class: center, middle, inverse, title-slide .title[ # Advanced plots and inference ] .subtitle[ ## SISBID 2023
https://github.com/dicook/SISBID
] .author[ ### Di Cook (
<EMAIL_ADDRESS>)
Heike Hofmann (
<EMAIL_ADDRESS>)
Susan Vanderplas (
<EMAIL_ADDRESS>) ] .date[ ### 07/26-28/2023 ] --- # Tidy data and random variables - The concept of tidy data matches elementary statistics - Tabular form puts variables in columns and observations in rows - Not all tabular data is in this form - This is the point of tidy data `$$X = \left[ \begin{array}{rrrr} X_1 & X_2 & ... & X_p \end{array} \right] \\ = \left[ \begin{array}{rrrr} X_{11} & X_{12} & ... & X_{1p} \\ X_{21} & X_{22} & ... & X_{2p} \\ \vdots & \vdots & \ddots& \vdots \\ X_{n1} & X_{n2} & ... & X_{np} \end{array} \right]$$` - We might even make assumptions about the distribution of each variable, e.g. `\(X_1 \sim N(0,1), ~~X_2 \sim \text{Exp}(1) ...\)` --- # Grammar of graphics and statistics - A statistic is a function on the values of items in a sample, e.g. for `\(n\)` iid random variates `\(\bar{X}_1=\sum_{i=1}^n X_{i1}\)`, `\(s_1^2=\frac{1}{n-1}\sum_{i=1}^n(X_{i1}-\bar{X}_1)^2\)` - We study the behaviour of the statistic over all possible samples of size `\(n\)`. - The grammar of graphics is the mapping of (random) variables to graphical elements, making plots of data into statistics --- # What is inference? Inferring that what we see in the data at hand holds more broadly in life, society and the world. .pull-left[ **Why do we need it for graphics?** Here's an example tweeted by David Robinson:  Based on an analysis in [Tick Tock blog, by Graham Tierney](https://ticktocksaythehandsoftheclock.wordpress.com/2018/01/11/capitals-and-good-governance/) ] .pull-right[ *Below is a simple scatterplot of the two variables of interest. A slight negative slope is observed, but it does not look very large. There are a lot of states whose capitals are less than 5% of the total population. The two outliers are Hawaii (government rank 33 and capital population 25%) and Arizona (government rank 26 and capital population 23%). Without those two the downward trend (an improvement in ranking) would be much stronger.* *I ran linear regressions of government rank on the percentage of each state’s population living in the capital city, state population (in 100,000s), and state GDP (in $100,000s).... The coefficient is not significant for any regression at the traditional 5% level.* *... I'm not convinced that the lack of significance is itself significant.* ] --- # To do *statistical* inference You need a: - null hypothesis, and alternative - statistic computed on the data - reference distribution on which to measure the statistic: if its extreme on this scale you would reject the null # Inference with *data plots* You need a: - plot description, as provided by the grammar (which is a statistic) - this would prescribe the null hypothesis - null generating mechanism, e.g. permutation, simulation from a distribution or model - human vision, to examine array of plots and decide if any are different from the others --- # Some examples Here are several plot descriptions. What would be the null hypothesis in each? .pull-left[ A ``` ggplot(data) + geom_point(aes(x=x1, y=x2)) ```

 B ``` ggplot(data) + geom_point(aes(x=x1, y=x2, colour=cl)) ``` ] .pull-right[ C ``` ggplot(data) + geom_histogram(aes(x=x1)) ```

 D ``` ggplot(data) + geom_boxplot(aes(x=cl, y=x1)) ``` ]

 .orange[Which plot definition would most match to a null hypothesis stating **there is no difference in the distribution between the groups?**] --- # Some examples Here are several plot descriptions. What would be the null hypothesis in each? .pull-left[ A `\(H_o:\)` no association between `x1` and `x2`

 B `\(H_o:\)` no difference between levels of `cl` ] .pull-right[ C `\(H_o:\)` the distribution of `x1` is XXX

 D `\(H_o:\)` no difference in the distribution of `x1` between levels of `cl` ] --- # Let's do it .pull-left[  Example from the nullabor package. The data plot is embedded randomly in a field of null plots, this is a **lineup**. Can you see which one is different? When you run the example yourself, you get a `decrypt` code line, that you run after deciding on a plot to print the location of the data plot amongst the nulls. - plot is a scatterplot, null hypothesis is *there is no association between the two variables mapped to the x, y axes* - null generating mechanism: permutation ] .pull-right[  ] --- .pull-left[ # Lineup Embed the data plot in a field of null plots Ask: Which plot is the most different? ] .pull-right[ # Null-generating mechanisms - Permutation: randomizing the order of one of the variables breaks association, but keeps marginal distributions the same - Simulation: from a given distribution, or model. Assumption is that the data comes from that model # Evaluation - Compute `\(p\)`-value - Power `\(=\)` signal strength ] --- # p-values Suppose `\(x\)` individuals selected the data plot from a lineup of `\(m\)` plots, shown to `\(K\)` independent observers, then simplistically we can think about the probability of this happening, if the data plot is from the same distribution as the null plots. Assuming that all plots in a lineup are equally likely to be selected, this yields a binomial formula: `$$P(X\geq x) = \sum_{i=x}^{K} \binom{K}{i} \left(\frac{1}{m}\right)^i\left(\frac{m-1}{m}\right)^{K-i}$$` For `\(x=4, K=17, m=20\)` ``` x simulated binom [1,] 4 0.0225 0.008800605 ``` Assuming some null plots are visually more salient than others, we can introduce parameter `\(\alpha\)` to get to more realistic `\(p\)`-values: ``` alpha = 0.01 alpha = 0.15 alpha = 1 0.03397441 0.25942593 0.47220162 ``` `$$P(X \geq x) = \sum_{i = x}^{K} \binom{K}{x} \frac{1}{B(\alpha, (m-1)\alpha)}\cdot B(x+\alpha, K-x+(m-1)\alpha)$$`, where `\(B(.,.)\)` is the Beta function. Large values of `\(\alpha\)` indicate that several null plots are 'interesting'. Generally, `\(\alpha = 0.15\)` indicates that 1 or 2 null plots are interesting enough for viewers to pick them. --- **Evaluating goodness-of-fit with a residual plot** - plot is a residual vs fitted scatterplot, null hypothesis is *there is no association between the two statistics* - null generating mechanism: residual rotation  --- **Assessing temporal dependence** - plot is a lineplot, null hypothesis is *there is no temporal trend* - null generating mechanism: simulation from a time series model  --- # Let's talk TB  There are many aspects of this plot, this is what we said earlier: - *Across all ages, and years, the proportion of males having TB is higher than females* - These proportions tend to be higher in the middle age groups, for all years. - Relatively similar proportions across years. --- # Null hypothesis The plot is constructed by plotting count against year, separately by age group, and coloured by sex. - By colouring by sex, we make this a primary comparison - Proportion of sex, conditional on age group and year is the query being addressed by this plot. *Null hypothesis*: TB incidence is spread equally among men and women, regardless of age and year. *Alternative hypothesis*: It isn't. --- ```r # Make expanded rows of categorical variables # matching the counts of aggregated data. # sex needs to be converted to 0, 1 to # match binomial simulations tb_us_long <- expandRows(tb_us, "count") tb_us_long <- tb_us_long %>% mutate(sex01 = ifelse(sex=="m", 0, 1)) %>% select(-sex) # Generate a lineup of three, randomly choose one of the # positions to place true data. # Compute counts again. pos = sample(1:3, 1) l <- lineup(null_dist(var="sex01", dist="binom", list(size=1, p=0.5)), true=tb_us_long, n=3, pos=pos) l <- l %>% group_by(.sample, year, age) %>% dplyr::count(sex01) ``` ---  --- # A more complicated null *Null hypothesis*: TB incidence is has the same proportion of men and women, regardless of age and year. *Alternative hypothesis*: It isn't. ```r # Compute proportion across all data tbl <- tb_us %>% group_by(sex) %>% summarise(count=sum(count)) tbl # A tibble: 2 × 2 sex count <chr> <dbl> 1 f 25915 2 m 55640 p <- tbl$count[1]/sum(tbl$count) pos = sample(1:3, 1) l <- lineup(null_dist(var="sex01", dist="binom", list(size=1, p=p)), true=tb_us_long, n=3, pos=pos) l <- l %>% group_by(.sample, year, age) %>% dplyr::count(sex01) ``` ---  --- class: inverse middle # Danger zone -
The null hypothesis is determined based on the plot type
-- -
It is not based on the structure seen in a data set
--- # A map lineup example Cancer incidence across the US 2010-2014, all cancer types, per 100k. Data from American Cancer Society, https://cancerstatisticscenter.cancer.org. Does one map show a spatial trend?  --- background-image: \url(https://pbs.twimg.com/profile_images/1092451626781163523/0YzJMi-8_400x400.jpg) background-size: 12% background-position: 100% 0% # Your turn .pull-left[ Time to play Sesame Street, with your study group. Each person can 1. run this code, 2. look at the lineup of plots, and choose which plot shows the most separation between classes (DON'T LOOK AT ANYONE ELSE's!) 3. then run the `decrypt` line, produced when the `lineup` function was called 4. tally up the number of your group members who picked the data plot, this is `x` 5. use the `pvisual` function to compute the p-value, `K=` the number of people in your group, `m=12` ] .pull-right[ ```r library(MASS) data(wasps) lda_pred <- function(x) { d <- predict(lda(Group~., data=x[,-43]))$x[,1:2] %>% as_tibble() %>% mutate(Group = x$Group) return(d) } wasps_lineup <- lineup(null_permute('Group'), wasps[,-1], n=12) %>% as_tibble() wasps_lineup_lda <- wasps_lineup %>% split(.$.sample) %>% map_df(~lda_pred(.)) %>% mutate(.sample = wasps_lineup$.sample) ggplot(wasps_lineup_lda, aes(x=LD1, y=LD2, colour=Group)) + geom_point() + facet_wrap(~.sample, ncol=4) + scale_colour_brewer(palette="Dark2") + theme(legend.position="none") ``` ] .orange[Enter the p-value in the chat!] --- # Resources - VanderPlas S., Röttger Chr., Cook D., Hofmann H.: Statistical Significance Calculations for Scenarios in Visual Inference, STAT, 2020, doi:http://dx.doi.org/10.1002/sta4.337. - Hofmann, H., Follett, L., Majumder, M. and Cook, D. (2012) Graphical Tests for Power Comparison of Competing Designs, http://doi.ieeecomputersociety.org/10.1109/TVCG.2012.230. - Wickham, H., Cook, D., Hofmann, H. and Buja, A. (2010) Graphical Inference for Infovis, http://doi.ieeecomputersociety.org/10.1109/TVCG.2010.161. - Sievert, C. (2019) Interactive web-based data visualization with R, plotly, and shiny, https://plotly-r.com/index.html --- # Share and share alike
This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.


Lil Xan Claims He Suffered Seizures Trying to Quit Drugs Cold Turkey, Completely Sober Now
by C. Vernon Coleman IILil Xan's struggle with drug addiction has been a reoccurring theme. Now the rapper claims he is completely sober following a fight to kick drugs cold turkey that had ill effects on his body.
On Monday (Nov. 18), TMZ interviewed the rapper about his harrowing ordeal with trying to get sober.
"You guys remember back when everyone was like, 'Oh, Diego's M.I.A. Where's Diego at' and stuff?" Xan asked. "I was in the hospital..because I stopped taking drugs...I went cold turkey 'cause I didn't want to be on drugs no more. But the withdrawals actually gave me seizures, you know what I mean? It was a wake-up call."
He added, "They said it was from going cold turkey off Xanax. I wanted to stop drugs completely, but I did it the wrong way, you know. I did it cold turkey and I had a couple seizures.  Yea, I'm completely sober now."
He went on to say the overdose deaths of Lil Peep and Mac Miller were the main factors in him deciding to be drug free.
Xan checked himself into rehab last December. After two weeks in the facility, he checked out claiming to be clean. "Out And Sober 🙏🏼💔," he posted on social media.
After relapsing again, it sounds like the rapper doing better.
See Xan talk about his struggle sobriety below.
Read 10 Wild Stories That Took Place During Hip-Hop Tours
24hrs
“We did this show in Atlanta on Ty [Dolla $ign]’s tour. We was all on the tour bus and it was a bunch of people I didn’t meet. Ty’s tour bus was like the fuckin’ Source Awards. But everybody was on there—Trouble, Migos, everybody. I’m going to introduce myself to everybody and everybody would be like, ‘Come on, dog, we know who you are. When we workin’? What we doin’?’ That was crazy, but I brought Jermaine [Dupri] out. I brought Hoodrich Pablo Juan out and he did a song; I brought [Lil] Scrappy out and he did 'Head Bussa.' And then I closed my set with Jermaine and he did 'Welcome to Atlanta' in Atlanta and the whole crowd went crazy. Like, no one really brings JD out to do that, so I’m thinking that’s Jay-Z level. That’s crazy. Do we have footage? I need that.”
Blac Youngsta
"I had a room, right? So I go in my hotel room and it was a girl that worked there. I don't know if she got drunk and got bold to do it, but when I go back in the room, she came out the drawer. She hid in my room until I got back and came out of the drawer. I think we were in Houston. I put her out—you gotta roll."
Emilio Rojas
“We were on the road with Dizzy Wright. I think it was like, Albuquerque, N.M. Or it might have been El Paso—one of those fuckin’ places. There were a bunch of fans arguing and screaming in the parking lot. Our security—his name is DJ—came and got me and was like, ‘Yo, you gotta check this out.’
My first reaction is to jump in between them and try to make sure that everything’s cool, you know? Didn’t realize these kids were heavily gang-affiliated. You ever seen that movie, Only the Strong, where they sing, ‘Ba, na, na, weh,’ and they’re kinda stabbing at each other with knives and shit?  They were trying to poke each other so I was like, ‘Y’all wanna keep having hip-hop shows?   Two seconds later, you hear tires screeching—one of the kids who was involved in the tussle, his peoples had pulled up. Crowd scatters.   I was duckin’ and then the fighting still kept on going.
Have you ever seen a hockey fight where they pull the shirt over? So these kids, one of them pulled the shirt over, he finally cut the other kid and he was just laying, bleeding in the parking lot. It was crazy. The police were on their own schedule. It was a good five, six minutes that passed. At a Dizzy Wright show, too. We’re like, peaceful; everybody’s chillin’.”
Flipp Dinero
"I was fuckin' performing and I just see something fly on the stage. This huge-ass item fly on the stage. Boom! I stopped the show.   I see a group of dudes ganged up in the corner.  I continue the show, go home. I go in my Instagram messages, Some girl hits me up.   You threw a cookie of all things. You had niggas in the crowd like, 'Ohhh,' why you do that shit?' I followed her right after though. We're friends now. I just hit her up the other day like, 'Yo, you good?'"
J.I.D
"Losing a homie. We lost a whole friend for a whole day in Barcelona, Spain. It was absinthe. It was crazy."
Lil Keke
"I've had crazy things happen.  Sometimes it won't be they wife. A muhfucka just be [like], 'This my gal, you can have her tonight Ke!' I be like, 'Nah'. I'm scared of that move really—that's too strong right there. I don't want that one. As far as like an autograph, women take they bra off, took they titty out: 'I want it on here!' I be like damn! Boy, I've had some women go far. They'll say any goddamn thing."
Nue
“A whole show getting shut down because I guess it was getting too crazy. They pepper sprayed everyone in the crowd. It was an all-ages show. I had to sneak everyone out the back; it was getting too wild and people were climbing on the stage. It was crazy. It was in [Greater] Sudbury, Canada.”
Roy Woods
"This [Aubrey & the Three Migos Tour] is crazy. They got a Ferrari in the sky, bro.
Tokyo Jetz
“My first tour, I went on the road with Tip. I’ve seen people fake pass out so he could run to their aide and try to save them. Then when he come over, they’re perfectly fine. Somebody peed on our tour bus before [laughs]. That’s probably the top two moments.”
Tyla Yaweh
"When I was in Sweden, I stayed an extra day to two days. We were like, 'Fuck it, I'ma just see how many fans I have out here.' I was like, 'Yo, meet me here.' About 80 kids pulled up to my hotel. They're waiting to just take a picture with me and then dip. Some stayed, brought me weed, smoked it with me."


[§572D-4] Effect of marriage. A premarital agreement becomes effective upon marriage of the parties to each other. [L 1987, c 321, pt of §1]


jQuery Mobile is a touch-optimized HTML5 UI framework built with jQuery and is the easiest way to build sites and apps that are accessible on all popular smartphone, tablet and desktop devices. Welcome to the demo center.
New to jQuery Mobile? Start here.
Examples of how to customize and extend jQuery Mobile.
Common issues and questions, explained.
Visit the API site for full technical info.


Changing a login name will affect the user's ability to log in! Not only will they have to use a different username, but it will seriously break their windows profiles, and will confuse some email clients. This should only be done if absolutely necessary
To correct Windows user profiles, you will need to edit the logon name stored in the registry.


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teams works! This will display the list of all teams we are playing or have played in the current competition year. By default the current year, but could change to another year?


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[gecode-users] svn download of release
Kish Shen
kisshen at cisco.com
Fri Sep 25 19:03:53 CEST 2015
Hi,
Does the Gecode source downloaded via svn:
svn --username anonymous checkout \
https://svn.gecode.org/svn/gecode/tags/release-4.4.0
include the bug fixes since the official release?
I am planning to build the latest released Gecode for all the platforms
we support for ECLiPSe. I have already downloaded this source several
months ago, and so am wondering if it make sense to download this again.
Thanks and cheers,
Kish
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This page shows automated results of the Southern-Taurids 2007, based on visual data entered through the IMO electronic report form. Send your feedback regarding this page to Geert Barentsen or visit the project pages. Note that these automated results may not be suitable for scientific use!
Page generated: 2008 November 10 at 2:58 UT.
ZHRmax = 7 based on 247 Southern-Taurids in 571 data intervals, assuming fixed population index r = 2.3 and zenith correction 1/sin(hR).
| Time (UT) | Solarlon | nINT | nSTA | ZHR | |
|---|---|---|---|---|---|
| 2007-10-03 19:56 | 190.123 | 1 | 2 | 7 | +-4 |
| 2007-10-07 16:35 | 193.928 | 4 | 9 | 3 | +-1 |
| 2007-10-09 11:38 | 195.699 | 4 | 5 | 2 | +-1 |
| 2007-10-12 04:08 | 198.356 | 7 | 6 | 2 | +-1 |
| 2007-10-14 16:27 | 200.844 | 53 | 30 | 1 | +-0 |
| 2007-10-15 10:02 | 201.570 | 7 | 2 | 1 | +-1 |
| 2007-10-18 10:33 | 204.567 | 9 | 7 | 2 | +-1 |
| 2007-10-20 01:32 | 206.180 | 48 | 30 | 2 | +-0 |
| 2007-10-21 07:39 | 207.428 | 90 | 30 | 1 | +-0 |
| 2007-10-22 02:58 | 208.228 | 95 | 30 | 2 | +-0 |
| 2007-10-22 15:19 | 208.739 | 48 | 14 | 1 | +-0 |
| 2007-10-24 18:45 | 210.872 | 3 | 0 | 3 | +-3 |
| 2007-11-03 18:00 | 220.827 | 6 | 7 | 4 | +-1 |
| 2007-11-07 23:53 | 225.084 | 10 | 12 | 4 | +-1 |
| 2007-11-11 11:50 | 228.599 | 7 | 2 | 1 | +-1 |
| 2007-11-14 15:42 | 231.780 | 24 | 14 | 2 | +-1 |
| 2007-11-17 00:42 | 234.173 | 18 | 13 | 1 | +-0 |
| 2007-11-18 05:53 | 235.399 | 134 | 32 | 1 | +-0 |
| 2007-11-20 07:50 | 237.498 | 3 | 2 | 4 | +-2 |
For each estimation interval: time is the middle of the interval, nINT is the number of observing periods and nSTA is the number of Southern-Taurids involved. ZHR = (1 + sum nSTA) / sum(Teff/C) where Teff is the effective observing time and C is the total correction for limiting magnitude, clouds and zenith correction. The solar longitudes refer to equinox J2000.0.
Data has been received from 53 observers in 17 countries. Thank you for your efforts!
| Observer | Country | Teff | nSTA |
|---|---|---|---|
| Salvador Aguirre | Mexico | 22.90h | 12 |
| Salvador Aguirre | Mexico | 2.00h | 1 |
| Salvador Aguirre | Mexico | 2.00h | 0 |
| Salvador Aguirre | Mexico | 2.50h | 0 |
| Geert Barentsen | Belgium | 2.10h | 0 |
| Felix Bettonvil | Netherlands | 1.72h | 3 |
| Jean Marie Biets | Belgium | 2.33h | 0 |
| Jean-marie Biets | Belgium | 2.58h | 0 |
| Mark Davis | United States | 1.00h | 1 |
| Shishir Deshmukh | India | 3.75h | 2 |
| Sietse Dijkstra | Netherlands | 7.55h | 10 |
| Audrius Dubietis | Lithuania | 1.00h | 0 |
| Frank Enzlein | Germany | 5.56h | 3 |
| William Godley | United States | 3.04h | 0 |
| Mitja Govedic | Slovenia | 2.17h | 8 |
| Wayne T. Hally | United States | 26.15h | 25 |
| Vilem Heblik | Czech Republic | 7.50h | 5 |
| Carl Hergenrother | United States | 5.46h | 5 |
| Jiawei Ma Jiawei Ma | China | 0.37h | 9 |
| Carl Johannink | Netherlands | 11.63h | 12 |
| Roy Keeris | Netherlands | 0.90h | 0 |
| Andre Knofel | Germany | 8.97h | 7 |
| Nikhil Kolthankar | India | 1.50h | 1 |
| Peter Kozich | unknown | 2.27h | 3 |
| Dovile Krauleidiene | Lithuania | 1.00h | 0 |
| Jens Lacorne | France | 1.20h | 0 |
| Greet Lembregts | Belgium | 0.86h | 1 |
| Jan Lembregts | Belgium | 0.73h | 0 |
| Peter Van Leuteren | Netherlands | 14.25h | 13 |
| Alister Ling | Canada | 2.61h | 3 |
| Pierre Martin | Canada | 5.84h | 4 |
| Bruce Mccurdy | Canada | 6.00h | 9 |
| Frederic Merlin | France | 1.75h | 2 |
| Vandeputte Michel | Belgium | 4.25h | 0 |
| Koen Miskotte | Netherlands | 32.11h | 22 |
| Sabine Wachter | Germany | 4.29h | 4 |
| Sven Nather | Germany | 22.50h | 13 |
| Mohammad Nilforoushan | Iran | 1.53h | 2 |
| Shridhar Pophali | India | 3.07h | 1 |
| Jurgen Rendtel | Germany | 22.70h | 24 |
| Nathan Schulpen | Belgium | 1.08h | 1 |
| Nathan Schulpen | Belgium | 1.08h | 1 |
| Rene Scurbecq | Belgium | 6.29h | 4 |
| Ulrich Sperberg | Germany | 2.30h | 0 |
| Wesley Stone | United States | 1.79h | 1 |
| Blanca Troughton Luque | Spain | 2.33h | 0 |
| Shigeo Uchiyama | Japan | 7.55h | 6 |
| Michel Vandeputte | Belgium | 43.30h | 20 |
| Sicco Van Hoegee | Netherlands | 12.23h | 8 |
| Myriam Vingerhoets | Belgium | 2.69h | 2 |
| William Watson | United States | 5.94h | 4 |
| Thomas Weiland | Austria | 4.90h | 10 |
| Jurga Zieniute | Lithuania | 1.00h | 0 |
Create your own analysis!
Rate intervals: sta2007_rate.csv (CSV-format).
Magnitude distributions: sta2007_magn.csv (CSV-format).


The restaurant suffering from some 'uneven' cooking which saved itself with a 'humdinger' of a pie
The steak and ale pie was worth returning for alone
by Mark TaylorLondon pub chain Young’s should be commended for the way it has gone about the careful transformation of Bristol’s landmark riverstation restaurant.
When it bought the property three years ago, after two decades under independent ownership, there must have been a few worried regulars who thought the former river police station would end up as yet another homogenous pub chain operation.
It is to their credit that Young’s have treated the riverstation with the kid gloves it deserves and they clearly realised that it’s a special venue and one close to the hearts of many Bristolians.
Since the takeover, it has been pretty much business as usual in the light-filled upstairs restaurant with its views across the water towards the colourful townhouses of Redcliffe.
There have been more noticeable changes in the renamed Pontoon bar on the ground floor, which is a lot more informal than the fine dining restaurant upstairs.
What had been the old bar and kitchen at riverstation has been refurbished and named after the docking bay and terrace outside.
Over the summer, Pontoon was certainly a go-to venue for sun seekers in search of al fresco opportunities, whether it was a leisurely breakfast, weekend brunch, business lunch, romantic dinner or simply drinks in between.
The new look boasts glazed tiles and wood panels on the walls, toffee-colour leather upholstery and a teal blue colour scheme to reflect the waterside location.
With its armchairs and sofas, the space looks and feels warmer and it’s more welcoming than before when the hard edges resulted in a more clackety ambience.
One of the real draws of the Pontoon is the fact it serves its main menu from 12pm through to the evening (and brunch until 4pm).
As we hurtle towards the December office party season, the work/life balance can start to get out of sync as short days meld into long nights. It’s always useful to find good quality restaurants that serve food in the late afternoon, even if it’s to soak up the booze before carrying on.
Rushing between a school thing and a gig, I found myself at Pontoon just after 5.30pm on a mid-week afternoon, which is usually a terrible time to hunt down a decent meal in central Bristol. It’s that hinterland between lunch and dinner when most of the best places are either closed or changing staff before the evening session.
But I wasn’t alone. A couple of women were killing time before a networking event and there were a few clusters of post-work drinkers and solo diners, perhaps refuelling before making the journey home.
With Billie Holiday, Frank Sinatra and Bing Crosby crooning in the background, there was even an early hint of Christmas in the air and that extended to the food on offer, even though I didn’t spot a festive menu.
Still, I started with the oh-so-festive turkey and cranberry Scotch egg (£6) which arrived about two minutes later after I ordered it.
The waiter had assured me it would be served hot rather than cold. He was half right. It could have done with an extra minute in the oven.
The crisp, crunchy crumb exterior was certainly warmer than the fridge-cold egg in the centre. The minced turkey layer was herby and there was a gentle sweetness from the cranberry that was countered by the salty bacon mayonnaise.
'The sides arrived a good five minutes later than the pie'
There were certainly no temperature issues with a main course of steak and ale short crust pie (£14.50). Plumes of steam emerged from the shiny pastry lid as soon as I cut into it and the chunks of steak in the flood of gravy were seriously tender.
It was surrounded by diced swede and carrot with a nice bite to them and strips of squeaky Savoy cabbage in a moat of rosemary-scented ale and onion gravy so dark brown and luscious that it would make a decent substitute for Creosote when painting the garden fence before winter sets in.
The sides arrived a good five minutes later than the pie, which was pretty inexcusable considering how few people were actually eating at the time. This was the quiet period, the calm before the storm.
The skinny fries (£3.50) were crisp, salty and greaseless and the dish of Brussels sprouts, chestnuts and orange (£3) was large enough to feed a family on December 25. A few of the sprouts were hard enough to last a vigorous round of golf but others were more tender. The chestnuts contributed a nice chew.
I ordered the vegan sticky toffee pudding (£5.50) really out of curiosity because experience has shown that desserts lacking animal fats and cream are inevitably a dreary and depressing disappointment.
The cake-like toffee pudding was on the dry side but the caramel sauce was decent enough and the soya caramel ice cream was surprisingly good. I may not have even realised it was vegan had I not known in advance.
THE VERDICT:
Despite some uneven cooking to start and finish, the meal was saved by the steak and ale pie, which was a humdinger of a main course and worth returning for alone.
RATINGS:
Overall: 3.5
Food: 3
Ambience: 4
Service: 3.5
Value: 4
Pontoon at riverstation, The Grove, Bristol, BS1 4RB. Tel: 0117 9144434. www.riverstation.co.uk
For the latest news in and around Bristol, visit and bookmark Bristol Live's homepage.


To populate your Apache Geode cache and fine-tune its storage and distribution behavior, you need to define cached data regions and provide custom configuration for the cache and regions.
Cache configuration properties define:
- Cache-wide settings such as disk stores, communication timeouts, and settings designating the member as a server
- Cache data regions
Configure the cache and its data regions through one or more of these methods:
Through a persistent configuration that you define when issuing commands that use the gfsh command line utility. The gfsh utility supports the administration, debugging, and deployment of Apache Geode processes and applications. You can use gfsh to configure regions, locators, servers, disk stores, event queues, and other objects.
As you issue commands, gfsh saves a set of configurations that apply to the entire cluster and also saves configurations that only apply to defined groups of members within the cluster. You can re-use these configurations to create a distributed system. See Overview of the Cluster Configuration Service.
Through declarations in the XML file named in the
cache-xml-file
gemfire.properties
setting. This file is generally referred to as thecache.xml
file, but it can have any name. See cache.xml.Through application calls to the
org.apache.geode.cache.CacheFactory
,org.apache.geode.cache.Cache
andorg.apache.geode.cache.Region
APIs.


This lesson has two sections. First demonstrates a method to simulate one-participant data. The function, simulate, in the ggdmc package creates a data frame based on the parameter vector and the model (both are defined by a user) with nsim observations for each row in model. ps is the true parameter vector.
Second section shows a method to conduct a process model. Specifically, the section conducts a simulation experiment to describe the British tea example on p 37 in Maxwell & Delaney (2004). See Maxwell and Delaney (2004) for an analytic method to calculate the same probabilities. Here I directly model the Britich tea example, approximating the same probabilities. The analytic method is just to use a binominal distribution and the idea of combinations and permutations.
One-participant simulation
This line define one S (stimulus) factor with two levels. So this model defines one two experimental conditions.
factors = list(S = c(“s1”, “s2”)),
Below are the R codes for defining a model and for simulating data from the model.
require(ggdmc)
model <- BuildModel(
p.map = list(A = "1", B = "R", t0 = "1", mean_v = "M", sd_v = "M", st0 = "1"),
match.map = list(M = list(s1 = "r1", s2 = "r2")),
factors = list(S = c("s1", "s2")), ## one factor with two levels, so only
constants = c(sd_v.false = 1, st0 = 0),
responses = c("r1", "r2"),
type = "norm")
p.vector <- c(A = .75, B.r1 = .25, B.r2 = .15, t0 = .2, mean_v.true = 2.5,
mean_v.false = 1.5, sd_v.true = 0.5)
This just is to simulate only one observation per condition to check the function.
set.seed(123) ## Set seed to get the same simulation
dat <- simulate(model, nsim = 1, ps = p.vector)
## S R RT
## 1 s1 r1 0.3327392
## 2 s2 r1 0.3797985
The following simulates 500 observations per condition. So in total, there are 1000 observations.
ntrial <- 5e2 ## number of trials per condition
dat <- simulate(model, nsim = ntrial, ps = p.vector)
dplyr::tbl_df(dat)
## A tibble: 1,000 x 3
## S R RT
## <fct> <fct> <dbl>
## 1 s1 r2 0.533
## 2 s1 r2 0.494
## 3 s1 r1 0.497
## 4 s1 r2 0.310
## 5 s1 r1 0.462
## 6 s1 r2 0.345
## 7 s1 r2 0.430
## 8 s1 r1 0.384
## 9 s1 r2 0.310
## 10 s1 r1 0.302
# # ... with 990 more rows
Note that model and data are in fact two separate objects. To fit data with certain models, we need to bind them together with BuildDMI. This is useful to facilitate model comparison. That is, a data set can bind with many different models, so we can compare them to see which model may fit the data better so perhaps provide a better account. I used a term, data-model instance (dmi), coined by Matthew Gretton.
dmi <- BuildDMI(dat, model)
We can the codes introduced in the “Descriptive Statistics” to check the correct 10%, 50%, 90% quantile RTs and accuracy, separately, for each level of the stimulus factor.
First I convert the dmi data frame to a data table and then create a new accuracy (logical) column, C.
require(data.table)
d <- data.table(dmi)
d$C <- ifelse(d$S == "s1" & d$R == "r1", TRUE,
ifelse(d$S == "s2" & d$R == "r2", TRUE,
ifelse(d$S == "s1" & d$R == "r2", FALSE,
ifelse(d$S == "s2" & d$R == "r1", FALSE, NA))))
d[, .(q1 = round(quantile(RT, .1), 2),
q5 = round(quantile(RT, .5), 2),
q9 = round(quantile(RT, .9), 2)), .(C, S)]
## C S q1 q5 q9
## 1: TRUE s1 0.32 0.42 0.56
## 2: TRUE s2 0.28 0.39 0.52
## 3: FALSE s1 0.27 0.37 0.50
## 4: FALSE s2 0.32 0.39 0.54
pro <- d[, .N, .(C, S)]
pro[, NN := sum(N), .(S)]
pro[, value := N/NN]
cp <- pro[C == TRUE] ## correct percentage
## C S N NN value
## 1: TRUE s1 333 500 0.666
## 2: TRUE s2 391 500 0.782
ep <- pro[C == FALSE] ## error percentage
## C S N NN value
## 1: FALSE s1 167 500 0.334
## 2: FALSE s2 109 500 0.218
Plot the RT distributions
require(ggplot2)
bw <- .01 ## 10 ms binwidth
p0 <- ggplot(d, aes(RT)) +
geom_histogram(binwidth = bw, fill = "white", colour = "black") +
facet_grid(.~C) +
theme_bw(base_size = 18)
print(p0)
British tea example
This section shows how we may test an hypothetical question directly via a simulation. Quoted from Maxwell and Delaney (p. 37, 2004)
“A lady declares that by tasting a cup of tea made with milk, she can discriminate whether the milk or the tea infusion was first added to the cup. We will consider the problem of designing an experiment by means of which this assertion can be tested. (Fisher, 1935/1971, p. 11)”
This is essential a binominal decision making. That is, the decision maker (“the lady”) in question will be presented one cup of tea after another and then her task is to decide if the cup is made by milk or tea is added first.
This following function, British.tea implements a process model to describe the above “British tea example”. That is, it conducts a simulation experiment of presenting 8 (i.e., n) cups of tea to a participant. The n equals 8 is decided arbitrarily here.
One additional information (i.e., assumption) is that the participant is told half of the cups are milk first and tea and vice versa. So when simulating the chance only scenario, we need also to take this into consideration. That is, after making a decision for a cup (either MT or TM), the (chance) probability state should adjust accordingly.
##' British tea example
##'
##' The function runs a simulation study to test the British tea example
##'
##' @param the number of observation (cups of tea)
##' @param correct correct sequence: First four cups are tea and milk
## (TM = 1), the next four cups are milk and then tea (MT = 0).
##' @param verbose print more information
##'
##' @export
British.tea <- function(n = 8, correct = c(1,1,1,1, 0,0,0,0),
verbose = TRUE) {
MT <- n/2 ## 0 indicates milk and then tea (MT)
TM <- n/2 ## 1 indicates tea and then milk (TM)
## Create three containers
## 1. x0 is a "n x 2" matrix to store the evolution of chance probabilities
## 2. res is a n-element numeric vector
## 3. acc is a n logical vector; default value is FALSE
x0 <- matrix(numeric(n*2), ncol = 2)
res <- numeric(n)
acc <- rep(FALSE, n)
## Begin the experiment, presenting one cup after another
for (i in 1:n) {
if (verbose) cat("Cup", i, "in total", sum(MT, TM), " cup(s)\n")
## store the chance probabilities of MT and TM in probs
probs <- c(MT / (MT + TM), TM / (MT + TM))
if (verbose) cat("Chances probabilities of (MT, TM): ", probs, "\n")
x0[i, ] <- probs
decision <- sample(c(0, 1), 1, prob = probs);
if (decision == 0) {
if (verbose) cat("This cup is made by adding milk first\n")
MT <- MT - 1
res[i] <- decision
if (decision == correct[i]) acc[i] <- TRUE
} else if (decision == 1) {
if (verbose) cat("This cup is made by adding tea first\n")
TM <- TM - 1
res[i] <- decision
if (decision == correct[i]) acc[i] <- TRUE
} else cat("Unexpected situation\n")
if (verbose) cat("Current state", i, ": ", c(MT, TM), "\n\n")
}
if (verbose) cat("Done\n")
return(list(x0, res, correct, acc))
}
The simulation starts from the for loop.
for (i in 1:n) {…}
The for loop presents a cup of tea after another until the nth cup. Before the participant make a decision, the chance probabilities of the two possible outcomes are stored in x0 variable.
probs <- c(probMT, probTM)
x0[i, ] <- probs
And then the sample function acts as a chance mechanism to simulate the participant’s (chance) decision making process.
decision <- sample(c(0, 1), 1, replace = TRUE, prob = probs);
The function randomly choose two numbers, c(0, 1), with the probabilities, probs to for the first and second number.
## Begin the experiment, presenting one cup after another
for (i in 1:n) {
if (verbose) cat("Cup", i, "in total", sum(MT, TM), " cup(s)\n")
probMT <- MT / (MT + TM) ## chance probability of MT 0
probTM <- TM / (MT + TM) ## chance probability of TM 1
probs <- c(probMT, probTM)
if (verbose) cat("Chances probabilities of (MT, TM): ", probs, "\n")
x0[i, ] <- probs
decision <- sample(c(0, 1), 1, prob = probs);
if (decision == 0) {
if (verbose) cat("This cup is made by adding milk first\n")
MT <- MT - 1
res[i] <- decision
if (decision == correct[i]) acc[i] <- TRUE
} else if (decision == 1) {
if (verbose) cat("This cup is made by adding tea first\n")
TM <- TM - 1
res[i] <- decision
if (decision == correct[i]) acc[i] <- TRUE
} else cat("Unexpected situation\n")
if (verbose) cat("Current state", i, ": ", c(MT, TM), "\n\n")
}
Conduct one experiment and print information
ncup <- 8
cor <- c(rep(1, 4), rep(0, 4));
res <- British.tea(ncup, cor, TRUE)
Cup 1 in total 8 cup(s).
Chances probabilities of (MT, TM): 0.5 0.5 This cup is made by adding tea first Current state 1 : 4 3
Cup 2 in total 7 cup(s).
Chances probabilities of (MT, TM): 0.5714286 0.4285714 This cup is made by adding milk first Current state 2 : 3 3
Cup 3 in total 6 cup(s).
Chances probabilities of (MT, TM): 0.5 0.5 This cup is made by adding tea first Current state 3 : 3 2
Cup 4 in total 5 cup(s).
Chances probabilities of (MT, TM): 0.6 0.4 This cup is made by adding milk first Current state 4 : 2 2
Cup 5 in total 4 cup(s).
Chances probabilities of (MT, TM): 0.5 0.5 This cup is made by adding milk first Current state 5 : 1 2
Cup 6 in total 3 cup(s).
Chances probabilities of (MT, TM): 0.3333333 0.6666667 This cup is made by adding tea first Current state 6 : 1 1
Cup 7 in total 2 cup(s).
Chances probabilities of (MT, TM): 0.5 0.5 This cup is made by adding tea first Current state 7 : 1 0
Cup 8 in total 1 cup(s).
Chances probabilities of (MT, TM): 1 0 This cup is made by adding milk first Current state 8 : 0 0
Done
Now I replicate the experiments separately for 512, 4096, 32768, 262144, and 2097152 times and store each result in a list, called exp.
n <- 8^(3:7);
exp <- vector("list", length(n))
## Use parallel package to conduct experiments
## 100.636 s
library(parallel)
cl <- makeCluster(detectCores())
clusterExport(cl, c("British.tea", "ncup", "cor"))
system.time(
for (i in 1:length(n)) {
exp[[i]] <- parSapply(cl, 1:n[i], function(i, ...) {British.tea(ncup, cor, FALSE)} )
}
)
stopCluster(cl)
## Without using parallel
## for(i in 1:length(n)) {
## exp[[i]] <- replicate(n[i], British.tea(ncup, cor, FALSE))
## }
res3 <- numeric(length(n)); res3 ## to store the result when 6 corrects
res4 <- numeric(length(n)); res4 ## to store the result when 8 corrects
## Collect results
for(i in 1:length(n)) {
c3 <- 0
c4 <- 0
for(j in 1:n[i]) {
## Calculate exactly 4 corrects
if(all(exp[[i]][,j][[4]])) c4 <- c4 + 1
if(sum(exp[[i]][,j][[4]]) == 6) c3 <- c3 + 1
}
res3[i] <- c3 / n[i]
res4[i] <- c4 / n[i]
}
round(res3, 4) ## [1] 0.2578 0.2324 0.2306 0.2288 0.2286
round(res4, 4) ## [1] 0.0137 0.0137 0.0140 0.0141 0.0143
require(ggplot2); require(data.table)
## Plot the result
## (How to add differernt horizontal lines on each facet)
DT <- data.table(x= rep(n, 2), y = c(res3, res4), gp = rep(c("6", "8"), each = 5),
ref = rep(c(16/70, 1/70), each = 5))
## Dashlines show theoretically probabilities
p0 <- ggplot(DT, aes(x, y)) +
geom_point(size = 3) +
geom_hline(aes(yintercept = ref), linetype = "dashed") +
## scale_x_log10(name = "N") +
xlab("N") + ylab("Probability") +
facet_grid(gp~., scales = "free") +
theme_bw(base_size = 22)
print(p0)


compute Bardeen potentials for metric. @ Currently works in synchronous gauge only; ie. with dust field. More...
#include "../../cosmo_includes.h"
#include "../../cosmo_macros.h"
#include "../../cosmo_types.h"
#include "../../utils/Fourier.h"
#include "bssn.h"
Go to the source code of this file.
Classes | |
| class | cosmo::Bardeen |
Macros | |
| #define | NUM_BARDEEN_VIOLS 17 |
compute Bardeen potentials for metric. @ Currently works in synchronous gauge only; ie. with dust field.


Always wanted to spend more time being creative? Just choose from the options below & you will be directed to a bespoke page with amazing artwork of all kinds to inspire & motivate you. Refresh your options at any time.


org
.
fusesource
.
scalate
.
spring
view
package
view
Visibility
Public
All
Type Members
trait
AbstractScalateView
extends AbstractView with ServletConfigAware
trait
DefaultScalateRenderStrategy
extends AbstractTemplateView with ScalateRenderStrategy
trait
LayoutScalateRenderStrategy
extends AbstractTemplateView with ScalateRenderStrategy
trait
ScalateRenderStrategy
extends AnyRef
class
ScalateUrlView
extends AbstractTemplateView with AbstractScalateView with ServletConfigAware with LayoutScalateRenderStrategy
class
ScalateView
extends AbstractView with AbstractScalateView with ViewScalateRenderStrategy
class
ScalateViewResolver
extends AbstractTemplateViewResolver
trait
ViewScalateRenderStrategy
extends ScalateRenderStrategy


- Try to click on the locations before your friends do!
- You score points the closer you click and lose points if you click too far away.
- You can't get points for the same location twice.
| Challengers: | Scores: | {{#each players}} {{> player }} {{/each}}
|---|
| User: | Latest Points | {{#each pointfeed}} {{> score }} {{/each}}
|---|


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Welcome to the Simple Map Viewer.
This simple interface is designed to help inform conservation decisions by
allowing you to explore a suite of data layers and models.
You’ll find information on priority natural resources, biodiversity richness,
landscape connectivity and integrity, surface waters and factors
contributing to potential landscape change (sea level rise, urban development).
Use one of the tabs above to explore different combinations of key characteristics assessed at the watershed scale across the state. Click on the bars in the charts to filter the map by that class. You can combine filters across tabs and across factors within a tab. You can also display that factor on the map by clicking the map icon next to the name.
To find out more information about a specific watershed, click on it in the map. To select and aggregate details for multiple watersheds, hold down your Ctrl or Cmd (Mac only) keys, then click on the watersheds you wish to select. The detailed data for each watershed will be aggregated into a single aggregate set of results.
You can save your current view, including any filters and selected watersheds, to a detailed PDF report.
We welcome your feedback. Please contact us to let us know what we can do to make this viewer more useful for you, or to report any bugs.
Read more about conservation assets and access data.


On a never ending journey into the world of coding and web development. I have a background in education and nonprofit work. Whether on the rock wall or playing board games with friends, I am constantly finding problems to solve, and love new challenges. I am currently attending General Assembly as part of their Web Development Immersive Program. I look forward to starting my career in web development, and am eager to work on a mission-driven team.
Go There, Do This! is a travel app for users to log trips. I learned React in a matter of days, and then built a full stack application with Rails for the backend, and React for the client side. In the future, I would like to grow the site to let users log recommendations for each location, like activities and restaurants, and then be able to share their trips with friends. View the live site and the code. If you do not want to create an account, simply login with email: demo@demo, password: demo
This or That is a survey app that allows registered users to create short polls to share with their friends and family. Results from polls are displayed in pie charts to create a seamless user experience. This was a group project where I served as project lead. Check out the code and the live site If you do not want to create an account, simply login with email: demo@demo, password: demo
GameTracker is a full stack app to track board games and comments. One of my favorite things to do is play board games, but there are so many, and they often have really obscure / similar / confusing names. I wanted to make an app where people can keep track of games, and write comments about them. In future versions, I would want there to be more information about each game, and for users to also have the ability to create a wishlist of games. I built an API that is hosted on heroku, and a front end where registered users can create games. See my live client site in action. Want to see my code? Here is my Github front end repository and my Github back end repository. If you do not want to create an account, simply login with email: demo@demo, password: demo
This app is a simple tic tac toe game and my first front end project. I wrote all of the game logic, and it communicates with an API. I love sushi, and also wanted to write a backstory to put my creative spin on such a classic game. Check out my github repo and the live site If you do not want to create an account, simply login with email: demo@demo, password: demo
Please feel free to reach out, or have a look at my code


I hear this a lot, “Do you use Agile?” “Yes, we use Scrum”
I believe most Development shops don’t truly understand what it means to be Agile and Scrum doesn’t accomplish what many of them think it does. I believe that a lot of teams start off using Agile with the best intentions but slowly revert back to traditional non-agile methodologies. This usually happens when things goes wrong during the Software Development Lifecycle and when the Development Teams are forced to be reactive instead of proactive.
Many Software Development Teams work within the constraints of a non-software companies that do not understand how to develop software. Scrum is typically viewed as a fad or as a way to put off doing work. Scrum should be seen as a Project Management solution for running software development projects. Scrum includes using a product backlog, defining iterations, and using stand ups. You can even use it to better understand the roles that exist beyond titles within a team. Scrum is strictly how to define what work is going to be done and when it will be worked on. Scrum does not provide guidance on how to actually do the work.
What inevitably happens while using scrum is that non-technical stakeholders put too much focus on the burndown chart. They seem to get tunnel vision on this one metric. The tasks created and estimated for a User Story are the development teams best guess at what it will take to complete the work. I have noticed that although the development team has every intention to focus on the work defined in their scrum, they usually have to perform some work that was neither defined nor captured.
This additional work is also a metric that should be captured. It is directly related to defects in requirements, defects in code, and defects in design. By capturing these additional metrics a Team can better understand or explain the impact on the burndown chart.
To summarize my thoughts, Scrum is merely a means to manage a software development project. If you’re going to focus on metrics, include more than just the hours that are completed and the hours remaining. Using additional metrics is needed to view the project status as a whole. I believe if you include additional metrics you’ll gain valuable insight into your Software Development Process. Also, just because you say you’re Agile doesn’t make you Agile.


The main problem with education
Whether it's at school or university, the one thing no one ever teaches you is how to teach yourself. All you actually do is memorize information in order to regurgitate it when you take the test. You get drilled on how to solve specific exercises so that while you know exactly what you need to do when exam time comes around, do you truly understand the subject material? All you have really learned is how to pass that one test. Once that test is over, the information in your head has outlived its usefulness.
Conclusion: Knowing how to study is absolutely essential, but for some reason, this point is too often overlooked. So, what can we do to remedy this?
There are certain techniques and exercises that can be applied to the learning process to help make it more efficient and engaging. This is exactly what we do at Practicum.


Samaritan UI elements used to display or contain textual content are required to meet WCAG Contrast Level AA standards. Samaritan Brand and UI Themes have been tested against both Level AA and Level AAA standards.
Use this theme throughout every App and web page to drive brand unity and continuity.
The Marketing Theme is used primarily with print material, but can be extended to the web through icons, illustrations and graphical elements.
Use the UI Theme when interacting with actionable items such as buttons, links and clickable banners.
Use the Extended Theme for illustrations, graphs and components. Be sure to test contrast ratios before utilizing text in these colors.


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League - Term 1 - 2020 Team Ladder to 11/03/2020 |
||||
|---|---|---|---|---|
| Rank | Team | Points | Average score | Games |
| 1 | 1+1=5 | 84 | 42012 | 18 |
| 2 | Will Wheaton Warriors | 78 | 41762 | 18 |
| 3 | Just Wingin' It | 72 | 41362 | 18 |
| 4 | Bretts Extra Hair | 70 | 39512 | 18 |
| 5 | Beard & The Beardless | 66 | 40404 | 18 |
| 6 | Bag of Mixed Nuts | 62 | 34335 | 18 |
League - Term 1 - 2020 Solo Ladder to 11/03/2020 |
||||||||
|---|---|---|---|---|---|---|---|---|
| Rank | Player | Team | Average Score | Average Rank | Score Ratio | Games | ||
| 1 | Camsta | Just Wingin' It, Bag of Mixed Nuts | 13576 | 2.4 | 174% | 15 | ||
| 2 | Packing | Will Wheaton Warriors, Bag of Mixed Nuts | 12410 | 3.0 | 157% | 12 | ||
| 3 | 1+1=5, Just Wingin' It, Beard & The Beardless, Will Wheaton Warriors | 12375 | 2.4 | 157% | 22 | |||
| 4 | M2Sloter | 1+1=5, Just Wingin' It, Beard & The Beardless, Will Wheaton Warriors, Bag of Mixed Nuts | 11208 | 3.4 | 142% | 23 | ||
| 5 | Molten | Bag of Mixed Nuts | 11132 | 3.0 | 144% | 2 | ||
| 6 | M2Skyros | Just Wingin' It, Bretts Extra Hair, Will Wheaton Warriors, Bag of Mixed Nuts | 10033 | 5.1 | 130% | 25 | ||
| 7 | Whippet | Just Wingin' It, Beard & The Beardless | 9696 | 5.0 | 125% | 7 | ||
| 8 | Vexim | 1+1=5, Just Wingin' It, Beard & The Beardless, Bretts Extra Hair, Bag of Mixed Nuts | 9684 | 5.4 | 124% | 32 | ||
| 9 | Strik3r | 1+1=5, Just Wingin' It, Beard & The Beardless, Bretts Extra Hair, Bag of Mixed Nuts | 9093 | 6.2 | 116% | 32 | ||
| 10 | BROCKER | Bretts Extra Hair, Will Wheaton Warriors, Bag of Mixed Nuts | 8847 | 6.6 | 122% | 5 | ||
| 11 | Bigfish | 1+1=5, Beard & The Beardless, Bretts Extra Hair, Will Wheaton Warriors | 8840 | 7.1 | 114% | 16 | ||
| 12 | Wookie | 1+1=5, Beard & The Beardless | 8817 | 7.5 | 107% | 4 | ||
| 13 | Fluffy | 1+1=5, Beard & The Beardless, Bretts Extra Hair, Bag of Mixed Nuts | 8064 | 8.0 | 100% | 24 | ||
| 14 | Browny | Beard & The Beardless, Bretts Extra Hair, Will Wheaton Warriors, Bag of Mixed Nuts | 7659 | 9.0 | 93% | 21 | ||
| 15 | Im A C | Just Wingin' It, Beard & The Beardless, Bretts Extra Hair, Will Wheaton Warriors, Bag of Mixed Nuts | 7542 | 8.6 | 93% | 25 | ||
| 16 | ICUP | Just Wingin' It, Beard & The Beardless, Bretts Extra Hair, Bag of Mixed Nuts | 7287 | 9.0 | 95% | 6 | ||
| 17 | Bicky | 1+1=5, Beard & The Beardless, Bretts Extra Hair, Will Wheaton Warriors, Bag of Mixed Nuts | 7285 | 9.1 | 89% | 27 | ||
| 18 | LazerLim | 1+1=5, Just Wingin' It, Beard & The Beardless, Bretts Extra Hair, Will Wheaton Warriors, Bag of Mixed Nuts | 7180 | 9.1 | 89% | 21 | ||
| 19 | Echo | 1+1=5, Just Wingin' It, Beard & The Beardless, Bretts Extra Hair, Bag of Mixed Nuts | 7022 | 9.7 | 88% | 28 | ||
| 20 | Brisngr | Just Wingin' It, Bretts Extra Hair | 6986 | 9.3 | 86% | 8 | ||
| 21 | ICUP | 1+1=5, Just Wingin' It, Beard & The Beardless, Bretts Extra Hair, Bag of Mixed Nuts | 6938 | 8.6 | 87% | 12 | ||
| 22 | Kakarot | Beard & The Beardless | 6782 | 9.0 | 94% | 2 | ||
| 23 | TACO_ | 1+1=5, Just Wingin' It, Beard & The Beardless, Bretts Extra Hair, Will Wheaton Warriors, Bag of Mixed Nuts | 6648 | 10.2 | 83% | 28 | ||
| 24 | Hornet | Bretts Extra Hair, Will Wheaton Warriors, Bag of Mixed Nuts | 6066 | 10.9 | 72% | 16 | ||
| 25 | MindMojo | 1+1=5, Just Wingin' It, Beard & The Beardless, Bretts Extra Hair, Will Wheaton Warriors | 6053 | 10.2 | 76% | 17 | ||
| 26 | El Mayo | 1+1=5, Just Wingin' It, Bretts Extra Hair, Bag of Mixed Nuts | 5832 | 10.6 | 73% | 17 | ||
| 27 | RektNoob | 1+1=5, Just Wingin' It, Beard & The Beardless, Bag of Mixed Nuts | 5453 | 12.1 | 66% | 15 | ||
| 28 | Whatzee | 1+1=5, Just Wingin' It, Beard & The Beardless, Will Wheaton Warriors | 4374 | 11.8 | 56% | 15 | ||
| 29 | JrTrex | Just Wingin' It, Bag of Mixed Nuts | 4001 | 11.0 | 53% | 2 | ||
| 30 | Bubbles | Just Wingin' It, Beard & The Beardless, Bretts Extra Hair, Bag of Mixed Nuts | 3671 | 13.4 | 45% | 28 | ||
| 31 | Balgruf | Bretts Extra Hair | 1634 | 14.3 | 20% | 3 | ||
| 32 | Ulfric | Bretts Extra Hair | 1290 | 14.3 | 15% | 3 |
This report ranks individual players. Tag Ratio is the number of people you shot divided by the number of people that shot you. Score ratio is your score in each game divided by the average score for that game. TR x SR is tag ratio multiplied by score ratio. Players are ranked on their average score.
League - Term 1 - 2020 Games for 11/03/2020 |
||||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Game | Team | Score | Pts | Team | Score | Pts | Team | Score | Pts | |||
| 7:07 PM | 1+1=5 | 56960 | 6 | Bretts Extra Hair | 43919 | 4 | Bag of Mixed Nuts | 22306 | 2 | |||
| 7:26 PM | Bag of Mixed Nuts | 47740 | 6 | Beard & The Beardless | 45439 | 4 | Just Wingin' It | 41388 | 2 | |||
| 7:44 PM | Will Wheaton Warriors | 41298 | 6 | Just Wingin' It | 34396 | 4 | Bretts Extra Hair | 21234 | 2 | |||
| 8:02 PM | Beard & The Beardless | 46450 | 6 | Will Wheaton Warriors | 43219 | 4 | Bag of Mixed Nuts | 34496 | 2 | |||
| 8:20 PM | Bretts Extra Hair | 46038 | 6 | 1+1=5 | 28806 | 4 | Beard & The Beardless | 21242 | 2 | |||
| 8:39 PM | Just Wingin' It | 46008 | 6 | Will Wheaton Warriors | 42349 | 4 | 1+1=5 | 36709 | 2 |
This is a list of games. Each row in the table is one game. Across each row, you see the teams that were in that game (with the team that placed first listed first, and so on), and the score for each team.
Game 11/03/2020 7:07 PM |
|||
|---|---|---|---|
| Rank | Name | Score | Score Ratio |
| 1 | 17242 | 2.10 | |
| 3 | MindMojo | 10502 | 1.28 |
| 4 | Vexim | 10272 | 1.25 |
| 5 | Whatzee | 9642 | 1.17 |
| 6 | Echo | 9302 | 1.13 |
| 1+1=5 | 56960 | 1.39 | |
| 2 | M2Skyros | 12232 | 1.49 |
| 8 | TACO_ | 8182 | 1.00 |
| 9 | LazerLim | 8072 | 0.98 |
| 10 | Strik3r | 7732 | 0.94 |
| 11 | BROCKER | 7701 | 0.94 |
| Bretts Extra Hair | 43919 | 1.07 | |
| 7 | ICUP | 8702 | 1.06 |
| 12 | Fluffy | 7352 | 0.90 |
| 13 | El Mayo | 4871 | 0.59 |
| 14 | Bicky | 2671 | 0.33 |
| 15 | Bubbles | -1290 | -0.16 |
| Bag of Mixed Nuts | 22306 | 0.54 | |
| Team Average | 41062 | 1.00 |
Game 11/03/2020 7:26 PM |
|||
|---|---|---|---|
| Rank | Name | Score | Score Ratio |
| 1 | Packing | 14552 | 1.62 |
| 4 | Fluffy | 10142 | 1.13 |
| 9 | El Mayo | 8542 | 0.95 |
| 11 | Bicky | 8302 | 0.93 |
| 12 | Bubbles | 7202 | 0.80 |
| Bag of Mixed Nuts | 47740 | 1.06 | |
| 3 | M2Sloter | 11982 | 1.34 |
| 5 | MindMojo | 10022 | 1.12 |
| 8 | Strik3r | 8902 | 0.99 |
| 10 | TACO_ | 8352 | 0.93 |
| 13 | LazerLim | 6181 | 0.69 |
| Beard & The Beardless | 45439 | 1.01 | |
| 2 | Camsta | 13922 | 1.55 |
| 6 | ICUP | 9872 | 1.10 |
| 7 | Vexim | 9052 | 1.01 |
| 14 | Echo | 5251 | 0.59 |
| 15 | Whatzee | 3291 | 0.37 |
| Just Wingin' It | 41388 | 0.92 | |
| Team Average | 44856 | 1.00 |
Game 11/03/2020 7:44 PM |
|||
|---|---|---|---|
| Rank | Name | Score | Score Ratio |
| 2 | Packing | 13352 | 2.07 |
| 3 | BROCKER | 11952 | 1.85 |
| 6 | TACO_ | 8592 | 1.33 |
| 9 | Whatzee | 5532 | 0.86 |
| 11 | Bicky | 3870 | 0.60 |
| Will Wheaton Warriors | 41298 | 1.28 | |
| 1 | Camsta | 14742 | 2.28 |
| 4 | Vexim | 10802 | 1.67 |
| 10 | ICUP | 4571 | 0.71 |
| 12 | MindMojo | 2861 | 0.44 |
| 13 | Echo | 1420 | 0.22 |
| Just Wingin' It | 34396 | 1.06 | |
| 5 | M2Skyros | 9132 | 1.41 |
| 7 | Strik3r | 5971 | 0.92 |
| 8 | LazerLim | 5831 | 0.90 |
| 14 | El Mayo | 590 | 0.09 |
| 15 | Bubbles | -290 | -0.04 |
| Bretts Extra Hair | 21234 | 0.66 | |
| Team Average | 32309 | 1.00 |
Game 11/03/2020 8:02 PM |
|||
|---|---|---|---|
| Rank | Name | Score | Score Ratio |
| 1 | 12172 | 1.47 | |
| 3 | M2Sloter | 10002 | 1.21 |
| 5 | Strik3r | 9132 | 1.10 |
| 10 | LazerLim | 7982 | 0.96 |
| 12 | ICUP | 7162 | 0.87 |
| Beard & The Beardless | 46450 | 1.12 | |
| 2 | Packing | 10392 | 1.26 |
| 4 | BROCKER | 9331 | 1.13 |
| 7 | MindMojo | 8602 | 1.04 |
| 8 | Bicky | 8272 | 1.00 |
| 14 | TACO_ | 6622 | 0.80 |
| Will Wheaton Warriors | 43219 | 1.04 | |
| 6 | Camsta | 8741 | 1.06 |
| 9 | El Mayo | 8062 | 0.97 |
| 11 | Echo | 7532 | 0.91 |
| 13 | Fluffy | 6820 | 0.82 |
| 15 | Bubbles | 3341 | 0.40 |
| Bag of Mixed Nuts | 34496 | 0.83 | |
| Team Average | 41388 | 1.00 |
Game 11/03/2020 8:20 PM |
|||
|---|---|---|---|
| Rank | Name | Score | Score Ratio |
| 1 | Strik3r | 12282 | 1.92 |
| 2 | BROCKER | 10212 | 1.59 |
| 3 | M2Skyros | 9581 | 1.50 |
| 5 | ICUP | 8912 | 1.39 |
| 9 | Bicky | 5051 | 0.79 |
| Bretts Extra Hair | 46038 | 1.44 | |
| 6 | MindMojo | 8532 | 1.33 |
| 7 | Vexim | 8182 | 1.28 |
| 8 | 7341 | 1.15 | |
| 10 | Echo | 4971 | 0.78 |
| 15 | Whatzee | -220 | -0.03 |
| 1+1=5 | 28806 | 0.90 | |
| 4 | M2Sloter | 9301 | 1.45 |
| 11 | Fluffy | 4360 | 0.68 |
| 12 | TACO_ | 3590 | 0.56 |
| 13 | LazerLim | 2811 | 0.44 |
| 14 | Bubbles | 1180 | 0.18 |
| Beard & The Beardless | 21242 | 0.66 | |
| Team Average | 32029 | 1.00 |
Game 11/03/2020 8:39 PM |
|||
|---|---|---|---|
| Rank | Name | Score | Score Ratio |
| 1 | Camsta | 15362 | 1.97 |
| 3 | Strik3r | 10552 | 1.35 |
| 7 | Echo | 8892 | 1.14 |
| 11 | LazerLim | 6781 | 0.87 |
| 14 | ICUP | 4501 | 0.58 |
| 16 | Strik3r | -80 | -0.01 |
| Just Wingin' It | 46008 | 1.10 | |
| 4 | Packing | 10482 | 1.34 |
| 5 | M2Skyros | 9932 | 1.27 |
| 6 | Bicky | 9412 | 1.20 |
| 9 | TACO_ | 7482 | 0.96 |
| 13 | BROCKER | 5041 | 0.64 |
| Will Wheaton Warriors | 42349 | 1.02 | |
| 2 | 11242 | 1.44 | |
| 8 | MindMojo | 8642 | 1.11 |
| 10 | Vexim | 7052 | 0.90 |
| 12 | Whatzee | 6162 | 0.79 |
| 15 | El Mayo | 3611 | 0.46 |
| 1+1=5 | 36709 | 0.88 | |
| Team Average | 41689 | 1.00 |


Interactively Exploring Publications for Open Celled Metal Foam
Jessica Hills and Rebecca Mih
The chord diagram is useful to visualize the contents of documents. In this case, academic publications for metal foams
are classified by the materials used to make the foam (Aluminum, Copper, etc), and materials properties (such as strength, elasticity, conductivity etc.)
that were tested by the authors.
In this way, it's easy to what materials and properties researchers have studied the most, by the relative size of the arcs and chords in the diagram.
The chord diagram was created using D3.js library.
Analysis of the most prolific authors, and the most popular journals, by material and properties, is helpful for identifying potential collaborators
and their areas of expertise. These plots were created using Chart.js library.
Select Property Category:
Select Composition:
This interactive bubble chart shows the evolution of research interest in metal foams, over time. Citations are the number of times
an article has be referenced in a later publication. The number of citations by others indicates the importance of the articles content.
Seminal, ground-breaking articles may have hundreds or even thousands of citations.
The plot was created using the plotly library.


The upcoming termination of NSF funding for NCEAS has recently been receiving a bit of attention, including this nice piece in Science (Stokstad, 2011). I was particularly intrigued by Oikos’s treatment of this, in which Oikos editor Jeremy Fox writes,
that’s probably NCEAS’ biggest legacy-the sense that the answers to all, or at least many, of our questions are already out there, in existing data that just needs to be pulled together"
and then argues that this may have gone to far,
As the saying goes, if all you have is a hammer, everything looks like a nail. Similarly, if all you have is the data you’ve currently got, every question looks like one that can be answered with those data.
I think this is an important point to raise, but it misses the mark. I don’t think anyone meant we should never collect new data. I think the issue is rather that we never look back at the data we collect, as Jeremy very aptly acknowledges:
The sort of datasets I (and everyone I know) work with can be handled easily on an ordinary laptop with ordinary software like Excel and R. […] we’re getting along just fine without any ecoinformatics or high performance computing resources.
As he says, if all you have is a hammer, everything looks like a nail. This is exactly why efforts such as NCEAS are so important. Darwin didn’t need a laptop with Excel and R, he got along just fine. So why is everyone using fancy laptops?
I don’t think we’re at any risk of all new data collection ceasing and everyone pouring over the same tired old few studies. Rather we’re in an era of being flooded with data, from GenBank to NCEAS to the thousands of individual studies becoming papers in our ever-expanding journals. We will always need new data, and we’ll always collect it, even though it is hard work and expensive, as Jeremy says.
Do we check that our questions cannot be answered by combining existing data before we set off to get more? Do we design our collection in a way that others can maximally reuse the data to answer other questions, or combine it with other existing data to answer the kinds of questions that a small, short-term, or system-specific dataset cannot? Do we recognize the contributions of excellent, careful data collection the way we do those brilliantly clever insights backed by the most tenuous available evidence? Do we have the tools to do this?
Jeremy has argued that many centers have attempted to mimic NCEAS, and it is worth reflecting on exactly what is successful. Another of NSF’s short-term initiative centers, NESCent, (National Evolutionary Synthesis Center) holds an equally synthetic charge which was excellently enunciated by it’s own independent post-docs in an article called Linking Big, (Sidlauskas et. al. 2009), emerging in the aftermath of an NSF program review that critiqued the center early on for being insufficiently synthetic. NESCent will soon face similar challenges to NCEAS, figuring out how to continue its mission and value on a model that goes beyond 5-year program grants.
Their recent piece in Nature (Piwowar et. al. 2011) emphasizes the importance of funding data-archiving infastructure, which, at a cost of $400,000 annually may generate much more research for the dollar than the current NSF funding average of over $25,000 per paper ((or the 3.7 million budget of NCEAS, though admittedly was charged with a much greater scope)).
As NCEAS authors Reichman, Jones & Schildhauer eloquently state, (Reichman et. al. 2011)
Ecology is a synthetic discipline benefiting from open access to data from the earth, life, and social sciences. Technological challenges exist, however, due to the dispersed and heterogeneous nature of these data. Standardization of methods and development of robust metadata can increase data access but are not sufficient. Reproducibility of analyses is also important, and executable workflows are addressing this issue by capturing data provenance. Sociological challenges, including inadequate rewards for sharing data, must also be resolved. The establishment of well-curated, federated data repositories will provide a means to preserve data while promoting attribution and acknowledgement of its use.
Reichman, Jones & Schildhauer are putting their money on federated repositories such as DataONE to address these challenges, which may be a key as well as economical solution. But will such programs be funded more continuously than the synthesis centers that have jump-started them? What role will future centers play in helping us address those challenges?
References
Stokstad E (2011). “Pioneering Center Ponders Future as Nsf Pulls Out.” Science, 332. ISSN 0036-8075, https://dx.doi.org/10.1126/science.332.6032.905.
Sidlauskas B, Ganapathy G, Hazkani-Covo E, Jenkins K, Lapp H, McCall L, Price S, Scherle R, Spaeth P and Kidd D (2009). “Linking Big: The Continuing Promise of Evolutionary Synthesis.” Evolution, 64. https://dx.doi.org/10.1111/j.1558-5646.2009.00892.x.
Piwowar H, Vision T and Whitlock M (2011). “Data Archiving is A Good Investment.” Nature, 473. ISSN 0028-0836, https://dx.doi.org/10.1038/473285a.
Reichman O, Jones M and Schildhauer M (2011). “Challenges And Opportunities of Open Data in Ecology.” Science, 331. ISSN 0036-8075, https://dx.doi.org/10.1126/science.1197962.


We organize dialogues across a variety of interfaith topics, from discussing the interrelation between faith and sexuality to sharing ideas about faith and art. We aim to create a safe and engaging space for our participants to feel comfortable sharing on topics that they otherwise would not have avenues to express their thoughts about, as these topics are often sensitive or rarely talked about. We are constantly introducing new topics in our series of dialogues, so be sure to keep your eyes out for them!
The UnConference, also know as "Open Space", is an innovative way for us to bring people together to work on issues facing their communities. It empowers participants to set agenda that are relevant, dynamic and flexible. Multiple track discussions circles balanced with larger group dialogues are also the hallmarks of the UnConference. Open to an extraordinary, highly engaging and deeply meaningful experience? Join us in our next UnConference to find out more!
We invest time into training our volunteers to be skilled facilitators. Given the sensitive nature of the topics in our dialogues as well as the high level of engagement required of participants in our UnConference, we have to ensure that our facilitators are able to effectively and meaningfully motivate and mediate. Our facilitators are empowered to motivate participants to be active contributors in discussions, and are confident in creating a fun and supportive environment for sharing.


A datepicker instantiated with no options
A datepicker instantiated with just one date
A datepicker instantiated with the time support plugin which allows you to specify time
A datepicker instantiated with a start and end date
A datepicker instantiated with a list of preset date ranges


PERLUNITUT
Section: Perl Programmers Reference Guide (1)Updated: 2009-02-12
Index Return to Main Contents
NAME
perlunitut - Perl Unicode TutorialDESCRIPTION
The days of just flinging strings around are over. It's well established that modern programs need to be capable of communicating funny accented letters, and things like euro symbols. This means that programmers need new habits. It's easy to program Unicode capable software, but it does require discipline to do it right.There's a lot to know about character sets, and text encodings. It's probably best to spend a full day learning all this, but the basics can be learned in minutes.
These are not the very basics, though. It is assumed that you already know the difference between bytes and characters, and realise (and accept!) that there are many different character sets and encodings, and that your program has to be explicit about them. Recommended reading is ``The Absolute Minimum Every Software Developer Absolutely, Positively Must Know About Unicode and Character Sets (No Excuses!)'' by Joel Spolsky, at <http://joelonsoftware.com/articles/Unicode.html>.
This tutorial speaks in rather absolute terms, and provides only a limited view of the wealth of character string related features that Perl has to offer. For most projects, this information will probably suffice.
Definitions
It's important to set a few things straight first. This is the most important part of this tutorial. This view may conflict with other information that you may have found on the web, but that's mostly because many sources are wrong.You may have to re-read this entire section a few times...
Unicode is a character set with room for lots of characters. The ordinal value of a character is called a code point.
There are many, many code points, but computers work with bytes, and a byte can have only 256 values. Unicode has many more characters, so you need a method to make these accessible.
Unicode is encoded using several competing encodings, of which UTF-8 is the most used. In a Unicode encoding, multiple subsequent bytes can be used to store a single code point, or simply: character.
UTF-8 is a Unicode encoding. Many people think that Unicode and UTF-8 are the same thing, but they're not. There are more Unicode encodings, but much of the world has standardized on UTF-8.
UTF-8 treats the first 128 codepoints, 0..127, the same as ASCII. They take only one byte per character. All other characters are encoded as two or more (up to six) bytes using a complex scheme. Fortunately, Perl handles this for us, so we don't have to worry about this.
Text strings (character strings)
Text strings, or character strings are made of characters. Bytes are irrelevant here, and so are encodings. Each character is just that: the character.
Text strings are also called Unicode strings, because in Perl, every text string is a Unicode string.
On a text string, you would do things like:
$text =~ s/foo/bar/; if ($string =~ /^\d+$/) { ... } $text = ucfirst $text; my $character_count = length $text;
The value of a character ("ord", "chr") is the corresponding Unicode code point.
Binary strings, or byte strings are made of bytes. Here, you don't have characters, just bytes. All communication with the outside world (anything outside of your current Perl process) is done in binary.
On a binary string, you would do things like:
my (@length_content) = unpack "(V/a)*", $binary; $binary =~ s/\x00\x0F/\xFF\xF0/; # for the brave :) print {$fh} $binary; my $byte_count = length $binary;
Encoding (as a verb) is the conversion from text to binary. To encode, you have to supply the target encoding, for example "iso-8859-1" or "UTF-8". Some encodings, like the "iso-8859" (``latin'') range, do not support the full Unicode standard; characters that can't be represented are lost in the conversion.
Decoding is the conversion from binary to text. To decode, you have to know what encoding was used during the encoding phase. And most of all, it must be something decodable. It doesn't make much sense to decode a PNG image into a text string.
Perl has an internal format, an encoding that it uses to encode text strings so it can store them in memory. All text strings are in this internal format. In fact, text strings are never in any other format!
You shouldn't worry about what this format is, because conversion is automatically done when you decode or encode.
Your new toolkit
Add to your standard heading the following line:
use Encode qw(encode decode);
Or, if you're lazy, just:
use Encode;
I/O flow (the actual 5 minute tutorial)
The typical input/output flow of a program is:
1. Receive and decode 2. Process 3. Encode and output
If your input is binary, and is supposed to remain binary, you shouldn't decode it to a text string, of course. But in all other cases, you should decode it.
Decoding can't happen reliably if you don't know how the data was encoded. If you get to choose, it's a good idea to standardize on UTF-8.
my $foo = decode('UTF-8', get 'http://example.com/'); my $bar = decode('ISO-8859-1', readline STDIN); my $xyzzy = decode('Windows-1251', $cgi->param('foo'));
Processing happens as you knew before. The only difference is that you're now using characters instead of bytes. That's very useful if you use things like "substr", or "length".
It's important to realize that there are no bytes in a text string. Of course, Perl has its internal encoding to store the string in memory, but ignore that. If you have to do anything with the number of bytes, it's probably best to move that part to step 3, just after you've encoded the string. Then you know exactly how many bytes it will be in the destination string.
The syntax for encoding text strings to binary strings is as simple as decoding:
$body = encode('UTF-8', $body);
If you needed to know the length of the string in bytes, now's the perfect time for that. Because $body is now a byte string, "length" will report the number of bytes, instead of the number of characters. The number of characters is no longer known, because characters only exist in text strings.
my $byte_count = length $body;
And if the protocol you're using supports a way of letting the recipient know which character encoding you used, please help the receiving end by using that feature! For example, E-mail and HTTP support MIME headers, so you can use the "Content-Type" header. They can also have "Content-Length" to indicate the number of bytes, which is always a good idea to supply if the number is known.
"Content-Type: text/plain; charset=UTF-8", "Content-Length: $byte_count"
SUMMARY
Decode everything you receive, encode everything you send out. (If it's text data.)Q and A (or FAQ)
After reading this document, you ought to read perlunifaq too.ACKNOWLEDGEMENTS
Thanks to Johan Vromans from Squirrel Consultancy. His UTF-8 rants during the Amsterdam Perl Mongers meetings got me interested and determined to find out how to use character encodings in Perl in ways that don't break easily.Thanks to Gerard Goossen from TTY. His presentation ``UTF-8 in the wild'' (Dutch Perl Workshop 2006) inspired me to publish my thoughts and write this tutorial.
Thanks to the people who asked about this kind of stuff in several Perl IRC channels, and have constantly reminded me that a simpler explanation was needed.
Thanks to the people who reviewed this document for me, before it went public. They are: Benjamin Smith, Jan-Pieter Cornet, Johan Vromans, Lukas Mai, Nathan Gray.
AUTHOR
Juerd Waalboer<EMAIL_ADDRESS>ALSO
perlunifaq, perlunicode, perluniintro, Encode
Index
This document was created by man2html, using the manual pages.
Time: 05:29:09 GMT, December 24, 2015


What Month Were You Born in?
What Year Were You Born? Try


| 028:008 Khan | : |
Then the household of Fir'aun (Pharaoh) picked him up, that he might become for them an enemy and a (cause of) grief. Verily! Fir'aun (Pharaoh), Haman and their hosts were sinners. |
| 028:008 Maulana | : |
So Pharaoh's people took him up that he might be en enemy and a grief for them. Surely Pharaoh and Haman and their hosts were wrongdoers. |
| 028:008 Pickthal | : |
And the family of Pharaoh took him up, that he might become for them an enemy and a sorrow, Lo! Pharaoh and Haman and their hosts were ever sinning. |
| 028:008 Rashad | : |
Pharaoh's family picked him up, only to have him lead the opposition and to be a source of grief for them. That is because Pharaoh, Hamaan, and their troops were transgressors. |
| 028:008 Sarwar | : |
The people of the Pharaoh picked him up (without realizing) that he would become their enemy and a source of their sorrow. The Pharaoh, Haman, and their army were sinful people. |
| 028:008 Shakir | : |
And Firon's family took him up that he might be an enemy and a grief for them; surely Firon and Haman and their hosts were wrongdoers. |
| 028:008 Sherali | : |
She did accordingly and one of the family of Pharaoh picked him up so that he became for them an enemy and a source of sorrow. Verily, Pharaoh and Haman and their hosts were wrongdoers. |
| 028:008 Yusufali | : |
Then the people of Pharaoh picked him up (from the river): (It was intended) that (Moses) should be to them an adversary and a cause of sorrow: for Pharaoh and Haman and (all) their hosts were men of sin. |
Concepts/Themes Covered: القصص والتاريخ > فرعون > قوم فرعون The Stories and the History>Pharaoh>Pharaoh's People
This tool uses eight English parallel translations for the Holy Quran imported from Mr. Clay Smith. You can also visit Qurany Concepts for browsing the Quran themes or topics. If you want to search the Holy Quran for keywords, please visit Qurany Keywords.


--- ---
This tip is something that I've wanted to do for a while, but kept on forgetting to write the article for it. It involves adding a menu choice to the system menu of an application. For the most part, you'll never have a need to do this. But there are some things like setting a form style, or some other action that is more system oriented than application oriented that just belong in the system menu. Well, here it is folks, and as usual, it's pretty incredibly easy to implement.
If you've tried to do this before but couldn't, it's because there is no way to add a menu item with standard Delphi calls. You have to trap Windows the windows message WM_SYSCOMMAND and evaluate the wParam message element to see if your added menu item was selected. Really folks, it's not that hard, and a little digging in the API help was all I needed to do find out how to implement this in a program. Basically, what you have to do is this:
Create a new form. Override the OnMessage event by assigning a new event handler procedure for the OnMessage event. Create a constant that will be used as the ordinal identifier for your menu choice. In the FormCreate, make your menu choice with the AppendMenu API call. Here's the code to show you how to do it:
uses SysUtils, WinTypes, WinProcs, Messages, Classes, Graphics, Controls, Forms, Dialogs, Menus; type TForm1 = class(TForm) procedure FormCreate(Sender: TObject); private { ... } public { This declaration is of the type TMessageEvent which is a pointer to a procedure that takes two variable arguments of type TMsg and Boolean, respectively } procedure WinMsgHandler(var Msg : TMsg; var Handled : Boolean); end; var Form1: TForm1; const MyItem = 100; // Here's the menu identifier. It can be any WORD value implementation {$R *.DFM} procedure TForm1.FormCreate(Sender: TObject); begin { First, tell the application that its message handler is different from the default } Application.OnMessage := WinMsgHandler; { Add a separator } AppendMenu(GetSystemMenu(Self.Handle, False), MF_SEPARATOR, 0, ''); { Add your menu choice. Since the Item ID is high, using the MF_BYPOSITION constant will place it last on the system menu } AppendMenu(GetSystemMenu(Self.Handle, False), MF_BYPOSITION, MyItem, 'My Men&u Choice'); end; procedure TForm1.WinMsgHandler(var Msg: TMsg; var Handled: Boolean); begin { If the message is a system one... } if Msg.Message=WM_SYSCOMMAND then if Msg.wParam = MyItem then { Put handling code here. I've opted for a ShowMessage for demonstration purposes } ShowMessage('You picked my menu!!!'); end; end.
As you can see, this is fairly straight-forward. Granted, the tip is not very complicated. However, it does open up many doors to things you can do. In anticipation of some questions you might have later, The AppendMenu command can also be used with minimized apps. For instance, if you minimize your app, the icon represents the application, not your form. Therefore in order to make the system menu with your changes visible when in minimized form you would use Application.Handle instead of Self.Handle to deal with the application's system menu.


CookCC documentation¶
CookCC is a lexer and parser (LALR (1)) generator project, combined. It is written in Java, but the target languages can vary.
CookCC comes with two unique features, which were the original motivations for this work.
- CookCC uses a unique approach of storing and loading DFA tables in Java that significantly reduces the starting up time. Many efforts have been made to maximize the generated Java lexer and parser performances, painstakingly line-by-line, case-by-case fine turning the lexer and parser code. I believe that CookCC is the fastest lexer for Java (see the performance test).
- CookCC allows lexer/parser patterns and rules to be specified using Java annotation. This feature greatly simplifies and eases the writing of lexer and parser for Java.
Other Features
- CookCC can produce highly compressed DFA tables for both the lexer and parser, using the similar compression algorithm found in Flex.
- For the lexer, DFA states constructed were minimal. In contrast, Flex does not construct minimal DFA states.
The current release is 0.4 which requires 1.7+ to run, mainly due to the annotation processing API. The generated Java code can be compiled and run with earlier versions of Java. There are zero dependencies for the generated Java code unless you use extended grammar. So it is light and fast.
Before 0.4, CookCC uses the new BSD license. For 0.4+, it is moved to APL 2.0 license. It should be noted that the license only applies to CookCC itself. The code generated belongs to you.


Im just kiding btw , html is pretty simple, infact all you really need to make a site are 6 tags(head,body,title,h1-h6,p)
As you go through this quick guide you will notice that the pages quaility improves, this is suppose to show what you can do with the things you have learned up to that point. I will be hooping around so if you have questions on things that I have displayed on this page some great sites to learn Html/css are
Now we could be lame and just edit html through a web browser but, im telling you right now that when you refresh your code will be lost so we will download a compiler
This tells the compiler that, your using the language html or HYPER_TEXT_MARKUP_LANUAGE
<DOCTYPE html>   <title>Site Name</title>
 is the site retrieving data like <meta> tags which can be sent to browsers telling the web browser what langauge your document is in this case english(NOT NECCESARY AT ALL).
Body is the is like your store front, what people will see, and depending on how you use your body your can attract alot of attention, or ward people away.
Now that you have your template lets mess around with what your body is showing currently

<Title Name

The text here will be displayed to the site. Notice how its this paragraph is smushed toghther with two
 tags, that called opening and closing tags and is very important to keeping your code working/p>
The text here will be displayed to the site. Notice how its this paragraph is smushed toghther with two p tags, that called opening and closing tags and is very important to keeping your code working
Close all your tags starting with
 and ending with
 or in this case any element, like
 and

BTW!! -- Elements are just what your call things like
 which display a paragraph
But your asking how do your use these things...?

Item #1

Item #2

Last item

In this case the parent
 holds or contains the child

Another type of list is
 . Its a parent too! Which means that it controls what kind of list it is.
 creates bulleted lists

 and

 Table Item

 Row with 2 colums	2nd colum

| Table item | |
| Row with 2 colums | 2nd Colum |
<DOCTYPE html>   <title>Site Name</title>
This is where your code belongs, in betweeen your body tags

When you indent correctly you can save yourself, and others much time finding certain blocks of your code.
<Parent> <Child>  </Child> </Parent>
Links consist of two components a and href=, once tells the landguage that its an anchor and it staing put , and href tell the langauge that its pointing to a different location.
Link text< href="http://google.com/">Link text
Css is the thing that makes your boring old HTML look nice and fancy going as far as changing the whole looks of as small section. This is the first part of this page, but styled
Im just kiding btw , html is pretty simple, infact all you really need to make a site are 6 tags(head,body,title,h1-h6,p)
As you go through this quick guide you will notice that the pages quaility improves, this is suppose to show what you can do with the things you have learned up to that point. I will be hooping around so if you have questions on things that I have displayed on this page some great sites to learn Html/css are
Not much can be done with just HTML so that why we use Css to style it and make it look pretty.


There are 256 possible elementary cellular automata, but this simulator only supports 128 even automata that preserve empty field. You can either enter automata code or choose one from the map.
Code:
Code:
Use # character to indicate "ON" cells, and any other characters for "OFF". Press [Enter] to update.
Simulate over steps.


Delta consists of 4 projects that all work on Discord in one way or another.
Delta uses the "." prefix for all the commands from the projects below.
Click on the button "Commands" to get a list of commands Delta can do from that project.
NadekoBot offers more then 430+ commands of all kinds!
This version contains art pieces from Delta and some minor changes!
AvaIre is a high quality Music bot, with many other commandes aswell.
This version contains only the music features.
Discord.RSS is focused on delivering and customizing all forms of RSS-feeds straight to your Server!
This version has a higher feed limit.
D-Zone is an ambient life simulation of the Discord servers it is in.
Use: ".pixel" to get the link to your Discord server!
Without the help of my best friends listed below, I wouldn't be able to make Delta as she is today. I hope to add more of my friends into this section, by encouraging me in what I do while I know you for so long means for me your part of the team.
Main Motive & Designer
Young and joyful artist growing to be where most dream of.
The one that brings respect.
Motive & Main Designer
Confident, understanding of other concerns and an amazing friend to have.
The one that brings reason.
Designer
A long-term friend who can be tougher than a diamond but as sweet as pie.
The one that brings emotion.
Designer & Inspiration
The longest friend I ever had, the one that started it all. The one that got it all.
The one that brings passion & purpose.
Technical Advisor
A developer taking his ideas and feelings into a deeply personalized bot called Ene.
The one that brings consciousness.
Assistant Web Developer
A confident front-end developer with an extraordinary passion for code and art.
The one that brings love.


{% extends "kalite_zim/base.html" %} {% load i18n %} {% block title %}{% trans "Welcome" %}{% endblock %} {% block content %}
{% trans "Khan Academy: For free. For everyone. Forever." %}
{% trans "No ads, no subscriptions. We are a not‑for‑profit because we believe in a free, world-class education for anyone, anywhere. " %}