractmethods__
= frozenset({})¶
-
__init__
(v_2, omega, phi_0, beta_x_RFQ, beta_y_RFQ)¶ - An RFQ element is fully characterized by the parameters
- v_2: quadrupolar expansion coefficient of the accelerating
voltage (~strength of the RFQ), in [V/m^2]. One-turn value.
omega: Angular frequency of the RF wave, in [rad/s]. phi_0: Constant phase offset wrt. bunch center (z=0), in
[rad].
beta_x_RFQ and beta_y_RFQ are the beta functions at the position of the RFQ, although in the detuner model of the RFQ, the RFQ should not actually be understood as being localized.
-
__module__
= 'PyHEADTAIL.rfq.rfq'¶
-
_abc_impl
= <_abc_data object>¶
-
generate_segment_detuner
(dmu_x, dmu_y, **kwargs)¶ Instantiate a RFQTransverseSegmentDetuner for the specified segment of the accelerator ring. Note that the bare betatron phase advances over the current segment, dmu_x and dmu_y, are given as relative values, i.e. in units of the overall phase advance around the whole accelerator (the betatron tune). The method is called by the TransverseMap object which manages the creation of a detuner for every defined segment.
-
-
class
PyHEADTAIL.rfq.rfq.
RFQTransverseDetunerSegment
(dapp_xz, dapp_yz, omega, phi_0)¶ Python implementation of the RFQ element acting directly on the particles’ betatron tunes (i.e. RFQ detuner model).
-
__dict__
= mappingproxy({'__module__': 'PyHEADTAIL.rfq.rfq', '__doc__': "Python implementation of the RFQ element acting directly on the\n particles' betatron tunes (i.e. RFQ detuner model).\n ", '__init__': <function RFQTransverseDetunerSegment.__init__>, 'detune': <function RFQTransverseDetunerSegment.detune>, '__dict__': <attribute '__dict__' of 'RFQTransverseDetunerSegment' objects>, '__weakref__': <attribute '__weakref__' of 'RFQTransverseDetunerSegment' objects>})¶
-
__init__
(dapp_xz, dapp_yz, omega, phi_0)¶ Creates an instance of the RFQTransverseDetunerSegment class. The RFQ is characterized by
omega: Angular frequency of the RF wave, in [rad/s]. phi_0: Constant phase offset wrt. bunch center (z=0), in
[rad].
- dapp_xz: Strength of detuning in the horizontal plane, scaled
to the relative bare betatron phase advance in x.
- dapp_yz: Strength of detuning in the vertical plane, scaled
to the relative bare betatron phase advance in y.
-
__module__
= 'PyHEADTAIL.rfq.rfq'¶
-
__weakref__
¶ list of weak references to the object (if defined)
-
detune
(beam)¶ Calculates for each particle its betatron detuning dQ_x, dQ_y according to formulae taken from [1] (see above).
dQ_x = dapp_xz / p * cos(omega / (beta c) z + phi_0) dQ_y = dapp_yz / p * cos(omega / (beta c) z + phi_0)
- with
dapp_xz = beta_x_RFQ * v_2 * e / (2 Pi * omega) dapp_yz = -beta_y_RFQ * v_2 * e / (2 Pi * omega)
and p the particle momentum p = (1 + dp) p0. (Probably, it would make sense to approximate p by p0 for better performance).
-
-
class
PyHEADTAIL.rfq.rfq.
RFQTransverseKick
(v_2, omega, phi_0)¶ Python implementation of the RFQ element acting on the particles’ transverse coordinates (i.e. localized kick model).
-
__abstractmethods__
= frozenset({})¶
-
__init__
(v_2, omega, phi_0)¶ An RFQ element is fully characterized by the parameters v_2: quadrupolar expansion coefficient of the
accelerating voltage (~strength of the RFQ), in [V/m^2].
omega: Angular frequency of the RF wave, in [rad/s]. phi_0: Constant phase offset wrt. bunch center (z=0), in
[rad].
-
__module__
= 'PyHEADTAIL.rfq.rfq'¶
-
_abc_impl
= <_abc_data object>¶
-
track
(beam)¶ The formula that describes the transverse kick experienced by an ultra-relativistic particle traversing the RFQ longitudinally is based on the thin-lens approximation
- Delta p_x = -x*(2 e v_2 / omega) *
cos(omega z / (beta c) + phi_0),
- Delta p_y = y*(2 e v_2 / omega) *
cos(omega z / (beta c) + phi_0).
-


JavaScript is disabled on your browser.
Skip navigation links
Package
Class
Use
Tree
Deprecated
Index
Help
Prev
Next
Frames
No Frames
All Classes
E
G
I
L
S
E
execute()
- Method in class in.kncsolutions.dhelm.launcher.
LauncherCreator
G
getDesktopFileNameCommand(String)
- Static method in class in.kncsolutions.dhelm.launcher.
ShellScriptTemplate
getdesktopIconCommand(String)
- Static method in class in.kncsolutions.dhelm.launcher.
ShellScriptTemplate
getExecutable(String)
- Static method in class in.kncsolutions.dhelm.launcher.
ShellScriptTemplate
getFinalCommand(String)
- Static method in class in.kncsolutions.dhelm.launcher.
ShellScriptTemplate
getFirstLine()
- Static method in class in.kncsolutions.dhelm.launcher.
ShellScriptTemplate
getHeader(String)
- Static method in class in.kncsolutions.dhelm.launcher.
ShellScriptTemplate
getIconCommand(String)
- Static method in class in.kncsolutions.dhelm.launcher.
ShellScriptTemplate
getLicenseText()
- Static method in class in.kncsolutions.dhelm.launcher.
ShellScriptTemplate
getLicenseText(File)
- Static method in class in.kncsolutions.dhelm.launcher.
ShellScriptTemplate
getParentDirCommand()
- Static method in class in.kncsolutions.dhelm.launcher.
ShellScriptTemplate
getSecondSet(String, String, String)
- Static method in class in.kncsolutions.dhelm.launcher.
ShellScriptTemplate
getTerminalCommand(String)
- Static method in class in.kncsolutions.dhelm.launcher.
ShellScriptTemplate
I
in.kncsolutions.dhelm.launcher
- package in.kncsolutions.dhelm.launcher
L
LauncherCreator
- Class in
in.kncsolutions.dhelm.launcher
Goal which creates Desktop launcher for Ubuntu.
LauncherCreator()
- Constructor for class in.kncsolutions.dhelm.launcher.
LauncherCreator
S
ShellScriptTemplate
- Class in
in.kncsolutions.dhelm.launcher
Copyright 2018 Pallav@KNC Solutions Private Limited Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with the License.
ShellScriptTemplate()
- Constructor for class in.kncsolutions.dhelm.launcher.
ShellScriptTemplate
E
G
I
L
S
Skip navigation links
Package
Class
Use
Tree
Deprecated
Index
Help
Prev
Next
Frames
No Frames
All Classes
Copyright © 2018 KNC Solutions Private Limited.. All rights reserved.


Name
HPL_dlaswp10N performs a series column interchanges.
Synopsis
#include "hpl.h"
void
HPL_dlaswp10N(
const int
M
,
const int
N
,
double *
A
,
const int
LDA
,
const int *
IPIV
);
Description
HPL_dlaswp10N
performs a sequence of local column interchanges on a
matrix A. One column interchange is initiated for columns 0 through
N-1 of A.
Arguments
M (local input) const int
__arg0__
N (local input) const int
On entry, M specifies the number of rows of the array A. M
must be at least zero.
A (local input/output) double *
On entry, N specifies the number of columns of the array A. N
must be at least zero.
LDA (local input) const int
On entry, A points to an array of dimension (LDA,N). This
array contains the columns onto which the interchanges should
be applied. On exit, A contains the permuted matrix.
IPIV (local input) const int *
On entry, LDA specifies the leading dimension of the array A.
LDA must be at least MAX(1,M).
See Also
HPL_dlaswp00N,
HPL_dlaswp10N,
HPL_dlaswp01N,
HPL_dlaswp01T,
HPL_dlaswp02N,
HPL_dlaswp03N,
HPL_dlaswp03T,
HPL_dlaswp04N,
HPL_dlaswp04T,
HPL_dlaswp05N,
HPL_dlaswp05T,
HPL_dlaswp06N,
HPL_dlaswp06T.


Collection of your Personal Information
NADIA collects personal information, such as your e-mail address, name, home or work address or telephone number, work experience and other such data associated with a resume for job applications in the Middle East.
Please keep in mind that, when you apply for jobs advertised on NADIA, your resume data is stored in your MyNADIA account and any other information that you disclosed during your job application will be made available to the companies you applied to. It is our policy to discuss specific vacancies with candidates prior to submitting their CV to the client, but this may not always be possible.
Use of your Personal Information
NADIA collects and uses your personal information to operate nadia-me.com and deliver the services you have requested. NADIA also uses your personal information to inform you of other products or services available from NADIA and its affiliates. NADIA may also contact you via surveys to conduct research about your opinion of current services or of potential new services that may be offered.
NADIA does not sell, rent or lease its user or registered member lists to third parties. NADIA may, from time to time, contact you on behalf of external business partners about a particular offering that may be of interest to you. In those cases, your personal information (e-mail, name, address, telephone number) is not transferred to the third party. NADIA may access and/or disclose your personal information if required to do so by law or in the good faith belief that such action is necessary to: (a) conform to the edicts of the law or comply with legal process served on NADIA or the site; (b) protect and defend the rights or property of NADIA and its family of websites; or (c) act under exigent circumstances to protect the personal safety of users of NADIA, its websites, or the public.
Personal information collected on this site may be stored and processed at any country in which NADIA or its affiliates, subsidiaries or agents maintain facilities, and by using this site, you consent to any such transfer of information outside of your country and of the UAE.
Security of your Personal Information
NADIA is committed to protecting the security of your personal information. We use a variety of security technologies and procedures to help protect your personal information from unauthorized access, use, or disclosure.
Changes to These Terms
NADIA will occasionally update these Terms to reflect company and user feedback. When NADIA posts changes to these Terms, you will see the word "updated" next to the Privacy Statement link on the front page of NADIA. If there are material changes to these Terms or in how NADIA will use your personal information, NADIA will prominently post such changes prior to implementing the change. NADIA encourages you to periodically review these Terms to be informed of how NADIA is protecting your information.


Reports assignment expressions nested inside other expressions to use the assigned value immediately. Such expressions may be confusing and violating the general design principle that a given construct should do precisely one thing.


During last four decades sea ice extent declines, sea ice becomes younger and thinner (Stroeve and Notz, 2018). In this issue we focused on earlier sea ice break-up and later sea ice advance.
Passive microwave sea ice concentration data (Cavalieri, 1996) is selected for long-term estimating of variability and trends.
Isotonic unimodal regression (Turner and Wollan, 1997) is used for fitting of SIC time-series. Implemented in package Iso
(Turner, 2020) for R statistical software (R Core Team, 2022). Isotonic fitted curve excludes double or multiple crossing threshold value, which was reached in 4.8 % cases of fitted smoothed regional SIC time-series (Stern and Laidre, 2016).
In practice, out of 2736 crossing dates (36 years × 2 seasons × 19 regions × 2 time series per region), only 131 dates (4.8 %) had any potential for ambiguity. In more than 95 % of the cases there was clearly only a single crossing date. (Stern and Laidre, 2016)
Less sensitive for period subset in comparison with asymmetrical Gaussian
Overlapped periods (Figure 1).
“max”: winter, period of maximal sea ice extent between 15 January and 15 May.
“mlt”: Spring, period of sea ice retreat between 15 February and 30 September.
“min”: Summer, perod of minimal sea ice extent between 15 July and 15 November.
“frz”: Autumn, period of sea ice advance between 15 August and 31 March.
Seasonal sequence for SID is “max”, “mlt”, “min”.
Seasonal sequence for SIA is “min”, “frz”, “max”.
We selected SIC 15 %, which is abopted from Parkinson et al. (1999). This is common value for estimating sea ice extent (NSIDC, 2020).
2013:
Resident ice – status of grid cell, for which the ice-free conditions are absent during the considered period. The extent of resident ice is less than or equal to extent of multiyear ice. Resident open water – status of grid cell, for which the icy conditions are absent during the considered period. Ice presence, icy conditions mean that sea ice concentration is ≥15%. Ice-free conditions mean that sea ice concentration is <15%.
Resident open water where ice is absent during the entire winter period
Resident ice where ice is present all summer.
We distinguish summer minimum extent and extent of resident ice. The former is set of cells with ice for the date of mimimal seasonal extent. The later is set of cells without SID or SIA detection.
Comparing summer minimal extent and extent of resident sea ice, we estimated 7 as a minimal duration for open-water period during summer. Comparing winter maximal extent and outer extent without resident open water, we estimated 21 as a minimal duration for sea ice period during winter. If SIC is <15 % for fitted timeseries during 7 days or more, then SID and SIA are detected. If SIC is <15 % for fitted time-series during less than 7 days, then resident ice is detected. If SIC is ≥15 % for fitted timeseries during 21 days or more, then SID and SIA are detected. If SIC is ≥15 % for fitted time-series during less than21 days, then resident open water is detected.
Spatial (focal) and temporal filling data gaps.
2013: We developed an algorithm to estimate SID and SIA onset dates using daily SIC data derived from 25-km x 25-km pixel resolution SSM/I and SSMIS passive microwave satellite imagery (Maslanik et al. 1999). We preprocessed the SIC data in two steps: 1) spatial filtering to fill no-data pixels using Gaussian smoothing with a 3x3 pixel window size and σ=0.75; and 2) temporal linear interpolation to fill remaining no-data pixels using three sequential sliding windows with sizes 3x3, 5x5, and 7x7 pixels.
“Maslanik et al. 1999” is Maslanik and Stroeve (1999). Change to (Cavalieri, 1996).
+ GSFC NASA Team (Cavalieri, 1996)
+ GSFC Bootstrap (Comiso, 2017)
+ NRT NASA Team (Maslanik and Stroeve, 1999)
Invisible for SSM/I and SSMIS scanning instrument Polar belt (approximately north of 86.5°N) is filled by SIC value 100%. For our data () There is no SID or SIA onsets, which are close to polar belt for our data (1988-2021). Each map contains 66285 cells. Coastal buffer zone (1-2 pixel or 25-50 km) are (can be) applied for output.
Each phenological state (SID or SIA) is considered independently. For each phenological state seasons are considered independently: “max”, “mlt”, and “min” for SID; “min”, “frz”, and “max” for SIA. For each season, SIC time-series is approximated by isotonic function with a single mode. Based on fitted time-series, SID is last date when SIC ≥15 %, SIA is first date when SIC ≥15 %.
Generally, two time-series: original x and inverted 100 − x are fitted. For “max” season only original x is considered. For “min” season only inverted 100 − x is considered. For “mlt” and “frz” seasons both fittings compete, and fitting with minimal RMSE is selected. Backward transition 100 − x is applied for inverted fitting.
Additional numeric value for resilience of onset date, proximity to ends of time-series, is included to interim output for estimating quality of fitting. Proximity to ends is estimated as a fraction between 0.0 and 0.5, where 0.0 means position in the head or tail of time-series, 0.5 means position in the middle of time-series. There is more uncertainty in the proximity to the boundaries of time-series period because of the uncertain time-series behavior beyond considered period.
Because output has length 3, only one value is selected based on maximal value of onset resilience.
Deprecated:
For SID “mlt” has higher priority. For SIA “frz” has higher priority. If SIA, then “max” is selected between “min” and “max” competition.
Raster maps with two kinds of information.
Values between 15 and 600 denote DOY of SID onset or SIA onset.
Values between 600 and 1000 are flags.
990: resident sea ice
980: resident open water
930: transition
Below are interim flags for flexible detection of short-term phenological stages:
After excluding of short-term phenological stages (21 days for sea ice and 7 days for open water), values between 701 and 720 are reclassified to 980, values between 721 and 750 are reclassified to 930, values between 801 and 806 are reclassified to 990, values between 807 and 850 are reclassified to 930.
Values greater than 6500 are dates with origin 1970-01-01 00:00:00.
Based on available SIC (Cavalieri, 1996) the interleaving starts by 1988 SID and ends 2001 SID. For demonstration purposed four maps in the tail of sequence are used: 2019 SIA, 2020 SID, 2020 SIA, 2021 SID
We developed two kind of maps for consequent interleaving.
Difference between consequent SIA dates and SID dates – duration of open water season. For all 33 maps during 1988-2020, based on consequent SIA after SID, with total 524971 cells there are 10 cells (19ppm) with negative values of season duration: 4 for 2014, 1 for 2005, 3 for 2000 and 1 for 1996.
Difference between consequent SID dates and SIA dates – duration of ice season. For all maps there was not detection of negative values.
For any date (in units of DOY) of transitional period SIE is restored by overlapping mask with values ≤DOY and mask with flag of resident ice.
Single map instead of seasonal set (about three hundred maps) of sea ice extent maps. See comparison on Figure 3. Tails of isotonic SID and SIA curvesare limited by minimal and maximal values of onset.
As a metric for polar bear habitat.
Polar bear subpopulation choropleths.
Scatterplot (Figure 4), X-value is date of polar bear location, Y-value is SID/SIA.
This is mapping too (Figure 5).
Direction of SID/SIA
Speed of SID/SIA (km day-1).
Contour lines, where units are dates or DOY.
Early freeze onset and freeze onset estimates (Markus and Miller, 2022) based on passive microwave data (Markus et al., 2009). Similar to MDSDA (Rigor et al., 2000; Belchansky et al., 2004).
Current state: PMW freeze onset verification by SIA dates.
Proposed state: Masking of seasonal and resident ice. Freeze onset makes sense only for resident ice, whereas SIA can be used for seasonal ice.
Near real-time data for monitoring tasks. For mapping “near” means couple weeks for regions with seasonal ice and months for regions with close proximity to MYI
SIC in 25 km grid (Maslanik and Stroeve, 1999).
SIC in 6.25 km grid (Spreen et al., 2008; Melsheimer, 2019).
Time-series of ice-charts (e.g., US NIC (2022), Norwegian1 and American2 ice charts) can be applied too BUT with additional expert conversion quantitative data (ice type, category of ice fraction) to qualitative data (ice concentration)
Github nplatonov/phenology repository with GeoTiff/NetCDF dataset and source R (R Core Team, 2022) code. Assigned Zenodo DOI. Dependence from package ursa
(Platonov, 2022), version ≥ 3.9.7.
R package on github with vignette, then CRAN. Only sia2019.tif, sia2020.tif, sid2020.tif, sid2021.tif. Because:
As a general rule, neither data nor documentation should exceed 5MB (which covers several books). A CRAN package is not an appropriate way to distribute course notes, and authors will be asked to trim their documentation to a maximum of 5MB.
After publication: add publication citing to package’s “how to cite”.
NSIDC dataset?
I would like to declare on behalf of my co‐authors that the work described was original research that has not been published previously, and not under consideration for publication elsewhere, in whole or in part. The manuscript is approved by all authors for publication.
NP:
DD: